nloptr/0000755000176000001440000000000013355073540011625 5ustar ripleyusersnloptr/inst/0000755000176000001440000000000013332372163012600 5ustar ripleyusersnloptr/inst/CITATION0000644000176000001440000000141412553776710013747 0ustar ripleyuserscitHeader("If you use NLopt in work that leads to a publication, we would appreciate it if you would kindly cite NLopt in your manuscript. Please cite both the NLopt library and the authors of the specific algorithm(s) that you employed in your work. Cite NLopt as:") citEntry(entry="Article", title = "The NLopt nonlinear-optimization package", author = personList(as.person("Steven G. Johnson")), year = "?", journal = "?", volume = "?", number = "?", pages = "?", textVersion = "Steven G. Johnson, The NLopt nonlinear-optimization package, http://ab-initio.mit.edu/nlopt" ) citFooter("The authors and appropriate citations for the specific optimization algorithms in NLopt are listed in the NLopt Algorithms page.") nloptr/inst/doc/0000755000176000001440000000000013332372163013345 5ustar ripleyusersnloptr/inst/doc/nloptr.R0000644000176000001440000001230613332372160015005 0ustar ripleyusers## ----setSweaveOptions,echo=FALSE-------------------------- # have an (invisible) initialization noweb chunk # to remove the default continuation prompt '>' options(continue = " ") options(width = 60) # eliminate margin space above plots options(SweaveHooks=list(fig=function() par(mar=c(5.1, 4.1, 1.1, 2.1)))) ## ----installNLopt, eval=FALSE----------------------------- # install.packages("nloptr") ## ----testNLoptInstallation, eval=FALSE-------------------- # library('nloptr') # ?nloptr ## ----installNLoptRForge, eval=FALSE----------------------- # install.packages("nloptr",repos="http://R-Forge.R-project.org") ## ----installNLoptRForgeSource, eval=FALSE----------------- # install.packages("nloptr",type="source",repos="http://R-Forge.R-project.org") ## ----loadLibrary------------------------------------------ library(nloptr) ## ----defineRosenbrockBanana------------------------------- ## Rosenbrock Banana function eval_f <- function(x) { return( 100 * (x[2] - x[1] * x[1])^2 + (1 - x[1])^2 ) } ## Gradient of Rosenbrock Banana function eval_grad_f <- function(x) { return( c( -400 * x[1] * (x[2] - x[1] * x[1]) - 2 * (1 - x[1]), 200 * (x[2] - x[1] * x[1]) ) ) } ## ----setRosenbrockBananaInitialValues--------------------- # initial values x0 <- c( -1.2, 1 ) ## ----setRosenbrockBananaOptions--------------------------- opts <- list("algorithm"="NLOPT_LD_LBFGS", "xtol_rel"=1.0e-8) ## ----solveRosenbrockBanana-------------------------------- # solve Rosenbrock Banana function res <- nloptr( x0=x0, eval_f=eval_f, eval_grad_f=eval_grad_f, opts=opts) ## ----printRosenbrockBanana-------------------------------- print( res ) ## ----defineRosenbrockBananaList--------------------------- ## Rosenbrock Banana function and gradient in one function eval_f_list <- function(x) { common_term <- x[2] - x[1] * x[1] return( list( "objective" = 100 * common_term^2 + (1 - x[1])^2, "gradient" = c( -400 * x[1] * common_term - 2 * (1 - x[1]), 200 * common_term) ) ) } ## ----solveRosenbrockBananaList---------------------------- res <- nloptr( x0=x0, eval_f=eval_f_list, opts=opts) print( res ) ## ----defineTutorialObjective------------------------------ # objective function eval_f0 <- function( x, a, b ){ return( sqrt(x[2]) ) } # gradient of objective function eval_grad_f0 <- function( x, a, b ){ return( c( 0, .5/sqrt(x[2]) ) ) } ## ----defineTutorialConstraints---------------------------- # constraint function eval_g0 <- function( x, a, b ) { return( (a*x[1] + b)^3 - x[2] ) } # jacobian of constraint eval_jac_g0 <- function( x, a, b ) { return( rbind( c( 3*a[1]*(a[1]*x[1] + b[1])^2, -1.0 ), c( 3*a[2]*(a[2]*x[1] + b[2])^2, -1.0 ) ) ) } ## ----defineTutorialParameters----------------------------- # define parameters a <- c(2,-1) b <- c(0, 1) ## ----solveTutorialWithGradient---------------------------- # Solve using NLOPT_LD_MMA with gradient information supplied in separate function res0 <- nloptr( x0=c(1.234,5.678), eval_f=eval_f0, eval_grad_f=eval_grad_f0, lb = c(-Inf,0), ub = c(Inf,Inf), eval_g_ineq = eval_g0, eval_jac_g_ineq = eval_jac_g0, opts = list("algorithm" = "NLOPT_LD_MMA", "xtol_rel"=1.0e-8, "print_level" = 2, "check_derivatives" = TRUE, "check_derivatives_print" = "all"), a = a, b = b ) print( res0 ) ## ----solveTutorialWithoutGradient------------------------- # Solve using NLOPT_LN_COBYLA without gradient information res1 <- nloptr( x0=c(1.234,5.678), eval_f=eval_f0, lb = c(-Inf,0), ub = c(Inf,Inf), eval_g_ineq = eval_g0, opts = list("algorithm"="NLOPT_LN_COBYLA", "xtol_rel"=1.0e-8), a = a, b = b ) print( res1 ) ## ----derivativeCheckerDefineFunctions--------------------- g <- function( x, a ) { return( c( x[1] - a[1], x[2] - a[2], (x[1] - a[1])^2, (x[2] - a[2])^2, (x[1] - a[1])^3, (x[2] - a[2])^3 ) ) } g_grad <- function( x, a ) { return( rbind( c( 1, 0 ), c( 0, 1 ), c( 2*(x[1] - a[1]), 0 ), c( 2*(x[1] - a[1]), 2*(x[2] - a[2]) ), c( 3*(x[1] - a[2])^2, 0 ), c( 0, 3*(x[2] - a[2])^2 ) ) ) } ## ----derivativeCheckerPrint------------------------------- res <- check.derivatives( .x=c(1,2), func=g, func_grad=g_grad, check_derivatives_print='all', a=c(.3, .8) ) ## ----derivativeCheckerResult------------------------------ res ## ----printAllOptions-------------------------------------- nloptr::nloptr.print.options() nloptr/inst/doc/nloptr.Rmd0000644000176000001440000003513013150612565015332 0ustar ripleyusers--- title: "Introduction to `nloptr`: an R interface to NLopt^[This package should be considered in beta and comments about any aspect of the package are welcome. This document is an R vignette prepared with the aid of `knitr` [@Xie:2014; @Xie:2015; @Xie:2016]. Financial support of the UK Economic and Social Research Council through a grant (RES-589-28-0001) to the ESRC Centre for Microdata Methods and Practice (CeMMAP) is gratefully acknowledged.]" author: "Jelmer Ypma" date: "`r Sys.Date()`" output: rmarkdown::pdf_document bibliography: reflist.bib nocite: | @NLopt:website vignette: > %\VignetteIndexEntry{Introduction to nloptr: an R interface to NLopt} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setSweaveOptions,echo=FALSE} # have an (invisible) initialization noweb chunk # to remove the default continuation prompt '>' options(continue = " ") options(width = 60) # eliminate margin space above plots options(SweaveHooks=list(fig=function() par(mar=c(5.1, 4.1, 1.1, 2.1)))) ``` This document describes how to use `nloptr`, which is an R interface to NLopt. NLopt is a free/open-source library for nonlinear optimization started by Steven G. Johnson, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. The NLopt library is available under the GNU Lesser General Public License (LGPL), and the copyrights are owned by a variety of authors. # Introduction NLopt addresses general nonlinear optimization problems of the form: $$ \begin{aligned} &\min_{x \in R^n} f(x) \\ s.t.& g(x) \leq 0 \\ & h(x) = 0 \\ & x_L \leq x \leq x_U \end{aligned} $$ where $f(\cdot)$ is the objective function and $x$ represents the $n$ optimization parameters. This problem may optionally be subject to the bound constraints (also called box constraints), $x_L$ and $x_U$. For partially or totally unconstrained problems the bounds can take values $-\infty$ or $\infty$. One may also optionally have $m$ nonlinear inequality constraints (sometimes called a nonlinear programming problem), which can be specified in $g(\cdot)$, and equality constraints that can be specified in $h(\cdot)$. Note that not all of the algorithms in NLopt can handle constraints. This vignette describes how to formulate minimization problems to be solved with the R interface to NLopt. If you want to use the C interface directly or are interested in the Matlab interface, there are other sources of documentation avialable. Some of the information here has been taken from the [NLopt website](\texttt{http://ab-initio.mit.edu/nlopt), where more details are available. All credit for implementing the C code for the different algorithms available in NLopt should go to the respective authors. See the [website](http://ab-initio.mit.edu/wiki/index.php/Citing\_NLopt) for information on how to cite NLopt and the algorithms you use. # Installation This package is on CRAN and can be installed from within R using ```{r installNLopt, eval=FALSE} install.packages("nloptr") ``` You should now be able to load the R interface to NLopt and read the help. ```{r testNLoptInstallation, eval=FALSE} library('nloptr') ?nloptr ``` The most recent experimental version of `nloptr` can be installed from R-Forge using ```{r installNLoptRForge, eval=FALSE} install.packages("nloptr",repos="http://R-Forge.R-project.org") ``` or from source using ```{r installNLoptRForgeSource, eval=FALSE} install.packages("nloptr",type="source",repos="http://R-Forge.R-project.org") ``` # Minimizing the Rosenbrock Banana function As a first example we will solve an unconstrained minimization problem. The function we look at is the Rosenbrock Banana function $$ f( x ) = 100 \left( x_2 - x_1^2 \right)^2 + \left(1 - x_1 \right)^2, $$ which is also used as an example in the documentation for the standard R optimizer `optim`. The gradient of the objective function is given by $$ \nabla f( x ) = \left( \begin{array}[1]{c} -400 \cdot x_1 \cdot (x_2 - x_1^2) - 2 \cdot (1 - x_1) \\ 200 \cdot (x_2 - x_1^2) \end{array} \right). $$ Not all of the algorithms in NLopt need gradients to be supplied by the user. We will show examples with and without supplying the gradient. After loading the library ```{r loadLibrary} library(nloptr) ``` we start by specifying the objective function and its gradient ```{r defineRosenbrockBanana} ## Rosenbrock Banana function eval_f <- function(x) { return( 100 * (x[2] - x[1] * x[1])^2 + (1 - x[1])^2 ) } ## Gradient of Rosenbrock Banana function eval_grad_f <- function(x) { return( c( -400 * x[1] * (x[2] - x[1] * x[1]) - 2 * (1 - x[1]), 200 * (x[2] - x[1] * x[1]) ) ) } ``` We define initial values ```{r setRosenbrockBananaInitialValues} # initial values x0 <- c( -1.2, 1 ) ``` and then minimize the function using the `nloptr` command. This command runs some checks on the supplied inputs and returns an object with the exit code of the solver, the optimal value of the objective function and the solution. Before we can minimize the function we need to specify which algorithm we want to use ```{r setRosenbrockBananaOptions} opts <- list("algorithm"="NLOPT_LD_LBFGS", "xtol_rel"=1.0e-8) ``` Here we use the L-BFGS algorithm [@Nocedal:1980; @LiuNocedal:1989]. The characters `LD` in the algorithm show that this algorithm looks for local minima (`L`) using a derivative-based (`D`) algorithm. Other algorithms look for global (`G`) minima, or they don't need derivatives (`N`). We also specified the termination criterium in terms of the relative x-tolerance. Other termination criteria are available (see Appendix `\ref{sec:descoptions}` for a full list of options). We then solve the minimization problem using ```{r solveRosenbrockBanana} # solve Rosenbrock Banana function res <- nloptr( x0=x0, eval_f=eval_f, eval_grad_f=eval_grad_f, opts=opts) ``` We can see the results by printing the resulting object. ```{r printRosenbrockBanana} print( res ) ``` Sometimes the objective function and its gradient contain common terms. To economize on calculations, we can return the objective and its gradient in a list. For the Rosenbrock Banana function we have for instance ```{r defineRosenbrockBananaList} ## Rosenbrock Banana function and gradient in one function eval_f_list <- function(x) { common_term <- x[2] - x[1] * x[1] return( list( "objective" = 100 * common_term^2 + (1 - x[1])^2, "gradient" = c( -400 * x[1] * common_term - 2 * (1 - x[1]), 200 * common_term) ) ) } ``` which we minimize using ```{r solveRosenbrockBananaList} res <- nloptr( x0=x0, eval_f=eval_f_list, opts=opts) print( res ) ``` This gives the same results as before. # Minimization with inequality constraints This section shows how to minimize a function subject to inequality constraints. This example is the same as the one used in the tutorial on the NLopt website. The problem we want to solve is $$ \begin{aligned} &\min_{x \in R^n} \sqrt{x_2} \\ s.t.& x_2 \geq 0 \\ & x_2 \geq ( a_1 x_1 + b_1 )^3 \\ & x_2 \geq ( a_2 x_1 + b_2 )^3, \end{aligned} $$ where $a_1 = 2$, $b_1 = 0$, $a_2 = -1$, and $b_2 = 1$. In order to solve this problem, we first have to re-formulate the constraints to be of the form $g(x) \leq 0$. Note that the first constraint is a bound on $x_2$, which we will add later. The other two constraints can be re-written as $$ \begin{aligned} ( a_1 x_1 + b_1 )^3 - x_2 &\leq 0 \\ ( a_2 x_1 + b_2 )^3 - x_2 &\leq 0 \end{aligned} $$ First we define R functions to calculate the objective function and its gradient ```{r defineTutorialObjective} # objective function eval_f0 <- function( x, a, b ){ return( sqrt(x[2]) ) } # gradient of objective function eval_grad_f0 <- function( x, a, b ){ return( c( 0, .5/sqrt(x[2]) ) ) } ``` If needed, these can of course be calculated in the same function as before. Then we define the two constraints and the jacobian of the constraints ```{r defineTutorialConstraints} # constraint function eval_g0 <- function( x, a, b ) { return( (a*x[1] + b)^3 - x[2] ) } # jacobian of constraint eval_jac_g0 <- function( x, a, b ) { return( rbind( c( 3*a[1]*(a[1]*x[1] + b[1])^2, -1.0 ), c( 3*a[2]*(a[2]*x[1] + b[2])^2, -1.0 ) ) ) } ``` Note that all of the functions above depend on additional parameters, `a` and `b`. We have to supply specific values for these when we invoke the optimization command. The constraint function `eval_g0` returns a vector with in this case the same length as the vectors `a` and `b`. The function calculating the jacobian of the constraint should return a matrix where the number of rows equal the number of constraints (in this case two). The number of columns should equal the number of control variables (two in this case as well). After defining values for the parameters ```{r defineTutorialParameters} # define parameters a <- c(2,-1) b <- c(0, 1) ``` we can minimize the function subject to the constraints with the following command ```{r solveTutorialWithGradient} # Solve using NLOPT_LD_MMA with gradient information supplied in separate function res0 <- nloptr( x0=c(1.234,5.678), eval_f=eval_f0, eval_grad_f=eval_grad_f0, lb = c(-Inf,0), ub = c(Inf,Inf), eval_g_ineq = eval_g0, eval_jac_g_ineq = eval_jac_g0, opts = list("algorithm" = "NLOPT_LD_MMA", "xtol_rel"=1.0e-8, "print_level" = 2, "check_derivatives" = TRUE, "check_derivatives_print" = "all"), a = a, b = b ) print( res0 ) ``` Here we supplied lower bounds for $x_2$ in `lb`. There are no upper bounds for both control variables, so we supply `Inf` values. If we don't supply lower or upper bounds, plus or minus infinity is chosen by default. The inequality constraints and its jacobian are defined using `eval_g_ineq` and `eval_jac_g_ineq`. Not all algorithms can handle inequality constraints, so we have to specifiy one that does, `NLOPT_LD_MMA` [@Svanberg:2002]. We also specify the option `print_level` to obtain output during the optimization process. For the available `print_level` values, see `?nloptr`. Setting the `check_derivatives` option to `TRUE`, compares the gradients supplied by the user with a finite difference approximation in the initial point (`x0`). When this check is run, the option `check_derivatives_print` can be used to print all values of the derivative checker (`all` (default)), only those values that result in an error (`errors`) or no output (`none`), in which case only the number of errors is shown. The tolerance that determines if a difference between the analytic gradient and the finite difference approximation results in an error can be set using the option `check_derivatives_tol` (default = 1e-04). The first column shows the value of the analytic gradient, the second column shows the value of the finite difference approximation, and the third column shows the relative error. Stars are added at the front of a line if the relative error is larger than the specified tolerance. Finally, we add all the parameters that have to be passed on to the objective and constraint functions, `a` and `b`. We can also use a different algorithm to solve the same minimization problem. The only thing we have to change is the algorithm that we want to use, in this case `NLOPT_LN_COBYLA`, which is an algorithm that doesn't need gradient information [@Powell:1994; @Powell:1998]. ```{r solveTutorialWithoutGradient} # Solve using NLOPT_LN_COBYLA without gradient information res1 <- nloptr( x0=c(1.234,5.678), eval_f=eval_f0, lb = c(-Inf,0), ub = c(Inf,Inf), eval_g_ineq = eval_g0, opts = list("algorithm"="NLOPT_LN_COBYLA", "xtol_rel"=1.0e-8), a = a, b = b ) print( res1 ) ``` # Derivative checker The derivative checker can be called when supplying a minimization problem to `nloptr`, using the options `check_derivatives`, `check_derivatives_tol` and `check_derivatives_print`, but it can also be used separately. For example, define the function `g`, with vector outcome, and its gradient `g_grad` ```{r derivativeCheckerDefineFunctions} g <- function( x, a ) { return( c( x[1] - a[1], x[2] - a[2], (x[1] - a[1])^2, (x[2] - a[2])^2, (x[1] - a[1])^3, (x[2] - a[2])^3 ) ) } g_grad <- function( x, a ) { return( rbind( c( 1, 0 ), c( 0, 1 ), c( 2*(x[1] - a[1]), 0 ), c( 2*(x[1] - a[1]), 2*(x[2] - a[2]) ), c( 3*(x[1] - a[2])^2, 0 ), c( 0, 3*(x[2] - a[2])^2 ) ) ) } ``` `a` is some vector containing data. The gradient contains some errors in this case. By calling the function `check.derivatives` we can check the user-supplied analytic gradients with a finite difference approximation at a point `.x`. ```{r derivativeCheckerPrint} res <- check.derivatives( .x=c(1,2), func=g, func_grad=g_grad, check_derivatives_print='all', a=c(.3, .8) ) ``` The errors are shown on screen, where the option `check_derivatives_print` determines the amount of output you see. The value of the analytic gradient and the value of the finite difference approximation at the supplied point is returned in a list. ```{r derivativeCheckerResult} res ``` Note that not all errors will be picked up by the derivative checker. For instance, if we run the check with `a = c(.5, .5)`, one of the errors is not flagged as an error. # Notes The `.R` scripts in the `tests` directory contain more examples. For instance, `hs071.R` and `systemofeq.R` show how to solve problems with equality constraints. See the [NLopt website](http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms#Augmented_Lagrangian_algorithm) for more details. Please let me know if you're missing any of the features that are implemented in NLopt. Sometimes the optimization procedure terminates with a message `maxtime was reached` without evaluating the objective function. 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P8Cȶj}EXQL]nKJE)nd`?X&,ے4TzsGQH7TXL@m̨0mXuJ YeVTi Ctnu].?9 |/,*u[QJLCކ]h#`Km0fo}l\e@͋ .u`IHj⥇*s0B!ą y!lF׊WpSU%%f16g~pz{}E< eFN5)Nd\ϵb`Ժ+rg3ty2{o;ZydPɵ$a sqgL=A6f)Kj>_xV"msw3k%4JOCIRO>ޝڽ1h1P<;-T7HQ{Z wG|jzugfaձ8O!n&II)ɭJIZ)dѬt%.] fVH78HVΠ,[& -_}W@^/ԕA VgN*"Q):uIQyb}TG !7--8У/ =h(n-!^Нҝ fu![;"@Vb.a|ݢ?F[)EwtZjHYxNQPra$%(t^L2 Gpؠo?78@"En@KbF-=(>6ָ9PU u_Bs)rCgwKo?F@^8эAvq~5>*,;endstream endobj 151 0 obj << /Filter /FlateDecode /Length 1391 >> stream xXM6Wrk+gH е{Wm%u%jFu6%f͛,,Ta{ֶw+L+"X g1j+wz̷fZ%w#ϝ3s:MOl~/|{mt=O9bTRܚ8g̪f~ߛAHvS%x,߫=sgz~oS` w4!=Ş-ogFtCX #d/h_\@>5\ DWc1!Ia#$TjHOmq >( i.bT_f*˺UgV hZp?~,t|=p§8FǤAO;MXѸjM4"S.|?S`i|S;.Q3 zQEP[XǘF~ҦǺ .q#Ja'Ttd$c5h _sy?2d|x~MKj5vFSKmP ;F nD[,:L B.6εSɓ`iAo2H{NuG,KeTWTLPi0E^%{KLa'S<Y5D;(5oTڦ¢h~WRf@ʥ"0oiR,"<`h ܕpA8+Km>#tUSc;IA OZut.R:#ѐm!JsbO([ \y>(٢^ژp%U&ᕐ~f,m5J:4k ,)SL6{k#vuHZg{+KLs[Rh3ޤiosg e[fdžu@džL< r8*U*rQ^sr*8(:qGu9 =bW46.^}vd:bKrDTfK!5duF!+p F5s6<]PͲFce4?is <5@~7aZn6"ES J) zc'b?Ur 7endstream endobj 152 0 obj << /Filter /FlateDecode /Subtype /Type1C /Length 4717 >> stream xX XSW1*M%S{/b+YNgqQ.@ d? HNI@QZvک:ڙ7tN2|'@;Nϙ>> s,bbٺmgVF\e {cyO>e^ x |ŝl9A梷 labEd.姤Xleh(Bp|a+K7%K- ޞ%C2Rғw% 32x~M2g{hC^¸- ۊ''GG 3J p"XH ""]DC U:U+ Hn M&' J}`39l5oAHı3/IceRI`9b^0tk4;f^E*.Akby/'rPXMɳ@WU\bm0D~˵Jq ) gJ=Vɩ׻LTSn [ S&ֈbz-sNԩ@ÓY5: u2 &y5MqCL I]הAy-]huuoєq5#7V13\fhm-@OSa}\_(ܵ7fO <3|Sm#h\#Y;l\} $*JA2jk;3&2kQ( PIJT\Ce^flY+/h =ggW5bђ; kA}ܿ ϤC1(Ċ,D CBaRYZ1htiJ3${\aWh9,afff3ذ@(m5+}v]:*v ⲙ[Y>5},7˷#ڻ|/ br2I+Թ[HZf;"+0A5.q(^˅L&|v 9`z3(iax_p>÷jqUxol_D)ٹĺvx&hzIL#YbԺ+W=F7K/o\rm-˜bcdS֞D;%P˳jZ61㟍UI:^:P IAK wΈ3&87^W,*>zfTb"$mɵMe| X1)/USנ@jʳWheYI@.yMjB+?@B0ROЄS4@ >{gr~E ǁtڱFn%E 1 /wR+S2-PmiN1@LA!`T[Z,YS-nr3Gtj$΄yfBZ熫.C߾[WRHV>9Z9Q7gW 3r- 3*mY OqȘÝ4S%ԑܱKS ! vMΡlI"UCuut4Q)ܵKXbuox#:Cm`Xʫ(1ۮb8 k״]혘fWP2r~89=;YeÕ?ޙRwf^n^^EaVPM9FVL93mtN۔\5H_- 䲑̋ZY^W^u+oo%צ4R4bۯvƔGxj C% ,7 dE,D/7Oٻh4ݛniXWegηY>p{۷G EQ׀fW?j25;"KW(K_' ~<[fCdo9Ԁ;;T%ɾh[}h)#/G^O}5)Qcj yY,Z=nSGh Uf+l{q;Nڣt~lQqShlID_}ePFƺ74 =ԐIQ<*xl:]sK2A7VzvAD:⍉Qo>kpvdTpXmfTKF }xŠ"HҕRwzځY˫J>B@N~@Nl1L ol.y_xWj_z)};LUMN'ļ`4%&,zmrB5 R&i22ן~y06ۛ5$:]ZB]܁sm9Iw_=}x+À*>Z b{*g˷!2ߣG>s\eIT_}7qsއ+hN>h@V|>v9(;uiDG$i' KG8Okf^J4h[ `.F:Q>RRrae ۳l3^e(:E;w|p~pWF7XeEr7/e+fJ&)Ue+cӥJ<9$*JAdUu*tVss1 zG,LIxA=sB)PI̯-nn?ツtwWz60,9OyV9+a@q\_tze+ŋ&F1rFQsL?]^Lr˻ ͓g)I{v<4&yb 9pKY|^J VGtgY]Na$1_{:Jp2n{hz |KF}A=>ʸЮ亥%m"SMeWyw}múBO$Ϣ:aN;߼bͰ(מlڬq)ԧ$AgBĞ)䲗^vm׍V{vT$QLV84J- g@bIm9#q")mRҦ7ѦЃFZ+Zg/ *:NJVR'6.O$Ż$h-ȖK@A{T# Pk$8ן9ꬳSa0b$1t|ܣبsL*%4"(L9cؕUB(N>[jWdpTzkG3(}i_Ǧhh?}l8[-73塟b7,Xl{7d 2rjZܝ-AYxUb K?@sYT`?]W-lr{~IHD `?`6t(&#}DT6+Rhp g廼萡q=ta]Μ3gqendstream endobj 153 0 obj << /Type /XRef /Length 177 /Filter /FlateDecode /DecodeParms << /Columns 5 /Predictor 12 >> /W [ 1 3 1 ] /Info 3 0 R /Root 2 0 R /Size 154 /ID [] >> stream xcb&F~0 $8JRSpf_ ۷aKͰ [EP %HAI)/"OHYGɫ "YMA$~$&@${X\D<ܻ&y"9H``M$#4+M3ԁ R,lLH`r'G' endstream endobj startxref 95204 %%EOF nloptr/inst/include/0000755000176000001440000000000013263213124014215 5ustar ripleyusersnloptr/inst/include/nlopt.h0000644000176000001440000003320712336720216015534 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #ifndef NLOPT_H #define NLOPT_H #include /* for ptrdiff_t and size_t */ /* Change 0 to 1 to use stdcall convention under Win32 */ #if 0 && (defined(_WIN32) || defined(__WIN32__)) # if defined(__GNUC__) # define NLOPT_STDCALL __attribute__((stdcall)) # elif defined(_MSC_VER) || defined(_ICC) || defined(_STDCALL_SUPPORTED) # define NLOPT_STDCALL __stdcall # else # define NLOPT_STDCALL # endif #else # define NLOPT_STDCALL #endif /* for Windows compilers, you should add a line #define NLOPT_DLL when using NLopt from a DLL, in order to do the proper Windows importing nonsense. */ #if defined(NLOPT_DLL) && (defined(_WIN32) || defined(__WIN32__)) && !defined(__LCC__) /* annoying Windows syntax for calling functions in a DLL */ # if defined(NLOPT_DLL_EXPORT) # define NLOPT_EXTERN(T) extern __declspec(dllexport) T NLOPT_STDCALL # else # define NLOPT_EXTERN(T) extern __declspec(dllimport) T NLOPT_STDCALL # endif #else # define NLOPT_EXTERN(T) extern T NLOPT_STDCALL #endif #ifdef __cplusplus extern "C" { #endif /* __cplusplus */ typedef double (*nlopt_func)(unsigned n, const double *x, double *gradient, /* NULL if not needed */ void *func_data); typedef void (*nlopt_mfunc)(unsigned m, double *result, unsigned n, const double *x, double *gradient, /* NULL if not needed */ void *func_data); /* A preconditioner, which preconditions v at x to return vpre. (The meaning of "preconditioning" is algorithm-dependent.) */ typedef void (*nlopt_precond)(unsigned n, const double *x, const double *v, double *vpre, void *data); typedef enum { /* Naming conventions: NLOPT_{G/L}{D/N}_* = global/local derivative/no-derivative optimization, respectively *_RAND algorithms involve some randomization. *_NOSCAL algorithms are *not* scaled to a unit hypercube (i.e. they are sensitive to the units of x) */ NLOPT_GN_DIRECT = 0, NLOPT_GN_DIRECT_L, NLOPT_GN_DIRECT_L_RAND, NLOPT_GN_DIRECT_NOSCAL, NLOPT_GN_DIRECT_L_NOSCAL, NLOPT_GN_DIRECT_L_RAND_NOSCAL, NLOPT_GN_ORIG_DIRECT, NLOPT_GN_ORIG_DIRECT_L, NLOPT_GD_STOGO, NLOPT_GD_STOGO_RAND, NLOPT_LD_LBFGS_NOCEDAL, NLOPT_LD_LBFGS, NLOPT_LN_PRAXIS, NLOPT_LD_VAR1, NLOPT_LD_VAR2, NLOPT_LD_TNEWTON, NLOPT_LD_TNEWTON_RESTART, NLOPT_LD_TNEWTON_PRECOND, NLOPT_LD_TNEWTON_PRECOND_RESTART, NLOPT_GN_CRS2_LM, NLOPT_GN_MLSL, NLOPT_GD_MLSL, NLOPT_GN_MLSL_LDS, NLOPT_GD_MLSL_LDS, NLOPT_LD_MMA, NLOPT_LN_COBYLA, NLOPT_LN_NEWUOA, NLOPT_LN_NEWUOA_BOUND, NLOPT_LN_NELDERMEAD, NLOPT_LN_SBPLX, NLOPT_LN_AUGLAG, NLOPT_LD_AUGLAG, NLOPT_LN_AUGLAG_EQ, NLOPT_LD_AUGLAG_EQ, NLOPT_LN_BOBYQA, NLOPT_GN_ISRES, /* new variants that require local_optimizer to be set, not with older constants for backwards compatibility */ NLOPT_AUGLAG, NLOPT_AUGLAG_EQ, NLOPT_G_MLSL, NLOPT_G_MLSL_LDS, NLOPT_LD_SLSQP, NLOPT_LD_CCSAQ, NLOPT_GN_ESCH, NLOPT_NUM_ALGORITHMS /* not an algorithm, just the number of them */ } nlopt_algorithm; NLOPT_EXTERN(const char *) nlopt_algorithm_name(nlopt_algorithm a); typedef enum { NLOPT_FAILURE = -1, /* generic failure code */ NLOPT_INVALID_ARGS = -2, NLOPT_OUT_OF_MEMORY = -3, NLOPT_ROUNDOFF_LIMITED = -4, NLOPT_FORCED_STOP = -5, NLOPT_SUCCESS = 1, /* generic success code */ NLOPT_STOPVAL_REACHED = 2, NLOPT_FTOL_REACHED = 3, NLOPT_XTOL_REACHED = 4, NLOPT_MAXEVAL_REACHED = 5, NLOPT_MAXTIME_REACHED = 6 } nlopt_result; #define NLOPT_MINF_MAX_REACHED NLOPT_STOPVAL_REACHED NLOPT_EXTERN(void) nlopt_srand(unsigned long seed); NLOPT_EXTERN(void) nlopt_srand_time(void); NLOPT_EXTERN(void) nlopt_version(int *major, int *minor, int *bugfix); /*************************** OBJECT-ORIENTED API **************************/ /* The style here is that we create an nlopt_opt "object" (an opaque pointer), then set various optimization parameters, and then execute the algorithm. In this way, we can add more and more optimization parameters (including algorithm-specific ones) without breaking backwards compatibility, having functions with zillions of parameters, or relying non-reentrantly on global variables.*/ struct nlopt_opt_s; /* opaque structure, defined internally */ typedef struct nlopt_opt_s *nlopt_opt; /* the only immutable parameters of an optimization are the algorithm and the dimension n of the problem, since changing either of these could have side-effects on lots of other parameters */ NLOPT_EXTERN(nlopt_opt) nlopt_create(nlopt_algorithm algorithm, unsigned n); NLOPT_EXTERN(void) nlopt_destroy(nlopt_opt opt); NLOPT_EXTERN(nlopt_opt) nlopt_copy(const nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_optimize(nlopt_opt opt, double *x, double *opt_f); NLOPT_EXTERN(nlopt_result) nlopt_set_min_objective(nlopt_opt opt, nlopt_func f, void *f_data); NLOPT_EXTERN(nlopt_result) nlopt_set_max_objective(nlopt_opt opt, nlopt_func f, void *f_data); NLOPT_EXTERN(nlopt_result) nlopt_set_precond_min_objective(nlopt_opt opt, nlopt_func f, nlopt_precond pre, void *f_data); NLOPT_EXTERN(nlopt_result) nlopt_set_precond_max_objective(nlopt_opt opt, nlopt_func f, nlopt_precond pre, void *f_data); NLOPT_EXTERN(nlopt_algorithm) nlopt_get_algorithm(const nlopt_opt opt); NLOPT_EXTERN(unsigned) nlopt_get_dimension(const nlopt_opt opt); /* constraints: */ NLOPT_EXTERN(nlopt_result) nlopt_set_lower_bounds(nlopt_opt opt, const double *lb); NLOPT_EXTERN(nlopt_result) nlopt_set_lower_bounds1(nlopt_opt opt, double lb); NLOPT_EXTERN(nlopt_result) nlopt_get_lower_bounds(const nlopt_opt opt, double *lb); NLOPT_EXTERN(nlopt_result) nlopt_set_upper_bounds(nlopt_opt opt, const double *ub); NLOPT_EXTERN(nlopt_result) nlopt_set_upper_bounds1(nlopt_opt opt, double ub); NLOPT_EXTERN(nlopt_result) nlopt_get_upper_bounds(const nlopt_opt opt, double *ub); NLOPT_EXTERN(nlopt_result) nlopt_remove_inequality_constraints(nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_add_inequality_constraint(nlopt_opt opt, nlopt_func fc, void *fc_data, double tol); NLOPT_EXTERN(nlopt_result) nlopt_add_precond_inequality_constraint( nlopt_opt opt, nlopt_func fc, nlopt_precond pre, void *fc_data, double tol); NLOPT_EXTERN(nlopt_result) nlopt_add_inequality_mconstraint(nlopt_opt opt, unsigned m, nlopt_mfunc fc, void *fc_data, const double *tol); NLOPT_EXTERN(nlopt_result) nlopt_remove_equality_constraints(nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_add_equality_constraint(nlopt_opt opt, nlopt_func h, void *h_data, double tol); NLOPT_EXTERN(nlopt_result) nlopt_add_precond_equality_constraint( nlopt_opt opt, nlopt_func h, nlopt_precond pre, void *h_data, double tol); NLOPT_EXTERN(nlopt_result) nlopt_add_equality_mconstraint(nlopt_opt opt, unsigned m, nlopt_mfunc h, void *h_data, const double *tol); /* stopping criteria: */ NLOPT_EXTERN(nlopt_result) nlopt_set_stopval(nlopt_opt opt, double stopval); NLOPT_EXTERN(double) nlopt_get_stopval(const nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_set_ftol_rel(nlopt_opt opt, double tol); NLOPT_EXTERN(double) nlopt_get_ftol_rel(const nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_set_ftol_abs(nlopt_opt opt, double tol); NLOPT_EXTERN(double) nlopt_get_ftol_abs(const nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_set_xtol_rel(nlopt_opt opt, double tol); NLOPT_EXTERN(double) nlopt_get_xtol_rel(const nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_set_xtol_abs1(nlopt_opt opt, double tol); NLOPT_EXTERN(nlopt_result) nlopt_set_xtol_abs(nlopt_opt opt, const double *tol); NLOPT_EXTERN(nlopt_result) nlopt_get_xtol_abs(const nlopt_opt opt, double *tol); NLOPT_EXTERN(nlopt_result) nlopt_set_maxeval(nlopt_opt opt, int maxeval); NLOPT_EXTERN(int) nlopt_get_maxeval(const nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_set_maxtime(nlopt_opt opt, double maxtime); NLOPT_EXTERN(double) nlopt_get_maxtime(const nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_force_stop(nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_set_force_stop(nlopt_opt opt, int val); NLOPT_EXTERN(int) nlopt_get_force_stop(const nlopt_opt opt); /* more algorithm-specific parameters */ NLOPT_EXTERN(nlopt_result) nlopt_set_local_optimizer(nlopt_opt opt, const nlopt_opt local_opt); NLOPT_EXTERN(nlopt_result) nlopt_set_population(nlopt_opt opt, unsigned pop); NLOPT_EXTERN(unsigned) nlopt_get_population(const nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_set_vector_storage(nlopt_opt opt, unsigned dim); NLOPT_EXTERN(unsigned) nlopt_get_vector_storage(const nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_set_default_initial_step(nlopt_opt opt, const double *x); NLOPT_EXTERN(nlopt_result) nlopt_set_initial_step(nlopt_opt opt, const double *dx); NLOPT_EXTERN(nlopt_result) nlopt_set_initial_step1(nlopt_opt opt, double dx); NLOPT_EXTERN(nlopt_result) nlopt_get_initial_step(const nlopt_opt opt, const double *x, double *dx); /* the following are functions mainly designed to be used internally by the Fortran and SWIG wrappers, allow us to tel nlopt_destroy and nlopt_copy to do something to the f_data pointers (e.g. free or duplicate them, respectively) */ typedef void* (*nlopt_munge)(void *p); NLOPT_EXTERN(void) nlopt_set_munge(nlopt_opt opt, nlopt_munge munge_on_destroy, nlopt_munge munge_on_copy); typedef void* (*nlopt_munge2)(void *p, void *data); NLOPT_EXTERN(void) nlopt_munge_data(nlopt_opt opt, nlopt_munge2 munge, void *data); /*************************** DEPRECATED API **************************/ /* The new "object-oriented" API is preferred, since it allows us to gracefully add new features and algorithm-specific options in a re-entrant way, and we can automatically assume reasonable defaults for unspecified parameters. */ /* Where possible (e.g. for gcc >= 3.1), enable a compiler warning for code that uses a deprecated function */ #if defined(__GNUC__) && (__GNUC__ > 3 || (__GNUC__==3 && __GNUC_MINOR__ > 0)) # define NLOPT_DEPRECATED __attribute__((deprecated)) #else # define NLOPT_DEPRECATED #endif typedef double (*nlopt_func_old)(int n, const double *x, double *gradient, /* NULL if not needed */ void *func_data); NLOPT_EXTERN(nlopt_result) nlopt_minimize( nlopt_algorithm algorithm, int n, nlopt_func_old f, void *f_data, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, /* out: minimum */ double minf_max, double ftol_rel, double ftol_abs, double xtol_rel, const double *xtol_abs, int maxeval, double maxtime) NLOPT_DEPRECATED; NLOPT_EXTERN(nlopt_result) nlopt_minimize_constrained( nlopt_algorithm algorithm, int n, nlopt_func_old f, void *f_data, int m, nlopt_func_old fc, void *fc_data, ptrdiff_t fc_datum_size, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, /* out: minimum */ double minf_max, double ftol_rel, double ftol_abs, double xtol_rel, const double *xtol_abs, int maxeval, double maxtime) NLOPT_DEPRECATED; NLOPT_EXTERN(nlopt_result) nlopt_minimize_econstrained( nlopt_algorithm algorithm, int n, nlopt_func_old f, void *f_data, int m, nlopt_func_old fc, void *fc_data, ptrdiff_t fc_datum_size, int p, nlopt_func_old h, void *h_data, ptrdiff_t h_datum_size, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, /* out: minimum */ double minf_max, double ftol_rel, double ftol_abs, double xtol_rel, const double *xtol_abs, double htol_rel, double htol_abs, int maxeval, double maxtime) NLOPT_DEPRECATED; NLOPT_EXTERN(void) nlopt_get_local_search_algorithm(nlopt_algorithm *deriv, nlopt_algorithm *nonderiv, int *maxeval) NLOPT_DEPRECATED; NLOPT_EXTERN(void) nlopt_set_local_search_algorithm(nlopt_algorithm deriv, nlopt_algorithm nonderiv, int maxeval) NLOPT_DEPRECATED; NLOPT_EXTERN(int) nlopt_get_stochastic_population(void) NLOPT_DEPRECATED; NLOPT_EXTERN(void) nlopt_set_stochastic_population(int pop) NLOPT_DEPRECATED; /*********************************************************************/ #ifdef __cplusplus } /* extern "C" */ #endif /* __cplusplus */ #endif nloptr/inst/include/nlopt.hpp0000644000176000001440000005113412336720325016074 0ustar ripleyusers/* Copyright (c) 2007-2011 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ // C++ style wrapper around NLopt API // nlopt.hpp is AUTOMATICALLY GENERATED from nlopt-in.hpp - edit the latter! #ifndef NLOPT_HPP #define NLOPT_HPP #include #include #include #include #include #include #include // convenience overloading for below (not in nlopt:: since has nlopt_ prefix) inline nlopt_result nlopt_get_initial_step(const nlopt_opt opt, double *dx) { return nlopt_get_initial_step(opt, (const double *) NULL, dx); } namespace nlopt { ////////////////////////////////////////////////////////////////////// // nlopt::* namespace versions of the C enumerated types // AUTOMATICALLY GENERATED, DO NOT EDIT // GEN_ENUMS_HERE enum algorithm { GN_DIRECT = 0, GN_DIRECT_L, GN_DIRECT_L_RAND, GN_DIRECT_NOSCAL, GN_DIRECT_L_NOSCAL, GN_DIRECT_L_RAND_NOSCAL, GN_ORIG_DIRECT, GN_ORIG_DIRECT_L, GD_STOGO, GD_STOGO_RAND, LD_LBFGS_NOCEDAL, LD_LBFGS, LN_PRAXIS, LD_VAR1, LD_VAR2, LD_TNEWTON, LD_TNEWTON_RESTART, LD_TNEWTON_PRECOND, LD_TNEWTON_PRECOND_RESTART, GN_CRS2_LM, GN_MLSL, GD_MLSL, GN_MLSL_LDS, GD_MLSL_LDS, LD_MMA, LN_COBYLA, LN_NEWUOA, LN_NEWUOA_BOUND, LN_NELDERMEAD, LN_SBPLX, LN_AUGLAG, LD_AUGLAG, LN_AUGLAG_EQ, LD_AUGLAG_EQ, LN_BOBYQA, GN_ISRES, AUGLAG, AUGLAG_EQ, G_MLSL, G_MLSL_LDS, LD_SLSQP, LD_CCSAQ, GN_ESCH, NUM_ALGORITHMS /* not an algorithm, just the number of them */ }; enum result { FAILURE = -1, /* generic failure code */ INVALID_ARGS = -2, OUT_OF_MEMORY = -3, ROUNDOFF_LIMITED = -4, FORCED_STOP = -5, SUCCESS = 1, /* generic success code */ STOPVAL_REACHED = 2, FTOL_REACHED = 3, XTOL_REACHED = 4, MAXEVAL_REACHED = 5, MAXTIME_REACHED = 6 }; // GEN_ENUMS_HERE ////////////////////////////////////////////////////////////////////// typedef nlopt_func func; // nlopt::func synoynm typedef nlopt_mfunc mfunc; // nlopt::mfunc synoynm // alternative to nlopt_func that takes std::vector // ... unfortunately requires a data copy typedef double (*vfunc)(const std::vector &x, std::vector &grad, void *data); ////////////////////////////////////////////////////////////////////// // NLopt-specific exceptions (corresponding to error codes): class roundoff_limited : public std::runtime_error { public: roundoff_limited() : std::runtime_error("nlopt roundoff-limited") {} }; class forced_stop : public std::runtime_error { public: forced_stop() : std::runtime_error("nlopt forced stop") {} }; ////////////////////////////////////////////////////////////////////// class opt { private: nlopt_opt o; void mythrow(nlopt_result ret) const { switch (ret) { case NLOPT_FAILURE: throw std::runtime_error("nlopt failure"); case NLOPT_OUT_OF_MEMORY: throw std::bad_alloc(); case NLOPT_INVALID_ARGS: throw std::invalid_argument("nlopt invalid argument"); case NLOPT_ROUNDOFF_LIMITED: throw roundoff_limited(); case NLOPT_FORCED_STOP: throw forced_stop(); default: break; } } typedef struct { opt *o; mfunc mf; func f; void *f_data; vfunc vf; nlopt_munge munge_destroy, munge_copy; // non-NULL for SWIG wrappers } myfunc_data; // free/destroy f_data in nlopt_destroy and nlopt_copy, respectively static void *free_myfunc_data(void *p) { myfunc_data *d = (myfunc_data *) p; if (d) { if (d->f_data && d->munge_destroy) d->munge_destroy(d->f_data); delete d; } return NULL; } static void *dup_myfunc_data(void *p) { myfunc_data *d = (myfunc_data *) p; if (d) { void *f_data; if (d->f_data && d->munge_copy) { f_data = d->munge_copy(d->f_data); if (!f_data) return NULL; } else f_data = d->f_data; myfunc_data *dnew = new myfunc_data; if (dnew) { *dnew = *d; dnew->f_data = f_data; } return (void*) dnew; } else return NULL; } // nlopt_func wrapper that catches exceptions static double myfunc(unsigned n, const double *x, double *grad, void *d_) { myfunc_data *d = reinterpret_cast(d_); try { return d->f(n, x, grad, d->f_data); } catch (std::bad_alloc&) { d->o->forced_stop_reason = NLOPT_OUT_OF_MEMORY; } catch (std::invalid_argument&) { d->o->forced_stop_reason = NLOPT_INVALID_ARGS; } catch (roundoff_limited&) { d->o->forced_stop_reason = NLOPT_ROUNDOFF_LIMITED; } catch (forced_stop&) { d->o->forced_stop_reason = NLOPT_FORCED_STOP; } catch (...) { d->o->forced_stop_reason = NLOPT_FAILURE; } d->o->force_stop(); // stop gracefully, opt::optimize will re-throw return HUGE_VAL; } // nlopt_mfunc wrapper that catches exceptions static void mymfunc(unsigned m, double *result, unsigned n, const double *x, double *grad, void *d_) { myfunc_data *d = reinterpret_cast(d_); try { d->mf(m, result, n, x, grad, d->f_data); return; } catch (std::bad_alloc&) { d->o->forced_stop_reason = NLOPT_OUT_OF_MEMORY; } catch (std::invalid_argument&) { d->o->forced_stop_reason = NLOPT_INVALID_ARGS; } catch (roundoff_limited&) { d->o->forced_stop_reason = NLOPT_ROUNDOFF_LIMITED; } catch (forced_stop&) { d->o->forced_stop_reason = NLOPT_FORCED_STOP; } catch (...) { d->o->forced_stop_reason = NLOPT_FAILURE; } d->o->force_stop(); // stop gracefully, opt::optimize will re-throw for (unsigned i = 0; i < m; ++i) result[i] = HUGE_VAL; } std::vector xtmp, gradtmp, gradtmp0; // scratch for myvfunc // nlopt_func wrapper, using std::vector static double myvfunc(unsigned n, const double *x, double *grad, void *d_){ myfunc_data *d = reinterpret_cast(d_); try { std::vector &xv = d->o->xtmp; if (n) std::memcpy(&xv[0], x, n * sizeof(double)); double val=d->vf(xv, grad ? d->o->gradtmp : d->o->gradtmp0, d->f_data); if (grad && n) { std::vector &gradv = d->o->gradtmp; std::memcpy(grad, &gradv[0], n * sizeof(double)); } return val; } catch (std::bad_alloc&) { d->o->forced_stop_reason = NLOPT_OUT_OF_MEMORY; } catch (std::invalid_argument&) { d->o->forced_stop_reason = NLOPT_INVALID_ARGS; } catch (roundoff_limited&) { d->o->forced_stop_reason = NLOPT_ROUNDOFF_LIMITED; } catch (forced_stop&) { d->o->forced_stop_reason = NLOPT_FORCED_STOP; } catch (...) { d->o->forced_stop_reason = NLOPT_FAILURE; } d->o->force_stop(); // stop gracefully, opt::optimize will re-throw return HUGE_VAL; } void alloc_tmp() { if (xtmp.size() != nlopt_get_dimension(o)) { xtmp = std::vector(nlopt_get_dimension(o)); gradtmp = std::vector(nlopt_get_dimension(o)); } } result last_result; double last_optf; nlopt_result forced_stop_reason; public: // Constructors etc. opt() : o(NULL), xtmp(0), gradtmp(0), gradtmp0(0), last_result(nlopt::FAILURE), last_optf(HUGE_VAL), forced_stop_reason(NLOPT_FORCED_STOP) {} ~opt() { nlopt_destroy(o); } opt(algorithm a, unsigned n) : o(nlopt_create(nlopt_algorithm(a), n)), xtmp(0), gradtmp(0), gradtmp0(0), last_result(nlopt::FAILURE), last_optf(HUGE_VAL), forced_stop_reason(NLOPT_FORCED_STOP) { if (!o) throw std::bad_alloc(); nlopt_set_munge(o, free_myfunc_data, dup_myfunc_data); } opt(const opt& f) : o(nlopt_copy(f.o)), xtmp(f.xtmp), gradtmp(f.gradtmp), gradtmp0(0), last_result(f.last_result), last_optf(f.last_optf), forced_stop_reason(f.forced_stop_reason) { if (f.o && !o) throw std::bad_alloc(); } opt& operator=(opt const& f) { if (this == &f) return *this; // self-assignment nlopt_destroy(o); o = nlopt_copy(f.o); if (f.o && !o) throw std::bad_alloc(); xtmp = f.xtmp; gradtmp = f.gradtmp; last_result = f.last_result; last_optf = f.last_optf; forced_stop_reason = f.forced_stop_reason; return *this; } // Do the optimization: result optimize(std::vector &x, double &opt_f) { if (o && nlopt_get_dimension(o) != x.size()) throw std::invalid_argument("dimension mismatch"); forced_stop_reason = NLOPT_FORCED_STOP; nlopt_result ret = nlopt_optimize(o, x.empty() ? NULL : &x[0], &opt_f); last_result = result(ret); last_optf = opt_f; if (ret == NLOPT_FORCED_STOP) mythrow(forced_stop_reason); mythrow(ret); return last_result; } // variant mainly useful for SWIG wrappers: std::vector optimize(const std::vector &x0) { std::vector x(x0); last_result = optimize(x, last_optf); return x; } result last_optimize_result() const { return last_result; } double last_optimum_value() const { return last_optf; } // accessors: algorithm get_algorithm() const { if (!o) throw std::runtime_error("uninitialized nlopt::opt"); return algorithm(nlopt_get_algorithm(o)); } const char *get_algorithm_name() const { if (!o) throw std::runtime_error("uninitialized nlopt::opt"); return nlopt_algorithm_name(nlopt_get_algorithm(o)); } unsigned get_dimension() const { if (!o) throw std::runtime_error("uninitialized nlopt::opt"); return nlopt_get_dimension(o); } // Set the objective function void set_min_objective(func f, void *f_data) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_set_min_objective(o, myfunc, d)); // d freed via o } void set_min_objective(vfunc vf, void *f_data) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = NULL; d->f_data = f_data; d->mf = NULL; d->vf = vf; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_set_min_objective(o, myvfunc, d)); // d freed via o alloc_tmp(); } void set_max_objective(func f, void *f_data) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_set_max_objective(o, myfunc, d)); // d freed via o } void set_max_objective(vfunc vf, void *f_data) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = NULL; d->f_data = f_data; d->mf = NULL; d->vf = vf; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_set_max_objective(o, myvfunc, d)); // d freed via o alloc_tmp(); } // for internal use in SWIG wrappers -- variant that // takes ownership of f_data, with munging for destroy/copy void set_min_objective(func f, void *f_data, nlopt_munge md, nlopt_munge mc) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = md; d->munge_copy = mc; mythrow(nlopt_set_min_objective(o, myfunc, d)); // d freed via o } void set_max_objective(func f, void *f_data, nlopt_munge md, nlopt_munge mc) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = md; d->munge_copy = mc; mythrow(nlopt_set_max_objective(o, myfunc, d)); // d freed via o } // Nonlinear constraints: void remove_inequality_constraints() { nlopt_result ret = nlopt_remove_inequality_constraints(o); mythrow(ret); } void add_inequality_constraint(func f, void *f_data, double tol=0) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_add_inequality_constraint(o, myfunc, d, tol)); } void add_inequality_constraint(vfunc vf, void *f_data, double tol=0) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = NULL; d->f_data = f_data; d->mf = NULL; d->vf = vf; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_add_inequality_constraint(o, myvfunc, d, tol)); alloc_tmp(); } void add_inequality_mconstraint(mfunc mf, void *f_data, const std::vector &tol) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->mf = mf; d->f_data = f_data; d->f = NULL; d->vf = NULL; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_add_inequality_mconstraint(o, tol.size(), mymfunc, d, tol.empty() ? NULL : &tol[0])); } void remove_equality_constraints() { nlopt_result ret = nlopt_remove_equality_constraints(o); mythrow(ret); } void add_equality_constraint(func f, void *f_data, double tol=0) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_add_equality_constraint(o, myfunc, d, tol)); } void add_equality_constraint(vfunc vf, void *f_data, double tol=0) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = NULL; d->f_data = f_data; d->mf = NULL; d->vf = vf; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_add_equality_constraint(o, myvfunc, d, tol)); alloc_tmp(); } void add_equality_mconstraint(mfunc mf, void *f_data, const std::vector &tol) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->mf = mf; d->f_data = f_data; d->f = NULL; d->vf = NULL; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_add_equality_mconstraint(o, tol.size(), mymfunc, d, tol.empty() ? NULL : &tol[0])); } // For internal use in SWIG wrappers (see also above) void add_inequality_constraint(func f, void *f_data, nlopt_munge md, nlopt_munge mc, double tol=0) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = md; d->munge_copy = mc; mythrow(nlopt_add_inequality_constraint(o, myfunc, d, tol)); } void add_equality_constraint(func f, void *f_data, nlopt_munge md, nlopt_munge mc, double tol=0) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = md; d->munge_copy = mc; mythrow(nlopt_add_equality_constraint(o, myfunc, d, tol)); } void add_inequality_mconstraint(mfunc mf, void *f_data, nlopt_munge md, nlopt_munge mc, const std::vector &tol) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->mf = mf; d->f_data = f_data; d->f = NULL; d->vf = NULL; d->munge_destroy = md; d->munge_copy = mc; mythrow(nlopt_add_inequality_mconstraint(o, tol.size(), mymfunc, d, tol.empty() ? NULL : &tol[0])); } void add_equality_mconstraint(mfunc mf, void *f_data, nlopt_munge md, nlopt_munge mc, const std::vector &tol) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->mf = mf; d->f_data = f_data; d->f = NULL; d->vf = NULL; d->munge_destroy = md; d->munge_copy = mc; mythrow(nlopt_add_equality_mconstraint(o, tol.size(), mymfunc, d, tol.empty() ? NULL : &tol[0])); } #define NLOPT_GETSET_VEC(name) \ void set_##name(double val) { \ mythrow(nlopt_set_##name##1(o, val)); \ } \ void get_##name(std::vector &v) const { \ if (o && nlopt_get_dimension(o) != v.size()) \ throw std::invalid_argument("dimension mismatch"); \ mythrow(nlopt_get_##name(o, v.empty() ? NULL : &v[0])); \ } \ std::vector get_##name() const { \ if (!o) throw std::runtime_error("uninitialized nlopt::opt"); \ std::vector v(nlopt_get_dimension(o)); \ get_##name(v); \ return v; \ } \ void set_##name(const std::vector &v) { \ if (o && nlopt_get_dimension(o) != v.size()) \ throw std::invalid_argument("dimension mismatch"); \ mythrow(nlopt_set_##name(o, v.empty() ? NULL : &v[0])); \ } NLOPT_GETSET_VEC(lower_bounds) NLOPT_GETSET_VEC(upper_bounds) // stopping criteria: #define NLOPT_GETSET(T, name) \ T get_##name() const { \ if (!o) throw std::runtime_error("uninitialized nlopt::opt"); \ return nlopt_get_##name(o); \ } \ void set_##name(T name) { \ mythrow(nlopt_set_##name(o, name)); \ } NLOPT_GETSET(double, stopval) NLOPT_GETSET(double, ftol_rel) NLOPT_GETSET(double, ftol_abs) NLOPT_GETSET(double, xtol_rel) NLOPT_GETSET_VEC(xtol_abs) NLOPT_GETSET(int, maxeval) NLOPT_GETSET(double, maxtime) NLOPT_GETSET(int, force_stop) void force_stop() { set_force_stop(1); } // algorithm-specific parameters: void set_local_optimizer(const opt &lo) { nlopt_result ret = nlopt_set_local_optimizer(o, lo.o); mythrow(ret); } NLOPT_GETSET(unsigned, population) NLOPT_GETSET(unsigned, vector_storage) NLOPT_GETSET_VEC(initial_step) void set_default_initial_step(const std::vector &x) { nlopt_result ret = nlopt_set_default_initial_step(o, x.empty() ? NULL : &x[0]); mythrow(ret); } void get_initial_step(const std::vector &x, std::vector &dx) const { if (o && (nlopt_get_dimension(o) != x.size() || nlopt_get_dimension(o) != dx.size())) throw std::invalid_argument("dimension mismatch"); nlopt_result ret = nlopt_get_initial_step(o, x.empty() ? NULL : &x[0], dx.empty() ? NULL : &dx[0]); mythrow(ret); } std::vector get_initial_step_(const std::vector &x) const { if (!o) throw std::runtime_error("uninitialized nlopt::opt"); std::vector v(nlopt_get_dimension(o)); get_initial_step(x, v); return v; } }; #undef NLOPT_GETSET #undef NLOPT_GETSET_VEC ////////////////////////////////////////////////////////////////////// inline void srand(unsigned long seed) { nlopt_srand(seed); } inline void srand_time() { nlopt_srand_time(); } inline void version(int &major, int &minor, int &bugfix) { nlopt_version(&major, &minor, &bugfix); } inline int version_major() { int major, minor, bugfix; nlopt_version(&major, &minor, &bugfix); return major; } inline int version_minor() { int major, minor, bugfix; nlopt_version(&major, &minor, &bugfix); return minor; } inline int version_bugfix() { int major, minor, bugfix; nlopt_version(&major, &minor, &bugfix); return bugfix; } inline const char *algorithm_name(algorithm a) { return nlopt_algorithm_name(nlopt_algorithm(a)); } ////////////////////////////////////////////////////////////////////// } // namespace nlopt #endif /* NLOPT_HPP */ nloptr/inst/include/nloptrAPI.h0000644000176000001440000004377313262730360016261 0ustar ripleyusers/* * Copyright (C) 2017 Jelmer Ypma. All Rights Reserved. * This code is published under the L-GPL. * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU Lesser General Public License as published * by the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU Lesser General Public License for more details. * * You should have received a copy of the GNU Lesser General Public License * along with this program. If not, see . * * File: nloptrAPI.h * Author: Jelmer Ypma * Date: 3 October 2017 * * This file provides an API for calling internal NLopt code from C within * R packages. The C functions that are registered in init_nloptr.c can be * accessed by external R packages. * * 03/10/2017: Initial version exposing nlopt_version. */ #ifndef __NLOPTRAPI_H__ #define __NLOPTRAPI_H__ #include #include #include #include "nlopt.h" /* * C functions can be exposed using the following template: * * RET_TYPE FUNCNAME(ARGTYPE_1 ARGNAME 1, ARGTYPE_2 ARGNAME_2) * { * static RET_TYPE(*fun)(ARGTYPE_1, ARGTYPE_2) = NULL; * if (fun == NULL) fun = (RET_TYPE(*)(ARGTYPE_1, ARGTYPE_2)) R_GetCCallable("nloptr","FUNCNAME"); * return fun(ARGNAME_1, ARGNAME_2); * } * */ NLOPT_EXTERN(const char *) nlopt_algorithm_name(nlopt_algorithm a) { static const char *(*fun)(nlopt_algorithm) = NULL; if (fun == NULL) fun = (const char *(*)(nlopt_algorithm)) R_GetCCallable("nloptr","nlopt_algorithm_name"); return fun(a); } NLOPT_EXTERN(void) nlopt_srand(unsigned long seed) { static void(*fun)(unsigned long) = NULL; if (fun == NULL) fun = (void(*)(unsigned long)) R_GetCCallable("nloptr","nlopt_srand"); return fun(seed); } NLOPT_EXTERN(void) nlopt_srand_time(void) { static void(*fun)(void) = NULL; if (fun == NULL) fun = (void(*)(void)) R_GetCCallable("nloptr","nlopt_srand_time"); return fun(); } NLOPT_EXTERN(void) nlopt_version(int *major, int *minor, int *bugfix) { static void(*fun)(int *, int *, int *) = NULL; if (fun == NULL) fun = (void(*)(int *, int *, int *)) R_GetCCallable("nloptr","nlopt_version"); return fun(major, minor, major); } NLOPT_EXTERN(nlopt_opt) nlopt_create(nlopt_algorithm algorithm, unsigned n) { static nlopt_opt(*fun)(nlopt_algorithm, unsigned) = NULL; if (fun == NULL) fun = (nlopt_opt(*)(nlopt_algorithm, unsigned)) R_GetCCallable("nloptr","nlopt_create"); return fun(algorithm, n); } NLOPT_EXTERN(void) nlopt_destroy(nlopt_opt opt) { static void(*fun)(nlopt_opt) = NULL; if (fun == NULL) fun = (void(*)(nlopt_opt)) R_GetCCallable("nloptr","nlopt_destroy"); return fun(opt); } NLOPT_EXTERN(nlopt_opt) nlopt_copy(const nlopt_opt opt) { static nlopt_opt(*fun)(const nlopt_opt) = NULL; if (fun == NULL) fun = (nlopt_opt(*)(const nlopt_opt)) R_GetCCallable("nloptr","nlopt_copy"); return fun(opt); } NLOPT_EXTERN(nlopt_result) nlopt_optimize(nlopt_opt opt, double *x, double *opt_f) { static nlopt_result(*fun)(nlopt_opt, double *, double *) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, double *, double *)) R_GetCCallable("nloptr","nlopt_optimize"); return fun(opt, x, opt_f); } NLOPT_EXTERN(nlopt_result) nlopt_set_min_objective(nlopt_opt opt, nlopt_func f, void *f_data) { static nlopt_result(*fun)(nlopt_opt, nlopt_func, void *) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, nlopt_func, void *)) R_GetCCallable("nloptr","nlopt_set_min_objective"); return fun(opt, f, f_data); } NLOPT_EXTERN(nlopt_result) nlopt_set_max_objective(nlopt_opt opt, nlopt_func f, void *f_data) { static nlopt_result(*fun)(nlopt_opt, nlopt_func, void *) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, nlopt_func, void *)) R_GetCCallable("nloptr","nlopt_set_max_objective"); return fun(opt, f, f_data); } NLOPT_EXTERN(nlopt_result) nlopt_set_precond_min_objective(nlopt_opt opt, nlopt_func f, nlopt_precond pre, void *f_data) { static nlopt_result(*fun)(nlopt_opt, nlopt_func, nlopt_precond, void *) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, nlopt_func, nlopt_precond, void *)) R_GetCCallable("nloptr","nlopt_set_precond_min_objective"); return fun(opt, f, pre, f_data); } NLOPT_EXTERN(nlopt_result) nlopt_set_precond_max_objective(nlopt_opt opt, nlopt_func f, nlopt_precond pre, void *f_data) { static nlopt_result(*fun)(nlopt_opt, nlopt_func, nlopt_precond, void *) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, nlopt_func, nlopt_precond, void *)) R_GetCCallable("nloptr","nlopt_set_precond_max_objective"); return fun(opt, f, pre, f_data); } NLOPT_EXTERN(nlopt_algorithm) nlopt_get_algorithm(const nlopt_opt opt) { static nlopt_algorithm(*fun)(const nlopt_opt) = NULL; if (fun == NULL) fun = (nlopt_algorithm(*)(const nlopt_opt)) R_GetCCallable("nloptr","nlopt_get_algorithm"); return fun(opt); } NLOPT_EXTERN(unsigned) nlopt_get_dimension(const nlopt_opt opt) { static unsigned(*fun)(const nlopt_opt) = NULL; if (fun == NULL) fun = (unsigned(*)(const nlopt_opt)) R_GetCCallable("nloptr","nlopt_get_dimension"); return fun(opt); } /* constraints: */ NLOPT_EXTERN(nlopt_result) nlopt_set_lower_bounds(nlopt_opt opt, const double *lb) { static nlopt_result(*fun)(nlopt_opt, const double *) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, const double *)) R_GetCCallable("nloptr","nlopt_set_lower_bounds"); return fun(opt, lb); } NLOPT_EXTERN(nlopt_result) nlopt_set_lower_bounds1(nlopt_opt opt, double lb) { static nlopt_result(*fun)(nlopt_opt, double) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, double)) R_GetCCallable("nloptr","nlopt_set_lower_bounds1"); return fun(opt, lb); } NLOPT_EXTERN(nlopt_result) nlopt_get_lower_bounds(const nlopt_opt opt, double *lb) { static nlopt_result(*fun)(const nlopt_opt, double *) = NULL; if (fun == NULL) fun = (nlopt_result(*)(const nlopt_opt, double *)) R_GetCCallable("nloptr","nlopt_get_lower_bounds"); return fun(opt, lb); } NLOPT_EXTERN(nlopt_result) nlopt_set_upper_bounds(nlopt_opt opt, const double *ub) { static nlopt_result(*fun)(nlopt_opt, const double *) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, const double *)) R_GetCCallable("nloptr","nlopt_set_upper_bounds"); return fun(opt, ub); } NLOPT_EXTERN(nlopt_result) nlopt_set_upper_bounds1(nlopt_opt opt, double ub) { static nlopt_result(*fun)(nlopt_opt, double) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, double)) R_GetCCallable("nloptr","nlopt_set_upper_bounds1"); return fun(opt, ub); } NLOPT_EXTERN(nlopt_result) nlopt_get_upper_bounds(const nlopt_opt opt, double *ub) { static nlopt_result(*fun)(const nlopt_opt, double *) = NULL; if (fun == NULL) fun = (nlopt_result(*)(const nlopt_opt, double *)) R_GetCCallable("nloptr","nlopt_get_upper_bounds"); return fun(opt, ub); } NLOPT_EXTERN(nlopt_result) nlopt_remove_inequality_constraints(nlopt_opt opt) { static nlopt_result(*fun)(nlopt_opt) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt)) R_GetCCallable("nloptr","nlopt_remove_inequality_constraints"); return fun(opt); } NLOPT_EXTERN(nlopt_result) nlopt_add_inequality_constraint(nlopt_opt opt, nlopt_func fc, void *fc_data, double tol) { static nlopt_result(*fun)(nlopt_opt, nlopt_func, void *, double) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, nlopt_func, void *, double)) R_GetCCallable("nloptr","nlopt_add_inequality_constraint"); return fun(opt, fc, fc_data, tol); } NLOPT_EXTERN(nlopt_result) nlopt_add_precond_inequality_constraint( nlopt_opt opt, nlopt_func fc, nlopt_precond pre, void *fc_data, double tol) { static nlopt_result(*fun)(nlopt_opt, nlopt_func, nlopt_precond, void *, double) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, nlopt_func, nlopt_precond, void *, double)) R_GetCCallable("nloptr","nlopt_add_precond_inequality_constraint"); return fun(opt, fc, pre, fc_data, tol); } NLOPT_EXTERN(nlopt_result) nlopt_add_inequality_mconstraint(nlopt_opt opt, unsigned m, nlopt_mfunc fc, void *fc_data, const double *tol) { static nlopt_result(*fun)(nlopt_opt, unsigned, nlopt_mfunc, void *, const double *) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, unsigned, nlopt_mfunc, void *, const double *)) R_GetCCallable("nloptr","nlopt_add_inequality_mconstraint"); return fun(opt, m, fc, fc_data, tol); } NLOPT_EXTERN(nlopt_result) nlopt_remove_equality_constraints(nlopt_opt opt) { static nlopt_result(*fun)(nlopt_opt) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt)) R_GetCCallable("nloptr","nlopt_remove_equality_constraints"); return fun(opt); } NLOPT_EXTERN(nlopt_result) nlopt_add_equality_constraint(nlopt_opt opt, nlopt_func h, void *h_data, double tol) { static nlopt_result(*fun)(nlopt_opt, nlopt_func, void *, double) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, nlopt_func, void *, double)) R_GetCCallable("nloptr","nlopt_add_equality_constraint"); return fun(opt, h, h_data, tol); } NLOPT_EXTERN(nlopt_result) nlopt_add_precond_equality_constraint( nlopt_opt opt, nlopt_func h, nlopt_precond pre, void *h_data, double tol) { static nlopt_result(*fun)(nlopt_opt, nlopt_func, nlopt_precond, void *, double) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, nlopt_func, nlopt_precond, void *, double)) R_GetCCallable("nloptr","nlopt_add_precond_equality_constraint"); return fun(opt, h, pre, h_data, tol); } NLOPT_EXTERN(nlopt_result) nlopt_add_equality_mconstraint(nlopt_opt opt, unsigned m, nlopt_mfunc h, void *h_data, const double *tol) { static nlopt_result(*fun)(nlopt_opt, unsigned, nlopt_mfunc, void *, const double *) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, unsigned, nlopt_mfunc, void *, const double *)) R_GetCCallable("nloptr","nlopt_add_equality_mconstraint"); return fun(opt, m, h, h_data, tol); } /* stopping criteria: */ NLOPT_EXTERN(nlopt_result) nlopt_set_stopval(nlopt_opt opt, double stopval) { static nlopt_result(*fun)(nlopt_opt, double) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, double)) R_GetCCallable("nloptr","nlopt_set_stopval"); return fun(opt, stopval); } NLOPT_EXTERN(double) nlopt_get_stopval(const nlopt_opt opt) { static double(*fun)(const nlopt_opt) = NULL; if (fun == NULL) fun = (double(*)(const nlopt_opt)) R_GetCCallable("nloptr","nlopt_get_stopval"); return fun(opt); } NLOPT_EXTERN(nlopt_result) nlopt_set_ftol_rel(nlopt_opt opt, double tol) { static nlopt_result(*fun)(nlopt_opt, double) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, double)) R_GetCCallable("nloptr","nlopt_set_ftol_rel"); return fun(opt, tol); } NLOPT_EXTERN(double) nlopt_get_ftol_rel(const nlopt_opt opt) { static double(*fun)(const nlopt_opt) = NULL; if (fun == NULL) fun = (double(*)(const nlopt_opt)) R_GetCCallable("nloptr","nlopt_get_ftol_rel"); return fun(opt); } NLOPT_EXTERN(nlopt_result) nlopt_set_ftol_abs(nlopt_opt opt, double tol) { static nlopt_result(*fun)(nlopt_opt, double) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, double)) R_GetCCallable("nloptr","nlopt_set_ftol_abs"); return fun(opt, tol); } NLOPT_EXTERN(double) nlopt_get_ftol_abs(const nlopt_opt opt) { static double(*fun)(const nlopt_opt) = NULL; if (fun == NULL) fun = (double(*)(const nlopt_opt)) R_GetCCallable("nloptr","nlopt_get_ftol_abs"); return fun(opt); } NLOPT_EXTERN(nlopt_result) nlopt_set_xtol_rel(nlopt_opt opt, double tol) { static nlopt_result(*fun)(nlopt_opt, double) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, double)) R_GetCCallable("nloptr","nlopt_set_xtol_rel"); return fun(opt, tol); } NLOPT_EXTERN(double) nlopt_get_xtol_rel(const nlopt_opt opt) { static double(*fun)(const nlopt_opt) = NULL; if (fun == NULL) fun = (double(*)(const nlopt_opt)) R_GetCCallable("nloptr","nlopt_get_xtol_rel"); return fun(opt); } NLOPT_EXTERN(nlopt_result) nlopt_set_xtol_abs1(nlopt_opt opt, double tol) { static nlopt_result(*fun)(nlopt_opt, double) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, double)) R_GetCCallable("nloptr","nlopt_set_xtol_abs1"); return fun(opt, tol); } NLOPT_EXTERN(nlopt_result) nlopt_set_xtol_abs(nlopt_opt opt, const double *tol) { static nlopt_result(*fun)(nlopt_opt, const double *) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, const double *)) R_GetCCallable("nloptr","nlopt_set_xtol_abs"); return fun(opt, tol); } NLOPT_EXTERN(nlopt_result) nlopt_get_xtol_abs(const nlopt_opt opt, double *tol) { static nlopt_result(*fun)(nlopt_opt, double *) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, double *)) R_GetCCallable("nloptr","nlopt_get_xtol_abs"); return fun(opt, tol); } NLOPT_EXTERN(nlopt_result) nlopt_set_maxeval(nlopt_opt opt, int maxeval) { static nlopt_result(*fun)(nlopt_opt, int) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, int)) R_GetCCallable("nloptr","nlopt_set_maxeval"); return fun(opt, maxeval); } NLOPT_EXTERN(int) nlopt_get_maxeval(const nlopt_opt opt) { static int(*fun)(const nlopt_opt) = NULL; if (fun == NULL) fun = (int(*)(const nlopt_opt)) R_GetCCallable("nloptr","nlopt_get_maxeval"); return fun(opt); } NLOPT_EXTERN(nlopt_result) nlopt_set_maxtime(nlopt_opt opt, double maxtime) { static nlopt_result(*fun)(nlopt_opt, double) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, double)) R_GetCCallable("nloptr","nlopt_set_maxtime"); return fun(opt, maxtime); } NLOPT_EXTERN(double) nlopt_get_maxtime(const nlopt_opt opt) { static double(*fun)(nlopt_opt) = NULL; if (fun == NULL) fun = (double(*)(nlopt_opt)) R_GetCCallable("nloptr","nlopt_get_maxtime"); return fun(opt); } NLOPT_EXTERN(nlopt_result) nlopt_force_stop(nlopt_opt opt) { static nlopt_result(*fun)(nlopt_opt) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt)) R_GetCCallable("nloptr","nlopt_force_stop"); return fun(opt); } NLOPT_EXTERN(nlopt_result) nlopt_set_force_stop(nlopt_opt opt, int val) { static nlopt_result(*fun)(nlopt_opt, int) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, int)) R_GetCCallable("nloptr","nlopt_set_force_stop"); return fun(opt, val); } NLOPT_EXTERN(int) nlopt_get_force_stop(const nlopt_opt opt) { static int(*fun)(const nlopt_opt) = NULL; if (fun == NULL) fun = (int(*)(const nlopt_opt)) R_GetCCallable("nloptr","nlopt_get_force_stop"); return fun(opt); } /* more algorithm-specific parameters */ NLOPT_EXTERN(nlopt_result) nlopt_set_local_optimizer(nlopt_opt opt, const nlopt_opt local_opt) { static nlopt_result(*fun)(nlopt_opt, const nlopt_opt) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, const nlopt_opt)) R_GetCCallable("nloptr","nlopt_set_local_optimizer"); return fun(opt, local_opt); } NLOPT_EXTERN(nlopt_result) nlopt_set_population(nlopt_opt opt, unsigned pop) { static nlopt_result(*fun)(nlopt_opt, unsigned) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, unsigned)) R_GetCCallable("nloptr","nlopt_set_population"); return fun(opt, pop); } NLOPT_EXTERN(unsigned) nlopt_get_population(const nlopt_opt opt) { static unsigned(*fun)(const nlopt_opt) = NULL; if (fun == NULL) fun = (unsigned(*)(const nlopt_opt)) R_GetCCallable("nloptr","nlopt_get_population"); return fun(opt); } NLOPT_EXTERN(nlopt_result) nlopt_set_vector_storage(nlopt_opt opt, unsigned dim) { static nlopt_result(*fun)(nlopt_opt, unsigned) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, unsigned)) R_GetCCallable("nloptr","nlopt_set_vector_storage"); return fun(opt, dim); } NLOPT_EXTERN(unsigned) nlopt_get_vector_storage(const nlopt_opt opt) { static unsigned(*fun)(const nlopt_opt) = NULL; if (fun == NULL) fun = (unsigned(*)(const nlopt_opt)) R_GetCCallable("nloptr","nlopt_get_vector_storage"); return fun(opt); } NLOPT_EXTERN(nlopt_result) nlopt_set_default_initial_step(nlopt_opt opt, const double *x) { static nlopt_result(*fun)(nlopt_opt, const double *) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, const double *)) R_GetCCallable("nloptr","nlopt_set_default_initial_step"); return fun(opt, x); } NLOPT_EXTERN(nlopt_result) nlopt_set_initial_step(nlopt_opt opt, const double *dx) { static nlopt_result(*fun)(nlopt_opt, const double *) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, const double *)) R_GetCCallable("nloptr","nlopt_set_initial_step"); return fun(opt, dx); } NLOPT_EXTERN(nlopt_result) nlopt_set_initial_step1(nlopt_opt opt, double dx) { static nlopt_result(*fun)(nlopt_opt, double) = NULL; if (fun == NULL) fun = (nlopt_result(*)(nlopt_opt, double)) R_GetCCallable("nloptr","nlopt_set_initial_step1"); return fun(opt, dx); } NLOPT_EXTERN(nlopt_result) nlopt_get_initial_step(const nlopt_opt opt, const double *x, double *dx) { static nlopt_result(*fun)(const nlopt_opt, const double *, double *) = NULL; if (fun == NULL) fun = (nlopt_result(*)(const nlopt_opt, const double *, double *)) R_GetCCallable("nloptr","nlopt_get_initial_step"); return fun(opt, x, dx); } #endif /* __NLOPTRAPI_H__ */ nloptr/configure.ac0000644000176000001440000001334313355070622014115 0ustar ripleyusers## -*- mode: autoconf; autoconf-indentation: 4; -*- ## ## nloptr configure.ac ## ## nloptr -- R interface to NLopt ## ## Copyright (C) 2014 Dirk Eddelbuettel ## ## This file is licensed under the GPL-2 or later just like most of my ## Open Source code, and is granted an exemption (should it be needed) ## for inclusion into nloptr # require at least autoconf 2.61 AC_PREREQ(2.61) # Process this file with autoconf to produce a configure script. AC_INIT([nloptr], [1.0.1]) ## consider command-line options if given ## AC_ARG_WITH([nlopt-cflags], AC_HELP_STRING([--with-nlopt-cflags=CFLAGS], [Supply compiler flags such as the location of NLopt header files]), [nlopt_cflags=$withval], [nlopt_cflags=""]) AC_ARG_WITH([nlopt-libs], AC_HELP_STRING([--with-nlopt-libs=LIBS], [Supply the linker flags such as the location and name of NLopt libraries]), [nlopt_libs=$withval], [nlopt_libs=""]) ## Set R_HOME, respecting an environment variable if set : ${R_HOME=$(R RHOME)} if test -z "${R_HOME}"; then AC_MSG_ERROR([Could not determine R_HOME.]) fi ## Get R compilers and flags. ## Add user-supplied flags to flags specified by R if they have been set. CC=$("${R_HOME}/bin/R" CMD config CC) CFLAGS="$("${R_HOME}/bin/R" CMD config CFLAGS) ${CFLAGS}" CPP=$("${R_HOME}/bin/R" CMD config CPP) CPPFLAGS="$("${R_HOME}/bin/R" CMD config CPPFLAGS) ${CPPFLAGS}" CXX=$("${R_HOME}/bin/R" CMD config CXX) CXXFLAGS="$("${R_HOME}/bin/R" CMD config CXXFLAGS) ${CXXFLAGS}" CXXCPP=$("${R_HOME}/bin/R" CMD config CXXCPP) # We are using C++ AC_LANG(C++) AC_REQUIRE_CPP AC_PROG_CXX ## check for pkg-config AC_DEFUN([AC_PROG_PKGCONFIG], [AC_CHECK_PROG(PKGCONFIG,pkg-config,yes)]) AC_PROG_PKGCONFIG ## default to assuming no sufficient NLopt library has been found nlopt_good="no" nlopt_cflags="" nlopt_libs="" ## also use pkg-config to check for NLopt ## if test x"${nlopt_libs}" == x""; then if test x"${PKGCONFIG}" == x"yes"; then ## check via pkg-config for nlopt if pkg-config --exists nlopt; then ## obtain cflags and obtain libs nlopt_cflags=$(pkg-config --cflags nlopt) nlopt_libs=$(pkg-config --libs nlopt) nlopt_good="yes" fi fi fi ## And make sure these flags are used for the test below. CPPFLAGS="${nlopt_cflags} ${CPPFLAGS}" CXXFLAGS="${nlopt_cflags} ${CXXFLAGS}" ## check for headers Debian has in libhiredis-dev AC_MSG_NOTICE([Now testing for NLopt header file.]) nlopt_header=nlopt.h AC_CHECK_HEADER([$nlopt_header], [nlopt_good="yes"], # may want to check for minimal version here too [nlopt_good="no"], []) ## Check for NLopt version number using pkg-config. # Perform this check after the header check, # because the correct header with the wrong # version, does not work. if test x"${nlopt_good}" = x"yes"; then AC_MSG_NOTICE([Now testing for NLopt versison number.]) if test x"${PKGCONFIG}" == x"yes"; then ## check via pkg-config for version number of nlopt if pkg-config --exists nlopt; then ## obtain version number nlopt_version=$(pkg-config --modversion nlopt) case ${nlopt_version} in 1.*|2.0.*|2.1.*|2.2.*|2.3.*) AC_MSG_WARN([Only NLopt version 2.4.0 or greater can be used with nloptr.]) nlopt_good="no" ;; esac fi else AC_MSG_WARN([Could not test version of NLopt because pkg-config is not installed. Make sure the version number of NLopt is 2.4.0 or greater. Otherwise nloptr can not be used.]) fi fi ## in case neither of the two methods above worked, build NLopt locally ## from included source files. if test x"${nlopt_good}" = x"no"; then AC_MSG_NOTICE([Need to configure and build NLopt]) ## Libraries necessary to link with NLopt NLOPTR_LIBS="-lm $(pwd)/src/nlopt_src/lib/libnlopt_cxx.a" ## Necessary Include dirs NLOPTR_INCL="-I$(pwd)/src/nlopt_src/include" ## Get flags that have been re-defined above ## to test whether NLopt headers are present. #NLOPT_CPPFLAGS=$("${R_HOME}/bin/R" CMD config CPPFLAGS) #NLOPT_CXXFLAGS=$("${R_HOME}/bin/R" CMD config CXXFLAGS) ## Compile NLopt source code and clean up ## --prefix="`pwd`", which is the directory we want to ## install in, after we changed our current directory AC_MSG_NOTICE([Starting to install library to $(pwd)/src/nlopt_src]) (cd src/nlopt_src; \ ./configure --prefix="$(pwd)" --enable-shared --enable-static --without-octave \ --without-matlab --without-guile --without-python --with-cxx \ CC="${CC}" CFLAGS="${CFLAGS}" CPP="${CPP}" \ CPPFLAGS="${NLOPT_CPPFLAGS}" CXX="${CXX}" \ CXXFLAGS="${NLOPT_CXXFLAGS}" CXXCPP="${CXXCPP}" && \ make && make install && rm -rf .libs) || exit 1 AC_MSG_NOTICE([Done installing library to $(pwd)/src/nlopt_src]) ## Copy header files to inst/include such that they are available from R. mkdir -p inst/include cp src/nlopt_src/include/* inst/include ## Store compiler and linker flags makevars_nlopt_cflags="${CFLAGS} ${NLOPTR_INCL}" makevars_nlopt_libs="${NLOPTR_LIBS}" nlopt_libs="-lm" else AC_MSG_NOTICE([Suitable NLopt library found.]) fi ## could add a test of building a three-liner here ## now use all these AC_SUBST([MAKEVARS_PKG_CFLAGS],["${PKG_CFLAGS} $nlopt_cflags $makevars_nlopt_cflags"]) AC_SUBST([MAKEVARS_PKG_LIBS],["${PKG_LIBS} $nlopt_libs $makevars_nlopt_libs"]) AC_SUBST([PKG_CFLAGS],["${PKG_CFLAGS} $nlopt_cflags"]) AC_SUBST([PKG_LIBS],["${PKG_LIBS} $nlopt_libs"]) AC_CONFIG_FILES([src/Makevars R/PkgFlags.R]) AC_OUTPUT nloptr/tests/0000755000176000001440000000000012553776710012777 5ustar ripleyusersnloptr/tests/testthat/0000755000176000001440000000000013265444476014641 5ustar ripleyusersnloptr/tests/testthat/test-C-api.R0000644000176000001440000001015413262741611016657 0ustar ripleyusers# Copyright (C) 2010-17 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: test-C-api.R # Author: Jelmer Ypma # Date: 6 October 2017 # # Test API to expose NLopt C functions. # # CHANGELOG: # 05/10/2017: Initial version with test of exposed nlopt_version. # 06/10/2017: Define empty R_TESTS environment variable to fix error on Travis. # 06/10/2017: Add test for exposed NLopt C functions based on NLopt tutorial. context("NLopt-C-API") # Load inline package to compile C code. library('inline') # Define empty R_TESTS environment variable to fix error on Travis # See https://github.com/hadley/testthat/issues/144 for more information. Sys.setenv("R_TESTS" = "") # Define arguments for compiler and linker. cxxargs = nloptr:::CFlags(print = FALSE) libargs = nloptr:::LdFlags(print = FALSE) test_that( "Test exposing NLopt C function nlopt_version.", { # Define C function to return version number. code <- ' // get version of NLopt int major, minor, bugfix; nlopt_version(&major, &minor, &bugfix); SEXP retvec = PROTECT(allocVector(INTSXP, 3)); INTEGER(retvec)[0] = major; INTEGER(retvec)[1] = minor; INTEGER(retvec)[2] = bugfix; UNPROTECT(1); return retvec;' get_nlopt_version <- cfunction(sig = signature(), body = code, includes = '#include "nloptrAPI.h"', cxxargs = cxxargs, libargs = libargs) # Get nlopt version, which consists of an integer vector: # (major, minor, bugfix) res <- get_nlopt_version() # Check return value. expect_type(res, "integer") expect_length(res, 3) } ) test_that( "Test exposed NLopt C code using example from NLopt tutorial.", { # Example taken from: # http://ab-initio.mit.edu/wiki/index.php?title=NLopt_Tutorial include_code <- ' #include #include "nloptrAPI.h" double myfunc(unsigned n, const double *x, double *grad, void *my_func_data) { if (grad) { grad[0] = 0.0; grad[1] = 0.5 / sqrt(x[1]); } return sqrt(x[1]); } typedef struct { double a, b; } my_constraint_data; double myconstraint(unsigned n, const double *x, double *grad, void *data) { my_constraint_data *d = (my_constraint_data *) data; double a = d->a, b = d->b; if (grad) { grad[0] = 3 * a * (a*x[0] + b) * (a*x[0] + b); grad[1] = -1.0; } return ((a*x[0] + b) * (a*x[0] + b) * (a*x[0] + b) - x[1]); } ' # Define C function to solve optimization problem. code <- ' double lb[2] = { -HUGE_VAL, 0 }; /* lower bounds */ nlopt_opt opt; opt = nlopt_create(NLOPT_LD_MMA, 2); /* algorithm and dimensionality */ nlopt_set_lower_bounds(opt, lb); nlopt_set_min_objective(opt, myfunc, NULL); my_constraint_data data[2] = { {2,0}, {-1,1} }; nlopt_add_inequality_constraint(opt, myconstraint, &data[0], 1e-8); nlopt_add_inequality_constraint(opt, myconstraint, &data[1], 1e-8); nlopt_set_xtol_rel(opt, 1e-4); double x[2] = { 1.234, 5.678 }; /* some initial guess */ double minf; /* the minimum objective value, upon return */ if (nlopt_optimize(opt, x, &minf) < 0) { //Rprintf("nlopt failed!\\n"); } else { //Rprintf("found minimum at f(%g,%g) = %0.10g\\n", x[0], x[1], minf); } nlopt_destroy(opt); // Convert result to SEXP. SEXP result = PROTECT(allocVector(REALSXP, 2)); REAL(result)[0] = x[0]; REAL(result)[1] = x[1]; UNPROTECT(1); return result;' solve_example <- cfunction(sig = signature(), body = code, includes = include_code, cxxargs = cxxargs, libargs = libargs) # Get optimal x values. res <- solve_example() # Optimal solution as provided in the NLopt tutorial. solution.opt <- c( 1/3, 8/27 ) # Check return value. expect_equal(res, solution.opt) } ) nloptr/tests/testthat/test-banana-global.R0000644000176000001440000001136712553776710020425 0ustar ripleyusers# Copyright (C) 2011 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: test-banana-global.R # Author: Jelmer Ypma # Date: 8 August 2011 # # Example showing how to solve the Rosenbrock Banana function # using a global optimization algorithm. # # Changelog: # 27/10/2013: Changed example to use unit testing framework testthat. context("Banana Global") ## Rosenbrock Banana objective function eval_f <- function(x) { return( 100 * (x[2] - x[1] * x[1])^2 + (1 - x[1])^2 ) } eval_grad_f <- function(x) { return( c( -400 * x[1] * (x[2] - x[1] * x[1]) - 2 * (1 - x[1]), 200 * (x[2] - x[1] * x[1])) ) } # initial values x0 <- c( -1.2, 1 ) # lower and upper bounds lb <- c( -3, -3 ) ub <- c( 3, 3 ) test_that("Test Rosenbrock Banana optimization with global optimizer NLOPT_GD_MLSL.", { # Define optimizer options. local_opts <- list( "algorithm" = "NLOPT_LD_LBFGS", "xtol_rel" = 1e-4 ) opts <- list( "algorithm" = "NLOPT_GD_MLSL", "maxeval" = 10000, "population" = 4, "local_opts" = local_opts ) # Solve Rosenbrock Banana function. res <- nloptr( x0 = x0, lb = lb, ub = ub, eval_f = eval_f, eval_grad_f = eval_grad_f, opts = opts ) # Check results. expect_that( res$objective, equals( 0.0 ) ) expect_that( res$solution, equals( c( 1.0, 1.0 ) ) ) } ) test_that("Test Rosenbrock Banana optimization with global optimizer NLOPT_GN_ISRES.", { # Define optimizer options. # For unit testing we want to set the random seed for replicability. opts <- list( "algorithm" = "NLOPT_GN_ISRES", "maxeval" = 10000, "population" = 100, "ranseed" = 2718 ) # Solve Rosenbrock Banana function. res <- nloptr( x0 = x0, lb = lb, ub = ub, eval_f = eval_f, opts = opts ) # Check results. expect_that( res$objective, equals( 0.0, tolerance=1e-4 ) ) expect_that( res$solution, equals( c( 1.0, 1.0 ), tolerance=1e-2 ) ) } ) test_that("Test Rosenbrock Banana optimization with global optimizer NLOPT_GN_CRS2_LM with random seed defined.", { # Define optimizer options. # For unit testing we want to set the random seed for replicability. opts <- list( "algorithm" = "NLOPT_GN_CRS2_LM", "maxeval" = 10000, "population" = 100, "ranseed" = 2718 ) # Solve Rosenbrock Banana function. res1 <- nloptr( x0 = x0, lb = lb, ub = ub, eval_f = eval_f, opts = opts ) # Define optimizer options. # this optimization uses a different seed for the # random number generator and gives a different result opts <- list( "algorithm" = "NLOPT_GN_CRS2_LM", "maxeval" = 10000, "population" = 100, "ranseed" = 3141 ) # Solve Rosenbrock Banana function. res2 <- nloptr( x0 = x0, lb = lb, ub = ub, eval_f = eval_f, opts = opts ) # Define optimizer options. # this optimization uses the same seed for the random # number generator and gives the same results as res2 opts <- list( "algorithm" = "NLOPT_GN_CRS2_LM", "maxeval" = 10000, "population" = 100, "ranseed" = 3141 ) # Solve Rosenbrock Banana function. res3 <- nloptr( x0 = x0, lb = lb, ub = ub, eval_f = eval_f, opts = opts ) # Check results. expect_that( res1$objective, equals( 0.0, tolerance=1e-4 ) ) expect_that( res1$solution, equals( c( 1.0, 1.0 ), tolerance=1e-2 ) ) expect_that( res2$objective, equals( 0.0, tolerance=1e-4 ) ) expect_that( res2$solution, equals( c( 1.0, 1.0 ), tolerance=1e-2 ) ) expect_that( res3$objective, equals( 0.0, tolerance=1e-4 ) ) expect_that( res3$solution, equals( c( 1.0, 1.0 ), tolerance=1e-2 ) ) # Expect that the results are different for res1 and res2. expect_that( res1$objective == res2$objective, is_false() ) expect_that( all( res1$solution == res2$solution ), is_false() ) # Expect that the results are identical for res2 and res3. expect_that( res2$objective, is_identical_to( res3$objective ) ) expect_that( res2$solution, is_identical_to( res3$solution ) ) } ) nloptr/tests/testthat/test-simple.R0000644000176000001440000000555012553776710017235 0ustar ripleyusers# Copyright (C) 2010 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: simple.R # Author: Jelmer Ypma # Date: 20 June 2010 # # Example showing how to solve a simple constrained problem. # # min x^2 # s.t. x >= 5 # # re-formulate constraint to be of form g(x) <= 0 # 5 - x <= 0 # we could use a bound constraint as well here # # CHANGELOG: # 05/05/2014: Changed example to use unit testing framework testthat. context("Simple contstrained problem.") test_that( "Test simple constrained optimization problem with gradient information." , { # Objective function. eval_f <- function(x) { return( x^2 ) } # Gradient of objective function. eval_grad_f <- function(x) { return( 2*x ) } # Inequality constraint function. eval_g_ineq <- function( x ) { return( 5 - x ) } # Jacobian of constraint. eval_jac_g_ineq <- function( x ) { return( -1 ) } # Optimal solution. solution.opt <- 5 # Solve using NLOPT_LD_MMA with gradient information supplied in separate function res <- nloptr( x0 = 1, eval_f = eval_f, eval_grad_f = eval_grad_f, eval_g_ineq = eval_g_ineq, eval_jac_g_ineq = eval_jac_g_ineq, opts = list("algorithm"="NLOPT_LD_MMA", "xtol_rel" = 1e-4) ) # Run some checks on the optimal solution. expect_that( res$solution, equals( solution.opt ) ) # Check whether constraints are violated (up to specified tolerance). expect_that( eval_g_ineq( res$solution ) <= res$options$tol_constraints_ineq, is_true() ) } ) test_that( "Test simple constrained optimization problem without gradient information." , { # Objective function. eval_f <- function(x) { return( x^2 ) } # Inequality constraint function. eval_g_ineq <- function( x ) { return( 5 - x ) } # Optimal solution. solution.opt <- 5 # Solve using NLOPT_LN_COBYLA without gradient information res <- nloptr( x0 = 1, eval_f = eval_f, eval_g_ineq = eval_g_ineq, opts = list( "algorithm" = "NLOPT_LN_COBYLA", "xtol_rel" = 1e-6, "tol_constraints_ineq" = 1e-6 ) ) # Run some checks on the optimal solution. expect_that( res$solution, equals( solution.opt, tolerance = 1e-6 ) ) # Check whether constraints are violated (up to specified tolerance). expect_that( eval_g_ineq( res$solution ) <= res$options$tol_constraints_ineq, is_true() ) } ) nloptr/tests/testthat/test-parameters.R0000644000176000001440000000314512553776710020105 0ustar ripleyusers# Copyright (C) 2010 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: parameters.R # Author: Jelmer Ypma # Date: 17 August 2010 # # Example shows how we can have an objective function # depend on parameters or data. The objective function # is a simple polynomial. # # CHANGELOG: # 05/05/2014: Changed example to use unit testing framework testthat. context("Simple polynomial with additional data.") test_that( "Test simple polyonmial where parameters are supplied as additional data.", { # Objective function and gradient in terms of parameters. eval_f <- function(x, params) { return( params[1]*x^2 + params[2]*x + params[3] ) } eval_grad_f <- function(x, params) { return( 2*params[1]*x + params[2] ) } # Define parameters that we want to use. params <- c(1,2,3) # Define initial value of the optimzation problem. x0 <- 0 # solve using nloptr adding params as an additional parameter res <- nloptr( x0 = x0, eval_f = eval_f, eval_grad_f = eval_grad_f, opts = list("algorithm"="NLOPT_LD_MMA", "xtol_rel" = 1e-6), params = params ) # Solve using algebra # Minimize f(x) = ax^2 + bx + c. # Optimal value for control is -b/(2a). expect_that( res$solution, equals( -params[2]/(2*params[1]), tolerance = 1e-7 ) ) # With value of the objective function f(-b/(2a)). expect_that( res$objective, equals( eval_f( -params[2]/(2*params[1]), params ) ) ) } ) nloptr/tests/testthat/test-options-maxtime.R0000644000176000001440000000211112553776710021067 0ustar ripleyusers# Copyright (C) 2015 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: test-options-maxtime.R # Author: Jelmer Ypma # Date: 22 March 2015 # # Check whether the solver stop when maxtime # (set in the options) is reached. context("Options maxtime") test_that("Test that solver stops when maxtime is reached.", { # Objective function with sleep added # such that maxtime will be reached # when solving the optimization problem. eval_f <- function(x) { Sys.sleep( 1 ) return( x^2 ) } eval_grad_f <- function(x) { return( 2*x ) } # Initial values. x0 <- c( 5 ) # Define optimizer options. opts <- list( "algorithm" = "NLOPT_LD_LBFGS", "maxtime" = 1, "xtol_rel" = 1e-4 ) # Solve problem. res <- nloptr( x0 = x0, eval_f = eval_f, eval_grad_f = eval_grad_f, opts = opts ) # Check results. expect_that( res$status, equals( 6L ) ) } ) nloptr/tests/testthat/test-hs023.R0000644000176000001440000000512113265445111016562 0ustar ripleyusers# Copyright (C) 2010 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: hs023.R # Author: Jelmer Ypma # Date: 16 August 2010 # # Example problem, number 23 from the Hock-Schittkowsky test suite.. # # \min_{x} x1^2 + x2^2 # s.t. # x1 + x2 >= 1 # x1^2 + x2^2 >= 1 # 9*x1^2 + x2^2 >= 9 # x1^2 - x2 >= 0 # x2^2 - x1 >= 0 # # with bounds on the variables # -50 <= x1, x2 <= 50 # # we re-write the inequalities as # 1 - x1 - x2 <= 0 # 1 - x1^2 - x2^2 <= 0 # 9 - 9*x1^2 - x2^2 <= 0 # x2 - x1^2 <= 0 # x1 - x2^2 <= 0 # # the initial value is # x0 = (3, 1) # # Optimal solution = (1, 1) # # CHANGELOG: # 05/05/2014: Changed example to use unit testing framework testthat. context("HS023") test_that( "Test HS023.", { # # f(x) = x1^2 + x2^2 # eval_f <- function( x ) { return( list( "objective" = x[1]^2 + x[2]^2, "gradient" = c( 2*x[1], 2*x[2] ) ) ) } # Inequality constraints. eval_g_ineq <- function( x ) { constr <- c( 1 - x[1] - x[2], 1 - x[1]^2 - x[2]^2, 9 - 9*x[1]^2 - x[2]^2, x[2] - x[1]^2, x[1] - x[2]^2 ) grad <- rbind( c( -1, -1 ), c( -2*x[1], -2*x[2] ), c( -18*x[1], -2*x[2] ), c( -2*x[1], 1 ), c( 1, -2*x[2] ) ) return( list( "constraints"=constr, "jacobian"=grad ) ) } # Initial values. x0 <- c( 3, 1 ) # Lower and upper bounds of control. lb <- c( -50, -50 ) ub <- c( 50, 50 ) # Optimal solution. solution.opt <- c( 1, 1 ) # Solve with MMA. opts <- list( "algorithm" = "NLOPT_LD_MMA", "xtol_rel" = 1.0e-6, "tol_constraints_ineq" = rep( 1.0e-6, 5 ), "print_level" = 0 ) res <- nloptr( x0 = x0, eval_f = eval_f, lb = lb, ub = ub, eval_g_ineq = eval_g_ineq, opts = opts ) # Run some checks on the optimal solution. expect_that( res$solution, equals( solution.opt, tolerance = 1e-6 ) ) expect_that( all( res$solution >= lb ), is_true() ) expect_that( all( res$solution <= ub ), is_true() ) # Check whether constraints are violated (up to specified tolerance). expect_that( all( eval_g_ineq( res$solution )$constr <= res$options$tol_constraints_ineq ), is_true() ) } ) nloptr/tests/testthat/test-example.R0000644000176000001440000000751412553776710017401 0ustar ripleyusers# Copyright (C) 2010-14 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: test-example.R # Author: Jelmer Ypma # Date: 10 June 2010 # # Example showing how to solve the problem from the NLopt tutorial. # # min sqrt( x2 ) # s.t. x2 >= 0 # x2 >= ( a1*x1 + b1 )^3 # x2 >= ( a2*x1 + b2 )^3 # where # a1 = 2, b1 = 0, a2 = -1, b2 = 1 # # re-formulate constraints to be of form g(x) <= 0 # ( a1*x1 + b1 )^3 - x2 <= 0 # ( a2*x1 + b2 )^3 - x2 <= 0 # # Optimal solution: ( 1/3, 8/27 ) # # CHANGELOG: # 03/05/2014: Changed example to use unit testing framework testthat. context("Example-NLoptTutorial") # objective function eval_f0 <- function( x, a, b ) { return( sqrt(x[2]) ) } # constraint function eval_g0 <- function( x, a, b ) { return( (a*x[1] + b)^3 - x[2] ) } # gradient of objective function eval_grad_f0 <- function( x, a, b ){ return( c( 0, .5/sqrt(x[2]) ) ) } # jacobian of constraint eval_jac_g0 <- function( x, a, b ) { return( rbind( c( 3*a[1]*(a[1]*x[1] + b[1])^2, -1.0 ), c( 3*a[2]*(a[2]*x[1] + b[2])^2, -1.0 ) ) ) } # functions with gradients in objective and constraint function # this can be useful if the same calculations are needed for # the function value and the gradient eval_f1 <- function( x, a, b ){ return( list("objective"=sqrt(x[2]), "gradient"=c(0,.5/sqrt(x[2])) ) ) } eval_g1 <- function( x, a, b ) { return( list( "constraints"=(a*x[1] + b)^3 - x[2], "jacobian"=rbind( c( 3*a[1]*(a[1]*x[1] + b[1])^2, -1.0 ), c( 3*a[2]*(a[2]*x[1] + b[2])^2, -1.0 ) ) ) ) } # Define parameters. a <- c( 2, -1 ) b <- c( 0, 1 ) # Define optimal solution. solution.opt <- c( 1/3, 8/27 ) test_that( "Test NLopt tutorial example with NLOPT_LD_MMA with gradient information.", { # Solve using NLOPT_LD_MMA with gradient information supplied in separate function res0 <- nloptr( x0 = c( 1.234, 5.678 ), eval_f = eval_f0, eval_grad_f = eval_grad_f0, lb = c( -Inf, 0 ), ub = c( Inf, Inf ), eval_g_ineq = eval_g0, eval_jac_g_ineq = eval_jac_g0, opts = list("xtol_rel" = 1e-4, "algorithm" = "NLOPT_LD_MMA"), a = a, b = b ) expect_that( res0$solution, equals( solution.opt ) ) } ) test_that( "Test NLopt tutorial example with NLOPT_LN_COBYLA with gradient information.", { # Solve using NLOPT_LN_COBYLA without gradient information # A tighter convergence tolerance is used here (1e-6), to ensure # that the final solution is equal to the optimal solution (within some tolerance). res1 <- nloptr( x0 = c( 1.234, 5.678 ), eval_f = eval_f0, lb = c( -Inf, 0 ), ub = c( Inf, Inf ), eval_g_ineq = eval_g0, opts = list("xtol_rel" = 1e-6, "algorithm" = "NLOPT_LN_COBYLA"), a = a, b = b ) expect_that( res1$solution, equals( solution.opt ) ) } ) test_that( "Test NLopt tutorial example with NLOPT_LN_COBYLA with gradient information using combined function.", { # Solve using NLOPT_LD_MMA with gradient information in objective function res2 <- nloptr( x0 = c( 1.234, 5.678 ), eval_f = eval_f1, lb = c( -Inf, 0 ), ub = c( Inf, Inf ), eval_g_ineq = eval_g1, opts = list("xtol_rel" = 1e-4, "algorithm" = "NLOPT_LD_MMA"), a = a, b = b ) expect_that( res2$solution, equals( solution.opt ) ) } ) nloptr/tests/testthat/options.R0000644000176000001440000000060412553776710016455 0ustar ripleyusers# Copyright (C) 2011 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: test_options.R # Author: Jelmer Ypma # Date: 7 August 2011 # # Test printing of options. library('nloptr') opts <- list( "algorithm" = "NLOPT_LD_LBFGS", "xtol_rel" = 1.0e-8, "print_level" = 1 ) nloptr.print.options( opts ) nloptr/tests/testthat/test-systemofeq.R0000644000176000001440000000727612553776710020152 0ustar ripleyusers# Copyright (C) 2010 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: systemofeq.R # Author: Jelmer Ypma # Date: 20 June 2010 # # Example showing how to solve a system of equations. # # min 1 # s.t. x^2 + x - 1 = 0 # # Optimal solution for x: -1.61803398875 # # CHANGELOG: # 16/06/2011: added NLOPT_LD_SLSQP # 05/05/2014: Changed example to use unit testing framework testthat. context("System of equations.") test_that( "Solve system of equations using NLOPT_LD_MMA with local optimizer NLOPT_LD_MMA.", { # Objective function. eval_f0 <- function( x, params ) { return( 1 ) } # Gradient of objective function. eval_grad_f0 <- function( x, params ) { return( 0 ) } # Equality constraint function. eval_g0_eq <- function( x, params ) { return( params[1]*x^2 + params[2]*x + params[3] ) } # Jacobian of constraint. eval_jac_g0_eq <- function( x, params ) { return( 2*params[1]*x + params[2] ) } # Define vector with addiitonal data. params <- c(1, 1, -1) # Define optimal solution. solution.opt <- -1.61803398875 # # Solve using NLOPT_LD_MMA with local optimizer NLOPT_LD_MMA. # local_opts <- list( "algorithm" = "NLOPT_LD_MMA", "xtol_rel" = 1.0e-6 ) opts <- list( "algorithm" = "NLOPT_LD_AUGLAG", "xtol_rel" = 1.0e-6, "local_opts" = local_opts ) res <- nloptr( x0 = -5, eval_f = eval_f0, eval_grad_f = eval_grad_f0, eval_g_eq = eval_g0_eq, eval_jac_g_eq = eval_jac_g0_eq, opts = opts, params = params ) # Run some checks on the optimal solution. expect_that( res$solution, equals( solution.opt ) ) # Check whether constraints are violated (up to specified tolerance). expect_that( eval_g0_eq( res$solution, params = params ), equals( 0, tolerance = res$options$tol_constraints_eq ) ) } ) test_that( "Solve system of equations using NLOPT_LD_SLSQP.", { # Objective function. eval_f0 <- function( x, params ) { return( 1 ) } # Gradient of objective function. eval_grad_f0 <- function( x, params ) { return( 0 ) } # Equality constraint function. eval_g0_eq <- function( x, params ) { return( params[1]*x^2 + params[2]*x + params[3] ) } # Jacobian of constraint. eval_jac_g0_eq <- function( x, params ) { return( 2*params[1]*x + params[2] ) } # Define vector with addiitonal data. params <- c(1, 1, -1) # Define optimal solution. solution.opt <- -1.61803398875 # # Solve using NLOPT_LD_SLSQP. # opts <- list( "algorithm" = "NLOPT_LD_SLSQP", "xtol_rel" = 1.0e-6 ) res <- nloptr( x0 = -5, eval_f = eval_f0, eval_grad_f = eval_grad_f0, eval_g_eq = eval_g0_eq, eval_jac_g_eq = eval_jac_g0_eq, opts = opts, params = params ) # Run some checks on the optimal solution. expect_that( res$solution, equals( solution.opt ) ) # Check whether constraints are violated (up to specified tolerance). expect_that( eval_g0_eq( res$solution, params = params ), equals( 0, tolerance = res$options$tol_constraints_eq ) ) } ) nloptr/tests/testthat/test-banana.R0000644000176000001440000000456212553776710017166 0ustar ripleyusers# Copyright (C) 2010 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: test-banana.R # Author: Jelmer Ypma # Date: 10 June 2010 # # Example showing how to solve the Rosenbrock Banana function. # # Changelog: # 27/10/2013: Changed example to use unit testing framework testthat. context("Banana") test_that("Test Rosenbrock Banana optimization with objective and gradient in separate functions.", { # initial values x0 <- c( -1.2, 1 ) opts <- list( "algorithm" = "NLOPT_LD_LBFGS", "xtol_rel" = 1.0e-8, "print_level" = 0 ) ## Rosenbrock Banana function and gradient in separate functions eval_f <- function(x) { return( 100 * (x[2] - x[1] * x[1])^2 + (1 - x[1])^2 ) } eval_grad_f <- function(x) { return( c( -400 * x[1] * (x[2] - x[1] * x[1]) - 2 * (1 - x[1]), 200 * (x[2] - x[1] * x[1])) ) } # Solve Rosenbrock Banana function. res <- nloptr( x0 = x0, eval_f = eval_f, eval_grad_f = eval_grad_f, opts = opts ) # Check results. expect_that( res$objective, equals( 0.0 ) ) expect_that( res$solution, equals( c( 1.0, 1.0 ) ) ) } ) test_that("Test Rosenbrock Banana optimization with objective and gradient in the same function.", { # initial values x0 <- c( -1.2, 1 ) opts <- list( "algorithm" = "NLOPT_LD_LBFGS", "xtol_rel" = 1.0e-8, "print_level" = 0 ) ## Rosenbrock Banana function and gradient in one function # this can be used to economize on calculations eval_f_list <- function(x) { return( list( "objective" = 100 * (x[2] - x[1] * x[1])^2 + (1 - x[1])^2, "gradient" = c( -400 * x[1] * (x[2] - x[1] * x[1]) - 2 * (1 - x[1]), 200 * (x[2] - x[1] * x[1])) ) ) } # Solve Rosenbrock Banana function. using an objective function that # returns a list with the objective value and its gradient res <- nloptr( x0 = x0, eval_f = eval_f_list, opts = opts ) # Check results. expect_that( res$objective, equals( 0.0 ) ) expect_that( res$solution, equals( c( 1.0, 1.0 ) ) ) } ) nloptr/tests/testthat/test-hs071.R0000644000176000001440000000656212553776710016612 0ustar ripleyusers# Copyright (C) 2010 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: hs071.R # Author: Jelmer Ypma # Date: 10 June 2010 # # Example problem, number 71 from the Hock-Schittkowsky test suite. # # \min_{x} x1*x4*(x1 + x2 + x3) + x3 # s.t. # x1*x2*x3*x4 >= 25 # x1^2 + x2^2 + x3^2 + x4^2 = 40 # 1 <= x1,x2,x3,x4 <= 5 # # we re-write the inequality as # 25 - x1*x2*x3*x4 <= 0 # # and the equality as # x1^2 + x2^2 + x3^2 + x4^2 - 40 = 0 # # x0 = (1,5,5,1) # # Optimal solution = (1.00000000, 4.74299963, 3.82114998, 1.37940829) # # CHANGELOG: # 05/05/2014: Changed example to use unit testing framework testthat. context("HS071") test_that( "Test HS071.", { # # f(x) = x1*x4*(x1 + x2 + x3) + x3 # eval_f <- function( x ) { return( list( "objective" = x[1]*x[4]*(x[1] + x[2] + x[3]) + x[3], "gradient" = c( x[1] * x[4] + x[4] * (x[1] + x[2] + x[3]), x[1] * x[4], x[1] * x[4] + 1.0, x[1] * (x[1] + x[2] + x[3]) ) ) ) } # Inequality constraints. eval_g_ineq <- function( x ) { constr <- c( 25 - x[1] * x[2] * x[3] * x[4] ) grad <- c( -x[2]*x[3]*x[4], -x[1]*x[3]*x[4], -x[1]*x[2]*x[4], -x[1]*x[2]*x[3] ) return( list( "constraints"=constr, "jacobian"=grad ) ) } # Equality constraints. eval_g_eq <- function( x ) { constr <- c( x[1]^2 + x[2]^2 + x[3]^2 + x[4]^2 - 40 ) grad <- c( 2.0*x[1], 2.0*x[2], 2.0*x[3], 2.0*x[4] ) return( list( "constraints"=constr, "jacobian"=grad ) ) } # Initial values. x0 <- c( 1, 5, 5, 1 ) # Lower and upper bounds of control. lb <- c( 1, 1, 1, 1 ) ub <- c( 5, 5, 5, 5 ) # Optimal solution. solution.opt <- c(1.00000000, 4.74299963, 3.82114998, 1.37940829) # Set optimization options. local_opts <- list( "algorithm" = "NLOPT_LD_MMA", "xtol_rel" = 1.0e-7 ) opts <- list( "algorithm" = "NLOPT_LD_AUGLAG", "xtol_rel" = 1.0e-7, "maxeval" = 1000, "local_opts" = local_opts, "print_level" = 0 ) # Do optimization. res <- nloptr( x0 = x0, eval_f = eval_f, lb = lb, ub = ub, eval_g_ineq = eval_g_ineq, eval_g_eq = eval_g_eq, opts = opts ) # Run some checks on the optimal solution. expect_that( res$solution, equals( solution.opt, tolerance = 1e-6 ) ) expect_that( all( res$solution >= lb ), is_true() ) expect_that( all( res$solution <= ub ), is_true() ) # Check whether constraints are violated (up to specified tolerance). expect_that( eval_g_ineq( res$solution )$constr <= res$options$tol_constraints_ineq, is_true() ) expect_that( eval_g_eq( res$solution )$constr, equals( 0, tolerance = res$options$tol_constraints_eq ) ) } ) nloptr/tests/testthat/test-derivative-checker.R0000644000176000001440000000601713262730360021474 0ustar ripleyusers# Copyright (C) 201 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: test-derivative-checker.R # Author: Jelmer Ypma # Date: 24 July 2010 # # Example showing results of the derivative checker. # # Changelog: # 27/10/2013: Changed example to use unit testing framework testthat. context("Derivative checker") test_that("Test derivative checker.", { # Define objective function. f <- function( x, a ) { return( sum( ( x - a )^2 ) ) } # Define gradient function without errors. f_grad <- function( x, a ) { return( 2*( x - a ) ) } # Generated a using: # > set.seed(3141) # > a = runif(10) a <- c(0.754995959345251, 0.964991861954331, 0.0414307734463364, 0.42781219445169, 0.6517094373703, 0.838369226781651, 0.774285392835736, 0.531992698321119, 0.768715722020715, 0.785174649208784) res <- check.derivatives( .x=1:10, func=f, func_grad=f_grad, check_derivatives_print='none', a=a ) expect_that( sum( res$flag_derivative_warning ), equals( 0 ) ) # res <- check.derivatives( .x=1:10, func=f, func_grad=f_grad, check_derivatives_print='errors', a=runif(10) ) # expect_that( sum( res$flag_derivative_warning ), equals( 0 ) ) # # res <- check.derivatives( .x=1:10, func=f, func_grad=f_grad, check_derivatives_print='all', a=runif(10) ) # expect_that( sum( res$flag_derivative_warning ), equals( 0 ) ) # Define gradient function with 1 error. f_grad <- function( x, a ) { return( 2*( x - a ) + c(0,.1,rep(0,8)) ) } res <- check.derivatives( .x=1:10, func=f, func_grad=f_grad, check_derivatives_print='none', a=a ) expect_that( sum( res$flag_derivative_warning ), equals( 1 ) ) #res <- check.derivatives( .x=1:10, func=f, func_grad=f_grad, check_derivatives_print='errors', a=runif(10) ) #expect_that( sum( res$flag_derivative_warning ), equals( 1 ) ) # #res <- check.derivatives( .x=1:10, func=f, func_grad=f_grad, check_derivatives_print='all', a=runif(10) ) #expect_that( sum( res$flag_derivative_warning ), equals( 1 ) ) # Define objective function. g <- function( x, a ) { return( c( sum(x-a), sum( (x-a)^2 ) ) ) } # Define gradient function with 2 errors. g_grad <- function( x, a ) { return( rbind( rep(1,length(x)) + c(0,.01,rep(0,8)), 2*(x-a) + c(0,.1,rep(0,8)) ) ) } res <- check.derivatives( .x=1:10, func=g, func_grad=g_grad, check_derivatives_print='none', a=a ) expect_that( sum( res$flag_derivative_warning ), equals( 2 ) ) # res <- check.derivatives( .x=1:10, func=g, func_grad=g_grad, check_derivatives_print='errors', a=runif(10) ) # expect_that( sum( res$flag_derivative_warning ), equals( 2 ) ) # # res <- check.derivatives( .x=1:10, func=g, func_grad=g_grad, check_derivatives_print='all', a=runif(10) ) # expect_that( sum( res$flag_derivative_warning ), equals( 2 ) ) } ) nloptr/tests/test-all.R0000644000176000001440000000023212553776710014644 0ustar ripleyusersif(require("testthat", quietly = TRUE)) { test_check("nloptr") } else { print( "package 'testthat' not available, cannot run unit tests" ) } nloptr/src/0000755000176000001440000000000013332372165012414 5ustar ripleyusersnloptr/src/nlopt_src/0000755000176000001440000000000013354670113014415 5ustar ripleyusersnloptr/src/nlopt_src/neldermead/0000755000176000001440000000000013263734242016520 5ustar ripleyusersnloptr/src/nlopt_src/neldermead/neldermead.h0000644000176000001440000000435613355070635021002 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #ifndef NELDERMEAD_H #define NELDERMEAD_H #include "nlopt.h" #include "nlopt-util.h" #ifdef __cplusplus extern "C" { #endif /* __cplusplus */ nlopt_result nldrmd_minimize(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, const double *xstep, /* initial step sizes */ nlopt_stopping *stop); nlopt_result nldrmd_minimize_(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, /* bounds */ double *x,/* in: initial guess, out: minimizer */ double *minf, const double *xstep, /* initial step sizes */ nlopt_stopping *stop, double psi, double *scratch, double *fdiff); nlopt_result sbplx_minimize(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, const double *xstep0, /* initial step sizes */ nlopt_stopping *stop); #ifdef __cplusplus } /* extern "C" */ #endif /* __cplusplus */ #endif nloptr/src/nlopt_src/neldermead/nldrmd.c0000644000176000001440000002444613355070635020157 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include #include #include #include "neldermead.h" #include "redblack.h" /* Nelder-Mead simplex algorithm, used as a subroutine for the Rowan's subplex algorithm. Modified to handle bound constraints ala Richardson and Kuester (1973), as mentioned below. */ /* heuristic "strategy" constants: */ static const double alpha = 1, beta = 0.5, gamm = 2, delta = 0.5; /* sort order in red-black tree: keys [f(x), x] are sorted by f(x) */ static int simplex_compare(double *k1, double *k2) { if (*k1 < *k2) return -1; if (*k1 > *k2) return +1; return k1 - k2; /* tie-breaker */ } /* return 1 if a and b are approximately equal relative to floating-point precision, 0 otherwise */ static int close(double a, double b) { return (fabs(a - b) <= 1e-13 * (fabs(a) + fabs(b))); } /* Perform the reflection xnew = c + scale * (c - xold), returning 0 if xnew == c or xnew == xold (coincident points), 1 otherwise. The reflected point xnew is "pinned" to the lower and upper bounds (lb and ub), as suggested by J. A. Richardson and J. L. Kuester, "The complex method for constrained optimization," Commun. ACM 16(8), 487-489 (1973). This is probably a suboptimal way to handle bound constraints, but I don't know a better way. The main danger with this is that the simplex might collapse into a lower-dimensional hyperplane; this danger can be ameliorated by restarting (as in subplex), however. */ static int reflectpt(int n, double *xnew, const double *c, double scale, const double *xold, const double *lb, const double *ub) { int equalc = 1, equalold = 1, i; for (i = 0; i < n; ++i) { double newx = c[i] + scale * (c[i] - xold[i]); if (newx < lb[i]) newx = lb[i]; if (newx > ub[i]) newx = ub[i]; equalc = equalc && close(newx, c[i]); equalold = equalold && close(newx, xold[i]); xnew[i] = newx; } return !(equalc || equalold); } #define CHECK_EVAL(xc,fc) \ stop->nevals++; \ if (nlopt_stop_forced(stop)) { ret=NLOPT_FORCED_STOP; goto done; } \ if ((fc) <= *minf) { \ *minf = (fc); memcpy(x, (xc), n * sizeof(double)); \ if (*minf < stop->minf_max) { ret=NLOPT_MINF_MAX_REACHED; goto done; } \ } \ if (nlopt_stop_evals(stop)) { ret=NLOPT_MAXEVAL_REACHED; goto done; } \ if (nlopt_stop_time(stop)) { ret=NLOPT_MAXTIME_REACHED; goto done; } /* Internal version of nldrmd_minimize, intended to be used as a subroutine for the subplex method. Three differences compared to nldrmd_minimize: *minf should contain the value of f(x) (so that we don't have to re-evaluate f at the starting x). if psi > 0, then it *replaces* xtol and ftol in stop with the condition that the simplex diameter |xl - xh| must be reduced by a factor of psi ... this is for when nldrmd is used within the subplex method; for ordinary termination tests, set psi = 0. scratch should contain an array of length >= (n+1)*(n+1) + 2*n, used as scratch workspace. On output, *fdiff will contain the difference between the high and low function values of the last simplex. */ nlopt_result nldrmd_minimize_(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, const double *xstep, /* initial step sizes */ nlopt_stopping *stop, double psi, double *scratch, double *fdiff) { double *pts; /* (n+1) x (n+1) array of n+1 points plus function val [0] */ double *c; /* centroid * n */ double *xcur; /* current point */ rb_tree t; /* red-black tree of simplex, sorted by f(x) */ int i, j; double ninv = 1.0 / n; nlopt_result ret = NLOPT_SUCCESS; double init_diam = 0; pts = scratch; c = scratch + (n+1)*(n+1); xcur = c + n; rb_tree_init(&t, simplex_compare); *fdiff = HUGE_VAL; /* initialize the simplex based on the starting xstep */ memcpy(pts+1, x, sizeof(double)*n); pts[0] = *minf; if (*minf < stop->minf_max) { ret=NLOPT_MINF_MAX_REACHED; goto done; } for (i = 0; i < n; ++i) { double *pt = pts + (i+1)*(n+1); memcpy(pt+1, x, sizeof(double)*n); pt[1+i] += xstep[i]; if (pt[1+i] > ub[i]) { if (ub[i] - x[i] > fabs(xstep[i]) * 0.1) pt[1+i] = ub[i]; else /* ub is too close to pt, go in other direction */ pt[1+i] = x[i] - fabs(xstep[i]); } if (pt[1+i] < lb[i]) { if (x[i] - lb[i] > fabs(xstep[i]) * 0.1) pt[1+i] = lb[i]; else {/* lb is too close to pt, go in other direction */ pt[1+i] = x[i] + fabs(xstep[i]); if (pt[1+i] > ub[i]) /* go towards further of lb, ub */ pt[1+i] = 0.5 * ((ub[i] - x[i] > x[i] - lb[i] ? ub[i] : lb[i]) + x[i]); } } if (close(pt[1+i], x[i])) { ret=NLOPT_FAILURE; goto done; } pt[0] = f(n, pt+1, NULL, f_data); CHECK_EVAL(pt+1, pt[0]); } restart: for (i = 0; i < n + 1; ++i) if (!rb_tree_insert(&t, pts + i*(n+1))) { ret = NLOPT_OUT_OF_MEMORY; goto done; } while (1) { rb_node *low = rb_tree_min(&t); rb_node *high = rb_tree_max(&t); double fl = low->k[0], *xl = low->k + 1; double fh = high->k[0], *xh = high->k + 1; double fr; *fdiff = fh - fl; if (init_diam == 0) /* initialize diam. for psi convergence test */ for (i = 0; i < n; ++i) init_diam += fabs(xl[i] - xh[i]); if (psi <= 0 && nlopt_stop_ftol(stop, fl, fh)) { ret = NLOPT_FTOL_REACHED; goto done; } /* compute centroid ... if we cared about the perfomance of this, we could do it iteratively by updating the centroid on each step, but then we would have to be more careful about accumulation of rounding errors... anyway n is unlikely to be very large for Nelder-Mead in practical cases */ memset(c, 0, sizeof(double)*n); for (i = 0; i < n + 1; ++i) { double *xi = pts + i*(n+1) + 1; if (xi != xh) for (j = 0; j < n; ++j) c[j] += xi[j]; } for (i = 0; i < n; ++i) c[i] *= ninv; /* x convergence check: find xcur = max radius from centroid */ memset(xcur, 0, sizeof(double)*n); for (i = 0; i < n + 1; ++i) { double *xi = pts + i*(n+1) + 1; for (j = 0; j < n; ++j) { double dx = fabs(xi[j] - c[j]); if (dx > xcur[j]) xcur[j] = dx; } } for (i = 0; i < n; ++i) xcur[i] += c[i]; if (psi > 0) { double diam = 0; for (i = 0; i < n; ++i) diam += fabs(xl[i] - xh[i]); if (diam < psi * init_diam) { ret = NLOPT_XTOL_REACHED; goto done; } } else if (nlopt_stop_x(stop, c, xcur)) { ret = NLOPT_XTOL_REACHED; goto done; } /* reflection */ if (!reflectpt(n, xcur, c, alpha, xh, lb, ub)) { ret=NLOPT_XTOL_REACHED; goto done; } fr = f(n, xcur, NULL, f_data); CHECK_EVAL(xcur, fr); if (fr < fl) { /* new best point, expand simplex */ if (!reflectpt(n, xh, c, gamm, xh, lb, ub)) { ret=NLOPT_XTOL_REACHED; goto done; } fh = f(n, xh, NULL, f_data); CHECK_EVAL(xh, fh); if (fh >= fr) { /* expanding didn't improve */ fh = fr; memcpy(xh, xcur, sizeof(double)*n); } } else if (fr < rb_tree_pred(high)->k[0]) { /* accept new point */ memcpy(xh, xcur, sizeof(double)*n); fh = fr; } else { /* new worst point, contract */ double fc; if (!reflectpt(n,xcur,c, fh <= fr ? -beta : beta, xh, lb,ub)) { ret=NLOPT_XTOL_REACHED; goto done; } fc = f(n, xcur, NULL, f_data); CHECK_EVAL(xcur, fc); if (fc < fr && fc < fh) { /* successful contraction */ memcpy(xh, xcur, sizeof(double)*n); fh = fc; } else { /* failed contraction, shrink simplex */ rb_tree_destroy(&t); rb_tree_init(&t, simplex_compare); for (i = 0; i < n+1; ++i) { double *pt = pts + i * (n+1); if (pt+1 != xl) { if (!reflectpt(n,pt+1, xl,-delta,pt+1, lb,ub)) { ret = NLOPT_XTOL_REACHED; goto done; } pt[0] = f(n, pt+1, NULL, f_data); CHECK_EVAL(pt+1, pt[0]); } } goto restart; } } high->k[0] = fh; rb_tree_resort(&t, high); } done: rb_tree_destroy(&t); return ret; } nlopt_result nldrmd_minimize(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, const double *xstep, /* initial step sizes */ nlopt_stopping *stop) { nlopt_result ret; double *scratch, fdiff; *minf = f(n, x, NULL, f_data); stop->nevals++; if (nlopt_stop_forced(stop)) return NLOPT_FORCED_STOP; if (*minf < stop->minf_max) return NLOPT_MINF_MAX_REACHED; if (nlopt_stop_evals(stop)) return NLOPT_MAXEVAL_REACHED; if (nlopt_stop_time(stop)) return NLOPT_MAXTIME_REACHED; scratch = (double*) malloc(sizeof(double) * ((n+1)*(n+1) + 2*n)); if (!scratch) return NLOPT_OUT_OF_MEMORY; ret = nldrmd_minimize_(n, f, f_data, lb, ub, x, minf, xstep, stop, 0.0, scratch, &fdiff); free(scratch); return ret; } nloptr/src/nlopt_src/neldermead/Makefile.am0000644000176000001440000000025413263734242020555 0ustar ripleyusersAM_CPPFLAGS = -I$(top_srcdir)/util -I$(top_srcdir)/api noinst_LTLIBRARIES = libneldermead.la libneldermead_la_SOURCES = nldrmd.c neldermead.h sbplx.c EXTRA_DIST = README nloptr/src/nlopt_src/neldermead/sbplx.c0000644000176000001440000002012113355070635020011 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include #include #include #include #include "neldermead.h" /* subplex strategy constants: */ static const double psi = 0.25, omega = 0.1; static const int nsmin = 2, nsmax = 5; int sbplx_verbose = 0; /* for debugging */ /* qsort_r comparison function for sorting indices into delta-x array */ static int p_compare(void *dx_, const void *i_, const void *j_) { const double *dx = (const double *) dx_; int i = *((const int *) i_), j = *((const int *) j_); double dxi = fabs(dx[i]), dxj = fabs(dx[j]); return (dxi > dxj ? -1 : (dxi < dxj ? +1 : 0)); } typedef struct { const int *p; /* subspace index permutation */ int is; /* starting index for this subspace */ int n; /* dimension of underlying space */ double *x; /* current x vector */ nlopt_func f; void *f_data; /* the "actual" underlying function */ } subspace_data; /* wrapper around objective function for subspace optimization */ static double subspace_func(unsigned ns, const double *xs, double *grad, void *data) { subspace_data *d = (subspace_data *) data; int i, is = d->is; const int *p = d->p; double *x = d->x; (void) grad; /* should always be NULL here */ for (i = is; i < is + ((int) ns); ++i) x[p[i]] = xs[i-is]; return d->f(d->n, x, NULL, d->f_data); } nlopt_result sbplx_minimize(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, const double *xstep0, /* initial step sizes */ nlopt_stopping *stop) { nlopt_result ret = NLOPT_SUCCESS; double *xstep, *xprev, *dx, *xs, *lbs, *ubs, *xsstep, *scratch; int *p; /* permuted indices of x sorted by decreasing magnitude |dx| */ int i; subspace_data sd; double fprev; *minf = f(n, x, NULL, f_data); stop->nevals++; if (nlopt_stop_forced(stop)) return NLOPT_FORCED_STOP; if (*minf < stop->minf_max) return NLOPT_MINF_MAX_REACHED; if (nlopt_stop_evals(stop)) return NLOPT_MAXEVAL_REACHED; if (nlopt_stop_time(stop)) return NLOPT_MAXTIME_REACHED; xstep = (double*)malloc(sizeof(double) * (n*3 + nsmax*4 + (nsmax+1)*(nsmax+1)+2*nsmax)); if (!xstep) return NLOPT_OUT_OF_MEMORY; xprev = xstep + n; dx = xprev + n; xs = dx + n; xsstep = xs + nsmax; lbs = xsstep + nsmax; ubs = lbs + nsmax; scratch = ubs + nsmax; p = (int *) malloc(sizeof(int) * n); if (!p) { free(xstep); return NLOPT_OUT_OF_MEMORY; } memcpy(xstep, xstep0, n * sizeof(double)); memset(dx, 0, n * sizeof(double)); sd.p = p; sd.n = n; sd.x = x; sd.f = f; sd.f_data = f_data; while (1) { double normi = 0; double normdx = 0; int ns, nsubs = 0; int nevals = stop->nevals; double fdiff, fdiff_max = 0; memcpy(xprev, x, n * sizeof(double)); fprev = *minf; /* sort indices into the progress vector dx by decreasing order of magnitude |dx| */ for (i = 0; i < n; ++i) p[i] = i; nlopt_qsort_r(p, (size_t) n, sizeof(int), dx, p_compare); /* find the subspaces, and perform nelder-mead on each one */ for (i = 0; i < n; ++i) normdx += fabs(dx[i]); /* L1 norm */ for (i = 0; i + nsmin < n; i += ns) { /* find subspace starting at index i */ int k, nk; double ns_goodness = -HUGE_VAL, norm = normi; nk = i+nsmax > n ? n : i+nsmax; /* max k for this subspace */ for (k = i; k < i+nsmin-1; ++k) norm += fabs(dx[p[k]]); ns = nsmin; for (k = i+nsmin-1; k < nk; ++k) { double goodness; norm += fabs(dx[p[k]]); /* remaining subspaces must be big enough to partition */ if (n-(k+1) < nsmin) continue; /* maximize figure of merit defined by Rowan thesis: look for sudden drops in average |dx| */ if (k+1 < n) goodness = norm/(k+1) - (normdx-norm)/(n-(k+1)); else goodness = normdx/n; if (goodness > ns_goodness) { ns_goodness = goodness; ns = (k+1)-i; } } for (k = i; k < i+ns; ++k) normi += fabs(dx[p[k]]); /* do nelder-mead on subspace of dimension ns starting w/i */ sd.is = i; for (k = i; k < i+ns; ++k) { xs[k-i] = x[p[k]]; xsstep[k-i] = xstep[p[k]]; lbs[k-i] = lb[p[k]]; ubs[k-i] = ub[p[k]]; } ++nsubs; nevals = stop->nevals; ret = nldrmd_minimize_(ns, subspace_func, &sd, lbs,ubs,xs, minf, xsstep, stop, psi, scratch, &fdiff); if (fdiff > fdiff_max) fdiff_max = fdiff; if (sbplx_verbose) printf("%d NM iterations for (%d,%d) subspace\n", stop->nevals - nevals, sd.is, ns); for (k = i; k < i+ns; ++k) x[p[k]] = xs[k-i]; if (ret == NLOPT_FAILURE) { ret=NLOPT_XTOL_REACHED; goto done; } if (ret != NLOPT_XTOL_REACHED) goto done; } /* nelder-mead on last subspace */ ns = n - i; sd.is = i; for (; i < n; ++i) { xs[i-sd.is] = x[p[i]]; xsstep[i-sd.is] = xstep[p[i]]; lbs[i-sd.is] = lb[p[i]]; ubs[i-sd.is] = ub[p[i]]; } ++nsubs; nevals = stop->nevals; ret = nldrmd_minimize_(ns, subspace_func, &sd, lbs,ubs,xs, minf, xsstep, stop, psi, scratch, &fdiff); if (fdiff > fdiff_max) fdiff_max = fdiff; if (sbplx_verbose) printf("sbplx: %d NM iterations for (%d,%d) subspace\n", stop->nevals - nevals, sd.is, ns); for (i = sd.is; i < n; ++i) x[p[i]] = xs[i-sd.is]; if (ret == NLOPT_FAILURE) { ret=NLOPT_XTOL_REACHED; goto done; } if (ret != NLOPT_XTOL_REACHED) goto done; /* termination tests: */ if (nlopt_stop_ftol(stop, *minf, *minf + fdiff_max)) { ret = NLOPT_FTOL_REACHED; goto done; } if (nlopt_stop_x(stop, x, xprev)) { int j; /* as explained in Rowan's thesis, it is important to check |xstep| as well as |x-xprev|, since if the step size is too large (in early iterations), the inner Nelder-Mead may not make much progress */ for (j = 0; j < n; ++j) if (fabs(xstep[j]) * psi > stop->xtol_abs[j] && fabs(xstep[j]) * psi > stop->xtol_rel * fabs(x[j])) break; if (j == n) { ret = NLOPT_XTOL_REACHED; goto done; } } /* compute change in optimal point */ for (i = 0; i < n; ++i) dx[i] = x[i] - xprev[i]; /* setting stepsizes */ { double scale; if (nsubs == 1) scale = psi; else { double stepnorm = 0, dxnorm = 0; for (i = 0; i < n; ++i) { stepnorm += fabs(xstep[i]); dxnorm += fabs(dx[i]); } scale = dxnorm / stepnorm; if (scale < omega) scale = omega; if (scale > 1/omega) scale = 1/omega; 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Currently, I have implemented two algorithms, described below. The code in this directory is under the same MIT license as the rest of my code in NLopt (see ../COPYRIGHT). Steven G. Johnson November 2008 ----------------------------------------------------------------------- First, (almost) the original Nelder-Mead simplex algorithm (NLOPT_LN_NELDERMEAD), as described in: J. A. Nelder and R. Mead, "A simplex method for function minimization," The Computer Journal 7, p. 308-313 (1965). This method is simple and has demonstrated enduring popularity, despite the later discovery that it fails to converge at all for some functions. Anecdotal evidence suggests that it often performs well even for noisy and/or discontinuous objective functions. I would tend to recommend the Subplex method (below) instead, however. The main variation is that I implemented explicit support for bound constraints, using essentially the method described in: J. A. Richardson and J. L. Kuester, "The complex method for constrained optimization," Commun. ACM 16(8), 487-489 (1973). implementing the method described by: M. J. Box, "A new method of constrained optimization and a comparison with other methods," Computer J. 8 (1), 42-52 (1965). Whenever a new point would lie outside the bound constraints, Box advocates moving it "just inside" the constraints. I couldn't see any advantage to using a fixed distance inside the constraints, especially if the optimum is on the constraint, so instead I move the point exactly onto the constraint in that case. The danger with implementing bound constraints in this way (or by Box's method) is that you may collapse the simplex into a lower-dimensional subspace. I'm not aware of a better way, however. In any case, this collapse of the simplex is ameliorated by restarting, such as when Nelder-Mead is used within the Subplex algorithm below. ----------------------------------------------------------------------- Second, I re-implemented Tom Rowan's "Subplex" algorithm. As Rowan expressed a preference that other implementations of his algorithm use a different name, I called my implementation "Sbplx" (NLOPT_LN_SBPLX). Subplex (a variant of Nelder-Mead that uses Nelder-Mead on a sequence of subspaces) is claimed to be much more efficient and robust than the original Nelder-Mead, while retaining the latter's facility with discontinuous objectives, and in my experience these claims seem to be true. (However, I'm not aware of any proof that Subplex is globally convergent, and may fail for some objectives like Nelder-Mead; YMMV.) I used the description of Rowan's algorithm in his PhD thesis: T. Rowan, "Functional Stability Analysis of Numerical Algorithms", Ph.D. thesis, Department of Computer Sciences, University of Texas at Austin, 1990. I would have preferred to use Rowan's original implementation, posted by him on Netlib: http://www.netlib.org/opt/subplex.tgz Unfortunately, the legality of redistributing or modifying this code is unclear. Rowan didn't include any license statement at all with the original code, which makes it technically illegal to redistribute. I contacted Rowan about getting a clear open-source/free-software license for it, and he was very agreeable, but he said he had to think about the specific license choice and would get back to me. Unfortunately, a year later I still haven't heard from him, and his old email address no longer seems to work, so I don't know how to contact him for permission. Since the algorithm is not too complicated, however, I just rewrote it. There seem to be slight differences between the behavior of my implementation and his (probably due to different choices of initial subspace and other slight variations, where his paper was ambiguous), but the number of iterations to converge on my test problems seems to be quite close (within 10% for most problems). The only major difference between my implementation and Rowan's, as far as I can tell, is that I implemented explicit support for bound constraints (via the method in the Box paper as described above). This seems to be a big improvement in the case where the optimum lies against one of the constraints. ----------------------------------------------------------------------- Future possibilities: C. J. Price, I. D. Coope, and D. Byatt, "A convergent variant of the Nelder-Mead algorithm," J. Optim. Theory Appl. 113 (1), p. 5-19 (2002). A. Burmen, J. Puhan, and T. Tuma, "Grid restrained Nelder-Mead algorithm," Computational Optim. Appl. 34(3), 359-375 (2006). Both of these are provably convergent variations of Nelder-Mead; the latter authors claim that theirs is superior. nloptr/src/nlopt_src/crs/0000755000176000001440000000000013263734242015207 5ustar ripleyusersnloptr/src/nlopt_src/crs/crs.c0000644000176000001440000002230013355070635016140 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include #include #include "crs.h" #include "redblack.h" /* Controlled Random Search 2 (CRS2) with "local mutation", as defined by: P. Kaelo and M. M. Ali, "Some variants of the controlled random search algorithm for global optimization," J. Optim. Theory Appl. 130 (2), 253-264 (2006). */ typedef struct { int n; /* # dimensions */ const double *lb, *ub; nlopt_stopping *stop; /* stopping criteria */ nlopt_func f; void *f_data; int N; /* # points in population */ double *ps; /* population array N x (n+1) of tuples [f(x), x] */ double *p; /* single point array (length n+1), for temp use */ rb_tree t; /* red-black tree of population, sorted by f(x) */ nlopt_sobol s; /* sobol data for LDS point generation, or NULL to use pseudo-random numbers */ } crs_data; /* sort order in red-black tree: keys [f(x), x] are sorted by f(x) */ static int crs_compare(double *k1, double *k2) { if (*k1 < *k2) return -1; if (*k1 > *k2) return +1; return k1 - k2; /* tie-breaker */ } /* set x to a random trial value, as defined by CRS: x = 2G - x_n, where x_0 ... x_n are distinct points in the population with x_0 the current best point and the other points are random, and G is the centroid of x_0...x_{n-1} */ static void random_trial(crs_data *d, double *x, rb_node *best) { int n = d->n, n1 = n+1, i, k, i0, jn; double *ps = d->ps, *xi; /* initialize x to x_0 = best point */ memcpy(x, best->k + 1, sizeof(double) * n); i0 = (best->k - ps) / n1; jn = nlopt_iurand(n); /* which of remaining n points is "x_n", i.e. which to reflect through ... this is necessary since we generate the remaining points in order, so just picking the last point would not be very random */ /* use "method A" from Jeffrey Scott Vitter, "An efficient algorithm for sequential random sampling," ACM Trans. Math. Soft. 13 (1), 58--67 (1987). to randomly pick n distinct points out of the remaining N-1 (not including i0!). (The same as "method S" in Knuth vol. 2.) This method requires O(N) time, which is fine in our case (there are better methods if n << N). */ { int Nleft = d->N - 1, nleft = n; int Nfree = Nleft - nleft; i = 0; i += i == i0; while (nleft > 1) { double q = ((double) Nfree) / Nleft; double v = nlopt_urand(0., 1.); while (q > v) { ++i; i += i == i0; --Nfree; --Nleft; q = (q * Nfree) / Nleft; } xi = ps + n1 * i + 1; if (jn-- == 0) /* point to reflect through */ for (k = 0; k < n; ++k) x[k] -= xi[k] * (0.5*n); else /* point to include in centroid */ for (k = 0; k < n; ++k) x[k] += xi[k]; ++i; i += i == i0; --Nleft; --nleft; } i += nlopt_iurand(Nleft); i += i == i0; xi = ps + n1 * i + 1; if (jn-- == 0) /* point to reflect through */ for (k = 0; k < n; ++k) x[k] -= xi[k] * (0.5*n); else /* point to include in centroid */ for (k = 0; k < n; ++k) x[k] += xi[k]; } for (k = 0; k < n; ++k) { x[k] *= 2.0 / n; /* renormalize */ if (x[k] > d->ub[k]) x[k] = d->ub[k]; else if (x[k] < d->lb[k]) x[k] = d->lb[k]; } } #define NUM_MUTATION 1 /* # "local mutation" steps to try if trial fails */ static nlopt_result crs_trial(crs_data *d) { rb_node *best = rb_tree_min(&d->t); rb_node *worst = rb_tree_max(&d->t); int mutation = NUM_MUTATION; int i, n = d->n; random_trial(d, d->p + 1, best); do { d->p[0] = d->f(n, d->p + 1, NULL, d->f_data); d->stop->nevals++; if (nlopt_stop_forced(d->stop)) return NLOPT_FORCED_STOP; if (d->p[0] < worst->k[0]) break; if (nlopt_stop_evals(d->stop)) return NLOPT_MAXEVAL_REACHED; if (nlopt_stop_time(d->stop)) return NLOPT_MAXTIME_REACHED; if (mutation) { for (i = 0; i < n; ++i) { double w = nlopt_urand(0.,1.); d->p[1+i] = best->k[1+i] * (1 + w) - w * d->p[1+i]; if (d->p[1+i] > d->ub[i]) d->p[1+i] = d->ub[i]; else if (d->p[1+i] < d->lb[i]) d->p[1+i] = d->lb[i]; } mutation--; } else { random_trial(d, d->p + 1, best); mutation = NUM_MUTATION; } } while (1); memcpy(worst->k, d->p, sizeof(double) * (n+1)); rb_tree_resort(&d->t, worst); return NLOPT_SUCCESS; } static void crs_destroy(crs_data *d) { nlopt_sobol_destroy(d->s); rb_tree_destroy(&d->t); free(d->ps); } static nlopt_result crs_init(crs_data *d, int n, const double *x, const double *lb, const double *ub, nlopt_stopping *stop, nlopt_func f, void *f_data, int population, int lds) { int i; if (!population) { /* TODO: how should we set the default population size? the Kaelo and Ali paper suggests 10*(n+1), but should we add more random points if maxeval is large, or... ? */ d->N = 10 * (n + 1); /* heuristic initial population size */ } else d->N = population; if (d->N < n + 1) /* population must be big enough for a simplex */ return NLOPT_INVALID_ARGS; d->n = n; d->stop = stop; d->f = f; d->f_data = f_data; d->ub = ub; d->lb = lb; d->ps = (double *) malloc(sizeof(double) * (n + 1) * (d->N + 1)); if (!d->ps) return NLOPT_OUT_OF_MEMORY; d->p = d->ps + d->N * (n+1); rb_tree_init(&d->t, crs_compare); /* we can either use pseudorandom points, as in the original CRS algorithm, or use a low-discrepancy Sobol' sequence ... I tried the latter, however, and it doesn't seem to help, probably because we are only generating a small number of random points to start with */ d->s = lds ? nlopt_sobol_create((unsigned) n) : NULL; nlopt_sobol_skip(d->s, (unsigned) d->N, d->ps + 1); /* generate initial points randomly, plus starting guess x */ memcpy(d->ps + 1, x, sizeof(double) * n); d->ps[0] = f(n, x, NULL, f_data); stop->nevals++; if (!rb_tree_insert(&d->t, d->ps)) return NLOPT_OUT_OF_MEMORY; if (d->ps[0] < stop->minf_max) return NLOPT_MINF_MAX_REACHED; if (nlopt_stop_evals(stop)) return NLOPT_MAXEVAL_REACHED; if (nlopt_stop_time(stop)) return NLOPT_MAXTIME_REACHED; for (i = 1; i < d->N; ++i) { double *k = d->ps + i*(n+1); if (d->s) nlopt_sobol_next(d->s, k + 1, lb, ub); else { int j; for (j = 0; j < n; ++j) k[1 + j] = nlopt_urand(lb[j], ub[j]); } k[0] = f(n, k + 1, NULL, f_data); stop->nevals++; if (!rb_tree_insert(&d->t, k)) return NLOPT_OUT_OF_MEMORY; if (k[0] < stop->minf_max) return NLOPT_MINF_MAX_REACHED; if (nlopt_stop_evals(stop)) return NLOPT_MAXEVAL_REACHED; if (nlopt_stop_time(stop)) return NLOPT_MAXTIME_REACHED; } return NLOPT_SUCCESS;; } nlopt_result crs_minimize(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, nlopt_stopping *stop, int population, /* initial population (0=default) */ int lds) /* random or low-discrepancy seq. 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Kaelo and M. M. Ali, "Some variants of the controlled random search algorithm for global optimization," J. Optim. Theory Appl. 130 (2), 253-264 (2006). The original CRS2 algorithm was described by: W. L. Price, "A controlled random search procedure for global optimization," in Towards Global Optimization 2, p. 71-84 edited by L. C. W. Dixon and G. P. Szego (North-Holland Press, Amsterdam, 1978). It is under the same MIT license as the rest of my code in NLopt (see ../COPYRIGHT). Steven G. Johnson September 2007 nloptr/src/nlopt_src/crs/crs.h0000644000176000001440000000322613355070635016153 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #ifndef CRS_H #define CRS_H #include "nlopt.h" #include "nlopt-util.h" #ifdef __cplusplus extern "C" { #endif /* __cplusplus */ nlopt_result crs_minimize(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, nlopt_stopping *stop, int population, /* initial population (0=default) */ int random); /* random or low-discrepancy seq. */ #ifdef __cplusplus } /* extern "C" */ #endif /* __cplusplus */ #endif nloptr/src/nlopt_src/mma/0000755000176000001440000000000013263734242015172 5ustar ripleyusersnloptr/src/nlopt_src/mma/ccsa_quadratic.c0000644000176000001440000004450113355070635020311 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ /* In this file we implement Svanberg's CCSA algorithm with the simple linear approximation + quadratic penalty function. We also allow the user to specify an optional "preconditioner": an approximate Hessian (which must be symmetric & positive semidefinite) that can be added into the approximation. [X. Liang and I went through the convergence proof in Svanberg's paper and it does not seem to be modified by such preconditioning, as long as the preconditioner eigenvalues are bounded above for all x.] For the non-preconditioned case the trust-region subproblem is separable and can be solved by a dual method. For the preconditioned case the subproblem is still convex but in general is non-separable so we solve by calling the same algorithm recursively, under the assumption that the subproblem objective is cheap to evaluate. */ #include #include #include #include #include "mma.h" #include "nlopt-util.h" unsigned ccsa_verbose = 0; /* > 0 for verbose output */ #define MIN(a,b) ((a) < (b) ? (a) : (b)) #define MAX(a,b) ((a) > (b) ? (a) : (b)) /* magic minimum value for rho in CCSA ... the 2002 paper says it should be a "fixed, strictly positive `small' number, e.g. 1e-5" ... grrr, I hate these magic numbers, which seem like they should depend on the objective function in some way ... in particular, note that rho is dimensionful (= dimensions of objective function) */ #define CCSA_RHOMIN 1e-5 /***********************************************************************/ /* function for CCSA's dual solution of the approximate problem */ typedef struct { int count; /* evaluation count, incremented each call */ unsigned n; /* must be set on input to dimension of x */ const double *x, *lb, *ub, *sigma, *dfdx; /* arrays of length n */ const double *dfcdx; /* m-by-n array of fc gradients */ double fval, rho; /* must be set on input */ const double *fcval, *rhoc; /* arrays of length m */ double *xcur; /* array of length n, output each time */ double gval, wval, *gcval; /* output each time (array length m) */ nlopt_precond pre; void *pre_data; nlopt_precond *prec; void **prec_data; /* length = # constraints */ double *scratch; /* length = 2*n */ } dual_data; static double sqr(double x) { return x * x; } static double dual_func(unsigned m, const double *y, double *grad, void *d_) { dual_data *d = (dual_data *) d_; unsigned n = d->n; const double *x = d->x, *lb = d->lb, *ub = d->ub, *sigma = d->sigma, *dfdx = d->dfdx; const double *dfcdx = d->dfcdx; double rho = d->rho, fval = d->fval; const double *rhoc = d->rhoc, *fcval = d->fcval; double *xcur = d->xcur; double *gcval = d->gcval; unsigned i, j; double val; d->count++; val = d->gval = fval; d->wval = 0; for (i = 0; i < m; ++i) val += y[i] * (gcval[i] = fcval[i]); for (j = 0; j < n; ++j) { double u, v, dx, sigma2, dx2, dx2sig; /* first, compute xcur[j] = x+dx for y. Because this objective is separable, we can minimize over x analytically, and the minimum dx is given by the solution of a linear equation u dx + v sigma^2 = 0, i.e. dx = -sigma^2 v/u where u and v are defined by the sums below. However, we also have to check that |dx| <= sigma and that lb <= x+dx <= ub. */ if (sigma[j] == 0) { /* special case for lb[i] == ub[i] dims, dx=0 */ xcur[j] = x[j]; continue; } u = rho; v = dfdx[j]; for (i = 0; i < m; ++i) { u += rhoc[i] * y[i]; v += dfcdx[i*n + j] * y[i]; } dx = -(sigma2 = sqr(sigma[j])) * v/u; /* if dx is out of bounds, we are guaranteed by convexity that the minimum is at the bound on the side of dx */ if (fabs(dx) > sigma[j]) dx = copysign(sigma[j], dx); xcur[j] = x[j] + dx; if (xcur[j] > ub[j]) xcur[j] = ub[j]; else if (xcur[j] < lb[j]) xcur[j] = lb[j]; dx = xcur[j] - x[j]; /* function value: */ dx2 = dx * dx; val += v * dx + 0.5 * u * dx2 / sigma2; /* update gval, wval, gcval (approximant functions) */ d->gval += dfdx[j] * dx + rho * (dx2sig = 0.5*dx2/sigma2); d->wval += dx2sig; for (i = 0; i < m; ++i) gcval[i] += dfcdx[i*n+j] * dx + rhoc[i] * dx2sig; } /* gradient is easy to compute: since we are at a minimum x (dval/dx=0), we only need the partial derivative with respect to y, and we negate because we are maximizing: */ if (grad) for (i = 0; i < m; ++i) grad[i] = -gcval[i]; return -val; } /***********************************************************************/ /* compute g(x - x0) and its gradient */ static double gfunc(unsigned n, double f, const double *dfdx, double rho, const double *sigma, const double *x0, nlopt_precond pre, void *pre_data, double *scratch, const double *x, double *grad) { double *dx = scratch, *Hdx = scratch + n; double val = f; unsigned j; for (j = 0; j < n; ++j) { double sigma2inv = 1.0 / sqr(sigma[j]); dx[j] = x[j] - x0[j]; val += dfdx[j] * dx[j] + (0.5*rho) * sqr(dx[j]) * sigma2inv; if (grad) grad[j] = dfdx[j] + rho * dx[j] * sigma2inv; } if (pre) { pre(n, x0, dx, Hdx, pre_data); for (j = 0; j < n; ++j) val += 0.5 * dx[j] * Hdx[j]; if (grad) for (j = 0; j < n; ++j) grad[j] += Hdx[j]; } return val; } static double g0(unsigned n, const double *x, double *grad, void *d_) { dual_data *d = (dual_data *) d_; d->count++; return gfunc(n, d->fval, d->dfdx, d->rho, d->sigma, d->x, d->pre, d->pre_data, d->scratch, x, grad); } static void gi(unsigned m, double *result, unsigned n, const double *x, double *grad, void *d_) { dual_data *d = (dual_data *) d_; unsigned i; for (i = 0; i < m; ++i) result[i] = gfunc(n, d->fcval[i], d->dfcdx + i*n, d->rhoc[i], d->sigma, d->x, d->prec ? d->prec[i] : NULL, d->prec_data ? d->prec_data[i] : NULL, d->scratch, x, grad); } /***********************************************************************/ nlopt_result ccsa_quadratic_minimize( unsigned n, nlopt_func f, void *f_data, unsigned m, nlopt_constraint *fc, nlopt_precond pre, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, nlopt_stopping *stop, nlopt_opt dual_opt) { nlopt_result ret = NLOPT_SUCCESS; double *xcur, rho, *sigma, *dfdx, *dfdx_cur, *xprev, *xprevprev, fcur; double *dfcdx, *dfcdx_cur; double *fcval, *fcval_cur, *rhoc, *gcval, *y, *dual_lb, *dual_ub; double *pre_lb, *pre_ub; unsigned i, ifc, j, k = 0; dual_data dd; int feasible; double infeasibility; unsigned mfc; unsigned no_precond; nlopt_opt pre_opt = NULL; m = nlopt_count_constraints(mfc = m, fc); if (nlopt_get_dimension(dual_opt) != m) return NLOPT_INVALID_ARGS; sigma = (double *) malloc(sizeof(double) * (6*n + 2*m*n + m*7)); if (!sigma) return NLOPT_OUT_OF_MEMORY; dfdx = sigma + n; dfdx_cur = dfdx + n; xcur = dfdx_cur + n; xprev = xcur + n; xprevprev = xprev + n; fcval = xprevprev + n; fcval_cur = fcval + m; rhoc = fcval_cur + m; gcval = rhoc + m; dual_lb = gcval + m; dual_ub = dual_lb + m; y = dual_ub + m; dfcdx = y + m; dfcdx_cur = dfcdx + m*n; dd.n = n; dd.x = x; dd.lb = lb; dd.ub = ub; dd.sigma = sigma; dd.dfdx = dfdx; dd.dfcdx = dfcdx; dd.fcval = fcval; dd.rhoc = rhoc; dd.xcur = xcur; dd.gcval = gcval; dd.pre = pre; dd.pre_data = f_data; dd.prec = NULL; dd.prec_data = NULL; dd.scratch = NULL; if (m) { dd.prec = (nlopt_precond *) malloc(sizeof(nlopt_precond) * m); dd.prec_data = (void **) malloc(sizeof(void *) * m); if (!dd.prec || !dd.prec_data) { ret = NLOPT_OUT_OF_MEMORY; goto done; } for (i = ifc = 0; ifc < mfc; ++ifc) { unsigned inext = i + fc[ifc].m; for (; i < inext; ++i) { dd.prec[i] = fc[ifc].pre; dd.prec_data[i] = fc[ifc].f_data; } } } no_precond = pre == NULL; if (dd.prec) for (i = 0; i < m; ++i) no_precond = no_precond && dd.prec[i] == NULL; if (!no_precond) { dd.scratch = (double*) malloc(sizeof(double) * (4*n)); if (!dd.scratch) { free(sigma); return NLOPT_OUT_OF_MEMORY; } pre_lb = dd.scratch + 2*n; pre_ub = pre_lb + n; pre_opt = nlopt_create(nlopt_get_algorithm(dual_opt), n); if (!pre_opt) { ret = NLOPT_FAILURE; goto done; } ret = nlopt_set_min_objective(pre_opt, g0, &dd); if (ret < 0) goto done; ret = nlopt_add_inequality_mconstraint(pre_opt, m, gi, &dd, NULL); if (ret < 0) goto done; ret = nlopt_set_ftol_rel(pre_opt, nlopt_get_ftol_rel(dual_opt)); if (ret < 0) goto done; ret = nlopt_set_ftol_abs(pre_opt, nlopt_get_ftol_abs(dual_opt)); if (ret < 0) goto done; ret = nlopt_set_maxeval(pre_opt, nlopt_get_maxeval(dual_opt)); if (ret < 0) goto done; } for (j = 0; j < n; ++j) { if (nlopt_isinf(ub[j]) || nlopt_isinf(lb[j])) sigma[j] = 1.0; /* arbitrary default */ else sigma[j] = 0.5 * (ub[j] - lb[j]); } rho = 1.0; for (i = 0; i < m; ++i) { rhoc[i] = 1.0; dual_lb[i] = y[i] = 0.0; dual_ub[i] = HUGE_VAL; } dd.fval = fcur = *minf = f(n, x, dfdx, f_data); stop->nevals++; memcpy(xcur, x, sizeof(double) * n); if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } feasible = 1; infeasibility = 0; for (i = ifc = 0; ifc < mfc; ++ifc) { nlopt_eval_constraint(fcval + i, dfcdx + i*n, fc + ifc, n, x); i += fc[ifc].m; if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } } for (i = 0; i < m; ++i) { feasible = feasible && fcval[i] <= 0; if (fcval[i] > infeasibility) infeasibility = fcval[i]; } /* For non-feasible initial points, set a finite (large) upper-bound on the dual variables. What this means is that, if no feasible solution is found from the dual problem, it will minimize the dual objective with the unfeasible constraint weighted by 1e40 -- basically, minimizing the unfeasible constraint until it becomes feasible or until we at least obtain a step towards a feasible point. Svanberg suggested a different approach in his 1987 paper, basically introducing additional penalty variables for unfeasible constraints, but this is easier to implement and at least as efficient. */ if (!feasible) for (i = 0; i < m; ++i) dual_ub[i] = 1e40; nlopt_set_min_objective(dual_opt, dual_func, &dd); nlopt_set_lower_bounds(dual_opt, dual_lb); nlopt_set_upper_bounds(dual_opt, dual_ub); nlopt_set_stopval(dual_opt, -HUGE_VAL); nlopt_remove_inequality_constraints(dual_opt); nlopt_remove_equality_constraints(dual_opt); while (1) { /* outer iterations */ double fprev = fcur; if (nlopt_stop_forced(stop)) ret = NLOPT_FORCED_STOP; else if (nlopt_stop_evals(stop)) ret = NLOPT_MAXEVAL_REACHED; else if (nlopt_stop_time(stop)) ret = NLOPT_MAXTIME_REACHED; else if (feasible && *minf < stop->minf_max) ret = NLOPT_MINF_MAX_REACHED; if (ret != NLOPT_SUCCESS) goto done; if (++k > 1) memcpy(xprevprev, xprev, sizeof(double) * n); memcpy(xprev, xcur, sizeof(double) * n); while (1) { /* inner iterations */ double min_dual, infeasibility_cur; int feasible_cur, inner_done; unsigned save_verbose; nlopt_result reti; if (no_precond) { /* solve dual problem */ dd.rho = rho; dd.count = 0; save_verbose = ccsa_verbose; ccsa_verbose = 0; /* no recursive verbosity */ reti = nlopt_optimize_limited(dual_opt, y, &min_dual, 0, stop->maxtime - (nlopt_seconds() - stop->start)); ccsa_verbose = save_verbose; if (reti < 0 || reti == NLOPT_MAXTIME_REACHED) { ret = reti; goto done; } dual_func(m, y, NULL, &dd); /* evaluate final xcur etc. */ } else { double pre_min; for (j = 0; j < n; ++j) { pre_lb[j] = MAX(lb[j], x[j] - sigma[j]); pre_ub[j] = MIN(ub[j], x[j] + sigma[j]); xcur[j] = x[j]; } nlopt_set_lower_bounds(pre_opt, pre_lb); nlopt_set_upper_bounds(pre_opt, pre_ub); dd.rho = rho; dd.count = 0; save_verbose = ccsa_verbose; ccsa_verbose = 0; /* no recursive verbosity */ reti = nlopt_optimize_limited(pre_opt, xcur, &pre_min, 0, stop->maxtime - (nlopt_seconds() - stop->start)); ccsa_verbose = save_verbose; if (reti < 0 || reti == NLOPT_MAXTIME_REACHED) { ret = reti; goto done; } /* evaluate final xcur etc */ dd.gval = g0(n, xcur, NULL, &dd); gi(m, dd.gcval, n, xcur, NULL, &dd); } if (ccsa_verbose) { printf("CCSA dual converged in %d iters to g=%g:\n", dd.count, dd.gval); for (i = 0; i < MIN(ccsa_verbose, m); ++i) printf(" CCSA y[%d]=%g, gc[%d]=%g\n", i, y[i], i, dd.gcval[i]); } fcur = f(n, xcur, dfdx_cur, f_data); stop->nevals++; if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } feasible_cur = 1; infeasibility_cur = 0; inner_done = dd.gval >= fcur; for (i = ifc = 0; ifc < mfc; ++ifc) { nlopt_eval_constraint(fcval_cur + i, dfcdx_cur + i*n, fc + ifc, n, xcur); i += fc[ifc].m; if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } } for (i = ifc = 0; ifc < mfc; ++ifc) { unsigned i0 = i, inext = i + fc[ifc].m; for (; i < inext; ++i) { feasible_cur = feasible_cur && fcval_cur[i] <= fc[ifc].tol[i-i0]; inner_done = inner_done && (dd.gcval[i] >= fcval_cur[i]); if (fcval_cur[i] > infeasibility_cur) infeasibility_cur = fcval_cur[i]; } } if ((fcur < *minf && (inner_done || feasible_cur || !feasible)) || (!feasible && infeasibility_cur < infeasibility)) { if (ccsa_verbose && !feasible_cur) printf("CCSA - using infeasible point?\n"); dd.fval = *minf = fcur; infeasibility = infeasibility_cur; memcpy(fcval, fcval_cur, sizeof(double)*m); memcpy(x, xcur, sizeof(double)*n); memcpy(dfdx, dfdx_cur, sizeof(double)*n); memcpy(dfcdx, dfcdx_cur, sizeof(double)*n*m); /* once we have reached a feasible solution, the algorithm should never make the solution infeasible again (if inner_done), although the constraints may be violated slightly by rounding errors etc. so we must be a little careful about checking feasibility */ if (infeasibility_cur == 0) { if (!feasible) { /* reset upper bounds to infin. */ for (i = 0; i < m; ++i) dual_ub[i] = HUGE_VAL; nlopt_set_upper_bounds(dual_opt, dual_ub); } feasible = 1; } } if (nlopt_stop_forced(stop)) ret = NLOPT_FORCED_STOP; else if (nlopt_stop_evals(stop)) ret = NLOPT_MAXEVAL_REACHED; else if (nlopt_stop_time(stop)) ret = NLOPT_MAXTIME_REACHED; else if (feasible && *minf < stop->minf_max) ret = NLOPT_MINF_MAX_REACHED; if (ret != NLOPT_SUCCESS) goto done; if (inner_done) break; if (fcur > dd.gval) rho = MIN(10*rho, 1.1 * (rho + (fcur-dd.gval) / dd.wval)); for (i = 0; i < m; ++i) if (fcval_cur[i] > dd.gcval[i]) rhoc[i] = MIN(10*rhoc[i], 1.1 * (rhoc[i] + (fcval_cur[i]-dd.gcval[i]) / dd.wval)); if (ccsa_verbose) printf("CCSA inner iteration: rho -> %g\n", rho); for (i = 0; i < MIN(ccsa_verbose, m); ++i) printf(" CCSA rhoc[%d] -> %g\n", i,rhoc[i]); } if (nlopt_stop_ftol(stop, fcur, fprev)) ret = NLOPT_FTOL_REACHED; if (nlopt_stop_x(stop, xcur, xprev)) ret = NLOPT_XTOL_REACHED; if (ret != NLOPT_SUCCESS) goto done; /* update rho and sigma for iteration k+1 */ rho = MAX(0.1 * rho, CCSA_RHOMIN); if (ccsa_verbose) printf("CCSA outer iteration: rho -> %g\n", rho); for (i = 0; i < m; ++i) rhoc[i] = MAX(0.1 * rhoc[i], CCSA_RHOMIN); for (i = 0; i < MIN(ccsa_verbose, m); ++i) printf(" CCSA rhoc[%d] -> %g\n", i, rhoc[i]); if (k > 1) { for (j = 0; j < n; ++j) { double dx2 = (xcur[j]-xprev[j]) * (xprev[j]-xprevprev[j]); double gam = dx2 < 0 ? 0.7 : (dx2 > 0 ? 1.2 : 1); sigma[j] *= gam; if (!nlopt_isinf(ub[j]) && !nlopt_isinf(lb[j])) { sigma[j] = MIN(sigma[j], 10*(ub[j]-lb[j])); /* use a smaller lower bound than Svanberg's 0.01*(ub-lb), which seems unnecessarily large */ sigma[j] = MAX(sigma[j], 1e-8*(ub[j]-lb[j])); } } for (j = 0; j < MIN(ccsa_verbose, n); ++j) printf(" CCSA sigma[%d] -> %g\n", j, sigma[j]); } } done: nlopt_destroy(pre_opt); if (dd.scratch) free(dd.scratch); if (m) { free(dd.prec_data); free(dd.prec); } free(sigma); return ret; } nloptr/src/nlopt_src/mma/mma.c0000644000176000001440000003443413355070635016121 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include #include #include #include #include "mma.h" #include "nlopt-util.h" unsigned mma_verbose = 0; /* > 0 for verbose output */ #define MIN(a,b) ((a) < (b) ? (a) : (b)) #define MAX(a,b) ((a) > (b) ? (a) : (b)) #ifndef HAVE_ISNAN static int my_isnan(double x) { return x != x; } # define isnan my_isnan #endif /* magic minimum value for rho in MMA ... the 2002 paper says it should be a "fixed, strictly positive `small' number, e.g. 1e-5" ... grrr, I hate these magic numbers, which seem like they should depend on the objective function in some way ... in particular, note that rho is dimensionful (= dimensions of objective function) */ #define MMA_RHOMIN 1e-5 /***********************************************************************/ /* function for MMA's dual solution of the approximate problem */ typedef struct { int count; /* evaluation count, incremented each call */ unsigned n; /* must be set on input to dimension of x */ const double *x, *lb, *ub, *sigma, *dfdx; /* arrays of length n */ const double *dfcdx; /* m-by-n array of fc gradients */ double fval, rho; /* must be set on input */ const double *fcval, *rhoc; /* arrays of length m */ double *xcur; /* array of length n, output each time */ double gval, wval, *gcval; /* output each time (array length m) */ } dual_data; static double sqr(double x) { return x * x; } static double dual_func(unsigned m, const double *y, double *grad, void *d_) { dual_data *d = (dual_data *) d_; unsigned n = d->n; const double *x = d->x, *lb = d->lb, *ub = d->ub, *sigma = d->sigma, *dfdx = d->dfdx; const double *dfcdx = d->dfcdx; double rho = d->rho, fval = d->fval; const double *rhoc = d->rhoc, *fcval = d->fcval; double *xcur = d->xcur; double *gcval = d->gcval; unsigned i, j; double val; d->count++; val = d->gval = fval; d->wval = 0; for (i = 0; i < m; ++i) val += y[i] * (gcval[i] = isnan(fcval[i]) ? 0 : fcval[i]); for (j = 0; j < n; ++j) { double u, v, dx, denominv, c, sigma2, dx2; /* first, compute xcur[j] for y. Because this objective is separable, we can minimize over x analytically, and the minimum dx is given by the solution of a quadratic equation: u dx^2 + 2 v sigma^2 dx + u sigma^2 = 0 where u and v are defined by the sums below. Because of the definitions, it is guaranteed that |u/v| <= sigma, and it follows that the only dx solution with |dx| <= sigma is given by: (v/u) sigma^2 (-1 + sqrt(1 - (u / v sigma)^2)) = (u/v) / (-1 - sqrt(1 - (u / v sigma)^2)) (which goes to zero as u -> 0). The latter expression is less susceptible to roundoff error. */ if (sigma[j] == 0) { /* special case for lb[i] == ub[i] dims, dx=0 */ xcur[j] = x[j]; continue; } u = dfdx[j]; v = fabs(dfdx[j]) * sigma[j] + 0.5 * rho; for (i = 0; i < m; ++i) if (!isnan(fcval[i])) { u += dfcdx[i*n + j] * y[i]; v += (fabs(dfcdx[i*n + j]) * sigma[j] + 0.5 * rhoc[i]) * y[i]; } u *= (sigma2 = sqr(sigma[j])); dx = (u/v) / (-1 - sqrt(fabs(1 - sqr(u/(v*sigma[j]))))); xcur[j] = x[j] + dx; if (xcur[j] > ub[j]) xcur[j] = ub[j]; else if (xcur[j] < lb[j]) xcur[j] = lb[j]; if (xcur[j] > x[j]+0.9*sigma[j]) xcur[j] = x[j]+0.9*sigma[j]; else if (xcur[j] < x[j]-0.9*sigma[j]) xcur[j] = x[j]-0.9*sigma[j]; dx = xcur[j] - x[j]; /* function value: */ dx2 = dx * dx; denominv = 1.0 / (sigma2 - dx2); val += (u * dx + v * dx2) * denominv; /* update gval, wval, gcval (approximant functions) */ c = sigma2 * dx; d->gval += (dfdx[j] * c + (fabs(dfdx[j])*sigma[j] + 0.5*rho) * dx2) * denominv; d->wval += 0.5 * dx2 * denominv; for (i = 0; i < m; ++i) if (!isnan(fcval[i])) gcval[i] += (dfcdx[i*n+j] * c + (fabs(dfcdx[i*n+j])*sigma[j] + 0.5*rhoc[i]) * dx2) * denominv; } /* gradient is easy to compute: since we are at a minimum x (dval/dx=0), we only need the partial derivative with respect to y, and we negate because we are maximizing: */ if (grad) for (i = 0; i < m; ++i) grad[i] = -gcval[i]; return -val; } /***********************************************************************/ /* note that we implement a hidden feature not in the standard nlopt_minimize_constrained interface: whenever the constraint function returns NaN, that constraint becomes inactive. */ nlopt_result mma_minimize(unsigned n, nlopt_func f, void *f_data, unsigned m, nlopt_constraint *fc, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, nlopt_stopping *stop, nlopt_opt dual_opt) { nlopt_result ret = NLOPT_SUCCESS; double *xcur, rho, *sigma, *dfdx, *dfdx_cur, *xprev, *xprevprev, fcur; double *dfcdx, *dfcdx_cur; double *fcval, *fcval_cur, *rhoc, *gcval, *y, *dual_lb, *dual_ub; unsigned i, ifc, j, k = 0; dual_data dd; int feasible; double infeasibility; unsigned mfc; m = nlopt_count_constraints(mfc = m, fc); if (nlopt_get_dimension(dual_opt) != m) return NLOPT_INVALID_ARGS; sigma = (double *) malloc(sizeof(double) * (6*n + 2*m*n + m*7)); if (!sigma) return NLOPT_OUT_OF_MEMORY; dfdx = sigma + n; dfdx_cur = dfdx + n; xcur = dfdx_cur + n; xprev = xcur + n; xprevprev = xprev + n; fcval = xprevprev + n; fcval_cur = fcval + m; rhoc = fcval_cur + m; gcval = rhoc + m; dual_lb = gcval + m; dual_ub = dual_lb + m; y = dual_ub + m; dfcdx = y + m; dfcdx_cur = dfcdx + m*n; dd.n = n; dd.x = x; dd.lb = lb; dd.ub = ub; dd.sigma = sigma; dd.dfdx = dfdx; dd.dfcdx = dfcdx; dd.fcval = fcval; dd.rhoc = rhoc; dd.xcur = xcur; dd.gcval = gcval; for (j = 0; j < n; ++j) { if (nlopt_isinf(ub[j]) || nlopt_isinf(lb[j])) sigma[j] = 1.0; /* arbitrary default */ else sigma[j] = 0.5 * (ub[j] - lb[j]); } rho = 1.0; for (i = 0; i < m; ++i) { rhoc[i] = 1.0; dual_lb[i] = y[i] = 0.0; dual_ub[i] = HUGE_VAL; } dd.fval = fcur = *minf = f(n, x, dfdx, f_data); stop->nevals++; memcpy(xcur, x, sizeof(double) * n); if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } feasible = 1; infeasibility = 0; for (i = ifc = 0; ifc < mfc; ++ifc) { nlopt_eval_constraint(fcval + i, dfcdx + i*n, fc + ifc, n, x); i += fc[ifc].m; if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } } for (i = 0; i < m; ++i) { feasible = feasible && (fcval[i] <= 0 || isnan(fcval[i])); if (fcval[i] > infeasibility) infeasibility = fcval[i]; } /* For non-feasible initial points, set a finite (large) upper-bound on the dual variables. What this means is that, if no feasible solution is found from the dual problem, it will minimize the dual objective with the unfeasible constraint weighted by 1e40 -- basically, minimizing the unfeasible constraint until it becomes feasible or until we at least obtain a step towards a feasible point. Svanberg suggested a different approach in his 1987 paper, basically introducing additional penalty variables for unfeasible constraints, but this is easier to implement and at least as efficient. */ if (!feasible) for (i = 0; i < m; ++i) dual_ub[i] = 1e40; nlopt_set_min_objective(dual_opt, dual_func, &dd); nlopt_set_lower_bounds(dual_opt, dual_lb); nlopt_set_upper_bounds(dual_opt, dual_ub); nlopt_set_stopval(dual_opt, -HUGE_VAL); nlopt_remove_inequality_constraints(dual_opt); nlopt_remove_equality_constraints(dual_opt); while (1) { /* outer iterations */ double fprev = fcur; if (nlopt_stop_forced(stop)) ret = NLOPT_FORCED_STOP; else if (nlopt_stop_evals(stop)) ret = NLOPT_MAXEVAL_REACHED; else if (nlopt_stop_time(stop)) ret = NLOPT_MAXTIME_REACHED; else if (feasible && *minf < stop->minf_max) ret = NLOPT_MINF_MAX_REACHED; if (ret != NLOPT_SUCCESS) goto done; if (++k > 1) memcpy(xprevprev, xprev, sizeof(double) * n); memcpy(xprev, xcur, sizeof(double) * n); while (1) { /* inner iterations */ double min_dual, infeasibility_cur; int feasible_cur, inner_done; unsigned save_verbose; int new_infeasible_constraint; nlopt_result reti; /* solve dual problem */ dd.rho = rho; dd.count = 0; save_verbose = mma_verbose; mma_verbose = 0; /* no recursive verbosity */ reti = nlopt_optimize_limited(dual_opt, y, &min_dual, 0, stop->maxtime - (nlopt_seconds() - stop->start)); mma_verbose = save_verbose; if (reti < 0 || reti == NLOPT_MAXTIME_REACHED) { ret = reti; goto done; } dual_func(m, y, NULL, &dd); /* evaluate final xcur etc. */ if (mma_verbose) { printf("MMA dual converged in %d iterations to g=%g:\n", dd.count, dd.gval); for (i = 0; i < MIN(mma_verbose, m); ++i) printf(" MMA y[%d]=%g, gc[%d]=%g\n", i, y[i], i, dd.gcval[i]); } fcur = f(n, xcur, dfdx_cur, f_data); stop->nevals++; if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } feasible_cur = 1; infeasibility_cur = 0; new_infeasible_constraint = 0; inner_done = dd.gval >= fcur; for (i = ifc = 0; ifc < mfc; ++ifc) { nlopt_eval_constraint(fcval_cur + i, dfcdx_cur + i*n, fc + ifc, n, xcur); i += fc[ifc].m; if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } } for (i = ifc = 0; ifc < mfc; ++ifc) { unsigned i0 = i, inext = i + fc[ifc].m; for (; i < inext; ++i) if (!isnan(fcval_cur[i])) { feasible_cur = feasible_cur && (fcval_cur[i] <= fc[ifc].tol[i-i0]); if (!isnan(fcval[i])) inner_done = inner_done && (dd.gcval[i] >= fcval_cur[i]); else if (fcval_cur[i] > 0) new_infeasible_constraint = 1; if (fcval_cur[i] > infeasibility_cur) infeasibility_cur = fcval_cur[i]; } } if ((fcur < *minf && (inner_done || feasible_cur || !feasible)) || (!feasible && infeasibility_cur < infeasibility)) { if (mma_verbose && !feasible_cur) printf("MMA - using infeasible point?\n"); dd.fval = *minf = fcur; infeasibility = infeasibility_cur; memcpy(fcval, fcval_cur, sizeof(double)*m); memcpy(x, xcur, sizeof(double)*n); memcpy(dfdx, dfdx_cur, sizeof(double)*n); memcpy(dfcdx, dfcdx_cur, sizeof(double)*n*m); /* once we have reached a feasible solution, the algorithm should never make the solution infeasible again (if inner_done), although the constraints may be violated slightly by rounding errors etc. so we must be a little careful about checking feasibility */ if (infeasibility_cur == 0) { if (!feasible) { /* reset upper bounds to infin. */ for (i = 0; i < m; ++i) dual_ub[i] = HUGE_VAL; nlopt_set_upper_bounds(dual_opt, dual_ub); } feasible = 1; } else if (new_infeasible_constraint) feasible = 0; } if (nlopt_stop_forced(stop)) ret = NLOPT_FORCED_STOP; else if (nlopt_stop_evals(stop)) ret = NLOPT_MAXEVAL_REACHED; else if (nlopt_stop_time(stop)) ret = NLOPT_MAXTIME_REACHED; else if (feasible && *minf < stop->minf_max) ret = NLOPT_MINF_MAX_REACHED; if (ret != NLOPT_SUCCESS) goto done; if (inner_done) break; if (fcur > dd.gval) rho = MIN(10*rho, 1.1 * (rho + (fcur-dd.gval) / dd.wval)); for (i = 0; i < m; ++i) if (!isnan(fcval_cur[i]) && fcval_cur[i] > dd.gcval[i]) rhoc[i] = MIN(10*rhoc[i], 1.1 * (rhoc[i] + (fcval_cur[i]-dd.gcval[i]) / dd.wval)); if (mma_verbose) printf("MMA inner iteration: rho -> %g\n", rho); for (i = 0; i < MIN(mma_verbose, m); ++i) printf(" MMA rhoc[%d] -> %g\n", i,rhoc[i]); } if (nlopt_stop_ftol(stop, fcur, fprev)) ret = NLOPT_FTOL_REACHED; if (nlopt_stop_x(stop, xcur, xprev)) ret = NLOPT_XTOL_REACHED; if (ret != NLOPT_SUCCESS) goto done; /* update rho and sigma for iteration k+1 */ rho = MAX(0.1 * rho, MMA_RHOMIN); if (mma_verbose) printf("MMA outer iteration: rho -> %g\n", rho); for (i = 0; i < m; ++i) rhoc[i] = MAX(0.1 * rhoc[i], MMA_RHOMIN); for (i = 0; i < MIN(mma_verbose, m); ++i) printf(" MMA rhoc[%d] -> %g\n", i, rhoc[i]); if (k > 1) { for (j = 0; j < n; ++j) { double dx2 = (xcur[j]-xprev[j]) * (xprev[j]-xprevprev[j]); double gam = dx2 < 0 ? 0.7 : (dx2 > 0 ? 1.2 : 1); sigma[j] *= gam; if (!nlopt_isinf(ub[j]) && !nlopt_isinf(lb[j])) { sigma[j] = MIN(sigma[j], 10*(ub[j]-lb[j])); sigma[j] = MAX(sigma[j], 0.01*(ub[j]-lb[j])); } } for (j = 0; j < MIN(mma_verbose, n); ++j) printf(" MMA sigma[%d] -> %g\n", j, sigma[j]); } } done: free(sigma); return ret; } nloptr/src/nlopt_src/mma/Makefile.am0000644000176000001440000000023513263734242017226 0ustar ripleyusersAM_CPPFLAGS = -I$(top_srcdir)/util -I$(top_srcdir)/api noinst_LTLIBRARIES = libmma.la libmma_la_SOURCES = mma.c ccsa_quadratic.c mma.h EXTRA_DIST = README nloptr/src/nlopt_src/mma/Makefile.in0000644000176000001440000004357413355070635017255 0ustar ripleyusers# Makefile.in generated by automake 1.14 from Makefile.am. # @configure_input@ # Copyright (C) 1994-2013 Free Software Foundation, Inc. # This Makefile.in is free software; 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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #ifndef MMA_H #define MMA_H #include "nlopt.h" #include "nlopt-util.h" #ifdef __cplusplus extern "C" { #endif /* __cplusplus */ extern unsigned mma_verbose; nlopt_result mma_minimize(unsigned n, nlopt_func f, void *f_data, unsigned m, nlopt_constraint *fc, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, nlopt_stopping *stop, nlopt_opt dual_opt); nlopt_result ccsa_quadratic_minimize( unsigned n, nlopt_func f, void *f_data, unsigned m, nlopt_constraint *fc, nlopt_precond pre, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, nlopt_stopping *stop, nlopt_opt dual_opt); #ifdef __cplusplus } /* extern "C" */ #endif /* __cplusplus */ #endif nloptr/src/nlopt_src/mma/README0000644000176000001440000000321713263734242016055 0ustar ripleyusersImplementation of the globally-convergent method-of-moving-asymptotes (MMA) algorithm for gradient-based local optimization, as described in: Krister Svanberg, "A class of globally convergent optimization methods based on conservative convex separable approximations," SIAM J. Optim. 12 (2), p. 555-573 (2002). In fact, this algorithm is much more general than most of the other algorithms in NLopt, in that it handles an arbitrary set of nonlinear (differentiable) constraints as well, in a very efficient manner. I've implemented the full nonlinear-constrained MMA algorithm, and it is exported under the nlopt_minimize_constrained API. I also implemented another CCSA algorithm from the same paper: instead of constructing local MMA approximations, it constructs simple quadratic approximations (or rather, affine approximations plus a quadratic penalty term to stay conservative). This is the ccsa_quadratic code. It seems to have similar convergence rates to MMA for most problems, which is not surprising as they are both essentially similar. However, for the quadratic variant I implemented the possibility of preconditioning: including a user-supplied Hessian approximation in the local model. It is easy to incorporate this into the proof in Svanberg's paper, and to show that global convergence is still guaranteed as long as the user's "Hessian" is positive semidefinite, and it practice it can greatly improve convergence if the preconditioner is a good approximation for (at least for the largest eigenvectors) the real Hessian. It is under the same MIT license as the rest of my code in NLopt (see ../COPYRIGHT). Steven G. Johnson July 2008 - July 2012 nloptr/src/nlopt_src/COPYING0000644000176000001440000000367313263734241015463 0ustar ripleyusersNLopt combines several free/open-source nonlinear optimization libraries by various authors. See the COPYING, COPYRIGHT, and README files in the subdirectories for the original copyright and licensing information of these packages. The compiled NLopt library, i.e. the combined work of all of the included optimization routines, is licensed under the conjunction of all of these licensing terms. Currently, the most restrictive terms are for the code in the "luksan" directory, which is licensed under the GNU Lesser General Public License (GNU LGPL), version 2.1 or later (see luksan/COPYRIGHT). That means that the compiled NLopt library is governed by the terms of the LGPL. --------------------------------------------------------------------------- Other portions of NLopt, including any modifications to the abovementioned packages, are licensed under the standard "MIT License:" Copyright (c) 2007-2011 Massachusetts Institute of Technology Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. nloptr/src/nlopt_src/depcomp0000644000176000001440000005601613263734242016002 0ustar ripleyusers#! /bin/sh # depcomp - compile a program generating dependencies as side-effects scriptversion=2013-05-30.07; # UTC # Copyright (C) 1999-2013 Free Software Foundation, Inc. # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2, or (at your option) # any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that program. # Originally written by Alexandre Oliva . case $1 in '') echo "$0: No command. Try '$0 --help' for more information." 1>&2 exit 1; ;; -h | --h*) cat <<\EOF Usage: depcomp [--help] [--version] PROGRAM [ARGS] Run PROGRAMS ARGS to compile a file, generating dependencies as side-effects. Environment variables: depmode Dependency tracking mode. source Source file read by 'PROGRAMS ARGS'. object Object file output by 'PROGRAMS ARGS'. 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This case will never be run, # since it is checked for above. exit 1 ;; none) exec "$@" ;; *) echo "Unknown depmode $depmode" 1>&2 exit 1 ;; esac exit 0 # Local Variables: # mode: shell-script # sh-indentation: 2 # eval: (add-hook 'write-file-hooks 'time-stamp) # time-stamp-start: "scriptversion=" # time-stamp-format: "%:y-%02m-%02d.%02H" # time-stamp-time-zone: "UTC" # time-stamp-end: "; # UTC" # End: nloptr/src/nlopt_src/configure.ac0000644000176000001440000003452013263734242016712 0ustar ripleyusersdnl Process this file with autoconf to produce a configure script. AC_INIT(nlopt, 2.4.2, stevenj@alum.mit.edu) AC_CONFIG_SRCDIR(api/nlopt.h) SHARED_VERSION_INFO="8:2:8" # CURRENT:REVISION:AGE AM_INIT_AUTOMAKE(1.7) AM_CONFIG_HEADER(config.h) AC_CONFIG_MACRO_DIR([m4]) AM_MAINTAINER_MODE AC_SUBST(SHARED_VERSION_INFO) AC_DISABLE_SHARED dnl shared libraries are a PITA, disable by default dnl Checks for programs. AC_PROG_CC AC_PROG_CC_STDC AC_PROG_INSTALL AC_PROG_LN_S AC_PROG_MAKE_SET AC_LIBTOOL_WIN32_DLL AC_PROG_LIBTOOL NLOPT_SUFFIX="" AC_ARG_WITH(cxx, [AC_HELP_STRING([--with-cxx], [include C++-based routines])], with_cxx=$withval,with_cxx=no) AM_CONDITIONAL(WITH_CXX, test "x$with_cxx" = xyes) AC_PROG_CXX if test "x$with_cxx" = xyes; then AC_DEFINE([WITH_CXX], 1, [Define if compiled including C++-based routines]) NLOPT_SUFFIX="_cxx" CC=$CXX CFLAGS=$CXXFLAGS fi AC_SUBST(NLOPT_SUFFIX) AC_ARG_WITH(mthreads, [AC_HELP_STRING([--with-mthreads], [use -mthreads compiler flag])], with_mthrads=$withval,with_mthreads=no) if test "x$with_mthreads" = xyes; then CFLAGS="$CFLAGS -mthreads" CXXFLAGS="$CXXFLAGS -mthreads" fi dnl Checks for typedefs, structures, and compiler characteristics. AC_HEADER_STDC AC_HEADER_TIME AC_CHECK_HEADERS([unistd.h getopt.h stdint.h]) AC_C_CONST AC_C_INLINE AX_C_THREADLOCAL dnl find 32-bit unsigned integer type for random-number generator AC_CHECK_SIZEOF(unsigned int) AC_CHECK_SIZEOF(unsigned long) AC_CHECK_TYPES(uint32_t, [], [], [$ac_includes_default #ifdef HAVE_STDINT_H # include #endif]) dnl Checks for libraries and functions AC_CHECK_LIB(m, sin) AC_CHECK_FUNCS([BSDgettimeofday gettimeofday time qsort_r getpid]) AC_MSG_CHECKING([for gettid syscall]) AC_TRY_LINK([#include #include ], [syscall(SYS_gettid);], [ok=yes], [ok=no]) if test "$ok" = "yes"; then AC_DEFINE(HAVE_GETTID_SYSCALL,1,[Define if syscall(SYS_gettid) available.]) fi AC_MSG_RESULT(${ok}) AC_MSG_CHECKING([for isnan]) AC_TRY_LINK([#include ], [if (!isnan(3.14159)) isnan(2.7183);], ok=yes, ok=no) if test "$ok" = "yes"; then AC_DEFINE(HAVE_ISNAN,1,[Define if the isnan() function/macro is available.]) fi AC_MSG_RESULT(${ok}) AC_MSG_CHECKING([for isinf]) AC_TRY_LINK([#include ], [if (!isinf(3.14159)) isinf(2.7183);], ok=yes, ok=no) if test "$ok" = "yes"; then AC_DEFINE(HAVE_ISINF,1,[Define if the isinf() function/macro is available.]) fi AC_MSG_RESULT(${ok}) AC_MSG_CHECKING([for copysign]) AC_TRY_LINK([#include ], [double x = copysign(3.14159, -2.7183);], ok=yes, ok=no) if test "$ok" = "yes"; then AC_DEFINE(HAVE_COPYSIGN,1,[Define if the copysign function/macro is available.]) fi AC_MSG_RESULT(${ok}) dnl ----------------------------------------------------------------------- dnl SWIG wrappers AC_ARG_WITH(guile, [AC_HELP_STRING([--without-guile], [don't compile Guile plugin])], with_guile=$withval,with_guile=yes) AC_ARG_WITH(python, [AC_HELP_STRING([--without-python], [don't compile Python plugin])], with_python=$withval,with_python=yes) if test "$enable_shared" = no; then AC_MSG_WARN([Python and Guile wrappers require --enable-shared; disabling]) GUILE_CONFIG=unknown have_python=no else if test "x$with_guile" = xno; then GUILE_CONFIG=unknown else dnl Guile: AC_ARG_VAR(GUILE_INSTALL_DIR, [where to install Guile plug-ins]) AC_CHECK_PROG(GUILE_CONFIG, guile-config, guile-config, unknown) if test "x$GUILE_CONFIG" = "xunknown"; then AC_MSG_WARN([can't find guile-config, disabling Guile wrapper]) else save_CPPFLAGS=$CPPFLAGS save_LIBS=$LIBS GUILE_CPPFLAGS=`$GUILE_CONFIG compile` GUILE_LIBS=`$GUILE_CONFIG link` CPPFLAGS="$CPPFLAGS $GUILE_CPPFLAGS" LIBS="$GUILE_LIBS $LIBS" AC_MSG_CHECKING([if linking to guile works]) AC_TRY_LINK_FUNC(scm_is_vector, [AC_MSG_RESULT(yes)], [AC_MSG_RESULT(no) AC_MSG_WARN(guile-config is broken, disabling Guile wrapper) GUILE_CONFIG="unknown"]) CPPFLAGS=$save_CPPFLAGS LIBS=$save_LIBS fi AC_CHECK_PROG(GUILE, guile, guile, unknown) if test "x$GUILE" = xunknown; then AC_MSG_WARN([can't find guile, disabling Guile wrapper]) GUILE_CONFIG=unknown elif test x"$GUILE_INSTALL_DIR" = "x"; then AC_CHECK_PROGS(GUILE_CONFIG, guile-config, echo) AC_MSG_CHECKING(guile prefix) GUILE_PREFIX=`$GUILE_CONFIG info prefix` AC_MSG_RESULT($GUILE_PREFIX) AC_MSG_CHECKING([for Guile installation directory]) GUILE_INSTALL_DIR=`guile -c '(display (%site-dir))'` if test "$prefix" != "NONE"; then # strip guile install path to honor prefix GUILE_INSTALL_DIR=`echo "$GUILE_INSTALL_DIR" | sed "s|$GUILE_PREFIX|$prefix|g"` fi AC_MSG_RESULT([$GUILE_INSTALL_DIR]) fi fi # with_guile if test "x$with_python" = xno; then have_python=no else dnl Python: AM_PATH_PYTHON([],[have_python=yes],[have_python=no]) if test $have_python = yes; then AC_ARG_VAR([PYTHON_CONFIG], [python-config program]) AC_PATH_PROGS([PYTHON_CONFIG], [python$PYTHON_VERSION-config python-config], [unknown], [`dirname $PYTHON`:$PATH]) AC_MSG_CHECKING([for Python include flags]) if test "x$PYTHON_CONFIG" = "xunknown"; then pinc=-I`echo "import distutils.sysconfig; print (distutils.sysconfig.get_python_inc())" | $PYTHON - 2>/dev/null` test "x$pinc" = "x-I" && pinc="" else pinc=`$PYTHON_CONFIG --includes 2>/dev/null` fi AC_MSG_RESULT([${pinc:-unknown}]) PYTHON_INCLUDES="$pinc" save_CPPFLAGS=$CPPFLAGS CPPFLAGS="$CPPFLAGS $PYTHON_INCLUDES" AC_CHECK_HEADER([Python.h], [], [AC_MSG_WARN([disabling Python wrappers]) have_python=no]) if test $have_python = yes; then AC_MSG_CHECKING([for Numpy include directory]) pinc=`echo "import numpy; print (numpy.get_include())" | $PYTHON - 2>/dev/null` AC_MSG_RESULT([${pinc:-unknown}]) test -n "$pinc" && PYTHON_INCLUDES="$PYTHON_INCLUDES -I$pinc" CPPFLAGS="$save_CPPFLAGS $PYTHON_INCLUDES" AC_CHECK_HEADER([numpy/arrayobject.h],[],[ AC_MSG_WARN([disabling Python wrappers]) have_python=no],[#include ]) fi CPPFLAGS=$save_CPPFLAGS fi fi # with_python fi # if enable_shared AC_SUBST(GUILE_INSTALL_DIR) AC_SUBST(GUILE_CPPFLAGS) AC_SUBST(GUILE_LIBS) AC_SUBST(PYTHON_INCLUDES) AM_CONDITIONAL(WITH_GUILE, test x"$GUILE_CONFIG" != "xunknown") AM_CONDITIONAL(WITH_PYTHON, test x"$have_python" = "xyes") dnl ----------------------------------------------------------------------- dnl Compiling Octave plug-in AC_ARG_VAR(OCT_INSTALL_DIR, [where to install GNU Octave .oct plug-ins]) AC_ARG_VAR(M_INSTALL_DIR, [where to install GNU Octave .m plug-ins]) AC_ARG_VAR(MKOCTFILE, [name of mkoctfile program to compile Octave plug-ins]) AC_ARG_WITH(octave, [AC_HELP_STRING([--without-octave], [don't compile Octave plugin])], with_octave=$withval,with_octave=yes) AC_CHECK_PROGS(MKOCTFILE, mkoctfile, echo) if test x"$with_octave" = xno; then OCT_INSTALL_DIR="" elif test "$MKOCTFILE" = "echo"; then AC_MSG_WARN([can't find mkoctfile: won't be able to compile GNU Octave plugin]) OCT_INSTALL_DIR="" elif test x"$OCT_INSTALL_DIR" = "x"; then # try to find installation directory AC_CHECK_PROGS(OCTAVE, octave, echo) AC_CHECK_PROGS(OCTAVE_CONFIG, octave-config, echo) AC_MSG_CHECKING(octave prefix) OCTAVE_PREFIX=`$OCTAVE_CONFIG --print PREFIX 2> /dev/null` AC_MSG_RESULT($OCTAVE_PREFIX) AC_MSG_CHECKING(where octave plugins go) OCT_INSTALL_DIR=`$OCTAVE_CONFIG --oct-site-dir 2> /dev/null | grep '/'` if test -z "$OCT_INSTALL_DIR"; then OCT_INSTALL_DIR=`$OCTAVE_CONFIG --print OCTFILEDIR 2> /dev/null | grep '/'` fi if test -z "$OCT_INSTALL_DIR"; then OCT_INSTALL_DIR=`echo "path" | $OCTAVE -q 2> /dev/null | grep "/oct/" | head -1` fi if test -z "$OCT_INSTALL_DIR"; then OCT_INSTALL_DIR=`echo "DEFAULT_LOADPATH" | $OCTAVE -q 2> /dev/null | tr ':' '\n' | grep "site/oct" | head -1` fi if test -n "$OCT_INSTALL_DIR"; then if test "$prefix" != "NONE"; then # strip octave install path to honor prefix OCT_INSTALL_DIR=`echo "$OCT_INSTALL_DIR" | sed "s|$OCTAVE_PREFIX|$prefix|g"` fi AC_MSG_RESULT($OCT_INSTALL_DIR) else AC_MSG_RESULT(unknown) AC_MSG_WARN([can't find where to install octave plugins: won't be able to compile octave plugin]) fi AC_MSG_CHECKING(where octave scripts go) M_INSTALL_DIR=`$OCTAVE_CONFIG --m-site-dir 2> /dev/null | grep '/'` if test -z "$M_INSTALL_DIR"; then M_INSTALL_DIR=`$OCTAVE_CONFIG --print FCNFILEDIR 2> /dev/null | grep '/'` fi if test -z "$M_INSTALL_DIR"; then M_INSTALL_DIR=`echo "path" | $OCTAVE -q 2> /dev/null | grep "/m" | head -1` fi if test -z "$M_INSTALL_DIR"; then M_INSTALL_DIR=`echo "DEFAULT_LOADPATH" | $OCTAVE -q 2> /dev/null | tr ':' '\n' | grep "site/m" | head -1` fi if test -n "$M_INSTALL_DIR"; then if test "$prefix" != "NONE"; then # strip octave install path to honor prefix M_INSTALL_DIR=`echo "$M_INSTALL_DIR" | sed "s|$OCTAVE_PREFIX|$prefix|g"` fi AC_MSG_RESULT($M_INSTALL_DIR) else AC_MSG_RESULT(unknown) AC_MSG_WARN([can't find where to install octave scripts: won't be able to install octave plugin]) OCT_INSTALL_DIR="" fi elif test x"$M_INSTALL_DIR" = "x"; then # user-specified OCT_INSTALL_DIR M_INSTALL_DIR=$OCT_INSTALL_DIR fi if test x"$OCT_INSTALL_DIR" != "x"; then if test "$enable_shared" = no; then AC_MSG_WARN([mkoctfile requires --enable-shared; won't compile Octave plugin]) OCT_INSTALL_DIR="" fi fi AM_CONDITIONAL(WITH_OCTAVE, test x"$OCT_INSTALL_DIR" != "x") AC_SUBST(OCT_INSTALL_DIR) AC_SUBST(M_INSTALL_DIR) AC_SUBST(MKOCTFILE) dnl ----------------------------------------------------------------------- dnl Compiling Matlab plug-in AC_ARG_WITH(matlab, [AC_HELP_STRING([--without-matlab], [don't compile Matlab plugin])], with_matlab=$withval,with_matlab=yes) AC_ARG_VAR(MEX_INSTALL_DIR, [where to install Matlab .mex plug-ins]) AC_ARG_VAR(MEX, [name of mex program to compile Matlab plug-ins]) AC_CHECK_PROGS(MEX, mex, echo) if test x"$with_matlab" = xno; then MEX_INSTALL_DIR="" elif test "$MEX" = "echo"; then AC_MSG_WARN([can't find mex: won't be able to compile Matlab plugin]) MEX_INSTALL_DIR="" else AC_MSG_CHECKING([for extension of compiled mex files]) rm -f conftest* cat > conftest.c < void mexFunction(int nlhs, mxArray *plhs[[]], int nrhs, const mxArray *prhs[[]]) { } EOF if $MEX conftest.c >&AS_MESSAGE_LOG_FD 2>&AS_MESSAGE_LOG_FD; then MEXSUFF=`ls conftest.m* | head -1 | cut -d'.' -f2` AC_MSG_RESULT($MEXSUFF) AC_CHECK_PROGS(MATLAB, matlab, echo) else AC_MSG_WARN([$MEX failed to compile a simple file; won't compile Matlab plugin]) MEX_INSTALL_DIR="" MATLAB=echo fi if test x"$MATLAB" != xecho; then if test "$enable_shared" = no; then AC_MSG_RESULT(no) AC_MSG_WARN([mex requires --enable-shared; won't compile Matlab plugin]) MEX_INSTALL_DIR="" MATLAB=echo fi fi if test x"$MATLAB" != xecho; then # try to find installation directory if test x"$MEX_INSTALL_DIR" = "x"; then AC_MSG_CHECKING(for MATLAB mex installation dir) MEX_INSTALL_DIR=`matlab -nodisplay -nodesktop -nojvm -r 'path;quit' | grep toolbox/local |sed 's,^[[^/]]*,,g' |sort |head -1` AC_MSG_RESULT($MEX_INSTALL_DIR) if test x`basename "$MEX_INSTALL_DIR"` != xlocal; then MEX_INSTALL_DIR="" fi if test x"$MEX_INSTALL_DIR" = "x"; then AC_MSG_WARN([can't find reasonable Matlab installation directory; Matlab plugins will not be compiled unless you manually specify MEX_INSTALL_DIR]) fi fi else MEX_INSTALL_DIR="" fi fi AM_CONDITIONAL(WITH_MATLAB, test x"$MEX_INSTALL_DIR" != "x") AC_SUBST(MEX_INSTALL_DIR) AC_SUBST(MEX) AC_SUBST(MEXSUFF) dnl ----------------------------------------------------------------------- dnl Check for broken Solaris HUGE_VAL macro under gcc 3.4.x and similar AC_MSG_CHECKING([for working HUGE_VAL]) AC_TRY_COMPILE([#include ], [double x = -HUGE_VAL;], [AC_MSG_RESULT([ok])], [AC_TRY_COMPILE([#include #ifdef __GNUC__ #undef HUGE_VAL #define HUGE_VAL __builtin_huge_val() #endif], [double x = -HUGE_VAL;], [AC_MSG_RESULT([__builtin_huge_val()]) AC_DEFINE(REPLACEMENT_HUGE_VAL,[__builtin_huge_val()], [replacement for broken HUGE_VAL macro, if needed])], [AC_MSG_RESULT([unknown]) AC_MSG_ERROR([broken HUGE_VAL macro with this compiler, unknown workaround])])]) dnl ----------------------------------------------------------------------- dnl Debugging AC_ARG_ENABLE(debug, [AC_HELP_STRING([--enable-debug],[compile with extra runtime checks for debugging])], ok=$enableval, ok=no) if test "$ok" = "yes"; then AC_DEFINE(DEBUG,1,[Define to enable extra debugging code.]) fi dnl override CFLAGS selection when debugging if test "${enable_debug}" = "yes"; then CFLAGS="-g" CXXFLAGS="-g" FFLAGS="-g" fi dnl add gcc warnings, in debug/maintainer mode only if test "$enable_debug" = yes || test "$USE_MAINTAINER_MODE" = yes; then if test $ac_cv_prog_gcc = yes; then if test "$ac_test_CFLAGS" != "set"; then CFLAGS="$CFLAGS -Wall -W -Wcast-qual -Wpointer-arith -Wcast-align -Wno-long-long -pedantic -Wshadow -Wbad-function-cast -Wwrite-strings -Wstrict-prototypes -Wredundant-decls -Wnested-externs -Wundef -Wconversion -Wmissing-prototypes -Wmissing-declarations" fi if test "$ac_test_CXXFLAGS" != "set"; then CXXFLAGS="$CXXFLAGS -Wall -W -Wcast-qual -Wpointer-arith -Wcast-align -Wno-long-long -pedantic -Wshadow -Wwrite-strings -Wredundant-decls -Wundef -Wconversion" fi if test "x$with_cxx" = xyes; then CFLAGS=$CXXFLAGS fi fi fi vers=`echo ${VERSION}.0.0 | cut -d. -f1` AC_DEFINE_UNQUOTED(MAJOR_VERSION, $vers, [Major version number.]) vers=`echo ${VERSION}.0.0 | cut -d. -f2` AC_DEFINE_UNQUOTED(MINOR_VERSION, $vers, [Minor version number.]) vers=`echo ${VERSION}.0.0 | cut -d. -f3` AC_DEFINE_UNQUOTED(BUGFIX_VERSION, $vers, [Bugfix version number.]) dnl ----------------------------------------------------------------------- AC_CONFIG_FILES([ Makefile nlopt.pc api/Makefile util/Makefile octave/Makefile direct/Makefile cdirect/Makefile stogo/Makefile praxis/Makefile luksan/Makefile crs/Makefile mlsl/Makefile mma/Makefile cobyla/Makefile newuoa/Makefile neldermead/Makefile auglag/Makefile bobyqa/Makefile isres/Makefile slsqp/Makefile esch/Makefile test/Makefile swig/Makefile swig/nlopt.scm ]) AC_OUTPUT nloptr/src/nlopt_src/direct/0000755000176000001440000000000013263734242015672 5ustar ripleyusersnloptr/src/nlopt_src/direct/COPYING0000644000176000001440000000300713263734242016725 0ustar ripleyusersThis code is based on the DIRECT 2.0.4 Fortran code by Gablonsky et al. at http://www4.ncsu.edu/~ctk/SOFTWARE/DIRECTv204.tar.gz The C version was initially converted via f2c and then cleaned up and reorganized by Steven G. Johnson (stevenj@alum.mit.edu), August 2007. ******** Copyright and license for the original Fortran DIRECT code ******** Copyright (c) 1999, 2000, 2001 North Carolina State University This program is distributed under the MIT License (see http://www.opensource.org/licenses/mit-license.php): Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. nloptr/src/nlopt_src/direct/direct.h0000644000176000001440000000267613355070635017331 0ustar ripleyusers#ifndef DIRECT_H #define DIRECT_H #include #include #ifdef __cplusplus extern "C" { #endif /* __cplusplus */ typedef double (*direct_objective_func)(int n, const double *x, int *undefined_flag, void *data); typedef enum { DIRECT_ORIGINAL, DIRECT_GABLONSKY } direct_algorithm; typedef enum { DIRECT_INVALID_BOUNDS = -1, DIRECT_MAXFEVAL_TOOBIG = -2, DIRECT_INIT_FAILED = -3, DIRECT_SAMPLEPOINTS_FAILED = -4, DIRECT_SAMPLE_FAILED = -5, DIRECT_MAXFEVAL_EXCEEDED = 1, DIRECT_MAXITER_EXCEEDED = 2, DIRECT_GLOBAL_FOUND = 3, DIRECT_VOLTOL = 4, DIRECT_SIGMATOL = 5, DIRECT_MAXTIME_EXCEEDED = 6, DIRECT_OUT_OF_MEMORY = -100, DIRECT_INVALID_ARGS = -101, DIRECT_FORCED_STOP = -102 } direct_return_code; #define DIRECT_UNKNOWN_FGLOBAL (-HUGE_VAL) #define DIRECT_UNKNOWN_FGLOBAL_RELTOL (0.0) extern direct_return_code direct_optimize( direct_objective_func f, void *f_data, int dimension, const double *lower_bounds, const double *upper_bounds, double *x, double *minf, int max_feval, int max_iter, double start, double maxtime, double magic_eps, double magic_eps_abs, double volume_reltol, double sigma_reltol, int *force_stop, double fglobal, double fglobal_reltol, FILE *logfile, direct_algorithm algorithm); #ifdef __cplusplus } /* extern "C" */ #endif /* __cplusplus */ #endif /* DIRECT_H */ nloptr/src/nlopt_src/direct/DIRsubrout.c0000644000176000001440000016520513355070635020112 0ustar ripleyusers/* DIRsubrout.f -- translated by f2c (version 20050501). f2c output hand-cleaned by SGJ (August 2007). */ #include #include "direct-internal.h" /* Table of constant values */ static integer c__1 = 1; static integer c__32 = 32; static integer c__0 = 0; /* +-----------------------------------------------------------------------+ */ /* | INTEGER Function DIRGetlevel | */ /* | Returns the level of the hyperrectangle. Depending on the value of the| */ /* | global variable JONES. IF JONES equals 0, the level is given by | */ /* | kN + p, where the rectangle has p sides with a length of| */ /* | 1/3^(k+1), and N-p sides with a length of 1/3^k. | */ /* | If JONES equals 1, the level is the power of 1/3 of the length of the | */ /* | longest side hyperrectangle. | */ /* | | */ /* | On Return : | */ /* | the maximal length | */ /* | | */ /* | pos -- the position of the midpoint in the array length | */ /* | length -- the array with the dimensions | */ /* | maxfunc -- the leading dimension of length | */ /* | n -- the dimension of the problem | */ /* | | */ /* +-----------------------------------------------------------------------+ */ integer direct_dirgetlevel_(integer *pos, integer *length, integer *maxfunc, integer *n, integer jones) { /* System generated locals */ integer length_dim1, length_offset, ret_val, i__1; /* Local variables */ integer i__, k, p, help; /* JG 09/15/00 Added variable JONES (see above) */ /* Parameter adjustments */ length_dim1 = *n; length_offset = 1 + length_dim1; length -= length_offset; /* Function Body */ if (jones == 0) { help = length[*pos * length_dim1 + 1]; k = help; p = 1; i__1 = *n; for (i__ = 2; i__ <= i__1; ++i__) { if (length[i__ + *pos * length_dim1] < k) { k = length[i__ + *pos * length_dim1]; } if (length[i__ + *pos * length_dim1] == help) { ++p; } /* L100: */ } if (k == help) { ret_val = k * *n + *n - p; } else { ret_val = k * *n + p; } } else { help = length[*pos * length_dim1 + 1]; i__1 = *n; for (i__ = 2; i__ <= i__1; ++i__) { if (length[i__ + *pos * length_dim1] < help) { help = length[i__ + *pos * length_dim1]; } /* L10: */ } ret_val = help; } return ret_val; } /* dirgetlevel_ */ /* +-----------------------------------------------------------------------+ */ /* | Program : Direct.f (subfile DIRsubrout.f) | */ /* | Last modified : 07-16-2001 | */ /* | Written by : Joerg Gablonsky | */ /* | Subroutines used by the algorithm DIRECT. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | SUBROUTINE DIRChoose | */ /* | Decide, which is the next sampling point. | */ /* | Changed 09/25/00 JG | */ /* | Added maxdiv to call and changed S to size maxdiv. | */ /* | Changed 01/22/01 JG | */ /* | Added Ifeasiblef to call to keep track if a feasible point has| */ /* | been found. | */ /* | Changed 07/16/01 JG | */ /* | Changed if statement to prevent run-time errors. | | */ /* +-----------------------------------------------------------------------+ */ /* Subroutine */ void direct_dirchoose_(integer *anchor, integer *s, integer *actdeep, doublereal *f, doublereal *minf, doublereal epsrel, doublereal epsabs, doublereal *thirds, integer *maxpos, integer *length, integer *maxfunc, const integer *maxdeep, const integer *maxdiv, integer *n, FILE *logfile, integer *cheat, doublereal *kmax, integer *ifeasiblef, integer jones) { /* System generated locals */ integer s_dim1, s_offset, length_dim1, length_offset, i__1; /* Local variables */ integer i__, j, k; doublereal helplower; integer i___, j___; doublereal helpgreater; integer novaluedeep = 0; doublereal help2; integer novalue; /* Parameter adjustments */ f -= 3; ++anchor; s_dim1 = *maxdiv; s_offset = 1 + s_dim1; s -= s_offset; length_dim1 = *n; length_offset = 1 + length_dim1; length -= length_offset; /* Function Body */ helplower = HUGE_VAL; helpgreater = 0.; k = 1; if (*ifeasiblef >= 1) { i__1 = *actdeep; for (j = 0; j <= i__1; ++j) { if (anchor[j] > 0) { s[k + s_dim1] = anchor[j]; s[k + (s_dim1 << 1)] = direct_dirgetlevel_(&s[k + s_dim1], &length[ length_offset], maxfunc, n, jones); goto L12; } /* L1001: */ } L12: ++k; *maxpos = 1; return; } else { i__1 = *actdeep; for (j = 0; j <= i__1; ++j) { if (anchor[j] > 0) { s[k + s_dim1] = anchor[j]; s[k + (s_dim1 << 1)] = direct_dirgetlevel_(&s[k + s_dim1], &length[ length_offset], maxfunc, n, jones); ++k; } /* L10: */ } } novalue = 0; if (anchor[-1] > 0) { novalue = anchor[-1]; novaluedeep = direct_dirgetlevel_(&novalue, &length[length_offset], maxfunc, n, jones); } *maxpos = k - 1; i__1 = *maxdeep; for (j = k - 1; j <= i__1; ++j) { s[k + s_dim1] = 0; /* L11: */ } for (j = *maxpos; j >= 1; --j) { helplower = HUGE_VAL; helpgreater = 0.; j___ = s[j + s_dim1]; i__1 = j - 1; for (i__ = 1; i__ <= i__1; ++i__) { i___ = s[i__ + s_dim1]; /* +-----------------------------------------------------------------------+ */ /* | JG 07/16/01 Changed IF statement into two to prevent run-time errors | */ /* | which could occur if the compiler checks the second | */ /* | expression in an .AND. statement although the first | */ /* | statement is already not true. | */ /* +-----------------------------------------------------------------------+ */ if (i___ > 0 && ! (i__ == j)) { if (f[(i___ << 1) + 2] <= 1.) { help2 = thirds[s[i__ + (s_dim1 << 1)]] - thirds[s[j + ( s_dim1 << 1)]]; help2 = (f[(i___ << 1) + 1] - f[(j___ << 1) + 1]) / help2; if (help2 <= 0.) { if (logfile) fprintf(logfile, "thirds > 0, help2 <= 0\n"); goto L60; } if (help2 < helplower) { if (logfile) fprintf(logfile, "helplower = %g\n", help2); helplower = help2; } } } /* L30: */ } i__1 = *maxpos; for (i__ = j + 1; i__ <= i__1; ++i__) { i___ = s[i__ + s_dim1]; /* +-----------------------------------------------------------------------+ */ /* | JG 07/16/01 Changed IF statement into two to prevent run-time errors | */ /* | which could occur if the compiler checks the second | */ /* | expression in an .AND. statement although the first | */ /* | statement is already not true. | */ /* +-----------------------------------------------------------------------+ */ if (i___ > 0 && ! (i__ == j)) { if (f[(i___ << 1) + 2] <= 1.) { help2 = thirds[s[i__ + (s_dim1 << 1)]] - thirds[s[j + ( s_dim1 << 1)]]; help2 = (f[(i___ << 1) + 1] - f[(j___ << 1) + 1]) / help2; if (help2 <= 0.) { if (logfile) fprintf(logfile, "thirds < 0, help2 <= 0\n"); goto L60; } if (help2 > helpgreater) { if (logfile) fprintf(logfile, "helpgreater = %g\n", help2); helpgreater = help2; } } } /* L31: */ } if (helpgreater <= helplower) { if (*cheat == 1 && helplower > *kmax) { helplower = *kmax; } if (f[(j___ << 1) + 1] - helplower * thirds[s[j + (s_dim1 << 1)]] > MIN(*minf - epsrel * fabs(*minf), *minf - epsabs)) { if (logfile) fprintf(logfile, "> minf - epslminfl\n"); goto L60; } } else { if (logfile) fprintf(logfile, "helpgreater > helplower: %g %g %g\n", helpgreater, helplower, helpgreater - helplower); goto L60; } goto L40; L60: s[j + s_dim1] = 0; L40: ; } if (novalue > 0) { ++(*maxpos); s[*maxpos + s_dim1] = novalue; s[*maxpos + (s_dim1 << 1)] = novaluedeep; } } /* dirchoose_ */ /* +-----------------------------------------------------------------------+ */ /* | SUBROUTINE DIRDoubleInsert | */ /* | Routine to make sure that if there are several potential optimal | */ /* | hyperrectangles of the same level (i.e. hyperrectangles that have| */ /* | the same level and the same function value at the center), all of| */ /* | them are divided. This is the way as originally described in | */ /* | Jones et.al. | */ /* | JG 07/16/01 Added errorflag to calling sequence. We check if more | */ /* | we reach the capacity of the array S. If this happens, we | */ /* | return to the main program with an error. | */ /* +-----------------------------------------------------------------------+ */ /* Subroutine */ void direct_dirdoubleinsert_(integer *anchor, integer *s, integer * maxpos, integer *point, doublereal *f, const integer *maxdeep, integer * maxfunc, const integer *maxdiv, integer *ierror) { /* System generated locals */ integer s_dim1, s_offset, i__1; /* Local variables */ integer i__, oldmaxpos, pos, help, iflag, actdeep; /* +-----------------------------------------------------------------------+ */ /* | JG 07/16/01 Added flag to prevent run time-errors on some systems. | */ /* +-----------------------------------------------------------------------+ */ /* Parameter adjustments */ ++anchor; f -= 3; --point; s_dim1 = *maxdiv; s_offset = 1 + s_dim1; s -= s_offset; /* Function Body */ oldmaxpos = *maxpos; i__1 = oldmaxpos; for (i__ = 1; i__ <= i__1; ++i__) { if (s[i__ + s_dim1] > 0) { actdeep = s[i__ + (s_dim1 << 1)]; help = anchor[actdeep]; pos = point[help]; iflag = 0; /* +-----------------------------------------------------------------------+ */ /* | JG 07/16/01 Added flag to prevent run time-errors on some systems. On | */ /* | some systems the second conditions in an AND statement is | */ /* | evaluated even if the first one is already not true. | */ /* +-----------------------------------------------------------------------+ */ while(pos > 0 && iflag == 0) { if (f[(pos << 1) + 1] - f[(help << 1) + 1] <= 1e-13) { if (*maxpos < *maxdiv) { ++(*maxpos); s[*maxpos + s_dim1] = pos; s[*maxpos + (s_dim1 << 1)] = actdeep; pos = point[pos]; } else { /* +-----------------------------------------------------------------------+ */ /* | JG 07/16/01 Maximum number of elements possible in S has been reached!| */ /* +-----------------------------------------------------------------------+ */ *ierror = -6; return; } } else { iflag = 1; } } } /* L10: */ } } /* dirdoubleinsert_ */ /* +-----------------------------------------------------------------------+ */ /* | INTEGER Function GetmaxDeep | */ /* | function to get the maximal length (1/length) of the n-dimensional | */ /* | rectangle with midpoint pos. | */ /* | | */ /* | On Return : | */ /* | the maximal length | */ /* | | */ /* | pos -- the position of the midpoint in the array length | */ /* | length -- the array with the dimensions | */ /* | maxfunc -- the leading dimension of length | */ /* | n -- the dimension of the problem | */ /* | | */ /* +-----------------------------------------------------------------------+ */ integer direct_dirgetmaxdeep_(integer *pos, integer *length, integer *maxfunc, integer *n) { /* System generated locals */ integer length_dim1, length_offset, i__1, i__2, i__3; /* Local variables */ integer i__, help; /* Parameter adjustments */ length_dim1 = *n; length_offset = 1 + length_dim1; length -= length_offset; /* Function Body */ help = length[*pos * length_dim1 + 1]; i__1 = *n; for (i__ = 2; i__ <= i__1; ++i__) { /* Computing MIN */ i__2 = help, i__3 = length[i__ + *pos * length_dim1]; help = MIN(i__2,i__3); /* L10: */ } return help; } /* dirgetmaxdeep_ */ static integer isinbox_(doublereal *x, doublereal *a, doublereal *b, integer *n, integer *lmaxdim) { /* System generated locals */ integer ret_val, i__1; /* Local variables */ integer outofbox, i__; /* Parameter adjustments */ --b; --a; --x; /* Function Body */ outofbox = 1; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (a[i__] > x[i__] || b[i__] < x[i__]) { outofbox = 0; goto L1010; } /* L1000: */ } L1010: ret_val = outofbox; return ret_val; } /* isinbox_ */ /* +-----------------------------------------------------------------------+ */ /* | JG Added 09/25/00 | */ /* | | */ /* | SUBROUTINE DIRResortlist | */ /* | | */ /* | Resort the list so that the infeasible point is in the list with the | */ /* | replaced value. | */ /* +-----------------------------------------------------------------------+ */ /* Subroutine */ static void dirresortlist_(integer *replace, integer *anchor, doublereal *f, integer *point, integer *length, integer *n, integer * maxfunc, integer *maxdim, const integer *maxdeep, FILE *logfile, integer jones) { /* System generated locals */ integer length_dim1, length_offset, i__1; /* Local variables */ integer i__, l, pos; integer start; /* +-----------------------------------------------------------------------+ */ /* | Get the length of the hyper rectangle with infeasible mid point and | */ /* | Index of the corresponding list. | */ /* +-----------------------------------------------------------------------+ */ /* JG 09/25/00 Replaced with DIRgetlevel */ /* l = DIRgetmaxDeep(replace,length,maxfunc,n) */ /* Parameter adjustments */ --point; f -= 3; length_dim1 = *n; length_offset = 1 + length_dim1; length -= length_offset; ++anchor; /* Function Body */ l = direct_dirgetlevel_(replace, &length[length_offset], maxfunc, n, jones); start = anchor[l]; /* +-----------------------------------------------------------------------+ */ /* | If the hyper rectangle with infeasibel midpoint is already the start | */ /* | of the list, give out message, nothing to do. | */ /* +-----------------------------------------------------------------------+ */ if (*replace == start) { /* write(logfile,*) 'No resorting of list necessarry, since new ', */ /* + 'point is already anchor of list .',l */ } else { /* +-----------------------------------------------------------------------+ */ /* | Take the hyper rectangle with infeasible midpoint out of the list. | */ /* +-----------------------------------------------------------------------+ */ pos = start; i__1 = *maxfunc; for (i__ = 1; i__ <= i__1; ++i__) { if (point[pos] == *replace) { point[pos] = point[*replace]; goto L20; } else { pos = point[pos]; } if (pos == 0) { if (logfile) fprintf(logfile, "Error in DIRREsortlist: " "We went through the whole list\n" "and could not find the point to replace!!\n"); goto L20; } /* L10: */ } /* +-----------------------------------------------------------------------+ */ /* | If the anchor of the list has a higher value than the value of a | */ /* | nearby point, put the infeasible point at the beginning of the list. | */ /* +-----------------------------------------------------------------------+ */ L20: if (f[(start << 1) + 1] > f[(*replace << 1) + 1]) { anchor[l] = *replace; point[*replace] = start; /* write(logfile,*) 'Point is replacing current anchor for ' */ /* + , 'this list ',l,replace,start */ } else { /* +-----------------------------------------------------------------------+ */ /* | Insert the point into the list according to its (replaced) function | */ /* | value. | */ /* +-----------------------------------------------------------------------+ */ pos = start; i__1 = *maxfunc; for (i__ = 1; i__ <= i__1; ++i__) { /* +-----------------------------------------------------------------------+ */ /* | The point has to be added at the end of the list. | */ /* +-----------------------------------------------------------------------+ */ if (point[pos] == 0) { point[*replace] = point[pos]; point[pos] = *replace; /* write(logfile,*) 'Point is added at the end of the ' */ /* + , 'list ',l, replace */ goto L40; } else { if (f[(point[pos] << 1) + 1] > f[(*replace << 1) + 1]) { point[*replace] = point[pos]; point[pos] = *replace; /* write(logfile,*) 'There are points with a higher ' */ /* + ,'f-value in the list ',l,replace, pos */ goto L40; } pos = point[pos]; } /* L30: */ } L40: pos = pos; } } } /* dirresortlist_ */ /* +-----------------------------------------------------------------------+ */ /* | JG Added 09/25/00 | */ /* | SUBROUTINE DIRreplaceInf | */ /* | | */ /* | Find out if there are infeasible points which are near feasible ones. | */ /* | If this is the case, replace the function value at the center of the | */ /* | hyper rectangle by the lowest function value of a nearby function. | */ /* +-----------------------------------------------------------------------+ */ /* Subroutine */ void direct_dirreplaceinf_(integer *free, integer *freeold, doublereal *f, doublereal *c__, doublereal *thirds, integer *length, integer *anchor, integer *point, doublereal *c1, doublereal *c2, integer *maxfunc, const integer *maxdeep, integer *maxdim, integer *n, FILE *logfile, doublereal *fmax, integer jones) { /* System generated locals */ integer c_dim1, c_offset, length_dim1, length_offset, i__1, i__2, i__3; doublereal d__1, d__2; /* Local variables */ doublereal a[32], b[32]; integer i__, j, k, l; doublereal x[32], sidelength; integer help; /* +-----------------------------------------------------------------------+ */ /* | JG 01/22/01 Added variable to keep track of the maximum value found. | */ /* +-----------------------------------------------------------------------+ */ /* Parameter adjustments */ --point; f -= 3; ++anchor; length_dim1 = *maxdim; length_offset = 1 + length_dim1; length -= length_offset; c_dim1 = *maxdim; c_offset = 1 + c_dim1; c__ -= c_offset; --c2; --c1; /* Function Body */ i__1 = *free - 1; for (i__ = 1; i__ <= i__1; ++i__) { if (f[(i__ << 1) + 2] > 0.) { /* +-----------------------------------------------------------------------+ */ /* | Get the maximum side length of the hyper rectangle and then set the | */ /* | new side length to this lengths times the growth factor. | */ /* +-----------------------------------------------------------------------+ */ help = direct_dirgetmaxdeep_(&i__, &length[length_offset], maxfunc, n); sidelength = thirds[help] * 2.; /* +-----------------------------------------------------------------------+ */ /* | Set the Center and the upper and lower bounds of the rectangles. | */ /* +-----------------------------------------------------------------------+ */ i__2 = *n; for (j = 1; j <= i__2; ++j) { sidelength = thirds[length[i__ + j * length_dim1]]; a[j - 1] = c__[j + i__ * c_dim1] - sidelength; b[j - 1] = c__[j + i__ * c_dim1] + sidelength; /* L20: */ } /* +-----------------------------------------------------------------------+ */ /* | The function value is reset to 'Inf', since it may have been changed | */ /* | in an earlier iteration and now the feasible point which was close | */ /* | is not close anymore (since the hyper rectangle surrounding the | */ /* | current point may have shrunk). | */ /* +-----------------------------------------------------------------------+ */ f[(i__ << 1) + 1] = HUGE_VAL; f[(i__ << 1) + 2] = 2.; /* +-----------------------------------------------------------------------+ */ /* | Check if any feasible point is near this infeasible point. | */ /* +-----------------------------------------------------------------------+ */ i__2 = *free - 1; for (k = 1; k <= i__2; ++k) { /* +-----------------------------------------------------------------------+ */ /* | If the point k is feasible, check if it is near. | */ /* +-----------------------------------------------------------------------+ */ if (f[(k << 1) + 2] == 0.) { /* +-----------------------------------------------------------------------+ */ /* | Copy the coordinates of the point k into x. | */ /* +-----------------------------------------------------------------------+ */ i__3 = *n; for (l = 1; l <= i__3; ++l) { x[l - 1] = c__[l + k * c_dim1]; /* L40: */ } /* +-----------------------------------------------------------------------+ */ /* | Check if the point k is near the infeasible point, if so, replace the | */ /* | value at */ /* +-----------------------------------------------------------------------+ */ if (isinbox_(x, a, b, n, &c__32) == 1) { /* Computing MIN */ d__1 = f[(i__ << 1) + 1], d__2 = f[(k << 1) + 1]; f[(i__ << 1) + 1] = MIN(d__1,d__2); f[(i__ << 1) + 2] = 1.; } } /* L30: */ } if (f[(i__ << 1) + 2] == 1.) { f[(i__ << 1) + 1] += (d__1 = f[(i__ << 1) + 1], fabs(d__1)) * 1e-6f; i__2 = *n; for (l = 1; l <= i__2; ++l) { x[l - 1] = c__[l + i__ * c_dim1] * c1[l] + c__[l + i__ * c_dim1] * c2[l]; /* L200: */ } dirresortlist_(&i__, &anchor[-1], &f[3], &point[1], &length[length_offset], n, maxfunc, maxdim, maxdeep, logfile, jones); } else { /* +-----------------------------------------------------------------------+ */ /* | JG 01/22/01 | */ /* | Replaced fixed value for infeasible points with maximum value found, | */ /* | increased by 1. | */ /* +-----------------------------------------------------------------------+ */ if (! (*fmax == f[(i__ << 1) + 1])) { /* Computing MAX */ d__1 = *fmax + 1., d__2 = f[(i__ << 1) + 1]; f[(i__ << 1) + 1] = MAX(d__1,d__2); } } } /* L10: */ } /* L1000: */ } /* dirreplaceinf_ */ /* +-----------------------------------------------------------------------+ */ /* | SUBROUTINE DIRInsert | */ /* +-----------------------------------------------------------------------+ */ /* Subroutine */ static void dirinsert_(integer *start, integer *ins, integer *point, doublereal *f, integer *maxfunc) { /* System generated locals */ integer i__1; /* Local variables */ integer i__, help; /* JG 09/17/00 Rewrote this routine. */ /* DO 10,i = 1,maxfunc */ /* IF (f(ins,1) .LT. f(point(start),1)) THEN */ /* help = point(start) */ /* point(start) = ins */ /* point(ins) = help */ /* GOTO 20 */ /* END IF */ /* IF (point(start) .EQ. 0) THEN */ /* point(start) = ins */ /* point(ins) = 0 */ /* GOTO 20 */ /* END IF */ /* start = point(start) */ /* 10 CONTINUE */ /* 20 END */ /* Parameter adjustments */ f -= 3; --point; /* Function Body */ i__1 = *maxfunc; for (i__ = 1; i__ <= i__1; ++i__) { if (point[*start] == 0) { point[*start] = *ins; point[*ins] = 0; return; } else if (f[(*ins << 1) + 1] < f[(point[*start] << 1) + 1]) { help = point[*start]; point[*start] = *ins; point[*ins] = help; return; } *start = point[*start]; /* L10: */ } } /* dirinsert_ */ /* +-----------------------------------------------------------------------+ */ /* | SUBROUTINE DIRInsertList | */ /* | Changed 02-24-2000 | */ /* | Got rid of the distinction between feasible and infeasible points| */ /* | I could do this since infeasible points get set to a high | */ /* | function value, which may be replaced by a function value of a | */ /* | nearby function at the end of the main loop. | */ /* +-----------------------------------------------------------------------+ */ /* Subroutine */ void direct_dirinsertlist_(integer *new__, integer *anchor, integer * point, doublereal *f, integer *maxi, integer *length, integer * maxfunc, const integer *maxdeep, integer *n, integer *samp, integer jones) { /* System generated locals */ integer length_dim1, length_offset, i__1; /* Local variables */ integer j; integer pos; integer pos1, pos2, deep; /* JG 09/24/00 Changed this to Getlevel */ /* Parameter adjustments */ f -= 3; --point; ++anchor; length_dim1 = *n; length_offset = 1 + length_dim1; length -= length_offset; /* Function Body */ i__1 = *maxi; for (j = 1; j <= i__1; ++j) { pos1 = *new__; pos2 = point[pos1]; *new__ = point[pos2]; /* JG 09/24/00 Changed this to Getlevel */ /* deep = DIRGetMaxdeep(pos1,length,maxfunc,n) */ deep = direct_dirgetlevel_(&pos1, &length[length_offset], maxfunc, n, jones); if (anchor[deep] == 0) { if (f[(pos2 << 1) + 1] < f[(pos1 << 1) + 1]) { anchor[deep] = pos2; point[pos2] = pos1; point[pos1] = 0; } else { anchor[deep] = pos1; point[pos2] = 0; } } else { pos = anchor[deep]; if (f[(pos2 << 1) + 1] < f[(pos1 << 1) + 1]) { if (f[(pos2 << 1) + 1] < f[(pos << 1) + 1]) { anchor[deep] = pos2; /* JG 08/30/00 Fixed bug. Sorting was not correct when */ /* f(1,pos2) < f(1,pos1) < f(1,pos) */ if (f[(pos1 << 1) + 1] < f[(pos << 1) + 1]) { point[pos2] = pos1; point[pos1] = pos; } else { point[pos2] = pos; dirinsert_(&pos, &pos1, &point[1], &f[3], maxfunc); } } else { dirinsert_(&pos, &pos2, &point[1], &f[3], maxfunc); dirinsert_(&pos, &pos1, &point[1], &f[3], maxfunc); } } else { if (f[(pos1 << 1) + 1] < f[(pos << 1) + 1]) { /* JG 08/30/00 Fixed bug. Sorting was not correct when */ /* f(pos1,1) < f(pos2,1) < f(pos,1) */ anchor[deep] = pos1; if (f[(pos << 1) + 1] < f[(pos2 << 1) + 1]) { point[pos1] = pos; dirinsert_(&pos, &pos2, &point[1], &f[3], maxfunc); } else { point[pos1] = pos2; point[pos2] = pos; } } else { dirinsert_(&pos, &pos1, &point[1], &f[3], maxfunc); dirinsert_(&pos, &pos2, &point[1], &f[3], maxfunc); } } } /* L10: */ } /* JG 09/24/00 Changed this to Getlevel */ /* deep = DIRGetMaxdeep(samp,length,maxfunc,n) */ deep = direct_dirgetlevel_(samp, &length[length_offset], maxfunc, n, jones); pos = anchor[deep]; if (f[(*samp << 1) + 1] < f[(pos << 1) + 1]) { anchor[deep] = *samp; point[*samp] = pos; } else { dirinsert_(&pos, samp, &point[1], &f[3], maxfunc); } } /* dirinsertlist_ */ /* +-----------------------------------------------------------------------+ */ /* | SUBROUTINE DIRInsertList2 (Old way to do it.) | */ /* +-----------------------------------------------------------------------+ */ /* Subroutine */ static void dirinsertlist_2__(integer *start, integer *j, integer *k, integer *list2, doublereal *w, integer *maxi, integer *n) { /* System generated locals */ integer list2_dim1, list2_offset, i__1; /* Local variables */ integer i__, pos; /* Parameter adjustments */ --w; list2_dim1 = *n; list2_offset = 1 + list2_dim1; list2 -= list2_offset; /* Function Body */ pos = *start; if (*start == 0) { list2[*j + list2_dim1] = 0; *start = *j; goto L50; } if (w[*start] > w[*j]) { list2[*j + list2_dim1] = *start; *start = *j; } else { i__1 = *maxi; for (i__ = 1; i__ <= i__1; ++i__) { if (list2[pos + list2_dim1] == 0) { list2[*j + list2_dim1] = 0; list2[pos + list2_dim1] = *j; goto L50; } else { if (w[*j] < w[list2[pos + list2_dim1]]) { list2[*j + list2_dim1] = list2[pos + list2_dim1]; list2[pos + list2_dim1] = *j; goto L50; } } pos = list2[pos + list2_dim1]; /* L10: */ } } L50: list2[*j + (list2_dim1 << 1)] = *k; } /* dirinsertlist_2__ */ /* +-----------------------------------------------------------------------+ */ /* | SUBROUTINE DIRSearchmin | */ /* | Search for the minimum in the list. ! */ /* +-----------------------------------------------------------------------+ */ /* Subroutine */ static void dirsearchmin_(integer *start, integer *list2, integer * pos, integer *k, integer *n) { /* System generated locals */ integer list2_dim1, list2_offset; /* Parameter adjustments */ list2_dim1 = *n; list2_offset = 1 + list2_dim1; list2 -= list2_offset; /* Function Body */ *k = *start; *pos = list2[*start + (list2_dim1 << 1)]; *start = list2[*start + list2_dim1]; } /* dirsearchmin_ */ /* +-----------------------------------------------------------------------+ */ /* | SUBROUTINE DIRSamplepoints | */ /* | Subroutine to sample the new points. | */ /* +-----------------------------------------------------------------------+ */ /* Subroutine */ void direct_dirsamplepoints_(doublereal *c__, integer *arrayi, doublereal *delta, integer *sample, integer *start, integer *length, FILE *logfile, doublereal *f, integer *free, integer *maxi, integer *point, doublereal *x, doublereal *l, doublereal *minf, integer *minpos, doublereal *u, integer *n, integer *maxfunc, const integer *maxdeep, integer *oops) { /* System generated locals */ integer length_dim1, length_offset, c_dim1, c_offset, i__1, i__2; /* Local variables */ integer j, k, pos; /* Parameter adjustments */ --u; --l; --x; --arrayi; --point; f -= 3; length_dim1 = *n; length_offset = 1 + length_dim1; length -= length_offset; c_dim1 = *n; c_offset = 1 + c_dim1; c__ -= c_offset; /* Function Body */ *oops = 0; pos = *free; *start = *free; i__1 = *maxi + *maxi; for (k = 1; k <= i__1; ++k) { i__2 = *n; for (j = 1; j <= i__2; ++j) { length[j + *free * length_dim1] = length[j + *sample * length_dim1]; c__[j + *free * c_dim1] = c__[j + *sample * c_dim1]; /* L20: */ } pos = *free; *free = point[*free]; if (*free == 0) { if (logfile) fprintf(logfile, "Error, no more free positions! " "Increase maxfunc!\n"); *oops = 1; return; } /* L10: */ } point[pos] = 0; pos = *start; i__1 = *maxi; for (j = 1; j <= i__1; ++j) { c__[arrayi[j] + pos * c_dim1] = c__[arrayi[j] + *sample * c_dim1] + * delta; pos = point[pos]; c__[arrayi[j] + pos * c_dim1] = c__[arrayi[j] + *sample * c_dim1] - * delta; pos = point[pos]; /* L30: */ } ASRT(pos <= 0); } /* dirsamplepoints_ */ /* +-----------------------------------------------------------------------+ */ /* | SUBROUTINE DIRDivide | */ /* | Subroutine to divide the hyper rectangles according to the rules. | */ /* | Changed 02-24-2000 | */ /* | Replaced if statement by min (line 367) | */ /* +-----------------------------------------------------------------------+ */ /* Subroutine */ void direct_dirdivide_(integer *new__, integer *currentlength, integer *length, integer *point, integer *arrayi, integer *sample, integer *list2, doublereal *w, integer *maxi, doublereal *f, integer * maxfunc, const integer *maxdeep, integer *n) { /* System generated locals */ integer length_dim1, length_offset, list2_dim1, list2_offset, i__1, i__2; doublereal d__1, d__2; /* Local variables */ integer i__, j, k, pos, pos2; integer start; /* Parameter adjustments */ f -= 3; --point; --w; list2_dim1 = *n; list2_offset = 1 + list2_dim1; list2 -= list2_offset; --arrayi; length_dim1 = *n; length_offset = 1 + length_dim1; length -= length_offset; /* Function Body */ start = 0; pos = *new__; i__1 = *maxi; for (i__ = 1; i__ <= i__1; ++i__) { j = arrayi[i__]; w[j] = f[(pos << 1) + 1]; k = pos; pos = point[pos]; /* Computing MIN */ d__1 = f[(pos << 1) + 1], d__2 = w[j]; w[j] = MIN(d__1,d__2); pos = point[pos]; dirinsertlist_2__(&start, &j, &k, &list2[list2_offset], &w[1], maxi, n); /* L10: */ } ASRT(pos <= 0); i__1 = *maxi; for (j = 1; j <= i__1; ++j) { dirsearchmin_(&start, &list2[list2_offset], &pos, &k, n); pos2 = start; length[k + *sample * length_dim1] = *currentlength + 1; i__2 = *maxi - j + 1; for (i__ = 1; i__ <= i__2; ++i__) { length[k + pos * length_dim1] = *currentlength + 1; pos = point[pos]; length[k + pos * length_dim1] = *currentlength + 1; /* JG 07/10/01 pos2 = 0 at the end of the 30-loop. Since we end */ /* the loop now, we do not need to reassign pos and pos2. */ if (pos2 > 0) { pos = list2[pos2 + (list2_dim1 << 1)]; pos2 = list2[pos2 + list2_dim1]; } /* L30: */ } /* L20: */ } } /* dirdivide_ */ /* +-----------------------------------------------------------------------+ */ /* | | */ /* | SUBROUTINE DIRINFCN | */ /* | | */ /* | Subroutine DIRinfcn unscales the variable x for use in the | */ /* | user-supplied function evaluation subroutine fcn. After fcn returns | */ /* | to DIRinfcn, DIRinfcn then rescales x for use by DIRECT. | */ /* | | */ /* | On entry | */ /* | | */ /* | fcn -- The argument containing the name of the user-supplied | */ /* | subroutine that returns values for the function to be | */ /* | minimized. | */ /* | | */ /* | x -- A double-precision vector of length n. The point at | */ /* | which the derivative is to be evaluated. | */ /* | | */ /* | xs1 -- A double-precision vector of length n. Used for | */ /* | scaling and unscaling the vector x by DIRinfcn. | */ /* | | */ /* | xs2 -- A double-precision vector of length n. Used for | */ /* | scaling and unscaling the vector x by DIRinfcn. | */ /* | | */ /* | n -- An integer. The dimension of the problem. | */ /* | kret -- An Integer. If kret = 1, the point is infeasible, | */ /* | kret = -1, bad problem set up, | */ /* | kret = 0, feasible. | */ /* | | */ /* | On return | */ /* | | */ /* | f -- A double-precision scalar. | */ /* | | */ /* | Subroutines and Functions | */ /* | | */ /* | The subroutine whose name is passed through the argument fcn. | */ /* | | */ /* +-----------------------------------------------------------------------+ */ /* Subroutine */ void direct_dirinfcn_(fp fcn, doublereal *x, doublereal *c1, doublereal *c2, integer *n, doublereal *f, integer *flag__, void *fcn_data) { /* System generated locals */ integer i__1; /* Local variables */ integer i__; /* +-----------------------------------------------------------------------+ */ /* | Variables to pass user defined data to the function to be optimized. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | Unscale the variable x. | */ /* +-----------------------------------------------------------------------+ */ /* Parameter adjustments */ --c2; --c1; --x; /* Function Body */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { x[i__] = (x[i__] + c2[i__]) * c1[i__]; /* L20: */ } /* +-----------------------------------------------------------------------+ */ /* | Call the function-evaluation subroutine fcn. | */ /* +-----------------------------------------------------------------------+ */ *flag__ = 0; *f = fcn(*n, &x[1], flag__, fcn_data); /* +-----------------------------------------------------------------------+ */ /* | Rescale the variable x. | */ /* +-----------------------------------------------------------------------+ */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { x[i__] = x[i__] / c1[i__] - c2[i__]; /* L30: */ } } /* dirinfcn_ */ /* +-----------------------------------------------------------------------+ */ /* | SUBROUTINE DIRGet_I | */ /* +-----------------------------------------------------------------------+ */ /* Subroutine */ void direct_dirget_i__(integer *length, integer *pos, integer * arrayi, integer *maxi, integer *n, integer *maxfunc) { /* System generated locals */ integer length_dim1, length_offset, i__1; /* Local variables */ integer i__, j, help; /* Parameter adjustments */ --arrayi; length_dim1 = *n; length_offset = 1 + length_dim1; length -= length_offset; /* Function Body */ j = 1; help = length[*pos * length_dim1 + 1]; i__1 = *n; for (i__ = 2; i__ <= i__1; ++i__) { if (length[i__ + *pos * length_dim1] < help) { help = length[i__ + *pos * length_dim1]; } /* L10: */ } i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (length[i__ + *pos * length_dim1] == help) { arrayi[j] = i__; ++j; } /* L20: */ } *maxi = j - 1; } /* dirget_i__ */ /* +-----------------------------------------------------------------------+ */ /* | SUBROUTINE DIRInit | */ /* | Initialise all needed variables and do the first run of the | */ /* | algorithm. | */ /* | Changed 02/24/2000 | */ /* | Changed fcn Double precision to fcn external! | */ /* | Changed 09/15/2000 | */ /* | Added distinction between Jones way to characterize rectangles | */ /* | and our way. Common variable JONES controls which way we use. | */ /* | JONES = 0 Jones way (Distance from midpoint to corner) | */ /* | JONES = 1 Our way (Length of longest side) | */ /* | Changed 09/24/00 | */ /* | Added array levels. Levels contain the values to characterize | */ /* | the hyperrectangles. | */ /* | Changed 01/22/01 | */ /* | Added variable fmax to keep track of maximum value found. | */ /* | Added variable Ifeasiblef to keep track if feasibel point has | */ /* | been found. | */ /* | Changed 01/23/01 | */ /* | Added variable Ierror to keep track of errors. | */ /* +-----------------------------------------------------------------------+ */ /* Subroutine */ void direct_dirinit_(doublereal *f, fp fcn, doublereal *c__, integer *length, integer *actdeep, integer *point, integer *anchor, integer *free, FILE *logfile, integer *arrayi, integer *maxi, integer *list2, doublereal *w, doublereal *x, doublereal *l, doublereal *u, doublereal *minf, integer *minpos, doublereal *thirds, doublereal *levels, integer *maxfunc, const integer * maxdeep, integer *n, integer *maxor, doublereal *fmax, integer * ifeasiblef, integer *iinfeasible, integer *ierror, void *fcndata, integer jones, double starttime, double maxtime, int *force_stop) { /* System generated locals */ integer c_dim1, c_offset, length_dim1, length_offset, list2_dim1, list2_offset, i__1, i__2; /* Local variables */ integer i__, j; integer new__, help, oops; doublereal help2, delta; doublereal costmin; /* +-----------------------------------------------------------------------+ */ /* | JG 01/22/01 Added variable to keep track of the maximum value found. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | JG 01/22/01 Added variable Ifeasiblef to keep track if feasibel point | */ /* | has been found. | */ /* | JG 01/23/01 Added variable Ierror to keep track of errors. | */ /* | JG 03/09/01 Added IInfeasible to keep track if an infeasible point has| */ /* | been found. | */ /* +-----------------------------------------------------------------------+ */ /* JG 09/15/00 Added variable JONES (see above) */ /* +-----------------------------------------------------------------------+ */ /* | Variables to pass user defined data to the function to be optimized. | */ /* +-----------------------------------------------------------------------+ */ /* Parameter adjustments */ --point; f -= 3; ++anchor; --u; --l; --x; --w; list2_dim1 = *maxor; list2_offset = 1 + list2_dim1; list2 -= list2_offset; --arrayi; length_dim1 = *n; length_offset = 1 + length_dim1; length -= length_offset; c_dim1 = *maxor; c_offset = 1 + c_dim1; c__ -= c_offset; /* Function Body */ *minf = HUGE_VAL; costmin = *minf; /* JG 09/15/00 If Jones way of characterising rectangles is used, */ /* initialise thirds to reflect this. */ if (jones == 0) { i__1 = *n - 1; for (j = 0; j <= i__1; ++j) { w[j + 1] = sqrt(*n - j + j / 9.) * .5; /* L5: */ } help2 = 1.; i__1 = *maxdeep / *n; for (i__ = 1; i__ <= i__1; ++i__) { i__2 = *n - 1; for (j = 0; j <= i__2; ++j) { levels[(i__ - 1) * *n + j] = w[j + 1] / help2; /* L8: */ } help2 *= 3.; /* L10: */ } } else { /* JG 09/15/00 Initialiase levels to contain 1/j */ help2 = 3.; i__1 = *maxdeep; for (i__ = 1; i__ <= i__1; ++i__) { levels[i__] = 1. / help2; help2 *= 3.; /* L11: */ } levels[0] = 1.; } help2 = 3.; i__1 = *maxdeep; for (i__ = 1; i__ <= i__1; ++i__) { thirds[i__] = 1. / help2; help2 *= 3.; /* L21: */ } thirds[0] = 1.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { c__[i__ + c_dim1] = .5; x[i__] = .5; length[i__ + length_dim1] = 0; /* L20: */ } direct_dirinfcn_(fcn, &x[1], &l[1], &u[1], n, &f[3], &help, fcndata); if (force_stop && *force_stop) { *ierror = -102; return; } f[4] = (doublereal) help; *iinfeasible = help; *fmax = f[3]; /* 09/25/00 Added this */ /* if (f(1,1) .ge. 1.E+6) then */ if (f[4] > 0.) { f[3] = HUGE_VAL; *fmax = f[3]; *ifeasiblef = 1; } else { *ifeasiblef = 0; } /* JG 09/25/00 Remove IF */ *minf = f[3]; costmin = f[3]; *minpos = 1; *actdeep = 2; point[1] = 0; *free = 2; delta = thirds[1]; if (nlopt_stop_time_(starttime, maxtime)) { *ierror = DIRECT_MAXTIME_EXCEEDED; return; } direct_dirget_i__(&length[length_offset], &c__1, &arrayi[1], maxi, n, maxfunc); new__ = *free; direct_dirsamplepoints_(&c__[c_offset], &arrayi[1], &delta, &c__1, &new__, & length[length_offset], logfile, &f[3], free, maxi, & point[1], &x[1], &l[1], minf, minpos, &u[1], n, maxfunc, maxdeep, &oops); /* +-----------------------------------------------------------------------+ */ /* | JG 01/23/01 Added error checking. | */ /* +-----------------------------------------------------------------------+ */ if (oops > 0) { *ierror = -4; return; } /* +-----------------------------------------------------------------------+ */ /* | JG 01/22/01 Added variable to keep track of the maximum value found. | */ /* | Added variable to keep track if feasible point was found. | */ /* +-----------------------------------------------------------------------+ */ direct_dirsamplef_(&c__[c_offset], &arrayi[1], &delta, &c__1, &new__, &length[ length_offset], logfile, &f[3], free, maxi, &point[ 1], fcn, &x[1], &l[1], minf, minpos, &u[1], n, maxfunc, maxdeep, &oops, fmax, ifeasiblef, iinfeasible, fcndata, force_stop); if (force_stop && *force_stop) { *ierror = -102; return; } if (nlopt_stop_time_(starttime, maxtime)) { *ierror = DIRECT_MAXTIME_EXCEEDED; return; } /* +-----------------------------------------------------------------------+ */ /* | JG 01/23/01 Added error checking. | */ /* +-----------------------------------------------------------------------+ */ if (oops > 0) { *ierror = -5; return; } direct_dirdivide_(&new__, &c__0, &length[length_offset], &point[1], &arrayi[1], & c__1, &list2[list2_offset], &w[1], maxi, &f[3], maxfunc, maxdeep, n); direct_dirinsertlist_(&new__, &anchor[-1], &point[1], &f[3], maxi, & length[length_offset], maxfunc, maxdeep, n, &c__1, jones); } /* dirinit_ */ /* +-----------------------------------------------------------------------+ */ /* | SUBROUTINE DIRInitList | */ /* | Initialise the list. | */ /* +-----------------------------------------------------------------------+ */ /* Subroutine */ void direct_dirinitlist_(integer *anchor, integer *free, integer * point, doublereal *f, integer *maxfunc, const integer *maxdeep) { /* System generated locals */ integer i__1; /* Local variables */ integer i__; /* f -- values of functions. */ /* anchor -- anchors of lists with deep i */ /* point -- lists */ /* free -- first free position */ /* Parameter adjustments */ f -= 3; --point; ++anchor; /* Function Body */ i__1 = *maxdeep; for (i__ = -1; i__ <= i__1; ++i__) { anchor[i__] = 0; /* L10: */ } i__1 = *maxfunc; for (i__ = 1; i__ <= i__1; ++i__) { f[(i__ << 1) + 1] = 0.; f[(i__ << 1) + 2] = 0.; point[i__] = i__ + 1; /* point(i) = 0 */ /* L20: */ } point[*maxfunc] = 0; *free = 1; } /* dirinitlist_ */ /* +-----------------------------------------------------------------------+ */ /* | | */ /* | SUBROUTINE DIRPREPRC | */ /* | | */ /* | Subroutine DIRpreprc uses an afine mapping to map the hyper-box given | */ /* | by the constraints on the variable x onto the n-dimensional unit cube.| */ /* | This mapping is done using the following equation: | */ /* | | */ /* | x(i)=x(i)/(u(i)-l(i))-l(i)/(u(i)-l(i)). | */ /* | | */ /* | DIRpreprc checks if the bounds l and u are well-defined. That is, if | */ /* | | */ /* | l(i) < u(i) forevery i. | */ /* | | */ /* | On entry | */ /* | | */ /* | u -- A double-precision vector of length n. The vector | */ /* | containing the upper bounds for the n independent | */ /* | variables. | */ /* | | */ /* | l -- A double-precision vector of length n. The vector | */ /* | containing the lower bounds for the n independent | */ /* | variables. | */ /* | | */ /* | n -- An integer. The dimension of the problem. | */ /* | | */ /* | On return | */ /* | | */ /* | xs1 -- A double-precision vector of length n, used for scaling | */ /* | and unscaling the vector x. | */ /* | | */ /* | xs2 -- A double-precision vector of length n, used for scaling | */ /* | and unscaling the vector x. | */ /* | | */ /* | | */ /* | oops -- An integer. If an upper bound is less than a lower | */ /* | bound or if the initial point is not in the | */ /* | hyper-box oops is set to 1 and iffco terminates. | */ /* | | */ /* +-----------------------------------------------------------------------+ */ /* Subroutine */ void direct_dirpreprc_(doublereal *u, doublereal *l, integer *n, doublereal *xs1, doublereal *xs2, integer *oops) { /* System generated locals */ integer i__1; /* Local variables */ integer i__; doublereal help; /* Parameter adjustments */ --xs2; --xs1; --l; --u; /* Function Body */ *oops = 0; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* +-----------------------------------------------------------------------+ */ /* | Check if the hyper-box is well-defined. | */ /* +-----------------------------------------------------------------------+ */ if (u[i__] <= l[i__]) { *oops = 1; return; } /* L20: */ } /* +-----------------------------------------------------------------------+ */ /* | Scale the initial iterate so that it is in the unit cube. | */ /* +-----------------------------------------------------------------------+ */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { help = u[i__] - l[i__]; xs2[i__] = l[i__] / help; xs1[i__] = help; /* L50: */ } } /* dirpreprc_ */ /* Subroutine */ void direct_dirheader_(FILE *logfile, integer *version, doublereal *x, integer *n, doublereal *eps, integer *maxf, integer * maxt, doublereal *l, doublereal *u, integer *algmethod, integer * maxfunc, const integer *maxdeep, doublereal *fglobal, doublereal *fglper, integer *ierror, doublereal *epsfix, integer *iepschange, doublereal * volper, doublereal *sigmaper) { /* System generated locals */ integer i__1; /* Local variables */ integer imainver, i__, numerrors, isubsubver, ihelp, isubver; /* +-----------------------------------------------------------------------+ */ /* | Variables to pass user defined data to the function to be optimized. | */ /* +-----------------------------------------------------------------------+ */ /* Parameter adjustments */ --u; --l; --x; /* Function Body */ if (logfile) fprintf(logfile, "------------------- Log file ------------------\n"); numerrors = 0; *ierror = 0; imainver = *version / 100; ihelp = *version - imainver * 100; isubver = ihelp / 10; ihelp -= isubver * 10; isubsubver = ihelp; /* +-----------------------------------------------------------------------+ */ /* | JG 01/13/01 Added check for epsilon. If epsilon is smaller 0, we use | */ /* | the update formula from Jones. We then set the flag | */ /* | iepschange to 1, and store the absolute value of eps in | */ /* | epsfix. epsilon is then changed after each iteration. | */ /* +-----------------------------------------------------------------------+ */ if (*eps < 0.) { *iepschange = 1; *epsfix = -(*eps); *eps = -(*eps); } else { *iepschange = 0; *epsfix = 1e100; } /* +-----------------------------------------------------------------------+ */ /* | JG 07/16/01 Removed printout of contents in cdata(1). | */ /* +-----------------------------------------------------------------------+ */ /* write(logfile,*) cdata(1) */ if (logfile) { fprintf(logfile, "DIRECT Version %d.%d.%d\n" " Problem dimension n: %d\n" " Eps value: %e\n" " Maximum number of f-evaluations (maxf): %d\n" " Maximum number of iterations (MaxT): %d\n" " Value of f_global: %e\n" " Global percentage wanted: %e\n" " Volume percentage wanted: %e\n" " Measure percentage wanted: %e\n", imainver, isubver, isubsubver, *n, *eps, *maxf, *maxt, *fglobal, *fglper, *volper, *sigmaper); fprintf(logfile, *iepschange == 1 ? "Epsilon is changed using the Jones formula.\n" : "Epsilon is constant.\n"); fprintf(logfile, *algmethod == 0 ? "Jones original DIRECT algorithm is used.\n" : "Our modification of the DIRECT algorithm is used.\n"); } i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (u[i__] <= l[i__]) { *ierror = -1; if (logfile) fprintf(logfile, "WARNING: bounds on variable x%d: " "%g <= xi <= %g\n", i__, l[i__], u[i__]); ++numerrors; } else { if (logfile) fprintf(logfile, "Bounds on variable x%d: " "%g <= xi <= %g\n", i__, l[i__], u[i__]); } /* L1010: */ } /* +-----------------------------------------------------------------------+ */ /* | If there are to many function evaluations or to many iteration, note | */ /* | this and set the error flag accordingly. Note: If more than one error | */ /* | occurred, we give out an extra message. | */ /* +-----------------------------------------------------------------------+ */ if (*maxf + 20 > *maxfunc) { if (logfile) fprintf(logfile, "WARNING: The maximum number of function evaluations (%d) is higher than\n" " the constant maxfunc (%d). Increase maxfunc in subroutine DIRECT\n" " or decrease the maximum number of function evaluations.\n", *maxf, *maxfunc); ++numerrors; *ierror = -2; } if (*ierror < 0) { if (logfile) fprintf(logfile, "----------------------------------\n"); if (numerrors == 1) { if (logfile) fprintf(logfile, "WARNING: One error in the input!\n"); } else { if (logfile) fprintf(logfile, "WARNING: %d errors in the input!\n", numerrors); } } if (logfile) fprintf(logfile, "----------------------------------\n"); if (*ierror >= 0) { if (logfile) fprintf(logfile, "Iteration # of f-eval. minf\n"); } /* L10005: */ } /* dirheader_ */ /* Subroutine */ void direct_dirsummary_(FILE *logfile, doublereal *x, doublereal * l, doublereal *u, integer *n, doublereal *minf, doublereal *fglobal, integer *numfunc, integer *ierror) { /* Local variables */ integer i__; /* Parameter adjustments */ --u; --l; --x; /* Function Body */ if (logfile) { fprintf(logfile, "-----------------------Summary------------------\n" "Final function value: %g\n" "Number of function evaluations: %d\n", *minf, *numfunc); if (*fglobal > -1e99) fprintf(logfile, "Final function value is within %g%% of global optimum\n", 100*(*minf - *fglobal) / MAX(1.0, fabs(*fglobal))); fprintf(logfile, "Index, final solution, x(i)-l(i), u(i)-x(i)\n"); for (i__ = 1; i__ <= *n; ++i__) fprintf(logfile, "%d, %g, %g, %g\n", i__, x[i__], x[i__]-l[i__], u[i__] - x[i__]); fprintf(logfile, "-----------------------------------------------\n"); } } /* dirsummary_ */ nloptr/src/nlopt_src/direct/direct_wrap.c0000644000176000001440000000672613355070635020355 0ustar ripleyusers/* C-style API for DIRECT functions. SGJ (August 2007). */ #include "direct-internal.h" /* Perform global minimization using (Gablonsky implementation of) DIRECT algorithm. Arguments: f, f_data: the objective function and any user data -- the objective function f(n, x, undefined_flag, data) takes 4 args: int n: the dimension, same as dimension arg. to direct_optimize const double *x: array x[n] of point to evaluate int *undefined_flag: set to 1 on return if x violates constraints or don't touch otherwise void *data: same as f_data passed to direct_optimize return value = value of f(x) dimension: the number of minimization variable dimensions lower_bounds, upper_bounds: arrays of length dimension of variable bounds x: an array of length dimension, set to optimum variables upon return minf: on return, set to minimum f value magic_eps, magic_eps_abs: Jones' "magic" epsilon parameter, and also an absolute version of the same (not multipled by minf). Jones suggests setting this to 1e-4, but 0 also works... max_feval, max_iter: maximum number of function evaluations & DIRECT iters volume_reltol: relative tolerance on hypercube volume (0 if none) sigma_reltol: relative tolerance on hypercube "measure" (??) (0 if none) fglobal: the global minimum of f, if known ahead of time -- this is mainly for benchmarking, in most cases it is not known and you should pass DIRECT_UNKNOWN_FGLOBAL fglobal_reltol: relative tolerance on how close we should find fglobal -- ignored if fglobal is DIRECT_UNKNOWN_FGLOBAL logfile: an output file to write diagnostic info to (NULL for no I/O) algorithm: whether to use the original DIRECT algorithm (DIRECT_ORIGINAL) or Gablonsky's "improved" version (DIRECT_GABLONSKY) */ direct_return_code direct_optimize( direct_objective_func f, void *f_data, int dimension, const double *lower_bounds, const double *upper_bounds, double *x, double *minf, int max_feval, int max_iter, double start, double maxtime, double magic_eps, double magic_eps_abs, double volume_reltol, double sigma_reltol, int *force_stop, double fglobal, double fglobal_reltol, FILE *logfile, direct_algorithm algorithm) { integer algmethod = algorithm == DIRECT_GABLONSKY; integer ierror; doublereal *l, *u; int i; /* convert to percentages: */ volume_reltol *= 100; sigma_reltol *= 100; fglobal_reltol *= 100; /* make sure these are ignored if <= 0 */ if (volume_reltol <= 0) volume_reltol = -1; if (sigma_reltol <= 0) sigma_reltol = -1; if (fglobal == DIRECT_UNKNOWN_FGLOBAL) fglobal_reltol = DIRECT_UNKNOWN_FGLOBAL_RELTOL; if (dimension < 1) return DIRECT_INVALID_ARGS; l = (doublereal *) malloc(sizeof(doublereal) * dimension * 2); if (!l) return DIRECT_OUT_OF_MEMORY; u = l + dimension; for (i = 0; i < dimension; ++i) { l[i] = lower_bounds[i]; u[i] = upper_bounds[i]; } direct_direct_(f, x, &dimension, &magic_eps, magic_eps_abs, &max_feval, &max_iter, start, maxtime, force_stop, minf, l, u, &algmethod, &ierror, logfile, &fglobal, &fglobal_reltol, &volume_reltol, &sigma_reltol, f_data); free(l); return (direct_return_code) ierror; } nloptr/src/nlopt_src/direct/DIRect.c0000644000176000001440000012661313355070635017162 0ustar ripleyusers/* DIRect-transp.f -- translated by f2c (version 20050501). f2c output hand-cleaned by SGJ (August 2007). */ #include #include "direct-internal.h" /* Common Block Declarations */ /* Table of constant values */ /* +-----------------------------------------------------------------------+ */ /* | Program : Direct.f | */ /* | Last modified : 07-16-2001 | */ /* | Written by : Joerg Gablonsky (jmgablon@unity.ncsu.edu) | */ /* | North Carolina State University | */ /* | Dept. of Mathematics | */ /* | DIRECT is a method to solve problems of the form: | */ /* | min f: Q --> R, | */ /* | where f is the function to be minimized and Q is an n-dimensional | */ /* | hyperrectangle given by the the following equation: | */ /* | | */ /* | Q={ x : l(i) <= x(i) <= u(i), i = 1,...,n }. | */ /* | Note: This version of DIRECT can also handle hidden constraints. By | */ /* | this we mean that the function may not be defined over the whole| */ /* | hyperrectangle Q, but only over a subset, which is not given | */ /* | analytically. | */ /* | | */ /* | We now give a brief outline of the algorithm: | */ /* | | */ /* | The algorithm starts with mapping the hyperrectangle Q to the | */ /* | n-dimensional unit hypercube. DIRECT then samples the function at | */ /* | the center of this hypercube and at 2n more points, 2 in each | */ /* | coordinate direction. Uisng these function values, DIRECT then | */ /* | divides the domain into hyperrectangles, each having exactly one of | */ /* | the sampling points as its center. In each iteration, DIRECT chooses| */ /* | some of the existing hyperrectangles to be further divided. | */ /* | We provide two different strategies of how to decide which | */ /* | hyperrectangles DIRECT divides and several different convergence | */ /* | criteria. | */ /* | | */ /* | DIRECT was designed to solve problems where the function f is | */ /* | Lipschitz continues. However, DIRECT has proven to be effective on | */ /* | more complex problems than these. | */ /* +-----------------------------------------------------------------------+ */ /* Subroutine */ void direct_direct_(fp fcn, doublereal *x, integer *n, doublereal *eps, doublereal epsabs, integer *maxf, integer *maxt, double starttime, double maxtime, int *force_stop, doublereal *minf, doublereal *l, doublereal *u, integer *algmethod, integer *ierror, FILE *logfile, doublereal *fglobal, doublereal *fglper, doublereal *volper, doublereal *sigmaper, void *fcn_data) { /* System generated locals */ integer i__1, i__2; doublereal d__1; /* changed by SGJ to be dynamically allocated ... would be even better to use realloc, below, to grow these as needed */ integer MAXFUNC = *maxf <= 0 ? 101000 : (*maxf + 1000 + *maxf / 2); integer MAXDEEP = *maxt <= 0 ? MAXFUNC/5: *maxt + 1000; const integer MAXDIV = 5000; /* Local variables */ integer increase; doublereal *c__ = 0 /* was [90000][64] */, *f = 0 /* was [90000][2] */; integer i__, j, *s = 0 /* was [3000][2] */, t; doublereal *w = 0; doublereal divfactor; integer ifeasiblef, iepschange, actmaxdeep; integer actdeep_div__, iinfesiblef; integer pos1, newtosample; integer ifree, help; doublereal *oldl = 0, fmax; integer maxi; doublereal kmax, *oldu = 0; integer oops, *list2 = 0 /* was [64][2] */, cheat; doublereal delta; integer mdeep, *point = 0, start; integer *anchor = 0, *length = 0 /* was [90000][64] */, *arrayi = 0; doublereal *levels = 0, *thirds = 0; integer writed; doublereal epsfix; integer oldpos, minpos, maxpos, tstart, actdeep, ifreeold, oldmaxf; integer numfunc, version; integer jones; /* FIXME: change sizes dynamically? */ #define MY_ALLOC(p, t, n) p = (t *) malloc(sizeof(t) * (n)); \ if (!(p)) { *ierror = -100; goto cleanup; } /* Note that I've transposed c__, length, and f relative to the original Fortran code. e.g. length was length(maxfunc,n) in Fortran [ or actually length(maxfunc, maxdims), but by using malloc I can just allocate n ], corresponding to length[n][maxfunc] in C, but I've changed the code to access it as length[maxfunc][n]. That is, the maxfunc direction is the discontiguous one. This makes it easier to resize dynamically (by adding contiguous rows) using realloc, without having to move data around manually. */ MY_ALLOC(c__, doublereal, MAXFUNC * (*n)); MY_ALLOC(length, integer, MAXFUNC * (*n)); MY_ALLOC(f, doublereal, MAXFUNC * 2); MY_ALLOC(point, integer, MAXFUNC); if (*maxf <= 0) *maxf = MAXFUNC - 1000; MY_ALLOC(s, integer, MAXDIV * 2); MY_ALLOC(anchor, integer, MAXDEEP + 2); MY_ALLOC(levels, doublereal, MAXDEEP + 1); MY_ALLOC(thirds, doublereal, MAXDEEP + 1); if (*maxt <= 0) *maxt = MAXDEEP; MY_ALLOC(w, doublereal, (*n)); MY_ALLOC(oldl, doublereal, (*n)); MY_ALLOC(oldu, doublereal, (*n)); MY_ALLOC(list2, integer, (*n) * 2); MY_ALLOC(arrayi, integer, (*n)); /* +-----------------------------------------------------------------------+ */ /* | SUBROUTINE Direct | */ /* | On entry | */ /* | fcn -- The argument containing the name of the user-supplied | */ /* | SUBROUTINE that returns values for the function to be | */ /* | minimized. | */ /* | n -- The dimension of the problem. | */ /* | eps -- Exceeding value. If eps > 0, we use the same epsilon for | */ /* | all iterations. If eps < 0, we use the update formula from | */ /* | Jones: | */ /* | eps = max(1.D-4*abs(minf),epsfix), | */ /* | where epsfix = abs(eps), the absolute value of eps which is| */ /* | passed to the function. | */ /* | maxf -- The maximum number of function evaluations. | */ /* | maxT -- The maximum number of iterations. | */ /* | Direct stops when either the maximum number of iterations | */ /* | is reached or more than maxf function-evalutions were made.| */ /* | l -- The lower bounds of the hyperbox. | */ /* | u -- The upper bounds of the hyperbox. | */ /* |algmethod-- Choose the method, that is either use the original method | */ /* | as described by Jones et.al. (0) or use our modification(1)| */ /* | logfile -- File-Handle for the logfile. DIRECT expects this file to be| */ /* | opened and closed by the user outside of DIRECT. We moved | */ /* | this to the outside so the user can add extra informations | */ /* | to this file before and after the call to DIRECT. | */ /* | fglobal -- Function value of the global optimum. If this value is not | */ /* | known (that is, we solve a real problem, not a testproblem)| */ /* | set this value to -1.D100 and fglper (see below) to 0.D0. | */ /* | fglper -- Terminate the optimization when the percent error | */ /* | 100(f_min - fglobal)/max(1,abs(fglobal)) < fglper. | */ /* | volper -- Terminate the optimization when the volume of the | */ /* | hyperrectangle S with f(c(S)) = minf is less then volper | */ /* | percent of the volume of the original hyperrectangle. | */ /* |sigmaper -- Terminate the optimization when the measure of the | */ /* | hyperrectangle S with f(c(S)) = minf is less then sigmaper.| */ /* | | */ /* | User data that is passed through without being changed: | */ /* | fcn_data - opaque pointer to any user data | */ /* | | */ /* | On return | */ /* | | */ /* | x -- The final point obtained in the optimization process. | */ /* | X should be a good approximation to the global minimum | */ /* | for the function within the hyper-box. | */ /* | | */ /* | minf -- The value of the function at x. | */ /* | Ierror -- Error flag. If Ierror is lower 0, an error has occured. The| */ /* | values of Ierror mean | */ /* | Fatal errors : | */ /* | -1 u(i) <= l(i) for some i. | */ /* | -2 maxf is too large. | */ /* | -3 Initialization in DIRpreprc failed. | */ /* | -4 Error in DIRSamplepoints, that is there was an error | */ /* | in the creation of the sample points. | */ /* | -5 Error in DIRSamplef, that is an error occured while | */ /* | the function was sampled. | */ /* | -6 Error in DIRDoubleInsert, that is an error occured | */ /* | DIRECT tried to add all hyperrectangles with the same| */ /* | size and function value at the center. Either | */ /* | increase maxdiv or use our modification (Jones = 1). | */ /* | Termination values : | */ /* | 1 Number of function evaluations done is larger then | */ /* | maxf. | */ /* | 2 Number of iterations is equal to maxT. | */ /* | 3 The best function value found is within fglper of | */ /* | the (known) global optimum, that is | */ /* | 100(minf - fglobal/max(1,|fglobal|)) < fglper. | */ /* | Note that this termination signal only occurs when | */ /* | the global optimal value is known, that is, a test | */ /* | function is optimized. | */ /* | 4 The volume of the hyperrectangle with minf at its | */ /* | center is less than volper percent of the volume of | */ /* | the original hyperrectangle. | */ /* | 5 The measure of the hyperrectangle with minf at its | */ /* | center is less than sigmaper. | */ /* | | */ /* | SUBROUTINEs used : | */ /* | | */ /* | DIRheader, DIRInitSpecific, DIRInitList, DIRpreprc, DIRInit, DIRChoose| */ /* | DIRDoubleInsert, DIRGet_I, DIRSamplepoints, DIRSamplef, DIRDivide | */ /* | DIRInsertList, DIRreplaceInf, DIRWritehistbox, DIRsummary, Findareas | */ /* | | */ /* | Functions used : | */ /* | | */ /* | DIRgetMaxdeep, DIRgetlevel | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | Parameters | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | The maximum of function evaluations allowed. | */ /* | The maximum dept of the algorithm. | */ /* | The maximum number of divisions allowed. | */ /* | The maximal dimension of the problem. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | Global Variables. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | EXTERNAL Variables. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | User Variables. | */ /* | These can be used to pass user defined data to the function to be | */ /* | optimized. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | Place to define, if needed, some application-specific variables. | */ /* | Note: You should try to use the arrays defined above for this. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | End of application - specific variables ! | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | Internal variables : | */ /* | f -- values of functions. | */ /* |divfactor-- Factor used for termination with known global minimum. | */ /* | anchor -- anchors of lists with deepness i, -1 is anchor for list of | */ /* | NaN - values. | */ /* | S -- List of potentially optimal points. | */ /* | point -- lists | */ /* | ifree -- first free position | */ /* | c -- midpoints of arrays | */ /* | thirds -- Precalculated values of 1/3^i. | */ /* | levels -- Length of intervals. | */ /* | length -- Length of intervall (index) | */ /* | t -- actual iteration | */ /* | j -- loop-variable | */ /* | actdeep -- the actual minimal interval-length index | */ /* | Minpos -- position of the actual minimum | */ /* | file -- The filehandle for a datafile. | */ /* | maxpos -- The number of intervalls, which are truncated. | */ /* | help -- A help variable. | */ /* | numfunc -- The actual number of function evaluations. | */ /* | file2 -- The filehandle for an other datafile. | */ /* | ArrayI -- Array with the indexes of the sides with maximum length. | */ /* | maxi -- Number of directions with maximal side length. | */ /* | oops -- Flag which shows if anything went wrong in the | */ /* | initialisation. | */ /* | cheat -- Obsolete. If equal 1, we don't allow Ktilde > kmax. | */ /* | writed -- If writed=1, store final division to plot with Matlab. | */ /* | List2 -- List of indicies of intervalls, which are to be truncated. | */ /* | i -- Another loop-variable. | */ /* |actmaxdeep-- The actual maximum (minimum) of possible Interval length. | */ /* | oldpos -- The old index of the minimum. Used to print only, if there | */ /* | is a new minimum found. | */ /* | tstart -- The start of the outer loop. | */ /* | start -- The postion of the starting point in the inner loop. | */ /* | Newtosample -- The total number of points to sample in the inner loop.| */ /* | w -- Array used to divide the intervalls | */ /* | kmax -- Obsolete. If cheat = 1, Ktilde was not allowed to be larger| */ /* | than kmax. If Ktilde > kmax, we set ktilde = kmax. | */ /* | delta -- The distance to new points from center of old hyperrec. | */ /* | pos1 -- Help variable used as an index. | */ /* | version -- Store the version number of DIRECT. | */ /* | oldmaxf -- Store the original function budget. | */ /* |increase -- Flag used to keep track if function budget was increased | */ /* | because no feasible point was found. | */ /* | ifreeold -- Keep track which index was free before. Used with | */ /* | SUBROUTINE DIRReplaceInf. | */ /* |actdeep_div-- Keep track of the current depths for divisions. | */ /* | oldl -- Array used to store the original bounds of the domain. | */ /* | oldu -- Array used to store the original bounds of the domain. | */ /* | epsfix -- If eps < 0, we use Jones update formula. epsfix stores the | */ /* | absolute value of epsilon. | */ /* |iepschange-- flag iepschange to store if epsilon stays fixed or is | */ /* | changed. | */ /* |DIRgetMaxdeep-- Function to calculate the level of a hyperrectangle. | */ /* |DIRgetlevel-- Function to calculate the level and stage of a hyperrec. | */ /* | fmax -- Keep track of the maximum value of the function found. | */ /* |Ifeasiblef-- Keep track if a feasible point has been found so far. | */ /* | Ifeasiblef = 0 means a feasible point has been found, | */ /* | Ifeasiblef = 1 no feasible point has been found. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | JG 09/25/00 Version counter. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | JG 09/24/00 Add another actdeep to keep track of the current depths | */ /* | for divisions. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* |JG 01/13/01 Added epsfix for epsilon update. If eps < 0, we use Jones | */ /* | update formula. epsfix stores the absolute value of epsilon| */ /* | then. Also added flag iepschange to store if epsilon stays | */ /* | fixed or is changed. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | JG 01/22/01 fmax is used to keep track of the maximum value found. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | JG 01/22/01 Ifeasiblef is used to keep track if a feasible point has | */ /* | been found so far. Ifeasiblef = 0 means a feasible point | */ /* | has been found, Ifeasiblef = 1 if not. | */ /* | JG 03/09/01 IInfeasible is used to keep track if an infeasible point | */ /* | has been found. IInfeasible > 0 means a infeasible point | */ /* | has been found, IInfeasible = 0 if not. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | Start of code. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* Parameter adjustments */ --u; --l; --x; /* Function Body */ writed = 0; jones = *algmethod; /* +-----------------------------------------------------------------------+ */ /* | Save the upper and lower bounds. | */ /* +-----------------------------------------------------------------------+ */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { oldu[i__ - 1] = u[i__]; oldl[i__ - 1] = l[i__]; /* L150: */ } /* +-----------------------------------------------------------------------+ */ /* | Set version. | */ /* +-----------------------------------------------------------------------+ */ version = 204; /* +-----------------------------------------------------------------------+ */ /* | Set parameters. | */ /* | If cheat > 0, we do not allow \tilde{K} to be larger than kmax, and| */ /* | set \tilde{K} to set value if necessary. Not used anymore. | */ /* +-----------------------------------------------------------------------+ */ cheat = 0; kmax = 1e10; mdeep = MAXDEEP; /* +-----------------------------------------------------------------------+ */ /* | Write the header of the logfile. | */ /* +-----------------------------------------------------------------------+ */ direct_dirheader_(logfile, &version, &x[1], n, eps, maxf, maxt, &l[1], &u[1], algmethod, &MAXFUNC, &MAXDEEP, fglobal, fglper, ierror, &epsfix, & iepschange, volper, sigmaper); /* +-----------------------------------------------------------------------+ */ /* | If an error has occured while writing the header (we do some checking | */ /* | of variables there), return to the main program. | */ /* +-----------------------------------------------------------------------+ */ if (*ierror < 0) { goto cleanup; } /* +-----------------------------------------------------------------------+ */ /* | If the known global minimum is equal 0, we cannot divide by it. | */ /* | Therefore we set it to 1. If not, we set the divisionfactor to the | */ /* | absolute value of the global minimum. | */ /* +-----------------------------------------------------------------------+ */ if (*fglobal == 0.) { divfactor = 1.; } else { divfactor = fabs(*fglobal); } /* +-----------------------------------------------------------------------+ */ /* | Save the budget given by the user. The variable maxf will be changed | */ /* | if in the beginning no feasible points are found. | */ /* +-----------------------------------------------------------------------+ */ oldmaxf = *maxf; increase = 0; /* +-----------------------------------------------------------------------+ */ /* | Initialiase the lists. | */ /* +-----------------------------------------------------------------------+ */ direct_dirinitlist_(anchor, &ifree, point, f, &MAXFUNC, &MAXDEEP); /* +-----------------------------------------------------------------------+ */ /* | Call the routine to initialise the mapping of x from the n-dimensional| */ /* | unit cube to the hypercube given by u and l. If an error occured, | */ /* | give out a error message and return to the main program with the error| */ /* | flag set. | */ /* | JG 07/16/01 Changed call to remove unused data. | */ /* +-----------------------------------------------------------------------+ */ direct_dirpreprc_(&u[1], &l[1], n, &l[1], &u[1], &oops); if (oops > 0) { if (logfile) fprintf(logfile,"WARNING: Initialization in DIRpreprc failed.\n"); *ierror = -3; goto cleanup; } tstart = 2; /* +-----------------------------------------------------------------------+ */ /* | Initialise the algorithm DIRECT. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | Added variable to keep track of the maximum value found. | */ /* +-----------------------------------------------------------------------+ */ direct_dirinit_(f, fcn, c__, length, &actdeep, point, anchor, &ifree, logfile, arrayi, &maxi, list2, w, &x[1], &l[1], &u[1], minf, &minpos, thirds, levels, &MAXFUNC, &MAXDEEP, n, n, & fmax, &ifeasiblef, &iinfesiblef, ierror, fcn_data, jones, starttime, maxtime, force_stop); /* +-----------------------------------------------------------------------+ */ /* | Added error checking. | */ /* +-----------------------------------------------------------------------+ */ if (*ierror < 0) { if (*ierror == -4) { if (logfile) fprintf(logfile, "WARNING: Error occured in routine DIRsamplepoints.\n"); goto cleanup; } if (*ierror == -5) { if (logfile) fprintf(logfile, "WARNING: Error occured in routine DIRsamplef..\n"); goto cleanup; } if (*ierror == -102) goto L100; } else if (*ierror == DIRECT_MAXTIME_EXCEEDED) goto L100; numfunc = maxi + 1 + maxi; actmaxdeep = 1; oldpos = 0; tstart = 2; /* +-----------------------------------------------------------------------+ */ /* | If no feasible point has been found, give out the iteration, the | */ /* | number of function evaluations and a warning. Otherwise, give out | */ /* | the iteration, the number of function evaluations done and minf. | */ /* +-----------------------------------------------------------------------+ */ if (ifeasiblef > 0) { if (logfile) fprintf(logfile, "No feasible point found in %d iterations " "and %d function evaluations.\n", tstart-1, numfunc); } else { if (logfile) fprintf(logfile, "%d, %d, %g, %g\n", tstart-1, numfunc, *minf, fmax); } /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | Main loop! | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ i__1 = *maxt; for (t = tstart; t <= i__1; ++t) { /* +-----------------------------------------------------------------------+ */ /* | Choose the sample points. The indices of the sample points are stored | */ /* | in the list S. | */ /* +-----------------------------------------------------------------------+ */ actdeep = actmaxdeep; direct_dirchoose_(anchor, s, &MAXDEEP, f, minf, *eps, epsabs, levels, &maxpos, length, &MAXFUNC, &MAXDEEP, &MAXDIV, n, logfile, &cheat, & kmax, &ifeasiblef, jones); /* +-----------------------------------------------------------------------+ */ /* | Add other hyperrectangles to S, which have the same level and the same| */ /* | function value at the center as the ones found above (that are stored | */ /* | in S). This is only done if we use the original DIRECT algorithm. | */ /* | JG 07/16/01 Added Errorflag. | */ /* +-----------------------------------------------------------------------+ */ if (*algmethod == 0) { direct_dirdoubleinsert_(anchor, s, &maxpos, point, f, &MAXDEEP, &MAXFUNC, &MAXDIV, ierror); if (*ierror == -6) { if (logfile) fprintf(logfile, "WARNING: Capacity of array S in DIRDoubleInsert reached. Increase maxdiv.\n" "This means that there are a lot of hyperrectangles with the same function\n" "value at the center. We suggest to use our modification instead (Jones = 1)\n" ); goto cleanup; } } oldpos = minpos; /* +-----------------------------------------------------------------------+ */ /* | Initialise the number of sample points in this outer loop. | */ /* +-----------------------------------------------------------------------+ */ newtosample = 0; i__2 = maxpos; for (j = 1; j <= i__2; ++j) { actdeep = s[j + MAXDIV-1]; /* +-----------------------------------------------------------------------+ */ /* | If the actual index is a point to sample, do it. | */ /* +-----------------------------------------------------------------------+ */ if (s[j - 1] > 0) { /* +-----------------------------------------------------------------------+ */ /* | JG 09/24/00 Calculate the value delta used for sampling points. | */ /* +-----------------------------------------------------------------------+ */ actdeep_div__ = direct_dirgetmaxdeep_(&s[j - 1], length, &MAXFUNC, n); delta = thirds[actdeep_div__ + 1]; actdeep = s[j + MAXDIV-1]; /* +-----------------------------------------------------------------------+ */ /* | If the current dept of division is only one under the maximal allowed | */ /* | dept, stop the computation. | */ /* +-----------------------------------------------------------------------+ */ if (actdeep + 1 >= mdeep) { if (logfile) fprintf(logfile, "WARNING: Maximum number of levels reached. Increase maxdeep.\n"); *ierror = -6; goto L100; } actmaxdeep = MAX(actdeep,actmaxdeep); help = s[j - 1]; if (! (anchor[actdeep + 1] == help)) { pos1 = anchor[actdeep + 1]; while(! (point[pos1 - 1] == help)) { pos1 = point[pos1 - 1]; } point[pos1 - 1] = point[help - 1]; } else { anchor[actdeep + 1] = point[help - 1]; } if (actdeep < 0) { actdeep = (integer) f[(help << 1) - 2]; } /* +-----------------------------------------------------------------------+ */ /* | Get the Directions in which to decrease the intervall-length. | */ /* +-----------------------------------------------------------------------+ */ direct_dirget_i__(length, &help, arrayi, &maxi, n, &MAXFUNC); /* +-----------------------------------------------------------------------+ */ /* | Sample the function. To do this, we first calculate the points where | */ /* | we need to sample the function. After checking for errors, we then do | */ /* | the actual evaluation of the function, again followed by checking for | */ /* | errors. | */ /* +-----------------------------------------------------------------------+ */ direct_dirsamplepoints_(c__, arrayi, &delta, &help, &start, length, logfile, f, &ifree, &maxi, point, &x[ 1], &l[1], minf, &minpos, &u[1], n, &MAXFUNC, & MAXDEEP, &oops); if (oops > 0) { if (logfile) fprintf(logfile, "WARNING: Error occured in routine DIRsamplepoints.\n"); *ierror = -4; goto cleanup; } newtosample += maxi; /* +-----------------------------------------------------------------------+ */ /* | JG 01/22/01 Added variable to keep track of the maximum value found. | */ /* +-----------------------------------------------------------------------+ */ direct_dirsamplef_(c__, arrayi, &delta, &help, &start, length, logfile, f, &ifree, &maxi, point, fcn, &x[ 1], &l[1], minf, &minpos, &u[1], n, &MAXFUNC, & MAXDEEP, &oops, &fmax, &ifeasiblef, &iinfesiblef, fcn_data, force_stop); if (force_stop && *force_stop) { *ierror = -102; goto L100; } if (nlopt_stop_time_(starttime, maxtime)) { *ierror = DIRECT_MAXTIME_EXCEEDED; goto L100; } if (oops > 0) { if (logfile) fprintf(logfile, "WARNING: Error occured in routine DIRsamplef.\n"); *ierror = -5; goto cleanup; } /* +-----------------------------------------------------------------------+ */ /* | Divide the intervalls. | */ /* +-----------------------------------------------------------------------+ */ direct_dirdivide_(&start, &actdeep_div__, length, point, arrayi, & help, list2, w, &maxi, f, &MAXFUNC, &MAXDEEP, n); /* +-----------------------------------------------------------------------+ */ /* | Insert the new intervalls into the list (sorted). | */ /* +-----------------------------------------------------------------------+ */ direct_dirinsertlist_(&start, anchor, point, f, &maxi, length, & MAXFUNC, &MAXDEEP, n, &help, jones); /* +-----------------------------------------------------------------------+ */ /* | Increase the number of function evaluations. | */ /* +-----------------------------------------------------------------------+ */ numfunc = numfunc + maxi + maxi; } /* +-----------------------------------------------------------------------+ */ /* | End of main loop. | */ /* +-----------------------------------------------------------------------+ */ /* L20: */ } /* +-----------------------------------------------------------------------+ */ /* | If there is a new minimum, show the actual iteration, the number of | */ /* | function evaluations, the minimum value of f (so far) and the position| */ /* | in the array. | */ /* +-----------------------------------------------------------------------+ */ if (oldpos < minpos) { if (logfile) fprintf(logfile, "%d, %d, %g, %g\n", t, numfunc, *minf, fmax); } /* +-----------------------------------------------------------------------+ */ /* | If no feasible point has been found, give out the iteration, the | */ /* | number of function evaluations and a warning. | */ /* +-----------------------------------------------------------------------+ */ if (ifeasiblef > 0) { if (logfile) fprintf(logfile, "No feasible point found in %d iterations " "and %d function evaluations\n", t, numfunc); } /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | Termination Checks | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | JG 01/22/01 Calculate the index for the hyperrectangle at which | */ /* | minf is assumed. We then calculate the volume of this | */ /* | hyperrectangle and store it in delta. This delta can be | */ /* | used to stop DIRECT once the volume is below a certain | */ /* | percentage of the original volume. Since the original | */ /* | is 1 (scaled), we can stop once delta is below a certain | */ /* | percentage, given by volper. | */ /* +-----------------------------------------------------------------------+ */ *ierror = jones; jones = 0; actdeep_div__ = direct_dirgetlevel_(&minpos, length, &MAXFUNC, n, jones); jones = *ierror; /* +-----------------------------------------------------------------------+ */ /* | JG 07/16/01 Use precalculated values to calculate volume. | */ /* +-----------------------------------------------------------------------+ */ delta = thirds[actdeep_div__] * 100; if (delta <= *volper) { *ierror = 4; if (logfile) fprintf(logfile, "DIRECT stopped: Volume of S_min is " "%g%% < %g%% of the original volume.\n", delta, *volper); goto L100; } /* +-----------------------------------------------------------------------+ */ /* | JG 01/23/01 Calculate the measure for the hyperrectangle at which | */ /* | minf is assumed. If this measure is smaller then sigmaper,| */ /* | we stop DIRECT. | */ /* +-----------------------------------------------------------------------+ */ actdeep_div__ = direct_dirgetlevel_(&minpos, length, &MAXFUNC, n, jones); delta = levels[actdeep_div__]; if (delta <= *sigmaper) { *ierror = 5; if (logfile) fprintf(logfile, "DIRECT stopped: Measure of S_min " "= %g < %g.\n", delta, *sigmaper); goto L100; } /* +-----------------------------------------------------------------------+ */ /* | If the best found function value is within fglper of the (known) | */ /* | global minimum value, terminate. This only makes sense if this optimal| */ /* | value is known, that is, in test problems. | */ /* +-----------------------------------------------------------------------+ */ if ((*minf - *fglobal) * 100 / divfactor <= *fglper) { *ierror = 3; if (logfile) fprintf(logfile, "DIRECT stopped: minf within fglper of global minimum.\n"); goto L100; } /* +-----------------------------------------------------------------------+ */ /* | Find out if there are infeasible points which are near feasible ones. | */ /* | If this is the case, replace the function value at the center of the | */ /* | hyper rectangle by the lowest function value of a nearby function. | */ /* | If no infeasible points exist (IInfesiblef = 0), skip this. | */ /* +-----------------------------------------------------------------------+ */ if (iinfesiblef > 0) { direct_dirreplaceinf_(&ifree, &ifreeold, f, c__, thirds, length, anchor, point, &u[1], &l[1], &MAXFUNC, &MAXDEEP, n, n, logfile, &fmax, jones); } ifreeold = ifree; /* +-----------------------------------------------------------------------+ */ /* | If iepschange = 1, we use the epsilon change formula from Jones. | */ /* +-----------------------------------------------------------------------+ */ if (iepschange == 1) { /* Computing MAX */ d__1 = fabs(*minf) * 1e-4; *eps = MAX(d__1,epsfix); } /* +-----------------------------------------------------------------------+ */ /* | If no feasible point has been found yet, set the maximum number of | */ /* | function evaluations to the number of evaluations already done plus | */ /* | the budget given by the user. | */ /* | If the budget has already be increased, increase it again. If a | */ /* | feasible point has been found, remark that and reset flag. No further | */ /* | increase is needed. | */ /* +-----------------------------------------------------------------------+ */ if (increase == 1) { *maxf = numfunc + oldmaxf; if (ifeasiblef == 0) { if (logfile) fprintf(logfile, "DIRECT found a feasible point. The " "adjusted budget is now set to %d.\n", *maxf); increase = 0; } } /* +-----------------------------------------------------------------------+ */ /* | Check if the number of function evaluations done is larger than the | */ /* | allocated budget. If this is the case, check if a feasible point was | */ /* | found. If this is a case, terminate. If no feasible point was found, | */ /* | increase the budget and set flag increase. | */ /* +-----------------------------------------------------------------------+ */ if (numfunc > *maxf) { if (ifeasiblef == 0) { *ierror = 1; if (logfile) fprintf(logfile, "DIRECT stopped: numfunc >= maxf.\n"); goto L100; } else { increase = 1; if (logfile) fprintf(logfile, "DIRECT could not find a feasible point after %d function evaluations.\n" "DIRECT continues until a feasible point is found.\n", numfunc); *maxf = numfunc + oldmaxf; } } /* L10: */ } /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | End of main loop. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | The algorithm stopped after maxT iterations. | */ /* +-----------------------------------------------------------------------+ */ *ierror = 2; if (logfile) fprintf(logfile, "DIRECT stopped: maxT iterations.\n"); L100: /* +-----------------------------------------------------------------------+ */ /* | Store the position of the minimum in x. | */ /* +-----------------------------------------------------------------------+ */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { x[i__] = c__[i__ + minpos * i__1 - i__1-1] * l[i__] + l[i__] * u[i__]; u[i__] = oldu[i__ - 1]; l[i__] = oldl[i__ - 1]; /* L50: */ } /* +-----------------------------------------------------------------------+ */ /* | Store the number of function evaluations in maxf. | */ /* +-----------------------------------------------------------------------+ */ *maxf = numfunc; /* +-----------------------------------------------------------------------+ */ /* | Give out a summary of the run. | */ /* +-----------------------------------------------------------------------+ */ direct_dirsummary_(logfile, &x[1], &l[1], &u[1], n, minf, fglobal, &numfunc, ierror); /* +-----------------------------------------------------------------------+ */ /* | Format statements. | */ /* +-----------------------------------------------------------------------+ */ cleanup: #define MY_FREE(p) if (p) free(p) MY_FREE(c__); MY_FREE(f); MY_FREE(s); MY_FREE(w); MY_FREE(oldl); MY_FREE(oldu); MY_FREE(list2); MY_FREE(point); MY_FREE(anchor); MY_FREE(length); MY_FREE(arrayi); MY_FREE(levels); MY_FREE(thirds); } /* direct_ */ nloptr/src/nlopt_src/direct/AUTHORS0000644000176000001440000000017313263734242016743 0ustar ripleyusersC conversion: Steven G. 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doublereal d__1; /* Local variables */ integer i__, j, helppoint, pos, kret; /* +-----------------------------------------------------------------------+ */ /* | JG 07/16/01 fcn must be declared external. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | JG 07/16/01 Removed fcn. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | JG 01/22/01 Added variable to keep track of the maximum value found. | */ /* | Added variable to keep track IF feasible point was found. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | Variables to pass user defined data to the function to be optimized. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | Set the pointer to the first function to be evaluated, | */ /* | store this position also in helppoint. | */ /* +-----------------------------------------------------------------------+ */ /* Parameter adjustments */ --u; --l; --x; --arrayi; --point; f -= 3; length_dim1 = *n; length_offset = 1 + length_dim1; length -= length_offset; c_dim1 = *n; c_offset = 1 + c_dim1; c__ -= c_offset; /* Function Body */ pos = *new__; helppoint = pos; /* +-----------------------------------------------------------------------+ */ /* | Iterate over all points, where the function should be | */ /* | evaluated. | */ /* +-----------------------------------------------------------------------+ */ i__1 = *maxi + *maxi; for (j = 1; j <= i__1; ++j) { /* +-----------------------------------------------------------------------+ */ /* | Copy the position into the helparrayy x. | */ /* +-----------------------------------------------------------------------+ */ i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { x[i__] = c__[i__ + pos * c_dim1]; /* L60: */ } /* +-----------------------------------------------------------------------+ */ /* | Call the function. | */ /* +-----------------------------------------------------------------------+ */ if (force_stop && *force_stop) /* skip eval after forced stop */ f[(pos << 1) + 1] = *fmax; else direct_dirinfcn_(fcn, &x[1], &l[1], &u[1], n, &f[(pos << 1) + 1], &kret, fcn_data); if (force_stop && *force_stop) kret = -1; /* mark as invalid point */ /* +-----------------------------------------------------------------------+ */ /* | Remember IF an infeasible point has been found. | */ /* +-----------------------------------------------------------------------+ */ *iinfesiblef = MAX(*iinfesiblef,kret); if (kret == 0) { /* +-----------------------------------------------------------------------+ */ /* | IF the function evaluation was O.K., set the flag in | */ /* | f(2,pos). Also mark that a feasible point has been found. | */ /* +-----------------------------------------------------------------------+ */ f[(pos << 1) + 2] = 0.; *ifeasiblef = 0; /* +-----------------------------------------------------------------------+ */ /* | JG 01/22/01 Added variable to keep track of the maximum value found. | */ /* +-----------------------------------------------------------------------+ */ /* Computing MAX */ d__1 = f[(pos << 1) + 1]; *fmax = MAX(d__1,*fmax); } if (kret >= 1) { /* +-----------------------------------------------------------------------+ */ /* | IF the function could not be evaluated at the given point, | */ /* | set flag to mark this (f(2,pos) and store the maximum | */ /* | box-sidelength in f(1,pos). | */ /* +-----------------------------------------------------------------------+ */ f[(pos << 1) + 2] = 2.; f[(pos << 1) + 1] = *fmax; } /* +-----------------------------------------------------------------------+ */ /* | IF the function could not be evaluated due to a failure in | */ /* | the setup, mark this. | */ /* +-----------------------------------------------------------------------+ */ if (kret == -1) { f[(pos << 1) + 2] = -1.; } /* +-----------------------------------------------------------------------+ */ /* | Set the position to the next point, at which the function | */ /* | should e evaluated. | */ /* +-----------------------------------------------------------------------+ */ pos = point[pos]; /* L40: */ } pos = helppoint; /* +-----------------------------------------------------------------------+ */ /* | Iterate over all evaluated points and see, IF the minimal | */ /* | value of the function has changed. IF this has happEND, | */ /* | store the minimal value and its position in the array. | */ /* | Attention: Only valid values are checked!! | */ /* +-----------------------------------------------------------------------+ */ i__1 = *maxi + *maxi; for (j = 1; j <= i__1; ++j) { if (f[(pos << 1) + 1] < *minf && f[(pos << 1) + 2] == 0.) { *minf = f[(pos << 1) + 1]; *minpos = pos; } pos = point[pos]; /* L50: */ } } /* dirsamplef_ */ nloptr/src/nlopt_src/direct/tstc.c0000644000176000001440000000203413355070635017013 0ustar ripleyusers#include #include #include "direct.h" /* has two global minima at (0.09,-0.71) and (-0.09,0.71), plus 4 additional local minima */ static int cnt=0; double tst_obj(int n, const double *xy, int *undefined_flag, void *unused) { double x, y, f; x = xy[0]; y = xy[1]; f = ((x*x)*(4-2.1*(x*x)+((x*x)*(x*x))/3) + x*y + (y*y)*(-4+4*(y*y))); printf("feval:, %d, %g, %g, %g\n", ++cnt, x,y, f); return f; } int main(int argc, char **argv) { int n = 2; double x[2], l[2], u[2]; long int maxits = 0; int info; double minf; int force_stop = 0; maxits = argc < 2 ? 100 : atoi(argv[1]); l[0] = -3; l[1] = -3; u[0] = 3; u[1] = 3; info = direct_optimize(tst_obj, NULL, n, l, u, x, &minf, maxits, 500, 0, 0, 0, 0, 0.0, -1.0, &force_stop, DIRECT_UNKNOWN_FGLOBAL, 0, stdout, DIRECT_GABLONSKY); printf("min f = %g at (%g,%g) after %d evals, return value %d\n", minf, x[0], x[1], cnt, info); return EXIT_SUCCESS; } nloptr/src/nlopt_src/direct/README0000644000176000001440000000314513263734242016555 0ustar ripleyusersThe DIRECT algorithm (DIviding RECTangles) is a derivative-free global optimization algorithm invented by Jones et al.: D. R. Jones, C. D. Perttunen, and B. E. Stuckmann, "Lipschitzian optimization without the lipschitz constant," J. Optimization Theory and Applications, vol. 79, p. 157 (1993). This is a deterministic-search algorithm based on systematic division of the search domain into smaller and smaller hyperrectangles. The implementation is based on the 1998-2001 Fortran version by J. M. Gablonsky at North Carolina State University, converted to C by Steven G. Johnson. The Fortran source was downloaded from: http://www4.ncsu.edu/~ctk/SOFTWARE/DIRECTv204.tar.gz Gablonsky et al implemented a modified version of the original DIRECT algorithm, as described in: J. M. Gablonsky and C. T. Kelley, "A locally-biased form of the DIRECT algorithm," J. Global Optimization 21 (1), p. 27-37 (2001). Both the original Jones algorithm (NLOPT_GLOBAL_DIRECT) and the Gablonsky modified version (NLOPT_GLOBAL_DIRECT_L) are implemented and available from the NLopt interface. The Gablonsky version makes the algorithm "more biased towards local search" so that it is more efficient for functions without too many local minima. Also, Gablonsky et al. extended the algorithm to handle "hidden constraints", i.e. arbitrary nonlinear constraints. In NLopt, a hidden constraint is represented by returning NaN (or Inf, or HUGE_VAL) from the objective function at any points violating the constraint. Further information on the DIRECT algorithm and Gablonsky's implementation can be found in the included userguide.pdf file. nloptr/src/nlopt_src/direct/direct-internal.h0000644000176000001440000001261413355070635021134 0ustar ripleyusers#ifndef DIRECT_INTERNAL_H #define DIRECT_INTERNAL_H #include #include #include #include "direct.h" #include "nlopt-util.h" #ifdef __cplusplus extern "C" { #endif /* __cplusplus */ typedef int integer; typedef double doublereal; typedef direct_objective_func fp; #define ASRT(c) if (!(c)) { fprintf(stderr, "DIRECT assertion failure at " __FILE__ ":%d -- " #c "\n", __LINE__); exit(EXIT_FAILURE); } #define MIN(a,b) ((a) < (b) ? (a) : (b)) #define MAX(a,b) ((a) > (b) ? (a) : (b)) /* DIRsubrout.c */ extern void direct_dirheader_( FILE *logfile, integer *version, doublereal *x, integer *n, doublereal *eps, integer *maxf, integer * maxt, doublereal *l, doublereal *u, integer *algmethod, integer * maxfunc, const integer *maxdeep, doublereal *fglobal, doublereal *fglper, integer *ierror, doublereal *epsfix, integer *iepschange, doublereal * volper, doublereal *sigmaper); extern void direct_dirinit_( doublereal *f, fp fcn, doublereal *c__, integer *length, integer *actdeep, integer *point, integer *anchor, integer *free, FILE *logfile, integer *arrayi, integer *maxi, integer *list2, doublereal *w, doublereal *x, doublereal *l, doublereal *u, doublereal *minf, integer *minpos, doublereal *thirds, doublereal *levels, integer *maxfunc, const integer * maxdeep, integer *n, integer *maxor, doublereal *fmax, integer * ifeasiblef, integer *iinfeasible, integer *ierror, void *fcndata, integer jones, double starttime, double maxtime, int *force_stop); extern void direct_dirinitlist_( integer *anchor, integer *free, integer * point, doublereal *f, integer *maxfunc, const integer *maxdeep); extern void direct_dirpreprc_(doublereal *u, doublereal *l, integer *n, doublereal *xs1, doublereal *xs2, integer *oops); extern void direct_dirchoose_( integer *anchor, integer *s, integer *actdeep, doublereal *f, doublereal *minf, doublereal epsrel, doublereal epsabs, doublereal *thirds, integer *maxpos, integer *length, integer *maxfunc, const integer *maxdeep, const integer *maxdiv, integer *n, FILE *logfile, integer *cheat, doublereal *kmax, integer *ifeasiblef, integer jones); extern void direct_dirdoubleinsert_( integer *anchor, integer *s, integer *maxpos, integer *point, doublereal *f, const integer *maxdeep, integer *maxfunc, const integer *maxdiv, integer *ierror); extern integer direct_dirgetmaxdeep_(integer *pos, integer *length, integer *maxfunc, integer *n); extern void direct_dirget_i__( integer *length, integer *pos, integer *arrayi, integer *maxi, integer *n, integer *maxfunc); extern void direct_dirsamplepoints_( doublereal *c__, integer *arrayi, doublereal *delta, integer *sample, integer *start, integer *length, FILE *logfile, doublereal *f, integer *free, integer *maxi, integer *point, doublereal *x, doublereal *l, doublereal *minf, integer *minpos, doublereal *u, integer *n, integer *maxfunc, const integer *maxdeep, integer *oops); extern void direct_dirdivide_( integer *new__, integer *currentlength, integer *length, integer *point, integer *arrayi, integer *sample, integer *list2, doublereal *w, integer *maxi, doublereal *f, integer *maxfunc, const integer *maxdeep, integer *n); extern void direct_dirinsertlist_( integer *new__, integer *anchor, integer *point, doublereal *f, integer *maxi, integer *length, integer *maxfunc, const integer *maxdeep, integer *n, integer *samp, integer jones); extern void direct_dirreplaceinf_( integer *free, integer *freeold, doublereal *f, doublereal *c__, doublereal *thirds, integer *length, integer *anchor, integer *point, doublereal *c1, doublereal *c2, integer *maxfunc, const integer *maxdeep, integer *maxdim, integer *n, FILE *logfile, doublereal *fmax, integer jones); extern void direct_dirsummary_( FILE *logfile, doublereal *x, doublereal *l, doublereal *u, integer *n, doublereal *minf, doublereal *fglobal, integer *numfunc, integer *ierror); extern integer direct_dirgetlevel_( integer *pos, integer *length, integer *maxfunc, integer *n, integer jones); extern void direct_dirinfcn_( fp fcn, doublereal *x, doublereal *c1, doublereal *c2, integer *n, doublereal *f, integer *flag__, void *fcn_data); /* DIRserial.c / DIRparallel.c */ extern void direct_dirsamplef_( doublereal *c__, integer *arrayi, doublereal *delta, integer *sample, integer *new__, integer *length, FILE *logfile, doublereal *f, integer *free, integer *maxi, integer *point, fp fcn, doublereal *x, doublereal *l, doublereal * minf, integer *minpos, doublereal *u, integer *n, integer *maxfunc, const integer *maxdeep, integer *oops, doublereal *fmax, integer * ifeasiblef, integer *iinfesiblef, void *fcn_data, int *force_stop); /* DIRect.c */ extern void direct_direct_( fp fcn, doublereal *x, integer *n, doublereal *eps, doublereal epsabs, integer *maxf, integer *maxt, double starttime, double maxtime, int *force_stop, doublereal *minf, doublereal *l, doublereal *u, integer *algmethod, integer *ierror, FILE *logfile, doublereal *fglobal, doublereal *fglper, doublereal *volper, doublereal *sigmaper, void *fcn_data); #ifdef __cplusplus } /* extern "C" */ #endif /* __cplusplus */ #endif /* DIRECT_INTERNAL_H */ nloptr/src/nlopt_src/direct/userguide.pdf0000644000176000001440000372667513263734242020413 0ustar ripleyusers%PDF-1.4 %쏢 5 0 obj <> stream x]S]O@|#Jr_GR'6mnٙٙ?Fhҽ˵&۩G[ۓ1Hԛ"RKF8%QcH7U$ߋp8hfmqr!Zp4%.Hep&(CY"}. ~/OTQATC]X菈qByԲy?tbdgeC0;B2[%;48NhP<dɌ:coc3})s.FE'hؤ2c3ﺾ];j$7q> 'P.nꚱńJwi 0bIuQ]E6肮NT 4,LJzHFpZ!Gy;H{iO+RƹV{ *j%𝜆 WdQ16X-'Ovendstream endobj 6 0 obj 485 endobj 78 0 obj <> stream xZ]o6}7# aPs5͒Hk{pXղ]YQ$%]Zr`iFP$/e{Ͻ>F{~~'m/տySh5M/I?! ioo5# TQopG.L(G|;~gzTI]y˳6I>2_ FC>TW!p-y>zWVLi10$H s_H56l0 n'j-77=Cނ|m8 Q/@ڐn Nj \YiD[~a2NDrx;͒^Pj뫽BPZ$P3YS wSIY]]&oռA[#Z3mz6.DFmFݿa}D1!еtHܫ6 1^ːWO:P vGzP87) :h)H!~(MJe39FFa&`(Ɲ"QIH1֤(L7K\ :K '4ApYӫPQ:1F#s#k_gXg hT.фF3Eђ(T⡔\(zYrΒ|Y WHxwTA0 ZiMI ʍ VTT.lPݫߚڣu鸅qR';2.ˠ"V+(ELqŰEEm~*3pV?<4K0 ^g2xgݭ-?MGJW ;'7y/ >Kht3,Y.d֫.5DSFDi:m`ARI.|Y~<NpN=> hqLyvD;T6pS(Hx1VvXS`-Dfb0htq!d,&d*noKm_{zWAQP[hPOuWJ~TMќ:W#=6@oLy؈ }kd+Pl|@3e""i'S98 u_S(1\~NpXM,;Qp,!ok0CR{dH[}ºfb/@ݞ i(疃8ҭ!YbSPxN(\7sψ'DFN\?mv;<6.A4i5I"YZ=弌yc`.^'^_$5'XX:@# eʚ vJe}@7zѕG#od}v"Kˉu~.3sX˝Wj_fs.d@'>׈w"Z˱4VY¸pԙ}4?tiOerG'qدendstream endobj 79 0 obj 1772 endobj 203 0 obj <> stream xW]o6ݳ0`A^E">mM.ҥ<yPdVG_IWtCsϹ|]BmW1Iaͯn,~֣E$nB.yKgԦ%ah7]\.N_aEḊ \XYΓG\9c$gF?7iF4.[:uǀ^IhdY=Bpۇi !NsmODBM[lFKΪ:yQ_8IWƃr]Hw)S(EXEô:}ʚ*+aTXQ\Q]uO?º TXN@c$'~Cb:,}XU_<ɡppI\L6nQz)KΘ b Oi%P…4N̘bC|jŅ)"M\S?fA# ;`$@@u]ā+;zgTmhQT^\lH]qTN)72ɻ! 1TTu8?oҕL"rbcǍ+4m:7 '])o^.>U$'Uin jLyI7G$>7j*mZHOa~d0pk)mlҥ$UٴuD|cxj:1(i粍8SE'FqjfEXu>Y}NIPjsdš_\mݥ֦OϻS8/O/vd[2p]!yEaplbXgE\+d JE&D'yׇf-p3jF.Drg%1ɡM9ܷg}vC;KAwZӄ@oHfڼcj} Dfb\9:6&ޯ`7 6:mҸNp6P2ntXKLExZN \~N{Ӹr]V4s=KAoV ҷFy Cqf'>SI'xB T nDb([W 6gi134iAUfx@K&t%' eZ!!涋״YC5-nﲰeMGt83?s *PTiFç66q]o%p݀Go{a& ЧR \W?#֩9`߃lbuB ߞ|m@ߥsmGcIDIoQt&_CI{ PnM\Ӯx 1NW Jm%Ye<@:YZ]8G8x=읯u#n;ئs{zSbLЉ ݰ>YEFgendstream endobj 204 0 obj 1649 endobj 225 0 obj <> stream xZK>6KN9sA^k!8LÌnMj-wf]|A`,]IUW_qHzb;%ُgTX% *s1-.o]P\%Iܟ-W<fT) Hļ##8Q>-U~}l7zM YY̤JKFr䰄*Ug3 +^-q8_$IH-7ߜU,KKx+g` VT{k'h|InORJr7YpY38xM:kJ%)-OC'M+x5pfx롟nsʬl( anO6 o07?-=V*R=z֧aVttMQ35醃ȗ qIfOڟ }3LaLhNEqD9wJRl,e1 m@+*G;TȀ}̈4x1Z͘Y!歂Kg}8tY %( q}CB*~O#fr앶r#()k.W6ףA˾ P9VD4Wi RTrD"We)ÈViQxfQN1܍us?7FAu0gkwyb$;#]٨b8qޤ_#_:/(ĮYF<,OxWիP$i񨰯FmG,0~/$ScEATpog=58uv8a AЊLI ʬ+h`B/ Ko8nW!E%+~MNȑCaG?(ޚe,r\b:_:H)5MtfMNGHUX d%"Wm. &"d#qvFsB5H:p #!ȭ8v ?$EIꞈ 6oX/e 9v' r0RE‘2y`NZeجfTL~jد) K(ӰWA(qJ;N=6ӱ'(0`KWg3*H9;U䪠jփ_i?_Rj9wjbHS!o~7lw;tH%(VmTԹW4qNeUF`S@@BZFuE^/i.*J@5 k <ܼ Df$weug 59  *{37HVI?ĸ½8a_@΄luB&ఇ\x/T(^lx$jKO:0[NIM/**pP(ё!OoMr&{zL&IS0>=L];k~C?q8*hug6,:u'qH]@zGT8q# &OxajE&0.}4(#qmzPDnh%If1d9; 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Table of constant values */ static integer c__0 = 0; static integer c_n1 = -1; /* +-----------------------------------------------------------------------+ */ /* | Program : Direct.f (subfile DIRseriell.f) | */ /* | Last modified : 02-22-01 | */ /* | Written by : Joerg Gablonsky | */ /* | Subroutines, which differ depending on the serial or parallel version.| */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | Parallel Direct. This routine replaces the normal main routine DIRect.| */ /* | In it, we find out if this pe is the master or slave. If it is the | */ /* | master, it calls the serial DIRect main routine. The only routine that| */ /* | has to change for parallel Direct is DIRSamplef, where the actual | */ /* | sampling of the function is done. If we are on the slave, wait for | */ /* | either the coordinates of a point to sample the function or the | */ /* | termination signal. | */ /* +-----------------------------------------------------------------------+ */ /* Subroutine */ int direct_pardirect_(fp fcn, doublereal *x, integer *n, doublereal *eps, integer *maxf, integer *maxt, doublereal *minf, doublereal *l, doublereal *u, integer *algmethod, integer *ierror, FILE *logfile, doublereal *fglobal, doublereal *fglper, doublereal *volper, doublereal *sigmaper, void *fcn_data) { /* System generated locals */ integer i__1; /* Local variables */ integer i__, k; integer tid; integer flag__; doublereal fval; integer tids[360], kret; integer mytid; doublereal fscale; integer nprocs; /* +-----------------------------------------------------------------------+ */ /* | Parameters | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | The maximum of function evaluations allowed. | */ /* | The maximum dept of the algorithm. | */ /* | The maximum number of divisions allowed. | */ /* | The maximal dimension of the problem. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | Global Variables. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | External Variables. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | User Variables. | */ /* | These can be used to pass user defined data to the function to be | */ /* | optimized. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | Parallel programming variables | */ /* +-----------------------------------------------------------------------+ */ /* maxprocs should be >= the number of processes used for DIRECT */ /* +-----------------------------------------------------------------------+ */ /* | End of parallel programming variables | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | Internal variables | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | JG 02/28/01 Begin of parallel additions | */ /* | DETERMINE MASTER PROCESSOR. GET TIDS OF ALL PROCESSORS. | */ /* +-----------------------------------------------------------------------+ */ /* Parameter adjustments */ --u; --l; --x; /* Function Body */ getmytidif_(&mytid); getnprocsif_(&nprocs); gettidif_(&c__0, tids); /* +-----------------------------------------------------------------------+ */ /* | If I am the master get the other tids and start running DIRECT. | */ /* | Otherwise, branch off to do function evaluations. | */ /* +-----------------------------------------------------------------------+ */ if (mytid == tids[0]) { i__1 = nprocs - 1; for (i__ = 1; i__ <= i__1; ++i__) { gettidif_(&i__, &tids[i__]); /* L46: */ } /* +-----------------------------------------------------------------------+ */ /* | Call Direct main routine. This routine calls DIRSamplef for the | */ /* | function evaluations, which are then done in parallel. | */ /* +-----------------------------------------------------------------------+ */ direct_direct_(fcn, &x[1], n, eps, maxf, maxt, minf, &l[1], &u[1], algmethod, ierror, logfile, fglobal, fglper, volper, sigmaper, fcn_data); /* +-----------------------------------------------------------------------+ */ /* | Send exit message to rest of pe's. | */ /* +-----------------------------------------------------------------------+ */ flag__ = 0; i__1 = nprocs; for (tid = 2; tid <= i__1; ++tid) { mastersendif_(&tids[tid - 1], &tids[tid - 1], n, &flag__, &flag__, &x[1], &u[1], &l[1], &x[1]); /* L200: */ } } else { /* +-----------------------------------------------------------------------+ */ /* | This is what the slaves do!! | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | Receive the first point from the master processor. | */ /* +-----------------------------------------------------------------------+ */ slaverecvif_(tids, &c_n1, n, &flag__, &k, &fscale, &u[1], &l[1], &x[1] ); /* +-----------------------------------------------------------------------+ */ /* | Repeat until master signals to stop. | */ /* +-----------------------------------------------------------------------+ */ while(flag__ > 0) { /* +-----------------------------------------------------------------------+ */ /* | Evaluate f(x). | */ /* +-----------------------------------------------------------------------+ */ direct_dirinfcn_(fcn, &x[1], &l[1], &u[1], n, &fval, &kret, & fcn_data); /* +-----------------------------------------------------------------------+ */ /* | Send result and wait for next point / message with signal to stop. | */ /* +-----------------------------------------------------------------------+ */ slavesendif_(tids, &mytid, &k, &mytid, &fval, &kret); slaverecvif_(tids, &c_n1, n, &flag__, &k, &fscale, &u[1], &l[1], & x[1]); } } return 0; } /* pardirect_ */ /* +-----------------------------------------------------------------------+ */ /* | Subroutine for sampling. This sampling is done in parallel, the master| */ /* | prozessor is also evaluating the function sometimes. | */ /* +-----------------------------------------------------------------------+ */ /* Subroutine */ void direct_dirsamplef_(doublereal *c__, integer *arrayi, doublereal *delta, integer *sample, integer *new__, integer *length, FILE *logfile, doublereal *f, integer *free, integer *maxi, integer *point, fp fcn, doublereal *x, doublereal *l, doublereal * minf, integer *minpos, doublereal *u, integer *n, integer *maxfunc, integer *maxdeep, integer *oops, doublereal *fmax, integer * ifeasiblef, integer *iinfesiblef, void *fcn_data) { /* System generated locals */ integer length_dim1, length_offset, c_dim1, c_offset, f_dim1, f_offset, i__1; doublereal d__1; /* Local variables */ integer i__, j, k, helppoint, tid, pos; integer flag__, tids[360], kret, npts; doublereal fhelp; integer oldpos, nprocs, datarec; /* +-----------------------------------------------------------------------+ */ /* | JG 07/16/01 fcn must be declared external. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | JG 07/16/01 Removed fcn. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | JG 01/22/01 Added variable to keep track of the maximum value found. | */ /* | Added variable to keep track if feasible point was found. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | Variables to pass user defined data to the function to be optimized. | */ /* +-----------------------------------------------------------------------+ */ /* +-----------------------------------------------------------------------+ */ /* | Parallel programming variables. | */ /* +-----------------------------------------------------------------------+ */ /* JG 09/05/00 Increase this if more processors are used. */ /* +-----------------------------------------------------------------------+ */ /* | Find out the id's of all processors. | */ /* +-----------------------------------------------------------------------+ */ /* Parameter adjustments */ --u; --l; --x; --arrayi; --point; f_dim1 = *maxfunc; f_offset = 1 + f_dim1; f -= f_offset; length_dim1 = *maxfunc; length_offset = 1 + length_dim1; length -= length_offset; c_dim1 = *maxfunc; c_offset = 1 + c_dim1; c__ -= c_offset; /* Function Body */ getnprocsif_(&nprocs); i__1 = nprocs - 1; for (i__ = 0; i__ <= i__1; ++i__) { gettidif_(&i__, &tids[i__]); /* L46: */ } /* +-----------------------------------------------------------------------+ */ /* | Set the pointer to the first function to be evaluated, | */ /* | store this position also in helppoint. | */ /* +-----------------------------------------------------------------------+ */ pos = *new__; helppoint = pos; /* +-----------------------------------------------------------------------+ */ /* | Iterate over all points, where the function should be | */ /* | evaluated. | */ /* +-----------------------------------------------------------------------+ */ flag__ = 1; npts = *maxi + *maxi; k = 1; while(k <= npts && k < nprocs) { /* +-----------------------------------------------------------------------+ */ /* | tid is the id of the prozessor the next points is send to. | */ /* +-----------------------------------------------------------------------+ */ tid = k + 1; /* +-----------------------------------------------------------------------+ */ /* | Copy the position into the helparray x. | */ /* +-----------------------------------------------------------------------+ */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { x[i__] = c__[pos + i__ * c_dim1]; /* L60: */ } /* +-----------------------------------------------------------------------+ */ /* | Send the point. | */ /* +-----------------------------------------------------------------------+ */ mastersendif_(&tids[tid - 1], &tids[tid - 1], n, &flag__, &pos, &x[1], &u[1], &l[1], &x[1]); ++k; pos = point[pos]; /* +-----------------------------------------------------------------------+ */ /* | Get the next point. | */ /* +-----------------------------------------------------------------------+ */ } /* +-----------------------------------------------------------------------+ */ /* | Get data until it is all received. | */ /* +-----------------------------------------------------------------------+ */ datarec = 0; while(datarec < npts) { if ((doublereal) datarec / (doublereal) nprocs - datarec / nprocs < 1e-5 && k <= npts) { i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { x[i__] = c__[pos + i__ * c_dim1]; /* L165: */ } direct_dirinfcn_(fcn, &x[1], &l[1], &u[1], n, &fhelp, &kret, fcn_data); oldpos = pos; f[oldpos + f_dim1] = fhelp; ++datarec; /* +-----------------------------------------------------------------------+ */ /* | Remember if an infeasible point has been found. | */ /* +-----------------------------------------------------------------------+ */ *iinfesiblef = MAX(*iinfesiblef,kret); if (kret == 0) { /* +-----------------------------------------------------------------------+ */ /* | if the function evaluation was O.K., set the flag in | */ /* | f(pos,2). | */ /* +-----------------------------------------------------------------------+ */ f[oldpos + (f_dim1 << 1)] = 0.; *ifeasiblef = 0; /* +-----------------------------------------------------------------------+ */ /* | JG 01/22/01 Added variable to keep track of the maximum value found. | */ /* +-----------------------------------------------------------------------+ */ /* Computing MAX */ d__1 = f[pos + f_dim1]; *fmax = MAX(d__1,*fmax); } /* +-----------------------------------------------------------------------+ */ /* | Remember if an infeasible point has been found. | */ /* +-----------------------------------------------------------------------+ */ *iinfesiblef = MAX(*iinfesiblef,kret); if (kret == 1) { /* +-----------------------------------------------------------------------+ */ /* | If the function could not be evaluated at the given point, | */ /* | set flag to mark this (f(pos,2) and store the maximum | */ /* | box-sidelength in f(pos,1). | */ /* +-----------------------------------------------------------------------+ */ f[oldpos + (f_dim1 << 1)] = 2.; f[oldpos + f_dim1] = *fmax; } /* +-----------------------------------------------------------------------+ */ /* | If the function could not be evaluated due to a failure in | */ /* | the setup, mark this. | */ /* +-----------------------------------------------------------------------+ */ if (kret == -1) { f[oldpos + (f_dim1 << 1)] = -1.; } ++k; pos = point[pos]; } /* +-----------------------------------------------------------------------+ */ /* | Recover where to store the value. | */ /* +-----------------------------------------------------------------------+ */ masterrecvif_(&c_n1, &c_n1, &oldpos, &tid, &fhelp, &kret); f[oldpos + f_dim1] = fhelp; ++datarec; /* +-----------------------------------------------------------------------+ */ /* | Remember if an infeasible point has been found. | */ /* +-----------------------------------------------------------------------+ */ *iinfesiblef = MAX(*iinfesiblef,kret); if (kret == 0) { /* +-----------------------------------------------------------------------+ */ /* | if the function evaluation was O.K., set the flag in | */ /* | f(pos,2). | */ /* +-----------------------------------------------------------------------+ */ f[oldpos + (f_dim1 << 1)] = 0.; *ifeasiblef = 0; /* +-----------------------------------------------------------------------+ */ /* | JG 01/22/01 Added variable to keep track of the maximum value found. | */ /* +-----------------------------------------------------------------------+ */ /* Computing MAX */ d__1 = f[oldpos + f_dim1]; *fmax = MAX(d__1,*fmax); } if (kret == 1) { /* +-----------------------------------------------------------------------+ */ /* | If the function could not be evaluated at the given point, | */ /* | set flag to mark this (f(pos,2) and store the maximum | */ /* | box-sidelength in f(pos,1). | */ /* +-----------------------------------------------------------------------+ */ f[oldpos + (f_dim1 << 1)] = 2.; f[oldpos + f_dim1] = *fmax; } /* +-----------------------------------------------------------------------+ */ /* | If the function could not be evaluated due to a failure in | */ /* | the setup, mark this. | */ /* +-----------------------------------------------------------------------+ */ if (kret == -1) { f[oldpos + (f_dim1 << 1)] = -1.; } /* +-----------------------------------------------------------------------+ */ /* | Send data until it is all sent. | */ /* +-----------------------------------------------------------------------+ */ if (k <= npts) { /* +-----------------------------------------------------------------------+ */ /* | Copy the position into the helparray x. | */ /* +-----------------------------------------------------------------------+ */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { x[i__] = c__[pos + i__ * c_dim1]; /* L160: */ } mastersendif_(&tid, &tid, n, &flag__, &pos, &x[1], &u[1], &l[1], & x[1]); ++k; pos = point[pos]; } } pos = helppoint; /* +-----------------------------------------------------------------------+ */ /* | Iterate over all evaluated points and see, if the minimal | */ /* | value of the function has changed. If this has happend, | */ /* | store the minimal value and its position in the array. | */ /* | Attention: Only valied values are checked!! | */ /* +-----------------------------------------------------------------------+ */ i__1 = *maxi + *maxi; for (j = 1; j <= i__1; ++j) { if (f[pos + f_dim1] < *minf && f[pos + (f_dim1 << 1)] == 0.) { *minf = f[pos + f_dim1]; *minpos = pos; } pos = point[pos]; /* L50: */ } } /* dirsamplef_ */ nloptr/src/nlopt_src/COPYRIGHT0000644000176000001440000000217713263734241015721 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ nloptr/src/nlopt_src/aclocal.m40000644000176000001440000015065213263734242016271 0ustar ripleyusers# generated automatically by aclocal 1.14 -*- Autoconf -*- # Copyright (C) 1996-2013 Free Software Foundation, Inc. # This file is free software; the Free Software Foundation # gives unlimited permission to copy and/or distribute it, # with or without modifications, as long as this notice is preserved. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY, to the extent permitted by law; without # even the implied warranty of MERCHANTABILITY or FITNESS FOR A # PARTICULAR PURPOSE. m4_ifndef([AC_CONFIG_MACRO_DIRS], [m4_defun([_AM_CONFIG_MACRO_DIRS], [])m4_defun([AC_CONFIG_MACRO_DIRS], [_AM_CONFIG_MACRO_DIRS($@)])]) m4_ifndef([AC_AUTOCONF_VERSION], [m4_copy([m4_PACKAGE_VERSION], [AC_AUTOCONF_VERSION])])dnl m4_if(m4_defn([AC_AUTOCONF_VERSION]), [2.69],, [m4_warning([this file was generated for autoconf 2.69. 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Powell (mjdp@cam.ac.uk) * Copyright (c) 2007-2011 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ nloptr/src/nlopt_src/newuoa/Makefile.am0000644000176000001440000000025613263734242017755 0ustar ripleyusersAM_CPPFLAGS = -I$(top_srcdir)/util -I$(top_srcdir)/api noinst_LTLIBRARIES = libnewuoa.la libnewuoa_la_SOURCES = newuoa.c newuoa.h EXTRA_DIST = README README.orig COPYRIGHT nloptr/src/nlopt_src/newuoa/newuoa.c0000644000176000001440000020475613355070635017377 0ustar ripleyusers/* Copyright (c) 2004 M. J. D. Powell (mjdp@cam.ac.uk) * Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ /* NEWUOA derivative-free optimization algorithm by M. J. D. Powell. Original Fortran code by Powell (2004). Converted via f2c, cleaned up, and incorporated into NLopt by S. G. Johnson (2008). See README. */ #include #include #include #include #include "newuoa.h" #define MIN2(a,b) ((a) <= (b) ? (a) : (b)) #define MAX2(a,b) ((a) >= (b) ? (a) : (b)) /*************************************************************************/ /* trsapp.f */ typedef struct { int npt; double *xpt, *pq, *hq, *gq, *xopt; double *hd; int iter; } quad_model_data; static double quad_model(int n, const double *x, double *grad, void *data) { quad_model_data *d = (quad_model_data *) data; const double *xpt = d->xpt, *pq = d->pq, *hq = d->hq, *gq = d->gq, *xopt = d->xopt; double *hd = d->hd; int npt = d->npt; int i, j, k; double val = 0; /* first, set hd to be Hessian matrix times x */ memset(hd, 0, sizeof(double) * n); /* implicit Hessian terms (a sum of outer products of xpt vectors) */ for (k = 0; k < npt; ++k) { double temp = 0; for (j = 0; j < n; ++j) temp += xpt[k + j * npt] * (xopt[j] + x[j]); temp *= pq[k]; for (i = 0; i < n; ++i) hd[i] += temp * xpt[k + i * npt]; } /* explicit Hessian terms (stored as compressed lower triangle hq) */ k = 0; for (j = 0; j < n; ++j) { for (i = 0; i < j; ++i) { hd[j] += hq[k] * (xopt[i] + x[i]); hd[i] += hq[k] * (xopt[j] + x[j]); ++k; } hd[j] += hq[k++] * (xopt[j] + x[j]); } for (i = 0; i < n; ++i) { val += (gq[i] + 0.5 * hd[i]) * (xopt[i] + x[i]); if (grad) grad[i] = gq[i] + hd[i]; } d->iter++; return val; } /* constraint function to enforce |x|^2 <= rho*rho */ static double rho_constraint(int n, const double *x, double *grad, void *data) { double rho = *((double *) data); double val = - rho*rho; int i; for (i = 0; i < n; ++i) val += x[i] * x[i]; if (grad) for (i = 0; i < n; ++i) grad[i] = 2*x[i]; return val; } static nlopt_result trsapp_(int *n, int *npt, double *xopt, double *xpt, double *gq, double *hq, double *pq, double *delta, double *step, double *d__, double *g, double *hd, double *hs, double *crvmin, const double *xbase, const double *lb, const double *ub) { /* System generated locals */ int xpt_dim1, xpt_offset, i__1, i__2; double d__1, d__2; /* Local variables */ int i__, j, k; double dd, cf, dg, gg; int ih; double ds, sg; int iu; double ss, dhd, dhs, cth, sgk, shs, sth, qadd, half, qbeg, qred, qmin, temp, qsav, qnew, zero, ggbeg, alpha, angle, reduc; int iterc; double ggsav, delsq, tempa, tempb; int isave; double bstep, ratio, twopi; int itersw; double angtest; int itermax; /* N is the number of variables of a quadratic objective function, Q say. */ /* The arguments NPT, XOPT, XPT, GQ, HQ and PQ have their usual meanings, */ /* in order to define the current quadratic model Q. */ /* DELTA is the trust region radius, and has to be positive. */ /* STEP will be set to the calculated trial step. */ /* The arrays D, G, HD and HS will be used for working space. */ /* CRVMIN will be set to the least curvature of H along the conjugate */ /* directions that occur, except that it is set to zero if STEP goes */ /* all the way to the trust region boundary. */ /* The calculation of STEP begins with the truncated conjugate gradient */ /* method. If the boundary of the trust region is reached, then further */ /* changes to STEP may be made, each one being in the 2D space spanned */ /* by the current STEP and the corresponding gradient of Q. Thus STEP */ /* should provide a substantial reduction to Q within the trust region. */ /* Initialization, which includes setting HD to H times XOPT. */ /* Parameter adjustments */ xpt_dim1 = *npt; xpt_offset = 1 + xpt_dim1; xpt -= xpt_offset; --xopt; --gq; --hq; --pq; --step; --d__; --g; --hd; --hs; if (lb && ub) { double *slb, *sub, *xtol, minf, crv; nlopt_result ret; quad_model_data qmd; qmd.npt = *npt; qmd.xpt = &xpt[1 + 1 * xpt_dim1]; qmd.pq = &pq[1]; qmd.hq = &hq[1]; qmd.gq = &gq[1]; qmd.xopt = &xopt[1]; qmd.hd = &hd[1]; qmd.iter = 0; slb = &g[1]; sub = &hs[1]; xtol = &d__[1]; for (j = 0; j < *n; ++j) { slb[j] = -(sub[j] = *delta); if (slb[j] < lb[j] - xbase[j] - xopt[j+1]) slb[j] = lb[j] - xbase[j] - xopt[j+1]; if (sub[j] > ub[j] - xbase[j] - xopt[j+1]) sub[j] = ub[j] - xbase[j] - xopt[j+1]; if (slb[j] > 0) slb[j] = 0; if (sub[j] < 0) sub[j] = 0; xtol[j] = 1e-7 * *delta; /* absolute x tolerance */ } memset(&step[1], 0, sizeof(double) * *n); ret = nlopt_minimize_constrained(NLOPT_LD_MMA, *n, quad_model, &qmd, 1, rho_constraint, delta, sizeof(double), slb, sub, &step[1], &minf, -HUGE_VAL, 0., 0., 0., xtol, 1000, 0.); if (rho_constraint(*n, &step[1], 0, delta) > -1e-6*(*delta)*(*delta)) crv = 0; else { for (j = 1; j <= *n; ++j) d__[j] = step[j] - xopt[j]; quad_model(*n, &d__[1], &g[1], &qmd); crv = gg = 0; for (j = 1; j <= *n; ++j) { crv += step[j] * (g[j] - gq[j]); gg += step[j] * step[j]; } if (gg <= 1e-16 * crv) crv = 1e16; else crv = crv / gg; } *crvmin = crv; return ret; } /* Function Body */ half = .5; zero = 0.; twopi = atan(1.) * 8.; delsq = *delta * *delta; iterc = 0; itermax = *n; itersw = itermax; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* L10: */ d__[i__] = xopt[i__]; } goto L170; /* Prepare for the first line search. */ L20: qred = zero; dd = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { step[i__] = zero; hs[i__] = zero; g[i__] = gq[i__] + hd[i__]; d__[i__] = -g[i__]; /* L30: */ /* Computing 2nd power */ d__1 = d__[i__]; dd += d__1 * d__1; } *crvmin = zero; if (dd == zero) { goto L160; } ds = zero; ss = zero; gg = dd; ggbeg = gg; /* Calculate the step to the trust region boundary and the product HD. */ L40: ++iterc; temp = delsq - ss; bstep = temp / (ds + sqrt(ds * ds + dd * temp)); goto L170; L50: dhd = zero; i__1 = *n; for (j = 1; j <= i__1; ++j) { /* L60: */ dhd += d__[j] * hd[j]; } /* Update CRVMIN and set the step-length ALPHA. */ alpha = bstep; if (dhd > zero) { temp = dhd / dd; if (iterc == 1) { *crvmin = temp; } *crvmin = MIN2(*crvmin,temp); /* Computing MIN */ d__1 = alpha, d__2 = gg / dhd; alpha = MIN2(d__1,d__2); } qadd = alpha * (gg - half * alpha * dhd); qred += qadd; /* Update STEP and HS. */ ggsav = gg; gg = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { step[i__] += alpha * d__[i__]; hs[i__] += alpha * hd[i__]; /* L70: */ /* Computing 2nd power */ d__1 = g[i__] + hs[i__]; gg += d__1 * d__1; } /* Begin another conjugate direction iteration if required. */ if (alpha < bstep) { if (qadd <= qred * .01) { goto L160; } if (gg <= ggbeg * 1e-4) { goto L160; } if (iterc == itermax) { goto L160; } temp = gg / ggsav; dd = zero; ds = zero; ss = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { d__[i__] = temp * d__[i__] - g[i__] - hs[i__]; /* Computing 2nd power */ d__1 = d__[i__]; dd += d__1 * d__1; ds += d__[i__] * step[i__]; /* L80: */ /* Computing 2nd power */ d__1 = step[i__]; ss += d__1 * d__1; } if (ds <= zero) { goto L160; } if (ss < delsq) { goto L40; } } *crvmin = zero; itersw = iterc; /* Test whether an alternative iteration is required. */ L90: if (gg <= ggbeg * 1e-4) { goto L160; } sg = zero; shs = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { sg += step[i__] * g[i__]; /* L100: */ shs += step[i__] * hs[i__]; } sgk = sg + shs; angtest = sgk / sqrt(gg * delsq); if (angtest <= -.99) { goto L160; } /* Begin the alternative iteration by calculating D and HD and some */ /* scalar products. */ ++iterc; temp = sqrt(delsq * gg - sgk * sgk); tempa = delsq / temp; tempb = sgk / temp; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* L110: */ d__[i__] = tempa * (g[i__] + hs[i__]) - tempb * step[i__]; } goto L170; L120: dg = zero; dhd = zero; dhs = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { dg += d__[i__] * g[i__]; dhd += hd[i__] * d__[i__]; /* L130: */ dhs += hd[i__] * step[i__]; } /* Seek the value of the angle that minimizes Q. */ cf = half * (shs - dhd); qbeg = sg + cf; qsav = qbeg; qmin = qbeg; isave = 0; iu = 49; temp = twopi / (double) (iu + 1); i__1 = iu; for (i__ = 1; i__ <= i__1; ++i__) { angle = (double) i__ * temp; cth = cos(angle); sth = sin(angle); qnew = (sg + cf * cth) * cth + (dg + dhs * cth) * sth; if (qnew < qmin) { qmin = qnew; isave = i__; tempa = qsav; } else if (i__ == isave + 1) { tempb = qnew; } /* L140: */ qsav = qnew; } if ((double) isave == zero) { tempa = qnew; } if (isave == iu) { tempb = qbeg; } angle = zero; if (tempa != tempb) { tempa -= qmin; tempb -= qmin; angle = half * (tempa - tempb) / (tempa + tempb); } angle = temp * ((double) isave + angle); /* Calculate the new STEP and HS. Then test for convergence. */ cth = cos(angle); sth = sin(angle); reduc = qbeg - (sg + cf * cth) * cth - (dg + dhs * cth) * sth; gg = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { step[i__] = cth * step[i__] + sth * d__[i__]; hs[i__] = cth * hs[i__] + sth * hd[i__]; /* L150: */ /* Computing 2nd power */ d__1 = g[i__] + hs[i__]; gg += d__1 * d__1; } qred += reduc; ratio = reduc / qred; if (iterc < itermax && ratio > .01) { goto L90; } L160: return NLOPT_SUCCESS; /* The following instructions act as a subroutine for setting the vector */ /* HD to the vector D multiplied by the second derivative matrix of Q. */ /* They are called from three different places, which are distinguished */ /* by the value of ITERC. */ L170: i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* L180: */ hd[i__] = zero; } i__1 = *npt; for (k = 1; k <= i__1; ++k) { temp = zero; i__2 = *n; for (j = 1; j <= i__2; ++j) { /* L190: */ temp += xpt[k + j * xpt_dim1] * d__[j]; } temp *= pq[k]; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { /* L200: */ hd[i__] += temp * xpt[k + i__ * xpt_dim1]; } } ih = 0; i__2 = *n; for (j = 1; j <= i__2; ++j) { i__1 = j; for (i__ = 1; i__ <= i__1; ++i__) { ++ih; if (i__ < j) { hd[j] += hq[ih] * d__[i__]; } /* L210: */ hd[i__] += hq[ih] * d__[j]; } } if (iterc == 0) { goto L20; } if (iterc <= itersw) { goto L50; } goto L120; } /* trsapp_ */ /*************************************************************************/ /* bigden.f */ static nlopt_result bigden_(int *n, int *npt, double *xopt, double *xpt, double *bmat, double *zmat, int *idz, int *ndim, int *kopt, int *knew, double *d__, double *w, double *vlag, double *beta, double *s, double *wvec, double *prod, const double *xbase, const double *lb, const double *ub) { /* System generated locals */ int xpt_dim1, xpt_offset, bmat_dim1, bmat_offset, zmat_dim1, zmat_offset, wvec_dim1, wvec_offset, prod_dim1, prod_offset, i__1, i__2; double d__1; /* Local variables */ int i__, j, k; double dd; int jc; double ds; int ip, iu, nw; double ss, den[9], one, par[9], tau, sum, two, diff, half, temp; int ksav; double step; int nptm; double zero, alpha, angle, denex[9]; int iterc; double tempa, tempb, tempc; int isave; double ssden, dtest, quart, xoptd, twopi, xopts, denold, denmax, densav, dstemp, sumold, sstemp, xoptsq; /* N is the number of variables. */ /* NPT is the number of interpolation equations. */ /* XOPT is the best interpolation point so far. */ /* XPT contains the coordinates of the current interpolation points. */ /* BMAT provides the last N columns of H. */ /* ZMAT and IDZ give a factorization of the first NPT by NPT submatrix of H. */ /* NDIM is the first dimension of BMAT and has the value NPT+N. */ /* KOPT is the index of the optimal interpolation point. */ /* KNEW is the index of the interpolation point that is going to be moved. */ /* D will be set to the step from XOPT to the new point, and on entry it */ /* should be the D that was calculated by the last call of BIGLAG. The */ /* length of the initial D provides a trust region bound on the final D. */ /* W will be set to Wcheck for the final choice of D. */ /* VLAG will be set to Theta*Wcheck+e_b for the final choice of D. */ /* BETA will be set to the value that will occur in the updating formula */ /* when the KNEW-th interpolation point is moved to its new position. */ /* S, WVEC, PROD and the private arrays DEN, DENEX and PAR will be used */ /* for working space. */ /* D is calculated in a way that should provide a denominator with a large */ /* modulus in the updating formula when the KNEW-th interpolation point is */ /* shifted to the new position XOPT+D. */ /* Set some constants. */ /* Parameter adjustments */ zmat_dim1 = *npt; zmat_offset = 1 + zmat_dim1; zmat -= zmat_offset; xpt_dim1 = *npt; xpt_offset = 1 + xpt_dim1; xpt -= xpt_offset; --xopt; prod_dim1 = *ndim; prod_offset = 1 + prod_dim1; prod -= prod_offset; wvec_dim1 = *ndim; wvec_offset = 1 + wvec_dim1; wvec -= wvec_offset; bmat_dim1 = *ndim; bmat_offset = 1 + bmat_dim1; bmat -= bmat_offset; --d__; --w; --vlag; --s; /* Function Body */ half = .5; one = 1.; quart = .25; two = 2.; zero = 0.; twopi = atan(one) * 8.; nptm = *npt - *n - 1; /* Store the first NPT elements of the KNEW-th column of H in W(N+1) */ /* to W(N+NPT). */ i__1 = *npt; for (k = 1; k <= i__1; ++k) { /* L10: */ w[*n + k] = zero; } i__1 = nptm; for (j = 1; j <= i__1; ++j) { temp = zmat[*knew + j * zmat_dim1]; if (j < *idz) { temp = -temp; } i__2 = *npt; for (k = 1; k <= i__2; ++k) { /* L20: */ w[*n + k] += temp * zmat[k + j * zmat_dim1]; } } alpha = w[*n + *knew]; /* The initial search direction D is taken from the last call of BIGLAG, */ /* and the initial S is set below, usually to the direction from X_OPT */ /* to X_KNEW, but a different direction to an interpolation point may */ /* be chosen, in order to prevent S from being nearly parallel to D. */ dd = zero; ds = zero; ss = zero; xoptsq = zero; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { /* Computing 2nd power */ d__1 = d__[i__]; dd += d__1 * d__1; s[i__] = xpt[*knew + i__ * xpt_dim1] - xopt[i__]; ds += d__[i__] * s[i__]; /* Computing 2nd power */ d__1 = s[i__]; ss += d__1 * d__1; /* L30: */ /* Computing 2nd power */ d__1 = xopt[i__]; xoptsq += d__1 * d__1; } if (ds * ds > dd * .99 * ss) { ksav = *knew; dtest = ds * ds / ss; i__2 = *npt; for (k = 1; k <= i__2; ++k) { if (k != *kopt) { dstemp = zero; sstemp = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { diff = xpt[k + i__ * xpt_dim1] - xopt[i__]; dstemp += d__[i__] * diff; /* L40: */ sstemp += diff * diff; } if (sstemp == 0) return NLOPT_ROUNDOFF_LIMITED; if (dstemp * dstemp / sstemp < dtest) { ksav = k; dtest = dstemp * dstemp / sstemp; ds = dstemp; ss = sstemp; } } /* L50: */ } i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { /* L60: */ s[i__] = xpt[ksav + i__ * xpt_dim1] - xopt[i__]; } } ssden = dd * ss - ds * ds; iterc = 0; densav = zero; /* Begin the iteration by overwriting S with a vector that has the */ /* required length and direction. */ L70: ++iterc; if (ssden < 0) return NLOPT_ROUNDOFF_LIMITED; temp = one / sqrt(ssden); xoptd = zero; xopts = zero; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { s[i__] = temp * (dd * s[i__] - ds * d__[i__]); xoptd += xopt[i__] * d__[i__]; /* L80: */ if (nlopt_isinf(s[i__])) return NLOPT_ROUNDOFF_LIMITED; xopts += xopt[i__] * s[i__]; } /* Set the coefficients of the first two terms of BETA. */ tempa = half * xoptd * xoptd; tempb = half * xopts * xopts; den[0] = dd * (xoptsq + half * dd) + tempa + tempb; den[1] = two * xoptd * dd; den[2] = two * xopts * dd; den[3] = tempa - tempb; den[4] = xoptd * xopts; for (i__ = 6; i__ <= 9; ++i__) { /* L90: */ den[i__ - 1] = zero; } /* Put the coefficients of Wcheck in WVEC. */ i__2 = *npt; for (k = 1; k <= i__2; ++k) { tempa = zero; tempb = zero; tempc = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { tempa += xpt[k + i__ * xpt_dim1] * d__[i__]; tempb += xpt[k + i__ * xpt_dim1] * s[i__]; /* L100: */ tempc += xpt[k + i__ * xpt_dim1] * xopt[i__]; } wvec[k + wvec_dim1] = quart * (tempa * tempa + tempb * tempb); wvec[k + (wvec_dim1 << 1)] = tempa * tempc; wvec[k + wvec_dim1 * 3] = tempb * tempc; wvec[k + (wvec_dim1 << 2)] = quart * (tempa * tempa - tempb * tempb); /* L110: */ wvec[k + wvec_dim1 * 5] = half * tempa * tempb; } i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { ip = i__ + *npt; wvec[ip + wvec_dim1] = zero; wvec[ip + (wvec_dim1 << 1)] = d__[i__]; wvec[ip + wvec_dim1 * 3] = s[i__]; wvec[ip + (wvec_dim1 << 2)] = zero; /* L120: */ wvec[ip + wvec_dim1 * 5] = zero; } /* Put the coefficents of THETA*Wcheck in PROD. */ for (jc = 1; jc <= 5; ++jc) { nw = *npt; if (jc == 2 || jc == 3) { nw = *ndim; } i__2 = *npt; for (k = 1; k <= i__2; ++k) { /* L130: */ prod[k + jc * prod_dim1] = zero; } i__2 = nptm; for (j = 1; j <= i__2; ++j) { sum = zero; i__1 = *npt; for (k = 1; k <= i__1; ++k) { /* L140: */ sum += zmat[k + j * zmat_dim1] * wvec[k + jc * wvec_dim1]; } if (j < *idz) { sum = -sum; } i__1 = *npt; for (k = 1; k <= i__1; ++k) { /* L150: */ prod[k + jc * prod_dim1] += sum * zmat[k + j * zmat_dim1]; } } if (nw == *ndim) { i__1 = *npt; for (k = 1; k <= i__1; ++k) { sum = zero; i__2 = *n; for (j = 1; j <= i__2; ++j) { /* L160: */ sum += bmat[k + j * bmat_dim1] * wvec[*npt + j + jc * wvec_dim1]; } /* L170: */ prod[k + jc * prod_dim1] += sum; } } i__1 = *n; for (j = 1; j <= i__1; ++j) { sum = zero; i__2 = nw; for (i__ = 1; i__ <= i__2; ++i__) { /* L180: */ sum += bmat[i__ + j * bmat_dim1] * wvec[i__ + jc * wvec_dim1]; } /* L190: */ prod[*npt + j + jc * prod_dim1] = sum; } } /* Include in DEN the part of BETA that depends on THETA. */ i__1 = *ndim; for (k = 1; k <= i__1; ++k) { sum = zero; for (i__ = 1; i__ <= 5; ++i__) { par[i__ - 1] = half * prod[k + i__ * prod_dim1] * wvec[k + i__ * wvec_dim1]; /* L200: */ sum += par[i__ - 1]; } den[0] = den[0] - par[0] - sum; tempa = prod[k + prod_dim1] * wvec[k + (wvec_dim1 << 1)] + prod[k + ( prod_dim1 << 1)] * wvec[k + wvec_dim1]; tempb = prod[k + (prod_dim1 << 1)] * wvec[k + (wvec_dim1 << 2)] + prod[k + (prod_dim1 << 2)] * wvec[k + (wvec_dim1 << 1)]; tempc = prod[k + prod_dim1 * 3] * wvec[k + wvec_dim1 * 5] + prod[k + prod_dim1 * 5] * wvec[k + wvec_dim1 * 3]; den[1] = den[1] - tempa - half * (tempb + tempc); den[5] -= half * (tempb - tempc); tempa = prod[k + prod_dim1] * wvec[k + wvec_dim1 * 3] + prod[k + prod_dim1 * 3] * wvec[k + wvec_dim1]; tempb = prod[k + (prod_dim1 << 1)] * wvec[k + wvec_dim1 * 5] + prod[k + prod_dim1 * 5] * wvec[k + (wvec_dim1 << 1)]; tempc = prod[k + prod_dim1 * 3] * wvec[k + (wvec_dim1 << 2)] + prod[k + (prod_dim1 << 2)] * wvec[k + wvec_dim1 * 3]; den[2] = den[2] - tempa - half * (tempb - tempc); den[6] -= half * (tempb + tempc); tempa = prod[k + prod_dim1] * wvec[k + (wvec_dim1 << 2)] + prod[k + ( prod_dim1 << 2)] * wvec[k + wvec_dim1]; den[3] = den[3] - tempa - par[1] + par[2]; tempa = prod[k + prod_dim1] * wvec[k + wvec_dim1 * 5] + prod[k + prod_dim1 * 5] * wvec[k + wvec_dim1]; tempb = prod[k + (prod_dim1 << 1)] * wvec[k + wvec_dim1 * 3] + prod[k + prod_dim1 * 3] * wvec[k + (wvec_dim1 << 1)]; den[4] = den[4] - tempa - half * tempb; den[7] = den[7] - par[3] + par[4]; tempa = prod[k + (prod_dim1 << 2)] * wvec[k + wvec_dim1 * 5] + prod[k + prod_dim1 * 5] * wvec[k + (wvec_dim1 << 2)]; /* L210: */ den[8] -= half * tempa; } /* Extend DEN so that it holds all the coefficients of DENOM. */ sum = zero; for (i__ = 1; i__ <= 5; ++i__) { /* Computing 2nd power */ d__1 = prod[*knew + i__ * prod_dim1]; par[i__ - 1] = half * (d__1 * d__1); /* L220: */ sum += par[i__ - 1]; } denex[0] = alpha * den[0] + par[0] + sum; tempa = two * prod[*knew + prod_dim1] * prod[*knew + (prod_dim1 << 1)]; tempb = prod[*knew + (prod_dim1 << 1)] * prod[*knew + (prod_dim1 << 2)]; tempc = prod[*knew + prod_dim1 * 3] * prod[*knew + prod_dim1 * 5]; denex[1] = alpha * den[1] + tempa + tempb + tempc; denex[5] = alpha * den[5] + tempb - tempc; tempa = two * prod[*knew + prod_dim1] * prod[*knew + prod_dim1 * 3]; tempb = prod[*knew + (prod_dim1 << 1)] * prod[*knew + prod_dim1 * 5]; tempc = prod[*knew + prod_dim1 * 3] * prod[*knew + (prod_dim1 << 2)]; denex[2] = alpha * den[2] + tempa + tempb - tempc; denex[6] = alpha * den[6] + tempb + tempc; tempa = two * prod[*knew + prod_dim1] * prod[*knew + (prod_dim1 << 2)]; denex[3] = alpha * den[3] + tempa + par[1] - par[2]; tempa = two * prod[*knew + prod_dim1] * prod[*knew + prod_dim1 * 5]; denex[4] = alpha * den[4] + tempa + prod[*knew + (prod_dim1 << 1)] * prod[ *knew + prod_dim1 * 3]; denex[7] = alpha * den[7] + par[3] - par[4]; denex[8] = alpha * den[8] + prod[*knew + (prod_dim1 << 2)] * prod[*knew + prod_dim1 * 5]; /* Seek the value of the angle that maximizes the modulus of DENOM. */ sum = denex[0] + denex[1] + denex[3] + denex[5] + denex[7]; denold = sum; denmax = sum; isave = 0; iu = 49; temp = twopi / (double) (iu + 1); par[0] = one; i__1 = iu; for (i__ = 1; i__ <= i__1; ++i__) { angle = (double) i__ * temp; par[1] = cos(angle); par[2] = sin(angle); for (j = 4; j <= 8; j += 2) { par[j - 1] = par[1] * par[j - 3] - par[2] * par[j - 2]; /* L230: */ par[j] = par[1] * par[j - 2] + par[2] * par[j - 3]; } sumold = sum; sum = zero; for (j = 1; j <= 9; ++j) { /* L240: */ sum += denex[j - 1] * par[j - 1]; } if (fabs(sum) > fabs(denmax)) { denmax = sum; isave = i__; tempa = sumold; } else if (i__ == isave + 1) { tempb = sum; } /* L250: */ } if (isave == 0) { tempa = sum; } if (isave == iu) { tempb = denold; } step = zero; if (tempa != tempb) { tempa -= denmax; tempb -= denmax; step = half * (tempa - tempb) / (tempa + tempb); } angle = temp * ((double) isave + step); /* Calculate the new parameters of the denominator, the new VLAG vector */ /* and the new D. Then test for convergence. */ par[1] = cos(angle); par[2] = sin(angle); for (j = 4; j <= 8; j += 2) { par[j - 1] = par[1] * par[j - 3] - par[2] * par[j - 2]; /* L260: */ par[j] = par[1] * par[j - 2] + par[2] * par[j - 3]; } *beta = zero; denmax = zero; for (j = 1; j <= 9; ++j) { *beta += den[j - 1] * par[j - 1]; /* L270: */ denmax += denex[j - 1] * par[j - 1]; } i__1 = *ndim; for (k = 1; k <= i__1; ++k) { vlag[k] = zero; for (j = 1; j <= 5; ++j) { /* L280: */ vlag[k] += prod[k + j * prod_dim1] * par[j - 1]; } } tau = vlag[*knew]; dd = zero; tempa = zero; tempb = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { d__[i__] = par[1] * d__[i__] + par[2] * s[i__]; w[i__] = xopt[i__] + d__[i__]; /* Computing 2nd power */ d__1 = d__[i__]; dd += d__1 * d__1; tempa += d__[i__] * w[i__]; /* L290: */ tempb += w[i__] * w[i__]; } if (iterc >= *n) { goto L340; } if (iterc > 1) { densav = MAX2(densav,denold); } if (fabs(denmax) <= fabs(densav) * 1.1) { goto L340; } densav = denmax; /* Set S to half the gradient of the denominator with respect to D. */ /* Then branch for the next iteration. */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp = tempa * xopt[i__] + tempb * d__[i__] - vlag[*npt + i__]; /* L300: */ s[i__] = tau * bmat[*knew + i__ * bmat_dim1] + alpha * temp; } i__1 = *npt; for (k = 1; k <= i__1; ++k) { sum = zero; i__2 = *n; for (j = 1; j <= i__2; ++j) { /* L310: */ sum += xpt[k + j * xpt_dim1] * w[j]; } if (nlopt_isinf(tau * w[*n + k]) || nlopt_isinf(alpha * vlag[k])) return NLOPT_ROUNDOFF_LIMITED; temp = (tau * w[*n + k] - alpha * vlag[k]) * sum; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { /* L320: */ s[i__] += temp * xpt[k + i__ * xpt_dim1]; } } ss = zero; ds = zero; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { /* Computing 2nd power */ d__1 = s[i__]; ss += d__1 * d__1; /* L330: */ ds += d__[i__] * s[i__]; } ssden = dd * ss - ds * ds; if (ssden >= dd * 1e-8 * ss) { goto L70; } /* Set the vector W before the RETURN from the subroutine. */ L340: /* SGJ, 2008: crude hack: truncate d to [lb,ub] bounds if given */ if (lb && ub) { for (k = 1; k <= *n; ++k) { if (d__[k] > ub[k-1] - xbase[k-1] - xopt[k]) d__[k] = ub[k-1] - xbase[k-1] - xopt[k]; else if (d__[k] < lb[k-1] - xbase[k-1] - xopt[k]) d__[k] = lb[k-1] - xbase[k-1] - xopt[k]; } } i__2 = *ndim; for (k = 1; k <= i__2; ++k) { w[k] = zero; for (j = 1; j <= 5; ++j) { /* L350: */ w[k] += wvec[k + j * wvec_dim1] * par[j - 1]; } } vlag[*kopt] += one; return NLOPT_SUCCESS; } /* bigden_ */ /*************************************************************************/ /* biglag.f */ typedef struct { int npt, ndim, iter; double *hcol, *xpt, *bmat, *xopt; int flipsign; } lag_data; /* the Lagrange function, whose absolute value biglag maximizes */ static double lag(int n, const double *dx, double *grad, void *data) { lag_data *d = (lag_data *) data; int i, j, npt = d->npt, ndim = d->ndim; const double *hcol = d->hcol, *xpt = d->xpt, *bmat = d->bmat, *xopt = d->xopt; double val = 0; for (j = 0; j < n; ++j) { val += bmat[j * ndim] * (xopt[j] + dx[j]); if (grad) grad[j] = bmat[j * ndim]; } for (i = 0; i < npt; ++i) { double dot = 0; for (j = 0; j < n; ++j) dot += xpt[i + j * npt] * (xopt[j] + dx[j]); val += 0.5 * hcol[i] * (dot * dot); dot *= hcol[i]; if (grad) for (j = 0; j < n; ++j) grad[j] += dot * xpt[i + j * npt]; } if (d->flipsign) { val = -val; if (grad) for (j = 0; j < n; ++j) grad[j] = -grad[j]; } d->iter++; return val; } static nlopt_result biglag_(int *n, int *npt, double *xopt, double *xpt, double *bmat, double *zmat, int *idz, int *ndim, int *knew, double *delta, double *d__, double *alpha, double *hcol, double *gc, double *gd, double *s, double *w, const double *xbase, const double *lb, const double *ub) { /* System generated locals */ int xpt_dim1, xpt_offset, bmat_dim1, bmat_offset, zmat_dim1, zmat_offset, i__1, i__2; double d__1; /* Local variables */ int i__, j, k; double dd, gg; int iu; double sp, ss, cf1, cf2, cf3, cf4, cf5, dhd, cth, one, tau, sth, sum, half, temp, step; int nptm; double zero, angle, scale, denom; int iterc, isave; double delsq, tempa, tempb, twopi, taubeg, tauold, taumax; /* N is the number of variables. */ /* NPT is the number of interpolation equations. */ /* XOPT is the best interpolation point so far. */ /* XPT contains the coordinates of the current interpolation points. */ /* BMAT provides the last N columns of H. */ /* ZMAT and IDZ give a factorization of the first NPT by NPT submatrix of H. */ /* NDIM is the first dimension of BMAT and has the value NPT+N. */ /* KNEW is the index of the interpolation point that is going to be moved. */ /* DELTA is the current trust region bound. */ /* D will be set to the step from XOPT to the new point. */ /* ALPHA will be set to the KNEW-th diagonal element of the H matrix. */ /* HCOL, GC, GD, S and W will be used for working space. */ /* The step D is calculated in a way that attempts to maximize the modulus */ /* of LFUNC(XOPT+D), subject to the bound ||D|| .LE. DELTA, where LFUNC is */ /* the KNEW-th Lagrange function. */ /* Set some constants. */ /* Parameter adjustments */ zmat_dim1 = *npt; zmat_offset = 1 + zmat_dim1; zmat -= zmat_offset; xpt_dim1 = *npt; xpt_offset = 1 + xpt_dim1; xpt -= xpt_offset; --xopt; bmat_dim1 = *ndim; bmat_offset = 1 + bmat_dim1; bmat -= bmat_offset; --d__; --hcol; --gc; --gd; --s; --w; /* Function Body */ half = .5; one = 1.; zero = 0.; twopi = atan(one) * 8.; delsq = *delta * *delta; nptm = *npt - *n - 1; /* Set the first NPT components of HCOL to the leading elements of the */ /* KNEW-th column of H. */ iterc = 0; i__1 = *npt; for (k = 1; k <= i__1; ++k) { /* L10: */ hcol[k] = zero; } i__1 = nptm; for (j = 1; j <= i__1; ++j) { temp = zmat[*knew + j * zmat_dim1]; if (j < *idz) { temp = -temp; } i__2 = *npt; for (k = 1; k <= i__2; ++k) { /* L20: */ hcol[k] += temp * zmat[k + j * zmat_dim1]; if (nlopt_isinf(hcol[k])) return NLOPT_ROUNDOFF_LIMITED; } } *alpha = hcol[*knew]; /* Set the unscaled initial direction D. Form the gradient of LFUNC at */ /* XOPT, and multiply D by the second derivative matrix of LFUNC. */ dd = zero; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { d__[i__] = xpt[*knew + i__ * xpt_dim1] - xopt[i__]; gc[i__] = bmat[*knew + i__ * bmat_dim1]; gd[i__] = zero; /* L30: */ /* Computing 2nd power */ d__1 = d__[i__]; dd += d__1 * d__1; } i__2 = *npt; for (k = 1; k <= i__2; ++k) { temp = zero; sum = zero; i__1 = *n; for (j = 1; j <= i__1; ++j) { temp += xpt[k + j * xpt_dim1] * xopt[j]; /* L40: */ sum += xpt[k + j * xpt_dim1] * d__[j]; } temp = hcol[k] * temp; sum = hcol[k] * sum; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { gc[i__] += temp * xpt[k + i__ * xpt_dim1]; /* L50: */ gd[i__] += sum * xpt[k + i__ * xpt_dim1]; } } /* Scale D and GD, with a sign change if required. Set S to another */ /* vector in the initial two dimensional subspace. */ gg = zero; sp = zero; dhd = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* Computing 2nd power */ d__1 = gc[i__]; gg += d__1 * d__1; sp += d__[i__] * gc[i__]; /* L60: */ dhd += d__[i__] * gd[i__]; } scale = *delta / sqrt(dd); if (sp * dhd < zero) { scale = -scale; } temp = zero; if (sp * sp > dd * .99 * gg) { temp = one; } tau = scale * (fabs(sp) + half * scale * fabs(dhd)); if (gg * delsq < tau * .01 * tau) { temp = one; } i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { d__[i__] = scale * d__[i__]; gd[i__] = scale * gd[i__]; /* L70: */ s[i__] = gc[i__] + temp * gd[i__]; } if (lb && ub) { double minf, *dlb, *dub, *xtol; lag_data ld; ld.npt = *npt; ld.ndim = *ndim; ld.iter = 0; ld.hcol = &hcol[1]; ld.xpt = &xpt[1 + 1 * xpt_dim1]; ld.bmat = &bmat[*knew + 1 * bmat_dim1]; ld.xopt = &xopt[1]; ld.flipsign = 0; dlb = &gc[1]; dub = &gd[1]; xtol = &s[1]; /* make sure rounding errors don't push initial |d| > delta */ for (j = 1; j <= *n; ++j) d__[j] *= 0.99999; for (j = 0; j < *n; ++j) { dlb[j] = -(dub[j] = *delta); if (dlb[j] < lb[j] - xbase[j] - xopt[j+1]) dlb[j] = lb[j] - xbase[j] - xopt[j+1]; if (dub[j] > ub[j] - xbase[j] - xopt[j+1]) dub[j] = ub[j] - xbase[j] - xopt[j+1]; if (dlb[j] > 0) dlb[j] = 0; if (dub[j] < 0) dub[j] = 0; if (d__[j+1] < dlb[j]) d__[j+1] = dlb[j]; else if (d__[j+1] > dub[j]) d__[j+1] = dub[j]; xtol[j] = 1e-5 * *delta; } ld.flipsign = lag(*n, &d__[1], 0, &ld) > 0; /* maximize if > 0 */ return nlopt_minimize_constrained(NLOPT_LD_MMA, *n, lag, &ld, 1, rho_constraint, delta, sizeof(double), dlb, dub, &d__[1], &minf, -HUGE_VAL, 0., 0., 0., xtol, 1000, 0.); } /* Begin the iteration by overwriting S with a vector that has the */ /* required length and direction, except that termination occurs if */ /* the given D and S are nearly parallel. */ L80: ++iterc; dd = zero; sp = zero; ss = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* Computing 2nd power */ d__1 = d__[i__]; dd += d__1 * d__1; sp += d__[i__] * s[i__]; /* L90: */ /* Computing 2nd power */ d__1 = s[i__]; ss += d__1 * d__1; } temp = dd * ss - sp * sp; if (temp <= dd * 1e-8 * ss) { goto L160; } denom = sqrt(temp); i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { s[i__] = (dd * s[i__] - sp * d__[i__]) / denom; /* L100: */ w[i__] = zero; } /* Calculate the coefficients of the objective function on the circle, */ /* beginning with the multiplication of S by the second derivative matrix. */ i__1 = *npt; for (k = 1; k <= i__1; ++k) { sum = zero; i__2 = *n; for (j = 1; j <= i__2; ++j) { /* L110: */ sum += xpt[k + j * xpt_dim1] * s[j]; } sum = hcol[k] * sum; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { /* L120: */ w[i__] += sum * xpt[k + i__ * xpt_dim1]; } } cf1 = zero; cf2 = zero; cf3 = zero; cf4 = zero; cf5 = zero; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { cf1 += s[i__] * w[i__]; cf2 += d__[i__] * gc[i__]; cf3 += s[i__] * gc[i__]; cf4 += d__[i__] * gd[i__]; /* L130: */ cf5 += s[i__] * gd[i__]; } cf1 = half * cf1; cf4 = half * cf4 - cf1; /* Seek the value of the angle that maximizes the modulus of TAU. */ taubeg = cf1 + cf2 + cf4; taumax = taubeg; tauold = taubeg; isave = 0; iu = 49; temp = twopi / (double) (iu + 1); i__2 = iu; for (i__ = 1; i__ <= i__2; ++i__) { angle = (double) i__ * temp; cth = cos(angle); sth = sin(angle); tau = cf1 + (cf2 + cf4 * cth) * cth + (cf3 + cf5 * cth) * sth; if (fabs(tau) > fabs(taumax)) { taumax = tau; isave = i__; tempa = tauold; } else if (i__ == isave + 1) { tempb = tau; } /* L140: */ tauold = tau; } if (isave == 0) { tempa = tau; } if (isave == iu) { tempb = taubeg; } step = zero; if (tempa != tempb) { tempa -= taumax; tempb -= taumax; step = half * (tempa - tempb) / (tempa + tempb); } angle = temp * ((double) isave + step); /* Calculate the new D and GD. Then test for convergence. */ cth = cos(angle); sth = sin(angle); tau = cf1 + (cf2 + cf4 * cth) * cth + (cf3 + cf5 * cth) * sth; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { d__[i__] = cth * d__[i__] + sth * s[i__]; gd[i__] = cth * gd[i__] + sth * w[i__]; /* L150: */ s[i__] = gc[i__] + gd[i__]; } if (fabs(tau) <= fabs(taubeg) * 1.1) { goto L160; } if (iterc < *n) { goto L80; } L160: return NLOPT_SUCCESS; } /* biglag_ */ /*************************************************************************/ /* update.f */ static void update_(int *n, int *npt, double *bmat, double *zmat, int *idz, int *ndim, double *vlag, double *beta, int *knew, double *w) { /* System generated locals */ int bmat_dim1, bmat_offset, zmat_dim1, zmat_offset, i__1, i__2; double d__1, d__2; /* Local variables */ int i__, j, ja, jb, jl, jp; double one, tau, temp; int nptm; double zero; int iflag; double scala, scalb_, alpha, denom, tempa, tempb, tausq; /* The arrays BMAT and ZMAT with IDZ are updated, in order to shift the */ /* interpolation point that has index KNEW. On entry, VLAG contains the */ /* components of the vector Theta*Wcheck+e_b of the updating formula */ /* (6.11), and BETA holds the value of the parameter that has this name. */ /* The vector W is used for working space. */ /* Set some constants. */ /* Parameter adjustments */ zmat_dim1 = *npt; zmat_offset = 1 + zmat_dim1; zmat -= zmat_offset; bmat_dim1 = *ndim; bmat_offset = 1 + bmat_dim1; bmat -= bmat_offset; --vlag; --w; /* Function Body */ one = 1.; zero = 0.; nptm = *npt - *n - 1; /* Apply the rotations that put zeros in the KNEW-th row of ZMAT. */ jl = 1; i__1 = nptm; for (j = 2; j <= i__1; ++j) { if (j == *idz) { jl = *idz; } else if (zmat[*knew + j * zmat_dim1] != zero) { /* Computing 2nd power */ d__1 = zmat[*knew + jl * zmat_dim1]; /* Computing 2nd power */ d__2 = zmat[*knew + j * zmat_dim1]; temp = sqrt(d__1 * d__1 + d__2 * d__2); tempa = zmat[*knew + jl * zmat_dim1] / temp; tempb = zmat[*knew + j * zmat_dim1] / temp; i__2 = *npt; for (i__ = 1; i__ <= i__2; ++i__) { temp = tempa * zmat[i__ + jl * zmat_dim1] + tempb * zmat[i__ + j * zmat_dim1]; zmat[i__ + j * zmat_dim1] = tempa * zmat[i__ + j * zmat_dim1] - tempb * zmat[i__ + jl * zmat_dim1]; /* L10: */ zmat[i__ + jl * zmat_dim1] = temp; } zmat[*knew + j * zmat_dim1] = zero; } /* L20: */ } /* Put the first NPT components of the KNEW-th column of HLAG into W, */ /* and calculate the parameters of the updating formula. */ tempa = zmat[*knew + zmat_dim1]; if (*idz >= 2) { tempa = -tempa; } if (jl > 1) { tempb = zmat[*knew + jl * zmat_dim1]; } i__1 = *npt; for (i__ = 1; i__ <= i__1; ++i__) { w[i__] = tempa * zmat[i__ + zmat_dim1]; if (jl > 1) { w[i__] += tempb * zmat[i__ + jl * zmat_dim1]; } /* L30: */ } alpha = w[*knew]; tau = vlag[*knew]; tausq = tau * tau; denom = alpha * *beta + tausq; vlag[*knew] -= one; /* Complete the updating of ZMAT when there is only one nonzero element */ /* in the KNEW-th row of the new matrix ZMAT, but, if IFLAG is set to one, */ /* then the first column of ZMAT will be exchanged with another one later. */ iflag = 0; if (jl == 1) { temp = sqrt((fabs(denom))); tempb = tempa / temp; tempa = tau / temp; i__1 = *npt; for (i__ = 1; i__ <= i__1; ++i__) { /* L40: */ zmat[i__ + zmat_dim1] = tempa * zmat[i__ + zmat_dim1] - tempb * vlag[i__]; } if (*idz == 1 && temp < zero) { *idz = 2; } if (*idz >= 2 && temp >= zero) { iflag = 1; } } else { /* Complete the updating of ZMAT in the alternative case. */ ja = 1; if (*beta >= zero) { ja = jl; } jb = jl + 1 - ja; temp = zmat[*knew + jb * zmat_dim1] / denom; tempa = temp * *beta; tempb = temp * tau; temp = zmat[*knew + ja * zmat_dim1]; scala = one / sqrt(fabs(*beta) * temp * temp + tausq); scalb_ = scala * sqrt((fabs(denom))); i__1 = *npt; for (i__ = 1; i__ <= i__1; ++i__) { zmat[i__ + ja * zmat_dim1] = scala * (tau * zmat[i__ + ja * zmat_dim1] - temp * vlag[i__]); /* L50: */ zmat[i__ + jb * zmat_dim1] = scalb_ * (zmat[i__ + jb * zmat_dim1] - tempa * w[i__] - tempb * vlag[i__]); } if (denom <= zero) { if (*beta < zero) { ++(*idz); } if (*beta >= zero) { iflag = 1; } } } /* IDZ is reduced in the following case, and usually the first column */ /* of ZMAT is exchanged with a later one. */ if (iflag == 1) { --(*idz); i__1 = *npt; for (i__ = 1; i__ <= i__1; ++i__) { temp = zmat[i__ + zmat_dim1]; zmat[i__ + zmat_dim1] = zmat[i__ + *idz * zmat_dim1]; /* L60: */ zmat[i__ + *idz * zmat_dim1] = temp; } } /* Finally, update the matrix BMAT. */ i__1 = *n; for (j = 1; j <= i__1; ++j) { jp = *npt + j; w[jp] = bmat[*knew + j * bmat_dim1]; tempa = (alpha * vlag[jp] - tau * w[jp]) / denom; tempb = (-(*beta) * w[jp] - tau * vlag[jp]) / denom; i__2 = jp; for (i__ = 1; i__ <= i__2; ++i__) { bmat[i__ + j * bmat_dim1] = bmat[i__ + j * bmat_dim1] + tempa * vlag[i__] + tempb * w[i__]; if (i__ > *npt) { bmat[jp + (i__ - *npt) * bmat_dim1] = bmat[i__ + j * bmat_dim1]; } /* L70: */ } } return; } /* update_ */ /*************************************************************************/ /* newuob.f */ static nlopt_result newuob_(int *n, int *npt, double *x, double *rhobeg, const double *lb, const double *ub, nlopt_stopping *stop, double *minf, newuoa_func calfun, void *calfun_data, double *xbase, double *xopt, double *xnew, double *xpt, double *fval, double *gq, double *hq, double *pq, double *bmat, double *zmat, int *ndim, double *d__, double *vlag, double *w) { /* System generated locals */ int xpt_dim1, xpt_offset, bmat_dim1, bmat_offset, zmat_dim1, zmat_offset, i__1, i__2, i__3; double d__1, d__2, d__3; /* Local variables */ double f; int i__, j, k, ih, nf, nh, ip, jp; double dx; int np, nfm; double one; int idz; double dsq, rho; int ipt, jpt; double sum, fbeg, diff, half, beta; int nfmm; double gisq; int knew; double temp, suma, sumb, fopt = HUGE_VAL, bsum, gqsq; int kopt, nptm; double zero, xipt, xjpt, sumz, diffa, diffb, diffc, hdiag, alpha, delta, recip, reciq, fsave; int ksave, nfsav, itemp; double dnorm, ratio, dstep, tenth, vquad; int ktemp; double tempq; int itest; double rhosq; double detrat, crvmin; double distsq; double xoptsq; double rhoend; nlopt_result rc = NLOPT_SUCCESS, rc2; /* SGJ, 2008: compute rhoend from NLopt stop info */ rhoend = stop->xtol_rel * (*rhobeg); for (j = 0; j < *n; ++j) if (rhoend < stop->xtol_abs[j]) rhoend = stop->xtol_abs[j]; /* The arguments N, NPT, X, RHOBEG, RHOEND, IPRINT and MAXFUN are identical */ /* to the corresponding arguments in SUBROUTINE NEWUOA. */ /* XBASE will hold a shift of origin that should reduce the contributions */ /* from rounding errors to values of the model and Lagrange functions. */ /* XOPT will be set to the displacement from XBASE of the vector of */ /* variables that provides the least calculated F so far. */ /* XNEW will be set to the displacement from XBASE of the vector of */ /* variables for the current calculation of F. */ /* XPT will contain the interpolation point coordinates relative to XBASE. */ /* FVAL will hold the values of F at the interpolation points. */ /* GQ will hold the gradient of the quadratic model at XBASE. */ /* HQ will hold the explicit second derivatives of the quadratic model. */ /* PQ will contain the parameters of the implicit second derivatives of */ /* the quadratic model. */ /* BMAT will hold the last N columns of H. */ /* ZMAT will hold the factorization of the leading NPT by NPT submatrix of */ /* H, this factorization being ZMAT times Diag(DZ) times ZMAT^T, where */ /* the elements of DZ are plus or minus one, as specified by IDZ. */ /* NDIM is the first dimension of BMAT and has the value NPT+N. */ /* D is reserved for trial steps from XOPT. */ /* VLAG will contain the values of the Lagrange functions at a new point X. */ /* They are part of a product that requires VLAG to be of length NDIM. */ /* The array W will be used for working space. Its length must be at least */ /* 10*NDIM = 10*(NPT+N). */ /* Set some constants. */ /* Parameter adjustments */ zmat_dim1 = *npt; zmat_offset = 1 + zmat_dim1; zmat -= zmat_offset; xpt_dim1 = *npt; xpt_offset = 1 + xpt_dim1; xpt -= xpt_offset; --x; --xbase; --xopt; --xnew; --fval; --gq; --hq; --pq; bmat_dim1 = *ndim; bmat_offset = 1 + bmat_dim1; bmat -= bmat_offset; --d__; --vlag; --w; /* Function Body */ half = .5; one = 1.; tenth = .1; zero = 0.; np = *n + 1; nh = *n * np / 2; nptm = *npt - np; /* Set the initial elements of XPT, BMAT, HQ, PQ and ZMAT to zero. */ i__1 = *n; for (j = 1; j <= i__1; ++j) { xbase[j] = x[j]; i__2 = *npt; for (k = 1; k <= i__2; ++k) { /* L10: */ xpt[k + j * xpt_dim1] = zero; } i__2 = *ndim; for (i__ = 1; i__ <= i__2; ++i__) { /* L20: */ bmat[i__ + j * bmat_dim1] = zero; } } i__2 = nh; for (ih = 1; ih <= i__2; ++ih) { /* L30: */ hq[ih] = zero; } i__2 = *npt; for (k = 1; k <= i__2; ++k) { pq[k] = zero; i__1 = nptm; for (j = 1; j <= i__1; ++j) { /* L40: */ zmat[k + j * zmat_dim1] = zero; } } /* Begin the initialization procedure. NF becomes one more than the number */ /* of function values so far. The coordinates of the displacement of the */ /* next initial interpolation point from XBASE are set in XPT(NF,.). */ rhosq = *rhobeg * *rhobeg; recip = one / rhosq; reciq = sqrt(half) / rhosq; nf = 0; L50: nfm = nf; nfmm = nf - *n; ++nf; if (nfm <= *n << 1) { if (nfm >= 1 && nfm <= *n) { xpt[nf + nfm * xpt_dim1] = *rhobeg; } else if (nfm > *n) { xpt[nf + nfmm * xpt_dim1] = -(*rhobeg); } } else { itemp = (nfmm - 1) / *n; jpt = nfm - itemp * *n - *n; ipt = jpt + itemp; if (ipt > *n) { itemp = jpt; jpt = ipt - *n; ipt = itemp; } xipt = *rhobeg; if (fval[ipt + np] < fval[ipt + 1]) { xipt = -xipt; } xjpt = *rhobeg; if (fval[jpt + np] < fval[jpt + 1]) { xjpt = -xjpt; } xpt[nf + ipt * xpt_dim1] = xipt; xpt[nf + jpt * xpt_dim1] = xjpt; } /* Calculate the next value of F, label 70 being reached immediately */ /* after this calculation. The least function value so far and its index */ /* are required. */ i__1 = *n; for (j = 1; j <= i__1; ++j) { /* L60: */ x[j] = xpt[nf + j * xpt_dim1] + xbase[j]; } if (lb && ub) { /* SGJ, 2008: make sure we are within bounds */ for (j = 1; j <= i__1; ++j) { if (x[j] < lb[j-1]) x[j] = lb[j-1]; else if (x[j] > ub[j-1]) x[j] = ub[j-1]; } } goto L310; L70: fval[nf] = f; if (nf == 1) { fbeg = f; fopt = f; kopt = 1; } else if (f < fopt) { fopt = f; kopt = nf; } /* Set the nonzero initial elements of BMAT and the quadratic model in */ /* the cases when NF is at most 2*N+1. */ if (nfm <= *n << 1) { if (nfm >= 1 && nfm <= *n) { gq[nfm] = (f - fbeg) / *rhobeg; if (*npt < nf + *n) { bmat[nfm * bmat_dim1 + 1] = -one / *rhobeg; bmat[nf + nfm * bmat_dim1] = one / *rhobeg; bmat[*npt + nfm + nfm * bmat_dim1] = -half * rhosq; } } else if (nfm > *n) { bmat[nf - *n + nfmm * bmat_dim1] = half / *rhobeg; bmat[nf + nfmm * bmat_dim1] = -half / *rhobeg; zmat[nfmm * zmat_dim1 + 1] = -reciq - reciq; zmat[nf - *n + nfmm * zmat_dim1] = reciq; zmat[nf + nfmm * zmat_dim1] = reciq; ih = nfmm * (nfmm + 1) / 2; temp = (fbeg - f) / *rhobeg; hq[ih] = (gq[nfmm] - temp) / *rhobeg; gq[nfmm] = half * (gq[nfmm] + temp); } /* Set the off-diagonal second derivatives of the Lagrange functions and */ /* the initial quadratic model. */ } else { ih = ipt * (ipt - 1) / 2 + jpt; if (xipt < zero) { ipt += *n; } if (xjpt < zero) { jpt += *n; } zmat[nfmm * zmat_dim1 + 1] = recip; zmat[nf + nfmm * zmat_dim1] = recip; zmat[ipt + 1 + nfmm * zmat_dim1] = -recip; zmat[jpt + 1 + nfmm * zmat_dim1] = -recip; hq[ih] = (fbeg - fval[ipt + 1] - fval[jpt + 1] + f) / (xipt * xjpt); } if (nf < *npt) { goto L50; } /* Begin the iterative procedure, because the initial model is complete. */ rho = *rhobeg; delta = rho; idz = 1; diffa = zero; diffb = zero; itest = 0; xoptsq = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { xopt[i__] = xpt[kopt + i__ * xpt_dim1]; /* L80: */ /* Computing 2nd power */ d__1 = xopt[i__]; xoptsq += d__1 * d__1; } L90: nfsav = nf; /* Generate the next trust region step and test its length. Set KNEW */ /* to -1 if the purpose of the next F will be to improve the model. */ L100: knew = 0; rc2 = trsapp_(n, npt, &xopt[1], &xpt[xpt_offset], &gq[1], &hq[1], &pq[1], & delta, &d__[1], &w[1], &w[np], &w[np + *n], &w[np + (*n << 1)], & crvmin, &xbase[1], lb, ub); if (rc2 < 0) { rc = rc2; goto L530; } dsq = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* L110: */ /* Computing 2nd power */ d__1 = d__[i__]; dsq += d__1 * d__1; } /* Computing MIN */ d__1 = delta, d__2 = sqrt(dsq); dnorm = MIN2(d__1,d__2); if (dnorm < half * rho) { knew = -1; delta = tenth * delta; ratio = -1.; if (delta <= rho * 1.5) { delta = rho; } if (nf <= nfsav + 2) { goto L460; } temp = crvmin * .125 * rho * rho; /* Computing MAX */ d__1 = MAX2(diffa,diffb); if (temp <= MAX2(d__1,diffc)) { goto L460; } goto L490; } /* Shift XBASE if XOPT may be too far from XBASE. First make the changes */ /* to BMAT that do not depend on ZMAT. */ L120: if (dsq <= xoptsq * .001) { tempq = xoptsq * .25; i__1 = *npt; for (k = 1; k <= i__1; ++k) { sum = zero; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { /* L130: */ sum += xpt[k + i__ * xpt_dim1] * xopt[i__]; } temp = pq[k] * sum; sum -= half * xoptsq; w[*npt + k] = sum; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { gq[i__] += temp * xpt[k + i__ * xpt_dim1]; xpt[k + i__ * xpt_dim1] -= half * xopt[i__]; vlag[i__] = bmat[k + i__ * bmat_dim1]; w[i__] = sum * xpt[k + i__ * xpt_dim1] + tempq * xopt[i__]; ip = *npt + i__; i__3 = i__; for (j = 1; j <= i__3; ++j) { /* L140: */ bmat[ip + j * bmat_dim1] = bmat[ip + j * bmat_dim1] + vlag[i__] * w[j] + w[i__] * vlag[j]; } } } /* Then the revisions of BMAT that depend on ZMAT are calculated. */ i__3 = nptm; for (k = 1; k <= i__3; ++k) { sumz = zero; i__2 = *npt; for (i__ = 1; i__ <= i__2; ++i__) { sumz += zmat[i__ + k * zmat_dim1]; /* L150: */ w[i__] = w[*npt + i__] * zmat[i__ + k * zmat_dim1]; } i__2 = *n; for (j = 1; j <= i__2; ++j) { sum = tempq * sumz * xopt[j]; i__1 = *npt; for (i__ = 1; i__ <= i__1; ++i__) { /* L160: */ sum += w[i__] * xpt[i__ + j * xpt_dim1]; } vlag[j] = sum; if (k < idz) { sum = -sum; } i__1 = *npt; for (i__ = 1; i__ <= i__1; ++i__) { /* L170: */ bmat[i__ + j * bmat_dim1] += sum * zmat[i__ + k * zmat_dim1]; } } i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { ip = i__ + *npt; temp = vlag[i__]; if (k < idz) { temp = -temp; } i__2 = i__; for (j = 1; j <= i__2; ++j) { /* L180: */ bmat[ip + j * bmat_dim1] += temp * vlag[j]; } } } /* The following instructions complete the shift of XBASE, including */ /* the changes to the parameters of the quadratic model. */ ih = 0; i__2 = *n; for (j = 1; j <= i__2; ++j) { w[j] = zero; i__1 = *npt; for (k = 1; k <= i__1; ++k) { w[j] += pq[k] * xpt[k + j * xpt_dim1]; /* L190: */ xpt[k + j * xpt_dim1] -= half * xopt[j]; } i__1 = j; for (i__ = 1; i__ <= i__1; ++i__) { ++ih; if (i__ < j) { gq[j] += hq[ih] * xopt[i__]; } gq[i__] += hq[ih] * xopt[j]; hq[ih] = hq[ih] + w[i__] * xopt[j] + xopt[i__] * w[j]; /* L200: */ bmat[*npt + i__ + j * bmat_dim1] = bmat[*npt + j + i__ * bmat_dim1]; } } i__1 = *n; for (j = 1; j <= i__1; ++j) { xbase[j] += xopt[j]; /* L210: */ xopt[j] = zero; } xoptsq = zero; } /* Pick the model step if KNEW is positive. A different choice of D */ /* may be made later, if the choice of D by BIGLAG causes substantial */ /* cancellation in DENOM. */ if (knew > 0) { rc2 = biglag_(n, npt, &xopt[1], &xpt[xpt_offset], &bmat[bmat_offset], &zmat[ zmat_offset], &idz, ndim, &knew, &dstep, &d__[1], &alpha, & vlag[1], &vlag[*npt + 1], &w[1], &w[np], &w[np + *n], &xbase[1], lb, ub); if (rc2 < 0) { rc = rc2; goto L530; } } /* Calculate VLAG and BETA for the current choice of D. The first NPT */ /* components of W_check will be held in W. */ i__1 = *npt; for (k = 1; k <= i__1; ++k) { suma = zero; sumb = zero; sum = zero; i__2 = *n; for (j = 1; j <= i__2; ++j) { suma += xpt[k + j * xpt_dim1] * d__[j]; sumb += xpt[k + j * xpt_dim1] * xopt[j]; /* L220: */ sum += bmat[k + j * bmat_dim1] * d__[j]; } w[k] = suma * (half * suma + sumb); /* L230: */ vlag[k] = sum; } beta = zero; i__1 = nptm; for (k = 1; k <= i__1; ++k) { sum = zero; i__2 = *npt; for (i__ = 1; i__ <= i__2; ++i__) { /* L240: */ sum += zmat[i__ + k * zmat_dim1] * w[i__]; } if (k < idz) { beta += sum * sum; sum = -sum; } else { beta -= sum * sum; } i__2 = *npt; for (i__ = 1; i__ <= i__2; ++i__) { /* L250: */ vlag[i__] += sum * zmat[i__ + k * zmat_dim1]; } } bsum = zero; dx = zero; i__2 = *n; for (j = 1; j <= i__2; ++j) { sum = zero; i__1 = *npt; for (i__ = 1; i__ <= i__1; ++i__) { /* L260: */ sum += w[i__] * bmat[i__ + j * bmat_dim1]; } bsum += sum * d__[j]; jp = *npt + j; i__1 = *n; for (k = 1; k <= i__1; ++k) { /* L270: */ sum += bmat[jp + k * bmat_dim1] * d__[k]; } vlag[jp] = sum; bsum += sum * d__[j]; /* L280: */ dx += d__[j] * xopt[j]; } beta = dx * dx + dsq * (xoptsq + dx + dx + half * dsq) + beta - bsum; vlag[kopt] += one; /* If KNEW is positive and if the cancellation in DENOM is unacceptable, */ /* then BIGDEN calculates an alternative model step, XNEW being used for */ /* working space. */ if (knew > 0) { /* Computing 2nd power */ d__1 = vlag[knew]; if (d__1 == 0) { rc = NLOPT_ROUNDOFF_LIMITED; goto L530; } temp = one + alpha * beta / (d__1 * d__1); if (fabs(temp) <= .8) { rc2 = bigden_(n, npt, &xopt[1], &xpt[xpt_offset], &bmat[bmat_offset], & zmat[zmat_offset], &idz, ndim, &kopt, &knew, &d__[1], &w[ 1], &vlag[1], &beta, &xnew[1], &w[*ndim + 1], &w[*ndim * 6 + 1], &xbase[1], lb, ub); if (rc2 < 0) { rc = rc2; goto L530; } } } /* Calculate the next value of the objective function. */ L290: i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { xnew[i__] = xopt[i__] + d__[i__]; /* L300: */ x[i__] = xbase[i__] + xnew[i__]; } if (lb && ub) { /* SGJ, 2008: make sure we are within bounds, since roundoff errors can push us slightly outside */ for (j = 1; j <= i__1; ++j) { if (x[j] < lb[j-1]) x[j] = lb[j-1]; else if (x[j] > ub[j-1]) x[j] = ub[j-1]; } } ++nf; L310: if (nlopt_stop_forced(stop)) rc = NLOPT_FORCED_STOP; else if (stop->nevals > 0) { if (nlopt_stop_evals(stop)) rc = NLOPT_MAXEVAL_REACHED; else if (nlopt_stop_time(stop)) rc = NLOPT_MAXTIME_REACHED; } if (rc != NLOPT_SUCCESS) goto L530; stop->nevals++; f = calfun(*n, &x[1], calfun_data); if (f < stop->minf_max) { rc = NLOPT_MINF_MAX_REACHED; goto L530; } if (nf <= *npt) { goto L70; } if (knew == -1) { goto L530; } /* Use the quadratic model to predict the change in F due to the step D, */ /* and set DIFF to the error of this prediction. */ vquad = zero; ih = 0; i__2 = *n; for (j = 1; j <= i__2; ++j) { vquad += d__[j] * gq[j]; i__1 = j; for (i__ = 1; i__ <= i__1; ++i__) { ++ih; temp = d__[i__] * xnew[j] + d__[j] * xopt[i__]; if (i__ == j) { temp = half * temp; } /* L340: */ vquad += temp * hq[ih]; } } i__1 = *npt; for (k = 1; k <= i__1; ++k) { /* L350: */ vquad += pq[k] * w[k]; } diff = f - fopt - vquad; diffc = diffb; diffb = diffa; diffa = fabs(diff); if (dnorm > rho) { nfsav = nf; } /* Update FOPT and XOPT if the new F is the least value of the objective */ /* function so far. The branch when KNEW is positive occurs if D is not */ /* a trust region step. */ fsave = fopt; if (f < fopt) { fopt = f; xoptsq = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { xopt[i__] = xnew[i__]; /* L360: */ /* Computing 2nd power */ d__1 = xopt[i__]; xoptsq += d__1 * d__1; } if (nlopt_stop_ftol(stop, fopt, fsave)) { rc = NLOPT_FTOL_REACHED; goto L530; } } ksave = knew; if (knew > 0) { goto L410; } /* Pick the next value of DELTA after a trust region step. */ if (vquad >= zero) { goto L530; } ratio = (f - fsave) / vquad; if (ratio <= tenth) { delta = half * dnorm; } else if (ratio <= .7) { /* Computing MAX */ d__1 = half * delta; delta = MAX2(d__1,dnorm); } else { /* Computing MAX */ d__1 = half * delta, d__2 = dnorm + dnorm; delta = MAX2(d__1,d__2); } if (delta <= rho * 1.5) { delta = rho; } /* Set KNEW to the index of the next interpolation point to be deleted. */ /* Computing MAX */ d__2 = tenth * delta; /* Computing 2nd power */ d__1 = MAX2(d__2,rho); rhosq = d__1 * d__1; ktemp = 0; detrat = zero; if (f >= fsave) { ktemp = kopt; detrat = one; } i__1 = *npt; for (k = 1; k <= i__1; ++k) { hdiag = zero; i__2 = nptm; for (j = 1; j <= i__2; ++j) { temp = one; if (j < idz) { temp = -one; } /* L380: */ /* Computing 2nd power */ d__1 = zmat[k + j * zmat_dim1]; hdiag += temp * (d__1 * d__1); } /* Computing 2nd power */ d__2 = vlag[k]; temp = (d__1 = beta * hdiag + d__2 * d__2, fabs(d__1)); distsq = zero; i__2 = *n; for (j = 1; j <= i__2; ++j) { /* L390: */ /* Computing 2nd power */ d__1 = xpt[k + j * xpt_dim1] - xopt[j]; distsq += d__1 * d__1; } if (distsq > rhosq) { /* Computing 3rd power */ d__1 = distsq / rhosq; temp *= d__1 * (d__1 * d__1); } if (temp > detrat && k != ktemp) { detrat = temp; knew = k; } /* L400: */ } if (knew == 0) { goto L460; } /* Update BMAT, ZMAT and IDZ, so that the KNEW-th interpolation point */ /* can be moved. Begin the updating of the quadratic model, starting */ /* with the explicit second derivative term. */ L410: update_(n, npt, &bmat[bmat_offset], &zmat[zmat_offset], &idz, ndim, &vlag[ 1], &beta, &knew, &w[1]); fval[knew] = f; ih = 0; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp = pq[knew] * xpt[knew + i__ * xpt_dim1]; i__2 = i__; for (j = 1; j <= i__2; ++j) { ++ih; /* L420: */ hq[ih] += temp * xpt[knew + j * xpt_dim1]; } } pq[knew] = zero; /* Update the other second derivative parameters, and then the gradient */ /* vector of the model. Also include the new interpolation point. */ i__2 = nptm; for (j = 1; j <= i__2; ++j) { temp = diff * zmat[knew + j * zmat_dim1]; if (j < idz) { temp = -temp; } i__1 = *npt; for (k = 1; k <= i__1; ++k) { /* L440: */ pq[k] += temp * zmat[k + j * zmat_dim1]; } } gqsq = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { gq[i__] += diff * bmat[knew + i__ * bmat_dim1]; /* Computing 2nd power */ d__1 = gq[i__]; gqsq += d__1 * d__1; /* L450: */ xpt[knew + i__ * xpt_dim1] = xnew[i__]; } /* If a trust region step makes a small change to the objective function, */ /* then calculate the gradient of the least Frobenius norm interpolant at */ /* XBASE, and store it in W, using VLAG for a vector of right hand sides. */ if (ksave == 0 && delta == rho) { if (fabs(ratio) > .01) { itest = 0; } else { i__1 = *npt; for (k = 1; k <= i__1; ++k) { /* L700: */ vlag[k] = fval[k] - fval[kopt]; } gisq = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { sum = zero; i__2 = *npt; for (k = 1; k <= i__2; ++k) { /* L710: */ sum += bmat[k + i__ * bmat_dim1] * vlag[k]; } gisq += sum * sum; /* L720: */ w[i__] = sum; } /* Test whether to replace the new quadratic model by the least Frobenius */ /* norm interpolant, making the replacement if the test is satisfied. */ ++itest; if (gqsq < gisq * 100.) { itest = 0; } if (itest >= 3) { i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* L730: */ gq[i__] = w[i__]; } i__1 = nh; for (ih = 1; ih <= i__1; ++ih) { /* L740: */ hq[ih] = zero; } i__1 = nptm; for (j = 1; j <= i__1; ++j) { w[j] = zero; i__2 = *npt; for (k = 1; k <= i__2; ++k) { /* L750: */ w[j] += vlag[k] * zmat[k + j * zmat_dim1]; } /* L760: */ if (j < idz) { w[j] = -w[j]; } } i__1 = *npt; for (k = 1; k <= i__1; ++k) { pq[k] = zero; i__2 = nptm; for (j = 1; j <= i__2; ++j) { /* L770: */ pq[k] += zmat[k + j * zmat_dim1] * w[j]; } } itest = 0; } } } if (f < fsave) { kopt = knew; } /* If a trust region step has provided a sufficient decrease in F, then */ /* branch for another trust region calculation. The case KSAVE>0 occurs */ /* when the new function value was calculated by a model step. */ if (f <= fsave + tenth * vquad) { goto L100; } if (ksave > 0) { goto L100; } /* Alternatively, find out if the interpolation points are close enough */ /* to the best point so far. */ knew = 0; L460: distsq = delta * 4. * delta; i__2 = *npt; for (k = 1; k <= i__2; ++k) { sum = zero; i__1 = *n; for (j = 1; j <= i__1; ++j) { /* L470: */ /* Computing 2nd power */ d__1 = xpt[k + j * xpt_dim1] - xopt[j]; sum += d__1 * d__1; } if (sum > distsq) { knew = k; distsq = sum; } /* L480: */ } /* If KNEW is positive, then set DSTEP, and branch back for the next */ /* iteration, which will generate a "model step". */ if (knew > 0) { /* Computing MAX */ /* Computing MIN */ d__2 = tenth * sqrt(distsq), d__3 = half * delta; d__1 = MIN2(d__2,d__3); dstep = MAX2(d__1,rho); dsq = dstep * dstep; goto L120; } if (ratio > zero) { goto L100; } if (MAX2(delta,dnorm) > rho) { goto L100; } /* The calculations with the current value of RHO are complete. Pick the */ /* next values of RHO and DELTA. */ L490: if (rho > rhoend) { delta = half * rho; ratio = rho / rhoend; if (ratio <= 16.) { rho = rhoend; } else if (ratio <= 250.) { rho = sqrt(ratio) * rhoend; } else { rho = tenth * rho; } delta = MAX2(delta,rho); goto L90; } /* Return from the calculation, after another Newton-Raphson step, if */ /* it is too short to have been tried before. */ if (knew == -1) { goto L290; } rc = NLOPT_XTOL_REACHED; L530: if (fopt <= f) { i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { /* L540: */ x[i__] = xbase[i__] + xopt[i__]; } f = fopt; } *minf = f; return rc; } /* newuob_ */ /*************************************************************************/ /* newuoa.f */ nlopt_result newuoa(int n, int npt, double *x, const double *lb, const double *ub, double rhobeg, nlopt_stopping *stop, double *minf, newuoa_func calfun, void *calfun_data) { /* Local variables */ int id, np, iw, igq, ihq, ixb, ifv, ipq, ivl, ixn, ixo, ixp, ndim, nptm, ibmat, izmat; nlopt_result ret; double *w; /* This subroutine seeks the least value of a function of many variables, */ /* by a trust region method that forms quadratic models by interpolation. */ /* There can be some freedom in the interpolation conditions, which is */ /* taken up by minimizing the Frobenius norm of the change to the second */ /* derivative of the quadratic model, beginning with a zero matrix. The */ /* arguments of the subroutine are as follows. */ /* N must be set to the number of variables and must be at least two. */ /* NPT is the number of interpolation conditions. Its value must be in the */ /* interval [N+2,(N+1)(N+2)/2]. */ /* Initial values of the variables must be set in X(1),X(2),...,X(N). They */ /* will be changed to the values that give the least calculated F. */ /* RHOBEG and RHOEND must be set to the initial and final values of a trust */ /* region radius, so both must be positive with RHOEND<=RHOBEG. Typically */ /* RHOBEG should be about one tenth of the greatest expected change to a */ /* variable, and RHOEND should indicate the accuracy that is required in */ /* the final values of the variables. */ /* The value of IPRINT should be set to 0, 1, 2 or 3, which controls the */ /* amount of printing. Specifically, there is no output if IPRINT=0 and */ /* there is output only at the return if IPRINT=1. Otherwise, each new */ /* value of RHO is printed, with the best vector of variables so far and */ /* the corresponding value of the objective function. Further, each new */ /* value of F with its variables are output if IPRINT=3. */ /* MAXFUN must be set to an upper bound on the number of calls of CALFUN. */ /* The array W will be used for working space. Its length must be at least */ /* (NPT+13)*(NPT+N)+3*N*(N+3)/2. */ /* SUBROUTINE CALFUN (N,X,F) must be provided by the user. It must set F to */ /* the value of the objective function for the variables X(1),X(2),...,X(N). */ /* Partition the working space array, so that different parts of it can be */ /* treated separately by the subroutine that performs the main calculation. */ /* Parameter adjustments */ --x; /* Function Body */ np = n + 1; nptm = npt - np; if (n < 2 || npt < n + 2 || npt > (n + 2) * np / 2) { return NLOPT_INVALID_ARGS; } ndim = npt + n; ixb = 1; ixo = ixb + n; ixn = ixo + n; ixp = ixn + n; ifv = ixp + n * npt; igq = ifv + npt; ihq = igq + n; ipq = ihq + n * np / 2; ibmat = ipq + npt; izmat = ibmat + ndim * n; id = izmat + npt * nptm; ivl = id + n; iw = ivl + ndim; w = (double *) malloc(sizeof(double) * ((npt+13)*(npt+n) + 3*(n*(n+3))/2)); if (!w) return NLOPT_OUT_OF_MEMORY; --w; /* The above settings provide a partition of W for subroutine NEWUOB. */ /* The partition requires the first NPT*(NPT+N)+5*N*(N+3)/2 elements of */ /* W plus the space that is needed by the last array of NEWUOB. */ ret = newuob_(&n, &npt, &x[1], &rhobeg, lb, ub, stop, minf, calfun, calfun_data, &w[ixb], &w[ixo], &w[ixn], &w[ixp], &w[ifv], &w[igq], &w[ihq], &w[ipq], &w[ibmat], &w[izmat], &ndim, &w[id], &w[ivl], &w[iw]); 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J. D. Powell, which performs derivative-free unconstrained optimization using an iteratively constructred quadratic approximation for the objective function. See: M. J. D. Powell, "The NEWUOA software for unconstrained optimization without derivatives," Proc. 40th Workshop on Large Scale Nonlinear Optimization (Erice, Italy, 2004). The C translation by S. G. Johnson (2008) includes a few minor modifications, mainly to use the NLopt stopping criteria (and to take the objective function as an argument rather than a global). The C translation also includes a variant (NEWUOA_BOUND, when the lb and ub parameters to newuoa are non-NULL) that is substantially modified in order to support bound constraints on the input variables. In the original NEWUOA algorithm, Powell solved the quadratic subproblems (in routines TRSAPP and BIGLAG) in a spherical trust region via a truncated conjugate-gradient algorithm. In the new variant, we use the MMA algorithm for these subproblems to solve them with both bound constraints and a spherical trust region. In principle, we should also change the BIGDEN subroutine in a similar way (since BIGDEN also approximately solves a trust-region subproblem), but instead I just truncated its result to the bounds (which probably gives suboptimal convergence, but BIGDEN is called only very rarely in practice). The original Fortran code was released by Powell with "no restrictions or charges", and the C translation by S. G. Johnson is released in a similar spirit under the MIT License (see the COPYRIGHT file in this directory). nloptr/src/nlopt_src/newuoa/README.orig0000644000176000001440000000467613263734242017552 0ustar ripleyusers This is the Fortran version of NEWUOA. Its purpose is to seek the least value of a function F of several variables, when derivatives are not available, where F is specified by the user through a subroutine called CALFUN. The algorithm is intended to change the variables to values that are close to a local minimum of F. The user, however, should assume responsibility for finding out if the calculations are satisfactory, by considering carefully the values of F that occur. The method is described in the report "The NEWUOA software for unconstrained optimization without derivatives", which is available on the web at www.damtp.cam.ac.uk, where you have to click on Numerical Analysis and then on Reports, the number of the report being NA2004/08. Let N be the number of variables. The main new feature of the method is that quadratic models are updated using only about NPT=2N+1 interpolation conditions, the remaining freedom being taken up by minimizing the Frobenius norm of the change to the second derivative matrix of the model. The new software was developed from UOBYQA, which also forms quadratic models from interpolation conditions. That method requires NPT=(N+1)(N+2)/2 conditions, however, because they have to define all the parameters of the model. The least Frobenius norm updating procedure with NPT=2N+1 is usually much more efficient when N is large, because the work of each iteration is much less than before, and in some experiments the number of calculations of the objective function seems to be only of magnitude N. The attachments in sequence are a suitable Makefile, followed by a main program and a CALFUN routine for the Chebyquad problems, in order to provide an example for testing. Then NEWUOA and its five auxiliary routines, namely NEWUOB, BIGDEN, BIGLAG, TRSAPP and UPDATE, are given. Finally, the computed output that the author obtained for the Chebyquad problems is listed. The way of calling NEWUOA should be clear from the Chebyquad example and from the comments of that subroutine. It is hoped that the software will be helpful to much future research and to many applications. There are no restrictions on or charges for its use. If you wish to refer to it, please cite the DAMTP report that is mentioned above, which has been submitted for publication in the proceedings of the 40th Workshop on Large Scale Nonlinear Optimization (Erice, Italy, 2004). December 16th, 2004 M.J.D. Powell (mjdp@cam.ac.uk) nloptr/src/nlopt_src/newuoa/newuoa.h0000644000176000001440000000057113355070635017371 0ustar ripleyusers#ifndef NEWUOA_H #define NEWUOA_H 1 #include "nlopt-util.h" #include "nlopt.h" typedef double (*newuoa_func)(int n, const double *x, void *func_data); extern nlopt_result newuoa(int n, int npt, double *x, const double *lb, const double *ub, double rhobeg, nlopt_stopping *stop, double *minf, newuoa_func calfun, void *calfun_data); #endif /* NEWUOA_H */ nloptr/src/nlopt_src/py-compile0000644000176000001440000001107613263734242016426 0ustar ripleyusers#!/bin/sh # py-compile - Compile a Python program scriptversion=2011-06-08.12; # UTC # Copyright (C) 2000-2013 Free Software Foundation, Inc. # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2, or (at your option) # any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that program. # This file is maintained in Automake, please report # bugs to or send patches to # . if [ -z "$PYTHON" ]; then PYTHON=python fi me=py-compile usage_error () { echo "$me: $*" >&2 echo "Try '$me --help' for more information." >&2 exit 1 } basedir= destdir= while test $# -ne 0; do case "$1" in --basedir) if test $# -lt 2; then usage_error "option '--basedir' requires an argument" else basedir=$2 fi shift ;; --destdir) if test $# -lt 2; then usage_error "option '--destdir' requires an argument" else destdir=$2 fi shift ;; -h|--help) cat <<\EOF Usage: py-compile [--help] [--version] [--basedir DIR] [--destdir DIR] FILES..." Byte compile some python scripts FILES. Use --destdir to specify any leading directory path to the FILES that you don't want to include in the byte compiled file. Specify --basedir for any additional path information you do want to be shown in the byte compiled file. Example: py-compile --destdir /tmp/pkg-root --basedir /usr/share/test test.py test2.py Report bugs to . EOF exit $? ;; -v|--version) echo "$me $scriptversion" exit $? ;; --) shift break ;; -*) usage_error "unrecognized option '$1'" ;; *) break ;; esac shift done files=$* if test -z "$files"; then usage_error "no files given" fi # if basedir was given, then it should be prepended to filenames before # byte compilation. if [ -z "$basedir" ]; then pathtrans="path = file" else pathtrans="path = os.path.join('$basedir', file)" fi # if destdir was given, then it needs to be prepended to the filename to # byte compile but not go into the compiled file. if [ -z "$destdir" ]; then filetrans="filepath = path" else filetrans="filepath = os.path.normpath('$destdir' + os.sep + path)" fi $PYTHON -c " import sys, os, py_compile, imp files = '''$files''' sys.stdout.write('Byte-compiling python modules...\n') for file in files.split(): $pathtrans $filetrans if not os.path.exists(filepath) or not (len(filepath) >= 3 and filepath[-3:] == '.py'): continue sys.stdout.write(file) sys.stdout.flush() if hasattr(imp, 'get_tag'): py_compile.compile(filepath, imp.cache_from_source(filepath), path) else: py_compile.compile(filepath, filepath + 'c', path) sys.stdout.write('\n')" || exit $? # this will fail for python < 1.5, but that doesn't matter ... $PYTHON -O -c " import sys, os, py_compile, imp # pypy does not use .pyo optimization if hasattr(sys, 'pypy_translation_info'): sys.exit(0) files = '''$files''' sys.stdout.write('Byte-compiling python modules (optimized versions) ...\n') for file in files.split(): $pathtrans $filetrans if not os.path.exists(filepath) or not (len(filepath) >= 3 and filepath[-3:] == '.py'): continue sys.stdout.write(file) sys.stdout.flush() if hasattr(imp, 'get_tag'): py_compile.compile(filepath, imp.cache_from_source(filepath, False), path) else: py_compile.compile(filepath, filepath + 'o', path) sys.stdout.write('\n')" 2>/dev/null || : # Local Variables: # mode: shell-script # sh-indentation: 2 # eval: (add-hook 'write-file-hooks 'time-stamp) # time-stamp-start: "scriptversion=" # time-stamp-format: "%:y-%02m-%02d.%02H" # time-stamp-time-zone: "UTC" # time-stamp-end: "; # UTC" # End: nloptr/src/nlopt_src/config.h.in0000644000176000001440000000676313263734242016457 0ustar ripleyusers/* config.h.in. Generated from configure.ac by autoheader. */ /* Bugfix version number. */ #undef BUGFIX_VERSION /* Define to enable extra debugging code. */ #undef DEBUG /* Define to 1 if you have the `BSDgettimeofday' function. */ #undef HAVE_BSDGETTIMEOFDAY /* Define if the copysign function/macro is available. */ #undef HAVE_COPYSIGN /* Define to 1 if you have the header file. */ #undef HAVE_DLFCN_H /* Define to 1 if you have the header file. */ #undef HAVE_GETOPT_H /* Define to 1 if you have the `getpid' function. */ #undef HAVE_GETPID /* Define if syscall(SYS_gettid) available. */ #undef HAVE_GETTID_SYSCALL /* Define to 1 if you have the `gettimeofday' function. */ #undef HAVE_GETTIMEOFDAY /* Define to 1 if you have the header file. */ #undef HAVE_INTTYPES_H /* Define if the isinf() function/macro is available. */ #undef HAVE_ISINF /* Define if the isnan() function/macro is available. */ #undef HAVE_ISNAN /* Define to 1 if you have the `m' library (-lm). */ #undef HAVE_LIBM /* Define to 1 if you have the header file. */ #undef HAVE_MEMORY_H /* Define to 1 if you have the `qsort_r' function. */ #undef HAVE_QSORT_R /* Define to 1 if you have the header file. */ #undef HAVE_STDINT_H /* Define to 1 if you have the header file. */ #undef HAVE_STDLIB_H /* Define to 1 if you have the header file. */ #undef HAVE_STRINGS_H /* Define to 1 if you have the header file. */ #undef HAVE_STRING_H /* Define to 1 if you have the header file. */ #undef HAVE_SYS_STAT_H /* Define to 1 if you have the header file. */ #undef HAVE_SYS_TYPES_H /* Define to 1 if you have the `time' function. */ #undef HAVE_TIME /* Define to 1 if the system has the type `uint32_t'. */ #undef HAVE_UINT32_T /* Define to 1 if you have the header file. */ #undef HAVE_UNISTD_H /* Define to the sub-directory in which libtool stores uninstalled libraries. */ #undef LT_OBJDIR /* Major version number. */ #undef MAJOR_VERSION /* Minor version number. */ #undef MINOR_VERSION /* Name of package */ #undef PACKAGE /* Define to the address where bug reports for this package should be sent. */ #undef PACKAGE_BUGREPORT /* Define to the full name of this package. */ #undef PACKAGE_NAME /* Define to the full name and version of this package. */ #undef PACKAGE_STRING /* Define to the one symbol short name of this package. */ #undef PACKAGE_TARNAME /* Define to the home page for this package. */ #undef PACKAGE_URL /* Define to the version of this package. */ #undef PACKAGE_VERSION /* replacement for broken HUGE_VAL macro, if needed */ #undef REPLACEMENT_HUGE_VAL /* The size of `unsigned int', as computed by sizeof. */ #undef SIZEOF_UNSIGNED_INT /* The size of `unsigned long', as computed by sizeof. */ #undef SIZEOF_UNSIGNED_LONG /* Define to 1 if you have the ANSI C header files. */ #undef STDC_HEADERS /* Define to C thread-local keyword, or to nothing if this is not supported in your compiler. */ #undef THREADLOCAL /* Define to 1 if you can safely include both and . */ #undef TIME_WITH_SYS_TIME /* Version number of package */ #undef VERSION /* Define if compiled including C++-based routines */ #undef WITH_CXX /* Define to empty if `const' does not conform to ANSI C. */ #undef const /* Define to `__inline__' or `__inline' if that's what the C compiler calls it, or to nothing if 'inline' is not supported under any name. */ #ifndef __cplusplus #undef inline #endif nloptr/src/nlopt_src/stogo/0000755000176000001440000000000013263734242015553 5ustar ripleyusersnloptr/src/nlopt_src/stogo/COPYRIGHT0000644000176000001440000000613013263734242017046 0ustar ripleyusersThe original StoGO code is: Copyright (c) 1998 by S. Zertchaninov and K. Madsen. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ---------------------------------------------------------------------- The StoGO source code on the authors' web site has no copyright or license information, but I (Steven G. Johnson) contacted the author K. Madsen for clarification and he gave me permission to distribute it under the MIT license above. See the correspondence below: ---------------------------------------------------------------------- Date: Fri, 24 Aug 2007 10:46:09 +0200 From: Kaj Madsen To: stevenj@math.mit.edu Subject: RE: open source license for StoGO software? The MIT licence is ok with me, and so is your copyright suggestion. Kaj Madsen. -----Original Message----- From: Steven G. Johnson [mailto:stevenj@fftw.org] Sent: 23. august 2007 22:23 To: Kaj Madsen Subject: RE: open source license for StoGO software? On Thu, 23 Aug 2007, Kaj Madsen wrote: > Many thanks for your mail. You can definitely consider the software as > open source, please use it as you like, however please refer to us if > you publish results based on the software. > > I am now in a heavy administrative position as a Head of Department > (has been since 1998, therefore I never really followed up on this). > The paper was never published, however I am attaching two fdf-files, > one with the paper, one with some descriptions of the code. I hope > this makes sense, otherwise please don't hesitate to contact me again. Thanks so much for your response! To be open source it needs some specific open-source license to specify the permissions in legal terms. The simplest open-source license is probably the MIT license, is this okay? http://opensource.org/licenses/mit-license.php Also, I need to know who the authors are for the copyright statement. Should I list it as: Copyright (c) 1998 by S. Zertchaninov and K. Madsen ? Thanks again! I will definitely cite it if I publish any results based on StoGO! (It's one of the few public global-optimization programs that is able to exploit gradient information.) Regards, Steven G. Johnson nloptr/src/nlopt_src/stogo/stogo_config.h0000644000176000001440000000065513355070635020413 0ustar ripleyusers#ifndef STOGO_CONFIG_H #define STOGO_CONFIG_H // Compiler flags to enable/disable various options in the code // To obtain extra information on the global search process: //#define GS_DEBUG // To obtain info on the local search process: //#define LS_DEBUG // Initialization strategy in the local search (INI1,INI2 or INI3) #define INI2 // Use the more pessimistic strategy for lower bound estimation //#define LB2 #endif nloptr/src/nlopt_src/stogo/tst.cc0000644000176000001440000000254513355070635016703 0ustar ripleyusers#include #include "global.h" #include "tools.h" #include "linalg.h" #include "tools.h" #include "stogo_config.h" void Domain_Mine(RTBox box) { box.lb=-3.0 ; box.ub=3.0; } static int cnt = 0; double Objective_Mine(RCRVector xy) { double x = xy(0); double y = xy(1); double f = ((x*x)*(4-2.1*(x*x)+((x*x)*(x*x))/3) + x*y + (y*y)*(-4+4*(y*y))); printf("feval:, %d, %g, %g, %g\n", ++cnt, x,y, f); return f; } void Gradient_Mine(RCRVector xy, RVector &grad) { double x = xy(0); double y = xy(1); grad(0) = /* df/dx */ ((2*x)*(4-2.1*(x*x)+((x*x)*(x*x))/3) + (x*x)*(-4.2*x+4*(x*(x*x))/3) + y); grad(1) = /* df/dy */ (x + (2*y)*(-4+4*(y*y)) + (y*y)*(8*(y))); } int main(int argc, char **argv) { int dim=2; Pdom Dom=Domain_Mine; Pobj Obj=Objective_Mine; Pgrad Grad=Gradient_Mine; GlobalParams params; params.det_pnts=2*dim+1; params.rnd_pnts=0; params.eps_cl=0.1; params.rshift=0.3; params.mu=1.0E-4; params.maxtime=0; params.maxeval = argc < 2 ? 100 : atoi(argv[1]); TBox D(dim); Dom(D); Global Problem(D,Obj, Grad, params); RVector dummyvec(dim); Problem.Search(-1, dummyvec); cout << "Minimizers found\n"; Problem.DispMinimizers(); cout << "dummyvec: " << dummyvec << "\n"; double val = Problem.OneMinimizer(dummyvec); cout << "one minimizer: " << val << ": " << dummyvec << "\n"; } nloptr/src/nlopt_src/stogo/Makefile.am0000644000176000001440000000050613263734242017610 0ustar ripleyusersAM_CPPFLAGS = -I$(top_srcdir)/util -I$(top_srcdir)/api noinst_LTLIBRARIES = libstogo.la libstogo_la_SOURCES = global.cc linalg.cc local.cc stogo.cc tools.cc \ global.h linalg.h local.h stogo_config.h stogo.h tools.h EXTRA_DIST = testros.cc tst.cc tstc.c prog.cc testfun.h rosen.h README \ paper.pdf techreport.pdf COPYRIGHT nloptr/src/nlopt_src/stogo/testfun.h0000644000176000001440000003300713355070635017420 0ustar ripleyusers#ifndef TESTFUN_H #define TESTFUN_H #include "linalg.h" #include "tools.h" #include "stogo_config.h" const double pi=fabs(acos(-1.)); /* The Matrix a and vector c are needed in the Shekel function */ static double a[10][4]={ { 4,4,4,4 } , { 1,1,1,1 } , { 8,8,8,8 } , { 6,6,6,6 } , { 3,7,3,7 } , { 2,9,2,9 } , { 5,5,3,3 } , { 8,1,8,1 } , { 6,2,6,2 } , {7,3.6,7,3.6} }; static double c[10]= { .1 , .2 , .2 , .4 , .4 , .6 , .3, .7 , .5 , .5 }; void Domain_Shekel(RTBox box) { box.lb=0.0 ; box.ub=10.0 ; } double Objective_Shekel(RCRVector x) { int n=x.GetLength() ; double R=0.0, S; for(int i=0;i<10;i++) { S=0; for(int j=0;j=0) grad(i)=-( sin(sqrt(x(i)))+sqrt(x(i))/2.0*cos(sqrt(x(i))) ); else grad(i)=-( sin(sqrt(-x(i)))+sqrt(-x(i))/2.0*cos(sqrt(-x(i))) ); } } /******* Difficult multimodal problem, PERM(n) *********/ void Domain_Perm(RTBox box) { int n=(box.lb).GetLength(); box.lb=double(-n) ; box.ub=double(n) ; } double ObjPerm(RCRVector x, double beta) { int n=x.GetLength(); double s1=0.0; for (int k=1; k<=n ; k++) { double s2=0.0; for (int i=1 ; i<=n; i++) // s2+=(i^k+beta)*((x(i-1)/i)^k-1); s2+=(pow(1.0*i,1.0*k)+beta)*(pow(x(i-1)/i,1.0*k)-1) ; s1+=s2*s2; } return s1; } void GradPerm(RCRVector x, RVector &grad, double beta) { int n=x.GetLength(); for (int j=1 ; j<=n ; j++) { double s1=0.0; for (int k=1 ; k<=n ; k++) { double s2=0.0; for (int i=1 ; i<=n; i++) // s2+=(i^k+beta)*((x(i-1)/i)^k-1); s2+=(pow(1.0*i,1.0*k)+beta)*(pow(x(i-1)/i,1.0*k)-1); // s1+=2*s2*(j^k+beta)/(x(j-1)*j^k)*k*x(j-1)^k; s1+=2*s2*(pow(1.0*j,1.0*k)+beta)/pow(1.0*j,1.0*k)*k*pow(x(j-1),k-1.0); } grad(j-1)=s1; } } double Objective_Perm_4_50(RCRVector x) { return ObjPerm(x,50); } void Gradient_Perm_4_50(RCRVector x, RVector &grad) { GradPerm(x,grad,50); } double Objective_Perm_4_05(RCRVector x) { return ObjPerm(x,0.5); } void Gradient_Perm_4_05(RCRVector x, RVector &grad) { GradPerm(x,grad,0.5); } /******************* Powersum **************/ void Domain_Powersum(RTBox box) { box.lb=0.0 ; box.ub=4.0 ; } double Objective_Powersum(RCRVector x) { int n=x.GetLength(); double b[]={8,18,44,114}; double s1=0.0; for (int k=1 ; k<=n ; k++) { double s2=0.0; for (int i=1 ; i<=n ; i++) s2+=pow(x(i-1),1.0*k); s1+=pow(s2-b[k-1],2.0); } return s1; } void Gradient_Powersum(RCRVector x, RVector &grad) { int n=x.GetLength(); double b[]={8,18,44,114}; for (int j=0 ; j #include #include #include #include #include "linalg.h" #ifndef FALSE const int FALSE=(1==0); // boolean FALSE #endif #ifndef FALSE const int TRUE=(1==1); // boolean TRUE #endif typedef const class Trial CTrial; typedef CTrial& RCTrial; typedef CTrial* PCTrial; class Trial{ public: RVector xvals; double objval; Trial(); Trial(int); Trial(RCTrial); // Copy constructor RCTrial operator=(RCTrial) ; // assignment operator friend ostream & operator << (ostream &, RCTrial) ; }; class TrialGT : public unary_function // Predicate for Trial (needed for remove_if) { public: explicit TrialGT(double val) : _val(val) {} bool operator()(Trial& foo) { return foo.objval > _val; } private: double _val; }; typedef class VBox& RVBox; typedef const class VBox CVBox; typedef CVBox* PCVBox; typedef CVBox& RCVBox; class VBox{ public: RVector lb, ub; VBox(); // Construct a box VBox(int); VBox(RCVBox); // Copy constructor RCVBox operator=(RCVBox); // assignment operator int GetDim(); // Returns the dimension of the box double Width(int) ; // Returns the width of the i-th interval void Midpoint(RCRVector); // Returns the midpoint friend ostream & operator << (ostream &, const VBox &); }; typedef class TBox& RTBox; typedef const class TBox CTBox; typedef CTBox* PCTBox; typedef CTBox& RCTBox; class TBox: public VBox { public: double minf; // Smallest function value found so far list TList; // List of trials TBox(); // Construct a box TBox(int); TBox(RCTBox); // Copy constructor RCTBox operator=(RCTBox); // assignment operator double GetMin(); // Returns 'minf' bool EmptyBox(); // Returns TRUE if Box contains no trials void AddTrial(RCTrial); // Add a trial to the (back of) box void RemoveTrial(Trial &); // Remove a trial from the (back of) box void GetLastTrial(Trial &); // Return a trial from the back of the box list::const_iterator FirstTrial(); list::const_iterator LastTrial(); void GetTrial(list::const_iterator, Trial&); void ClearBox(); bool CloseToMin(RVector&, double*, double); unsigned int NStationary(); // Returns the number of stationary points void split(RTBox, RTBox); // Split a box void dispTrials(); bool InsideBox(RCRVector); int OutsideBox(RCRVector, RCTBox); double ShortestSide(int*); // Returns the shortest side double LongestSide(int*); // Returns the longest side double ClosestSide(RCRVector x); double FarthestSide(RCRVector x); bool Intersection(RCRVector, RCRVector, RCRVector); double LowerBound(double); bool operator<(const TBox & x) const {return (minf>x.minf);} friend ostream & operator << (ostream &, const TBox &); }; #endif nloptr/src/nlopt_src/stogo/Makefile.in0000644000176000001440000004613113355070635017626 0ustar ripleyusers# Makefile.in generated by automake 1.14 from Makefile.am. # @configure_input@ # Copyright (C) 1994-2013 Free Software Foundation, Inc. # This Makefile.in is free software; the Free Software Foundation # gives unlimited permission to copy and/or distribute it, # with or without modifications, as long as this notice is preserved. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY, to the extent permitted by law; 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Johnson (stevenj@alum.mit.edu) in 2007. StoGO uses a gradient-based direct-search branch-and-bound algorithm, described in: S. Gudmundsson, "Parallel Global Optimization," M.Sc. Thesis, IMM, Technical University of Denmark, 1998. K. Madsen, S. Zertchaninov, and A. Zilinskas, "Global Optimization using Branch-and-Bound," Submitted to the Journal of Global Optimization, 1998. [ never published, but preprint is included as paper.pdf ] S. Zertchaninov and K. Madsen, "A C++ Programme for Global Optimization," IMM-REP-1998-04, Department of Mathematical Modelling, Technical University of Denmark, DK-2800 Lyngby, Denmark, 1998. [ included as techreport.pdf ] nloptr/src/nlopt_src/stogo/tools.cc0000644000176000001440000002303713355070635017230 0ustar ripleyusers #include #include #include "stogo_config.h" #include "tools.h" Trial::Trial():xvals(0) { objval=DBL_MAX; } Trial::Trial(int n):xvals(n) { objval=DBL_MAX; } Trial::Trial(RCTrial tr):xvals(tr.xvals) { objval=tr.objval ; } RCTrial Trial::operator=(RCTrial tr) { xvals=tr.xvals ; objval=tr.objval ; return *this ; } ostream & operator << (ostream & os, const Trial & T) { os << T.xvals << " " << "(" << T.objval << ")" << endl ; return os; } /********************* Vanilla Box **********************/ VBox::VBox():lb(0),ub(0) {} // Constructor VBox::VBox(int n):lb(n),ub(n) {} // Constructor VBox::VBox(RCVBox box):lb(box.lb), ub(box.ub) {} // Constructor RCVBox VBox::operator=(RCVBox box) { // Copy: Box1=Box2 lb=box.lb; ub=box.ub; return *this; } int VBox::GetDim() { return lb.GetLength(); } double VBox::Width(int i) { // Returns the width of the i-th interval, i between [0,...,dim-1] return ub(i)-lb(i); } void VBox::Midpoint(RCRVector x) { // Returns the midpoint of Box in x int n=GetDim(); for (int i=0 ; i::const_iterator TBox::FirstTrial() { return TList.begin(); } list::const_iterator TBox::LastTrial() { return TList.end(); } void TBox::GetTrial(list::const_iterator itr, Trial &T) { T.xvals=(*itr).xvals; T.objval=(*itr).objval; } void TBox::ClearBox() { TList.erase(TList.begin(), TList.end()); minf=DBL_MAX; } bool TBox::CloseToMin(RVector &vec, double *objval, double eps_cl) { // Returns TRUE if 'vec' is close to some of the trials in the box, // in this case, 'vec' and 'objval' are overwritten by the Trial data // otherwise 'vec' and 'objval' are not affected. // // Here "close" means norm2(T - some trial in box)<=eps_cl // // NB It might be better to accept Trial as argument instead of vector int n=GetDim(); RVector x(n), y(n); list::const_iterator itr; for ( itr = TList.begin(); itr != TList.end(); ++itr ) { y=vec ; // NB Should be possible to avoid this copying x=(*itr).xvals; axpy(-1,x,y); if (norm2(y)<=eps_cl) { vec=x; *objval=(*itr).objval; return TRUE; } } return FALSE; } unsigned int TBox::NStationary() { // Returns the number of trials in a box return TList.size() ; } void TBox::split(RTBox B1, RTBox B2) { list::const_iterator itr; double w,m,tmp; double fm1=DBL_MAX, fm2=DBL_MAX; int i, k, ns; int n=GetDim(); B1.lb=lb; B1.ub=ub; B2.lb=lb; B2.ub=ub; w=LongestSide(&i); ns=TList.size(); switch (ns) { case 0: case 1: // Bisection w=ub(i)-lb(i); // Length of interval m=lb(i)+w/2; // Midpoint B1.ub(i)=m; B2.lb(i)=m; break; default: // More elaborate split when there are more than one trials in B // See Serguies and Kaj's tech. report, page 11 // Compute the center point of all stationary points RVector center(n), x(n), dispers(n); center=0; dispers=0; for ( itr = TList.begin(); itr != TList.end(); ++itr ) axpy(1.0, (*itr).xvals, center); scal((double)(1.0/ns),center); // Compute the relative deviations for ( itr = TList.begin(); itr != TList.end(); ++itr ) { for (k = 0; ktmp) { tmp=dispers(k);i=k; } } B1.ub(i)=center(i) ; B2.lb(i)=center(i); break; } // Split the set of trials accordingly for ( itr = TList.begin(); itr != TList.end(); ++itr ) { if ( B1.InsideBox((*itr).xvals) ) { fm1=min(fm1,(*itr).objval); B1.AddTrial(*itr); } else { B2.AddTrial(*itr); fm2=min(fm2,(*itr).objval); } } // Set minf of B1 and B2 B1.minf=fm1 ; B2.minf=fm2; } void TBox::dispTrials() { // Display information about box #ifdef KCC copy(TList.begin(), TList.end(), ostream_iterator(cout)); #else copy(TList.begin(), TList.end(), ostream_iterator(cout)); #endif } ostream & operator << (ostream & os, const TBox & B) { int n=(B.lb).GetLength() ; for (int i=0 ; itmp ) { tmp=ub(i)-lb(i); j=i; } *idx=j; return tmp; } double TBox::ClosestSide(RCRVector x) { // Returns the shortest distance from point X to the box B. // Warning: The output of this functon is nonsense if the // point X lies outside B. Should we try to detect this case? double dist, tmp ; int n=GetDim(); dist=DBL_MAX; for (int i=0 ; iub(j)) { isect=0; break; } } } copy(z,tmpV); axpy(-1.0,x,tmpV); // tmpV=z-x if (isect==1 && dot(tmpV,h)>0) { done=TRUE; break; } } i++; } return done; } double TBox::LowerBound(double maxgrad) { // Lower bound estimation double lb=minf ; double f1,f2,est ; list::const_iterator itr1,itr2 ; int n=GetDim(); RVector x1(n), x2(n) ; #ifndef LB2 for ( itr1 = TList.begin(); itr1 != TList.end(); ++itr1 ) { itr2=itr1 ; while (++itr2 != TList.end()) { x1=(*itr1).xvals ; f1=(*itr1).objval ; x2=(*itr2).xvals ; f2=(*itr2).objval ; axpy(-1.0,x2,x1) ; // x1=x1-x2 est=0.5*(f1+f2-maxgrad*norm2(x1)) ; lb=min(lb,est) ; // cout << "est=" << est << " x1=" << x1 << " x2=" << x2 << endl ; } } #endif #ifdef LB2 for ( itr1 = TList.begin(); itr1 != TList.end(); ++itr1 ) { // Use also max distance to border x1=(*itr1).xvals; f1=(*itr1).objval; est=f1-FarthestSide(x1)*maxgrad; lb=min(lb,est); } #endif return lb ; } nloptr/src/nlopt_src/stogo/stogo.h0000644000176000001440000000414313355070635017062 0ustar ripleyusers/* A C-callable front-end to the StoGO global-optimization library. -- Steven G. Johnson */ #ifndef STOGO_H #define STOGO_H #include "nlopt-util.h" #ifdef __cplusplus extern "C" { #endif typedef double (*objective_func)(unsigned n, const double *x, double *grad, void *data); /* search for the global minimum of the function fgrad(n, x, grad, data) inside a simple n-dimensional hyperrectangle. Input: n: dimension of search space (number of decision variables) fgrad: the objective function of the form fgrad(n, x, grad, data), returning the objective function at x, where n: dimension of search space x: pointer to array of length n, point to evaluate grad: if non-NULL, an array of length n which should on return be the gradient d(fgrad)/dx [ if NULL, grad should be ignored ] data: arbitrary data pointer, whose value is the data argument of stogo_minimize data: arbitrary pointer to any auxiliary data needed by fgrad l, u: arrays of length n giving the lower and upper bounds of the search space maxeval: if nonzero, a maximum number of fgrad evaluations maxtime: if nonzero, a maximum time (in seconds) -- REPLACED in NLopt by nlopt_stopping *stop nrandom: number of randomized search points to use per box, in addition to 2*n+1 deterministic search points (0 for a deterministic algorithm). Output: minf: the minimum value of the objective function found x: pointer to array of length n, giving the location of the minimum Return value: 0 if no minimum found, 1 otherwise. */ int stogo_minimize(int n, objective_func fgrad, void *data, double *x, double *minf, const double *l, const double *u, #ifdef NLOPT_UTIL_H nlopt_stopping *stop, #else long int maxeval, double maxtime, #endif int nrandom); extern int stogo_verbose; /* set to nonzero for verbose output */ #ifdef __cplusplus } #endif #endif nloptr/src/nlopt_src/stogo/prog.cc0000644000176000001440000001571113355070635017037 0ustar ripleyusers/* A simple program to test the global optimizer. */ #include "global.h" #include "tools.h" #include "testfun.h" #define STRLEN_MAX 80 int main() { bool AVfail, AVflag; int testfnc, dim, axis, i; double AVbest; Pdom Dom; Pgrad Grad; Pobj Obj; cout << "Global Optimizer v0.1" << endl; cout << "1. Rosenbrock (min=0)" << endl; cout << "2. McCormick (min=-1.91)" << endl; cout << "3. Box & Betts (min=0)" << endl; cout << "4. Paviani (min=-45.7)" << endl; cout << "5. Extended Beale (min=0)" << endl; cout << "6. Three-hump Camel (min=0)" << endl; cout << "7. Jacobsen & Pedersen (min=3)" << endl; cout << "8. Poly1 (min=0)" << endl; cout << "9. Six-hump Camel (min=-1.03) " << endl; cout << "10. One-dimensional (min=-23.58)" << endl; cout << "11. Kowalik (min=0.0003075)" << endl; cout << "12. Hansen (min=-176.54)" << endl; cout << "13. Shubert (min=-24.06)" << endl; cout << "14. Griewank (min=0)" << endl; cout << "15. Levy4 (min=-21.502)" << endl; cout << "16. Levy5 (min=-11.504)" << endl; cout << "17. Levy6 (min=-11.504)" << endl; cout << "18. Levy7 (min=-11.504)" << endl; cout << "19. Rastrigin (min=0)" << endl; cout << "20. Trid" << endl; cout << "21. Perm(4,50)" << endl; cout << "22. Perm(4,0.5)" << endl; cout << "23. Powersum(8,18,44,114)" << endl; cout << "24. Schwefel (min=-12569.5)" << endl; cout << "25. Shekel (-10.0)" << endl; cout << endl << "Select test function :"; cin >> testfnc; switch (testfnc) { case 10: dim=1; Dom=Domain_OneDim; Obj=Objective_OneDim; Grad=Gradient_OneDim; break; case 1: dim=2; Dom=Domain_Rosenbrock; Obj=Objective_Rosenbrock; Grad=Gradient_Rosenbrock; break; case 8: dim=2; Dom=Domain_Poly1; Obj=Objective_Poly1; Grad=Gradient_Poly1; break; case 7: dim=2; Dom=Domain_Jacobsen; Obj=Objective_Jacobsen; Grad=Gradient_Jacobsen; break; case 6: dim=2; Dom=Domain_Camel3; Obj=Objective_Camel3; Grad=Gradient_Camel3; break; case 5: dim=2; Dom=Domain_Beale3; Obj=Objective_Beale3; Grad=Gradient_Beale3; break; case 4: dim=10; Dom=Domain_Paviani; Obj=Objective_Paviani; Grad=Gradient_Paviani; break; case 12: dim=2; Dom=Domain_Hansen; Obj=Objective_Hansen; Grad=Gradient_Hansen; break; case 13: dim=2; Dom=Domain_Shubert; Obj=Objective_Shubert; Grad=Gradient_Shubert; break; case 11: dim=4; Dom=Domain_Kowalik; Obj=Objective_Kowalik; Grad=Gradient_Kowalik; break; case 9: dim=2; Dom=Domain_Camel6; Obj=Objective_Camel6; Grad=Gradient_Camel6; break; case 2: dim=2; Dom=Domain_McCormick; Obj=Objective_McCormick; Grad=Gradient_McCormick; break; case 3: dim=3; Dom=Domain_BoxBetts; Obj=Objective_BoxBetts; Grad=Gradient_BoxBetts; break; case 14: dim=10; Dom=Domain_Griewank; Obj=Objective_Griewank; Grad=Gradient_Griewank; break; case 15: dim=4; Dom=Domain_Levy; Obj=Objective_Levy; Grad=Gradient_Levy; break; case 16: dim=5; Dom=Domain_Levy; Obj=Objective_Levy; Grad=Gradient_Levy; break; case 17: dim=6; Dom=Domain_Levy; Obj=Objective_Levy; Grad=Gradient_Levy; break; case 18: dim=7; Dom=Domain_Levy; Obj=Objective_Levy; Grad=Gradient_Levy; break; case 19: cout << "Enter problem dimension "; int rast_dim; cin >> rast_dim; dim=rast_dim; Dom=Domain_Rastrigin; Obj=Objective_Rastrigin; Grad=Gradient_Rastrigin; break; case 20: cout << "Enter problem dimension (two or larger) "; int trid_dim; cin >> trid_dim; dim=trid_dim; Dom=Domain_Trid; Obj=Objective_Trid; Grad=Gradient_Trid; break; case 21: dim=4; Dom=Domain_Perm; Obj=Objective_Perm_4_50; Grad=Gradient_Perm_4_50; break; case 22: dim=4; Dom=Domain_Perm; Obj=Objective_Perm_4_05; Grad=Gradient_Perm_4_05; break; case 23: dim=4; Dom=Domain_Powersum; Obj=Objective_Powersum; Grad=Gradient_Powersum; break; case 24: cout << "Enter problem dimension "; int schwef_dim; cin >> schwef_dim; dim=schwef_dim; Dom=Domain_Schwefel; Obj=Objective_Schwefel; Grad=Gradient_Schwefel; break; case 25: dim=4; Dom=Domain_Shekel; Obj=Objective_Shekel; Grad=Gradient_Shekel; break; default: cout << "Error : Function not defined" << endl; exit(1); } cout << "Dimension=" <> params.maxtime; if (params.maxtime<1) { cout << "Warning: time limit set to 1 second\n"; } params.maxeval = 0; cout << "Use factory settings (y/n) "; char str[STRLEN_MAX]; cin >> str; if (str[0]=='y') { params.det_pnts=2*dim+1; params.rnd_pnts=0; params.eps_cl=0.1; params.rshift=0.3; params.mu=1.0E-4; AVflag=FALSE; } else { cout << "Number of deterministic points "; cin >> params.det_pnts; cout << "Numer of stochastic points "; cin >> params.rnd_pnts; cout << "Radius of attraction "; cin >> params.eps_cl; cout << "Parameter rshift "; cin >> params.rshift; cout << "Parameter mu "; cin >> params.mu; cout << "Use the AV initialization (y/n) "; cin >> str; if (str[0]=='y') AVflag=TRUE; else AVflag=FALSE; } Global Problem(D,Obj, Grad, params); RVector x_av(dim); if (AVflag==TRUE) { cout << "Enter time limit for each coordinate direction (seconds) "; cin >> params.maxtime; if (params.maxtime<1) { cout << "Warning: time limit set to 1 second\n"; } params.maxeval = 0; params.det_pnts=3; TBox I(1); Global AV(I, Obj, Grad, params); x_av=0.0; AVfail=FALSE; for (axis=0; axis #include // for sqrt() #include "linalg.h" double eps() { /* Returns the machine precision : (min { x >= 0 : 1 + x > 1 }) NB This routine should be replaced by LAPACK_LAMCH */ double Current, Last, OnePlusCurrent ; Current = 1.0 ; do { Last = Current ; Current /= 2.0 ; OnePlusCurrent = 1.0 + Current ; } while (OnePlusCurrent > 1.0) ; return Last ; } RVector::RVector() { // Constructor len=0; elements=0; (*this)=0.; } RVector::RVector(int n) { // Constructor len=n; elements=new double[len]; (*this)=0.; } RVector::RVector(RCRVector vect) { // Constructor + Copy len=vect.len; elements=new double[len]; (*this)=vect; } RCRVector RVector::operator=(RCRVector vect) { // Copy for (int i=0;i0) os << "," ; os << v.elements[i] ; } return os << ']'; } /*************************** Matrix Class ***********************/ RMatrix::RMatrix() { // Constructor Dim=0 ; Vals=0 ; (*this)=0 ; } RMatrix::RMatrix(int dim) { // Constructor Dim=dim; Vals=new double[long(Dim)*long(Dim)]; (*this)=0.; } RMatrix::RMatrix(RCRMatrix matr) { // Constructor + Copy Dim=matr.Dim; Vals=new double[long(Dim)*long(Dim)]; (*this)=matr; } RCRMatrix RMatrix::operator=(RCRMatrix mat) { // Assignment, A=B long int Len=long(Dim)*long(Dim); for (int i=0;i #include #include "stogo_config.h" #include "global.h" #include "local.h" #include "tools.h" #ifdef NLOPT_UTIL_H # define IF_NLOPT_CHECK_EVALS stop->nevals++; \ if (nlopt_stop_evalstime(stop)) \ return LS_MaxEvalTime #else # define IF_NLOPT_CHECK_EVALS #endif //////////////////////////////////////////////////////////////////////// // SGJ, 2007: allow local to use local optimizers in NLopt, to compare // to the BFGS code below #if 0 #include "nlopt.h" typedef struct { Global *glob; double maxgrad; nlopt_stopping *stop; } f_local_data; static double f_local(int n, const double *x, double *grad, void *data_) { f_local_data *data = (f_local_data *) data_; double f; RVector xv, gv; // hack to avoid pointless copy of x and grad xv.len = gv.len = n; xv.elements = const_cast(x); gv.elements = grad; f=data->glob->ObjectiveGradient(xv, gv, grad?OBJECTIVE_AND_GRADIENT:OBJECTIVE_ONLY); if (grad) data->maxgrad = max(data->maxgrad, normInf(gv)); xv.elements = gv.elements = 0; // prevent deallocation data->stop->nevals++; return f; } #endif //////////////////////////////////////////////////////////////////////// int local(Trial &T, TBox &box, TBox &domain, double eps_cl, double *mgr, Global &glob, int axis, RCRVector x_av #ifdef NLOPT_UTIL_H , nlopt_stopping *stop #endif ) { int n=box.GetDim(); RVector x(n); double tmp, f; x=T.xvals ; #ifdef LS_DEBUG cout << "Local Search, x=" << x << endl; #endif if (box.OutsideBox(x, domain) != 0) { cout << "Starting point is not inside the boundary. Exiting...\n" ; exit(1) ; return LS_Out ; } // Check if we are close to a stationary point located previously if (box.CloseToMin(x, &tmp, eps_cl)) { #ifdef LS_DEBUG cout << "Close to a previously located stationary point, exiting" << endl; #endif T.objval=tmp; return LS_Old ; } #if 0 if (axis != -1) { cout << "NLopt code only works with axis == -1, exiting...\n" ; exit(EXIT_FAILURE); } f_local_data data; data.glob = &glob; data.maxgrad = *mgr; data.stop = stop; nlopt_result ret = nlopt_minimize(NLOPT_LOCAL_LBFGS, n, f_local, &data, box.lb.raw_data(), box.ub.raw_data(), x.raw_data(), &f, stop->minf_max, stop->ftol_rel, stop->ftol_abs, stop->xtol_rel, stop->xtol_abs, stop->maxeval - stop->nevals, stop->maxtime - stop->start); *mgr = data.maxgrad; T.xvals=x ; T.objval=f ; if (ret == NLOPT_MAXEVAL_REACHED || ret == NLOPT_MAXTIME_REACHED) return LS_MaxEvalTime; else if (ret > 0) return LS_New; else return LS_Out; // failure #else /* not using NLopt local optimizer ... use original STOgo BFGS code */ int k_max, info, outside = 0; int k, i, good_enough, iTmp ; double maxgrad, delta, f_new; double alpha, gamma, beta, d2, s2, nom, den, ro ; double nrm_sd, nrm_hn, snrm_hn, nrm_dl ; RVector g(n), h_sd(n), h_dl(n), h_n(n), x_new(n), g_new(n) ; RVector s(n),y(n),z(n),w(n) ; // Temporary vectors RMatrix B(n), H(n) ; // Hessian and it's inverse k_max = max_iter*n ; // Initially B and H are equal to the identity matrix B=0 ; H=0 ; for (i=0 ; ik_max) { #ifdef LS_DEBUG cout << "Maximum number of iterations reached\n" ; #endif info=LS_MaxIter ; break ; } // Update maximal gradient value maxgrad=max(maxgrad,normInf(g)) ; // Steepest descent, h_sd = -g copy(g,h_sd) ; scal(-1.0,h_sd) ; nrm_sd=norm2(h_sd) ; if (nrm_sd < epsilon) { // Stop criterion (gradient) fullfilled #ifdef LS_DEBUG cout << "Gradient small enough" << endl ; #endif good_enough = 1 ; break ; } // Compute Newton step, h_n = -H*g gemv('N',-1.0, H, g, 0.0, h_n) ; nrm_hn = norm2(h_n) ; if (nrm_hn < delta) { // Pure Newton step copy(h_n, h_dl) ; #ifdef LS_DEBUG cout << "[Newton step] " ; #endif } else { gemv('N',1.0,B,g,0.0,z) ; tmp=dot(g,z) ; if (tmp==0) { info = LS_Unstable ; break ; } alpha=(nrm_sd*nrm_sd)/tmp ; // Normalization (N38,eq. 3.30) scal(alpha,h_sd) ; nrm_sd=fabs(alpha)*nrm_sd ; if (nrm_sd >= delta) { gamma = delta/nrm_sd ; // Normalization (N38, eq. 3.33) copy(h_sd,h_dl) ; scal(gamma,h_dl) ; #ifdef LS_DEBUG cout << "[Steepest descent] " ; #endif } else { // Combination of Newton and SD steps d2 = delta*delta ; copy(h_sd,s) ; s2=nrm_sd*nrm_sd ; nom = d2 - s2 ; snrm_hn=nrm_hn*nrm_hn ; tmp = dot(h_n,s) ; den = tmp-s2 + sqrt((tmp-d2)*(tmp-d2)+(snrm_hn-d2)*(d2-s2)) ; if (den==0) { info = LS_Unstable ; break ; } // Normalization (N38, eq. 3.31) beta = nom/den ; copy(h_n,h_dl) ; scal(beta,h_dl) ; axpy((1-beta),h_sd,h_dl) ; #ifdef LS_DEBUG cout << "[Mixed step] " ; #endif } } nrm_dl=norm2(h_dl) ; //x_new = x+h_dl ; copy(x,x_new) ; axpy(1.0,h_dl,x_new) ; // Check if x_new is inside the box iTmp=box.OutsideBox(x_new, domain) ; if (iTmp == 1) { #ifdef LS_DEBUG cout << "x_new is outside the box " << endl ; #endif outside++ ; if (outside>max_outside_steps) { // Previous point was also outside, exit break ; } } else if (iTmp == 2) { #ifdef LS_DEBUG cout << " x_new is outside the domain" << endl ; #endif info=LS_Out ; break ; } else { outside=0 ; } // Compute the gain if (axis==-1) f_new=glob.ObjectiveGradient(x_new,g_new,OBJECTIVE_AND_GRADIENT); else { x_av(axis)=x_new(0); f_new=glob.ObjectiveGradient(x_av,g_av,OBJECTIVE_AND_GRADIENT); } IF_NLOPT_CHECK_EVALS; FC++; GC++; gemv('N',0.5,B,h_dl,0.0,z); ro = (f_new-f) / (dot(g,h_dl) + dot(h_dl,z)); // Quadratic model if (ro > 0.75) { delta = delta*2; } if (ro < 0.25) { delta = delta/3; } if (ro > 0) { // Update the Hessian and it's inverse using the BFGS formula #if 0 // changed by SGJ to compute OBJECTIVE_AND_GRADIENT above if (axis==-1) glob.ObjectiveGradient(x_new,g_new,GRADIENT_ONLY); else { x_av(axis)=x_new(0); glob.ObjectiveGradient(x_av,g_av,GRADIENT_ONLY); g_new(0)=g_av(axis); } GC++; IF_NLOPT_CHECK_EVALS; #else if (axis != -1) g_new(0)=g_av(axis); #endif // y=g_new-g copy(g_new,y); axpy(-1.0,g,y); // Check curvature condition alpha=dot(y,h_dl); if (alpha <= sqrt(MacEpsilon)*nrm_dl*norm2(y)) { #ifdef LS_DEBUG cout << "Curvature condition violated " ; #endif } else { // Update Hessian gemv('N',1.0,B,h_dl,0.0,z) ; // z=Bh_dl beta=-1/dot(h_dl,z) ; ger(1/alpha,y,y,B) ; ger(beta,z,z,B) ; // Update Hessian inverse gemv('N',1.0,H,y,0.0,z) ; // z=H*y gemv('T',1.0,H,y,0.0,w) ; // w=y'*H beta=dot(y,z) ; beta=(1+beta/alpha)/alpha ; // It should be possible to do this updating more efficiently, by // exploiting the fact that (h_dl*y'*H) = transpose(H*y*h_dl') ger(beta,h_dl,h_dl,H) ; ger(-1/alpha,z,h_dl,H) ; ger(-1/alpha,h_dl,w,H) ; } if (nrm_dl < norm2(x)*epsilon) { // Stop criterion (iteration progress) fullfilled #ifdef LS_DEBUG cout << "Progress is marginal" ; #endif good_enough = 1 ; } // Check if we are close to a stationary point located previously if (box.CloseToMin(x_new, &f_new, eps_cl)) { // Note that x_new and f_new may be overwritten on exit from CloseToMin #ifdef LS_DEBUG cout << "Close to a previously located stationary point, exiting" << endl; #endif info = LS_Old ; good_enough = 1 ; } // Update x, g and f copy(x_new,x) ; copy(g_new,g) ; f=f_new ; #ifdef LS_DEBUG cout << " x=" << x << endl ; #endif } else { #ifdef LS_DEBUG cout << "Step is no good, ro=" << ro << " delta=" << delta << endl ; #endif } } // wend // Make sure the routine returns correctly... // Check if last iterate is outside the boundary if (box.OutsideBox(x, domain) != 0) { info=LS_Out; f=DBL_MAX; } if (info == LS_Unstable) { cout << "Local search became unstable. No big deal but exiting anyway\n" ; exit(1); } *mgr=maxgrad ; T.xvals=x ; T.objval=f ; if (outside>0) return LS_Out ; else return info ; #endif } nloptr/src/nlopt_src/stogo/local.h0000644000176000001440000000137213355070635017022 0ustar ripleyusers/* Definitions of various variables used in the local search */ #ifndef LOCAL_H #define LOCAL_H #include "tools.h" #include "global.h" extern int FC, GC ; // Results of local search enum {LS_Unstable, LS_MaxIter, LS_Old, LS_New,LS_Out, LS_MaxEvalTime} ; const double delta_coef = 1.0/2.0; // Initialization of trust region const double epsilon = 1.0E-4 ; // Stopping criterion, var 1e-4 const int max_outside_steps=1 ; // Maximum number of steps outside the box const int max_iter=50 ; // Max iterations = max_iter*dim. of problem extern double MacEpsilon ; // min {x >= 0 : 1 + x > 1} int local(Trial &, TBox &, TBox &, double, double*, Global&, int, RCRVector #ifdef NLOPT_UTIL_H , nlopt_stopping *stop #endif ); #endif nloptr/src/nlopt_src/stogo/global.h0000644000176000001440000000352013355070635017165 0ustar ripleyusers#ifndef GLOBAL_H #define GLOBAL_H #include "nlopt-util.h" #include //#include "function.h" #include "tools.h" using namespace std; extern "C" int stogo_verbose; typedef void dom(RTBox) ; typedef dom* Pdom ; typedef double obj(RCRVector) ; typedef obj* Pobj ; typedef void grad(RCRVector,RVector&) ; typedef grad* Pgrad ; typedef enum { OBJECTIVE_ONLY, GRADIENT_ONLY, OBJECTIVE_AND_GRADIENT } whichO; typedef double objgrad(RCRVector,RCRVector,whichO) ; typedef objgrad* Pobjgrad ; class GlobalParams { public: #ifdef NLOPT_UTIL_H nlopt_stopping *stop; #else double maxtime; long int maxeval; #endif double eps_cl, mu, rshift; int det_pnts, rnd_pnts; }; class Global: public GlobalParams { public: // Problem specification int dim ; Pobj Objective ; Pgrad Gradient ; long int numeval; virtual double ObjectiveGradient(RCRVector xy, RVector&grad, whichO which){ ++numeval; switch (which) { case OBJECTIVE_AND_GRADIENT: Gradient(xy, grad); case OBJECTIVE_ONLY: return Objective(xy); case GRADIENT_ONLY: Gradient(xy, grad); } return 0.0; } Global(RTBox, Pobj, Pgrad, GlobalParams); // Global& operator=(const Global &); void Search(int, RCRVector); void DispMinimizers(); double OneMinimizer(RCRVector); bool NoMinimizers(); void SetDomain(RTBox); void GetDomain(RTBox); double GetMinValue(); void SetMinValue(double); void ClearSolSet(); void AddPoint(RCRVector, double); double GetTime(); bool InTime(); private: list SolSet; list::const_iterator titr; priority_queue CandSet; priority_queue Garbage; double fbound; TBox Domain; void FillRegular(RTBox, RTBox); void FillRandom(RTBox, RTBox); double NewtonTest(RTBox, int, RCRVector, int*); void ReduceOrSubdivide(RTBox, int, RCRVector); }; #endif nloptr/src/nlopt_src/stogo/rosen.h0000644000176000001440000000062113355070635017052 0ustar ripleyusers#include "linalg.h" #include "tools.h" #include "stogo_config.h" void Domain_Rosenbrock(RTBox box) { box.lb=-10.0 ; box.ub=10.0; } double Objective_Rosenbrock(RCRVector x) { double a=x(1)-x(0)*x(0); double b=1-x(0); return 100*a*a + b*b; } void Gradient_Rosenbrock(RCRVector x, RCRVector grad) { grad(0)=200*(x(1)-x(0)*x(0))*(-2*x(0))-2*(1-x(0)); grad(1)=200*(x(1)-x(0)*x(0)); } nloptr/src/nlopt_src/stogo/linalg.h0000644000176000001440000000442213355070635017175 0ustar ripleyusers/* Temporary implementation of vector and matrix classes This is more or less borrowed from Serguei's program */ #ifndef LINALG_H #define LINALG_H #include using namespace std; #include // for sqrt() #include typedef const class RVector CRVector; typedef CRVector& RCRVector; typedef const class RMatrix CRMatrix ; typedef CRMatrix& RCRMatrix; double eps() ; #define max(A,B) ((A) > (B) ? (A):(B)) #define min(A,B) ((A) < (B) ? (A):(B)) /********************* Class RVector *********************/ class RVector{ protected: public: int len; // size of array double* elements; // array of values RVector() ; RVector(int); // Constructor RVector(RCRVector); // copy constructor ~RVector() { delete[] elements; elements=0 ; len=0; } RCRVector operator=(double) ; RCRVector operator=(RCRVector); double & operator () (int i) const {return elements[i] ; } double nrm2() ; // Euclidian norm double *raw_data() { return elements; } const double *raw_data_const() const { return elements; } friend ostream & operator << (ostream &, const RVector &); friend double norm2(RCRVector) ; friend double normInf(RCRVector) ; friend double dot(RCRVector, RCRVector) ; friend void scal(double, RCRVector) ; friend void copy(RCRVector, RCRVector) ; friend void axpy(double, RCRVector, RCRVector) ; friend void gemv(char,double, RCRMatrix, RCRVector, double, RCRVector); friend void ger(double alpha, RCRVector, RCRVector, RCRMatrix); int GetLength() const { return len; }; // get vector size }; /******************* Class RMatrix *************************/ class RMatrix { protected: double* Vals; // array of values int Dim; // dimension public: RMatrix() ; RMatrix(int); // dimension ~RMatrix() { delete[] Vals; Vals=0 ; Dim=0; } RMatrix(RCRMatrix); // copy constructor RCRMatrix operator=(double num) ; RCRMatrix operator=(RCRMatrix) ; // (needed for template stuff) double& operator()(int vidx,int hidx) ; friend ostream & operator << (ostream &, const RMatrix &); friend void gemv(char,double, RCRMatrix, RCRVector, double, RCRVector); friend void ger(double alpha,RCRVector,RCRVector,RCRMatrix); int GetDim() { return Dim; }; // get dimension }; #endif nloptr/src/nlopt_src/stogo/stogo.cc0000644000176000001440000000304513355070635017220 0ustar ripleyusers// A C-callable front-end to the StoGO global-optimization library. // -- Steven G. Johnson #include "stogo.h" #include "global.h" class MyGlobal : public Global { protected: objective_func my_func; void *my_data; public: MyGlobal(RTBox D, GlobalParams P, objective_func func, void *data) : Global(D, 0, 0, P), my_func(func), my_data(data) {} virtual double ObjectiveGradient(RCRVector xy, RVector &grad, whichO which){ ++numeval; switch (which) { case GRADIENT_ONLY: case OBJECTIVE_AND_GRADIENT: return my_func((unsigned) xy.GetLength(), xy.raw_data_const(), grad.raw_data(), my_data); case OBJECTIVE_ONLY: return my_func((unsigned) xy.GetLength(), xy.raw_data_const(), NULL, my_data); } return 0.0; } }; int stogo_minimize(int n, objective_func fgrad, void *data, double *x, double *minf, const double *l, const double *u, #ifdef NLOPT_UTIL_H nlopt_stopping *stop, #else long int maxeval, double maxtime, #endif int nrandom) { GlobalParams params; // FIXME: WTF do these parameters mean? params.rnd_pnts=nrandom; params.det_pnts=2*n+1 - nrandom; params.eps_cl=0.1; params.rshift=0.3; params.mu=1.0E-4; params.stop = stop; TBox D(n); for (int i = 0; i < n; ++i) { D.lb(i) = l[i]; D.ub(i) = u[i]; } MyGlobal Problem(D, params, fgrad, data); RVector dummyvec(n); Problem.Search(-1, dummyvec); if (Problem.NoMinimizers()) return 0; *minf = Problem.OneMinimizer(dummyvec); for (int i = 0; i < n; ++i) x[i] = dummyvec(i); return 1; } nloptr/src/nlopt_src/stogo/global.cc0000644000176000001440000001776313355070635017341 0ustar ripleyusers/* Multi Dimensional Global Search. Author: Steinn Gudmundsson Email: steinng@hotmail.com This program is supplied without any warranty whatsoever. NB The RNGs seed should be initialized using some timer */ #include #include #include #include #include "stogo_config.h" #include "global.h" #include "local.h" #include "nlopt-util.h" // Timer stuff double StartTime; double MacEpsilon ; int FC=0, GC=0 ; int stogo_verbose = 0; /* set to nonzero for verbose output */ Global::Global(RTBox D, Pobj o, Pgrad g, GlobalParams P): Domain(D) { dim=Domain.GetDim(); Objective=o; Gradient=g; // Initialize parameters #ifdef NLOPT_UTIL_H stop = P.stop; #else maxtime=P.maxtime; maxeval = P.maxeval; #endif numeval = 0; eps_cl=P.eps_cl; mu=P.mu; rshift=P.rshift; det_pnts=P.det_pnts; rnd_pnts=P.rnd_pnts; fbound=DBL_MAX; } #if 0 // not necessary; default copy is sufficient Global& Global::operator=(const Global &G) { // Copy the problem info and parameter settings Domain=G.Domain; Objective=G.Objective; Gradient=G.Gradient; #ifdef NLOPT_UTIL_H stop = G.stop; #else maxtime=G.maxtime; maxeval = G.maxeval; #endif numeval = G.numeval; eps_cl=G.eps_cl; mu=G.mu; rshift=G.rshift; det_pnts=G.det_pnts; rnd_pnts=G.rnd_pnts; return *this; } #endif void Global::FillRegular(RTBox SampleBox, RTBox box) { // Generation of regular sampling points double w; int i, flag, dir; Trial tmpTrial(dim); RVector m(dim), x(dim); if (det_pnts>0) { box.Midpoint(m) ; tmpTrial.objval=DBL_MAX ; // Add the rest i=1 ; flag=1 ; dir=0 ; x=m ; while (iminf_max) { done = TRUE; break; } #endif if (!InTime()) { done=TRUE; if (stogo_verbose) cout << "The program has run out of time or function evaluations\n"; break; } } // inner while-loop if (stogo_verbose) cout << endl << "*** Inner loop completed ***" << endl ; // Reduce SolSet if necessary SolSet.erase(remove_if(SolSet.begin(), SolSet.end(), TrialGT(fbound+mu)),SolSet.end()); if (InTime()) { if (stogo_verbose) { cout << "Current set of minimizers (" << SolSet.size() << ")" << endl ; DispMinimizers() ; } while (!Garbage.empty()) { box=Garbage.top() ; Garbage.pop() ; // Split box B1.ClearBox() ; B2.ClearBox() ; box.split(B1,B2) ; // Add boxes to Candidate set CandSet.push(B1) ; CandSet.push(B2) ; } } } // Outer while-loop if (stogo_verbose) { cout << "Number of outer iterations : " << outer_iter << endl; cout << "Number of unexplored boxes : " << CandSet.size() << endl; cout << "Number of boxes in garbage : " << Garbage.size() << endl; cout << "Number of elements in SolSet : " << SolSet.size() << endl; cout << "Number of function evaluations : " << FC << endl; cout << "Number of gradient evaluations : " << GC << endl; } if (axis != -1) { // Return minimizer when doing the AV method tmpTrial=SolSet.back(); x_av(axis)=tmpTrial.xvals(0); } } /************* Various utility functions ****************/ double Global::GetTime() { return nlopt_seconds() - StartTime; } bool Global::InTime() { #ifdef NLOPT_UTIL_H return !nlopt_stop_evalstime(stop); #else return (maxtime <= 0.0 || GetTime()(cout)); } double Global::OneMinimizer(RCRVector x) { if (NoMinimizers()) return 0.0; for (int i=0;i> endobj xref 63 46 0000000016 00000 n 0000001866 00000 n 0000001946 00000 n 0000002075 00000 n 0000002279 00000 n 0000002625 00000 n 0000003368 00000 n 0000003521 00000 n 0000003958 00000 n 0000011249 00000 n 0000011728 00000 n 0000012117 00000 n 0000012476 00000 n 0000012552 00000 n 0000012961 00000 n 0000016041 00000 n 0000016680 00000 n 0000017307 00000 n 0000017526 00000 n 0000017911 00000 n 0000018193 00000 n 0000018403 00000 n 0000024458 00000 n 0000024793 00000 n 0000025184 00000 n 0000025397 00000 n 0000027955 00000 n 0000028365 00000 n 0000028799 00000 n 0000029197 00000 n 0000029521 00000 n 0000030115 00000 n 0000030349 00000 n 0000030643 00000 n 0000030735 00000 n 0000031081 00000 n 0000031209 00000 n 0000032616 00000 n 0000037419 00000 n 0000043183 00000 n 0000045368 00000 n 0000045610 00000 n 0000046166 00000 n 0000046373 00000 n 0000048864 00000 n 0000001216 00000 n trailer <]>> startxref 0 %%EOF 108 0 obj<>stream xb```f````c``Ib`@ $AW] yNlV3:+EޘẀ`lWp5̴LI449kl  m<W;evƯ74:$P15z岕rKo'M R2XNحMہ@]E<"Xx (#SYy,*t9+L3>iGJ5p7kp^nFS$SO{?ڕbʼ@nb?y?R.xh Ш@* ^ju ._Z'JJJj@u4 .PVKKKG2vq  BRh( 4"сiiV+U ;EPe`r4NY@e @Z}xW(0hnPk]p2  "WqqyϹ¸tCcAM\M-L\2 _D>`>q!Qʅ [00'i& Yl8/v ` endstream endobj 64 0 obj<> endobj 65 0 obj<> endobj 66 0 obj<>/Font<>/ProcSet[/PDF/Text/ImageB]/ExtGState<>>> endobj 67 0 obj<> endobj 68 0 obj<> endobj 69 0 obj<> endobj 70 0 obj<> endobj 71 0 obj<>stream HyPgƻfzfhn=Wʠn'za100  #àYt5$.1^.[?m?RZU<;0]Yb9#/&h6hwg=_b_dc/k1!0_8p9f:>w wyDDbuhT飯Y2e䱷-d+C<.yܽ;āt.ʃRt;^g %''M #ze XmVXlŸ_l$X:~hUZ[hJ9\Yo67G7Kӓ@C<=5EB t؜9Qx4M@ZU1ނpk\z֚FʚY! 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J. D. Powell (mjdp@cam.ac.uk) * Modifications Copyright (c) 2010 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ nloptr/src/nlopt_src/bobyqa/Makefile.am0000644000176000001440000000025613263734242017734 0ustar ripleyusersAM_CPPFLAGS = -I$(top_srcdir)/util -I$(top_srcdir)/api noinst_LTLIBRARIES = libbobyqa.la libbobyqa_la_SOURCES = bobyqa.c bobyqa.h EXTRA_DIST = README README.orig COPYRIGHT nloptr/src/nlopt_src/bobyqa/Makefile.in0000644000176000001440000004353713355070635017757 0ustar ripleyusers# Makefile.in generated by automake 1.14 from Makefile.am. # @configure_input@ # Copyright (C) 1994-2013 Free Software Foundation, Inc. # This Makefile.in is free software; the Free Software Foundation # gives unlimited permission to copy and/or distribute it, # with or without modifications, as long as this notice is preserved. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY, to the extent permitted by law; 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J. D. Powell, which performs derivative-free unconstrained optimization using an iteratively constructred quadratic approximation for the objective function. See: M. J. D. Powell, "The BOBYQA algorithm for bound constrained optimization without derivatives," Department of Applied Mathematics and Theoretical Physics, Cambridge England, technical report NA2009/06 (2009). http://www.damtp.cam.ac.uk/user/na/NA_papers/NA2009_06.pdf http://plato.asu.edu/ftp/other_software/bobyqa.zip The C translation by S. G. Johnson (2009) includes a few minor modifications, mainly to use the NLopt stopping criteria (and to take the objective function as an argument rather than a global). The original Fortran code was released by Powell with "no restrictions or charges", and the C translation by S. G. Johnson is released in a similar spirit under the MIT License (see the COPYRIGHT file in this directory). nloptr/src/nlopt_src/bobyqa/bobyqa.c0000644000176000001440000026547113355070635017336 0ustar ripleyusers/* BOBYQA derivative-free optimization code by M. J. D. Powell. Original Fortran code by Powell (2009). Converted via v2c, cleaned up, and incorporated into NLopt by S. G. Johnson (2009). See README. */ #include #include #include #include "bobyqa.h" typedef double (*bobyqa_func)(int n, const double *x, void *func_data); #define MIN2(a,b) ((a) <= (b) ? (a) : (b)) #define MAX2(a,b) ((a) >= (b) ? (a) : (b)) #define IABS(x) ((x) < 0 ? -(x) : (x)) static void update_(int *n, int *npt, double *bmat, double *zmat, int *ndim, double *vlag, double *beta, double *denom, int *knew, double *w) { /* System generated locals */ int bmat_dim1, bmat_offset, zmat_dim1, zmat_offset, i__1, i__2; double d__1, d__2, d__3; /* Local variables */ int i__, j, k, jl, jp; double one, tau, temp; int nptm; double zero, alpha, tempa, tempb, ztest; /* The arrays BMAT and ZMAT are updated, as required by the new position */ /* of the interpolation point that has the index KNEW. The vector VLAG has */ /* N+NPT components, set on entry to the first NPT and last N components */ /* of the product Hw in equation (4.11) of the Powell (2006) paper on */ /* NEWUOA. Further, BETA is set on entry to the value of the parameter */ /* with that name, and DENOM is set to the denominator of the updating */ /* formula. Elements of ZMAT may be treated as zero if their moduli are */ /* at most ZTEST. The first NDIM elements of W are used for working space. */ /* Set some constants. */ /* Parameter adjustments */ zmat_dim1 = *npt; zmat_offset = 1 + zmat_dim1; zmat -= zmat_offset; bmat_dim1 = *ndim; bmat_offset = 1 + bmat_dim1; bmat -= bmat_offset; --vlag; --w; /* Function Body */ one = 1.; zero = 0.; nptm = *npt - *n - 1; ztest = zero; i__1 = *npt; for (k = 1; k <= i__1; ++k) { i__2 = nptm; for (j = 1; j <= i__2; ++j) { /* L10: */ /* Computing MAX */ d__2 = ztest, d__3 = (d__1 = zmat[k + j * zmat_dim1], fabs(d__1)); ztest = MAX2(d__2,d__3); } } ztest *= 1e-20; /* Apply the rotations that put zeros in the KNEW-th row of ZMAT. */ jl = 1; i__2 = nptm; for (j = 2; j <= i__2; ++j) { if ((d__1 = zmat[*knew + j * zmat_dim1], fabs(d__1)) > ztest) { /* Computing 2nd power */ d__1 = zmat[*knew + zmat_dim1]; /* Computing 2nd power */ d__2 = zmat[*knew + j * zmat_dim1]; temp = sqrt(d__1 * d__1 + d__2 * d__2); tempa = zmat[*knew + zmat_dim1] / temp; tempb = zmat[*knew + j * zmat_dim1] / temp; i__1 = *npt; for (i__ = 1; i__ <= i__1; ++i__) { temp = tempa * zmat[i__ + zmat_dim1] + tempb * zmat[i__ + j * zmat_dim1]; zmat[i__ + j * zmat_dim1] = tempa * zmat[i__ + j * zmat_dim1] - tempb * zmat[i__ + zmat_dim1]; /* L20: */ zmat[i__ + zmat_dim1] = temp; } } zmat[*knew + j * zmat_dim1] = zero; /* L30: */ } /* Put the first NPT components of the KNEW-th column of HLAG into W, */ /* and calculate the parameters of the updating formula. */ i__2 = *npt; for (i__ = 1; i__ <= i__2; ++i__) { w[i__] = zmat[*knew + zmat_dim1] * zmat[i__ + zmat_dim1]; /* L40: */ } alpha = w[*knew]; tau = vlag[*knew]; vlag[*knew] -= one; /* Complete the updating of ZMAT. */ temp = sqrt(*denom); tempb = zmat[*knew + zmat_dim1] / temp; tempa = tau / temp; i__2 = *npt; for (i__ = 1; i__ <= i__2; ++i__) { /* L50: */ zmat[i__ + zmat_dim1] = tempa * zmat[i__ + zmat_dim1] - tempb * vlag[ i__]; } /* Finally, update the matrix BMAT. */ i__2 = *n; for (j = 1; j <= i__2; ++j) { jp = *npt + j; w[jp] = bmat[*knew + j * bmat_dim1]; tempa = (alpha * vlag[jp] - tau * w[jp]) / *denom; tempb = (-(*beta) * w[jp] - tau * vlag[jp]) / *denom; i__1 = jp; for (i__ = 1; i__ <= i__1; ++i__) { bmat[i__ + j * bmat_dim1] = bmat[i__ + j * bmat_dim1] + tempa * vlag[i__] + tempb * w[i__]; if (i__ > *npt) { bmat[jp + (i__ - *npt) * bmat_dim1] = bmat[i__ + j * bmat_dim1]; } /* L60: */ } } } /* update_ */ static nlopt_result rescue_(int *n, int *npt, const double *xl, const double *xu, /* int *maxfun */ nlopt_stopping *stop, bobyqa_func calfun, void *calfun_data, double *xbase, double *xpt, double *fval, double *xopt, double *gopt, double *hq, double *pq, double *bmat, double *zmat, int *ndim, double *sl, double *su, /* int *nf, */ double *delta, int *kopt, double *vlag, double * ptsaux, double *ptsid, double *w) { /* System generated locals */ int xpt_dim1, xpt_offset, bmat_dim1, bmat_offset, zmat_dim1, zmat_offset, i__1, i__2, i__3; double d__1, d__2, d__3, d__4; /* Local variables */ double f; int i__, j, k, ih, jp, ip, iq, np, iw; double xp, xq, den; int ihp; double one; int ihq, jpn, kpt; double sum, diff, half, beta; int kold; double winc; int nrem, knew; double temp, bsum; int nptm; double zero, hdiag, fbase, sfrac, denom, vquad, sumpq; double dsqmin, distsq, vlmxsq; /* The arguments N, NPT, XL, XU, MAXFUN, XBASE, XPT, FVAL, XOPT, */ /* GOPT, HQ, PQ, BMAT, ZMAT, NDIM, SL and SU have the same meanings as */ /* the corresponding arguments of BOBYQB on the entry to RESCUE. */ /* NF is maintained as the number of calls of CALFUN so far, except that */ /* NF is set to -1 if the value of MAXFUN prevents further progress. */ /* KOPT is maintained so that FVAL(KOPT) is the least calculated function */ /* value. Its correct value must be given on entry. It is updated if a */ /* new least function value is found, but the corresponding changes to */ /* XOPT and GOPT have to be made later by the calling program. */ /* DELTA is the current trust region radius. */ /* VLAG is a working space vector that will be used for the values of the */ /* provisional Lagrange functions at each of the interpolation points. */ /* They are part of a product that requires VLAG to be of length NDIM. */ /* PTSAUX is also a working space array. For J=1,2,...,N, PTSAUX(1,J) and */ /* PTSAUX(2,J) specify the two positions of provisional interpolation */ /* points when a nonzero step is taken along e_J (the J-th coordinate */ /* direction) through XBASE+XOPT, as specified below. Usually these */ /* steps have length DELTA, but other lengths are chosen if necessary */ /* in order to satisfy the given bounds on the variables. */ /* PTSID is also a working space array. It has NPT components that denote */ /* provisional new positions of the original interpolation points, in */ /* case changes are needed to restore the linear independence of the */ /* interpolation conditions. The K-th point is a candidate for change */ /* if and only if PTSID(K) is nonzero. In this case let p and q be the */ /* int parts of PTSID(K) and (PTSID(K)-p) multiplied by N+1. If p */ /* and q are both positive, the step from XBASE+XOPT to the new K-th */ /* interpolation point is PTSAUX(1,p)*e_p + PTSAUX(1,q)*e_q. Otherwise */ /* the step is PTSAUX(1,p)*e_p or PTSAUX(2,q)*e_q in the cases q=0 or */ /* p=0, respectively. */ /* The first NDIM+NPT elements of the array W are used for working space. */ /* The final elements of BMAT and ZMAT are set in a well-conditioned way */ /* to the values that are appropriate for the new interpolation points. */ /* The elements of GOPT, HQ and PQ are also revised to the values that are */ /* appropriate to the final quadratic model. */ /* Set some constants. */ /* Parameter adjustments */ zmat_dim1 = *npt; zmat_offset = 1 + zmat_dim1; zmat -= zmat_offset; xpt_dim1 = *npt; xpt_offset = 1 + xpt_dim1; xpt -= xpt_offset; --xl; --xu; --xbase; --fval; --xopt; --gopt; --hq; --pq; bmat_dim1 = *ndim; bmat_offset = 1 + bmat_dim1; bmat -= bmat_offset; --sl; --su; --vlag; ptsaux -= 3; --ptsid; --w; /* Function Body */ half = .5; one = 1.; zero = 0.; np = *n + 1; sfrac = half / (double) np; nptm = *npt - np; /* Shift the interpolation points so that XOPT becomes the origin, and set */ /* the elements of ZMAT to zero. The value of SUMPQ is required in the */ /* updating of HQ below. The squares of the distances from XOPT to the */ /* other interpolation points are set at the end of W. Increments of WINC */ /* may be added later to these squares to balance the consideration of */ /* the choice of point that is going to become current. */ sumpq = zero; winc = zero; i__1 = *npt; for (k = 1; k <= i__1; ++k) { distsq = zero; i__2 = *n; for (j = 1; j <= i__2; ++j) { xpt[k + j * xpt_dim1] -= xopt[j]; /* L10: */ /* Computing 2nd power */ d__1 = xpt[k + j * xpt_dim1]; distsq += d__1 * d__1; } sumpq += pq[k]; w[*ndim + k] = distsq; winc = MAX2(winc,distsq); i__2 = nptm; for (j = 1; j <= i__2; ++j) { /* L20: */ zmat[k + j * zmat_dim1] = zero; } } /* Update HQ so that HQ and PQ define the second derivatives of the model */ /* after XBASE has been shifted to the trust region centre. */ ih = 0; i__2 = *n; for (j = 1; j <= i__2; ++j) { w[j] = half * sumpq * xopt[j]; i__1 = *npt; for (k = 1; k <= i__1; ++k) { /* L30: */ w[j] += pq[k] * xpt[k + j * xpt_dim1]; } i__1 = j; for (i__ = 1; i__ <= i__1; ++i__) { ++ih; /* L40: */ hq[ih] = hq[ih] + w[i__] * xopt[j] + w[j] * xopt[i__]; } } /* Shift XBASE, SL, SU and XOPT. Set the elements of BMAT to zero, and */ /* also set the elements of PTSAUX. */ i__1 = *n; for (j = 1; j <= i__1; ++j) { xbase[j] += xopt[j]; sl[j] -= xopt[j]; su[j] -= xopt[j]; xopt[j] = zero; /* Computing MIN */ d__1 = *delta, d__2 = su[j]; ptsaux[(j << 1) + 1] = MIN2(d__1,d__2); /* Computing MAX */ d__1 = -(*delta), d__2 = sl[j]; ptsaux[(j << 1) + 2] = MAX2(d__1,d__2); if (ptsaux[(j << 1) + 1] + ptsaux[(j << 1) + 2] < zero) { temp = ptsaux[(j << 1) + 1]; ptsaux[(j << 1) + 1] = ptsaux[(j << 1) + 2]; ptsaux[(j << 1) + 2] = temp; } if ((d__2 = ptsaux[(j << 1) + 2], fabs(d__2)) < half * (d__1 = ptsaux[( j << 1) + 1], fabs(d__1))) { ptsaux[(j << 1) + 2] = half * ptsaux[(j << 1) + 1]; } i__2 = *ndim; for (i__ = 1; i__ <= i__2; ++i__) { /* L50: */ bmat[i__ + j * bmat_dim1] = zero; } } fbase = fval[*kopt]; /* Set the identifiers of the artificial interpolation points that are */ /* along a coordinate direction from XOPT, and set the corresponding */ /* nonzero elements of BMAT and ZMAT. */ ptsid[1] = sfrac; i__2 = *n; for (j = 1; j <= i__2; ++j) { jp = j + 1; jpn = jp + *n; ptsid[jp] = (double) j + sfrac; if (jpn <= *npt) { ptsid[jpn] = (double) j / (double) np + sfrac; temp = one / (ptsaux[(j << 1) + 1] - ptsaux[(j << 1) + 2]); bmat[jp + j * bmat_dim1] = -temp + one / ptsaux[(j << 1) + 1]; bmat[jpn + j * bmat_dim1] = temp + one / ptsaux[(j << 1) + 2]; bmat[j * bmat_dim1 + 1] = -bmat[jp + j * bmat_dim1] - bmat[jpn + j * bmat_dim1]; zmat[j * zmat_dim1 + 1] = sqrt(2.) / (d__1 = ptsaux[(j << 1) + 1] * ptsaux[(j << 1) + 2], fabs(d__1)); zmat[jp + j * zmat_dim1] = zmat[j * zmat_dim1 + 1] * ptsaux[(j << 1) + 2] * temp; zmat[jpn + j * zmat_dim1] = -zmat[j * zmat_dim1 + 1] * ptsaux[(j << 1) + 1] * temp; } else { bmat[j * bmat_dim1 + 1] = -one / ptsaux[(j << 1) + 1]; bmat[jp + j * bmat_dim1] = one / ptsaux[(j << 1) + 1]; /* Computing 2nd power */ d__1 = ptsaux[(j << 1) + 1]; bmat[j + *npt + j * bmat_dim1] = -half * (d__1 * d__1); } /* L60: */ } /* Set any remaining identifiers with their nonzero elements of ZMAT. */ if (*npt >= *n + np) { i__2 = *npt; for (k = np << 1; k <= i__2; ++k) { iw = (int) (((double) (k - np) - half) / (double) (*n) ); ip = k - np - iw * *n; iq = ip + iw; if (iq > *n) { iq -= *n; } ptsid[k] = (double) ip + (double) iq / (double) np + sfrac; temp = one / (ptsaux[(ip << 1) + 1] * ptsaux[(iq << 1) + 1]); zmat[(k - np) * zmat_dim1 + 1] = temp; zmat[ip + 1 + (k - np) * zmat_dim1] = -temp; zmat[iq + 1 + (k - np) * zmat_dim1] = -temp; /* L70: */ zmat[k + (k - np) * zmat_dim1] = temp; } } nrem = *npt; kold = 1; knew = *kopt; /* Reorder the provisional points in the way that exchanges PTSID(KOLD) */ /* with PTSID(KNEW). */ L80: i__2 = *n; for (j = 1; j <= i__2; ++j) { temp = bmat[kold + j * bmat_dim1]; bmat[kold + j * bmat_dim1] = bmat[knew + j * bmat_dim1]; /* L90: */ bmat[knew + j * bmat_dim1] = temp; } i__2 = nptm; for (j = 1; j <= i__2; ++j) { temp = zmat[kold + j * zmat_dim1]; zmat[kold + j * zmat_dim1] = zmat[knew + j * zmat_dim1]; /* L100: */ zmat[knew + j * zmat_dim1] = temp; } ptsid[kold] = ptsid[knew]; ptsid[knew] = zero; w[*ndim + knew] = zero; --nrem; if (knew != *kopt) { temp = vlag[kold]; vlag[kold] = vlag[knew]; vlag[knew] = temp; /* Update the BMAT and ZMAT matrices so that the status of the KNEW-th */ /* interpolation point can be changed from provisional to original. The */ /* branch to label 350 occurs if all the original points are reinstated. */ /* The nonnegative values of W(NDIM+K) are required in the search below. */ update_(n, npt, &bmat[bmat_offset], &zmat[zmat_offset], ndim, &vlag[1] , &beta, &denom, &knew, &w[1]); if (nrem == 0) { goto L350; } i__2 = *npt; for (k = 1; k <= i__2; ++k) { /* L110: */ w[*ndim + k] = (d__1 = w[*ndim + k], fabs(d__1)); } } /* Pick the index KNEW of an original interpolation point that has not */ /* yet replaced one of the provisional interpolation points, giving */ /* attention to the closeness to XOPT and to previous tries with KNEW. */ L120: dsqmin = zero; i__2 = *npt; for (k = 1; k <= i__2; ++k) { if (w[*ndim + k] > zero) { if (dsqmin == zero || w[*ndim + k] < dsqmin) { knew = k; dsqmin = w[*ndim + k]; } } /* L130: */ } if (dsqmin == zero) { goto L260; } /* Form the W-vector of the chosen original interpolation point. */ i__2 = *n; for (j = 1; j <= i__2; ++j) { /* L140: */ w[*npt + j] = xpt[knew + j * xpt_dim1]; } i__2 = *npt; for (k = 1; k <= i__2; ++k) { sum = zero; if (k == *kopt) { } else if (ptsid[k] == zero) { i__1 = *n; for (j = 1; j <= i__1; ++j) { /* L150: */ sum += w[*npt + j] * xpt[k + j * xpt_dim1]; } } else { ip = (int) ptsid[k]; if (ip > 0) { sum = w[*npt + ip] * ptsaux[(ip << 1) + 1]; } iq = (int) ((double) np * ptsid[k] - (double) (ip * np)); if (iq > 0) { iw = 1; if (ip == 0) { iw = 2; } sum += w[*npt + iq] * ptsaux[iw + (iq << 1)]; } } /* L160: */ w[k] = half * sum * sum; } /* Calculate VLAG and BETA for the required updating of the H matrix if */ /* XPT(KNEW,.) is reinstated in the set of interpolation points. */ i__2 = *npt; for (k = 1; k <= i__2; ++k) { sum = zero; i__1 = *n; for (j = 1; j <= i__1; ++j) { /* L170: */ sum += bmat[k + j * bmat_dim1] * w[*npt + j]; } /* L180: */ vlag[k] = sum; } beta = zero; i__2 = nptm; for (j = 1; j <= i__2; ++j) { sum = zero; i__1 = *npt; for (k = 1; k <= i__1; ++k) { /* L190: */ sum += zmat[k + j * zmat_dim1] * w[k]; } beta -= sum * sum; i__1 = *npt; for (k = 1; k <= i__1; ++k) { /* L200: */ vlag[k] += sum * zmat[k + j * zmat_dim1]; } } bsum = zero; distsq = zero; i__1 = *n; for (j = 1; j <= i__1; ++j) { sum = zero; i__2 = *npt; for (k = 1; k <= i__2; ++k) { /* L210: */ sum += bmat[k + j * bmat_dim1] * w[k]; } jp = j + *npt; bsum += sum * w[jp]; i__2 = *ndim; for (ip = *npt + 1; ip <= i__2; ++ip) { /* L220: */ sum += bmat[ip + j * bmat_dim1] * w[ip]; } bsum += sum * w[jp]; vlag[jp] = sum; /* L230: */ /* Computing 2nd power */ d__1 = xpt[knew + j * xpt_dim1]; distsq += d__1 * d__1; } beta = half * distsq * distsq + beta - bsum; vlag[*kopt] += one; /* KOLD is set to the index of the provisional interpolation point that is */ /* going to be deleted to make way for the KNEW-th original interpolation */ /* point. The choice of KOLD is governed by the avoidance of a small value */ /* of the denominator in the updating calculation of UPDATE. */ denom = zero; vlmxsq = zero; i__1 = *npt; for (k = 1; k <= i__1; ++k) { if (ptsid[k] != zero) { hdiag = zero; i__2 = nptm; for (j = 1; j <= i__2; ++j) { /* L240: */ /* Computing 2nd power */ d__1 = zmat[k + j * zmat_dim1]; hdiag += d__1 * d__1; } /* Computing 2nd power */ d__1 = vlag[k]; den = beta * hdiag + d__1 * d__1; if (den > denom) { kold = k; denom = den; } } /* L250: */ /* Computing MAX */ /* Computing 2nd power */ d__3 = vlag[k]; d__1 = vlmxsq, d__2 = d__3 * d__3; vlmxsq = MAX2(d__1,d__2); } if (denom <= vlmxsq * .01) { w[*ndim + knew] = -w[*ndim + knew] - winc; goto L120; } goto L80; /* When label 260 is reached, all the final positions of the interpolation */ /* points have been chosen although any changes have not been included yet */ /* in XPT. Also the final BMAT and ZMAT matrices are complete, but, apart */ /* from the shift of XBASE, the updating of the quadratic model remains to */ /* be done. The following cycle through the new interpolation points begins */ /* by putting the new point in XPT(KPT,.) and by setting PQ(KPT) to zero, */ /* except that a RETURN occurs if MAXFUN prohibits another value of F. */ L260: i__1 = *npt; for (kpt = 1; kpt <= i__1; ++kpt) { if (ptsid[kpt] == zero) { goto L340; } if (nlopt_stop_forced(stop)) return NLOPT_FORCED_STOP; else if (nlopt_stop_evals(stop)) return NLOPT_MAXEVAL_REACHED; else if (nlopt_stop_time(stop)) return NLOPT_MAXTIME_REACHED; ih = 0; i__2 = *n; for (j = 1; j <= i__2; ++j) { w[j] = xpt[kpt + j * xpt_dim1]; xpt[kpt + j * xpt_dim1] = zero; temp = pq[kpt] * w[j]; i__3 = j; for (i__ = 1; i__ <= i__3; ++i__) { ++ih; /* L270: */ hq[ih] += temp * w[i__]; } } pq[kpt] = zero; ip = (int) ptsid[kpt]; iq = (int) ((double) np * ptsid[kpt] - (double) (ip * np)) ; if (ip > 0) { xp = ptsaux[(ip << 1) + 1]; xpt[kpt + ip * xpt_dim1] = xp; } if (iq > 0) { xq = ptsaux[(iq << 1) + 1]; if (ip == 0) { xq = ptsaux[(iq << 1) + 2]; } xpt[kpt + iq * xpt_dim1] = xq; } /* Set VQUAD to the value of the current model at the new point. */ vquad = fbase; if (ip > 0) { ihp = (ip + ip * ip) / 2; vquad += xp * (gopt[ip] + half * xp * hq[ihp]); } if (iq > 0) { ihq = (iq + iq * iq) / 2; vquad += xq * (gopt[iq] + half * xq * hq[ihq]); if (ip > 0) { iw = MAX2(ihp,ihq) - (i__3 = ip - iq, IABS(i__3)); vquad += xp * xq * hq[iw]; } } i__3 = *npt; for (k = 1; k <= i__3; ++k) { temp = zero; if (ip > 0) { temp += xp * xpt[k + ip * xpt_dim1]; } if (iq > 0) { temp += xq * xpt[k + iq * xpt_dim1]; } /* L280: */ vquad += half * pq[k] * temp * temp; } /* Calculate F at the new interpolation point, and set DIFF to the factor */ /* that is going to multiply the KPT-th Lagrange function when the model */ /* is updated to provide interpolation to the new function value. */ i__3 = *n; for (i__ = 1; i__ <= i__3; ++i__) { /* Computing MIN */ /* Computing MAX */ d__3 = xl[i__], d__4 = xbase[i__] + xpt[kpt + i__ * xpt_dim1]; d__1 = MAX2(d__3,d__4), d__2 = xu[i__]; w[i__] = MIN2(d__1,d__2); if (xpt[kpt + i__ * xpt_dim1] == sl[i__]) { w[i__] = xl[i__]; } if (xpt[kpt + i__ * xpt_dim1] == su[i__]) { w[i__] = xu[i__]; } /* L290: */ } stop->nevals++; f = calfun(*n, &w[1], calfun_data); fval[kpt] = f; if (f < fval[*kopt]) { *kopt = kpt; } if (nlopt_stop_forced(stop)) return NLOPT_FORCED_STOP; else if (f < stop->minf_max) return NLOPT_MINF_MAX_REACHED; else if (nlopt_stop_evals(stop)) return NLOPT_MAXEVAL_REACHED; else if (nlopt_stop_time(stop)) return NLOPT_MAXTIME_REACHED; diff = f - vquad; /* Update the quadratic model. The RETURN from the subroutine occurs when */ /* all the new interpolation points are included in the model. */ i__3 = *n; for (i__ = 1; i__ <= i__3; ++i__) { /* L310: */ gopt[i__] += diff * bmat[kpt + i__ * bmat_dim1]; } i__3 = *npt; for (k = 1; k <= i__3; ++k) { sum = zero; i__2 = nptm; for (j = 1; j <= i__2; ++j) { /* L320: */ sum += zmat[k + j * zmat_dim1] * zmat[kpt + j * zmat_dim1]; } temp = diff * sum; if (ptsid[k] == zero) { pq[k] += temp; } else { ip = (int) ptsid[k]; iq = (int) ((double) np * ptsid[k] - (double) (ip * np)); ihq = (iq * iq + iq) / 2; if (ip == 0) { /* Computing 2nd power */ d__1 = ptsaux[(iq << 1) + 2]; hq[ihq] += temp * (d__1 * d__1); } else { ihp = (ip * ip + ip) / 2; /* Computing 2nd power */ d__1 = ptsaux[(ip << 1) + 1]; hq[ihp] += temp * (d__1 * d__1); if (iq > 0) { /* Computing 2nd power */ d__1 = ptsaux[(iq << 1) + 1]; hq[ihq] += temp * (d__1 * d__1); iw = MAX2(ihp,ihq) - (i__2 = iq - ip, IABS(i__2)); hq[iw] += temp * ptsaux[(ip << 1) + 1] * ptsaux[(iq << 1) + 1]; } } } /* L330: */ } ptsid[kpt] = zero; L340: ; } L350: return NLOPT_SUCCESS; } /* rescue_ */ static void altmov_(int *n, int *npt, double *xpt, double *xopt, double *bmat, double *zmat, int *ndim, double *sl, double *su, int *kopt, int *knew, double *adelt, double *xnew, double *xalt, double * alpha, double *cauchy, double *glag, double *hcol, double *w) { /* System generated locals */ int xpt_dim1, xpt_offset, bmat_dim1, bmat_offset, zmat_dim1, zmat_offset, i__1, i__2; double d__1, d__2, d__3, d__4; /* Local variables */ int i__, j, k; double ha, gw, one, diff, half; int ilbd, isbd; double slbd; int iubd; double vlag, subd, temp; int ksav; double step, zero, curv; int iflag; double scale, csave, tempa, tempb, tempd, const__, sumin, ggfree; int ibdsav; double dderiv, bigstp, predsq, presav, distsq, stpsav, wfixsq, wsqsav; /* The arguments N, NPT, XPT, XOPT, BMAT, ZMAT, NDIM, SL and SU all have */ /* the same meanings as the corresponding arguments of BOBYQB. */ /* KOPT is the index of the optimal interpolation point. */ /* KNEW is the index of the interpolation point that is going to be moved. */ /* ADELT is the current trust region bound. */ /* XNEW will be set to a suitable new position for the interpolation point */ /* XPT(KNEW,.). Specifically, it satisfies the SL, SU and trust region */ /* bounds and it should provide a large denominator in the next call of */ /* UPDATE. The step XNEW-XOPT from XOPT is restricted to moves along the */ /* straight lines through XOPT and another interpolation point. */ /* XALT also provides a large value of the modulus of the KNEW-th Lagrange */ /* function subject to the constraints that have been mentioned, its main */ /* difference from XNEW being that XALT-XOPT is a constrained version of */ /* the Cauchy step within the trust region. An exception is that XALT is */ /* not calculated if all components of GLAG (see below) are zero. */ /* ALPHA will be set to the KNEW-th diagonal element of the H matrix. */ /* CAUCHY will be set to the square of the KNEW-th Lagrange function at */ /* the step XALT-XOPT from XOPT for the vector XALT that is returned, */ /* except that CAUCHY is set to zero if XALT is not calculated. */ /* GLAG is a working space vector of length N for the gradient of the */ /* KNEW-th Lagrange function at XOPT. */ /* HCOL is a working space vector of length NPT for the second derivative */ /* coefficients of the KNEW-th Lagrange function. */ /* W is a working space vector of length 2N that is going to hold the */ /* constrained Cauchy step from XOPT of the Lagrange function, followed */ /* by the downhill version of XALT when the uphill step is calculated. */ /* Set the first NPT components of W to the leading elements of the */ /* KNEW-th column of the H matrix. */ /* Parameter adjustments */ zmat_dim1 = *npt; zmat_offset = 1 + zmat_dim1; zmat -= zmat_offset; xpt_dim1 = *npt; xpt_offset = 1 + xpt_dim1; xpt -= xpt_offset; --xopt; bmat_dim1 = *ndim; bmat_offset = 1 + bmat_dim1; bmat -= bmat_offset; --sl; --su; --xnew; --xalt; --glag; --hcol; --w; /* Function Body */ half = .5; one = 1.; zero = 0.; const__ = one + sqrt(2.); i__1 = *npt; for (k = 1; k <= i__1; ++k) { /* L10: */ hcol[k] = zero; } i__1 = *npt - *n - 1; for (j = 1; j <= i__1; ++j) { temp = zmat[*knew + j * zmat_dim1]; i__2 = *npt; for (k = 1; k <= i__2; ++k) { /* L20: */ hcol[k] += temp * zmat[k + j * zmat_dim1]; } } *alpha = hcol[*knew]; ha = half * *alpha; /* Calculate the gradient of the KNEW-th Lagrange function at XOPT. */ i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { /* L30: */ glag[i__] = bmat[*knew + i__ * bmat_dim1]; } i__2 = *npt; for (k = 1; k <= i__2; ++k) { temp = zero; i__1 = *n; for (j = 1; j <= i__1; ++j) { /* L40: */ temp += xpt[k + j * xpt_dim1] * xopt[j]; } temp = hcol[k] * temp; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* L50: */ glag[i__] += temp * xpt[k + i__ * xpt_dim1]; } } /* Search for a large denominator along the straight lines through XOPT */ /* and another interpolation point. SLBD and SUBD will be lower and upper */ /* bounds on the step along each of these lines in turn. PREDSQ will be */ /* set to the square of the predicted denominator for each line. PRESAV */ /* will be set to the largest admissible value of PREDSQ that occurs. */ presav = zero; i__1 = *npt; for (k = 1; k <= i__1; ++k) { if (k == *kopt) { goto L80; } dderiv = zero; distsq = zero; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { temp = xpt[k + i__ * xpt_dim1] - xopt[i__]; dderiv += glag[i__] * temp; /* L60: */ distsq += temp * temp; } subd = *adelt / sqrt(distsq); slbd = -subd; ilbd = 0; iubd = 0; sumin = MIN2(one,subd); /* Revise SLBD and SUBD if necessary because of the bounds in SL and SU. */ i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { temp = xpt[k + i__ * xpt_dim1] - xopt[i__]; if (temp > zero) { if (slbd * temp < sl[i__] - xopt[i__]) { slbd = (sl[i__] - xopt[i__]) / temp; ilbd = -i__; } if (subd * temp > su[i__] - xopt[i__]) { /* Computing MAX */ d__1 = sumin, d__2 = (su[i__] - xopt[i__]) / temp; subd = MAX2(d__1,d__2); iubd = i__; } } else if (temp < zero) { if (slbd * temp > su[i__] - xopt[i__]) { slbd = (su[i__] - xopt[i__]) / temp; ilbd = i__; } if (subd * temp < sl[i__] - xopt[i__]) { /* Computing MAX */ d__1 = sumin, d__2 = (sl[i__] - xopt[i__]) / temp; subd = MAX2(d__1,d__2); iubd = -i__; } } /* L70: */ } /* Seek a large modulus of the KNEW-th Lagrange function when the index */ /* of the other interpolation point on the line through XOPT is KNEW. */ if (k == *knew) { diff = dderiv - one; step = slbd; vlag = slbd * (dderiv - slbd * diff); isbd = ilbd; temp = subd * (dderiv - subd * diff); if (fabs(temp) > fabs(vlag)) { step = subd; vlag = temp; isbd = iubd; } tempd = half * dderiv; tempa = tempd - diff * slbd; tempb = tempd - diff * subd; if (tempa * tempb < zero) { temp = tempd * tempd / diff; if (fabs(temp) > fabs(vlag)) { step = tempd / diff; vlag = temp; isbd = 0; } } /* Search along each of the other lines through XOPT and another point. */ } else { step = slbd; vlag = slbd * (one - slbd); isbd = ilbd; temp = subd * (one - subd); if (fabs(temp) > fabs(vlag)) { step = subd; vlag = temp; isbd = iubd; } if (subd > half) { if (fabs(vlag) < .25) { step = half; vlag = .25; isbd = 0; } } vlag *= dderiv; } /* Calculate PREDSQ for the current line search and maintain PRESAV. */ temp = step * (one - step) * distsq; predsq = vlag * vlag * (vlag * vlag + ha * temp * temp); if (predsq > presav) { presav = predsq; ksav = k; stpsav = step; ibdsav = isbd; } L80: ; } /* Construct XNEW in a way that satisfies the bound constraints exactly. */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp = xopt[i__] + stpsav * (xpt[ksav + i__ * xpt_dim1] - xopt[i__]); /* L90: */ /* Computing MAX */ /* Computing MIN */ d__3 = su[i__]; d__1 = sl[i__], d__2 = MIN2(d__3,temp); xnew[i__] = MAX2(d__1,d__2); } if (ibdsav < 0) { xnew[-ibdsav] = sl[-ibdsav]; } if (ibdsav > 0) { xnew[ibdsav] = su[ibdsav]; } /* Prepare for the iterative method that assembles the constrained Cauchy */ /* step in W. The sum of squares of the fixed components of W is formed in */ /* WFIXSQ, and the free components of W are set to BIGSTP. */ bigstp = *adelt + *adelt; iflag = 0; L100: wfixsq = zero; ggfree = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { w[i__] = zero; /* Computing MIN */ d__1 = xopt[i__] - sl[i__], d__2 = glag[i__]; tempa = MIN2(d__1,d__2); /* Computing MAX */ d__1 = xopt[i__] - su[i__], d__2 = glag[i__]; tempb = MAX2(d__1,d__2); if (tempa > zero || tempb < zero) { w[i__] = bigstp; /* Computing 2nd power */ d__1 = glag[i__]; ggfree += d__1 * d__1; } /* L110: */ } if (ggfree == zero) { *cauchy = zero; goto L200; } /* Investigate whether more components of W can be fixed. */ L120: temp = *adelt * *adelt - wfixsq; if (temp > zero) { wsqsav = wfixsq; step = sqrt(temp / ggfree); ggfree = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (w[i__] == bigstp) { temp = xopt[i__] - step * glag[i__]; if (temp <= sl[i__]) { w[i__] = sl[i__] - xopt[i__]; /* Computing 2nd power */ d__1 = w[i__]; wfixsq += d__1 * d__1; } else if (temp >= su[i__]) { w[i__] = su[i__] - xopt[i__]; /* Computing 2nd power */ d__1 = w[i__]; wfixsq += d__1 * d__1; } else { /* Computing 2nd power */ d__1 = glag[i__]; ggfree += d__1 * d__1; } } /* L130: */ } if (wfixsq > wsqsav && ggfree > zero) { goto L120; } } /* Set the remaining free components of W and all components of XALT, */ /* except that W may be scaled later. */ gw = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (w[i__] == bigstp) { w[i__] = -step * glag[i__]; /* Computing MAX */ /* Computing MIN */ d__3 = su[i__], d__4 = xopt[i__] + w[i__]; d__1 = sl[i__], d__2 = MIN2(d__3,d__4); xalt[i__] = MAX2(d__1,d__2); } else if (w[i__] == zero) { xalt[i__] = xopt[i__]; } else if (glag[i__] > zero) { xalt[i__] = sl[i__]; } else { xalt[i__] = su[i__]; } /* L140: */ gw += glag[i__] * w[i__]; } /* Set CURV to the curvature of the KNEW-th Lagrange function along W. */ /* Scale W by a factor less than one if that can reduce the modulus of */ /* the Lagrange function at XOPT+W. Set CAUCHY to the final value of */ /* the square of this function. */ curv = zero; i__1 = *npt; for (k = 1; k <= i__1; ++k) { temp = zero; i__2 = *n; for (j = 1; j <= i__2; ++j) { /* L150: */ temp += xpt[k + j * xpt_dim1] * w[j]; } /* L160: */ curv += hcol[k] * temp * temp; } if (iflag == 1) { curv = -curv; } if (curv > -gw && curv < -const__ * gw) { scale = -gw / curv; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp = xopt[i__] + scale * w[i__]; /* L170: */ /* Computing MAX */ /* Computing MIN */ d__3 = su[i__]; d__1 = sl[i__], d__2 = MIN2(d__3,temp); xalt[i__] = MAX2(d__1,d__2); } /* Computing 2nd power */ d__1 = half * gw * scale; *cauchy = d__1 * d__1; } else { /* Computing 2nd power */ d__1 = gw + half * curv; *cauchy = d__1 * d__1; } /* If IFLAG is zero, then XALT is calculated as before after reversing */ /* the sign of GLAG. Thus two XALT vectors become available. The one that */ /* is chosen is the one that gives the larger value of CAUCHY. */ if (iflag == 0) { i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { glag[i__] = -glag[i__]; /* L180: */ w[*n + i__] = xalt[i__]; } csave = *cauchy; iflag = 1; goto L100; } if (csave > *cauchy) { i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* L190: */ xalt[i__] = w[*n + i__]; } *cauchy = csave; } L200: return; } /* altmov_ */ static void trsbox_(int *n, int *npt, double *xpt, double *xopt, double *gopt, double *hq, double *pq, double *sl, double *su, double *delta, double *xnew, double *d__, double *gnew, double *xbdi, double *s, double *hs, double *hred, double *dsq, double *crvmin) { /* System generated locals */ int xpt_dim1, xpt_offset, i__1, i__2; double d__1, d__2, d__3, d__4; /* Local variables */ int i__, j, k, ih; double ds; int iu; double dhd, dhs, cth, one, shs, sth, ssq, half, beta, sdec, blen; int iact, nact; double angt, qred; int isav; double temp, zero, xsav, xsum, angbd, dredg, sredg; int iterc; double resid, delsq, ggsav, tempa, tempb, ratio, sqstp, redmax, dredsq, redsav, onemin, gredsq, rednew; int itcsav; double rdprev, rdnext, stplen, stepsq; int itermax; /* The arguments N, NPT, XPT, XOPT, GOPT, HQ, PQ, SL and SU have the same */ /* meanings as the corresponding arguments of BOBYQB. */ /* DELTA is the trust region radius for the present calculation, which */ /* seeks a small value of the quadratic model within distance DELTA of */ /* XOPT subject to the bounds on the variables. */ /* XNEW will be set to a new vector of variables that is approximately */ /* the one that minimizes the quadratic model within the trust region */ /* subject to the SL and SU constraints on the variables. It satisfies */ /* as equations the bounds that become active during the calculation. */ /* D is the calculated trial step from XOPT, generated iteratively from an */ /* initial value of zero. Thus XNEW is XOPT+D after the final iteration. */ /* GNEW holds the gradient of the quadratic model at XOPT+D. It is updated */ /* when D is updated. */ /* XBDI is a working space vector. For I=1,2,...,N, the element XBDI(I) is */ /* set to -1.0, 0.0, or 1.0, the value being nonzero if and only if the */ /* I-th variable has become fixed at a bound, the bound being SL(I) or */ /* SU(I) in the case XBDI(I)=-1.0 or XBDI(I)=1.0, respectively. This */ /* information is accumulated during the construction of XNEW. */ /* The arrays S, HS and HRED are also used for working space. They hold the */ /* current search direction, and the changes in the gradient of Q along S */ /* and the reduced D, respectively, where the reduced D is the same as D, */ /* except that the components of the fixed variables are zero. */ /* DSQ will be set to the square of the length of XNEW-XOPT. */ /* CRVMIN is set to zero if D reaches the trust region boundary. Otherwise */ /* it is set to the least curvature of H that occurs in the conjugate */ /* gradient searches that are not restricted by any constraints. The */ /* value CRVMIN=-1.0D0 is set, however, if all of these searches are */ /* constrained. */ /* A version of the truncated conjugate gradient is applied. If a line */ /* search is restricted by a constraint, then the procedure is restarted, */ /* the values of the variables that are at their bounds being fixed. If */ /* the trust region boundary is reached, then further changes may be made */ /* to D, each one being in the two dimensional space that is spanned */ /* by the current D and the gradient of Q at XOPT+D, staying on the trust */ /* region boundary. Termination occurs when the reduction in Q seems to */ /* be close to the greatest reduction that can be achieved. */ /* Set some constants. */ /* Parameter adjustments */ xpt_dim1 = *npt; xpt_offset = 1 + xpt_dim1; xpt -= xpt_offset; --xopt; --gopt; --hq; --pq; --sl; --su; --xnew; --d__; --gnew; --xbdi; --s; --hs; --hred; /* Function Body */ half = .5; one = 1.; onemin = -1.; zero = 0.; /* The sign of GOPT(I) gives the sign of the change to the I-th variable */ /* that will reduce Q from its value at XOPT. Thus XBDI(I) shows whether */ /* or not to fix the I-th variable at one of its bounds initially, with */ /* NACT being set to the number of fixed variables. D and GNEW are also */ /* set for the first iteration. DELSQ is the upper bound on the sum of */ /* squares of the free variables. QRED is the reduction in Q so far. */ iterc = 0; nact = 0; sqstp = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { xbdi[i__] = zero; if (xopt[i__] <= sl[i__]) { if (gopt[i__] >= zero) { xbdi[i__] = onemin; } } else if (xopt[i__] >= su[i__]) { if (gopt[i__] <= zero) { xbdi[i__] = one; } } if (xbdi[i__] != zero) { ++nact; } d__[i__] = zero; /* L10: */ gnew[i__] = gopt[i__]; } delsq = *delta * *delta; qred = zero; *crvmin = onemin; /* Set the next search direction of the conjugate gradient method. It is */ /* the steepest descent direction initially and when the iterations are */ /* restarted because a variable has just been fixed by a bound, and of */ /* course the components of the fixed variables are zero. ITERMAX is an */ /* upper bound on the indices of the conjugate gradient iterations. */ L20: beta = zero; L30: stepsq = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (xbdi[i__] != zero) { s[i__] = zero; } else if (beta == zero) { s[i__] = -gnew[i__]; } else { s[i__] = beta * s[i__] - gnew[i__]; } /* L40: */ /* Computing 2nd power */ d__1 = s[i__]; stepsq += d__1 * d__1; } if (stepsq == zero) { goto L190; } if (beta == zero) { gredsq = stepsq; itermax = iterc + *n - nact; } if (gredsq * delsq <= qred * 1e-4 * qred) { goto L190; } /* Multiply the search direction by the second derivative matrix of Q and */ /* calculate some scalars for the choice of steplength. Then set BLEN to */ /* the length of the the step to the trust region boundary and STPLEN to */ /* the steplength, ignoring the simple bounds. */ goto L210; L50: resid = delsq; ds = zero; shs = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (xbdi[i__] == zero) { /* Computing 2nd power */ d__1 = d__[i__]; resid -= d__1 * d__1; ds += s[i__] * d__[i__]; shs += s[i__] * hs[i__]; } /* L60: */ } if (resid <= zero) { goto L90; } temp = sqrt(stepsq * resid + ds * ds); if (ds < zero) { blen = (temp - ds) / stepsq; } else { blen = resid / (temp + ds); } stplen = blen; if (shs > zero) { /* Computing MIN */ d__1 = blen, d__2 = gredsq / shs; stplen = MIN2(d__1,d__2); } /* Reduce STPLEN if necessary in order to preserve the simple bounds, */ /* letting IACT be the index of the new constrained variable. */ iact = 0; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (s[i__] != zero) { xsum = xopt[i__] + d__[i__]; if (s[i__] > zero) { temp = (su[i__] - xsum) / s[i__]; } else { temp = (sl[i__] - xsum) / s[i__]; } if (temp < stplen) { stplen = temp; iact = i__; } } /* L70: */ } /* Update CRVMIN, GNEW and D. Set SDEC to the decrease that occurs in Q. */ sdec = zero; if (stplen > zero) { ++iterc; temp = shs / stepsq; if (iact == 0 && temp > zero) { *crvmin = MIN2(*crvmin,temp); if (*crvmin == onemin) { *crvmin = temp; } } ggsav = gredsq; gredsq = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { gnew[i__] += stplen * hs[i__]; if (xbdi[i__] == zero) { /* Computing 2nd power */ d__1 = gnew[i__]; gredsq += d__1 * d__1; } /* L80: */ d__[i__] += stplen * s[i__]; } /* Computing MAX */ d__1 = stplen * (ggsav - half * stplen * shs); sdec = MAX2(d__1,zero); qred += sdec; } /* Restart the conjugate gradient method if it has hit a new bound. */ if (iact > 0) { ++nact; xbdi[iact] = one; if (s[iact] < zero) { xbdi[iact] = onemin; } /* Computing 2nd power */ d__1 = d__[iact]; delsq -= d__1 * d__1; if (delsq <= zero) { goto L90; } goto L20; } /* If STPLEN is less than BLEN, then either apply another conjugate */ /* gradient iteration or RETURN. */ if (stplen < blen) { if (iterc == itermax) { goto L190; } if (sdec <= qred * .01) { goto L190; } beta = gredsq / ggsav; goto L30; } L90: *crvmin = zero; /* Prepare for the alternative iteration by calculating some scalars */ /* and by multiplying the reduced D by the second derivative matrix of */ /* Q, where S holds the reduced D in the call of GGMULT. */ L100: if (nact >= *n - 1) { goto L190; } dredsq = zero; dredg = zero; gredsq = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (xbdi[i__] == zero) { /* Computing 2nd power */ d__1 = d__[i__]; dredsq += d__1 * d__1; dredg += d__[i__] * gnew[i__]; /* Computing 2nd power */ d__1 = gnew[i__]; gredsq += d__1 * d__1; s[i__] = d__[i__]; } else { s[i__] = zero; } /* L110: */ } itcsav = iterc; goto L210; /* Let the search direction S be a linear combination of the reduced D */ /* and the reduced G that is orthogonal to the reduced D. */ L120: ++iterc; temp = gredsq * dredsq - dredg * dredg; if (temp <= qred * 1e-4 * qred) { goto L190; } temp = sqrt(temp); i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (xbdi[i__] == zero) { s[i__] = (dredg * d__[i__] - dredsq * gnew[i__]) / temp; } else { s[i__] = zero; } /* L130: */ } sredg = -temp; /* By considering the simple bounds on the variables, calculate an upper */ /* bound on the tangent of half the angle of the alternative iteration, */ /* namely ANGBD, except that, if already a free variable has reached a */ /* bound, there is a branch back to label 100 after fixing that variable. */ angbd = one; iact = 0; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (xbdi[i__] == zero) { tempa = xopt[i__] + d__[i__] - sl[i__]; tempb = su[i__] - xopt[i__] - d__[i__]; if (tempa <= zero) { ++nact; xbdi[i__] = onemin; goto L100; } else if (tempb <= zero) { ++nact; xbdi[i__] = one; goto L100; } ratio = one; /* Computing 2nd power */ d__1 = d__[i__]; /* Computing 2nd power */ d__2 = s[i__]; ssq = d__1 * d__1 + d__2 * d__2; /* Computing 2nd power */ d__1 = xopt[i__] - sl[i__]; temp = ssq - d__1 * d__1; if (temp > zero) { temp = sqrt(temp) - s[i__]; if (angbd * temp > tempa) { angbd = tempa / temp; iact = i__; xsav = onemin; } } /* Computing 2nd power */ d__1 = su[i__] - xopt[i__]; temp = ssq - d__1 * d__1; if (temp > zero) { temp = sqrt(temp) + s[i__]; if (angbd * temp > tempb) { angbd = tempb / temp; iact = i__; xsav = one; } } } /* L140: */ } /* Calculate HHD and some curvatures for the alternative iteration. */ goto L210; L150: shs = zero; dhs = zero; dhd = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (xbdi[i__] == zero) { shs += s[i__] * hs[i__]; dhs += d__[i__] * hs[i__]; dhd += d__[i__] * hred[i__]; } /* L160: */ } /* Seek the greatest reduction in Q for a range of equally spaced values */ /* of ANGT in [0,ANGBD], where ANGT is the tangent of half the angle of */ /* the alternative iteration. */ redmax = zero; isav = 0; redsav = zero; iu = (int) (angbd * 17. + 3.1); i__1 = iu; for (i__ = 1; i__ <= i__1; ++i__) { angt = angbd * (double) i__ / (double) iu; sth = (angt + angt) / (one + angt * angt); temp = shs + angt * (angt * dhd - dhs - dhs); rednew = sth * (angt * dredg - sredg - half * sth * temp); if (rednew > redmax) { redmax = rednew; isav = i__; rdprev = redsav; } else if (i__ == isav + 1) { rdnext = rednew; } /* L170: */ redsav = rednew; } /* Return if the reduction is zero. Otherwise, set the sine and cosine */ /* of the angle of the alternative iteration, and calculate SDEC. */ if (isav == 0) { goto L190; } if (isav < iu) { temp = (rdnext - rdprev) / (redmax + redmax - rdprev - rdnext); angt = angbd * ((double) isav + half * temp) / (double) iu; } cth = (one - angt * angt) / (one + angt * angt); sth = (angt + angt) / (one + angt * angt); temp = shs + angt * (angt * dhd - dhs - dhs); sdec = sth * (angt * dredg - sredg - half * sth * temp); if (sdec <= zero) { goto L190; } /* Update GNEW, D and HRED. If the angle of the alternative iteration */ /* is restricted by a bound on a free variable, that variable is fixed */ /* at the bound. */ dredg = zero; gredsq = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { gnew[i__] = gnew[i__] + (cth - one) * hred[i__] + sth * hs[i__]; if (xbdi[i__] == zero) { d__[i__] = cth * d__[i__] + sth * s[i__]; dredg += d__[i__] * gnew[i__]; /* Computing 2nd power */ d__1 = gnew[i__]; gredsq += d__1 * d__1; } /* L180: */ hred[i__] = cth * hred[i__] + sth * hs[i__]; } qred += sdec; if (iact > 0 && isav == iu) { ++nact; xbdi[iact] = xsav; goto L100; } /* If SDEC is sufficiently small, then RETURN after setting XNEW to */ /* XOPT+D, giving careful attention to the bounds. */ if (sdec > qred * .01) { goto L120; } L190: *dsq = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* Computing MAX */ /* Computing MIN */ d__3 = xopt[i__] + d__[i__], d__4 = su[i__]; d__1 = MIN2(d__3,d__4), d__2 = sl[i__]; xnew[i__] = MAX2(d__1,d__2); if (xbdi[i__] == onemin) { xnew[i__] = sl[i__]; } if (xbdi[i__] == one) { xnew[i__] = su[i__]; } d__[i__] = xnew[i__] - xopt[i__]; /* L200: */ /* Computing 2nd power */ d__1 = d__[i__]; *dsq += d__1 * d__1; } return; /* The following instructions multiply the current S-vector by the second */ /* derivative matrix of the quadratic model, putting the product in HS. */ /* They are reached from three different parts of the software above and */ /* they can be regarded as an external subroutine. */ L210: ih = 0; i__1 = *n; for (j = 1; j <= i__1; ++j) { hs[j] = zero; i__2 = j; for (i__ = 1; i__ <= i__2; ++i__) { ++ih; if (i__ < j) { hs[j] += hq[ih] * s[i__]; } /* L220: */ hs[i__] += hq[ih] * s[j]; } } i__2 = *npt; for (k = 1; k <= i__2; ++k) { if (pq[k] != zero) { temp = zero; i__1 = *n; for (j = 1; j <= i__1; ++j) { /* L230: */ temp += xpt[k + j * xpt_dim1] * s[j]; } temp *= pq[k]; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* L240: */ hs[i__] += temp * xpt[k + i__ * xpt_dim1]; } } /* L250: */ } if (*crvmin != zero) { goto L50; } if (iterc > itcsav) { goto L150; } i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { /* L260: */ hred[i__] = hs[i__]; } goto L120; } /* trsbox_ */ static nlopt_result prelim_(int *n, int *npt, double *x, const double *xl, const double *xu, double *rhobeg, nlopt_stopping *stop, bobyqa_func calfun, void *calfun_data, double *xbase, double *xpt, double *fval, double *gopt, double *hq, double *pq, double *bmat, double *zmat, int *ndim, double *sl, double *su, int *kopt) { /* System generated locals */ int xpt_dim1, xpt_offset, bmat_dim1, bmat_offset, zmat_dim1, zmat_offset, i__1, i__2; double d__1, d__2, d__3, d__4; /* Local variables */ double f; int i__, j, k, ih, np, nfm; double one; int nfx, ipt, jpt; double two, fbeg, diff, half, temp, zero, recip, stepa, stepb; int itemp; double rhosq; int nf; /* The arguments N, NPT, X, XL, XU, RHOBEG, and MAXFUN are the */ /* same as the corresponding arguments in SUBROUTINE BOBYQA. */ /* The arguments XBASE, XPT, FVAL, HQ, PQ, BMAT, ZMAT, NDIM, SL and SU */ /* are the same as the corresponding arguments in BOBYQB, the elements */ /* of SL and SU being set in BOBYQA. */ /* GOPT is usually the gradient of the quadratic model at XOPT+XBASE, but */ /* it is set by PRELIM to the gradient of the quadratic model at XBASE. */ /* If XOPT is nonzero, BOBYQB will change it to its usual value later. */ /* NF is maintaned as the number of calls of CALFUN so far. */ /* KOPT will be such that the least calculated value of F so far is at */ /* the point XPT(KOPT,.)+XBASE in the space of the variables. */ /* SUBROUTINE PRELIM sets the elements of XBASE, XPT, FVAL, GOPT, HQ, PQ, */ /* BMAT and ZMAT for the first iteration, and it maintains the values of */ /* NF and KOPT. The vector X is also changed by PRELIM. */ /* Set some constants. */ /* Parameter adjustments */ zmat_dim1 = *npt; zmat_offset = 1 + zmat_dim1; zmat -= zmat_offset; xpt_dim1 = *npt; xpt_offset = 1 + xpt_dim1; xpt -= xpt_offset; --x; --xl; --xu; --xbase; --fval; --gopt; --hq; --pq; bmat_dim1 = *ndim; bmat_offset = 1 + bmat_dim1; bmat -= bmat_offset; --sl; --su; /* Function Body */ half = .5; one = 1.; two = 2.; zero = 0.; rhosq = *rhobeg * *rhobeg; recip = one / rhosq; np = *n + 1; /* Set XBASE to the initial vector of variables, and set the initial */ /* elements of XPT, BMAT, HQ, PQ and ZMAT to zero. */ i__1 = *n; for (j = 1; j <= i__1; ++j) { xbase[j] = x[j]; i__2 = *npt; for (k = 1; k <= i__2; ++k) { /* L10: */ xpt[k + j * xpt_dim1] = zero; } i__2 = *ndim; for (i__ = 1; i__ <= i__2; ++i__) { /* L20: */ bmat[i__ + j * bmat_dim1] = zero; } } i__2 = *n * np / 2; for (ih = 1; ih <= i__2; ++ih) { /* L30: */ hq[ih] = zero; } i__2 = *npt; for (k = 1; k <= i__2; ++k) { pq[k] = zero; i__1 = *npt - np; for (j = 1; j <= i__1; ++j) { /* L40: */ zmat[k + j * zmat_dim1] = zero; } } /* Begin the initialization procedure. NF becomes one more than the number */ /* of function values so far. The coordinates of the displacement of the */ /* next initial interpolation point from XBASE are set in XPT(NF+1,.). */ nf = 0; L50: nfm = nf; nfx = nf - *n; ++(nf); if (nfm <= *n << 1) { if (nfm >= 1 && nfm <= *n) { stepa = *rhobeg; if (su[nfm] == zero) { stepa = -stepa; } xpt[nf + nfm * xpt_dim1] = stepa; } else if (nfm > *n) { stepa = xpt[nf - *n + nfx * xpt_dim1]; stepb = -(*rhobeg); if (sl[nfx] == zero) { /* Computing MIN */ d__1 = two * *rhobeg, d__2 = su[nfx]; stepb = MIN2(d__1,d__2); } if (su[nfx] == zero) { /* Computing MAX */ d__1 = -two * *rhobeg, d__2 = sl[nfx]; stepb = MAX2(d__1,d__2); } xpt[nf + nfx * xpt_dim1] = stepb; } } else { itemp = (nfm - np) / *n; jpt = nfm - itemp * *n - *n; ipt = jpt + itemp; if (ipt > *n) { itemp = jpt; jpt = ipt - *n; ipt = itemp; } xpt[nf + ipt * xpt_dim1] = xpt[ipt + 1 + ipt * xpt_dim1]; xpt[nf + jpt * xpt_dim1] = xpt[jpt + 1 + jpt * xpt_dim1]; } /* Calculate the next value of F. The least function value so far and */ /* its index are required. */ i__1 = *n; for (j = 1; j <= i__1; ++j) { /* Computing MIN */ /* Computing MAX */ d__3 = xl[j], d__4 = xbase[j] + xpt[nf + j * xpt_dim1]; d__1 = MAX2(d__3,d__4), d__2 = xu[j]; x[j] = MIN2(d__1,d__2); if (xpt[nf + j * xpt_dim1] == sl[j]) { x[j] = xl[j]; } if (xpt[nf + j * xpt_dim1] == su[j]) { x[j] = xu[j]; } /* L60: */ } stop->nevals++; f = calfun(*n, &x[1], calfun_data); fval[nf] = f; if (nf == 1) { fbeg = f; *kopt = 1; } else if (f < fval[*kopt]) { *kopt = nf; } /* Set the nonzero initial elements of BMAT and the quadratic model in the */ /* cases when NF is at most 2*N+1. If NF exceeds N+1, then the positions */ /* of the NF-th and (NF-N)-th interpolation points may be switched, in */ /* order that the function value at the first of them contributes to the */ /* off-diagonal second derivative terms of the initial quadratic model. */ if (nf <= (*n << 1) + 1) { if (nf >= 2 && nf <= *n + 1) { gopt[nfm] = (f - fbeg) / stepa; if (*npt < nf + *n) { bmat[nfm * bmat_dim1 + 1] = -one / stepa; bmat[nf + nfm * bmat_dim1] = one / stepa; bmat[*npt + nfm + nfm * bmat_dim1] = -half * rhosq; } } else if (nf >= *n + 2) { ih = nfx * (nfx + 1) / 2; temp = (f - fbeg) / stepb; diff = stepb - stepa; hq[ih] = two * (temp - gopt[nfx]) / diff; gopt[nfx] = (gopt[nfx] * stepb - temp * stepa) / diff; if (stepa * stepb < zero) { if (f < fval[nf - *n]) { fval[nf] = fval[nf - *n]; fval[nf - *n] = f; if (*kopt == nf) { *kopt = nf - *n; } xpt[nf - *n + nfx * xpt_dim1] = stepb; xpt[nf + nfx * xpt_dim1] = stepa; } } bmat[nfx * bmat_dim1 + 1] = -(stepa + stepb) / (stepa * stepb); bmat[nf + nfx * bmat_dim1] = -half / xpt[nf - *n + nfx * xpt_dim1]; bmat[nf - *n + nfx * bmat_dim1] = -bmat[nfx * bmat_dim1 + 1] - bmat[nf + nfx * bmat_dim1]; zmat[nfx * zmat_dim1 + 1] = sqrt(two) / (stepa * stepb); zmat[nf + nfx * zmat_dim1] = sqrt(half) / rhosq; zmat[nf - *n + nfx * zmat_dim1] = -zmat[nfx * zmat_dim1 + 1] - zmat[nf + nfx * zmat_dim1]; } /* Set the off-diagonal second derivatives of the Lagrange functions and */ /* the initial quadratic model. */ } else { ih = ipt * (ipt - 1) / 2 + jpt; zmat[nfx * zmat_dim1 + 1] = recip; zmat[nf + nfx * zmat_dim1] = recip; zmat[ipt + 1 + nfx * zmat_dim1] = -recip; zmat[jpt + 1 + nfx * zmat_dim1] = -recip; temp = xpt[nf + ipt * xpt_dim1] * xpt[nf + jpt * xpt_dim1]; hq[ih] = (fbeg - fval[ipt + 1] - fval[jpt + 1] + f) / temp; } if (nlopt_stop_forced(stop)) return NLOPT_FORCED_STOP; else if (f < stop->minf_max) return NLOPT_MINF_MAX_REACHED; else if (nlopt_stop_evals(stop)) return NLOPT_MAXEVAL_REACHED; else if (nlopt_stop_time(stop)) return NLOPT_MAXTIME_REACHED; if (nf < *npt) { goto L50; } return NLOPT_SUCCESS; } /* prelim_ */ static nlopt_result bobyqb_(int *n, int *npt, double *x, const double *xl, const double *xu, double *rhobeg, double * rhoend, nlopt_stopping *stop, bobyqa_func calfun, void *calfun_data, double *minf, double *xbase, double *xpt, double *fval, double *xopt, double *gopt, double *hq, double *pq, double *bmat, double *zmat, int *ndim, double *sl, double *su, double *xnew, double *xalt, double *d__, double *vlag, double *w) { /* System generated locals */ int xpt_dim1, xpt_offset, bmat_dim1, bmat_offset, zmat_dim1, zmat_offset, i__1, i__2, i__3; double d__1, d__2, d__3, d__4; /* Local variables */ double f; int i__, j, k, ih, jj, nh, ip, jp; double dx; int np; double den, one, ten, dsq, rho, sum, two, diff, half, beta, gisq; int knew; double temp, suma, sumb, bsum, fopt; int kopt, nptm; double zero, curv; int ksav; double gqsq, dist, sumw, sumz, diffa, diffb, diffc, hdiag; int kbase; double alpha, delta, adelt, denom, fsave, bdtol, delsq; int nresc, nfsav; double ratio, dnorm, vquad, pqold, tenth; int itest; double sumpq, scaden; double errbig, cauchy, fracsq, biglsq, densav; double bdtest; double crvmin, frhosq; double distsq; int ntrits; double xoptsq; nlopt_result rc = NLOPT_SUCCESS, rc2; /* The arguments N, NPT, X, XL, XU, RHOBEG, RHOEND, and MAXFUN */ /* are identical to the corresponding arguments in SUBROUTINE BOBYQA. */ /* XBASE holds a shift of origin that should reduce the contributions */ /* from rounding errors to values of the model and Lagrange functions. */ /* XPT is a two-dimensional array that holds the coordinates of the */ /* interpolation points relative to XBASE. */ /* FVAL holds the values of F at the interpolation points. */ /* XOPT is set to the displacement from XBASE of the trust region centre. */ /* GOPT holds the gradient of the quadratic model at XBASE+XOPT. */ /* HQ holds the explicit second derivatives of the quadratic model. */ /* PQ contains the parameters of the implicit second derivatives of the */ /* quadratic model. */ /* BMAT holds the last N columns of H. */ /* ZMAT holds the factorization of the leading NPT by NPT submatrix of H, */ /* this factorization being ZMAT times ZMAT^T, which provides both the */ /* correct rank and positive semi-definiteness. */ /* NDIM is the first dimension of BMAT and has the value NPT+N. */ /* SL and SU hold the differences XL-XBASE and XU-XBASE, respectively. */ /* All the components of every XOPT are going to satisfy the bounds */ /* SL(I) .LEQ. XOPT(I) .LEQ. SU(I), with appropriate equalities when */ /* XOPT is on a constraint boundary. */ /* XNEW is chosen by SUBROUTINE TRSBOX or ALTMOV. Usually XBASE+XNEW is the */ /* vector of variables for the next call of CALFUN. XNEW also satisfies */ /* the SL and SU constraints in the way that has just been mentioned. */ /* XALT is an alternative to XNEW, chosen by ALTMOV, that may replace XNEW */ /* in order to increase the denominator in the updating of UPDATE. */ /* D is reserved for a trial step from XOPT, which is usually XNEW-XOPT. */ /* VLAG contains the values of the Lagrange functions at a new point X. */ /* They are part of a product that requires VLAG to be of length NDIM. */ /* W is a one-dimensional array that is used for working space. Its length */ /* must be at least 3*NDIM = 3*(NPT+N). */ /* Set some constants. */ /* Parameter adjustments */ zmat_dim1 = *npt; zmat_offset = 1 + zmat_dim1; zmat -= zmat_offset; xpt_dim1 = *npt; xpt_offset = 1 + xpt_dim1; xpt -= xpt_offset; --x; --xl; --xu; --xbase; --fval; --xopt; --gopt; --hq; --pq; bmat_dim1 = *ndim; bmat_offset = 1 + bmat_dim1; bmat -= bmat_offset; --sl; --su; --xnew; --xalt; --d__; --vlag; --w; /* Function Body */ half = .5; one = 1.; ten = 10.; tenth = .1; two = 2.; zero = 0.; np = *n + 1; nptm = *npt - np; nh = *n * np / 2; /* The call of PRELIM sets the elements of XBASE, XPT, FVAL, GOPT, HQ, PQ, */ /* BMAT and ZMAT for the first iteration, with the corresponding values of */ /* of NF and KOPT, which are the number of calls of CALFUN so far and the */ /* index of the interpolation point at the trust region centre. Then the */ /* initial XOPT is set too. The branch to label 720 occurs if MAXFUN is */ /* less than NPT. GOPT will be updated if KOPT is different from KBASE. */ rc2 = prelim_(n, npt, &x[1], &xl[1], &xu[1], rhobeg, stop, calfun, calfun_data, &xbase[1], &xpt[xpt_offset], &fval[1], &gopt[1], &hq[1], &pq[1], &bmat[ bmat_offset], &zmat[zmat_offset], ndim, &sl[1], &su[1], &kopt); xoptsq = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { xopt[i__] = xpt[kopt + i__ * xpt_dim1]; /* L10: */ /* Computing 2nd power */ d__1 = xopt[i__]; xoptsq += d__1 * d__1; } fsave = fval[1]; if (rc2 != NLOPT_SUCCESS) { rc = rc2; goto L720; } kbase = 1; /* Complete the settings that are required for the iterative procedure. */ rho = *rhobeg; delta = rho; nresc = stop->nevals; ntrits = 0; diffa = zero; diffb = zero; itest = 0; nfsav = stop->nevals; /* Update GOPT if necessary before the first iteration and after each */ /* call of RESCUE that makes a call of CALFUN. */ L20: if (kopt != kbase) { ih = 0; i__1 = *n; for (j = 1; j <= i__1; ++j) { i__2 = j; for (i__ = 1; i__ <= i__2; ++i__) { ++ih; if (i__ < j) { gopt[j] += hq[ih] * xopt[i__]; } /* L30: */ gopt[i__] += hq[ih] * xopt[j]; } } if (stop->nevals > *npt) { i__2 = *npt; for (k = 1; k <= i__2; ++k) { temp = zero; i__1 = *n; for (j = 1; j <= i__1; ++j) { /* L40: */ temp += xpt[k + j * xpt_dim1] * xopt[j]; } temp = pq[k] * temp; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* L50: */ gopt[i__] += temp * xpt[k + i__ * xpt_dim1]; } } } } /* Generate the next point in the trust region that provides a small value */ /* of the quadratic model subject to the constraints on the variables. */ /* The int NTRITS is set to the number "trust region" iterations that */ /* have occurred since the last "alternative" iteration. If the length */ /* of XNEW-XOPT is less than HALF*RHO, however, then there is a branch to */ /* label 650 or 680 with NTRITS=-1, instead of calculating F at XNEW. */ L60: trsbox_(n, npt, &xpt[xpt_offset], &xopt[1], &gopt[1], &hq[1], &pq[1], &sl[ 1], &su[1], &delta, &xnew[1], &d__[1], &w[1], &w[np], &w[np + *n], &w[np + (*n << 1)], &w[np + *n * 3], &dsq, &crvmin); /* Computing MIN */ d__1 = delta, d__2 = sqrt(dsq); dnorm = MIN2(d__1,d__2); if (dnorm < half * rho) { ntrits = -1; /* Computing 2nd power */ d__1 = ten * rho; distsq = d__1 * d__1; if (stop->nevals <= nfsav + 2) { goto L650; } /* The following choice between labels 650 and 680 depends on whether or */ /* not our work with the current RHO seems to be complete. Either RHO is */ /* decreased or termination occurs if the errors in the quadratic model at */ /* the last three interpolation points compare favourably with predictions */ /* of likely improvements to the model within distance HALF*RHO of XOPT. */ /* Computing MAX */ d__1 = MAX2(diffa,diffb); errbig = MAX2(d__1,diffc); frhosq = rho * .125 * rho; if (crvmin > zero && errbig > frhosq * crvmin) { goto L650; } bdtol = errbig / rho; i__1 = *n; for (j = 1; j <= i__1; ++j) { bdtest = bdtol; if (xnew[j] == sl[j]) { bdtest = w[j]; } if (xnew[j] == su[j]) { bdtest = -w[j]; } if (bdtest < bdtol) { curv = hq[(j + j * j) / 2]; i__2 = *npt; for (k = 1; k <= i__2; ++k) { /* L70: */ /* Computing 2nd power */ d__1 = xpt[k + j * xpt_dim1]; curv += pq[k] * (d__1 * d__1); } bdtest += half * curv * rho; if (bdtest < bdtol) { goto L650; } } /* L80: */ } goto L680; } ++ntrits; /* Severe cancellation is likely to occur if XOPT is too far from XBASE. */ /* If the following test holds, then XBASE is shifted so that XOPT becomes */ /* zero. The appropriate changes are made to BMAT and to the second */ /* derivatives of the current model, beginning with the changes to BMAT */ /* that do not depend on ZMAT. VLAG is used temporarily for working space. */ L90: if (dsq <= xoptsq * .001) { fracsq = xoptsq * .25; sumpq = zero; i__1 = *npt; for (k = 1; k <= i__1; ++k) { sumpq += pq[k]; sum = -half * xoptsq; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { /* L100: */ sum += xpt[k + i__ * xpt_dim1] * xopt[i__]; } w[*npt + k] = sum; temp = fracsq - half * sum; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { w[i__] = bmat[k + i__ * bmat_dim1]; vlag[i__] = sum * xpt[k + i__ * xpt_dim1] + temp * xopt[i__]; ip = *npt + i__; i__3 = i__; for (j = 1; j <= i__3; ++j) { /* L110: */ bmat[ip + j * bmat_dim1] = bmat[ip + j * bmat_dim1] + w[ i__] * vlag[j] + vlag[i__] * w[j]; } } } /* Then the revisions of BMAT that depend on ZMAT are calculated. */ i__3 = nptm; for (jj = 1; jj <= i__3; ++jj) { sumz = zero; sumw = zero; i__2 = *npt; for (k = 1; k <= i__2; ++k) { sumz += zmat[k + jj * zmat_dim1]; vlag[k] = w[*npt + k] * zmat[k + jj * zmat_dim1]; /* L120: */ sumw += vlag[k]; } i__2 = *n; for (j = 1; j <= i__2; ++j) { sum = (fracsq * sumz - half * sumw) * xopt[j]; i__1 = *npt; for (k = 1; k <= i__1; ++k) { /* L130: */ sum += vlag[k] * xpt[k + j * xpt_dim1]; } w[j] = sum; i__1 = *npt; for (k = 1; k <= i__1; ++k) { /* L140: */ bmat[k + j * bmat_dim1] += sum * zmat[k + jj * zmat_dim1]; } } i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { ip = i__ + *npt; temp = w[i__]; i__2 = i__; for (j = 1; j <= i__2; ++j) { /* L150: */ bmat[ip + j * bmat_dim1] += temp * w[j]; } } } /* The following instructions complete the shift, including the changes */ /* to the second derivative parameters of the quadratic model. */ ih = 0; i__2 = *n; for (j = 1; j <= i__2; ++j) { w[j] = -half * sumpq * xopt[j]; i__1 = *npt; for (k = 1; k <= i__1; ++k) { w[j] += pq[k] * xpt[k + j * xpt_dim1]; /* L160: */ xpt[k + j * xpt_dim1] -= xopt[j]; } i__1 = j; for (i__ = 1; i__ <= i__1; ++i__) { ++ih; hq[ih] = hq[ih] + w[i__] * xopt[j] + xopt[i__] * w[j]; /* L170: */ bmat[*npt + i__ + j * bmat_dim1] = bmat[*npt + j + i__ * bmat_dim1]; } } i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { xbase[i__] += xopt[i__]; xnew[i__] -= xopt[i__]; sl[i__] -= xopt[i__]; su[i__] -= xopt[i__]; /* L180: */ xopt[i__] = zero; } xoptsq = zero; } if (ntrits == 0) { goto L210; } goto L230; /* XBASE is also moved to XOPT by a call of RESCUE. This calculation is */ /* more expensive than the previous shift, because new matrices BMAT and */ /* ZMAT are generated from scratch, which may include the replacement of */ /* interpolation points whose positions seem to be causing near linear */ /* dependence in the interpolation conditions. Therefore RESCUE is called */ /* only if rounding errors have reduced by at least a factor of two the */ /* denominator of the formula for updating the H matrix. It provides a */ /* useful safeguard, but is not invoked in most applications of BOBYQA. */ L190: nfsav = stop->nevals; kbase = kopt; rc2 = rescue_(n, npt, &xl[1], &xu[1], stop, calfun, calfun_data, &xbase[1], &xpt[xpt_offset], &fval[1], &xopt[1], &gopt[1], &hq[1], &pq[1], &bmat[bmat_offset], &zmat[zmat_offset], ndim, &sl[1], &su[1], &delta, &kopt, &vlag[1], &w[1], &w[*n + np], &w[*ndim + np]); /* XOPT is updated now in case the branch below to label 720 is taken. */ /* Any updating of GOPT occurs after the branch below to label 20, which */ /* leads to a trust region iteration as does the branch to label 60. */ xoptsq = zero; if (kopt != kbase) { i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { xopt[i__] = xpt[kopt + i__ * xpt_dim1]; /* L200: */ /* Computing 2nd power */ d__1 = xopt[i__]; xoptsq += d__1 * d__1; } } if (rc2 != NLOPT_SUCCESS) { rc = rc2; goto L720; } nresc = stop->nevals; if (nfsav < stop->nevals) { nfsav = stop->nevals; goto L20; } if (ntrits > 0) { goto L60; } /* Pick two alternative vectors of variables, relative to XBASE, that */ /* are suitable as new positions of the KNEW-th interpolation point. */ /* Firstly, XNEW is set to the point on a line through XOPT and another */ /* interpolation point that minimizes the predicted value of the next */ /* denominator, subject to ||XNEW - XOPT|| .LEQ. ADELT and to the SL */ /* and SU bounds. Secondly, XALT is set to the best feasible point on */ /* a constrained version of the Cauchy step of the KNEW-th Lagrange */ /* function, the corresponding value of the square of this function */ /* being returned in CAUCHY. The choice between these alternatives is */ /* going to be made when the denominator is calculated. */ L210: altmov_(n, npt, &xpt[xpt_offset], &xopt[1], &bmat[bmat_offset], &zmat[ zmat_offset], ndim, &sl[1], &su[1], &kopt, &knew, &adelt, &xnew[1] , &xalt[1], &alpha, &cauchy, &w[1], &w[np], &w[*ndim + 1]); i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* L220: */ d__[i__] = xnew[i__] - xopt[i__]; } /* Calculate VLAG and BETA for the current choice of D. The scalar */ /* product of D with XPT(K,.) is going to be held in W(NPT+K) for */ /* use when VQUAD is calculated. */ L230: i__1 = *npt; for (k = 1; k <= i__1; ++k) { suma = zero; sumb = zero; sum = zero; i__2 = *n; for (j = 1; j <= i__2; ++j) { suma += xpt[k + j * xpt_dim1] * d__[j]; sumb += xpt[k + j * xpt_dim1] * xopt[j]; /* L240: */ sum += bmat[k + j * bmat_dim1] * d__[j]; } w[k] = suma * (half * suma + sumb); vlag[k] = sum; /* L250: */ w[*npt + k] = suma; } beta = zero; i__1 = nptm; for (jj = 1; jj <= i__1; ++jj) { sum = zero; i__2 = *npt; for (k = 1; k <= i__2; ++k) { /* L260: */ sum += zmat[k + jj * zmat_dim1] * w[k]; } beta -= sum * sum; i__2 = *npt; for (k = 1; k <= i__2; ++k) { /* L270: */ vlag[k] += sum * zmat[k + jj * zmat_dim1]; } } dsq = zero; bsum = zero; dx = zero; i__2 = *n; for (j = 1; j <= i__2; ++j) { /* Computing 2nd power */ d__1 = d__[j]; dsq += d__1 * d__1; sum = zero; i__1 = *npt; for (k = 1; k <= i__1; ++k) { /* L280: */ sum += w[k] * bmat[k + j * bmat_dim1]; } bsum += sum * d__[j]; jp = *npt + j; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* L290: */ sum += bmat[jp + i__ * bmat_dim1] * d__[i__]; } vlag[jp] = sum; bsum += sum * d__[j]; /* L300: */ dx += d__[j] * xopt[j]; } beta = dx * dx + dsq * (xoptsq + dx + dx + half * dsq) + beta - bsum; vlag[kopt] += one; /* If NTRITS is zero, the denominator may be increased by replacing */ /* the step D of ALTMOV by a Cauchy step. Then RESCUE may be called if */ /* rounding errors have damaged the chosen denominator. */ if (ntrits == 0) { /* Computing 2nd power */ d__1 = vlag[knew]; denom = d__1 * d__1 + alpha * beta; if (denom < cauchy && cauchy > zero) { i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { xnew[i__] = xalt[i__]; /* L310: */ d__[i__] = xnew[i__] - xopt[i__]; } cauchy = zero; goto L230; } /* Computing 2nd power */ d__1 = vlag[knew]; if (denom <= half * (d__1 * d__1)) { if (stop->nevals > nresc) { goto L190; } /* Return from BOBYQA because of much cancellation in a denominator. */ rc = NLOPT_ROUNDOFF_LIMITED; goto L720; } /* Alternatively, if NTRITS is positive, then set KNEW to the index of */ /* the next interpolation point to be deleted to make room for a trust */ /* region step. Again RESCUE may be called if rounding errors have damaged */ /* the chosen denominator, which is the reason for attempting to select */ /* KNEW before calculating the next value of the objective function. */ } else { delsq = delta * delta; scaden = zero; biglsq = zero; knew = 0; i__2 = *npt; for (k = 1; k <= i__2; ++k) { if (k == kopt) { goto L350; } hdiag = zero; i__1 = nptm; for (jj = 1; jj <= i__1; ++jj) { /* L330: */ /* Computing 2nd power */ d__1 = zmat[k + jj * zmat_dim1]; hdiag += d__1 * d__1; } /* Computing 2nd power */ d__1 = vlag[k]; den = beta * hdiag + d__1 * d__1; distsq = zero; i__1 = *n; for (j = 1; j <= i__1; ++j) { /* L340: */ /* Computing 2nd power */ d__1 = xpt[k + j * xpt_dim1] - xopt[j]; distsq += d__1 * d__1; } /* Computing MAX */ /* Computing 2nd power */ d__3 = distsq / delsq; d__1 = one, d__2 = d__3 * d__3; temp = MAX2(d__1,d__2); if (temp * den > scaden) { scaden = temp * den; knew = k; denom = den; } /* Computing MAX */ /* Computing 2nd power */ d__3 = vlag[k]; d__1 = biglsq, d__2 = temp * (d__3 * d__3); biglsq = MAX2(d__1,d__2); L350: ; } if (scaden <= half * biglsq) { if (stop->nevals > nresc) { goto L190; } /* Return from BOBYQA because of much cancellation in a denominator. */ rc = NLOPT_ROUNDOFF_LIMITED; goto L720; } } /* Put the variables for the next calculation of the objective function */ /* in XNEW, with any adjustments for the bounds. */ /* Calculate the value of the objective function at XBASE+XNEW, unless */ /* the limit on the number of calculations of F has been reached. */ L360: i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { /* Computing MIN */ /* Computing MAX */ d__3 = xl[i__], d__4 = xbase[i__] + xnew[i__]; d__1 = MAX2(d__3,d__4), d__2 = xu[i__]; x[i__] = MIN2(d__1,d__2); if (xnew[i__] == sl[i__]) { x[i__] = xl[i__]; } if (xnew[i__] == su[i__]) { x[i__] = xu[i__]; } /* L380: */ } if (nlopt_stop_forced(stop)) rc = NLOPT_FORCED_STOP; else if (nlopt_stop_evals(stop)) rc = NLOPT_MAXEVAL_REACHED; else if (nlopt_stop_time(stop)) rc = NLOPT_MAXTIME_REACHED; if (rc != NLOPT_SUCCESS) goto L720; stop->nevals++; f = calfun(*n, &x[1], calfun_data); if (ntrits == -1) { fsave = f; rc = NLOPT_XTOL_REACHED; if (fsave < fval[kopt]) { *minf = f; return rc; } goto L720; } if (f < stop->minf_max) { *minf = f; return NLOPT_MINF_MAX_REACHED; } /* Use the quadratic model to predict the change in F due to the step D, */ /* and set DIFF to the error of this prediction. */ fopt = fval[kopt]; vquad = zero; ih = 0; i__2 = *n; for (j = 1; j <= i__2; ++j) { vquad += d__[j] * gopt[j]; i__1 = j; for (i__ = 1; i__ <= i__1; ++i__) { ++ih; temp = d__[i__] * d__[j]; if (i__ == j) { temp = half * temp; } /* L410: */ vquad += hq[ih] * temp; } } i__1 = *npt; for (k = 1; k <= i__1; ++k) { /* L420: */ /* Computing 2nd power */ d__1 = w[*npt + k]; vquad += half * pq[k] * (d__1 * d__1); } diff = f - fopt - vquad; diffc = diffb; diffb = diffa; diffa = fabs(diff); if (dnorm > rho) { nfsav = stop->nevals; } /* Pick the next value of DELTA after a trust region step. */ if (ntrits > 0) { if (vquad >= zero) { /* Return from BOBYQA because a trust region step has failed to reduce Q. */ rc = NLOPT_ROUNDOFF_LIMITED; /* or FTOL_REACHED? */ goto L720; } ratio = (f - fopt) / vquad; if (ratio <= tenth) { /* Computing MIN */ d__1 = half * delta; delta = MIN2(d__1,dnorm); } else if (ratio <= .7) { /* Computing MAX */ d__1 = half * delta; delta = MAX2(d__1,dnorm); } else { /* Computing MAX */ d__1 = half * delta, d__2 = dnorm + dnorm; delta = MAX2(d__1,d__2); } if (delta <= rho * 1.5) { delta = rho; } /* Recalculate KNEW and DENOM if the new F is less than FOPT. */ if (f < fopt) { ksav = knew; densav = denom; delsq = delta * delta; scaden = zero; biglsq = zero; knew = 0; i__1 = *npt; for (k = 1; k <= i__1; ++k) { hdiag = zero; i__2 = nptm; for (jj = 1; jj <= i__2; ++jj) { /* L440: */ /* Computing 2nd power */ d__1 = zmat[k + jj * zmat_dim1]; hdiag += d__1 * d__1; } /* Computing 2nd power */ d__1 = vlag[k]; den = beta * hdiag + d__1 * d__1; distsq = zero; i__2 = *n; for (j = 1; j <= i__2; ++j) { /* L450: */ /* Computing 2nd power */ d__1 = xpt[k + j * xpt_dim1] - xnew[j]; distsq += d__1 * d__1; } /* Computing MAX */ /* Computing 2nd power */ d__3 = distsq / delsq; d__1 = one, d__2 = d__3 * d__3; temp = MAX2(d__1,d__2); if (temp * den > scaden) { scaden = temp * den; knew = k; denom = den; } /* L460: */ /* Computing MAX */ /* Computing 2nd power */ d__3 = vlag[k]; d__1 = biglsq, d__2 = temp * (d__3 * d__3); biglsq = MAX2(d__1,d__2); } if (scaden <= half * biglsq) { knew = ksav; denom = densav; } } } /* Update BMAT and ZMAT, so that the KNEW-th interpolation point can be */ /* moved. Also update the second derivative terms of the model. */ update_(n, npt, &bmat[bmat_offset], &zmat[zmat_offset], ndim, &vlag[1], & beta, &denom, &knew, &w[1]); ih = 0; pqold = pq[knew]; pq[knew] = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp = pqold * xpt[knew + i__ * xpt_dim1]; i__2 = i__; for (j = 1; j <= i__2; ++j) { ++ih; /* L470: */ hq[ih] += temp * xpt[knew + j * xpt_dim1]; } } i__2 = nptm; for (jj = 1; jj <= i__2; ++jj) { temp = diff * zmat[knew + jj * zmat_dim1]; i__1 = *npt; for (k = 1; k <= i__1; ++k) { /* L480: */ pq[k] += temp * zmat[k + jj * zmat_dim1]; } } /* Include the new interpolation point, and make the changes to GOPT at */ /* the old XOPT that are caused by the updating of the quadratic model. */ fval[knew] = f; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { xpt[knew + i__ * xpt_dim1] = xnew[i__]; /* L490: */ w[i__] = bmat[knew + i__ * bmat_dim1]; } i__1 = *npt; for (k = 1; k <= i__1; ++k) { suma = zero; i__2 = nptm; for (jj = 1; jj <= i__2; ++jj) { /* L500: */ suma += zmat[knew + jj * zmat_dim1] * zmat[k + jj * zmat_dim1]; } if (nlopt_isinf(suma)) { /* SGJ: detect singularity here (happend if we run for too many iterations) ... is there another way to recover? */ rc = NLOPT_ROUNDOFF_LIMITED; goto L720; } sumb = zero; i__2 = *n; for (j = 1; j <= i__2; ++j) { /* L510: */ sumb += xpt[k + j * xpt_dim1] * xopt[j]; } temp = suma * sumb; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { /* L520: */ w[i__] += temp * xpt[k + i__ * xpt_dim1]; } } i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { /* L530: */ gopt[i__] += diff * w[i__]; } /* Update XOPT, GOPT and KOPT if the new calculated F is less than FOPT. */ if (f < fopt) { kopt = knew; xoptsq = zero; ih = 0; i__2 = *n; for (j = 1; j <= i__2; ++j) { xopt[j] = xnew[j]; /* Computing 2nd power */ d__1 = xopt[j]; xoptsq += d__1 * d__1; i__1 = j; for (i__ = 1; i__ <= i__1; ++i__) { ++ih; if (i__ < j) { gopt[j] += hq[ih] * d__[i__]; } /* L540: */ gopt[i__] += hq[ih] * d__[j]; } } i__1 = *npt; for (k = 1; k <= i__1; ++k) { temp = zero; i__2 = *n; for (j = 1; j <= i__2; ++j) { /* L550: */ temp += xpt[k + j * xpt_dim1] * d__[j]; } temp = pq[k] * temp; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { /* L560: */ gopt[i__] += temp * xpt[k + i__ * xpt_dim1]; } } if (nlopt_stop_ftol(stop, f, fopt)) { rc = NLOPT_FTOL_REACHED; goto L720; } } /* Calculate the parameters of the least Frobenius norm interpolant to */ /* the current data, the gradient of this interpolant at XOPT being put */ /* into VLAG(NPT+I), I=1,2,...,N. */ if (ntrits > 0) { i__2 = *npt; for (k = 1; k <= i__2; ++k) { vlag[k] = fval[k] - fval[kopt]; /* L570: */ w[k] = zero; } i__2 = nptm; for (j = 1; j <= i__2; ++j) { sum = zero; i__1 = *npt; for (k = 1; k <= i__1; ++k) { /* L580: */ sum += zmat[k + j * zmat_dim1] * vlag[k]; } i__1 = *npt; for (k = 1; k <= i__1; ++k) { /* L590: */ w[k] += sum * zmat[k + j * zmat_dim1]; } } i__1 = *npt; for (k = 1; k <= i__1; ++k) { sum = zero; i__2 = *n; for (j = 1; j <= i__2; ++j) { /* L600: */ sum += xpt[k + j * xpt_dim1] * xopt[j]; } w[k + *npt] = w[k]; /* L610: */ w[k] = sum * w[k]; } gqsq = zero; gisq = zero; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { sum = zero; i__2 = *npt; for (k = 1; k <= i__2; ++k) { /* L620: */ sum = sum + bmat[k + i__ * bmat_dim1] * vlag[k] + xpt[k + i__ * xpt_dim1] * w[k]; } if (xopt[i__] == sl[i__]) { /* Computing MIN */ d__2 = zero, d__3 = gopt[i__]; /* Computing 2nd power */ d__1 = MIN2(d__2,d__3); gqsq += d__1 * d__1; /* Computing 2nd power */ d__1 = MIN2(zero,sum); gisq += d__1 * d__1; } else if (xopt[i__] == su[i__]) { /* Computing MAX */ d__2 = zero, d__3 = gopt[i__]; /* Computing 2nd power */ d__1 = MAX2(d__2,d__3); gqsq += d__1 * d__1; /* Computing 2nd power */ d__1 = MAX2(zero,sum); gisq += d__1 * d__1; } else { /* Computing 2nd power */ d__1 = gopt[i__]; gqsq += d__1 * d__1; gisq += sum * sum; } /* L630: */ vlag[*npt + i__] = sum; } /* Test whether to replace the new quadratic model by the least Frobenius */ /* norm interpolant, making the replacement if the test is satisfied. */ ++itest; if (gqsq < ten * gisq) { itest = 0; } if (itest >= 3) { i__1 = MAX2(*npt,nh); for (i__ = 1; i__ <= i__1; ++i__) { if (i__ <= *n) { gopt[i__] = vlag[*npt + i__]; } if (i__ <= *npt) { pq[i__] = w[*npt + i__]; } if (i__ <= nh) { hq[i__] = zero; } itest = 0; /* L640: */ } } } /* If a trust region step has provided a sufficient decrease in F, then */ /* branch for another trust region calculation. The case NTRITS=0 occurs */ /* when the new interpolation point was reached by an alternative step. */ if (ntrits == 0) { goto L60; } if (f <= fopt + tenth * vquad) { goto L60; } /* Alternatively, find out if the interpolation points are close enough */ /* to the best point so far. */ /* Computing MAX */ /* Computing 2nd power */ d__3 = two * delta; /* Computing 2nd power */ d__4 = ten * rho; d__1 = d__3 * d__3, d__2 = d__4 * d__4; distsq = MAX2(d__1,d__2); L650: knew = 0; i__1 = *npt; for (k = 1; k <= i__1; ++k) { sum = zero; i__2 = *n; for (j = 1; j <= i__2; ++j) { /* L660: */ /* Computing 2nd power */ d__1 = xpt[k + j * xpt_dim1] - xopt[j]; sum += d__1 * d__1; } if (sum > distsq) { knew = k; distsq = sum; } /* L670: */ } /* If KNEW is positive, then ALTMOV finds alternative new positions for */ /* the KNEW-th interpolation point within distance ADELT of XOPT. It is */ /* reached via label 90. Otherwise, there is a branch to label 60 for */ /* another trust region iteration, unless the calculations with the */ /* current RHO are complete. */ if (knew > 0) { dist = sqrt(distsq); if (ntrits == -1) { /* Computing MIN */ d__1 = tenth * delta, d__2 = half * dist; delta = MIN2(d__1,d__2); if (delta <= rho * 1.5) { delta = rho; } } ntrits = 0; /* Computing MAX */ /* Computing MIN */ d__2 = tenth * dist; d__1 = MIN2(d__2,delta); adelt = MAX2(d__1,rho); dsq = adelt * adelt; goto L90; } if (ntrits == -1) { goto L680; } if (ratio > zero) { goto L60; } if (MAX2(delta,dnorm) > rho) { goto L60; } /* The calculations with the current value of RHO are complete. Pick the */ /* next values of RHO and DELTA. */ L680: if (rho > *rhoend) { delta = half * rho; ratio = rho / *rhoend; if (ratio <= 16.) { rho = *rhoend; } else if (ratio <= 250.) { rho = sqrt(ratio) * *rhoend; } else { rho = tenth * rho; } delta = MAX2(delta,rho); ntrits = 0; nfsav = stop->nevals; goto L60; } /* Return from the calculation, after another Newton-Raphson step, if */ /* it is too short to have been tried before. */ if (ntrits == -1) { goto L360; } L720: /* originally: if (fval[kopt] <= fsave) -- changed by SGJ, since this seems like a slight optimization to not update x[] unnecessarily, at the expense of possibly not returning the best x[] found so far if the algorithm is stopped suddenly (e.g. runs out of time) ... it seems safer to execute this unconditionally, and the efficiency loss seems negligible. */ { i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* Computing MIN */ /* Computing MAX */ d__3 = xl[i__], d__4 = xbase[i__] + xopt[i__]; d__1 = MAX2(d__3,d__4), d__2 = xu[i__]; x[i__] = MIN2(d__1,d__2); if (xopt[i__] == sl[i__]) { x[i__] = xl[i__]; } if (xopt[i__] == su[i__]) { x[i__] = xu[i__]; } /* L730: */ } f = fval[kopt]; } *minf = f; return rc; } /* bobyqb_ */ /**************************************************************************/ #define U(n) ((unsigned) (n)) typedef struct { double *s, *xs; nlopt_func f; void *f_data; } rescale_fun_data; static double rescale_fun(int n, const double *x, void *d_) { rescale_fun_data *d = (rescale_fun_data*) d_; nlopt_unscale(U(n), d->s, x, d->xs); return d->f(U(n), d->xs, NULL, d->f_data); } nlopt_result bobyqa(int n, int npt, double *x, const double *xl, const double *xu, const double *dx, nlopt_stopping *stop, double *minf, nlopt_func f, void *f_data) { /* System generated locals */ int i__1; double d__1, d__2; /* Local variables */ int j, id, np, iw, igo, ihq, ixb, ixa, ifv, isl, jsl, ipq, ivl, ixn, ixo, ixp, isu, jsu, ndim; double temp, zero; int ibmat, izmat; double rhobeg, rhoend; double *w0 = NULL, *w; nlopt_result ret; double *s = NULL, *sxl = NULL, *sxu = NULL, *xs = NULL; rescale_fun_data calfun_data; /* SGJ 2010: rescale parameters to make the initial step sizes dx equal in all directions */ s = nlopt_compute_rescaling(U(n), dx); if (!s) return NLOPT_OUT_OF_MEMORY; /* this statement must go before goto done, so that --x occurs */ nlopt_rescale(U(n), s, x, x); --x; xs = (double *) malloc(sizeof(double) * (U(n))); sxl = nlopt_new_rescaled(U(n), s, xl); if (!sxl) { ret = NLOPT_OUT_OF_MEMORY; goto done; } xl = sxl; sxu = nlopt_new_rescaled(U(n), s, xu); if (!sxu) { ret = NLOPT_OUT_OF_MEMORY; goto done; } xu = sxu; nlopt_reorder_bounds(n, sxl, sxu); rhobeg = fabs(dx[0] / s[0]); /* equals all other dx[i] after rescaling */ calfun_data.s = s; calfun_data.xs = xs; calfun_data.f = f; calfun_data.f_data = f_data; /* SGJ, 2009: compute rhoend from NLopt stop info */ rhoend = stop->xtol_rel * (rhobeg); for (j = 0; j < n; ++j) if (rhoend < stop->xtol_abs[j] / fabs(s[j])) rhoend = stop->xtol_abs[j] / fabs(s[j]); /* This subroutine seeks the least value of a function of many variables, */ /* by applying a trust region method that forms quadratic models by */ /* interpolation. There is usually some freedom in the interpolation */ /* conditions, which is taken up by minimizing the Frobenius norm of */ /* the change to the second derivative of the model, beginning with the */ /* zero matrix. The values of the variables are constrained by upper and */ /* lower bounds. The arguments of the subroutine are as follows. */ /* N must be set to the number of variables and must be at least two. */ /* NPT is the number of interpolation conditions. Its value must be in */ /* the interval [N+2,(N+1)(N+2)/2]. Choices that exceed 2*N+1 are not */ /* recommended. */ /* Initial values of the variables must be set in X(1),X(2),...,X(N). They */ /* will be changed to the values that give the least calculated F. */ /* For I=1,2,...,N, XL(I) and XU(I) must provide the lower and upper */ /* bounds, respectively, on X(I). The construction of quadratic models */ /* requires XL(I) to be strictly less than XU(I) for each I. Further, */ /* the contribution to a model from changes to the I-th variable is */ /* damaged severely by rounding errors if XU(I)-XL(I) is too small. */ /* RHOBEG and RHOEND must be set to the initial and final values of a trust */ /* region radius, so both must be positive with RHOEND no greater than */ /* RHOBEG. Typically, RHOBEG should be about one tenth of the greatest */ /* expected change to a variable, while RHOEND should indicate the */ /* accuracy that is required in the final values of the variables. An */ /* error return occurs if any of the differences XU(I)-XL(I), I=1,...,N, */ /* is less than 2*RHOBEG. */ /* MAXFUN must be set to an upper bound on the number of calls of CALFUN. */ /* The array W will be used for working space. Its length must be at least */ /* (NPT+5)*(NPT+N)+3*N*(N+5)/2. */ /* SUBROUTINE CALFUN (N,X,F) has to be provided by the user. It must set */ /* F to the value of the objective function for the current values of the */ /* variables X(1),X(2),...,X(N), which are generated automatically in a */ /* way that satisfies the bounds given in XL and XU. */ /* Return if the value of NPT is unacceptable. */ /* Parameter adjustments */ --xu; --xl; /* Function Body */ np = n + 1; if (npt < n + 2 || npt > (n + 2) * np / 2) { /* Return from BOBYQA because NPT is not in the required interval */ ret = NLOPT_INVALID_ARGS; goto done; } /* Partition the working space array, so that different parts of it can */ /* be treated separately during the calculation of BOBYQB. The partition */ /* requires the first (NPT+2)*(NPT+N)+3*N*(N+5)/2 elements of W plus the */ /* space that is taken by the last array in the argument list of BOBYQB. */ ndim = npt + n; ixb = 1; ixp = ixb + n; ifv = ixp + n * npt; ixo = ifv + npt; igo = ixo + n; ihq = igo + n; ipq = ihq + n * np / 2; ibmat = ipq + npt; izmat = ibmat + ndim * n; isl = izmat + npt * (npt - np); isu = isl + n; ixn = isu + n; ixa = ixn + n; id = ixa + n; ivl = id + n; iw = ivl + ndim; w0 = (double *) malloc(sizeof(double) * U((npt+5)*(npt+n)+3*n*(n+5)/2)); if (!w0) { ret = NLOPT_OUT_OF_MEMORY; goto done; } w = w0 - 1; /* Return if there is insufficient space between the bounds. Modify the */ /* initial X if necessary in order to avoid conflicts between the bounds */ /* and the construction of the first quadratic model. The lower and upper */ /* bounds on moves from the updated X are set now, in the ISL and ISU */ /* partitions of W, in order to provide useful and exact information about */ /* components of X that become within distance RHOBEG from their bounds. */ zero = 0.; i__1 = n; for (j = 1; j <= i__1; ++j) { temp = xu[j] - xl[j]; if (temp < rhobeg + rhobeg) { /* Return from BOBYQA because one of the differences XU(I)-XL(I)s is less than 2*RHOBEG. */ ret = NLOPT_INVALID_ARGS; goto done; } jsl = isl + j - 1; jsu = jsl + n; w[jsl] = xl[j] - x[j]; w[jsu] = xu[j] - x[j]; if (w[jsl] >= -(rhobeg)) { if (w[jsl] >= zero) { x[j] = xl[j]; w[jsl] = zero; w[jsu] = temp; } else { x[j] = xl[j] + rhobeg; w[jsl] = -(rhobeg); /* Computing MAX */ d__1 = xu[j] - x[j]; w[jsu] = MAX2(d__1,rhobeg); } } else if (w[jsu] <= rhobeg) { if (w[jsu] <= zero) { x[j] = xu[j]; w[jsl] = -temp; w[jsu] = zero; } else { x[j] = xu[j] - rhobeg; /* Computing MIN */ d__1 = xl[j] - x[j], d__2 = -(rhobeg); w[jsl] = MIN2(d__1,d__2); w[jsu] = rhobeg; } } /* L30: */ } /* Make the call of BOBYQB. */ ret = bobyqb_(&n, &npt, &x[1], &xl[1], &xu[1], &rhobeg, &rhoend, stop, rescale_fun, &calfun_data, minf, &w[ixb], &w[ixp], &w[ifv], &w[ixo], &w[igo], &w[ihq], &w[ipq], &w[ibmat], &w[izmat], &ndim, &w[isl], &w[isu], &w[ixn], &w[ixa], &w[id], &w[ivl], &w[iw]); done: free(w0); free(sxl); free(sxu); free(xs); ++x; nlopt_unscale(U(n), s, x, x); free(s); return ret; } /* bobyqa_ */ nloptr/src/nlopt_src/bobyqa/README.orig0000644000176000001440000000737013263734242017523 0ustar ripleyusers=========================================================================== On 8/13/09 added the paper on BOBYQA =========================================================================== For simplicity, the Makefi;e has been replaced by a one-line compile script "comp" which needs to be adjusted if the compiler name is not f77. All Fortran files are in bobyla.f. Compiling and running bobyqa should produce results similar to those in RESULTS. Hans Mittelmann, Jan 2009 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The Fortran version of BOBYQA is attached. Its purpose is to seek the least value of a function F of several variables, when derivatives are not available, where F is specified by the user through a subroutine called CALFUN. The name BOBYQA denotes Bound Approximation BY Quadratic Approximation, the constraints being lower and upper bounds on every variable, which can be set to huge values for unconstrained variables. The algorithm is intended to change the variables to values that are close to a local minimum of F. The user, however, should assume responsibility for finding out if the calculations are satisfactory, by considering carefully the values of F that occur. The BOBYQA software has been developed from the method of the paper "The NEWUOA software for unconstrained minimization without derivatives", in Large-Scale Nonlinear Optimization, editors G. Di Pillo and M. Roma, Springer (2006), pages 255-297. A report that describes the details of the development is going to be written soon. The attachments in sequence are a suitable Makefile, followed by a main program and a CALFUN routine for the "Invdist2" problem, in order to provide an example for testing. Then BOBYQA and its six auxiliary routines, namely BOBYQB, ALTMOV, PRELIM, RESCUE, TRSBOX and UPDATE, are given. Finally, the computed output that the author obtained for the Invdist2 problems is listed. In addition to providing CALFUN, an initial vector of variables and the lower and upper bounds, the user has to set the values of the parameters RHOBEG, RHOEND and NPT. After scaling the individual variables if necessary, so that the magnitudes of their expected changes are similar, RHOBEG is the initial steplength for changes to the variables, a reasonable choice being the mesh size of a coarse grid search. Further, RHOEND should be suitable for a search on a very fine grid. Typically, the software calculates a vector of variables that is within distance 10*RHOEND of a local minimum. Another consideration is that every trial vector of variables is forced to satisfy the lower and upper bounds, but there has to be room to make a search in all directions. Therefore an error return occurs if the difference between the bounds on any variable is less than 2*RHOBEG. The parameter NPT specifies the number of interpolation conditions on each quadratic model, the value NPT=2*N+1 being recommended for a start, where N is the number of variables. It is often worthwhile to try other choices too, but much larger values tend to be inefficient, because the amount of routine work of each iteration is of magnitude NPT**2, and because the achievement of adequate accuracy in some matrix calculations becomes more difficult. Some excellent numerical results have been found in the case NPT=N+6 even with more than 100 variables. The way of calling BOBYQA should be clear from the Invdist2 examples and from the comments near the beginning of SUBROUTINE BOBYQA. There are no restrictions on or charges for the use of the software. I hope that the time and effort I have spent on developing the package will be helpful to much research and to many applications. January 5th, 2009 M.J.D. Powell (mjdp@cam.ac.uk) nloptr/src/nlopt_src/bobyqa/bobyqa.h0000644000176000001440000000045713355070635017332 0ustar ripleyusers#ifndef BOBYQA_H #define BOBYQA_H 1 #include "nlopt-util.h" #include "nlopt.h" extern nlopt_result bobyqa(int n, int npt, double *x, const double *lb, const double *ub, const double *dx, nlopt_stopping *stop, double *minf, nlopt_func f, void *f_data); #endif /* BOBYQA_H */ nloptr/src/nlopt_src/auglag/0000755000176000001440000000000013263734242015660 5ustar ripleyusersnloptr/src/nlopt_src/auglag/Makefile.am0000644000176000001440000000023013263734242017707 0ustar ripleyusersAM_CPPFLAGS = -I$(top_srcdir)/util -I$(top_srcdir)/api noinst_LTLIBRARIES = libauglag.la libauglag_la_SOURCES = auglag.c auglag.h EXTRA_DIST = README nloptr/src/nlopt_src/auglag/Makefile.in0000644000176000001440000004351113355070635017732 0ustar ripleyusers# Makefile.in generated by automake 1.14 from Makefile.am. # @configure_input@ # Copyright (C) 1994-2013 Free Software Foundation, Inc. # This Makefile.in is free software; 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I used the algorithm description (but no source code) from the outline in the paper: E. G. Birgin and J. M. Martinez, "Improving ultimate convergence of an augmented Lagrangian method," Optimization Methods and Software vol. 23, no. 2, p. 177-195 (2008). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.72.6121 (The authors of this paper have their own free-software implementation of this idea and its variations, in the TANGO project: http://www.ime.usp.br/~egbirgin/tango/ ....I did *not* use any code from TANGO here, or even look at the TANGO code. TANGO is GPLed, and I'm trying to keep NLopt at most LGPLed.) The code in this directory is under the same MIT license as the rest of my code in NLopt (see ../COPYRIGHT). Steven G. Johnson November 2008 nloptr/src/nlopt_src/auglag/auglag.h0000644000176000001440000000331313355070635017272 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #ifndef AUGLAG_H #define AUGLAG_H #include "nlopt.h" #include "nlopt-util.h" #ifdef __cplusplus extern "C" { #endif /* __cplusplus */ extern int auglag_verbose; nlopt_result auglag_minimize(int n, nlopt_func f, void *f_data, int m, nlopt_constraint *fc, int p, nlopt_constraint *h, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, nlopt_stopping *stop, nlopt_opt sub_opt, int sub_has_fc); #ifdef __cplusplus } /* extern "C" */ #endif /* __cplusplus */ #endif nloptr/src/nlopt_src/auglag/auglag.c0000644000176000001440000002224713355070635017274 0ustar ripleyusers#include #include #include #include #include "auglag.h" int auglag_verbose = 0; #define MIN(a,b) ((a) < (b) ? (a) : (b)) #define MAX(a,b) ((a) > (b) ? (a) : (b)) /***************************************************************************/ typedef struct { nlopt_func f; void *f_data; int m, mm; nlopt_constraint *fc; int p, pp; nlopt_constraint *h; double rho, *lambda, *mu; double *restmp, *gradtmp; nlopt_stopping *stop; } auglag_data; /* the augmented lagrangian objective function */ static double auglag(unsigned n, const double *x, double *grad, void *data) { auglag_data *d = (auglag_data *) data; double *gradtmp = grad ? d->gradtmp : NULL; double *restmp = d->restmp; double rho = d->rho; const double *lambda = d->lambda, *mu = d->mu; double L; int i, ii; unsigned j, k; L = d->f(n, x, grad, d->f_data); d->stop->nevals++; if (nlopt_stop_forced(d->stop)) return L; for (ii = i = 0; i < d->p; ++i) { nlopt_eval_constraint(restmp, gradtmp, d->h + i, n, x); if (nlopt_stop_forced(d->stop)) return L; for (k = 0; k < d->h[i].m; ++k) { double h = restmp[k] + lambda[ii++] / rho; L += 0.5 * rho * h*h; if (grad) for (j = 0; j < n; ++j) grad[j] += (rho * h) * gradtmp[k*n + j]; } } for (ii = i = 0; i < d->m; ++i) { nlopt_eval_constraint(restmp, gradtmp, d->fc + i, n, x); if (nlopt_stop_forced(d->stop)) return L; for (k = 0; k < d->fc[i].m; ++k) { double fc = restmp[k] + mu[ii++] / rho; if (fc > 0) { L += 0.5 * rho * fc*fc; if (grad) for (j = 0; j < n; ++j) grad[j] += (rho * fc) * gradtmp[k*n + j]; } } } return L; } /***************************************************************************/ nlopt_result auglag_minimize(int n, nlopt_func f, void *f_data, int m, nlopt_constraint *fc, int p, nlopt_constraint *h, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, nlopt_stopping *stop, nlopt_opt sub_opt, int sub_has_fc) { auglag_data d; nlopt_result ret = NLOPT_SUCCESS; double ICM = HUGE_VAL, minf_penalty = HUGE_VAL, penalty; double *xcur = NULL, fcur; int i, ii, k, feasible, minf_feasible = 0; int auglag_iters = 0; int max_constraint_dim; /* magic parameters from Birgin & Martinez */ const double tau = 0.5, gam = 10; const double lam_min = -1e20, lam_max = 1e20, mu_max = 1e20; d.f = f; d.f_data = f_data; d.m = m; d.fc = fc; d.p = p; d.h = h; d.stop = stop; /* whether we handle inequality constraints via the augmented Lagrangian penalty function, or directly in the sub-algorithm */ if (sub_has_fc) d.m = 0; else m = 0; max_constraint_dim = MAX(nlopt_max_constraint_dim(d.m, fc), nlopt_max_constraint_dim(d.p, h)); d.mm = nlopt_count_constraints(d.m, fc); d.pp = nlopt_count_constraints(d.p, h); ret = nlopt_set_min_objective(sub_opt, auglag, &d); if (ret<0) return ret; ret = nlopt_set_lower_bounds(sub_opt, lb); if (ret<0) return ret; ret = nlopt_set_upper_bounds(sub_opt, ub); if (ret<0) return ret; ret = nlopt_set_stopval(sub_opt, d.m==0 && d.p==0 ? stop->minf_max : -HUGE_VAL); if (ret<0) return ret; ret = nlopt_remove_inequality_constraints(sub_opt); if (ret<0) return ret; ret = nlopt_remove_equality_constraints(sub_opt); if (ret<0) return ret; for (i = 0; i < m; ++i) { if (fc[i].f) ret = nlopt_add_inequality_constraint(sub_opt, fc[i].f, fc[i].f_data, fc[i].tol[0]); else ret = nlopt_add_inequality_mconstraint(sub_opt, fc[i].m, fc[i].mf, fc[i].f_data, fc[i].tol); if (ret < 0) return ret; } xcur = (double *) malloc(sizeof(double) * (n + max_constraint_dim * (1 + n) + d.pp + d.mm)); if (!xcur) return NLOPT_OUT_OF_MEMORY; memcpy(xcur, x, sizeof(double) * n); d.restmp = xcur + n; d.gradtmp = d.restmp + max_constraint_dim; memset(d.gradtmp, 0, sizeof(double) * (n*max_constraint_dim + d.pp+d.mm)); d.lambda = d.gradtmp + n * max_constraint_dim; d.mu = d.lambda + d.pp; *minf = HUGE_VAL; /* starting rho suggested by B & M */ if (d.p > 0 || d.m > 0) { double con2 = 0; d.stop->nevals++; fcur = f(n, xcur, NULL, f_data); if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } penalty = 0; feasible = 1; for (i = 0; i < d.p; ++i) { nlopt_eval_constraint(d.restmp, NULL, d.h + i, n, xcur); if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } for (k = 0; k < d.h[i].m; ++k) { double hi = d.restmp[k]; penalty += fabs(hi); feasible = feasible && fabs(hi) <= h[i].tol[k]; con2 += hi * hi; } } for (i = 0; i < d.m; ++i) { nlopt_eval_constraint(d.restmp, NULL, d.fc + i, n, xcur); if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } for (k = 0; k < d.fc[i].m; ++k) { double fci = d.restmp[k]; penalty += fci > 0 ? fci : 0; feasible = feasible && fci <= fc[i].tol[k]; if (fci > 0) con2 += fci * fci; } } *minf = fcur; minf_penalty = penalty; minf_feasible = feasible; d.rho = MAX(1e-6, MIN(10, 2 * fabs(*minf) / con2)); } else d.rho = 1; /* whatever, doesn't matter */ if (auglag_verbose) { printf("auglag: initial rho=%g\nauglag initial lambda=", d.rho); for (i = 0; i < d.pp; ++i) printf(" %g", d.lambda[i]); printf("\nauglag initial mu = "); for (i = 0; i < d.mm; ++i) printf(" %g", d.mu[i]); printf("\n"); } do { double prev_ICM = ICM; ret = nlopt_optimize_limited(sub_opt, xcur, &fcur, stop->maxeval - stop->nevals, stop->maxtime - (nlopt_seconds() - stop->start)); if (auglag_verbose) printf("auglag: subopt return code %d\n", ret); if (ret < 0) break; d.stop->nevals++; fcur = f(n, xcur, NULL, f_data); if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } if (auglag_verbose) printf("auglag: fcur = %g\n", fcur); ICM = 0; penalty = 0; feasible = 1; for (i = ii = 0; i < d.p; ++i) { nlopt_eval_constraint(d.restmp, NULL, d.h + i, n, xcur); if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } for (k = 0; k < d.h[i].m; ++k) { double hi = d.restmp[k]; double newlam = d.lambda[ii] + d.rho * hi; penalty += fabs(hi); feasible = feasible && fabs(hi) <= h[i].tol[k]; ICM = MAX(ICM, fabs(hi)); d.lambda[ii++] = MIN(MAX(lam_min, newlam), lam_max); } } for (i = ii = 0; i < d.m; ++i) { nlopt_eval_constraint(d.restmp, NULL, d.fc + i, n, xcur); if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } for (k = 0; k < d.fc[i].m; ++k) { double fci = d.restmp[k]; double newmu = d.mu[ii] + d.rho * fci; penalty += fci > 0 ? fci : 0; feasible = feasible && fci <= fc[i].tol[k]; ICM = MAX(ICM, fabs(MAX(fci, -d.mu[ii] / d.rho))); d.mu[ii++] = MIN(MAX(0.0, newmu), mu_max); } } if (ICM > tau * prev_ICM) { d.rho *= gam; } auglag_iters++; if (auglag_verbose) { printf("auglag %d: ICM=%g (%sfeasible), rho=%g\nauglag lambda=", auglag_iters, ICM, feasible ? "" : "not ", d.rho); for (i = 0; i < d.pp; ++i) printf(" %g", d.lambda[i]); printf("\nauglag %d: mu = ", auglag_iters); for (i = 0; i < d.mm; ++i) printf(" %g", d.mu[i]); printf("\n"); } if ((feasible && (!minf_feasible || penalty < minf_penalty || fcur < *minf)) || (!minf_feasible && penalty < minf_penalty)) { ret = NLOPT_SUCCESS; if (feasible) { if (fcur < stop->minf_max) ret = NLOPT_MINF_MAX_REACHED; else if (nlopt_stop_ftol(stop, fcur, *minf)) ret = NLOPT_FTOL_REACHED; else if (nlopt_stop_x(stop, xcur, x)) ret = NLOPT_XTOL_REACHED; } *minf = fcur; minf_penalty = penalty; minf_feasible = feasible; memcpy(x, xcur, sizeof(double) * n); if (ret != NLOPT_SUCCESS) break; } if (nlopt_stop_forced(stop)) {ret = NLOPT_FORCED_STOP; break;} if (nlopt_stop_evals(stop)) {ret = NLOPT_MAXEVAL_REACHED; break;} if (nlopt_stop_time(stop)) {ret = NLOPT_MAXTIME_REACHED; break;} /* TODO: use some other stopping criterion on ICM? */ /* The paper uses ICM <= epsilon and DFM <= epsilon, where DFM is a measure of the size of the Lagrangian gradient. Besides the fact that these kinds of absolute tolerances (non-scale-invariant) are unsatisfying and it is not clear how the user should specify it, the ICM <= epsilon condition seems not too different from requiring feasibility, especially now that the user can provide constraint-specific tolerances analogous to epsilon. */ if (ICM == 0) return NLOPT_FTOL_REACHED; } while (1); done: free(xcur); return ret; } nloptr/src/nlopt_src/nlopt.pc.in0000644000176000001440000000032313263734242016503 0ustar ripleyusersprefix=@prefix@ exec_prefix=@exec_prefix@ libdir=@libdir@ includedir=@includedir@ Name: NLopt Description: nonlinear optimization libary Version: @VERSION@ Libs: -L${libdir} -lnlopt -lm Cflags: -I${includedir} nloptr/src/nlopt_src/install-sh0000755000176000001440000003325513263734242016434 0ustar ripleyusers#!/bin/sh # install - install a program, script, or datafile scriptversion=2011-11-20.07; # UTC # This originates from X11R5 (mit/util/scripts/install.sh), which was # later released in X11R6 (xc/config/util/install.sh) with the # following copyright and license. # # Copyright (C) 1994 X Consortium # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to # deal in the Software without restriction, including without limitation the # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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In this case, the final cleanup might fail but the new # file should still install successfully. { test ! -f "$dst" || $doit $rmcmd -f "$dst" 2>/dev/null || { $doit $mvcmd -f "$dst" "$rmtmp" 2>/dev/null && { $doit $rmcmd -f "$rmtmp" 2>/dev/null; :; } } || { echo "$0: cannot unlink or rename $dst" >&2 (exit 1); exit 1 } } && # Now rename the file to the real destination. $doit $mvcmd "$dsttmp" "$dst" } fi || exit 1 trap '' 0 fi done # Local variables: # eval: (add-hook 'write-file-hooks 'time-stamp) # time-stamp-start: "scriptversion=" # time-stamp-format: "%:y-%02m-%02d.%02H" # time-stamp-time-zone: "UTC" # time-stamp-end: "; # UTC" # End: nloptr/src/nlopt_src/AUTHORS0000644000176000001440000000026513263734241015472 0ustar ripleyusersNLopt was written/packaged by: Steven G. Johnson See the subdirectories for the authors of the original nonlinear optimization libraries utilized by NLopt. nloptr/src/nlopt_src/Makefile.am0000644000176000001440000000206413263734241016455 0ustar ripleyusersOPTIONS_AUTOMAKE=gnu lib_LTLIBRARIES = libnlopt@NLOPT_SUFFIX@.la ACLOCAL_AMFLAGS=-I m4 if WITH_CXX CXX_DIRS = stogo CXX_LIBS = stogo/libstogo.la endif SUBDIRS = util direct cdirect $(CXX_DIRS) praxis luksan crs mlsl mma cobyla newuoa neldermead auglag bobyqa isres slsqp esch api . octave test swig EXTRA_DIST = autogen.sh nlopt.pc.in m4 libnlopt@NLOPT_SUFFIX@_la_SOURCES = libnlopt@NLOPT_SUFFIX@_la_LIBADD = direct/libdirect.la \ cdirect/libcdirect.la $(CXX_LIBS) praxis/libpraxis.la \ luksan/libluksan.la crs/libcrs.la mlsl/libmlsl.la mma/libmma.la \ cobyla/libcobyla.la newuoa/libnewuoa.la neldermead/libneldermead.la \ auglag/libauglag.la bobyqa/libbobyqa.la isres/libisres.la \ slsqp/libslsqp.la esch/libesch.la api/libapi.la util/libutil.la if WITH_CXX libnlopt@NLOPT_SUFFIX@_la_LDFLAGS = -version-info @SHARED_VERSION_INFO@ else libnlopt@NLOPT_SUFFIX@_la_LDFLAGS = -no-undefined -version-info @SHARED_VERSION_INFO@ endif pkgconfigdir = $(libdir)/pkgconfig pkgconfig_DATA = nlopt.pc if MAINTAINER_MODE README: README.md cp -f $(srcdir)/README.md $@ endif nloptr/src/nlopt_src/slsqp/0000755000176000001440000000000013263734242015562 5ustar ripleyusersnloptr/src/nlopt_src/slsqp/slsqp.h0000644000176000001440000000067313355070635017104 0ustar ripleyusers#ifndef SLSQP_H #define SLSQP_H #include "nlopt.h" #include "nlopt-util.h" #ifdef __cplusplus extern "C" { #endif /* __cplusplus */ nlopt_result nlopt_slsqp(unsigned n, nlopt_func f, void *f_data, unsigned m, nlopt_constraint *fc, unsigned p, nlopt_constraint *h, const double *lb, const double *ub, double *x, double *minf, nlopt_stopping *stop); #ifdef __cplusplus } /* extern "C" */ #endif /* __cplusplus */ #endif nloptr/src/nlopt_src/slsqp/COPYRIGHT0000644000176000001440000000467713263734242017073 0ustar ripleyusersCopyright (c) 1988 Dieter Kraft Copyright (c) 1994 Association for Computing Machinery Copyright (c) 2001, 2002 Enthought, Inc. All rights reserved. Copyright (c) 2003-2009 SciPy Developers. All rights reserved. Copyright (c) 2010 Massachusetts Institute of Technology All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: a. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. b. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. c. Neither the name of the Enthought nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. C http://permalink.gmane.org/gmane.comp.python.scientific.devel/6725 C ------ C From: Deborah Cotton C Date: Fri, 14 Sep 2007 12:35:55 -0500 C Subject: RE: Algorithm License requested C To: Alan Isaac C C Prof. Issac, C C In that case, then because the author consents to [the ACM] releasing C the code currently archived at http://www.netlib.org/toms/733 under the C BSD license, the ACM hereby releases this code under the BSD license. C C Regards, C C Deborah Cotton, Copyright & Permissions C ACM Publications C 2 Penn Plaza, Suite 701** C New York, NY 10121-0701 C permissions@acm.org C 212.869.7440 ext. 652 C Fax. 212.869.0481 C ------ nloptr/src/nlopt_src/slsqp/Makefile.am0000644000176000001440000000023613263734242017617 0ustar ripleyusersAM_CPPFLAGS = -I$(top_srcdir)/util -I$(top_srcdir)/api noinst_LTLIBRARIES = libslsqp.la libslsqp_la_SOURCES = slsqp.c slsqp.h EXTRA_DIST = README COPYRIGHT nloptr/src/nlopt_src/slsqp/Makefile.in0000644000176000001440000004347613355070635017646 0ustar ripleyusers# Makefile.in generated by automake 1.14 from Makefile.am. # @configure_input@ # Copyright (C) 1994-2013 Free Software Foundation, Inc. # This Makefile.in is free software; the Free Software Foundation # gives unlimited permission to copy and/or distribute it, # with or without modifications, as long as this notice is preserved. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY, to the extent permitted by law; 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Dieter Kraft, "Algorithm 733: TOMP–Fortran modules for optimal control calculations," ACM Transactions on Mathematical Software, vol. 20, no. 3, pp. 262-281 (1994). (I believe that SLSQP stands for something like Sequential Least-Squares Quadratic Programming, because the problem is treated as a sequence of constrained least-squared problems, but such a least-squares problem is equivalent to a QP.) The actual Fortran file was obtained from the SciPy project, who are responsible for obtaining permission to distribute it under a free-software (3-clause BSD) license (see the permission email from ACM at the top of slsqp.c, and also projects.scipy.org/scipy/ticket/1056). The code was modified for inclusion in NLopt by S. G. Johnson in 2010, with the following changes. The code was converted to C and manually cleaned up. It was modified to be re-entrant, preserving the reverse-communication interface but explicitly saving the state in a data structure. The reverse-communication interface was wrapped with an NLopt-style inteface, with NLopt stopping conditions. The inexact line search was modified to evaluate the functions including gradients for the first step, since this removes the need to evaluate the function+gradient a second time for the same point in the common case when the inexact line search concludes after a single step, since NLopt's interface combines the function and gradient computations. Since roundoff errors sometimes pushed SLSQP's parameters slightly outside the bound constraints (not allowed by NLopt), we added checks to force the parameters within the bounds. Fixed a bug in LSEI (use of uninitialized variables) for the case where the number of equality constraints equals the dimension of the problem. The LSQ subroutine was modified to handle infinite lower/upper bounds (in which case those constraints are omitted). The exact line-search option is currently disabled; if we want to re-enable this (although exact line-search is usually overkill in these kinds of algorithms), we plan to do so using a recursive call to NLopt. (This will allow a user-specified line-search algorithm to be used, and will allow the gradient to be exploited in the exact line search, in contrast to the routine provided with SLSQP.)nloptr/src/nlopt_src/slsqp/slsqp.c0000644000176000001440000024003113355070635017071 0ustar ripleyusers/* SLSQP: Sequentional Least Squares Programming (aka sequential quadratic programming SQP) method for nonlinearly constrained nonlinear optimization, by Dieter Kraft (1991). Fortran released under a free (BSD) license by ACM to the SciPy project and used there. C translation via f2c + hand-cleanup and incorporation into NLopt by S. G. Johnson (2009). */ /* Table of constant values */ #include #include #include #include "slsqp.h" /* ALGORITHM 733, COLLECTED ALGORITHMS FROM ACM. */ /* TRANSACTIONS ON MATHEMATICAL SOFTWARE, */ /* VOL. 20, NO. 3, SEPTEMBER, 1994, PP. 262-281. */ /* http://doi.acm.org/10.1145/192115.192124 */ /* http://permalink.gmane.org/gmane.comp.python.scientific.devel/6725 */ /* ------ */ /* From: Deborah Cotton */ /* Date: Fri, 14 Sep 2007 12:35:55 -0500 */ /* Subject: RE: Algorithm License requested */ /* To: Alan Isaac */ /* Prof. Issac, */ /* In that case, then because the author consents to [the ACM] releasing */ /* the code currently archived at http://www.netlib.org/toms/733 under the */ /* BSD license, the ACM hereby releases this code under the BSD license. */ /* Regards, */ /* Deborah Cotton, Copyright & Permissions */ /* ACM Publications */ /* 2 Penn Plaza, Suite 701** */ /* New York, NY 10121-0701 */ /* permissions@acm.org */ /* 212.869.7440 ext. 652 */ /* Fax. 212.869.0481 */ /* ------ */ /********************************* BLAS1 routines *************************/ /* COPIES A VECTOR, X, TO A VECTOR, Y, with the given increments */ static void dcopy___(int *n_, const double *dx, int incx, double *dy, int incy) { int i, n = *n_; if (n <= 0) return; if (incx == 1 && incy == 1) memcpy(dy, dx, sizeof(double) * ((unsigned) n)); else if (incx == 0 && incy == 1) { double x = dx[0]; for (i = 0; i < n; ++i) dy[i] = x; } else { for (i = 0; i < n; ++i) dy[i*incy] = dx[i*incx]; } } /* dcopy___ */ /* CONSTANT TIMES A VECTOR PLUS A VECTOR. */ static void daxpy_sl__(int *n_, const double *da_, const double *dx, int incx, double *dy, int incy) { int n = *n_, i; double da = *da_; if (n <= 0 || da == 0) return; for (i = 0; i < n; ++i) dy[i*incy] += da * dx[i*incx]; } /* dot product dx dot dy. */ static double ddot_sl__(int *n_, double *dx, int incx, double *dy, int incy) { int n = *n_, i; long double sum = 0; if (n <= 0) return 0; for (i = 0; i < n; ++i) sum += dx[i*incx] * dy[i*incy]; return (double) sum; } /* compute the L2 norm of array DX of length N, stride INCX */ static double dnrm2___(int *n_, double *dx, int incx) { int i, n = *n_; double xmax = 0, scale; long double sum = 0; for (i = 0; i < n; ++i) { double xabs = fabs(dx[incx*i]); if (xmax < xabs) xmax = xabs; } if (xmax == 0) return 0; scale = 1.0 / xmax; for (i = 0; i < n; ++i) { double xs = scale * dx[incx*i]; sum += xs * xs; } return xmax * sqrt((double) sum); } /* apply Givens rotation */ static void dsrot_(int n, double *dx, int incx, double *dy, int incy, double *c__, double *s_) { int i; double c = *c__, s = *s_; for (i = 0; i < n; ++i) { double x = dx[incx*i], y = dy[incy*i]; dx[incx*i] = c * x + s * y; dy[incy*i] = c * y - s * x; } } /* construct Givens rotation */ static void dsrotg_(double *da, double *db, double *c, double *s) { double absa, absb, roe, scale; absa = fabs(*da); absb = fabs(*db); if (absa > absb) { roe = *da; scale = absa; } else { roe = *db; scale = absb; } if (scale != 0) { double r, iscale = 1 / scale; double tmpa = (*da) * iscale, tmpb = (*db) * iscale; r = (roe < 0 ? -scale : scale) * sqrt((tmpa * tmpa) + (tmpb * tmpb)); *c = *da / r; *s = *db / r; *da = r; if (*c != 0 && fabs(*c) <= *s) *db = 1 / *c; else *db = *s; } else { *c = 1; *s = *da = *db = 0; } } /* scales vector X(n) by constant da */ static void dscal_sl__(int *n_, const double *da, double *dx, int incx) { int i, n = *n_; double alpha = *da; for (i = 0; i < n; ++i) dx[i*incx] *= alpha; } /**************************************************************************/ static const int c__0 = 0; static const int c__1 = 1; static const int c__2 = 2; #define MIN2(a,b) ((a) <= (b) ? (a) : (b)) #define MAX2(a,b) ((a) >= (b) ? (a) : (b)) static void h12_(const int *mode, int *lpivot, int *l1, int *m, double *u, const int *iue, double *up, double *c__, const int *ice, const int *icv, const int *ncv) { /* Initialized data */ const double one = 1.; /* System generated locals */ int u_dim1, u_offset, i__1, i__2; double d__1; /* Local variables */ double b; int i__, j, i2, i3, i4; double cl, sm; int incr; double clinv; /* C.L.LAWSON AND R.J.HANSON, JET PROPULSION LABORATORY, 1973 JUN 12 */ /* TO APPEAR IN 'SOLVING LEAST SQUARES PROBLEMS', PRENTICE-HALL, 1974 */ /* CONSTRUCTION AND/OR APPLICATION OF A SINGLE */ /* HOUSEHOLDER TRANSFORMATION Q = I + U*(U**T)/B */ /* MODE = 1 OR 2 TO SELECT ALGORITHM H1 OR H2 . */ /* LPIVOT IS THE INDEX OF THE PIVOT ELEMENT. */ /* L1,M IF L1 <= M THE TRANSFORMATION WILL BE CONSTRUCTED TO */ /* ZERO ELEMENTS INDEXED FROM L1 THROUGH M. */ /* IF L1 > M THE SUBROUTINE DOES AN IDENTITY TRANSFORMATION. */ /* U(),IUE,UP */ /* ON ENTRY TO H1 U() STORES THE PIVOT VECTOR. */ /* IUE IS THE STORAGE INCREMENT BETWEEN ELEMENTS. */ /* ON EXIT FROM H1 U() AND UP STORE QUANTITIES DEFINING */ /* THE VECTOR U OF THE HOUSEHOLDER TRANSFORMATION. */ /* ON ENTRY TO H2 U() AND UP */ /* SHOULD STORE QUANTITIES PREVIOUSLY COMPUTED BY H1. */ /* THESE WILL NOT BE MODIFIED BY H2. */ /* C() ON ENTRY TO H1 OR H2 C() STORES A MATRIX WHICH WILL BE */ /* REGARDED AS A SET OF VECTORS TO WHICH THE HOUSEHOLDER */ /* TRANSFORMATION IS TO BE APPLIED. */ /* ON EXIT C() STORES THE SET OF TRANSFORMED VECTORS. */ /* ICE STORAGE INCREMENT BETWEEN ELEMENTS OF VECTORS IN C(). */ /* ICV STORAGE INCREMENT BETWEEN VECTORS IN C(). */ /* NCV NUMBER OF VECTORS IN C() TO BE TRANSFORMED. */ /* IF NCV <= 0 NO OPERATIONS WILL BE DONE ON C(). */ /* Parameter adjustments */ u_dim1 = *iue; u_offset = 1 + u_dim1; u -= u_offset; --c__; /* Function Body */ if (0 >= *lpivot || *lpivot >= *l1 || *l1 > *m) { goto L80; } cl = (d__1 = u[*lpivot * u_dim1 + 1], fabs(d__1)); if (*mode == 2) { goto L30; } /* ****** CONSTRUCT THE TRANSFORMATION ****** */ i__1 = *m; for (j = *l1; j <= i__1; ++j) { sm = (d__1 = u[j * u_dim1 + 1], fabs(d__1)); /* L10: */ cl = MAX2(sm,cl); } if (cl <= 0.0) { goto L80; } clinv = one / cl; /* Computing 2nd power */ d__1 = u[*lpivot * u_dim1 + 1] * clinv; sm = d__1 * d__1; i__1 = *m; for (j = *l1; j <= i__1; ++j) { /* L20: */ /* Computing 2nd power */ d__1 = u[j * u_dim1 + 1] * clinv; sm += d__1 * d__1; } cl *= sqrt(sm); if (u[*lpivot * u_dim1 + 1] > 0.0) { cl = -cl; } *up = u[*lpivot * u_dim1 + 1] - cl; u[*lpivot * u_dim1 + 1] = cl; goto L40; /* ****** APPLY THE TRANSFORMATION I+U*(U**T)/B TO C ****** */ L30: if (cl <= 0.0) { goto L80; } L40: if (*ncv <= 0) { goto L80; } b = *up * u[*lpivot * u_dim1 + 1]; if (b >= 0.0) { goto L80; } b = one / b; i2 = 1 - *icv + *ice * (*lpivot - 1); incr = *ice * (*l1 - *lpivot); i__1 = *ncv; for (j = 1; j <= i__1; ++j) { i2 += *icv; i3 = i2 + incr; i4 = i3; sm = c__[i2] * *up; i__2 = *m; for (i__ = *l1; i__ <= i__2; ++i__) { sm += c__[i3] * u[i__ * u_dim1 + 1]; /* L50: */ i3 += *ice; } if (sm == 0.0) { goto L70; } sm *= b; c__[i2] += sm * *up; i__2 = *m; for (i__ = *l1; i__ <= i__2; ++i__) { c__[i4] += sm * u[i__ * u_dim1 + 1]; /* L60: */ i4 += *ice; } L70: ; } L80: return; } /* h12_ */ static void nnls_(double *a, int *mda, int *m, int * n, double *b, double *x, double *rnorm, double *w, double *z__, int *indx, int *mode) { /* Initialized data */ const double one = 1.; const double factor = .01; /* System generated locals */ int a_dim1, a_offset, i__1, i__2; double d__1; /* Local variables */ double c__; int i__, j, k, l; double s, t; int ii, jj, ip, iz, jz; double up; int iz1, iz2, npp1, iter; double wmax, alpha, asave; int itmax, izmax, nsetp; double unorm; /* C.L.LAWSON AND R.J.HANSON, JET PROPULSION LABORATORY: */ /* 'SOLVING LEAST SQUARES PROBLEMS'. PRENTICE-HALL.1974 */ /* ********** NONNEGATIVE LEAST SQUARES ********** */ /* GIVEN AN M BY N MATRIX, A, AND AN M-VECTOR, B, COMPUTE AN */ /* N-VECTOR, X, WHICH SOLVES THE LEAST SQUARES PROBLEM */ /* A*X = B SUBJECT TO X >= 0 */ /* A(),MDA,M,N */ /* MDA IS THE FIRST DIMENSIONING PARAMETER FOR THE ARRAY,A(). */ /* ON ENTRY A() CONTAINS THE M BY N MATRIX,A. */ /* ON EXIT A() CONTAINS THE PRODUCT Q*A, */ /* WHERE Q IS AN M BY M ORTHOGONAL MATRIX GENERATED */ /* IMPLICITLY BY THIS SUBROUTINE. */ /* EITHER M>=N OR M iz2 || nsetp >= *m) { goto L280; } i__1 = iz2; for (iz = iz1; iz <= i__1; ++iz) { j = indx[iz]; /* L120: */ i__2 = *m - nsetp; w[j] = ddot_sl__(&i__2, &a[npp1 + j * a_dim1], 1, &b[npp1], 1) ; } /* STEP THREE (TEST DUAL VARIABLES) */ L130: wmax = 0.0; i__2 = iz2; for (iz = iz1; iz <= i__2; ++iz) { j = indx[iz]; if (w[j] <= wmax) { goto L140; } wmax = w[j]; izmax = iz; L140: ; } /* .....EXIT LOOP A */ if (wmax <= 0.0) { goto L280; } iz = izmax; j = indx[iz]; /* STEP FOUR (TEST INDX J FOR LINEAR DEPENDENCY) */ asave = a[npp1 + j * a_dim1]; i__2 = npp1 + 1; h12_(&c__1, &npp1, &i__2, m, &a[j * a_dim1 + 1], &c__1, &up, &z__[1], & c__1, &c__1, &c__0); unorm = dnrm2___(&nsetp, &a[j * a_dim1 + 1], 1); t = factor * (d__1 = a[npp1 + j * a_dim1], fabs(d__1)); d__1 = unorm + t; if (d__1 - unorm <= 0.0) { goto L150; } dcopy___(m, &b[1], 1, &z__[1], 1); i__2 = npp1 + 1; h12_(&c__2, &npp1, &i__2, m, &a[j * a_dim1 + 1], &c__1, &up, &z__[1], & c__1, &c__1, &c__1); if (z__[npp1] / a[npp1 + j * a_dim1] > 0.0) { goto L160; } L150: a[npp1 + j * a_dim1] = asave; w[j] = 0.0; goto L130; /* STEP FIVE (ADD COLUMN) */ L160: dcopy___(m, &z__[1], 1, &b[1], 1); indx[iz] = indx[iz1]; indx[iz1] = j; ++iz1; nsetp = npp1; ++npp1; i__2 = iz2; for (jz = iz1; jz <= i__2; ++jz) { jj = indx[jz]; /* L170: */ h12_(&c__2, &nsetp, &npp1, m, &a[j * a_dim1 + 1], &c__1, &up, &a[jj * a_dim1 + 1], &c__1, mda, &c__1); } k = MIN2(npp1,*mda); w[j] = 0.0; i__2 = *m - nsetp; dcopy___(&i__2, &w[j], 0, &a[k + j * a_dim1], 1); /* STEP SIX (SOLVE LEAST SQUARES SUB-PROBLEM) */ /* .....ENTRY LOOP B */ L180: for (ip = nsetp; ip >= 1; --ip) { if (ip == nsetp) { goto L190; } d__1 = -z__[ip + 1]; daxpy_sl__(&ip, &d__1, &a[jj * a_dim1 + 1], 1, &z__[1], 1); L190: jj = indx[ip]; /* L200: */ z__[ip] /= a[ip + jj * a_dim1]; } ++iter; if (iter <= itmax) { goto L220; } L210: *mode = 3; goto L280; /* STEP SEVEN TO TEN (STEP LENGTH ALGORITHM) */ L220: alpha = one; jj = 0; i__2 = nsetp; for (ip = 1; ip <= i__2; ++ip) { if (z__[ip] > 0.0) { goto L230; } l = indx[ip]; t = -x[l] / (z__[ip] - x[l]); if (alpha < t) { goto L230; } alpha = t; jj = ip; L230: ; } i__2 = nsetp; for (ip = 1; ip <= i__2; ++ip) { l = indx[ip]; /* L240: */ x[l] = (one - alpha) * x[l] + alpha * z__[ip]; } /* .....EXIT LOOP B */ if (jj == 0) { goto L110; } /* STEP ELEVEN (DELETE COLUMN) */ i__ = indx[jj]; L250: x[i__] = 0.0; ++jj; i__2 = nsetp; for (j = jj; j <= i__2; ++j) { ii = indx[j]; indx[j - 1] = ii; dsrotg_(&a[j - 1 + ii * a_dim1], &a[j + ii * a_dim1], &c__, &s); t = a[j - 1 + ii * a_dim1]; dsrot_(*n, &a[j - 1 + a_dim1], *mda, &a[j + a_dim1], *mda, &c__, &s); a[j - 1 + ii * a_dim1] = t; a[j + ii * a_dim1] = 0.0; /* L260: */ dsrot_(1, &b[j - 1], 1, &b[j], 1, &c__, &s); } npp1 = nsetp; --nsetp; --iz1; indx[iz1] = i__; if (nsetp <= 0) { goto L210; } i__2 = nsetp; for (jj = 1; jj <= i__2; ++jj) { i__ = indx[jj]; if (x[i__] <= 0.0) { goto L250; } /* L270: */ } dcopy___(m, &b[1], 1, &z__[1], 1); goto L180; /* STEP TWELVE (SOLUTION) */ L280: k = MIN2(npp1,*m); i__2 = *m - nsetp; *rnorm = dnrm2___(&i__2, &b[k], 1); if (npp1 > *m) { w[1] = 0.0; dcopy___(n, &w[1], 0, &w[1], 1); } /* END OF SUBROUTINE NNLS */ L290: return; } /* nnls_ */ static void ldp_(double *g, int *mg, int *m, int *n, double *h__, double *x, double *xnorm, double *w, int *indx, int *mode) { /* Initialized data */ const double one = 1.; /* System generated locals */ int g_dim1, g_offset, i__1, i__2; double d__1; /* Local variables */ int i__, j, n1, if__, iw, iy, iz; double fac; double rnorm; int iwdual; /* T */ /* MINIMIZE 1/2 X X SUBJECT TO G * X >= H. */ /* C.L. LAWSON, R.J. HANSON: 'SOLVING LEAST SQUARES PROBLEMS' */ /* PRENTICE HALL, ENGLEWOOD CLIFFS, NEW JERSEY, 1974. */ /* PARAMETER DESCRIPTION: */ /* G(),MG,M,N ON ENTRY G() STORES THE M BY N MATRIX OF */ /* LINEAR INEQUALITY CONSTRAINTS. G() HAS FIRST */ /* DIMENSIONING PARAMETER MG */ /* H() ON ENTRY H() STORES THE M VECTOR H REPRESENTING */ /* THE RIGHT SIDE OF THE INEQUALITY SYSTEM */ /* REMARK: G(),H() WILL NOT BE CHANGED DURING CALCULATIONS BY LDP */ /* X() ON ENTRY X() NEED NOT BE INITIALIZED. */ /* ON EXIT X() STORES THE SOLUTION VECTOR X IF MODE=1. */ /* XNORM ON EXIT XNORM STORES THE EUCLIDIAN NORM OF THE */ /* SOLUTION VECTOR IF COMPUTATION IS SUCCESSFUL */ /* W() W IS A ONE DIMENSIONAL WORKING SPACE, THE LENGTH */ /* OF WHICH SHOULD BE AT LEAST (M+2)*(N+1) + 2*M */ /* ON EXIT W() STORES THE LAGRANGE MULTIPLIERS */ /* ASSOCIATED WITH THE CONSTRAINTS */ /* AT THE SOLUTION OF PROBLEM LDP */ /* INDX() INDX() IS A ONE DIMENSIONAL INT WORKING SPACE */ /* OF LENGTH AT LEAST M */ /* MODE MODE IS A SUCCESS-FAILURE FLAG WITH THE FOLLOWING */ /* MEANINGS: */ /* MODE=1: SUCCESSFUL COMPUTATION */ /* 2: ERROR RETURN BECAUSE OF WRONG DIMENSIONS (N.LE.0) */ /* 3: ITERATION COUNT EXCEEDED BY NNLS */ /* 4: INEQUALITY CONSTRAINTS INCOMPATIBLE */ /* Parameter adjustments */ --indx; --h__; --x; g_dim1 = *mg; g_offset = 1 + g_dim1; g -= g_offset; --w; /* Function Body */ *mode = 2; if (*n <= 0) { goto L50; } /* STATE DUAL PROBLEM */ *mode = 1; x[1] = 0.0; dcopy___(n, &x[1], 0, &x[1], 1); *xnorm = 0.0; if (*m == 0) { goto L50; } iw = 0; i__1 = *m; for (j = 1; j <= i__1; ++j) { i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { ++iw; /* L10: */ w[iw] = g[j + i__ * g_dim1]; } ++iw; /* L20: */ w[iw] = h__[j]; } if__ = iw + 1; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { ++iw; /* L30: */ w[iw] = 0.0; } w[iw + 1] = one; n1 = *n + 1; iz = iw + 2; iy = iz + n1; iwdual = iy + *m; /* SOLVE DUAL PROBLEM */ nnls_(&w[1], &n1, &n1, m, &w[if__], &w[iy], &rnorm, &w[iwdual], &w[iz], & indx[1], mode); if (*mode != 1) { goto L50; } *mode = 4; if (rnorm <= 0.0) { goto L50; } /* COMPUTE SOLUTION OF PRIMAL PROBLEM */ fac = one - ddot_sl__(m, &h__[1], 1, &w[iy], 1); d__1 = one + fac; if (d__1 - one <= 0.0) { goto L50; } *mode = 1; fac = one / fac; i__1 = *n; for (j = 1; j <= i__1; ++j) { /* L40: */ x[j] = fac * ddot_sl__(m, &g[j * g_dim1 + 1], 1, &w[iy], 1); } *xnorm = dnrm2___(n, &x[1], 1); /* COMPUTE LAGRANGE MULTIPLIERS FOR PRIMAL PROBLEM */ w[1] = 0.0; dcopy___(m, &w[1], 0, &w[1], 1); daxpy_sl__(m, &fac, &w[iy], 1, &w[1], 1); /* END OF SUBROUTINE LDP */ L50: return; } /* ldp_ */ static void lsi_(double *e, double *f, double *g, double *h__, int *le, int *me, int *lg, int *mg, int *n, double *x, double *xnorm, double *w, int * jw, int *mode) { /* Initialized data */ const double epmach = 2.22e-16; const double one = 1.; /* System generated locals */ int e_dim1, e_offset, g_dim1, g_offset, i__1, i__2, i__3; double d__1; /* Local variables */ int i__, j; double t; /* FOR MODE=1, THE SUBROUTINE RETURNS THE SOLUTION X OF */ /* INEQUALITY CONSTRAINED LINEAR LEAST SQUARES PROBLEM: */ /* MIN ||E*X-F|| */ /* X */ /* S.T. G*X >= H */ /* THE ALGORITHM IS BASED ON QR DECOMPOSITION AS DESCRIBED IN */ /* CHAPTER 23.5 OF LAWSON & HANSON: SOLVING LEAST SQUARES PROBLEMS */ /* THE FOLLOWING DIMENSIONS OF THE ARRAYS DEFINING THE PROBLEM */ /* ARE NECESSARY */ /* DIM(E) : FORMAL (LE,N), ACTUAL (ME,N) */ /* DIM(F) : FORMAL (LE ), ACTUAL (ME ) */ /* DIM(G) : FORMAL (LG,N), ACTUAL (MG,N) */ /* DIM(H) : FORMAL (LG ), ACTUAL (MG ) */ /* DIM(X) : N */ /* DIM(W) : (N+1)*(MG+2) + 2*MG */ /* DIM(JW): LG */ /* ON ENTRY, THE USER HAS TO PROVIDE THE ARRAYS E, F, G, AND H. */ /* ON RETURN, ALL ARRAYS WILL BE CHANGED BY THE SUBROUTINE. */ /* X STORES THE SOLUTION VECTOR */ /* XNORM STORES THE RESIDUUM OF THE SOLUTION IN EUCLIDIAN NORM */ /* W STORES THE VECTOR OF LAGRANGE MULTIPLIERS IN ITS FIRST */ /* MG ELEMENTS */ /* MODE IS A SUCCESS-FAILURE FLAG WITH THE FOLLOWING MEANINGS: */ /* MODE=1: SUCCESSFUL COMPUTATION */ /* 2: ERROR RETURN BECAUSE OF WRONG DIMENSIONS (N<1) */ /* 3: ITERATION COUNT EXCEEDED BY NNLS */ /* 4: INEQUALITY CONSTRAINTS INCOMPATIBLE */ /* 5: MATRIX E IS NOT OF FULL RANK */ /* 03.01.1980, DIETER KRAFT: CODED */ /* 20.03.1987, DIETER KRAFT: REVISED TO FORTRAN 77 */ /* Parameter adjustments */ --f; --jw; --h__; --x; g_dim1 = *lg; g_offset = 1 + g_dim1; g -= g_offset; e_dim1 = *le; e_offset = 1 + e_dim1; e -= e_offset; --w; /* Function Body */ /* QR-FACTORS OF E AND APPLICATION TO F */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* Computing MIN */ i__2 = i__ + 1; j = MIN2(i__2,*n); i__2 = i__ + 1; i__3 = *n - i__; h12_(&c__1, &i__, &i__2, me, &e[i__ * e_dim1 + 1], &c__1, &t, &e[j * e_dim1 + 1], &c__1, le, &i__3); /* L10: */ i__2 = i__ + 1; h12_(&c__2, &i__, &i__2, me, &e[i__ * e_dim1 + 1], &c__1, &t, &f[1], & c__1, &c__1, &c__1); } /* TRANSFORM G AND H TO GET LEAST DISTANCE PROBLEM */ *mode = 5; i__2 = *mg; for (i__ = 1; i__ <= i__2; ++i__) { i__1 = *n; for (j = 1; j <= i__1; ++j) { if ((d__1 = e[j + j * e_dim1], fabs(d__1)) < epmach) { goto L50; } /* L20: */ i__3 = j - 1; g[i__ + j * g_dim1] = (g[i__ + j * g_dim1] - ddot_sl__(&i__3, &g[ i__ + g_dim1], *lg, &e[j * e_dim1 + 1], 1)) / e[j + j * e_dim1]; } /* L30: */ h__[i__] -= ddot_sl__(n, &g[i__ + g_dim1], *lg, &f[1], 1); } /* SOLVE LEAST DISTANCE PROBLEM */ ldp_(&g[g_offset], lg, mg, n, &h__[1], &x[1], xnorm, &w[1], &jw[1], mode); if (*mode != 1) { goto L50; } /* SOLUTION OF ORIGINAL PROBLEM */ daxpy_sl__(n, &one, &f[1], 1, &x[1], 1); for (i__ = *n; i__ >= 1; --i__) { /* Computing MIN */ i__2 = i__ + 1; j = MIN2(i__2,*n); /* L40: */ i__2 = *n - i__; x[i__] = (x[i__] - ddot_sl__(&i__2, &e[i__ + j * e_dim1], *le, &x[j], 1)) / e[i__ + i__ * e_dim1]; } /* Computing MIN */ i__2 = *n + 1; j = MIN2(i__2,*me); i__2 = *me - *n; t = dnrm2___(&i__2, &f[j], 1); *xnorm = sqrt(*xnorm * *xnorm + t * t); /* END OF SUBROUTINE LSI */ L50: return; } /* lsi_ */ static void hfti_(double *a, int *mda, int *m, int * n, double *b, int *mdb, const int *nb, double *tau, int *krank, double *rnorm, double *h__, double *g, int * ip) { /* Initialized data */ const double factor = .001; /* System generated locals */ int a_dim1, a_offset, b_dim1, b_offset, i__1, i__2, i__3; double d__1; /* Local variables */ int i__, j, k, l; int jb, kp1; double tmp, hmax; int lmax, ldiag; /* RANK-DEFICIENT LEAST SQUARES ALGORITHM AS DESCRIBED IN: */ /* C.L.LAWSON AND R.J.HANSON, JET PROPULSION LABORATORY, 1973 JUN 12 */ /* TO APPEAR IN 'SOLVING LEAST SQUARES PROBLEMS', PRENTICE-HALL, 1974 */ /* A(*,*),MDA,M,N THE ARRAY A INITIALLY CONTAINS THE M x N MATRIX A */ /* OF THE LEAST SQUARES PROBLEM AX = B. */ /* THE FIRST DIMENSIONING PARAMETER MDA MUST SATISFY */ /* MDA >= M. EITHER M >= N OR M < N IS PERMITTED. */ /* THERE IS NO RESTRICTION ON THE RANK OF A. */ /* THE MATRIX A WILL BE MODIFIED BY THE SUBROUTINE. */ /* B(*,*),MDB,NB IF NB = 0 THE SUBROUTINE WILL MAKE NO REFERENCE */ /* TO THE ARRAY B. IF NB > 0 THE ARRAY B() MUST */ /* INITIALLY CONTAIN THE M x NB MATRIX B OF THE */ /* THE LEAST SQUARES PROBLEM AX = B AND ON RETURN */ /* THE ARRAY B() WILL CONTAIN THE N x NB SOLUTION X. */ /* IF NB>1 THE ARRAY B() MUST BE DOUBLE SUBSCRIPTED */ /* WITH FIRST DIMENSIONING PARAMETER MDB>=MAX(M,N), */ /* IF NB=1 THE ARRAY B() MAY BE EITHER SINGLE OR */ /* DOUBLE SUBSCRIPTED. */ /* TAU ABSOLUTE TOLERANCE PARAMETER FOR PSEUDORANK */ /* DETERMINATION, PROVIDED BY THE USER. */ /* KRANK PSEUDORANK OF A, SET BY THE SUBROUTINE. */ /* RNORM ON EXIT, RNORM(J) WILL CONTAIN THE EUCLIDIAN */ /* NORM OF THE RESIDUAL VECTOR FOR THE PROBLEM */ /* DEFINED BY THE J-TH COLUMN VECTOR OF THE ARRAY B. */ /* H(), G() ARRAYS OF WORKING SPACE OF LENGTH >= N. */ /* IP() INT ARRAY OF WORKING SPACE OF LENGTH >= N */ /* RECORDING PERMUTATION INDICES OF COLUMN VECTORS */ /* Parameter adjustments */ --ip; --g; --h__; a_dim1 = *mda; a_offset = 1 + a_dim1; a -= a_offset; --rnorm; b_dim1 = *mdb; b_offset = 1 + b_dim1; b -= b_offset; /* Function Body */ k = 0; ldiag = MIN2(*m,*n); if (ldiag <= 0) { goto L270; } /* COMPUTE LMAX */ i__1 = ldiag; for (j = 1; j <= i__1; ++j) { if (j == 1) { goto L20; } lmax = j; i__2 = *n; for (l = j; l <= i__2; ++l) { /* Computing 2nd power */ d__1 = a[j - 1 + l * a_dim1]; h__[l] -= d__1 * d__1; /* L10: */ if (h__[l] > h__[lmax]) { lmax = l; } } d__1 = hmax + factor * h__[lmax]; if (d__1 - hmax > 0.0) { goto L50; } L20: lmax = j; i__2 = *n; for (l = j; l <= i__2; ++l) { h__[l] = 0.0; i__3 = *m; for (i__ = j; i__ <= i__3; ++i__) { /* L30: */ /* Computing 2nd power */ d__1 = a[i__ + l * a_dim1]; h__[l] += d__1 * d__1; } /* L40: */ if (h__[l] > h__[lmax]) { lmax = l; } } hmax = h__[lmax]; /* COLUMN INTERCHANGES IF NEEDED */ L50: ip[j] = lmax; if (ip[j] == j) { goto L70; } i__2 = *m; for (i__ = 1; i__ <= i__2; ++i__) { tmp = a[i__ + j * a_dim1]; a[i__ + j * a_dim1] = a[i__ + lmax * a_dim1]; /* L60: */ a[i__ + lmax * a_dim1] = tmp; } h__[lmax] = h__[j]; /* J-TH TRANSFORMATION AND APPLICATION TO A AND B */ L70: /* Computing MIN */ i__2 = j + 1; i__ = MIN2(i__2,*n); i__2 = j + 1; i__3 = *n - j; h12_(&c__1, &j, &i__2, m, &a[j * a_dim1 + 1], &c__1, &h__[j], &a[i__ * a_dim1 + 1], &c__1, mda, &i__3); /* L80: */ i__2 = j + 1; h12_(&c__2, &j, &i__2, m, &a[j * a_dim1 + 1], &c__1, &h__[j], &b[ b_offset], &c__1, mdb, nb); } /* DETERMINE PSEUDORANK */ i__2 = ldiag; for (j = 1; j <= i__2; ++j) { /* L90: */ if ((d__1 = a[j + j * a_dim1], fabs(d__1)) <= *tau) { goto L100; } } k = ldiag; goto L110; L100: k = j - 1; L110: kp1 = k + 1; /* NORM OF RESIDUALS */ i__2 = *nb; for (jb = 1; jb <= i__2; ++jb) { /* L130: */ i__1 = *m - k; rnorm[jb] = dnrm2___(&i__1, &b[kp1 + jb * b_dim1], 1); } if (k > 0) { goto L160; } i__1 = *nb; for (jb = 1; jb <= i__1; ++jb) { i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { /* L150: */ b[i__ + jb * b_dim1] = 0.0; } } goto L270; L160: if (k == *n) { goto L180; } /* HOUSEHOLDER DECOMPOSITION OF FIRST K ROWS */ for (i__ = k; i__ >= 1; --i__) { /* L170: */ i__2 = i__ - 1; h12_(&c__1, &i__, &kp1, n, &a[i__ + a_dim1], mda, &g[i__], &a[ a_offset], mda, &c__1, &i__2); } L180: i__2 = *nb; for (jb = 1; jb <= i__2; ++jb) { /* SOLVE K*K TRIANGULAR SYSTEM */ for (i__ = k; i__ >= 1; --i__) { /* Computing MIN */ i__1 = i__ + 1; j = MIN2(i__1,*n); /* L210: */ i__1 = k - i__; b[i__ + jb * b_dim1] = (b[i__ + jb * b_dim1] - ddot_sl__(&i__1, & a[i__ + j * a_dim1], *mda, &b[j + jb * b_dim1], 1)) / a[i__ + i__ * a_dim1]; } /* COMPLETE SOLUTION VECTOR */ if (k == *n) { goto L240; } i__1 = *n; for (j = kp1; j <= i__1; ++j) { /* L220: */ b[j + jb * b_dim1] = 0.0; } i__1 = k; for (i__ = 1; i__ <= i__1; ++i__) { /* L230: */ h12_(&c__2, &i__, &kp1, n, &a[i__ + a_dim1], mda, &g[i__], &b[jb * b_dim1 + 1], &c__1, mdb, &c__1); } /* REORDER SOLUTION ACCORDING TO PREVIOUS COLUMN INTERCHANGES */ L240: for (j = ldiag; j >= 1; --j) { if (ip[j] == j) { goto L250; } l = ip[j]; tmp = b[l + jb * b_dim1]; b[l + jb * b_dim1] = b[j + jb * b_dim1]; b[j + jb * b_dim1] = tmp; L250: ; } } L270: *krank = k; } /* hfti_ */ static void lsei_(double *c__, double *d__, double *e, double *f, double *g, double *h__, int *lc, int * mc, int *le, int *me, int *lg, int *mg, int *n, double *x, double *xnrm, double *w, int *jw, int * mode) { /* Initialized data */ const double epmach = 2.22e-16; /* System generated locals */ int c_dim1, c_offset, e_dim1, e_offset, g_dim1, g_offset, i__1, i__2, i__3; double d__1; /* Local variables */ int i__, j, k, l; double t; int ie, if__, ig, iw, mc1; int krank; /* FOR MODE=1, THE SUBROUTINE RETURNS THE SOLUTION X OF */ /* EQUALITY & INEQUALITY CONSTRAINED LEAST SQUARES PROBLEM LSEI : */ /* MIN ||E*X - F|| */ /* X */ /* S.T. C*X = D, */ /* G*X >= H. */ /* USING QR DECOMPOSITION & ORTHOGONAL BASIS OF NULLSPACE OF C */ /* CHAPTER 23.6 OF LAWSON & HANSON: SOLVING LEAST SQUARES PROBLEMS. */ /* THE FOLLOWING DIMENSIONS OF THE ARRAYS DEFINING THE PROBLEM */ /* ARE NECESSARY */ /* DIM(E) : FORMAL (LE,N), ACTUAL (ME,N) */ /* DIM(F) : FORMAL (LE ), ACTUAL (ME ) */ /* DIM(C) : FORMAL (LC,N), ACTUAL (MC,N) */ /* DIM(D) : FORMAL (LC ), ACTUAL (MC ) */ /* DIM(G) : FORMAL (LG,N), ACTUAL (MG,N) */ /* DIM(H) : FORMAL (LG ), ACTUAL (MG ) */ /* DIM(X) : FORMAL (N ), ACTUAL (N ) */ /* DIM(W) : 2*MC+ME+(ME+MG)*(N-MC) for LSEI */ /* +(N-MC+1)*(MG+2)+2*MG for LSI */ /* DIM(JW): MAX(MG,L) */ /* ON ENTRY, THE USER HAS TO PROVIDE THE ARRAYS C, D, E, F, G, AND H. */ /* ON RETURN, ALL ARRAYS WILL BE CHANGED BY THE SUBROUTINE. */ /* X STORES THE SOLUTION VECTOR */ /* XNORM STORES THE RESIDUUM OF THE SOLUTION IN EUCLIDIAN NORM */ /* W STORES THE VECTOR OF LAGRANGE MULTIPLIERS IN ITS FIRST */ /* MC+MG ELEMENTS */ /* MODE IS A SUCCESS-FAILURE FLAG WITH THE FOLLOWING MEANINGS: */ /* MODE=1: SUCCESSFUL COMPUTATION */ /* 2: ERROR RETURN BECAUSE OF WRONG DIMENSIONS (N<1) */ /* 3: ITERATION COUNT EXCEEDED BY NNLS */ /* 4: INEQUALITY CONSTRAINTS INCOMPATIBLE */ /* 5: MATRIX E IS NOT OF FULL RANK */ /* 6: MATRIX C IS NOT OF FULL RANK */ /* 7: RANK DEFECT IN HFTI */ /* 18.5.1981, DIETER KRAFT, DFVLR OBERPFAFFENHOFEN */ /* 20.3.1987, DIETER KRAFT, DFVLR OBERPFAFFENHOFEN */ /* Parameter adjustments */ --d__; --f; --h__; --x; g_dim1 = *lg; g_offset = 1 + g_dim1; g -= g_offset; e_dim1 = *le; e_offset = 1 + e_dim1; e -= e_offset; c_dim1 = *lc; c_offset = 1 + c_dim1; c__ -= c_offset; --w; --jw; /* Function Body */ *mode = 2; if (*mc > *n) { goto L75; } l = *n - *mc; mc1 = *mc + 1; iw = (l + 1) * (*mg + 2) + (*mg << 1) + *mc; ie = iw + *mc + 1; if__ = ie + *me * l; ig = if__ + *me; /* TRIANGULARIZE C AND APPLY FACTORS TO E AND G */ i__1 = *mc; for (i__ = 1; i__ <= i__1; ++i__) { /* Computing MIN */ i__2 = i__ + 1; j = MIN2(i__2,*lc); i__2 = i__ + 1; i__3 = *mc - i__; h12_(&c__1, &i__, &i__2, n, &c__[i__ + c_dim1], lc, &w[iw + i__], & c__[j + c_dim1], lc, &c__1, &i__3); i__2 = i__ + 1; h12_(&c__2, &i__, &i__2, n, &c__[i__ + c_dim1], lc, &w[iw + i__], &e[ e_offset], le, &c__1, me); /* L10: */ i__2 = i__ + 1; h12_(&c__2, &i__, &i__2, n, &c__[i__ + c_dim1], lc, &w[iw + i__], &g[ g_offset], lg, &c__1, mg); } /* SOLVE C*X=D AND MODIFY F */ *mode = 6; i__2 = *mc; for (i__ = 1; i__ <= i__2; ++i__) { if ((d__1 = c__[i__ + i__ * c_dim1], fabs(d__1)) < epmach) { goto L75; } i__1 = i__ - 1; x[i__] = (d__[i__] - ddot_sl__(&i__1, &c__[i__ + c_dim1], *lc, &x[1], 1)) / c__[i__ + i__ * c_dim1]; /* L15: */ } *mode = 1; w[mc1] = 0.0; i__2 = *mg; // BUGFIX for *mc == *n: changed from *mg - *mc, SGJ 2010 dcopy___(&i__2, &w[mc1], 0, &w[mc1], 1); if (*mc == *n) { goto L50; } i__2 = *me; for (i__ = 1; i__ <= i__2; ++i__) { /* L20: */ w[if__ - 1 + i__] = f[i__] - ddot_sl__(mc, &e[i__ + e_dim1], *le, &x[1], 1); } /* STORE TRANSFORMED E & G */ i__2 = *me; for (i__ = 1; i__ <= i__2; ++i__) { /* L25: */ dcopy___(&l, &e[i__ + mc1 * e_dim1], *le, &w[ie - 1 + i__], *me); } i__2 = *mg; for (i__ = 1; i__ <= i__2; ++i__) { /* L30: */ dcopy___(&l, &g[i__ + mc1 * g_dim1], *lg, &w[ig - 1 + i__], *mg); } if (*mg > 0) { goto L40; } /* SOLVE LS WITHOUT INEQUALITY CONSTRAINTS */ *mode = 7; k = MAX2(*le,*n); t = sqrt(epmach); hfti_(&w[ie], me, me, &l, &w[if__], &k, &c__1, &t, &krank, xnrm, &w[1], & w[l + 1], &jw[1]); dcopy___(&l, &w[if__], 1, &x[mc1], 1); if (krank != l) { goto L75; } *mode = 1; goto L50; /* MODIFY H AND SOLVE INEQUALITY CONSTRAINED LS PROBLEM */ L40: i__2 = *mg; for (i__ = 1; i__ <= i__2; ++i__) { /* L45: */ h__[i__] -= ddot_sl__(mc, &g[i__ + g_dim1], *lg, &x[1], 1); } lsi_(&w[ie], &w[if__], &w[ig], &h__[1], me, me, mg, mg, &l, &x[mc1], xnrm, &w[mc1], &jw[1], mode); if (*mc == 0) { goto L75; } t = dnrm2___(mc, &x[1], 1); *xnrm = sqrt(*xnrm * *xnrm + t * t); if (*mode != 1) { goto L75; } /* SOLUTION OF ORIGINAL PROBLEM AND LAGRANGE MULTIPLIERS */ L50: i__2 = *me; for (i__ = 1; i__ <= i__2; ++i__) { /* L55: */ f[i__] = ddot_sl__(n, &e[i__ + e_dim1], *le, &x[1], 1) - f[i__]; } i__2 = *mc; for (i__ = 1; i__ <= i__2; ++i__) { /* L60: */ d__[i__] = ddot_sl__(me, &e[i__ * e_dim1 + 1], 1, &f[1], 1) - ddot_sl__(mg, &g[i__ * g_dim1 + 1], 1, &w[mc1], 1); } for (i__ = *mc; i__ >= 1; --i__) { /* L65: */ i__2 = i__ + 1; h12_(&c__2, &i__, &i__2, n, &c__[i__ + c_dim1], lc, &w[iw + i__], &x[ 1], &c__1, &c__1, &c__1); } for (i__ = *mc; i__ >= 1; --i__) { /* Computing MIN */ i__2 = i__ + 1; j = MIN2(i__2,*lc); i__2 = *mc - i__; w[i__] = (d__[i__] - ddot_sl__(&i__2, &c__[j + i__ * c_dim1], 1, & w[j], 1)) / c__[i__ + i__ * c_dim1]; /* L70: */ } /* END OF SUBROUTINE LSEI */ L75: return; } /* lsei_ */ static void lsq_(int *m, int *meq, int *n, int *nl, int *la, double *l, double *g, double *a, double * b, const double *xl, const double *xu, double *x, double *y, double *w, int *jw, int *mode) { /* Initialized data */ const double one = 1.; /* System generated locals */ int a_dim1, a_offset, i__1, i__2; double d__1; /* Local variables */ int i__, i1, i2, i3, i4, m1, n1, n2, n3, ic, id, ie, if__, ig, ih, il, im, ip, iu, iw; double diag; int mineq; double xnorm; /* MINIMIZE with respect to X */ /* ||E*X - F|| */ /* 1/2 T */ /* WITH UPPER TRIANGULAR MATRIX E = +D *L , */ /* -1/2 -1 */ /* AND VECTOR F = -D *L *G, */ /* WHERE THE UNIT LOWER TRIDIANGULAR MATRIX L IS STORED COLUMNWISE */ /* DENSE IN THE N*(N+1)/2 ARRAY L WITH VECTOR D STORED IN ITS */ /* 'DIAGONAL' THUS SUBSTITUTING THE ONE-ELEMENTS OF L */ /* SUBJECT TO */ /* A(J)*X - B(J) = 0 , J=1,...,MEQ, */ /* A(J)*X - B(J) >=0, J=MEQ+1,...,M, */ /* XL(I) <= X(I) <= XU(I), I=1,...,N, */ /* ON ENTRY, THE USER HAS TO PROVIDE THE ARRAYS L, G, A, B, XL, XU. */ /* WITH DIMENSIONS: L(N*(N+1)/2), G(N), A(LA,N), B(M), XL(N), XU(N) */ /* THE WORKING ARRAY W MUST HAVE AT LEAST THE FOLLOWING DIMENSION: */ /* DIM(W) = (3*N+M)*(N+1) for LSQ */ /* +(N-MEQ+1)*(MINEQ+2) + 2*MINEQ for LSI */ /* +(N+MINEQ)*(N-MEQ) + 2*MEQ + N for LSEI */ /* with MINEQ = M - MEQ + 2*N */ /* ON RETURN, NO ARRAY WILL BE CHANGED BY THE SUBROUTINE. */ /* X STORES THE N-DIMENSIONAL SOLUTION VECTOR */ /* Y STORES THE VECTOR OF LAGRANGE MULTIPLIERS OF DIMENSION */ /* M+N+N (CONSTRAINTS+LOWER+UPPER BOUNDS) */ /* MODE IS A SUCCESS-FAILURE FLAG WITH THE FOLLOWING MEANINGS: */ /* MODE=1: SUCCESSFUL COMPUTATION */ /* 2: ERROR RETURN BECAUSE OF WRONG DIMENSIONS (N<1) */ /* 3: ITERATION COUNT EXCEEDED BY NNLS */ /* 4: INEQUALITY CONSTRAINTS INCOMPATIBLE */ /* 5: MATRIX E IS NOT OF FULL RANK */ /* 6: MATRIX C IS NOT OF FULL RANK */ /* 7: RANK DEFECT IN HFTI */ /* coded Dieter Kraft, april 1987 */ /* revised march 1989 */ /* Parameter adjustments */ --y; --x; --xu; --xl; --g; --l; --b; a_dim1 = *la; a_offset = 1 + a_dim1; a -= a_offset; --w; --jw; /* Function Body */ n1 = *n + 1; mineq = *m - *meq; m1 = mineq + *n + *n; /* determine whether to solve problem */ /* with inconsistent linerarization (n2=1) */ /* or not (n2=0) */ n2 = n1 * *n / 2 + 1; if (n2 == *nl) { n2 = 0; } else { n2 = 1; } n3 = *n - n2; /* RECOVER MATRIX E AND VECTOR F FROM L AND G */ i2 = 1; i3 = 1; i4 = 1; ie = 1; if__ = *n * *n + 1; i__1 = n3; for (i__ = 1; i__ <= i__1; ++i__) { i1 = n1 - i__; diag = sqrt(l[i2]); w[i3] = 0.0; dcopy___(&i1, &w[i3], 0, &w[i3], 1); i__2 = i1 - n2; dcopy___(&i__2, &l[i2], 1, &w[i3], *n); i__2 = i1 - n2; dscal_sl__(&i__2, &diag, &w[i3], *n); w[i3] = diag; i__2 = i__ - 1; w[if__ - 1 + i__] = (g[i__] - ddot_sl__(&i__2, &w[i4], 1, &w[if__] , 1)) / diag; i2 = i2 + i1 - n2; i3 += n1; i4 += *n; /* L10: */ } if (n2 == 1) { w[i3] = l[*nl]; w[i4] = 0.0; dcopy___(&n3, &w[i4], 0, &w[i4], 1); w[if__ - 1 + *n] = 0.0; } d__1 = -one; dscal_sl__(n, &d__1, &w[if__], 1); ic = if__ + *n; id = ic + *meq * *n; if (*meq > 0) { /* RECOVER MATRIX C FROM UPPER PART OF A */ i__1 = *meq; for (i__ = 1; i__ <= i__1; ++i__) { dcopy___(n, &a[i__ + a_dim1], *la, &w[ic - 1 + i__], *meq); /* L20: */ } /* RECOVER VECTOR D FROM UPPER PART OF B */ dcopy___(meq, &b[1], 1, &w[id], 1); d__1 = -one; dscal_sl__(meq, &d__1, &w[id], 1); } ig = id + *meq; if (mineq > 0) { /* RECOVER MATRIX G FROM LOWER PART OF A */ i__1 = mineq; for (i__ = 1; i__ <= i__1; ++i__) { dcopy___(n, &a[*meq + i__ + a_dim1], *la, &w[ig - 1 + i__], m1); /* L30: */ } } /* AUGMENT MATRIX G BY +I AND -I */ ip = ig + mineq; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { w[ip - 1 + i__] = 0.0; dcopy___(n, &w[ip - 1 + i__], 0, &w[ip - 1 + i__], m1); /* L40: */ } i__1 = m1 + 1; /* SGJ, 2010: skip constraints for infinite bounds */ for (i__ = 1; i__ <= *n; ++i__) if (!nlopt_isinf(xl[i__])) w[(ip - i__1) + i__ * i__1] = +1.0; /* Old code: w[ip] = one; dcopy___(n, &w[ip], 0, &w[ip], i__1); */ im = ip + *n; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { w[im - 1 + i__] = 0.0; dcopy___(n, &w[im - 1 + i__], 0, &w[im - 1 + i__], m1); /* L50: */ } i__1 = m1 + 1; /* SGJ, 2010: skip constraints for infinite bounds */ for (i__ = 1; i__ <= *n; ++i__) if (!nlopt_isinf(xu[i__])) w[(im - i__1) + i__ * i__1] = -1.0; /* Old code: w[im] = -one; dcopy___(n, &w[im], 0, &w[im], i__1); */ ih = ig + m1 * *n; if (mineq > 0) { /* RECOVER H FROM LOWER PART OF B */ dcopy___(&mineq, &b[*meq + 1], 1, &w[ih], 1); d__1 = -one; dscal_sl__(&mineq, &d__1, &w[ih], 1); } /* AUGMENT VECTOR H BY XL AND XU */ il = ih + mineq; iu = il + *n; /* SGJ, 2010: skip constraints for infinite bounds */ for (i__ = 1; i__ <= *n; ++i__) { w[(il-1) + i__] = nlopt_isinf(xl[i__]) ? 0 : xl[i__]; w[(iu-1) + i__] = nlopt_isinf(xu[i__]) ? 0 : -xu[i__]; } /* Old code: dcopy___(n, &xl[1], 1, &w[il], 1); dcopy___(n, &xu[1], 1, &w[iu], 1); d__1 = -one; dscal_sl__(n, &d__1, &w[iu], 1); */ iw = iu + *n; i__1 = MAX2(1,*meq); lsei_(&w[ic], &w[id], &w[ie], &w[if__], &w[ig], &w[ih], &i__1, meq, n, n, &m1, &m1, n, &x[1], &xnorm, &w[iw], &jw[1], mode); if (*mode == 1) { /* restore Lagrange multipliers */ dcopy___(m, &w[iw], 1, &y[1], 1); dcopy___(&n3, &w[iw + *m], 1, &y[*m + 1], 1); dcopy___(&n3, &w[iw + *m + *n], 1, &y[*m + n3 + 1], 1); /* SGJ, 2010: make sure bound constraints are satisfied, since roundoff error sometimes causes slight violations and NLopt guarantees that bounds are strictly obeyed */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (x[i__] < xl[i__]) x[i__] = xl[i__]; else if (x[i__] > xu[i__]) x[i__] = xu[i__]; } } /* END OF SUBROUTINE LSQ */ } /* lsq_ */ static void ldl_(int *n, double *a, double *z__, double *sigma, double *w) { /* Initialized data */ const double one = 1.; const double four = 4.; const double epmach = 2.22e-16; /* System generated locals */ int i__1, i__2; /* Local variables */ int i__, j; double t, u, v; int ij; double tp, beta, gamma_, alpha, delta; /* LDL LDL' - RANK-ONE - UPDATE */ /* PURPOSE: */ /* UPDATES THE LDL' FACTORS OF MATRIX A BY RANK-ONE MATRIX */ /* SIGMA*Z*Z' */ /* INPUT ARGUMENTS: (* MEANS PARAMETERS ARE CHANGED DURING EXECUTION) */ /* N : ORDER OF THE COEFFICIENT MATRIX A */ /* * A : POSITIVE DEFINITE MATRIX OF DIMENSION N; */ /* ONLY THE LOWER TRIANGLE IS USED AND IS STORED COLUMN BY */ /* COLUMN AS ONE DIMENSIONAL ARRAY OF DIMENSION N*(N+1)/2. */ /* * Z : VECTOR OF DIMENSION N OF UPDATING ELEMENTS */ /* SIGMA : SCALAR FACTOR BY WHICH THE MODIFYING DYADE Z*Z' IS */ /* MULTIPLIED */ /* OUTPUT ARGUMENTS: */ /* A : UPDATED LDL' FACTORS */ /* WORKING ARRAY: */ /* W : VECTOR OP DIMENSION N (USED ONLY IF SIGMA .LT. ZERO) */ /* METHOD: */ /* THAT OF FLETCHER AND POWELL AS DESCRIBED IN : */ /* FLETCHER,R.,(1974) ON THE MODIFICATION OF LDL' FACTORIZATION. */ /* POWELL,M.J.D. MATH.COMPUTATION 28, 1067-1078. */ /* IMPLEMENTED BY: */ /* KRAFT,D., DFVLR - INSTITUT FUER DYNAMIK DER FLUGSYSTEME */ /* D-8031 OBERPFAFFENHOFEN */ /* STATUS: 15. JANUARY 1980 */ /* SUBROUTINES REQUIRED: NONE */ /* Parameter adjustments */ --w; --z__; --a; /* Function Body */ if (*sigma == 0.0) { goto L280; } ij = 1; t = one / *sigma; if (*sigma > 0.0) { goto L220; } /* PREPARE NEGATIVE UPDATE */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* L150: */ w[i__] = z__[i__]; } i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { v = w[i__]; t += v * v / a[ij]; i__2 = *n; for (j = i__ + 1; j <= i__2; ++j) { ++ij; /* L160: */ w[j] -= v * a[ij]; } /* L170: */ ++ij; } if (t >= 0.0) { t = epmach / *sigma; } i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { j = *n + 1 - i__; ij -= i__; u = w[j]; w[j] = t; /* L210: */ t -= u * u / a[ij]; } L220: /* HERE UPDATING BEGINS */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { v = z__[i__]; delta = v / a[ij]; if (*sigma < 0.0) { tp = w[i__]; } else /* if (*sigma > 0.0), since *sigma != 0 from above */ { tp = t + delta * v; } alpha = tp / t; a[ij] = alpha * a[ij]; if (i__ == *n) { goto L280; } beta = delta / tp; if (alpha > four) { goto L240; } i__2 = *n; for (j = i__ + 1; j <= i__2; ++j) { ++ij; z__[j] -= v * a[ij]; /* L230: */ a[ij] += beta * z__[j]; } goto L260; L240: gamma_ = t / tp; i__2 = *n; for (j = i__ + 1; j <= i__2; ++j) { ++ij; u = a[ij]; a[ij] = gamma_ * u + beta * z__[j]; /* L250: */ z__[j] -= v * u; } L260: ++ij; /* L270: */ t = tp; } L280: return; /* END OF LDL */ } /* ldl_ */ #if 0 /* we don't want to use this linmin function, for two reasons: 1) it was apparently written assuming an old version of Fortran where all variables are saved by default, hence it was missing a "save" statement ... I would need to go through and figure out which variables need to be declared "static" (or, better yet, save them like I did in slsqp to make it re-entrant) 2) it doesn't exploit gradient information, which is stupid since we have this info 3) in the context of NLopt, it makes much more sence to use the local_opt algorithm to do the line minimization recursively by whatever algorithm the user wants (defaulting to a gradient-based method like LBFGS) */ static double d_sign(double a, double s) { return s < 0 ? -a : a; } static double linmin_(int *mode, const double *ax, const double *bx, double * f, double *tol) { /* Initialized data */ const double c__ = .381966011; const double eps = 1.5e-8; /* System generated locals */ double ret_val, d__1; /* Local variables */ double a, b, d__, e, m, p, q, r__, u, v, w, x, fu, fv, fw, fx, tol1, tol2; /* LINMIN LINESEARCH WITHOUT DERIVATIVES */ /* PURPOSE: */ /* TO FIND THE ARGUMENT LINMIN WHERE THE FUNCTION F TAKES ITS MINIMUM */ /* ON THE INTERVAL AX, BX. */ /* COMBINATION OF GOLDEN SECTION AND SUCCESSIVE QUADRATIC INTERPOLATION. */ /* INPUT ARGUMENTS: (* MEANS PARAMETERS ARE CHANGED DURING EXECUTION) */ /* *MODE SEE OUTPUT ARGUMENTS */ /* AX LEFT ENDPOINT OF INITIAL INTERVAL */ /* BX RIGHT ENDPOINT OF INITIAL INTERVAL */ /* F FUNCTION VALUE AT LINMIN WHICH IS TO BE BROUGHT IN BY */ /* REVERSE COMMUNICATION CONTROLLED BY MODE */ /* TOL DESIRED LENGTH OF INTERVAL OF UNCERTAINTY OF FINAL RESULT */ /* OUTPUT ARGUMENTS: */ /* LINMIN ABSCISSA APPROXIMATING THE POINT WHERE F ATTAINS A MINIMUM */ /* MODE CONTROLS REVERSE COMMUNICATION */ /* MUST BE SET TO 0 INITIALLY, RETURNS WITH INTERMEDIATE */ /* VALUES 1 AND 2 WHICH MUST NOT BE CHANGED BY THE USER, */ /* ENDS WITH CONVERGENCE WITH VALUE 3. */ /* WORKING ARRAY: */ /* NONE */ /* METHOD: */ /* THIS FUNCTION SUBPROGRAM IS A SLIGHTLY MODIFIED VERSION OF THE */ /* ALGOL 60 PROCEDURE LOCALMIN GIVEN IN */ /* R.P. BRENT: ALGORITHMS FOR MINIMIZATION WITHOUT DERIVATIVES, */ /* PRENTICE-HALL (1973). */ /* IMPLEMENTED BY: */ /* KRAFT, D., DFVLR - INSTITUT FUER DYNAMIK DER FLUGSYSTEME */ /* D-8031 OBERPFAFFENHOFEN */ /* STATUS: 31. AUGUST 1984 */ /* SUBROUTINES REQUIRED: NONE */ /* EPS = SQUARE - ROOT OF MACHINE PRECISION */ /* C = GOLDEN SECTION RATIO = (3-SQRT(5))/2 */ switch (*mode) { case 1: goto L10; case 2: goto L55; } /* INITIALIZATION */ a = *ax; b = *bx; e = 0.0; v = a + c__ * (b - a); w = v; x = w; ret_val = x; *mode = 1; goto L100; /* MAIN LOOP STARTS HERE */ L10: fx = *f; fv = fx; fw = fv; L20: m = (a + b) * .5; tol1 = eps * fabs(x) + *tol; tol2 = tol1 + tol1; /* TEST CONVERGENCE */ if ((d__1 = x - m, fabs(d__1)) <= tol2 - (b - a) * .5) { goto L90; } r__ = 0.0; q = r__; p = q; if (fabs(e) <= tol1) { goto L30; } /* FIT PARABOLA */ r__ = (x - w) * (fx - fv); q = (x - v) * (fx - fw); p = (x - v) * q - (x - w) * r__; q -= r__; q += q; if (q > 0.0) { p = -p; } if (q < 0.0) { q = -q; } r__ = e; e = d__; /* IS PARABOLA ACCEPTABLE */ L30: if (fabs(p) >= (d__1 = q * r__, fabs(d__1)) * .5 || p <= q * (a - x) || p >= q * (b - x)) { goto L40; } /* PARABOLIC INTERPOLATION STEP */ d__ = p / q; /* F MUST NOT BE EVALUATED TOO CLOSE TO A OR B */ if (u - a < tol2) { d__1 = m - x; d__ = d_sign(tol1, d__1); } if (b - u < tol2) { d__1 = m - x; d__ = d_sign(tol1, d__1); } goto L50; /* GOLDEN SECTION STEP */ L40: if (x >= m) { e = a - x; } if (x < m) { e = b - x; } d__ = c__ * e; /* F MUST NOT BE EVALUATED TOO CLOSE TO X */ L50: if (fabs(d__) < tol1) { d__ = d_sign(tol1, d__); } u = x + d__; ret_val = u; *mode = 2; goto L100; L55: fu = *f; /* UPDATE A, B, V, W, AND X */ if (fu > fx) { goto L60; } if (u >= x) { a = x; } if (u < x) { b = x; } v = w; fv = fw; w = x; fw = fx; x = u; fx = fu; goto L85; L60: if (u < x) { a = u; } if (u >= x) { b = u; } if (fu <= fw || w == x) { goto L70; } if (fu <= fv || v == x || v == w) { goto L80; } goto L85; L70: v = w; fv = fw; w = u; fw = fu; goto L85; L80: v = u; fv = fu; L85: goto L20; /* END OF MAIN LOOP */ L90: ret_val = x; *mode = 3; L100: return ret_val; /* END OF LINMIN */ } /* linmin_ */ #endif typedef struct { double t, f0, h1, h2, h3, h4; int n1, n2, n3; double t0, gs; double tol; int line; double alpha; int iexact; int incons, ireset, itermx; double *x0; } slsqpb_state; #define SS(var) state->var = var #define SAVE_STATE \ SS(t); SS(f0); SS(h1); SS(h2); SS(h3); SS(h4); \ SS(n1); SS(n2); SS(n3); \ SS(t0); SS(gs); \ SS(tol); \ SS(line); \ SS(alpha); \ SS(iexact); \ SS(incons); SS(ireset); SS(itermx) #define RS(var) var = state->var #define RESTORE_STATE \ RS(t); RS(f0); RS(h1); RS(h2); RS(h3); RS(h4); \ RS(n1); RS(n2); RS(n3); \ RS(t0); RS(gs); \ RS(tol); \ RS(line); \ RS(alpha); \ RS(iexact); \ RS(incons); RS(ireset); RS(itermx) static void slsqpb_(int *m, int *meq, int *la, int * n, double *x, const double *xl, const double *xu, double *f, double *c__, double *g, double *a, double *acc, int *iter, int *mode, double *r__, double *l, double *x0, double *mu, double *s, double *u, double *v, double *w, int *iw, slsqpb_state *state) { /* Initialized data */ const double one = 1.; const double alfmin = .1; const double hun = 100.; const double ten = 10.; const double two = 2.; /* System generated locals */ int a_dim1, a_offset, i__1, i__2; double d__1, d__2; /* Local variables */ int i__, j, k; /* saved state from one call to the next; SGJ 2010: save/restore via state parameter, to make re-entrant. */ double t, f0, h1, h2, h3, h4; int n1, n2, n3; double t0, gs; double tol; int line; double alpha; int iexact; int incons, ireset, itermx; RESTORE_STATE; /* NONLINEAR PROGRAMMING BY SOLVING SEQUENTIALLY QUADRATIC PROGRAMS */ /* - L1 - LINE SEARCH, POSITIVE DEFINITE BFGS UPDATE - */ /* BODY SUBROUTINE FOR SLSQP */ /* dim(W) = N1*(N1+1) + MEQ*(N1+1) + MINEQ*(N1+1) for LSQ */ /* +(N1-MEQ+1)*(MINEQ+2) + 2*MINEQ */ /* +(N1+MINEQ)*(N1-MEQ) + 2*MEQ + N1 for LSEI */ /* with MINEQ = M - MEQ + 2*N1 & N1 = N+1 */ /* Parameter adjustments */ --mu; --c__; --v; --u; --s; --x0; --l; --r__; a_dim1 = *la; a_offset = 1 + a_dim1; a -= a_offset; --g; --xu; --xl; --x; --w; --iw; /* Function Body */ if (*mode == -1) { goto L260; } else if (*mode == 0) { goto L100; } else { goto L220; } L100: itermx = *iter; if (*acc >= 0.0) { iexact = 0; } else { iexact = 1; } *acc = fabs(*acc); tol = ten * *acc; *iter = 0; ireset = 0; n1 = *n + 1; n2 = n1 * *n / 2; n3 = n2 + 1; s[1] = 0.0; mu[1] = 0.0; dcopy___(n, &s[1], 0, &s[1], 1); dcopy___(m, &mu[1], 0, &mu[1], 1); /* RESET BFGS MATRIX */ L110: ++ireset; if (ireset > 5) { goto L255; } l[1] = 0.0; dcopy___(&n2, &l[1], 0, &l[1], 1); j = 1; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { l[j] = one; j = j + n1 - i__; /* L120: */ } /* MAIN ITERATION : SEARCH DIRECTION, STEPLENGTH, LDL'-UPDATE */ L130: ++(*iter); *mode = 9; if (*iter > itermx && itermx > 0) { /* SGJ 2010: ignore if itermx <= 0 */ goto L330; } /* SEARCH DIRECTION AS SOLUTION OF QP - SUBPROBLEM */ dcopy___(n, &xl[1], 1, &u[1], 1); dcopy___(n, &xu[1], 1, &v[1], 1); d__1 = -one; daxpy_sl__(n, &d__1, &x[1], 1, &u[1], 1); d__1 = -one; daxpy_sl__(n, &d__1, &x[1], 1, &v[1], 1); h4 = one; lsq_(m, meq, n, &n3, la, &l[1], &g[1], &a[a_offset], &c__[1], &u[1], &v[1] , &s[1], &r__[1], &w[1], &iw[1], mode); /* AUGMENTED PROBLEM FOR INCONSISTENT LINEARIZATION */ if (*mode == 6) { if (*n == *meq) { *mode = 4; } } if (*mode == 4) { i__1 = *m; for (j = 1; j <= i__1; ++j) { if (j <= *meq) { a[j + n1 * a_dim1] = -c__[j]; } else { /* Computing MAX */ d__1 = -c__[j]; a[j + n1 * a_dim1] = MAX2(d__1,0.0); } /* L140: */ } s[1] = 0.0; dcopy___(n, &s[1], 0, &s[1], 1); h3 = 0.0; g[n1] = 0.0; l[n3] = hun; s[n1] = one; u[n1] = 0.0; v[n1] = one; incons = 0; L150: lsq_(m, meq, &n1, &n3, la, &l[1], &g[1], &a[a_offset], &c__[1], &u[1], &v[1], &s[1], &r__[1], &w[1], &iw[1], mode); h4 = one - s[n1]; if (*mode == 4) { l[n3] = ten * l[n3]; ++incons; if (incons > 5) { goto L330; } goto L150; } else if (*mode != 1) { goto L330; } } else if (*mode != 1) { goto L330; } /* UPDATE MULTIPLIERS FOR L1-TEST */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { v[i__] = g[i__] - ddot_sl__(m, &a[i__ * a_dim1 + 1], 1, &r__[1], 1); /* L160: */ } f0 = *f; dcopy___(n, &x[1], 1, &x0[1], 1); gs = ddot_sl__(n, &g[1], 1, &s[1], 1); h1 = fabs(gs); h2 = 0.0; i__1 = *m; for (j = 1; j <= i__1; ++j) { if (j <= *meq) { h3 = c__[j]; } else { h3 = 0.0; } /* Computing MAX */ d__1 = -c__[j]; h2 += MAX2(d__1,h3); h3 = (d__1 = r__[j], fabs(d__1)); /* Computing MAX */ d__1 = h3, d__2 = (mu[j] + h3) / two; mu[j] = MAX2(d__1,d__2); h1 += h3 * (d__1 = c__[j], fabs(d__1)); /* L170: */ } /* CHECK CONVERGENCE */ *mode = 0; if (h1 < *acc && h2 < *acc) { goto L330; } h1 = 0.0; i__1 = *m; for (j = 1; j <= i__1; ++j) { if (j <= *meq) { h3 = c__[j]; } else { h3 = 0.0; } /* Computing MAX */ d__1 = -c__[j]; h1 += mu[j] * MAX2(d__1,h3); /* L180: */ } t0 = *f + h1; h3 = gs - h1 * h4; *mode = 8; if (h3 >= 0.0) { goto L110; } /* LINE SEARCH WITH AN L1-TESTFUNCTION */ line = 0; alpha = one; if (iexact == 1) { goto L210; } /* INEXACT LINESEARCH */ L190: ++line; h3 = alpha * h3; dscal_sl__(n, &alpha, &s[1], 1); dcopy___(n, &x0[1], 1, &x[1], 1); daxpy_sl__(n, &one, &s[1], 1, &x[1], 1); /* SGJ 2010: ensure roundoff doesn't push us past bound constraints */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (x[i__] < xl[i__]) x[i__] = xl[i__]; else if (x[i__] > xu[i__]) x[i__] = xu[i__]; } /* SGJ 2010: optimizing for the common case where the inexact line search succeeds in one step, use special mode = -2 here to eliminate a a subsequent unnecessary mode = -1 call, at the expense of extra gradient evaluations when more than one inexact line-search step is required */ *mode = line == 1 ? -2 : 1; goto L330; L200: if (h1 <= h3 / ten || line > 10) { goto L240; } /* Computing MAX */ d__1 = h3 / (two * (h3 - h1)); alpha = MAX2(d__1,alfmin); goto L190; /* EXACT LINESEARCH */ L210: #if 0 /* SGJ: see comments by linmin_ above: if we want to do an exact linesearch (which usually we probably don't), we should call NLopt recursively */ if (line != 3) { alpha = linmin_(&line, &alfmin, &one, &t, &tol); dcopy___(n, &x0[1], 1, &x[1], 1); daxpy_sl__(n, &alpha, &s[1], 1, &x[1], 1); *mode = 1; goto L330; } #else *mode = 9 /* will yield nlopt_failure */; return; #endif dscal_sl__(n, &alpha, &s[1], 1); goto L240; /* CALL FUNCTIONS AT CURRENT X */ L220: t = *f; i__1 = *m; for (j = 1; j <= i__1; ++j) { if (j <= *meq) { h1 = c__[j]; } else { h1 = 0.0; } /* Computing MAX */ d__1 = -c__[j]; t += mu[j] * MAX2(d__1,h1); /* L230: */ } h1 = t - t0; switch (iexact + 1) { case 1: goto L200; case 2: goto L210; } /* CHECK CONVERGENCE */ L240: h3 = 0.0; i__1 = *m; for (j = 1; j <= i__1; ++j) { if (j <= *meq) { h1 = c__[j]; } else { h1 = 0.0; } /* Computing MAX */ d__1 = -c__[j]; h3 += MAX2(d__1,h1); /* L250: */ } if (((d__1 = *f - f0, fabs(d__1)) < *acc || dnrm2___(n, &s[1], 1) < * acc) && h3 < *acc) { *mode = 0; } else { *mode = -1; } goto L330; /* CHECK relaxed CONVERGENCE in case of positive directional derivative */ L255: if (((d__1 = *f - f0, fabs(d__1)) < tol || dnrm2___(n, &s[1], 1) < tol) && h3 < tol) { *mode = 0; } else { *mode = 8; } goto L330; /* CALL JACOBIAN AT CURRENT X */ /* UPDATE CHOLESKY-FACTORS OF HESSIAN MATRIX BY MODIFIED BFGS FORMULA */ L260: i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { u[i__] = g[i__] - ddot_sl__(m, &a[i__ * a_dim1 + 1], 1, &r__[1], 1) - v[i__]; /* L270: */ } /* L'*S */ k = 0; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { h1 = 0.0; ++k; i__2 = *n; for (j = i__ + 1; j <= i__2; ++j) { ++k; h1 += l[k] * s[j]; /* L280: */ } v[i__] = s[i__] + h1; /* L290: */ } /* D*L'*S */ k = 1; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { v[i__] = l[k] * v[i__]; k = k + n1 - i__; /* L300: */ } /* L*D*L'*S */ for (i__ = *n; i__ >= 1; --i__) { h1 = 0.0; k = i__; i__1 = i__ - 1; for (j = 1; j <= i__1; ++j) { h1 += l[k] * v[j]; k = k + *n - j; /* L310: */ } v[i__] += h1; /* L320: */ } h1 = ddot_sl__(n, &s[1], 1, &u[1], 1); h2 = ddot_sl__(n, &s[1], 1, &v[1], 1); h3 = h2 * .2; if (h1 < h3) { h4 = (h2 - h3) / (h2 - h1); h1 = h3; dscal_sl__(n, &h4, &u[1], 1); d__1 = one - h4; daxpy_sl__(n, &d__1, &v[1], 1, &u[1], 1); } d__1 = one / h1; ldl_(n, &l[1], &u[1], &d__1, &v[1]); d__1 = -one / h2; ldl_(n, &l[1], &v[1], &d__1, &u[1]); /* END OF MAIN ITERATION */ goto L130; /* END OF SLSQPB */ L330: SAVE_STATE; } /* slsqpb_ */ /* *********************************************************************** */ /* optimizer * */ /* *********************************************************************** */ static void slsqp(int *m, int *meq, int *la, int *n, double *x, const double *xl, const double *xu, double *f, double *c__, double *g, double *a, double *acc, int *iter, int *mode, double *w, int *l_w__, int * jw, int *l_jw__, slsqpb_state *state) { /* System generated locals */ int a_dim1, a_offset, i__1, i__2; /* Local variables */ int n1, il, im, ir, is, iu, iv, iw, ix, mineq; /* SLSQP S EQUENTIAL L EAST SQ UARES P ROGRAMMING */ /* TO SOLVE GENERAL NONLINEAR OPTIMIZATION PROBLEMS */ /* *********************************************************************** */ /* * * */ /* * * */ /* * A NONLINEAR PROGRAMMING METHOD WITH * */ /* * QUADRATIC PROGRAMMING SUBPROBLEMS * */ /* * * */ /* * * */ /* * THIS SUBROUTINE SOLVES THE GENERAL NONLINEAR PROGRAMMING PROBLEM * */ /* * * */ /* * MINIMIZE F(X) * */ /* * * */ /* * SUBJECT TO C (X) .EQ. 0 , J = 1,...,MEQ * */ /* * J * */ /* * * */ /* * C (X) .GE. 0 , J = MEQ+1,...,M * */ /* * J * */ /* * * */ /* * XL .LE. X .LE. XU , I = 1,...,N. * */ /* * I I I * */ /* * * */ /* * THE ALGORITHM IMPLEMENTS THE METHOD OF HAN AND POWELL * */ /* * WITH BFGS-UPDATE OF THE B-MATRIX AND L1-TEST FUNCTION * */ /* * WITHIN THE STEPLENGTH ALGORITHM. * */ /* * * */ /* * PARAMETER DESCRIPTION: * */ /* * ( * MEANS THIS PARAMETER WILL BE CHANGED DURING CALCULATION ) * */ /* * * */ /* * M IS THE TOTAL NUMBER OF CONSTRAINTS, M .GE. 0 * */ /* * MEQ IS THE NUMBER OF EQUALITY CONSTRAINTS, MEQ .GE. 0 * */ /* * LA SEE A, LA .GE. MAX(M,1) * */ /* * N IS THE NUMBER OF VARIBLES, N .GE. 1 * */ /* * * X() X() STORES THE CURRENT ITERATE OF THE N VECTOR X * */ /* * ON ENTRY X() MUST BE INITIALIZED. ON EXIT X() * */ /* * STORES THE SOLUTION VECTOR X IF MODE = 0. * */ /* * XL() XL() STORES AN N VECTOR OF LOWER BOUNDS XL TO X. * */ /* * XU() XU() STORES AN N VECTOR OF UPPER BOUNDS XU TO X. * */ /* * F IS THE VALUE OF THE OBJECTIVE FUNCTION. * */ /* * C() C() STORES THE M VECTOR C OF CONSTRAINTS, * */ /* * EQUALITY CONSTRAINTS (IF ANY) FIRST. * */ /* * DIMENSION OF C MUST BE GREATER OR EQUAL LA, * */ /* * which must be GREATER OR EQUAL MAX(1,M). * */ /* * G() G() STORES THE N VECTOR G OF PARTIALS OF THE * */ /* * OBJECTIVE FUNCTION; DIMENSION OF G MUST BE * */ /* * GREATER OR EQUAL N+1. * */ /* * A(),LA,M,N THE LA BY N + 1 ARRAY A() STORES * */ /* * THE M BY N MATRIX A OF CONSTRAINT NORMALS. * */ /* * A() HAS FIRST DIMENSIONING PARAMETER LA, * */ /* * WHICH MUST BE GREATER OR EQUAL MAX(1,M). * */ /* * F,C,G,A MUST ALL BE SET BY THE USER BEFORE EACH CALL. * */ /* * * ACC ABS(ACC) CONTROLS THE FINAL ACCURACY. * */ /* * IF ACC .LT. ZERO AN EXACT LINESEARCH IS PERFORMED,* */ /* * OTHERWISE AN ARMIJO-TYPE LINESEARCH IS USED. * */ /* * * ITER PRESCRIBES THE MAXIMUM NUMBER OF ITERATIONS. * */ /* * ON EXIT ITER INDICATES THE NUMBER OF ITERATIONS. * */ /* * * MODE MODE CONTROLS CALCULATION: * */ /* * REVERSE COMMUNICATION IS USED IN THE SENSE THAT * */ /* * THE PROGRAM IS INITIALIZED BY MODE = 0; THEN IT IS* */ /* * TO BE CALLED REPEATEDLY BY THE USER UNTIL A RETURN* */ /* * WITH MODE .NE. IABS(1) TAKES PLACE. * */ /* * IF MODE = -1 GRADIENTS HAVE TO BE CALCULATED, * */ /* * WHILE WITH MODE = 1 FUNCTIONS HAVE TO BE CALCULATED */ /* * MODE MUST NOT BE CHANGED BETWEEN SUBSEQUENT CALLS * */ /* * OF SQP. * */ /* * EVALUATION MODES: * */ /* * MODE = -2,-1: GRADIENT EVALUATION, (G&A) * */ /* * 0: ON ENTRY: INITIALIZATION, (F,G,C&A) * */ /* * ON EXIT : REQUIRED ACCURACY FOR SOLUTION OBTAINED * */ /* * 1: FUNCTION EVALUATION, (F&C) * */ /* * * */ /* * FAILURE MODES: * */ /* * 2: NUMBER OF EQUALITY CONTRAINTS LARGER THAN N * */ /* * 3: MORE THAN 3*N ITERATIONS IN LSQ SUBPROBLEM * */ /* * 4: INEQUALITY CONSTRAINTS INCOMPATIBLE * */ /* * 5: SINGULAR MATRIX E IN LSQ SUBPROBLEM * */ /* * 6: SINGULAR MATRIX C IN LSQ SUBPROBLEM * */ /* * 7: RANK-DEFICIENT EQUALITY CONSTRAINT SUBPROBLEM HFTI* */ /* * 8: POSITIVE DIRECTIONAL DERIVATIVE FOR LINESEARCH * */ /* * 9: MORE THAN ITER ITERATIONS IN SQP * */ /* * >=10: WORKING SPACE W OR JW TOO SMALL, * */ /* * W SHOULD BE ENLARGED TO L_W=MODE/1000 * */ /* * JW SHOULD BE ENLARGED TO L_JW=MODE-1000*L_W * */ /* * * W(), L_W W() IS A ONE DIMENSIONAL WORKING SPACE, * */ /* * THE LENGTH L_W OF WHICH SHOULD BE AT LEAST * */ /* * (3*N1+M)*(N1+1) for LSQ * */ /* * +(N1-MEQ+1)*(MINEQ+2) + 2*MINEQ for LSI * */ /* * +(N1+MINEQ)*(N1-MEQ) + 2*MEQ + N1 for LSEI * */ /* * + N1*N/2 + 2*M + 3*N + 3*N1 + 1 for SLSQPB * */ /* * with MINEQ = M - MEQ + 2*N1 & N1 = N+1 * */ /* * NOTICE: FOR PROPER DIMENSIONING OF W IT IS RECOMMENDED TO * */ /* * COPY THE FOLLOWING STATEMENTS INTO THE HEAD OF * */ /* * THE CALLING PROGRAM (AND REMOVE THE COMMENT C) * */ /* ####################################################################### */ /* INT LEN_W, LEN_JW, M, N, N1, MEQ, MINEQ */ /* PARAMETER (M=... , MEQ=... , N=... ) */ /* PARAMETER (N1= N+1, MINEQ= M-MEQ+N1+N1) */ /* PARAMETER (LEN_W= */ /* $ (3*N1+M)*(N1+1) */ /* $ +(N1-MEQ+1)*(MINEQ+2) + 2*MINEQ */ /* $ +(N1+MINEQ)*(N1-MEQ) + 2*MEQ + N1 */ /* $ +(N+1)*N/2 + 2*M + 3*N + 3*N1 + 1, */ /* $ LEN_JW=MINEQ) */ /* DOUBLE PRECISION W(LEN_W) */ /* INT JW(LEN_JW) */ /* ####################################################################### */ /* * THE FIRST M+N+N*N1/2 ELEMENTS OF W MUST NOT BE * */ /* * CHANGED BETWEEN SUBSEQUENT CALLS OF SLSQP. * */ /* * ON RETURN W(1) ... W(M) CONTAIN THE MULTIPLIERS * */ /* * ASSOCIATED WITH THE GENERAL CONSTRAINTS, WHILE * */ /* * W(M+1) ... W(M+N(N+1)/2) STORE THE CHOLESKY FACTOR* */ /* * L*D*L(T) OF THE APPROXIMATE HESSIAN OF THE * */ /* * LAGRANGIAN COLUMNWISE DENSE AS LOWER TRIANGULAR * */ /* * UNIT MATRIX L WITH D IN ITS 'DIAGONAL' and * */ /* * W(M+N(N+1)/2+N+2 ... W(M+N(N+1)/2+N+2+M+2N) * */ /* * CONTAIN THE MULTIPLIERS ASSOCIATED WITH ALL * */ /* * ALL CONSTRAINTS OF THE QUADRATIC PROGRAM FINDING * */ /* * THE SEARCH DIRECTION TO THE SOLUTION X* * */ /* * * JW(), L_JW JW() IS A ONE DIMENSIONAL INT WORKING SPACE * */ /* * THE LENGTH L_JW OF WHICH SHOULD BE AT LEAST * */ /* * MINEQ * */ /* * with MINEQ = M - MEQ + 2*N1 & N1 = N+1 * */ /* * * */ /* * THE USER HAS TO PROVIDE THE FOLLOWING SUBROUTINES: * */ /* * LDL(N,A,Z,SIG,W) : UPDATE OF THE LDL'-FACTORIZATION. * */ /* * LINMIN(A,B,F,TOL) : LINESEARCH ALGORITHM IF EXACT = 1 * */ /* * LSQ(M,MEQ,LA,N,NC,C,D,A,B,XL,XU,X,LAMBDA,W,....) : * */ /* * * */ /* * SOLUTION OF THE QUADRATIC PROGRAM * */ /* * QPSOL IS RECOMMENDED: * */ /* * PE GILL, W MURRAY, MA SAUNDERS, MH WRIGHT: * */ /* * USER'S GUIDE FOR SOL/QPSOL: * */ /* * A FORTRAN PACKAGE FOR QUADRATIC PROGRAMMING, * */ /* * TECHNICAL REPORT SOL 83-7, JULY 1983 * */ /* * DEPARTMENT OF OPERATIONS RESEARCH, STANFORD UNIVERSITY * */ /* * STANFORD, CA 94305 * */ /* * QPSOL IS THE MOST ROBUST AND EFFICIENT QP-SOLVER * */ /* * AS IT ALLOWS WARM STARTS WITH PROPER WORKING SETS * */ /* * * */ /* * IF IT IS NOT AVAILABLE USE LSEI, A CONSTRAINT LINEAR LEAST * */ /* * SQUARES SOLVER IMPLEMENTED USING THE SOFTWARE HFTI, LDP, NNLS * */ /* * FROM C.L. LAWSON, R.J.HANSON: SOLVING LEAST SQUARES PROBLEMS, * */ /* * PRENTICE HALL, ENGLEWOOD CLIFFS, 1974. * */ /* * LSEI COMES WITH THIS PACKAGE, together with all necessary SR's. * */ /* * * */ /* * TOGETHER WITH A COUPLE OF SUBROUTINES FROM BLAS LEVEL 1 * */ /* * * */ /* * SQP IS HEAD SUBROUTINE FOR BODY SUBROUTINE SQPBDY * */ /* * IN WHICH THE ALGORITHM HAS BEEN IMPLEMENTED. * */ /* * * */ /* * IMPLEMENTED BY: DIETER KRAFT, DFVLR OBERPFAFFENHOFEN * */ /* * as described in Dieter Kraft: A Software Package for * */ /* * Sequential Quadratic Programming * */ /* * DFVLR-FB 88-28, 1988 * */ /* * which should be referenced if the user publishes results of SLSQP * */ /* * * */ /* * DATE: APRIL - OCTOBER, 1981. * */ /* * STATUS: DECEMBER, 31-ST, 1984. * */ /* * STATUS: MARCH , 21-ST, 1987, REVISED TO FORTAN 77 * */ /* * STATUS: MARCH , 20-th, 1989, REVISED TO MS-FORTRAN * */ /* * STATUS: APRIL , 14-th, 1989, HESSE in-line coded * */ /* * STATUS: FEBRUARY, 28-th, 1991, FORTRAN/2 Version 1.04 * */ /* * accepts Statement Functions * */ /* * STATUS: MARCH , 1-st, 1991, tested with SALFORD * */ /* * FTN77/386 COMPILER VERS 2.40* */ /* * in protected mode * */ /* * * */ /* *********************************************************************** */ /* * * */ /* * Copyright 1991: Dieter Kraft, FHM * */ /* * * */ /* *********************************************************************** */ /* dim(W) = N1*(N1+1) + MEQ*(N1+1) + MINEQ*(N1+1) for LSQ */ /* +(N1-MEQ+1)*(MINEQ+2) + 2*MINEQ for LSI */ /* +(N1+MINEQ)*(N1-MEQ) + 2*MEQ + N1 for LSEI */ /* + N1*N/2 + 2*M + 3*N +3*N1 + 1 for SLSQPB */ /* with MINEQ = M - MEQ + 2*N1 & N1 = N+1 */ /* CHECK LENGTH OF WORKING ARRAYS */ /* Parameter adjustments */ --c__; a_dim1 = *la; a_offset = 1 + a_dim1; a -= a_offset; --g; --xu; --xl; --x; --w; --jw; /* Function Body */ n1 = *n + 1; mineq = *m - *meq + n1 + n1; il = (n1 * 3 + *m) * (n1 + 1) + (n1 - *meq + 1) * (mineq + 2) + (mineq << 1) + (n1 + mineq) * (n1 - *meq) + (*meq << 1) + n1 * *n / 2 + (*m << 1) + *n * 3 + (n1 << 2) + 1; /* Computing MAX */ i__1 = mineq, i__2 = n1 - *meq; im = MAX2(i__1,i__2); if (*l_w__ < il || *l_jw__ < im) { *mode = MAX2(10,il) * 1000; *mode += MAX2(10,im); return; } /* PREPARE DATA FOR CALLING SQPBDY - INITIAL ADDRESSES IN W */ im = 1; il = im + MAX2(1,*m); il = im + *la; ix = il + n1 * *n / 2 + 1; ir = ix + *n; is = ir + *n + *n + MAX2(1,*m); is = ir + *n + *n + *la; iu = is + n1; iv = iu + n1; iw = iv + n1; slsqpb_(m, meq, la, n, &x[1], &xl[1], &xu[1], f, &c__[1], &g[1], &a[ a_offset], acc, iter, mode, &w[ir], &w[il], &w[ix], &w[im], &w[is] , &w[iu], &w[iv], &w[iw], &jw[1], state); state->x0 = &w[ix]; return; } /* slsqp_ */ static void length_work(int *LEN_W, int *LEN_JW, int M, int MEQ, int N) { int N1 = N+1, MINEQ = M-MEQ+N1+N1; *LEN_W = (3*N1+M)*(N1+1) +(N1-MEQ+1)*(MINEQ+2) + 2*MINEQ +(N1+MINEQ)*(N1-MEQ) + 2*MEQ + N1 +(N+1)*N/2 + 2*M + 3*N + 3*N1 + 1; *LEN_JW = MINEQ; } nlopt_result nlopt_slsqp(unsigned n, nlopt_func f, void *f_data, unsigned m, nlopt_constraint *fc, unsigned p, nlopt_constraint *h, const double *lb, const double *ub, double *x, double *minf, nlopt_stopping *stop) { slsqpb_state state = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NULL}; unsigned mtot = nlopt_count_constraints(m, fc); unsigned ptot = nlopt_count_constraints(p, h); double *work, *cgrad, *c, *grad, *w, fcur, *xcur, fprev, *xprev, *cgradtmp; int mpi = (int) (mtot + ptot), pi = (int) ptot, ni = (int) n, mpi1 = mpi > 0 ? mpi : 1; int len_w, len_jw, *jw; int mode = 0, prev_mode = 0; double acc = 0; /* we do our own convergence tests below */ int iter = 0; /* tell sqsqp to ignore this check, since we check evaluation counts ourselves */ unsigned i, ii; nlopt_result ret = NLOPT_SUCCESS; int feasible, feasible_cur; double infeasibility = HUGE_VAL, infeasibility_cur = HUGE_VAL; unsigned max_cdim; max_cdim = MAX2(nlopt_max_constraint_dim(m, fc), nlopt_max_constraint_dim(p, h)); length_work(&len_w, &len_jw, mpi, pi, ni); #define U(n) ((unsigned) (n)) work = (double *) malloc(sizeof(double) * (U(mpi1) * (n + 1) + U(mpi) + n+1 + n + n + max_cdim*n + U(len_w)) + sizeof(int) * U(len_jw)); if (!work) return NLOPT_OUT_OF_MEMORY; cgrad = work; c = cgrad + U(mpi1) * (n + 1); grad = c + mpi; xcur = grad + n+1; xprev = xcur + n; cgradtmp = xprev + n; w = cgradtmp + max_cdim*n; jw = (int *) (w + len_w); memcpy(xcur, x, sizeof(double) * n); memcpy(xprev, x, sizeof(double) * n); fprev = fcur = *minf = HUGE_VAL; feasible = feasible_cur = 0; goto eval_f_and_grad; /* eval before calling slsqp the first time */ do { slsqp(&mpi, &pi, &mpi1, &ni, xcur, lb, ub, &fcur, c, grad, cgrad, &acc, &iter, &mode, w, &len_w, jw, &len_jw, &state); switch (mode) { case -1: /* objective & gradient evaluation */ if (prev_mode == -2) break; /* just evaluated this point */ case -2: eval_f_and_grad: feasible_cur = 1; infeasibility_cur = 0; fcur = f(n, xcur, grad, f_data); stop->nevals++; if (nlopt_stop_forced(stop)) { fcur = HUGE_VAL; ret = NLOPT_FORCED_STOP; goto done; } ii = 0; for (i = 0; i < p; ++i) { unsigned j, k; nlopt_eval_constraint(c+ii, cgradtmp, h+i, n, xcur); if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } for (k = 0; k < h[i].m; ++k, ++ii) { infeasibility_cur = MAX2(infeasibility_cur, fabs(c[ii])); feasible_cur = feasible_cur && fabs(c[ii]) <= h[i].tol[k]; for (j = 0; j < n; ++ j) cgrad[j*U(mpi1) + ii] = cgradtmp[k*n + j]; } } for (i = 0; i < m; ++i) { unsigned j, k; nlopt_eval_constraint(c+ii, cgradtmp, fc+i, n, xcur); if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } for (k = 0; k < fc[i].m; ++k, ++ii) { infeasibility_cur = MAX2(infeasibility_cur, c[ii]); feasible_cur = feasible_cur && c[ii] <= fc[i].tol[k]; for (j = 0; j < n; ++ j) cgrad[j*U(mpi1) + ii] = -cgradtmp[k*n + j]; c[ii] = -c[ii]; /* slsqp sign convention */ } } break; case 0: /* required accuracy for solution obtained */ goto done; case 1: /* objective evaluation only (no gradient) */ feasible_cur = 1; infeasibility_cur = 0; fcur = f(n, xcur, NULL, f_data); stop->nevals++; if (nlopt_stop_forced(stop)) { fcur = HUGE_VAL; ret = NLOPT_FORCED_STOP; goto done; } ii = 0; for (i = 0; i < p; ++i) { unsigned k; nlopt_eval_constraint(c+ii, NULL, h+i, n, xcur); if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } for (k = 0; k < h[i].m; ++k, ++ii) { infeasibility_cur = MAX2(infeasibility_cur, fabs(c[ii])); feasible_cur = feasible_cur && fabs(c[ii]) <= h[i].tol[k]; } } for (i = 0; i < m; ++i) { unsigned k; nlopt_eval_constraint(c+ii, NULL, fc+i, n, xcur); if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } for (k = 0; k < fc[i].m; ++k, ++ii) { infeasibility_cur = MAX2(infeasibility_cur, c[ii]); feasible_cur = feasible_cur && c[ii] <= fc[i].tol[k]; c[ii] = -c[ii]; /* slsqp sign convention */ } } break; case 8: /* positive directional derivative for linesearch */ /* relaxed convergence check for a feasible_cur point, as in the SLSQP code (except xtol as well as ftol) */ ret = NLOPT_ROUNDOFF_LIMITED; /* usually why deriv>0 */ if (feasible_cur) { double save_ftol_rel = stop->ftol_rel; double save_xtol_rel = stop->xtol_rel; double save_ftol_abs = stop->ftol_abs; stop->ftol_rel *= 10; stop->ftol_abs *= 10; stop->xtol_rel *= 10; if (nlopt_stop_ftol(stop, fcur, state.f0)) ret = NLOPT_FTOL_REACHED; else if (nlopt_stop_x(stop, xcur, state.x0)) ret = NLOPT_XTOL_REACHED; stop->ftol_rel = save_ftol_rel; stop->ftol_abs = save_ftol_abs; stop->xtol_rel = save_xtol_rel; } goto done; case 5: /* singular matrix E in LSQ subproblem */ case 6: /* singular matrix C in LSQ subproblem */ case 7: /* rank-deficient equality constraint subproblem HFTI */ ret = NLOPT_ROUNDOFF_LIMITED; goto done; case 4: /* inequality constraints incompatible */ case 3: /* more than 3*n iterations in LSQ subproblem */ case 9: /* more than iter iterations in SQP */ ret = NLOPT_FAILURE; goto done; case 2: /* number of equality constraints > n */ default: /* >= 10: working space w or jw too small */ ret = NLOPT_INVALID_ARGS; goto done; } prev_mode = mode; /* update best point so far */ if ((fcur < *minf && (feasible_cur || !feasible)) || (!feasible && infeasibility_cur < infeasibility)) { *minf = fcur; feasible = feasible_cur; infeasibility = infeasibility_cur; memcpy(x, xcur, sizeof(double)*n); } /* note: mode == -1 corresponds to the completion of a line search, and is the only time we should check convergence (as in original slsqp code) */ if (mode == -1) { if (!nlopt_isinf(fprev)) { if (nlopt_stop_ftol(stop, fcur, fprev)) ret = NLOPT_FTOL_REACHED; else if (nlopt_stop_x(stop, xcur, xprev)) ret = NLOPT_XTOL_REACHED; } fprev = fcur; memcpy(xprev, xcur, sizeof(double)*n); } /* do some additional termination tests */ if (nlopt_stop_evals(stop)) ret = NLOPT_MAXEVAL_REACHED; else if (nlopt_stop_time(stop)) ret = NLOPT_MAXTIME_REACHED; else if (feasible && *minf < stop->minf_max) ret = NLOPT_MINF_MAX_REACHED; } while (ret == NLOPT_SUCCESS); done: if (nlopt_isinf(*minf)) { /* didn't find any feasible points, just return last point evaluated */ if (nlopt_isinf(fcur)) { /* invalid cur. point, use previous pt. */ *minf = fprev; memcpy(x, xprev, sizeof(double)*n); } else { *minf = fcur; memcpy(x, xcur, sizeof(double)*n); } } free(work); return ret; } nloptr/src/nlopt_src/ltmain.sh0000644000176000001440000105152213263734242016246 0ustar ripleyusers # libtool (GNU libtool) 2.4.2 # Written by Gordon Matzigkeit , 1996 # Copyright (C) 1996, 1997, 1998, 1999, 2000, 2001, 2003, 2004, 2005, 2006, # 2007, 2008, 2009, 2010, 2011 Free Software Foundation, Inc. # This is free software; see the source for copying conditions. There is NO # warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # GNU Libtool is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # As a special exception to the GNU General Public License, # if you distribute this file as part of a program or library that # is built using GNU Libtool, you may include this file under the # same distribution terms that you use for the rest of that program. # # GNU Libtool is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with GNU Libtool; see the file COPYING. If not, a copy # can be downloaded from http://www.gnu.org/licenses/gpl.html, # or obtained by writing to the Free Software Foundation, Inc., # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # Usage: $progname [OPTION]... [MODE-ARG]... # # Provide generalized library-building support services. # # --config show all configuration variables # --debug enable verbose shell tracing # -n, --dry-run display commands without modifying any files # --features display basic configuration information and exit # --mode=MODE use operation mode MODE # --preserve-dup-deps don't remove duplicate dependency libraries # --quiet, --silent don't print informational messages # --no-quiet, --no-silent # print informational messages (default) # --no-warn don't display warning messages # --tag=TAG use configuration variables from tag TAG # -v, --verbose print more informational messages than default # --no-verbose don't print the extra informational messages # --version print version information # -h, --help, --help-all print short, long, or detailed help message # # MODE must be one of the following: # # clean remove files from the build directory # compile compile a source file into a libtool object # execute automatically set library path, then run a program # finish complete the installation of libtool libraries # install install libraries or executables # link create a library or an executable # uninstall remove libraries from an installed directory # # MODE-ARGS vary depending on the MODE. When passed as first option, # `--mode=MODE' may be abbreviated as `MODE' or a unique abbreviation of that. # Try `$progname --help --mode=MODE' for a more detailed description of MODE. # # When reporting a bug, please describe a test case to reproduce it and # include the following information: # # host-triplet: $host # shell: $SHELL # compiler: $LTCC # compiler flags: $LTCFLAGS # linker: $LD (gnu? $with_gnu_ld) # $progname: (GNU libtool) 2.4.2 # automake: $automake_version # autoconf: $autoconf_version # # Report bugs to . # GNU libtool home page: . # General help using GNU software: . PROGRAM=libtool PACKAGE=libtool VERSION=2.4.2 TIMESTAMP="" package_revision=1.3337 # Be Bourne compatible if test -n "${ZSH_VERSION+set}" && (emulate sh) >/dev/null 2>&1; then emulate sh NULLCMD=: # Zsh 3.x and 4.x performs word splitting on ${1+"$@"}, which # is contrary to our usage. Disable this feature. alias -g '${1+"$@"}'='"$@"' setopt NO_GLOB_SUBST else case `(set -o) 2>/dev/null` in *posix*) set -o posix;; esac fi BIN_SH=xpg4; export BIN_SH # for Tru64 DUALCASE=1; export DUALCASE # for MKS sh # A function that is used when there is no print builtin or printf. func_fallback_echo () { eval 'cat <<_LTECHO_EOF $1 _LTECHO_EOF' } # NLS nuisances: We save the old values to restore during execute mode. lt_user_locale= lt_safe_locale= for lt_var in LANG LANGUAGE LC_ALL LC_CTYPE LC_COLLATE LC_MESSAGES do eval "if test \"\${$lt_var+set}\" = set; then save_$lt_var=\$$lt_var $lt_var=C export $lt_var lt_user_locale=\"$lt_var=\\\$save_\$lt_var; \$lt_user_locale\" lt_safe_locale=\"$lt_var=C; \$lt_safe_locale\" fi" done LC_ALL=C LANGUAGE=C export LANGUAGE LC_ALL $lt_unset CDPATH # Work around backward compatibility issue on IRIX 6.5. On IRIX 6.4+, sh # is ksh but when the shell is invoked as "sh" and the current value of # the _XPG environment variable is not equal to 1 (one), the special # positional parameter $0, within a function call, is the name of the # function. progpath="$0" : ${CP="cp -f"} test "${ECHO+set}" = set || ECHO=${as_echo-'printf %s\n'} : ${MAKE="make"} : ${MKDIR="mkdir"} : ${MV="mv -f"} : ${RM="rm -f"} : ${SHELL="${CONFIG_SHELL-/bin/sh}"} : ${Xsed="$SED -e 1s/^X//"} # Global variables: EXIT_SUCCESS=0 EXIT_FAILURE=1 EXIT_MISMATCH=63 # $? = 63 is used to indicate version mismatch to missing. EXIT_SKIP=77 # $? = 77 is used to indicate a skipped test to automake. exit_status=$EXIT_SUCCESS # Make sure IFS has a sensible default lt_nl=' ' IFS=" $lt_nl" dirname="s,/[^/]*$,," basename="s,^.*/,," # func_dirname file append nondir_replacement # Compute the dirname of FILE. If nonempty, add APPEND to the result, # otherwise set result to NONDIR_REPLACEMENT. func_dirname () { func_dirname_result=`$ECHO "${1}" | $SED "$dirname"` if test "X$func_dirname_result" = "X${1}"; then func_dirname_result="${3}" else func_dirname_result="$func_dirname_result${2}" fi } # func_dirname may be replaced by extended shell implementation # func_basename file func_basename () { func_basename_result=`$ECHO "${1}" | $SED "$basename"` } # func_basename may be replaced by extended shell implementation # func_dirname_and_basename file append nondir_replacement # perform func_basename and func_dirname in a single function # call: # dirname: Compute the dirname of FILE. If nonempty, # add APPEND to the result, otherwise set result # to NONDIR_REPLACEMENT. # value returned in "$func_dirname_result" # basename: Compute filename of FILE. # value retuned in "$func_basename_result" # Implementation must be kept synchronized with func_dirname # and func_basename. For efficiency, we do not delegate to # those functions but instead duplicate the functionality here. func_dirname_and_basename () { # Extract subdirectory from the argument. func_dirname_result=`$ECHO "${1}" | $SED -e "$dirname"` if test "X$func_dirname_result" = "X${1}"; then func_dirname_result="${3}" else func_dirname_result="$func_dirname_result${2}" fi func_basename_result=`$ECHO "${1}" | $SED -e "$basename"` } # func_dirname_and_basename may be replaced by extended shell implementation # func_stripname prefix suffix name # strip PREFIX and SUFFIX off of NAME. # PREFIX and SUFFIX must not contain globbing or regex special # characters, hashes, percent signs, but SUFFIX may contain a leading # dot (in which case that matches only a dot). # func_strip_suffix prefix name func_stripname () { case ${2} in .*) func_stripname_result=`$ECHO "${3}" | $SED "s%^${1}%%; s%\\\\${2}\$%%"`;; *) func_stripname_result=`$ECHO "${3}" | $SED "s%^${1}%%; s%${2}\$%%"`;; esac } # func_stripname may be replaced by extended shell implementation # These SED scripts presuppose an absolute path with a trailing slash. pathcar='s,^/\([^/]*\).*$,\1,' pathcdr='s,^/[^/]*,,' removedotparts=':dotsl s@/\./@/@g t dotsl s,/\.$,/,' collapseslashes='s@/\{1,\}@/@g' finalslash='s,/*$,/,' # func_normal_abspath PATH # Remove doubled-up and trailing slashes, "." path components, # and cancel out any ".." path components in PATH after making # it an absolute path. # value returned in "$func_normal_abspath_result" func_normal_abspath () { # Start from root dir and reassemble the path. func_normal_abspath_result= func_normal_abspath_tpath=$1 func_normal_abspath_altnamespace= case $func_normal_abspath_tpath in "") # Empty path, that just means $cwd. func_stripname '' '/' "`pwd`" func_normal_abspath_result=$func_stripname_result return ;; # The next three entries are used to spot a run of precisely # two leading slashes without using negated character classes; # we take advantage of case's first-match behaviour. ///*) # Unusual form of absolute path, do nothing. ;; //*) # Not necessarily an ordinary path; POSIX reserves leading '//' # and for example Cygwin uses it to access remote file shares # over CIFS/SMB, so we conserve a leading double slash if found. func_normal_abspath_altnamespace=/ ;; /*) # Absolute path, do nothing. ;; *) # Relative path, prepend $cwd. func_normal_abspath_tpath=`pwd`/$func_normal_abspath_tpath ;; esac # Cancel out all the simple stuff to save iterations. We also want # the path to end with a slash for ease of parsing, so make sure # there is one (and only one) here. func_normal_abspath_tpath=`$ECHO "$func_normal_abspath_tpath" | $SED \ -e "$removedotparts" -e "$collapseslashes" -e "$finalslash"` while :; do # Processed it all yet? if test "$func_normal_abspath_tpath" = / ; then # If we ascended to the root using ".." the result may be empty now. if test -z "$func_normal_abspath_result" ; then func_normal_abspath_result=/ fi break fi func_normal_abspath_tcomponent=`$ECHO "$func_normal_abspath_tpath" | $SED \ -e "$pathcar"` func_normal_abspath_tpath=`$ECHO "$func_normal_abspath_tpath" | $SED \ -e "$pathcdr"` # Figure out what to do with it case $func_normal_abspath_tcomponent in "") # Trailing empty path component, ignore it. ;; ..) # Parent dir; strip last assembled component from result. func_dirname "$func_normal_abspath_result" func_normal_abspath_result=$func_dirname_result ;; *) # Actual path component, append it. func_normal_abspath_result=$func_normal_abspath_result/$func_normal_abspath_tcomponent ;; esac done # Restore leading double-slash if one was found on entry. func_normal_abspath_result=$func_normal_abspath_altnamespace$func_normal_abspath_result } # func_relative_path SRCDIR DSTDIR # generates a relative path from SRCDIR to DSTDIR, with a trailing # slash if non-empty, suitable for immediately appending a filename # without needing to append a separator. # value returned in "$func_relative_path_result" func_relative_path () { func_relative_path_result= func_normal_abspath "$1" func_relative_path_tlibdir=$func_normal_abspath_result func_normal_abspath "$2" func_relative_path_tbindir=$func_normal_abspath_result # Ascend the tree starting from libdir while :; do # check if we have found a prefix of bindir case $func_relative_path_tbindir in $func_relative_path_tlibdir) # found an exact match func_relative_path_tcancelled= break ;; $func_relative_path_tlibdir*) # found a matching prefix func_stripname "$func_relative_path_tlibdir" '' "$func_relative_path_tbindir" func_relative_path_tcancelled=$func_stripname_result if test -z "$func_relative_path_result"; then func_relative_path_result=. fi break ;; *) func_dirname $func_relative_path_tlibdir func_relative_path_tlibdir=${func_dirname_result} if test "x$func_relative_path_tlibdir" = x ; then # Have to descend all the way to the root! func_relative_path_result=../$func_relative_path_result func_relative_path_tcancelled=$func_relative_path_tbindir break fi func_relative_path_result=../$func_relative_path_result ;; esac done # Now calculate path; take care to avoid doubling-up slashes. func_stripname '' '/' "$func_relative_path_result" func_relative_path_result=$func_stripname_result func_stripname '/' '/' "$func_relative_path_tcancelled" if test "x$func_stripname_result" != x ; then func_relative_path_result=${func_relative_path_result}/${func_stripname_result} fi # Normalisation. If bindir is libdir, return empty string, # else relative path ending with a slash; either way, target # file name can be directly appended. if test ! -z "$func_relative_path_result"; then func_stripname './' '' "$func_relative_path_result/" func_relative_path_result=$func_stripname_result fi } # The name of this program: func_dirname_and_basename "$progpath" progname=$func_basename_result # Make sure we have an absolute path for reexecution: case $progpath in [\\/]*|[A-Za-z]:\\*) ;; *[\\/]*) progdir=$func_dirname_result progdir=`cd "$progdir" && pwd` progpath="$progdir/$progname" ;; *) save_IFS="$IFS" IFS=${PATH_SEPARATOR-:} for progdir in $PATH; do IFS="$save_IFS" test -x "$progdir/$progname" && break done IFS="$save_IFS" test -n "$progdir" || progdir=`pwd` progpath="$progdir/$progname" ;; esac # Sed substitution that helps us do robust quoting. It backslashifies # metacharacters that are still active within double-quoted strings. Xsed="${SED}"' -e 1s/^X//' sed_quote_subst='s/\([`"$\\]\)/\\\1/g' # Same as above, but do not quote variable references. double_quote_subst='s/\(["`\\]\)/\\\1/g' # Sed substitution that turns a string into a regex matching for the # string literally. sed_make_literal_regex='s,[].[^$\\*\/],\\&,g' # Sed substitution that converts a w32 file name or path # which contains forward slashes, into one that contains # (escaped) backslashes. A very naive implementation. lt_sed_naive_backslashify='s|\\\\*|\\|g;s|/|\\|g;s|\\|\\\\|g' # Re-`\' parameter expansions in output of double_quote_subst that were # `\'-ed in input to the same. If an odd number of `\' preceded a '$' # in input to double_quote_subst, that '$' was protected from expansion. # Since each input `\' is now two `\'s, look for any number of runs of # four `\'s followed by two `\'s and then a '$'. `\' that '$'. bs='\\' bs2='\\\\' bs4='\\\\\\\\' dollar='\$' sed_double_backslash="\ s/$bs4/&\\ /g s/^$bs2$dollar/$bs&/ s/\\([^$bs]\\)$bs2$dollar/\\1$bs2$bs$dollar/g s/\n//g" # Standard options: opt_dry_run=false opt_help=false opt_quiet=false opt_verbose=false opt_warning=: # func_echo arg... # Echo program name prefixed message, along with the current mode # name if it has been set yet. func_echo () { $ECHO "$progname: ${opt_mode+$opt_mode: }$*" } # func_verbose arg... # Echo program name prefixed message in verbose mode only. func_verbose () { $opt_verbose && func_echo ${1+"$@"} # A bug in bash halts the script if the last line of a function # fails when set -e is in force, so we need another command to # work around that: : } # func_echo_all arg... # Invoke $ECHO with all args, space-separated. func_echo_all () { $ECHO "$*" } # func_error arg... # Echo program name prefixed message to standard error. func_error () { $ECHO "$progname: ${opt_mode+$opt_mode: }"${1+"$@"} 1>&2 } # func_warning arg... # Echo program name prefixed warning message to standard error. func_warning () { $opt_warning && $ECHO "$progname: ${opt_mode+$opt_mode: }warning: "${1+"$@"} 1>&2 # bash bug again: : } # func_fatal_error arg... # Echo program name prefixed message to standard error, and exit. func_fatal_error () { func_error ${1+"$@"} exit $EXIT_FAILURE } # func_fatal_help arg... # Echo program name prefixed message to standard error, followed by # a help hint, and exit. func_fatal_help () { func_error ${1+"$@"} func_fatal_error "$help" } help="Try \`$progname --help' for more information." ## default # func_grep expression filename # Check whether EXPRESSION matches any line of FILENAME, without output. func_grep () { $GREP "$1" "$2" >/dev/null 2>&1 } # func_mkdir_p directory-path # Make sure the entire path to DIRECTORY-PATH is available. func_mkdir_p () { my_directory_path="$1" my_dir_list= if test -n "$my_directory_path" && test "$opt_dry_run" != ":"; then # Protect directory names starting with `-' case $my_directory_path in -*) my_directory_path="./$my_directory_path" ;; esac # While some portion of DIR does not yet exist... while test ! -d "$my_directory_path"; do # ...make a list in topmost first order. 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Don't # stop in that case! $MKDIR "$my_dir" 2>/dev/null || : done IFS="$save_mkdir_p_IFS" # Bail out if we (or some other process) failed to create a directory. test -d "$my_directory_path" || \ func_fatal_error "Failed to create \`$1'" fi } # func_mktempdir [string] # Make a temporary directory that won't clash with other running # libtool processes, and avoids race conditions if possible. If # given, STRING is the basename for that directory. func_mktempdir () { my_template="${TMPDIR-/tmp}/${1-$progname}" if test "$opt_dry_run" = ":"; then # Return a directory name, but don't create it in dry-run mode my_tmpdir="${my_template}-$$" else # If mktemp works, use that first and foremost my_tmpdir=`mktemp -d "${my_template}-XXXXXXXX" 2>/dev/null` if test ! -d "$my_tmpdir"; then # Failing that, at least try and use $RANDOM to avoid a race my_tmpdir="${my_template}-${RANDOM-0}$$" save_mktempdir_umask=`umask` umask 0077 $MKDIR "$my_tmpdir" umask $save_mktempdir_umask fi # If we're not in dry-run mode, bomb out on failure test -d "$my_tmpdir" || \ func_fatal_error "cannot create temporary directory \`$my_tmpdir'" fi $ECHO "$my_tmpdir" } # func_quote_for_eval arg # Aesthetically quote ARG to be evaled later. # This function returns two values: FUNC_QUOTE_FOR_EVAL_RESULT # is double-quoted, suitable for a subsequent eval, whereas # FUNC_QUOTE_FOR_EVAL_UNQUOTED_RESULT has merely all characters # which are still active within double quotes backslashified. func_quote_for_eval () { case $1 in *[\\\`\"\$]*) func_quote_for_eval_unquoted_result=`$ECHO "$1" | $SED "$sed_quote_subst"` ;; *) func_quote_for_eval_unquoted_result="$1" ;; esac case $func_quote_for_eval_unquoted_result in # Double-quote args containing shell metacharacters to delay # word splitting, command substitution and and variable # expansion for a subsequent eval. # Many Bourne shells cannot handle close brackets correctly # in scan sets, so we specify it separately. *[\[\~\#\^\&\*\(\)\{\}\|\;\<\>\?\'\ \ ]*|*]*|"") func_quote_for_eval_result="\"$func_quote_for_eval_unquoted_result\"" ;; *) func_quote_for_eval_result="$func_quote_for_eval_unquoted_result" esac } # func_quote_for_expand arg # Aesthetically quote ARG to be evaled later; same as above, # but do not quote variable references. func_quote_for_expand () { case $1 in *[\\\`\"]*) my_arg=`$ECHO "$1" | $SED \ -e "$double_quote_subst" -e "$sed_double_backslash"` ;; *) my_arg="$1" ;; esac case $my_arg in # Double-quote args containing shell metacharacters to delay # word splitting and command substitution for a subsequent eval. # Many Bourne shells cannot handle close brackets correctly # in scan sets, so we specify it separately. *[\[\~\#\^\&\*\(\)\{\}\|\;\<\>\?\'\ \ ]*|*]*|"") my_arg="\"$my_arg\"" ;; esac func_quote_for_expand_result="$my_arg" } # func_show_eval cmd [fail_exp] # Unless opt_silent is true, then output CMD. Then, if opt_dryrun is # not true, evaluate CMD. If the evaluation of CMD fails, and FAIL_EXP # is given, then evaluate it. func_show_eval () { my_cmd="$1" my_fail_exp="${2-:}" ${opt_silent-false} || { func_quote_for_expand "$my_cmd" eval "func_echo $func_quote_for_expand_result" } if ${opt_dry_run-false}; then :; else eval "$my_cmd" my_status=$? if test "$my_status" -eq 0; then :; else eval "(exit $my_status); $my_fail_exp" fi fi } # func_show_eval_locale cmd [fail_exp] # Unless opt_silent is true, then output CMD. Then, if opt_dryrun is # not true, evaluate CMD. If the evaluation of CMD fails, and FAIL_EXP # is given, then evaluate it. Use the saved locale for evaluation. func_show_eval_locale () { my_cmd="$1" my_fail_exp="${2-:}" ${opt_silent-false} || { func_quote_for_expand "$my_cmd" eval "func_echo $func_quote_for_expand_result" } if ${opt_dry_run-false}; then :; else eval "$lt_user_locale $my_cmd" my_status=$? eval "$lt_safe_locale" if test "$my_status" -eq 0; then :; else eval "(exit $my_status); $my_fail_exp" fi fi } # func_tr_sh # Turn $1 into a string suitable for a shell variable name. # Result is stored in $func_tr_sh_result. All characters # not in the set a-zA-Z0-9_ are replaced with '_'. Further, # if $1 begins with a digit, a '_' is prepended as well. func_tr_sh () { case $1 in [0-9]* | *[!a-zA-Z0-9_]*) func_tr_sh_result=`$ECHO "$1" | $SED 's/^\([0-9]\)/_\1/; s/[^a-zA-Z0-9_]/_/g'` ;; * ) func_tr_sh_result=$1 ;; esac } # func_version # Echo version message to standard output and exit. func_version () { $opt_debug $SED -n '/(C)/!b go :more /\./!{ N s/\n# / / b more } :go /^# '$PROGRAM' (GNU /,/# warranty; / { s/^# // s/^# *$// s/\((C)\)[ 0-9,-]*\( [1-9][0-9]*\)/\1\2/ p }' < "$progpath" exit $? } # func_usage # Echo short help message to standard output and exit. func_usage () { $opt_debug $SED -n '/^# Usage:/,/^# *.*--help/ { s/^# // s/^# *$// s/\$progname/'$progname'/ p }' < "$progpath" echo $ECHO "run \`$progname --help | more' for full usage" exit $? } # func_help [NOEXIT] # Echo long help message to standard output and exit, # unless 'noexit' is passed as argument. func_help () { $opt_debug $SED -n '/^# Usage:/,/# Report bugs to/ { :print s/^# // s/^# *$// s*\$progname*'$progname'* s*\$host*'"$host"'* s*\$SHELL*'"$SHELL"'* s*\$LTCC*'"$LTCC"'* s*\$LTCFLAGS*'"$LTCFLAGS"'* s*\$LD*'"$LD"'* s/\$with_gnu_ld/'"$with_gnu_ld"'/ s/\$automake_version/'"`(${AUTOMAKE-automake} --version) 2>/dev/null |$SED 1q`"'/ s/\$autoconf_version/'"`(${AUTOCONF-autoconf} --version) 2>/dev/null |$SED 1q`"'/ p d } /^# .* home page:/b print /^# General help using/b print ' < "$progpath" ret=$? if test -z "$1"; then exit $ret fi } # func_missing_arg argname # Echo program name prefixed message to standard error and set global # exit_cmd. func_missing_arg () { $opt_debug func_error "missing argument for $1." exit_cmd=exit } # func_split_short_opt shortopt # Set func_split_short_opt_name and func_split_short_opt_arg shell # variables after splitting SHORTOPT after the 2nd character. func_split_short_opt () { my_sed_short_opt='1s/^\(..\).*$/\1/;q' my_sed_short_rest='1s/^..\(.*\)$/\1/;q' func_split_short_opt_name=`$ECHO "$1" | $SED "$my_sed_short_opt"` func_split_short_opt_arg=`$ECHO "$1" | $SED "$my_sed_short_rest"` } # func_split_short_opt may be replaced by extended shell implementation # func_split_long_opt longopt # Set func_split_long_opt_name and func_split_long_opt_arg shell # variables after splitting LONGOPT at the `=' sign. func_split_long_opt () { my_sed_long_opt='1s/^\(--[^=]*\)=.*/\1/;q' my_sed_long_arg='1s/^--[^=]*=//' func_split_long_opt_name=`$ECHO "$1" | $SED "$my_sed_long_opt"` func_split_long_opt_arg=`$ECHO "$1" | $SED "$my_sed_long_arg"` } # func_split_long_opt may be replaced by extended shell implementation exit_cmd=: magic="%%%MAGIC variable%%%" magic_exe="%%%MAGIC EXE variable%%%" # Global variables. nonopt= preserve_args= lo2o="s/\\.lo\$/.${objext}/" o2lo="s/\\.${objext}\$/.lo/" extracted_archives= extracted_serial=0 # If this variable is set in any of the actions, the command in it # will be execed at the end. This prevents here-documents from being # left over by shells. exec_cmd= # func_append var value # Append VALUE to the end of shell variable VAR. func_append () { eval "${1}=\$${1}\${2}" } # func_append may be replaced by extended shell implementation # func_append_quoted var value # Quote VALUE and append to the end of shell variable VAR, separated # by a space. func_append_quoted () { func_quote_for_eval "${2}" eval "${1}=\$${1}\\ \$func_quote_for_eval_result" } # func_append_quoted may be replaced by extended shell implementation # func_arith arithmetic-term... func_arith () { func_arith_result=`expr "${@}"` } # func_arith may be replaced by extended shell implementation # func_len string # STRING may not start with a hyphen. func_len () { func_len_result=`expr "${1}" : ".*" 2>/dev/null || echo $max_cmd_len` } # func_len may be replaced by extended shell implementation # func_lo2o object func_lo2o () { func_lo2o_result=`$ECHO "${1}" | $SED "$lo2o"` } # func_lo2o may be replaced by extended shell implementation # func_xform libobj-or-source func_xform () { func_xform_result=`$ECHO "${1}" | $SED 's/\.[^.]*$/.lo/'` } # func_xform may be replaced by extended shell implementation # func_fatal_configuration arg... # Echo program name prefixed message to standard error, followed by # a configuration failure hint, and exit. func_fatal_configuration () { func_error ${1+"$@"} func_error "See the $PACKAGE documentation for more information." func_fatal_error "Fatal configuration error." } # func_config # Display the configuration for all the tags in this script. func_config () { re_begincf='^# ### BEGIN LIBTOOL' re_endcf='^# ### END LIBTOOL' # Default configuration. $SED "1,/$re_begincf CONFIG/d;/$re_endcf CONFIG/,\$d" < "$progpath" # Now print the configurations for the tags. for tagname in $taglist; do $SED -n "/$re_begincf TAG CONFIG: $tagname\$/,/$re_endcf TAG CONFIG: $tagname\$/p" < "$progpath" done exit $? } # func_features # Display the features supported by this script. func_features () { echo "host: $host" if test "$build_libtool_libs" = yes; then echo "enable shared libraries" else echo "disable shared libraries" fi if test "$build_old_libs" = yes; then echo "enable static libraries" else echo "disable static libraries" fi exit $? } # func_enable_tag tagname # Verify that TAGNAME is valid, and either flag an error and exit, or # enable the TAGNAME tag. We also add TAGNAME to the global $taglist # variable here. func_enable_tag () { # Global variable: tagname="$1" re_begincf="^# ### BEGIN LIBTOOL TAG CONFIG: $tagname\$" re_endcf="^# ### END LIBTOOL TAG CONFIG: $tagname\$" sed_extractcf="/$re_begincf/,/$re_endcf/p" # Validate tagname. case $tagname in *[!-_A-Za-z0-9,/]*) func_fatal_error "invalid tag name: $tagname" ;; esac # Don't test for the "default" C tag, as we know it's # there but not specially marked. case $tagname in CC) ;; *) if $GREP "$re_begincf" "$progpath" >/dev/null 2>&1; then taglist="$taglist $tagname" # Evaluate the configuration. Be careful to quote the path # and the sed script, to avoid splitting on whitespace, but # also don't use non-portable quotes within backquotes within # quotes we have to do it in 2 steps: extractedcf=`$SED -n -e "$sed_extractcf" < "$progpath"` eval "$extractedcf" else func_error "ignoring unknown tag $tagname" fi ;; esac } # func_check_version_match # Ensure that we are using m4 macros, and libtool script from the same # release of libtool. func_check_version_match () { if test "$package_revision" != "$macro_revision"; then if test "$VERSION" != "$macro_version"; then if test -z "$macro_version"; then cat >&2 <<_LT_EOF $progname: Version mismatch error. This is $PACKAGE $VERSION, but the $progname: definition of this LT_INIT comes from an older release. $progname: You should recreate aclocal.m4 with macros from $PACKAGE $VERSION $progname: and run autoconf again. _LT_EOF else cat >&2 <<_LT_EOF $progname: Version mismatch error. This is $PACKAGE $VERSION, but the $progname: definition of this LT_INIT comes from $PACKAGE $macro_version. $progname: You should recreate aclocal.m4 with macros from $PACKAGE $VERSION $progname: and run autoconf again. _LT_EOF fi else cat >&2 <<_LT_EOF $progname: Version mismatch error. This is $PACKAGE $VERSION, revision $package_revision, $progname: but the definition of this LT_INIT comes from revision $macro_revision. $progname: You should recreate aclocal.m4 with macros from revision $package_revision $progname: of $PACKAGE $VERSION and run autoconf again. _LT_EOF fi exit $EXIT_MISMATCH fi } # Shorthand for --mode=foo, only valid as the first argument case $1 in clean|clea|cle|cl) shift; set dummy --mode clean ${1+"$@"}; shift ;; compile|compil|compi|comp|com|co|c) shift; set dummy --mode compile ${1+"$@"}; shift ;; execute|execut|execu|exec|exe|ex|e) shift; set dummy --mode execute ${1+"$@"}; shift ;; finish|finis|fini|fin|fi|f) shift; set dummy --mode finish ${1+"$@"}; shift ;; install|instal|insta|inst|ins|in|i) shift; set dummy --mode install ${1+"$@"}; shift ;; link|lin|li|l) shift; set dummy --mode link ${1+"$@"}; shift ;; uninstall|uninstal|uninsta|uninst|unins|unin|uni|un|u) shift; set dummy --mode uninstall ${1+"$@"}; shift ;; esac # Option defaults: opt_debug=: opt_dry_run=false opt_config=false opt_preserve_dup_deps=false opt_features=false opt_finish=false opt_help=false opt_help_all=false opt_silent=: opt_warning=: opt_verbose=: opt_silent=false opt_verbose=false # Parse options once, thoroughly. This comes as soon as possible in the # script to make things like `--version' happen as quickly as we can. { # this just eases exit handling while test $# -gt 0; do opt="$1" shift case $opt in --debug|-x) opt_debug='set -x' func_echo "enabling shell trace mode" $opt_debug ;; --dry-run|--dryrun|-n) opt_dry_run=: ;; --config) opt_config=: func_config ;; --dlopen|-dlopen) optarg="$1" opt_dlopen="${opt_dlopen+$opt_dlopen }$optarg" shift ;; --preserve-dup-deps) opt_preserve_dup_deps=: ;; --features) opt_features=: func_features ;; --finish) opt_finish=: set dummy --mode finish ${1+"$@"}; shift ;; --help) opt_help=: ;; --help-all) opt_help_all=: opt_help=': help-all' ;; --mode) test $# = 0 && func_missing_arg $opt && break optarg="$1" opt_mode="$optarg" case $optarg in # Valid mode arguments: clean|compile|execute|finish|install|link|relink|uninstall) ;; # Catch anything else as an error *) func_error "invalid argument for $opt" exit_cmd=exit break ;; esac shift ;; --no-silent|--no-quiet) opt_silent=false func_append preserve_args " $opt" ;; --no-warning|--no-warn) opt_warning=false func_append preserve_args " $opt" ;; --no-verbose) opt_verbose=false func_append preserve_args " $opt" ;; --silent|--quiet) opt_silent=: func_append preserve_args " $opt" opt_verbose=false ;; --verbose|-v) opt_verbose=: func_append preserve_args " $opt" opt_silent=false ;; --tag) test $# = 0 && func_missing_arg $opt && break optarg="$1" opt_tag="$optarg" func_append preserve_args " $opt $optarg" func_enable_tag "$optarg" shift ;; -\?|-h) func_usage ;; --help) func_help ;; --version) func_version ;; # Separate optargs to long options: --*=*) func_split_long_opt "$opt" set dummy "$func_split_long_opt_name" "$func_split_long_opt_arg" ${1+"$@"} shift ;; # Separate non-argument short options: -\?*|-h*|-n*|-v*) func_split_short_opt "$opt" set dummy "$func_split_short_opt_name" "-$func_split_short_opt_arg" ${1+"$@"} shift ;; --) break ;; -*) func_fatal_help "unrecognized option \`$opt'" ;; *) set dummy "$opt" ${1+"$@"}; shift; break ;; esac done # Validate options: # save first non-option argument if test "$#" -gt 0; then nonopt="$opt" shift fi # preserve --debug test "$opt_debug" = : || func_append preserve_args " --debug" case $host in *cygwin* | *mingw* | *pw32* | *cegcc*) # don't eliminate duplications in $postdeps and $predeps opt_duplicate_compiler_generated_deps=: ;; *) opt_duplicate_compiler_generated_deps=$opt_preserve_dup_deps ;; esac $opt_help || { # Sanity checks first: func_check_version_match if test "$build_libtool_libs" != yes && test "$build_old_libs" != yes; then func_fatal_configuration "not configured to build any kind of library" fi # Darwin sucks eval std_shrext=\"$shrext_cmds\" # Only execute mode is allowed to have -dlopen flags. if test -n "$opt_dlopen" && test "$opt_mode" != execute; then func_error "unrecognized option \`-dlopen'" $ECHO "$help" 1>&2 exit $EXIT_FAILURE fi # Change the help message to a mode-specific one. generic_help="$help" help="Try \`$progname --help --mode=$opt_mode' for more information." } # Bail if the options were screwed $exit_cmd $EXIT_FAILURE } ## ----------- ## ## Main. ## ## ----------- ## # func_lalib_p file # True iff FILE is a libtool `.la' library or `.lo' object file. # This function is only a basic sanity check; it will hardly flush out # determined imposters. func_lalib_p () { test -f "$1" && $SED -e 4q "$1" 2>/dev/null \ | $GREP "^# Generated by .*$PACKAGE" > /dev/null 2>&1 } # func_lalib_unsafe_p file # True iff FILE is a libtool `.la' library or `.lo' object file. # This function implements the same check as func_lalib_p without # resorting to external programs. To this end, it redirects stdin and # closes it afterwards, without saving the original file descriptor. # As a safety measure, use it only where a negative result would be # fatal anyway. Works if `file' does not exist. func_lalib_unsafe_p () { lalib_p=no if test -f "$1" && test -r "$1" && exec 5<&0 <"$1"; then for lalib_p_l in 1 2 3 4 do read lalib_p_line case "$lalib_p_line" in \#\ Generated\ by\ *$PACKAGE* ) lalib_p=yes; break;; esac done exec 0<&5 5<&- fi test "$lalib_p" = yes } # func_ltwrapper_script_p file # True iff FILE is a libtool wrapper script # This function is only a basic sanity check; it will hardly flush out # determined imposters. func_ltwrapper_script_p () { func_lalib_p "$1" } # func_ltwrapper_executable_p file # True iff FILE is a libtool wrapper executable # This function is only a basic sanity check; it will hardly flush out # determined imposters. func_ltwrapper_executable_p () { func_ltwrapper_exec_suffix= case $1 in *.exe) ;; *) func_ltwrapper_exec_suffix=.exe ;; esac $GREP "$magic_exe" "$1$func_ltwrapper_exec_suffix" >/dev/null 2>&1 } # func_ltwrapper_scriptname file # Assumes file is an ltwrapper_executable # uses $file to determine the appropriate filename for a # temporary ltwrapper_script. func_ltwrapper_scriptname () { func_dirname_and_basename "$1" "" "." func_stripname '' '.exe' "$func_basename_result" func_ltwrapper_scriptname_result="$func_dirname_result/$objdir/${func_stripname_result}_ltshwrapper" } # func_ltwrapper_p file # True iff FILE is a libtool wrapper script or wrapper executable # This function is only a basic sanity check; it will hardly flush out # determined imposters. func_ltwrapper_p () { func_ltwrapper_script_p "$1" || func_ltwrapper_executable_p "$1" } # func_execute_cmds commands fail_cmd # Execute tilde-delimited COMMANDS. # If FAIL_CMD is given, eval that upon failure. # FAIL_CMD may read-access the current command in variable CMD! func_execute_cmds () { $opt_debug save_ifs=$IFS; IFS='~' for cmd in $1; do IFS=$save_ifs eval cmd=\"$cmd\" func_show_eval "$cmd" "${2-:}" done IFS=$save_ifs } # func_source file # Source FILE, adding directory component if necessary. # Note that it is not necessary on cygwin/mingw to append a dot to # FILE even if both FILE and FILE.exe exist: automatic-append-.exe # behavior happens only for exec(3), not for open(2)! Also, sourcing # `FILE.' does not work on cygwin managed mounts. func_source () { $opt_debug case $1 in */* | *\\*) . "$1" ;; *) . "./$1" ;; esac } # func_resolve_sysroot PATH # Replace a leading = in PATH with a sysroot. Store the result into # func_resolve_sysroot_result func_resolve_sysroot () { func_resolve_sysroot_result=$1 case $func_resolve_sysroot_result in =*) func_stripname '=' '' "$func_resolve_sysroot_result" func_resolve_sysroot_result=$lt_sysroot$func_stripname_result ;; esac } # func_replace_sysroot PATH # If PATH begins with the sysroot, replace it with = and # store the result into func_replace_sysroot_result. func_replace_sysroot () { case "$lt_sysroot:$1" in ?*:"$lt_sysroot"*) func_stripname "$lt_sysroot" '' "$1" func_replace_sysroot_result="=$func_stripname_result" ;; *) # Including no sysroot. func_replace_sysroot_result=$1 ;; esac } # func_infer_tag arg # Infer tagged configuration to use if any are available and # if one wasn't chosen via the "--tag" command line option. # Only attempt this if the compiler in the base compile # command doesn't match the default compiler. # arg is usually of the form 'gcc ...' func_infer_tag () { $opt_debug if test -n "$available_tags" && test -z "$tagname"; then CC_quoted= for arg in $CC; do func_append_quoted CC_quoted "$arg" done CC_expanded=`func_echo_all $CC` CC_quoted_expanded=`func_echo_all $CC_quoted` case $@ in # Blanks in the command may have been stripped by the calling shell, # but not from the CC environment variable when configure was run. " $CC "* | "$CC "* | " $CC_expanded "* | "$CC_expanded "* | \ " $CC_quoted"* | "$CC_quoted "* | " $CC_quoted_expanded "* | "$CC_quoted_expanded "*) ;; # Blanks at the start of $base_compile will cause this to fail # if we don't check for them as well. *) for z in $available_tags; do if $GREP "^# ### BEGIN LIBTOOL TAG CONFIG: $z$" < "$progpath" > /dev/null; then # Evaluate the configuration. eval "`${SED} -n -e '/^# ### BEGIN LIBTOOL TAG CONFIG: '$z'$/,/^# ### END LIBTOOL TAG CONFIG: '$z'$/p' < $progpath`" CC_quoted= for arg in $CC; do # Double-quote args containing other shell metacharacters. func_append_quoted CC_quoted "$arg" done CC_expanded=`func_echo_all $CC` CC_quoted_expanded=`func_echo_all $CC_quoted` case "$@ " in " $CC "* | "$CC "* | " $CC_expanded "* | "$CC_expanded "* | \ " $CC_quoted"* | "$CC_quoted "* | " $CC_quoted_expanded "* | "$CC_quoted_expanded "*) # The compiler in the base compile command matches # the one in the tagged configuration. # Assume this is the tagged configuration we want. tagname=$z break ;; esac fi done # If $tagname still isn't set, then no tagged configuration # was found and let the user know that the "--tag" command # line option must be used. if test -z "$tagname"; then func_echo "unable to infer tagged configuration" func_fatal_error "specify a tag with \`--tag'" # else # func_verbose "using $tagname tagged configuration" fi ;; esac fi } # func_write_libtool_object output_name pic_name nonpic_name # Create a libtool object file (analogous to a ".la" file), # but don't create it if we're doing a dry run. func_write_libtool_object () { write_libobj=${1} if test "$build_libtool_libs" = yes; then write_lobj=\'${2}\' else write_lobj=none fi if test "$build_old_libs" = yes; then write_oldobj=\'${3}\' else write_oldobj=none fi $opt_dry_run || { cat >${write_libobj}T </dev/null` if test "$?" -eq 0 && test -n "${func_convert_core_file_wine_to_w32_tmp}"; then func_convert_core_file_wine_to_w32_result=`$ECHO "$func_convert_core_file_wine_to_w32_tmp" | $SED -e "$lt_sed_naive_backslashify"` else func_convert_core_file_wine_to_w32_result= fi fi } # end: func_convert_core_file_wine_to_w32 # func_convert_core_path_wine_to_w32 ARG # Helper function used by path conversion functions when $build is *nix, and # $host is mingw, cygwin, or some other w32 environment. Relies on a correctly # configured wine environment available, with the winepath program in $build's # $PATH. Assumes ARG has no leading or trailing path separator characters. # # ARG is path to be converted from $build format to win32. # Result is available in $func_convert_core_path_wine_to_w32_result. # Unconvertible file (directory) names in ARG are skipped; if no directory names # are convertible, then the result may be empty. func_convert_core_path_wine_to_w32 () { $opt_debug # unfortunately, winepath doesn't convert paths, only file names func_convert_core_path_wine_to_w32_result="" if test -n "$1"; then oldIFS=$IFS IFS=: for func_convert_core_path_wine_to_w32_f in $1; do IFS=$oldIFS func_convert_core_file_wine_to_w32 "$func_convert_core_path_wine_to_w32_f" if test -n "$func_convert_core_file_wine_to_w32_result" ; then if test -z "$func_convert_core_path_wine_to_w32_result"; then func_convert_core_path_wine_to_w32_result="$func_convert_core_file_wine_to_w32_result" else func_append func_convert_core_path_wine_to_w32_result ";$func_convert_core_file_wine_to_w32_result" fi fi done IFS=$oldIFS fi } # end: func_convert_core_path_wine_to_w32 # func_cygpath ARGS... # Wrapper around calling the cygpath program via LT_CYGPATH. This is used when # when (1) $build is *nix and Cygwin is hosted via a wine environment; or (2) # $build is MSYS and $host is Cygwin, or (3) $build is Cygwin. In case (1) or # (2), returns the Cygwin file name or path in func_cygpath_result (input # file name or path is assumed to be in w32 format, as previously converted # from $build's *nix or MSYS format). In case (3), returns the w32 file name # or path in func_cygpath_result (input file name or path is assumed to be in # Cygwin format). Returns an empty string on error. # # ARGS are passed to cygpath, with the last one being the file name or path to # be converted. # # Specify the absolute *nix (or w32) name to cygpath in the LT_CYGPATH # environment variable; do not put it in $PATH. func_cygpath () { $opt_debug if test -n "$LT_CYGPATH" && test -f "$LT_CYGPATH"; then func_cygpath_result=`$LT_CYGPATH "$@" 2>/dev/null` if test "$?" -ne 0; then # on failure, ensure result is empty func_cygpath_result= fi else func_cygpath_result= func_error "LT_CYGPATH is empty or specifies non-existent file: \`$LT_CYGPATH'" fi } #end: func_cygpath # func_convert_core_msys_to_w32 ARG # Convert file name or path ARG from MSYS format to w32 format. Return # result in func_convert_core_msys_to_w32_result. func_convert_core_msys_to_w32 () { $opt_debug # awkward: cmd appends spaces to result func_convert_core_msys_to_w32_result=`( cmd //c echo "$1" ) 2>/dev/null | $SED -e 's/[ ]*$//' -e "$lt_sed_naive_backslashify"` } #end: func_convert_core_msys_to_w32 # func_convert_file_check ARG1 ARG2 # Verify that ARG1 (a file name in $build format) was converted to $host # format in ARG2. Otherwise, emit an error message, but continue (resetting # func_to_host_file_result to ARG1). func_convert_file_check () { $opt_debug if test -z "$2" && test -n "$1" ; then func_error "Could not determine host file name corresponding to" func_error " \`$1'" func_error "Continuing, but uninstalled executables may not work." # Fallback: func_to_host_file_result="$1" fi } # end func_convert_file_check # func_convert_path_check FROM_PATHSEP TO_PATHSEP FROM_PATH TO_PATH # Verify that FROM_PATH (a path in $build format) was converted to $host # format in TO_PATH. Otherwise, emit an error message, but continue, resetting # func_to_host_file_result to a simplistic fallback value (see below). func_convert_path_check () { $opt_debug if test -z "$4" && test -n "$3"; then func_error "Could not determine the host path corresponding to" func_error " \`$3'" func_error "Continuing, but uninstalled executables may not work." # Fallback. This is a deliberately simplistic "conversion" and # should not be "improved". See libtool.info. if test "x$1" != "x$2"; then lt_replace_pathsep_chars="s|$1|$2|g" func_to_host_path_result=`echo "$3" | $SED -e "$lt_replace_pathsep_chars"` else func_to_host_path_result="$3" fi fi } # end func_convert_path_check # func_convert_path_front_back_pathsep FRONTPAT BACKPAT REPL ORIG # Modifies func_to_host_path_result by prepending REPL if ORIG matches FRONTPAT # and appending REPL if ORIG matches BACKPAT. func_convert_path_front_back_pathsep () { $opt_debug case $4 in $1 ) func_to_host_path_result="$3$func_to_host_path_result" ;; esac case $4 in $2 ) func_append func_to_host_path_result "$3" ;; esac } # end func_convert_path_front_back_pathsep ################################################## # $build to $host FILE NAME CONVERSION FUNCTIONS # ################################################## # invoked via `$to_host_file_cmd ARG' # # In each case, ARG is the path to be converted from $build to $host format. # Result will be available in $func_to_host_file_result. # func_to_host_file ARG # Converts the file name ARG from $build format to $host format. Return result # in func_to_host_file_result. func_to_host_file () { $opt_debug $to_host_file_cmd "$1" } # end func_to_host_file # func_to_tool_file ARG LAZY # converts the file name ARG from $build format to toolchain format. Return # result in func_to_tool_file_result. If the conversion in use is listed # in (the comma separated) LAZY, no conversion takes place. func_to_tool_file () { $opt_debug case ,$2, in *,"$to_tool_file_cmd",*) func_to_tool_file_result=$1 ;; *) $to_tool_file_cmd "$1" func_to_tool_file_result=$func_to_host_file_result ;; esac } # end func_to_tool_file # func_convert_file_noop ARG # Copy ARG to func_to_host_file_result. func_convert_file_noop () { func_to_host_file_result="$1" } # end func_convert_file_noop # func_convert_file_msys_to_w32 ARG # Convert file name ARG from (mingw) MSYS to (mingw) w32 format; automatic # conversion to w32 is not available inside the cwrapper. Returns result in # func_to_host_file_result. func_convert_file_msys_to_w32 () { $opt_debug func_to_host_file_result="$1" if test -n "$1"; then func_convert_core_msys_to_w32 "$1" func_to_host_file_result="$func_convert_core_msys_to_w32_result" fi func_convert_file_check "$1" "$func_to_host_file_result" } # end func_convert_file_msys_to_w32 # func_convert_file_cygwin_to_w32 ARG # Convert file name ARG from Cygwin to w32 format. Returns result in # func_to_host_file_result. func_convert_file_cygwin_to_w32 () { $opt_debug func_to_host_file_result="$1" if test -n "$1"; then # because $build is cygwin, we call "the" cygpath in $PATH; no need to use # LT_CYGPATH in this case. func_to_host_file_result=`cygpath -m "$1"` fi func_convert_file_check "$1" "$func_to_host_file_result" } # end func_convert_file_cygwin_to_w32 # func_convert_file_nix_to_w32 ARG # Convert file name ARG from *nix to w32 format. Requires a wine environment # and a working winepath. Returns result in func_to_host_file_result. func_convert_file_nix_to_w32 () { $opt_debug func_to_host_file_result="$1" if test -n "$1"; then func_convert_core_file_wine_to_w32 "$1" func_to_host_file_result="$func_convert_core_file_wine_to_w32_result" fi func_convert_file_check "$1" "$func_to_host_file_result" } # end func_convert_file_nix_to_w32 # func_convert_file_msys_to_cygwin ARG # Convert file name ARG from MSYS to Cygwin format. Requires LT_CYGPATH set. # Returns result in func_to_host_file_result. func_convert_file_msys_to_cygwin () { $opt_debug func_to_host_file_result="$1" if test -n "$1"; then func_convert_core_msys_to_w32 "$1" func_cygpath -u "$func_convert_core_msys_to_w32_result" func_to_host_file_result="$func_cygpath_result" fi func_convert_file_check "$1" "$func_to_host_file_result" } # end func_convert_file_msys_to_cygwin # func_convert_file_nix_to_cygwin ARG # Convert file name ARG from *nix to Cygwin format. Requires Cygwin installed # in a wine environment, working winepath, and LT_CYGPATH set. Returns result # in func_to_host_file_result. func_convert_file_nix_to_cygwin () { $opt_debug func_to_host_file_result="$1" if test -n "$1"; then # convert from *nix to w32, then use cygpath to convert from w32 to cygwin. func_convert_core_file_wine_to_w32 "$1" func_cygpath -u "$func_convert_core_file_wine_to_w32_result" func_to_host_file_result="$func_cygpath_result" fi func_convert_file_check "$1" "$func_to_host_file_result" } # end func_convert_file_nix_to_cygwin ############################################# # $build to $host PATH CONVERSION FUNCTIONS # ############################################# # invoked via `$to_host_path_cmd ARG' # # In each case, ARG is the path to be converted from $build to $host format. # The result will be available in $func_to_host_path_result. # # Path separators are also converted from $build format to $host format. If # ARG begins or ends with a path separator character, it is preserved (but # converted to $host format) on output. # # All path conversion functions are named using the following convention: # file name conversion function : func_convert_file_X_to_Y () # path conversion function : func_convert_path_X_to_Y () # where, for any given $build/$host combination the 'X_to_Y' value is the # same. If conversion functions are added for new $build/$host combinations, # the two new functions must follow this pattern, or func_init_to_host_path_cmd # will break. # func_init_to_host_path_cmd # Ensures that function "pointer" variable $to_host_path_cmd is set to the # appropriate value, based on the value of $to_host_file_cmd. to_host_path_cmd= func_init_to_host_path_cmd () { $opt_debug if test -z "$to_host_path_cmd"; then func_stripname 'func_convert_file_' '' "$to_host_file_cmd" to_host_path_cmd="func_convert_path_${func_stripname_result}" fi } # func_to_host_path ARG # Converts the path ARG from $build format to $host format. Return result # in func_to_host_path_result. func_to_host_path () { $opt_debug func_init_to_host_path_cmd $to_host_path_cmd "$1" } # end func_to_host_path # func_convert_path_noop ARG # Copy ARG to func_to_host_path_result. func_convert_path_noop () { func_to_host_path_result="$1" } # end func_convert_path_noop # func_convert_path_msys_to_w32 ARG # Convert path ARG from (mingw) MSYS to (mingw) w32 format; automatic # conversion to w32 is not available inside the cwrapper. Returns result in # func_to_host_path_result. func_convert_path_msys_to_w32 () { $opt_debug func_to_host_path_result="$1" if test -n "$1"; then # Remove leading and trailing path separator characters from ARG. MSYS # behavior is inconsistent here; cygpath turns them into '.;' and ';.'; # and winepath ignores them completely. func_stripname : : "$1" func_to_host_path_tmp1=$func_stripname_result func_convert_core_msys_to_w32 "$func_to_host_path_tmp1" func_to_host_path_result="$func_convert_core_msys_to_w32_result" func_convert_path_check : ";" \ "$func_to_host_path_tmp1" "$func_to_host_path_result" func_convert_path_front_back_pathsep ":*" "*:" ";" "$1" fi } # end func_convert_path_msys_to_w32 # func_convert_path_cygwin_to_w32 ARG # Convert path ARG from Cygwin to w32 format. Returns result in # func_to_host_file_result. func_convert_path_cygwin_to_w32 () { $opt_debug func_to_host_path_result="$1" if test -n "$1"; then # See func_convert_path_msys_to_w32: func_stripname : : "$1" func_to_host_path_tmp1=$func_stripname_result func_to_host_path_result=`cygpath -m -p "$func_to_host_path_tmp1"` func_convert_path_check : ";" \ "$func_to_host_path_tmp1" "$func_to_host_path_result" func_convert_path_front_back_pathsep ":*" "*:" ";" "$1" fi } # end func_convert_path_cygwin_to_w32 # func_convert_path_nix_to_w32 ARG # Convert path ARG from *nix to w32 format. Requires a wine environment and # a working winepath. Returns result in func_to_host_file_result. func_convert_path_nix_to_w32 () { $opt_debug func_to_host_path_result="$1" if test -n "$1"; then # See func_convert_path_msys_to_w32: func_stripname : : "$1" func_to_host_path_tmp1=$func_stripname_result func_convert_core_path_wine_to_w32 "$func_to_host_path_tmp1" func_to_host_path_result="$func_convert_core_path_wine_to_w32_result" func_convert_path_check : ";" \ "$func_to_host_path_tmp1" "$func_to_host_path_result" func_convert_path_front_back_pathsep ":*" "*:" ";" "$1" fi } # end func_convert_path_nix_to_w32 # func_convert_path_msys_to_cygwin ARG # Convert path ARG from MSYS to Cygwin format. Requires LT_CYGPATH set. # Returns result in func_to_host_file_result. func_convert_path_msys_to_cygwin () { $opt_debug func_to_host_path_result="$1" if test -n "$1"; then # See func_convert_path_msys_to_w32: func_stripname : : "$1" func_to_host_path_tmp1=$func_stripname_result func_convert_core_msys_to_w32 "$func_to_host_path_tmp1" func_cygpath -u -p "$func_convert_core_msys_to_w32_result" func_to_host_path_result="$func_cygpath_result" func_convert_path_check : : \ "$func_to_host_path_tmp1" "$func_to_host_path_result" func_convert_path_front_back_pathsep ":*" "*:" : "$1" fi } # end func_convert_path_msys_to_cygwin # func_convert_path_nix_to_cygwin ARG # Convert path ARG from *nix to Cygwin format. Requires Cygwin installed in a # a wine environment, working winepath, and LT_CYGPATH set. Returns result in # func_to_host_file_result. func_convert_path_nix_to_cygwin () { $opt_debug func_to_host_path_result="$1" if test -n "$1"; then # Remove leading and trailing path separator characters from # ARG. msys behavior is inconsistent here, cygpath turns them # into '.;' and ';.', and winepath ignores them completely. func_stripname : : "$1" func_to_host_path_tmp1=$func_stripname_result func_convert_core_path_wine_to_w32 "$func_to_host_path_tmp1" func_cygpath -u -p "$func_convert_core_path_wine_to_w32_result" func_to_host_path_result="$func_cygpath_result" func_convert_path_check : : \ "$func_to_host_path_tmp1" "$func_to_host_path_result" func_convert_path_front_back_pathsep ":*" "*:" : "$1" fi } # end func_convert_path_nix_to_cygwin # func_mode_compile arg... func_mode_compile () { $opt_debug # Get the compilation command and the source file. base_compile= srcfile="$nonopt" # always keep a non-empty value in "srcfile" suppress_opt=yes suppress_output= arg_mode=normal libobj= later= pie_flag= for arg do case $arg_mode in arg ) # do not "continue". Instead, add this to base_compile lastarg="$arg" arg_mode=normal ;; target ) libobj="$arg" arg_mode=normal continue ;; normal ) # Accept any command-line options. case $arg in -o) test -n "$libobj" && \ func_fatal_error "you cannot specify \`-o' more than once" arg_mode=target continue ;; -pie | -fpie | -fPIE) func_append pie_flag " $arg" continue ;; -shared | -static | -prefer-pic | -prefer-non-pic) func_append later " $arg" continue ;; -no-suppress) suppress_opt=no continue ;; -Xcompiler) arg_mode=arg # the next one goes into the "base_compile" arg list continue # The current "srcfile" will either be retained or ;; # replaced later. I would guess that would be a bug. -Wc,*) func_stripname '-Wc,' '' "$arg" args=$func_stripname_result lastarg= save_ifs="$IFS"; IFS=',' for arg in $args; do IFS="$save_ifs" func_append_quoted lastarg "$arg" done IFS="$save_ifs" func_stripname ' ' '' "$lastarg" lastarg=$func_stripname_result # Add the arguments to base_compile. func_append base_compile " $lastarg" continue ;; *) # Accept the current argument as the source file. # The previous "srcfile" becomes the current argument. # lastarg="$srcfile" srcfile="$arg" ;; esac # case $arg ;; esac # case $arg_mode # Aesthetically quote the previous argument. func_append_quoted base_compile "$lastarg" done # for arg case $arg_mode in arg) func_fatal_error "you must specify an argument for -Xcompile" ;; target) func_fatal_error "you must specify a target with \`-o'" ;; *) # Get the name of the library object. test -z "$libobj" && { func_basename "$srcfile" libobj="$func_basename_result" } ;; esac # Recognize several different file suffixes. # If the user specifies -o file.o, it is replaced with file.lo case $libobj in *.[cCFSifmso] | \ *.ada | *.adb | *.ads | *.asm | \ *.c++ | *.cc | *.ii | *.class | *.cpp | *.cxx | \ *.[fF][09]? | *.for | *.java | *.go | *.obj | *.sx | *.cu | *.cup) func_xform "$libobj" libobj=$func_xform_result ;; esac case $libobj in *.lo) func_lo2o "$libobj"; obj=$func_lo2o_result ;; *) func_fatal_error "cannot determine name of library object from \`$libobj'" ;; esac func_infer_tag $base_compile for arg in $later; do case $arg in -shared) test "$build_libtool_libs" != yes && \ func_fatal_configuration "can not build a shared library" build_old_libs=no continue ;; -static) build_libtool_libs=no build_old_libs=yes continue ;; -prefer-pic) pic_mode=yes continue ;; -prefer-non-pic) pic_mode=no continue ;; esac done func_quote_for_eval "$libobj" test "X$libobj" != "X$func_quote_for_eval_result" \ && $ECHO "X$libobj" | $GREP '[]~#^*{};<>?"'"'"' &()|`$[]' \ && func_warning "libobj name \`$libobj' may not contain shell special characters." func_dirname_and_basename "$obj" "/" "" objname="$func_basename_result" xdir="$func_dirname_result" lobj=${xdir}$objdir/$objname test -z "$base_compile" && \ func_fatal_help "you must specify a compilation command" # Delete any leftover library objects. if test "$build_old_libs" = yes; then removelist="$obj $lobj $libobj ${libobj}T" else removelist="$lobj $libobj ${libobj}T" fi # On Cygwin there's no "real" PIC flag so we must build both object types case $host_os in cygwin* | mingw* | pw32* | os2* | cegcc*) pic_mode=default ;; esac if test "$pic_mode" = no && test "$deplibs_check_method" != pass_all; then # non-PIC code in shared libraries is not supported pic_mode=default fi # Calculate the filename of the output object if compiler does # not support -o with -c if test "$compiler_c_o" = no; then output_obj=`$ECHO "$srcfile" | $SED 's%^.*/%%; s%\.[^.]*$%%'`.${objext} lockfile="$output_obj.lock" else output_obj= need_locks=no lockfile= fi # Lock this critical section if it is needed # We use this script file to make the link, it avoids creating a new file if test "$need_locks" = yes; then until $opt_dry_run || ln "$progpath" "$lockfile" 2>/dev/null; do func_echo "Waiting for $lockfile to be removed" sleep 2 done elif test "$need_locks" = warn; then if test -f "$lockfile"; then $ECHO "\ *** ERROR, $lockfile exists and contains: `cat $lockfile 2>/dev/null` This indicates that another process is trying to use the same temporary object file, and libtool could not work around it because your compiler does not support \`-c' and \`-o' together. If you repeat this compilation, it may succeed, by chance, but you had better avoid parallel builds (make -j) in this platform, or get a better compiler." $opt_dry_run || $RM $removelist exit $EXIT_FAILURE fi func_append removelist " $output_obj" $ECHO "$srcfile" > "$lockfile" fi $opt_dry_run || $RM $removelist func_append removelist " $lockfile" trap '$opt_dry_run || $RM $removelist; exit $EXIT_FAILURE' 1 2 15 func_to_tool_file "$srcfile" func_convert_file_msys_to_w32 srcfile=$func_to_tool_file_result func_quote_for_eval "$srcfile" qsrcfile=$func_quote_for_eval_result # Only build a PIC object if we are building libtool libraries. if test "$build_libtool_libs" = yes; then # Without this assignment, base_compile gets emptied. fbsd_hideous_sh_bug=$base_compile if test "$pic_mode" != no; then command="$base_compile $qsrcfile $pic_flag" else # Don't build PIC code command="$base_compile $qsrcfile" fi func_mkdir_p "$xdir$objdir" if test -z "$output_obj"; then # Place PIC objects in $objdir func_append command " -o $lobj" fi func_show_eval_locale "$command" \ 'test -n "$output_obj" && $RM $removelist; exit $EXIT_FAILURE' if test "$need_locks" = warn && test "X`cat $lockfile 2>/dev/null`" != "X$srcfile"; then $ECHO "\ *** ERROR, $lockfile contains: `cat $lockfile 2>/dev/null` but it should contain: $srcfile This indicates that another process is trying to use the same temporary object file, and libtool could not work around it because your compiler does not support \`-c' and \`-o' together. If you repeat this compilation, it may succeed, by chance, but you had better avoid parallel builds (make -j) in this platform, or get a better compiler." $opt_dry_run || $RM $removelist exit $EXIT_FAILURE fi # Just move the object if needed, then go on to compile the next one if test -n "$output_obj" && test "X$output_obj" != "X$lobj"; then func_show_eval '$MV "$output_obj" "$lobj"' \ 'error=$?; $opt_dry_run || $RM $removelist; exit $error' fi # Allow error messages only from the first compilation. if test "$suppress_opt" = yes; then suppress_output=' >/dev/null 2>&1' fi fi # Only build a position-dependent object if we build old libraries. if test "$build_old_libs" = yes; then if test "$pic_mode" != yes; then # Don't build PIC code command="$base_compile $qsrcfile$pie_flag" else command="$base_compile $qsrcfile $pic_flag" fi if test "$compiler_c_o" = yes; then func_append command " -o $obj" fi # Suppress compiler output if we already did a PIC compilation. func_append command "$suppress_output" func_show_eval_locale "$command" \ '$opt_dry_run || $RM $removelist; exit $EXIT_FAILURE' if test "$need_locks" = warn && test "X`cat $lockfile 2>/dev/null`" != "X$srcfile"; then $ECHO "\ *** ERROR, $lockfile contains: `cat $lockfile 2>/dev/null` but it should contain: $srcfile This indicates that another process is trying to use the same temporary object file, and libtool could not work around it because your compiler does not support \`-c' and \`-o' together. If you repeat this compilation, it may succeed, by chance, but you had better avoid parallel builds (make -j) in this platform, or get a better compiler." $opt_dry_run || $RM $removelist exit $EXIT_FAILURE fi # Just move the object if needed if test -n "$output_obj" && test "X$output_obj" != "X$obj"; then func_show_eval '$MV "$output_obj" "$obj"' \ 'error=$?; $opt_dry_run || $RM $removelist; exit $error' fi fi $opt_dry_run || { func_write_libtool_object "$libobj" "$objdir/$objname" "$objname" # Unlock the critical section if it was locked if test "$need_locks" != no; then removelist=$lockfile $RM "$lockfile" fi } exit $EXIT_SUCCESS } $opt_help || { test "$opt_mode" = compile && func_mode_compile ${1+"$@"} } func_mode_help () { # We need to display help for each of the modes. case $opt_mode in "") # Generic help is extracted from the usage comments # at the start of this file. func_help ;; clean) $ECHO \ "Usage: $progname [OPTION]... --mode=clean RM [RM-OPTION]... FILE... Remove files from the build directory. RM is the name of the program to use to delete files associated with each FILE (typically \`/bin/rm'). RM-OPTIONS are options (such as \`-f') to be passed to RM. If FILE is a libtool library, object or program, all the files associated with it are deleted. Otherwise, only FILE itself is deleted using RM." ;; compile) $ECHO \ "Usage: $progname [OPTION]... --mode=compile COMPILE-COMMAND... SOURCEFILE Compile a source file into a libtool library object. This mode accepts the following additional options: -o OUTPUT-FILE set the output file name to OUTPUT-FILE -no-suppress do not suppress compiler output for multiple passes -prefer-pic try to build PIC objects only -prefer-non-pic try to build non-PIC objects only -shared do not build a \`.o' file suitable for static linking -static only build a \`.o' file suitable for static linking -Wc,FLAG pass FLAG directly to the compiler COMPILE-COMMAND is a command to be used in creating a \`standard' object file from the given SOURCEFILE. The output file name is determined by removing the directory component from SOURCEFILE, then substituting the C source code suffix \`.c' with the library object suffix, \`.lo'." ;; execute) $ECHO \ "Usage: $progname [OPTION]... --mode=execute COMMAND [ARGS]... Automatically set library path, then run a program. This mode accepts the following additional options: -dlopen FILE add the directory containing FILE to the library path This mode sets the library path environment variable according to \`-dlopen' flags. If any of the ARGS are libtool executable wrappers, then they are translated into their corresponding uninstalled binary, and any of their required library directories are added to the library path. Then, COMMAND is executed, with ARGS as arguments." ;; finish) $ECHO \ "Usage: $progname [OPTION]... --mode=finish [LIBDIR]... Complete the installation of libtool libraries. Each LIBDIR is a directory that contains libtool libraries. The commands that this mode executes may require superuser privileges. Use the \`--dry-run' option if you just want to see what would be executed." ;; install) $ECHO \ "Usage: $progname [OPTION]... --mode=install INSTALL-COMMAND... Install executables or libraries. INSTALL-COMMAND is the installation command. The first component should be either the \`install' or \`cp' program. The following components of INSTALL-COMMAND are treated specially: -inst-prefix-dir PREFIX-DIR Use PREFIX-DIR as a staging area for installation The rest of the components are interpreted as arguments to that command (only BSD-compatible install options are recognized)." ;; link) $ECHO \ "Usage: $progname [OPTION]... --mode=link LINK-COMMAND... Link object files or libraries together to form another library, or to create an executable program. LINK-COMMAND is a command using the C compiler that you would use to create a program from several object files. The following components of LINK-COMMAND are treated specially: -all-static do not do any dynamic linking at all -avoid-version do not add a version suffix if possible -bindir BINDIR specify path to binaries directory (for systems where libraries must be found in the PATH setting at runtime) -dlopen FILE \`-dlpreopen' FILE if it cannot be dlopened at runtime -dlpreopen FILE link in FILE and add its symbols to lt_preloaded_symbols -export-dynamic allow symbols from OUTPUT-FILE to be resolved with dlsym(3) -export-symbols SYMFILE try to export only the symbols listed in SYMFILE -export-symbols-regex REGEX try to export only the symbols matching REGEX -LLIBDIR search LIBDIR for required installed libraries -lNAME OUTPUT-FILE requires the installed library libNAME -module build a library that can dlopened -no-fast-install disable the fast-install mode -no-install link a not-installable executable -no-undefined declare that a library does not refer to external symbols -o OUTPUT-FILE create OUTPUT-FILE from the specified objects -objectlist FILE Use a list of object files found in FILE to specify objects -precious-files-regex REGEX don't remove output files matching REGEX -release RELEASE specify package release information -rpath LIBDIR the created library will eventually be installed in LIBDIR -R[ ]LIBDIR add LIBDIR to the runtime path of programs and libraries -shared only do dynamic linking of libtool libraries -shrext SUFFIX override the standard shared library file extension -static do not do any dynamic linking of uninstalled libtool libraries -static-libtool-libs do not do any dynamic linking of libtool libraries -version-info CURRENT[:REVISION[:AGE]] specify library version info [each variable defaults to 0] -weak LIBNAME declare that the target provides the LIBNAME interface -Wc,FLAG -Xcompiler FLAG pass linker-specific FLAG directly to the compiler -Wl,FLAG -Xlinker FLAG pass linker-specific FLAG directly to the linker -XCClinker FLAG pass link-specific FLAG to the compiler driver (CC) All other options (arguments beginning with \`-') are ignored. Every other argument is treated as a filename. Files ending in \`.la' are treated as uninstalled libtool libraries, other files are standard or library object files. If the OUTPUT-FILE ends in \`.la', then a libtool library is created, only library objects (\`.lo' files) may be specified, and \`-rpath' is required, except when creating a convenience library. If OUTPUT-FILE ends in \`.a' or \`.lib', then a standard library is created using \`ar' and \`ranlib', or on Windows using \`lib'. If OUTPUT-FILE ends in \`.lo' or \`.${objext}', then a reloadable object file is created, otherwise an executable program is created." ;; uninstall) $ECHO \ "Usage: $progname [OPTION]... --mode=uninstall RM [RM-OPTION]... FILE... Remove libraries from an installation directory. RM is the name of the program to use to delete files associated with each FILE (typically \`/bin/rm'). RM-OPTIONS are options (such as \`-f') to be passed to RM. If FILE is a libtool library, all the files associated with it are deleted. Otherwise, only FILE itself is deleted using RM." ;; *) func_fatal_help "invalid operation mode \`$opt_mode'" ;; esac echo $ECHO "Try \`$progname --help' for more information about other modes." } # Now that we've collected a possible --mode arg, show help if necessary if $opt_help; then if test "$opt_help" = :; then func_mode_help else { func_help noexit for opt_mode in compile link execute install finish uninstall clean; do func_mode_help done } | sed -n '1p; 2,$s/^Usage:/ or: /p' { func_help noexit for opt_mode in compile link execute install finish uninstall clean; do echo func_mode_help done } | sed '1d /^When reporting/,/^Report/{ H d } $x /information about other modes/d /more detailed .*MODE/d s/^Usage:.*--mode=\([^ ]*\) .*/Description of \1 mode:/' fi exit $? fi # func_mode_execute arg... func_mode_execute () { $opt_debug # The first argument is the command name. cmd="$nonopt" test -z "$cmd" && \ func_fatal_help "you must specify a COMMAND" # Handle -dlopen flags immediately. for file in $opt_dlopen; do test -f "$file" \ || func_fatal_help "\`$file' is not a file" dir= case $file in *.la) func_resolve_sysroot "$file" file=$func_resolve_sysroot_result # Check to see that this really is a libtool archive. func_lalib_unsafe_p "$file" \ || func_fatal_help "\`$lib' is not a valid libtool archive" # Read the libtool library. dlname= library_names= func_source "$file" # Skip this library if it cannot be dlopened. if test -z "$dlname"; then # Warn if it was a shared library. test -n "$library_names" && \ func_warning "\`$file' was not linked with \`-export-dynamic'" continue fi func_dirname "$file" "" "." dir="$func_dirname_result" if test -f "$dir/$objdir/$dlname"; then func_append dir "/$objdir" else if test ! -f "$dir/$dlname"; then func_fatal_error "cannot find \`$dlname' in \`$dir' or \`$dir/$objdir'" fi fi ;; *.lo) # Just add the directory containing the .lo file. func_dirname "$file" "" "." dir="$func_dirname_result" ;; *) func_warning "\`-dlopen' is ignored for non-libtool libraries and objects" continue ;; esac # Get the absolute pathname. absdir=`cd "$dir" && pwd` test -n "$absdir" && dir="$absdir" # Now add the directory to shlibpath_var. if eval "test -z \"\$$shlibpath_var\""; then eval "$shlibpath_var=\"\$dir\"" else eval "$shlibpath_var=\"\$dir:\$$shlibpath_var\"" fi done # This variable tells wrapper scripts just to set shlibpath_var # rather than running their programs. libtool_execute_magic="$magic" # Check if any of the arguments is a wrapper script. args= for file do case $file in -* | *.la | *.lo ) ;; *) # Do a test to see if this is really a libtool program. if func_ltwrapper_script_p "$file"; then func_source "$file" # Transform arg to wrapped name. file="$progdir/$program" elif func_ltwrapper_executable_p "$file"; then func_ltwrapper_scriptname "$file" func_source "$func_ltwrapper_scriptname_result" # Transform arg to wrapped name. file="$progdir/$program" fi ;; esac # Quote arguments (to preserve shell metacharacters). func_append_quoted args "$file" done if test "X$opt_dry_run" = Xfalse; then if test -n "$shlibpath_var"; then # Export the shlibpath_var. eval "export $shlibpath_var" fi # Restore saved environment variables for lt_var in LANG LANGUAGE LC_ALL LC_CTYPE LC_COLLATE LC_MESSAGES do eval "if test \"\${save_$lt_var+set}\" = set; then $lt_var=\$save_$lt_var; export $lt_var else $lt_unset $lt_var fi" done # Now prepare to actually exec the command. exec_cmd="\$cmd$args" else # Display what would be done. if test -n "$shlibpath_var"; then eval "\$ECHO \"\$shlibpath_var=\$$shlibpath_var\"" echo "export $shlibpath_var" fi $ECHO "$cmd$args" exit $EXIT_SUCCESS fi } test "$opt_mode" = execute && func_mode_execute ${1+"$@"} # func_mode_finish arg... func_mode_finish () { $opt_debug libs= libdirs= admincmds= for opt in "$nonopt" ${1+"$@"} do if test -d "$opt"; then func_append libdirs " $opt" elif test -f "$opt"; then if func_lalib_unsafe_p "$opt"; then func_append libs " $opt" else func_warning "\`$opt' is not a valid libtool archive" fi else func_fatal_error "invalid argument \`$opt'" fi done if test -n "$libs"; then if test -n "$lt_sysroot"; then sysroot_regex=`$ECHO "$lt_sysroot" | $SED "$sed_make_literal_regex"` sysroot_cmd="s/\([ ']\)$sysroot_regex/\1/g;" else sysroot_cmd= fi # Remove sysroot references if $opt_dry_run; then for lib in $libs; do echo "removing references to $lt_sysroot and \`=' prefixes from $lib" done else tmpdir=`func_mktempdir` for lib in $libs; do sed -e "${sysroot_cmd} s/\([ ']-[LR]\)=/\1/g; s/\([ ']\)=/\1/g" $lib \ > $tmpdir/tmp-la mv -f $tmpdir/tmp-la $lib done ${RM}r "$tmpdir" fi fi if test -n "$finish_cmds$finish_eval" && test -n "$libdirs"; then for libdir in $libdirs; do if test -n "$finish_cmds"; then # Do each command in the finish commands. func_execute_cmds "$finish_cmds" 'admincmds="$admincmds '"$cmd"'"' fi if test -n "$finish_eval"; then # Do the single finish_eval. eval cmds=\"$finish_eval\" $opt_dry_run || eval "$cmds" || func_append admincmds " $cmds" fi done fi # Exit here if they wanted silent mode. $opt_silent && exit $EXIT_SUCCESS if test -n "$finish_cmds$finish_eval" && test -n "$libdirs"; then echo "----------------------------------------------------------------------" echo "Libraries have been installed in:" for libdir in $libdirs; do $ECHO " $libdir" done echo echo "If you ever happen to want to link against installed libraries" echo "in a given directory, LIBDIR, you must either use libtool, and" echo "specify the full pathname of the library, or use the \`-LLIBDIR'" echo "flag during linking and do at least one of the following:" if test -n "$shlibpath_var"; then echo " - add LIBDIR to the \`$shlibpath_var' environment variable" echo " during execution" fi if test -n "$runpath_var"; then echo " - add LIBDIR to the \`$runpath_var' environment variable" echo " during linking" fi if test -n "$hardcode_libdir_flag_spec"; then libdir=LIBDIR eval flag=\"$hardcode_libdir_flag_spec\" $ECHO " - use the \`$flag' linker flag" fi if test -n "$admincmds"; then $ECHO " - have your system administrator run these commands:$admincmds" fi if test -f /etc/ld.so.conf; then echo " - have your system administrator add LIBDIR to \`/etc/ld.so.conf'" fi echo echo "See any operating system documentation about shared libraries for" case $host in solaris2.[6789]|solaris2.1[0-9]) echo "more information, such as the ld(1), crle(1) and ld.so(8) manual" echo "pages." ;; *) echo "more information, such as the ld(1) and ld.so(8) manual pages." ;; esac echo "----------------------------------------------------------------------" fi exit $EXIT_SUCCESS } test "$opt_mode" = finish && func_mode_finish ${1+"$@"} # func_mode_install arg... func_mode_install () { $opt_debug # There may be an optional sh(1) argument at the beginning of # install_prog (especially on Windows NT). if test "$nonopt" = "$SHELL" || test "$nonopt" = /bin/sh || # Allow the use of GNU shtool's install command. case $nonopt in *shtool*) :;; *) false;; esac; then # Aesthetically quote it. func_quote_for_eval "$nonopt" install_prog="$func_quote_for_eval_result " arg=$1 shift else install_prog= arg=$nonopt fi # The real first argument should be the name of the installation program. # Aesthetically quote it. func_quote_for_eval "$arg" func_append install_prog "$func_quote_for_eval_result" install_shared_prog=$install_prog case " $install_prog " in *[\\\ /]cp\ *) install_cp=: ;; *) install_cp=false ;; esac # We need to accept at least all the BSD install flags. dest= files= opts= prev= install_type= isdir=no stripme= no_mode=: for arg do arg2= if test -n "$dest"; then func_append files " $dest" dest=$arg continue fi case $arg in -d) isdir=yes ;; -f) if $install_cp; then :; else prev=$arg fi ;; -g | -m | -o) prev=$arg ;; -s) stripme=" -s" continue ;; -*) ;; *) # If the previous option needed an argument, then skip it. if test -n "$prev"; then if test "x$prev" = x-m && test -n "$install_override_mode"; then arg2=$install_override_mode no_mode=false fi prev= else dest=$arg continue fi ;; esac # Aesthetically quote the argument. func_quote_for_eval "$arg" func_append install_prog " $func_quote_for_eval_result" if test -n "$arg2"; then func_quote_for_eval "$arg2" fi func_append install_shared_prog " $func_quote_for_eval_result" done test -z "$install_prog" && \ func_fatal_help "you must specify an install program" test -n "$prev" && \ func_fatal_help "the \`$prev' option requires an argument" if test -n "$install_override_mode" && $no_mode; then if $install_cp; then :; else func_quote_for_eval "$install_override_mode" func_append install_shared_prog " -m $func_quote_for_eval_result" fi fi if test -z "$files"; then if test -z "$dest"; then func_fatal_help "no file or destination specified" else func_fatal_help "you must specify a destination" fi fi # Strip any trailing slash from the destination. func_stripname '' '/' "$dest" dest=$func_stripname_result # Check to see that the destination is a directory. test -d "$dest" && isdir=yes if test "$isdir" = yes; then destdir="$dest" destname= else func_dirname_and_basename "$dest" "" "." destdir="$func_dirname_result" destname="$func_basename_result" # Not a directory, so check to see that there is only one file specified. set dummy $files; shift test "$#" -gt 1 && \ func_fatal_help "\`$dest' is not a directory" fi case $destdir in [\\/]* | [A-Za-z]:[\\/]*) ;; *) for file in $files; do case $file in *.lo) ;; *) func_fatal_help "\`$destdir' must be an absolute directory name" ;; esac done ;; esac # This variable tells wrapper scripts just to set variables rather # than running their programs. libtool_install_magic="$magic" staticlibs= future_libdirs= current_libdirs= for file in $files; do # Do each installation. case $file in *.$libext) # Do the static libraries later. func_append staticlibs " $file" ;; *.la) func_resolve_sysroot "$file" file=$func_resolve_sysroot_result # Check to see that this really is a libtool archive. func_lalib_unsafe_p "$file" \ || func_fatal_help "\`$file' is not a valid libtool archive" library_names= old_library= relink_command= func_source "$file" # Add the libdir to current_libdirs if it is the destination. if test "X$destdir" = "X$libdir"; then case "$current_libdirs " in *" $libdir "*) ;; *) func_append current_libdirs " $libdir" ;; esac else # Note the libdir as a future libdir. case "$future_libdirs " in *" $libdir "*) ;; *) func_append future_libdirs " $libdir" ;; esac fi func_dirname "$file" "/" "" dir="$func_dirname_result" func_append dir "$objdir" if test -n "$relink_command"; then # Determine the prefix the user has applied to our future dir. inst_prefix_dir=`$ECHO "$destdir" | $SED -e "s%$libdir\$%%"` # Don't allow the user to place us outside of our expected # location b/c this prevents finding dependent libraries that # are installed to the same prefix. # At present, this check doesn't affect windows .dll's that # are installed into $libdir/../bin (currently, that works fine) # but it's something to keep an eye on. test "$inst_prefix_dir" = "$destdir" && \ func_fatal_error "error: cannot install \`$file' to a directory not ending in $libdir" if test -n "$inst_prefix_dir"; then # Stick the inst_prefix_dir data into the link command. relink_command=`$ECHO "$relink_command" | $SED "s%@inst_prefix_dir@%-inst-prefix-dir $inst_prefix_dir%"` else relink_command=`$ECHO "$relink_command" | $SED "s%@inst_prefix_dir@%%"` fi func_warning "relinking \`$file'" func_show_eval "$relink_command" \ 'func_fatal_error "error: relink \`$file'\'' with the above command before installing it"' fi # See the names of the shared library. set dummy $library_names; shift if test -n "$1"; then realname="$1" shift srcname="$realname" test -n "$relink_command" && srcname="$realname"T # Install the shared library and build the symlinks. func_show_eval "$install_shared_prog $dir/$srcname $destdir/$realname" \ 'exit $?' tstripme="$stripme" case $host_os in cygwin* | mingw* | pw32* | cegcc*) case $realname in *.dll.a) tstripme="" ;; esac ;; esac if test -n "$tstripme" && test -n "$striplib"; then func_show_eval "$striplib $destdir/$realname" 'exit $?' fi if test "$#" -gt 0; then # Delete the old symlinks, and create new ones. # Try `ln -sf' first, because the `ln' binary might depend on # the symlink we replace! Solaris /bin/ln does not understand -f, # so we also need to try rm && ln -s. for linkname do test "$linkname" != "$realname" \ && func_show_eval "(cd $destdir && { $LN_S -f $realname $linkname || { $RM $linkname && $LN_S $realname $linkname; }; })" done fi # Do each command in the postinstall commands. lib="$destdir/$realname" func_execute_cmds "$postinstall_cmds" 'exit $?' fi # Install the pseudo-library for information purposes. func_basename "$file" name="$func_basename_result" instname="$dir/$name"i func_show_eval "$install_prog $instname $destdir/$name" 'exit $?' # Maybe install the static library, too. test -n "$old_library" && func_append staticlibs " $dir/$old_library" ;; *.lo) # Install (i.e. copy) a libtool object. # Figure out destination file name, if it wasn't already specified. if test -n "$destname"; then destfile="$destdir/$destname" else func_basename "$file" destfile="$func_basename_result" destfile="$destdir/$destfile" fi # Deduce the name of the destination old-style object file. case $destfile in *.lo) func_lo2o "$destfile" staticdest=$func_lo2o_result ;; *.$objext) staticdest="$destfile" destfile= ;; *) func_fatal_help "cannot copy a libtool object to \`$destfile'" ;; esac # Install the libtool object if requested. test -n "$destfile" && \ func_show_eval "$install_prog $file $destfile" 'exit $?' # Install the old object if enabled. if test "$build_old_libs" = yes; then # Deduce the name of the old-style object file. func_lo2o "$file" staticobj=$func_lo2o_result func_show_eval "$install_prog \$staticobj \$staticdest" 'exit $?' fi exit $EXIT_SUCCESS ;; *) # Figure out destination file name, if it wasn't already specified. if test -n "$destname"; then destfile="$destdir/$destname" else func_basename "$file" destfile="$func_basename_result" destfile="$destdir/$destfile" fi # If the file is missing, and there is a .exe on the end, strip it # because it is most likely a libtool script we actually want to # install stripped_ext="" case $file in *.exe) if test ! -f "$file"; then func_stripname '' '.exe' "$file" file=$func_stripname_result stripped_ext=".exe" fi ;; esac # Do a test to see if this is really a libtool program. case $host in *cygwin* | *mingw*) if func_ltwrapper_executable_p "$file"; then func_ltwrapper_scriptname "$file" wrapper=$func_ltwrapper_scriptname_result else func_stripname '' '.exe' "$file" wrapper=$func_stripname_result fi ;; *) wrapper=$file ;; esac if func_ltwrapper_script_p "$wrapper"; then notinst_deplibs= relink_command= func_source "$wrapper" # Check the variables that should have been set. test -z "$generated_by_libtool_version" && \ func_fatal_error "invalid libtool wrapper script \`$wrapper'" finalize=yes for lib in $notinst_deplibs; do # Check to see that each library is installed. libdir= if test -f "$lib"; then func_source "$lib" fi libfile="$libdir/"`$ECHO "$lib" | $SED 's%^.*/%%g'` ### testsuite: skip nested quoting test if test -n "$libdir" && test ! -f "$libfile"; then func_warning "\`$lib' has not been installed in \`$libdir'" finalize=no fi done relink_command= func_source "$wrapper" outputname= if test "$fast_install" = no && test -n "$relink_command"; then $opt_dry_run || { if test "$finalize" = yes; then tmpdir=`func_mktempdir` func_basename "$file$stripped_ext" file="$func_basename_result" outputname="$tmpdir/$file" # Replace the output file specification. relink_command=`$ECHO "$relink_command" | $SED 's%@OUTPUT@%'"$outputname"'%g'` $opt_silent || { func_quote_for_expand "$relink_command" eval "func_echo $func_quote_for_expand_result" } if eval "$relink_command"; then : else func_error "error: relink \`$file' with the above command before installing it" $opt_dry_run || ${RM}r "$tmpdir" continue fi file="$outputname" else func_warning "cannot relink \`$file'" fi } else # Install the binary that we compiled earlier. file=`$ECHO "$file$stripped_ext" | $SED "s%\([^/]*\)$%$objdir/\1%"` fi fi # remove .exe since cygwin /usr/bin/install will append another # one anyway case $install_prog,$host in */usr/bin/install*,*cygwin*) case $file:$destfile in *.exe:*.exe) # this is ok ;; *.exe:*) destfile=$destfile.exe ;; *:*.exe) func_stripname '' '.exe' "$destfile" destfile=$func_stripname_result ;; esac ;; esac func_show_eval "$install_prog\$stripme \$file \$destfile" 'exit $?' $opt_dry_run || if test -n "$outputname"; then ${RM}r "$tmpdir" fi ;; esac done for file in $staticlibs; do func_basename "$file" name="$func_basename_result" # Set up the ranlib parameters. oldlib="$destdir/$name" func_to_tool_file "$oldlib" func_convert_file_msys_to_w32 tool_oldlib=$func_to_tool_file_result func_show_eval "$install_prog \$file \$oldlib" 'exit $?' if test -n "$stripme" && test -n "$old_striplib"; then func_show_eval "$old_striplib $tool_oldlib" 'exit $?' fi # Do each command in the postinstall commands. func_execute_cmds "$old_postinstall_cmds" 'exit $?' done test -n "$future_libdirs" && \ func_warning "remember to run \`$progname --finish$future_libdirs'" if test -n "$current_libdirs"; then # Maybe just do a dry run. $opt_dry_run && current_libdirs=" -n$current_libdirs" exec_cmd='$SHELL $progpath $preserve_args --finish$current_libdirs' else exit $EXIT_SUCCESS fi } test "$opt_mode" = install && func_mode_install ${1+"$@"} # func_generate_dlsyms outputname originator pic_p # Extract symbols from dlprefiles and create ${outputname}S.o with # a dlpreopen symbol table. func_generate_dlsyms () { $opt_debug my_outputname="$1" my_originator="$2" my_pic_p="${3-no}" my_prefix=`$ECHO "$my_originator" | sed 's%[^a-zA-Z0-9]%_%g'` my_dlsyms= if test -n "$dlfiles$dlprefiles" || test "$dlself" != no; then if test -n "$NM" && test -n "$global_symbol_pipe"; then my_dlsyms="${my_outputname}S.c" else func_error "not configured to extract global symbols from dlpreopened files" fi fi if test -n "$my_dlsyms"; then case $my_dlsyms in "") ;; *.c) # Discover the nlist of each of the dlfiles. nlist="$output_objdir/${my_outputname}.nm" func_show_eval "$RM $nlist ${nlist}S ${nlist}T" # Parse the name list into a source file. func_verbose "creating $output_objdir/$my_dlsyms" $opt_dry_run || $ECHO > "$output_objdir/$my_dlsyms" "\ /* $my_dlsyms - symbol resolution table for \`$my_outputname' dlsym emulation. */ /* Generated by $PROGRAM (GNU $PACKAGE$TIMESTAMP) $VERSION */ #ifdef __cplusplus extern \"C\" { #endif #if defined(__GNUC__) && (((__GNUC__ == 4) && (__GNUC_MINOR__ >= 4)) || (__GNUC__ > 4)) #pragma GCC diagnostic ignored \"-Wstrict-prototypes\" #endif /* Keep this code in sync between libtool.m4, ltmain, lt_system.h, and tests. */ #if defined(_WIN32) || defined(__CYGWIN__) || defined(_WIN32_WCE) /* DATA imports from DLLs on WIN32 con't be const, because runtime relocations are performed -- see ld's documentation on pseudo-relocs. */ # define LT_DLSYM_CONST #elif defined(__osf__) /* This system does not cope well with relocations in const data. */ # define LT_DLSYM_CONST #else # define LT_DLSYM_CONST const #endif /* External symbol declarations for the compiler. */\ " if test "$dlself" = yes; then func_verbose "generating symbol list for \`$output'" $opt_dry_run || echo ': @PROGRAM@ ' > "$nlist" # Add our own program objects to the symbol list. progfiles=`$ECHO "$objs$old_deplibs" | $SP2NL | $SED "$lo2o" | $NL2SP` for progfile in $progfiles; do func_to_tool_file "$progfile" func_convert_file_msys_to_w32 func_verbose "extracting global C symbols from \`$func_to_tool_file_result'" $opt_dry_run || eval "$NM $func_to_tool_file_result | $global_symbol_pipe >> '$nlist'" done if test -n "$exclude_expsyms"; then $opt_dry_run || { eval '$EGREP -v " ($exclude_expsyms)$" "$nlist" > "$nlist"T' eval '$MV "$nlist"T "$nlist"' } fi if test -n "$export_symbols_regex"; then $opt_dry_run || { eval '$EGREP -e "$export_symbols_regex" "$nlist" > "$nlist"T' eval '$MV "$nlist"T "$nlist"' } fi # Prepare the list of exported symbols if test -z "$export_symbols"; then export_symbols="$output_objdir/$outputname.exp" $opt_dry_run || { $RM $export_symbols eval "${SED} -n -e '/^: @PROGRAM@ $/d' -e 's/^.* \(.*\)$/\1/p' "'< "$nlist" > "$export_symbols"' case $host in *cygwin* | *mingw* | *cegcc* ) eval "echo EXPORTS "'> "$output_objdir/$outputname.def"' eval 'cat "$export_symbols" >> "$output_objdir/$outputname.def"' ;; esac } else $opt_dry_run || { eval "${SED} -e 's/\([].[*^$]\)/\\\\\1/g' -e 's/^/ /' -e 's/$/$/'"' < "$export_symbols" > "$output_objdir/$outputname.exp"' eval '$GREP -f "$output_objdir/$outputname.exp" < "$nlist" > "$nlist"T' eval '$MV "$nlist"T "$nlist"' case $host in *cygwin* | *mingw* | *cegcc* ) eval "echo EXPORTS "'> "$output_objdir/$outputname.def"' eval 'cat "$nlist" >> "$output_objdir/$outputname.def"' ;; esac } fi fi for dlprefile in $dlprefiles; do func_verbose "extracting global C symbols from \`$dlprefile'" func_basename "$dlprefile" name="$func_basename_result" case $host in *cygwin* | *mingw* | *cegcc* ) # if an import library, we need to obtain dlname if func_win32_import_lib_p "$dlprefile"; then func_tr_sh "$dlprefile" eval "curr_lafile=\$libfile_$func_tr_sh_result" dlprefile_dlbasename="" if test -n "$curr_lafile" && func_lalib_p "$curr_lafile"; then # Use subshell, to avoid clobbering current variable values dlprefile_dlname=`source "$curr_lafile" && echo "$dlname"` if test -n "$dlprefile_dlname" ; then func_basename "$dlprefile_dlname" dlprefile_dlbasename="$func_basename_result" else # no lafile. user explicitly requested -dlpreopen . $sharedlib_from_linklib_cmd "$dlprefile" dlprefile_dlbasename=$sharedlib_from_linklib_result fi fi $opt_dry_run || { if test -n "$dlprefile_dlbasename" ; then eval '$ECHO ": $dlprefile_dlbasename" >> "$nlist"' else func_warning "Could not compute DLL name from $name" eval '$ECHO ": $name " >> "$nlist"' fi func_to_tool_file "$dlprefile" func_convert_file_msys_to_w32 eval "$NM \"$func_to_tool_file_result\" 2>/dev/null | $global_symbol_pipe | $SED -e '/I __imp/d' -e 's/I __nm_/D /;s/_nm__//' >> '$nlist'" } else # not an import lib $opt_dry_run || { eval '$ECHO ": $name " >> "$nlist"' func_to_tool_file "$dlprefile" func_convert_file_msys_to_w32 eval "$NM \"$func_to_tool_file_result\" 2>/dev/null | $global_symbol_pipe >> '$nlist'" } fi ;; *) $opt_dry_run || { eval '$ECHO ": $name " >> "$nlist"' func_to_tool_file "$dlprefile" func_convert_file_msys_to_w32 eval "$NM \"$func_to_tool_file_result\" 2>/dev/null | $global_symbol_pipe >> '$nlist'" } ;; esac done $opt_dry_run || { # Make sure we have at least an empty file. test -f "$nlist" || : > "$nlist" if test -n "$exclude_expsyms"; then $EGREP -v " ($exclude_expsyms)$" "$nlist" > "$nlist"T $MV "$nlist"T "$nlist" fi # Try sorting and uniquifying the output. if $GREP -v "^: " < "$nlist" | if sort -k 3 /dev/null 2>&1; then sort -k 3 else sort +2 fi | uniq > "$nlist"S; then : else $GREP -v "^: " < "$nlist" > "$nlist"S fi if test -f "$nlist"S; then eval "$global_symbol_to_cdecl"' < "$nlist"S >> "$output_objdir/$my_dlsyms"' else echo '/* NONE */' >> "$output_objdir/$my_dlsyms" fi echo >> "$output_objdir/$my_dlsyms" "\ /* The mapping between symbol names and symbols. */ typedef struct { const char *name; void *address; } lt_dlsymlist; extern LT_DLSYM_CONST lt_dlsymlist lt_${my_prefix}_LTX_preloaded_symbols[]; LT_DLSYM_CONST lt_dlsymlist lt_${my_prefix}_LTX_preloaded_symbols[] = {\ { \"$my_originator\", (void *) 0 }," case $need_lib_prefix in no) eval "$global_symbol_to_c_name_address" < "$nlist" >> "$output_objdir/$my_dlsyms" ;; *) eval "$global_symbol_to_c_name_address_lib_prefix" < "$nlist" >> "$output_objdir/$my_dlsyms" ;; esac echo >> "$output_objdir/$my_dlsyms" "\ {0, (void *) 0} }; /* This works around a problem in FreeBSD linker */ #ifdef FREEBSD_WORKAROUND static const void *lt_preloaded_setup() { return lt_${my_prefix}_LTX_preloaded_symbols; } #endif #ifdef __cplusplus } #endif\ " } # !$opt_dry_run pic_flag_for_symtable= case "$compile_command " in *" -static "*) ;; *) case $host in # compiling the symbol table file with pic_flag works around # a FreeBSD bug that causes programs to crash when -lm is # linked before any other PIC object. But we must not use # pic_flag when linking with -static. The problem exists in # FreeBSD 2.2.6 and is fixed in FreeBSD 3.1. *-*-freebsd2.*|*-*-freebsd3.0*|*-*-freebsdelf3.0*) pic_flag_for_symtable=" $pic_flag -DFREEBSD_WORKAROUND" ;; *-*-hpux*) pic_flag_for_symtable=" $pic_flag" ;; *) if test "X$my_pic_p" != Xno; then pic_flag_for_symtable=" $pic_flag" fi ;; esac ;; esac symtab_cflags= for arg in $LTCFLAGS; do case $arg in -pie | -fpie | -fPIE) ;; *) func_append symtab_cflags " $arg" ;; esac done # Now compile the dynamic symbol file. func_show_eval '(cd $output_objdir && $LTCC$symtab_cflags -c$no_builtin_flag$pic_flag_for_symtable "$my_dlsyms")' 'exit $?' # Clean up the generated files. func_show_eval '$RM "$output_objdir/$my_dlsyms" "$nlist" "${nlist}S" "${nlist}T"' # Transform the symbol file into the correct name. symfileobj="$output_objdir/${my_outputname}S.$objext" case $host in *cygwin* | *mingw* | *cegcc* ) if test -f "$output_objdir/$my_outputname.def"; then compile_command=`$ECHO "$compile_command" | $SED "s%@SYMFILE@%$output_objdir/$my_outputname.def $symfileobj%"` finalize_command=`$ECHO "$finalize_command" | $SED "s%@SYMFILE@%$output_objdir/$my_outputname.def $symfileobj%"` else compile_command=`$ECHO "$compile_command" | $SED "s%@SYMFILE@%$symfileobj%"` finalize_command=`$ECHO "$finalize_command" | $SED "s%@SYMFILE@%$symfileobj%"` fi ;; *) compile_command=`$ECHO "$compile_command" | $SED "s%@SYMFILE@%$symfileobj%"` finalize_command=`$ECHO "$finalize_command" | $SED "s%@SYMFILE@%$symfileobj%"` ;; esac ;; *) func_fatal_error "unknown suffix for \`$my_dlsyms'" ;; esac else # We keep going just in case the user didn't refer to # lt_preloaded_symbols. The linker will fail if global_symbol_pipe # really was required. # Nullify the symbol file. compile_command=`$ECHO "$compile_command" | $SED "s% @SYMFILE@%%"` finalize_command=`$ECHO "$finalize_command" | $SED "s% @SYMFILE@%%"` fi } # func_win32_libid arg # return the library type of file 'arg' # # Need a lot of goo to handle *both* DLLs and import libs # Has to be a shell function in order to 'eat' the argument # that is supplied when $file_magic_command is called. # Despite the name, also deal with 64 bit binaries. func_win32_libid () { $opt_debug win32_libid_type="unknown" win32_fileres=`file -L $1 2>/dev/null` case $win32_fileres in *ar\ archive\ import\ library*) # definitely import win32_libid_type="x86 archive import" ;; *ar\ archive*) # could be an import, or static # Keep the egrep pattern in sync with the one in _LT_CHECK_MAGIC_METHOD. if eval $OBJDUMP -f $1 | $SED -e '10q' 2>/dev/null | $EGREP 'file format (pei*-i386(.*architecture: i386)?|pe-arm-wince|pe-x86-64)' >/dev/null; then func_to_tool_file "$1" func_convert_file_msys_to_w32 win32_nmres=`eval $NM -f posix -A \"$func_to_tool_file_result\" | $SED -n -e ' 1,100{ / I /{ s,.*,import, p q } }'` case $win32_nmres in import*) win32_libid_type="x86 archive import";; *) win32_libid_type="x86 archive static";; esac fi ;; *DLL*) win32_libid_type="x86 DLL" ;; *executable*) # but shell scripts are "executable" too... case $win32_fileres in *MS\ Windows\ PE\ Intel*) win32_libid_type="x86 DLL" ;; esac ;; esac $ECHO "$win32_libid_type" } # func_cygming_dll_for_implib ARG # # Platform-specific function to extract the # name of the DLL associated with the specified # import library ARG. # Invoked by eval'ing the libtool variable # $sharedlib_from_linklib_cmd # Result is available in the variable # $sharedlib_from_linklib_result func_cygming_dll_for_implib () { $opt_debug sharedlib_from_linklib_result=`$DLLTOOL --identify-strict --identify "$1"` } # func_cygming_dll_for_implib_fallback_core SECTION_NAME LIBNAMEs # # The is the core of a fallback implementation of a # platform-specific function to extract the name of the # DLL associated with the specified import library LIBNAME. # # SECTION_NAME is either .idata$6 or .idata$7, depending # on the platform and compiler that created the implib. # # Echos the name of the DLL associated with the # specified import library. func_cygming_dll_for_implib_fallback_core () { $opt_debug match_literal=`$ECHO "$1" | $SED "$sed_make_literal_regex"` $OBJDUMP -s --section "$1" "$2" 2>/dev/null | $SED '/^Contents of section '"$match_literal"':/{ # Place marker at beginning of archive member dllname section s/.*/====MARK====/ p d } # These lines can sometimes be longer than 43 characters, but # are always uninteresting /:[ ]*file format pe[i]\{,1\}-/d /^In archive [^:]*:/d # Ensure marker is printed /^====MARK====/p # Remove all lines with less than 43 characters /^.\{43\}/!d # From remaining lines, remove first 43 characters s/^.\{43\}//' | $SED -n ' # Join marker and all lines until next marker into a single line /^====MARK====/ b para H $ b para b :para x s/\n//g # Remove the marker s/^====MARK====// # Remove trailing dots and whitespace s/[\. \t]*$// # Print /./p' | # we now have a list, one entry per line, of the stringified # contents of the appropriate section of all members of the # archive which possess that section. Heuristic: eliminate # all those which have a first or second character that is # a '.' (that is, objdump's representation of an unprintable # character.) This should work for all archives with less than # 0x302f exports -- but will fail for DLLs whose name actually # begins with a literal '.' or a single character followed by # a '.'. # # Of those that remain, print the first one. $SED -e '/^\./d;/^.\./d;q' } # func_cygming_gnu_implib_p ARG # This predicate returns with zero status (TRUE) if # ARG is a GNU/binutils-style import library. Returns # with nonzero status (FALSE) otherwise. func_cygming_gnu_implib_p () { $opt_debug func_to_tool_file "$1" func_convert_file_msys_to_w32 func_cygming_gnu_implib_tmp=`$NM "$func_to_tool_file_result" | eval "$global_symbol_pipe" | $EGREP ' (_head_[A-Za-z0-9_]+_[ad]l*|[A-Za-z0-9_]+_[ad]l*_iname)$'` test -n "$func_cygming_gnu_implib_tmp" } # func_cygming_ms_implib_p ARG # This predicate returns with zero status (TRUE) if # ARG is an MS-style import library. Returns # with nonzero status (FALSE) otherwise. func_cygming_ms_implib_p () { $opt_debug func_to_tool_file "$1" func_convert_file_msys_to_w32 func_cygming_ms_implib_tmp=`$NM "$func_to_tool_file_result" | eval "$global_symbol_pipe" | $GREP '_NULL_IMPORT_DESCRIPTOR'` test -n "$func_cygming_ms_implib_tmp" } # func_cygming_dll_for_implib_fallback ARG # Platform-specific function to extract the # name of the DLL associated with the specified # import library ARG. # # This fallback implementation is for use when $DLLTOOL # does not support the --identify-strict option. # Invoked by eval'ing the libtool variable # $sharedlib_from_linklib_cmd # Result is available in the variable # $sharedlib_from_linklib_result func_cygming_dll_for_implib_fallback () { $opt_debug if func_cygming_gnu_implib_p "$1" ; then # binutils import library sharedlib_from_linklib_result=`func_cygming_dll_for_implib_fallback_core '.idata$7' "$1"` elif func_cygming_ms_implib_p "$1" ; then # ms-generated import library sharedlib_from_linklib_result=`func_cygming_dll_for_implib_fallback_core '.idata$6' "$1"` else # unknown sharedlib_from_linklib_result="" fi } # func_extract_an_archive dir oldlib func_extract_an_archive () { $opt_debug f_ex_an_ar_dir="$1"; shift f_ex_an_ar_oldlib="$1" if test "$lock_old_archive_extraction" = yes; then lockfile=$f_ex_an_ar_oldlib.lock until $opt_dry_run || ln "$progpath" "$lockfile" 2>/dev/null; do func_echo "Waiting for $lockfile to be removed" sleep 2 done fi func_show_eval "(cd \$f_ex_an_ar_dir && $AR x \"\$f_ex_an_ar_oldlib\")" \ 'stat=$?; rm -f "$lockfile"; exit $stat' if test "$lock_old_archive_extraction" = yes; then $opt_dry_run || rm -f "$lockfile" fi if ($AR t "$f_ex_an_ar_oldlib" | sort | sort -uc >/dev/null 2>&1); then : else func_fatal_error "object name conflicts in archive: $f_ex_an_ar_dir/$f_ex_an_ar_oldlib" fi } # func_extract_archives gentop oldlib ... func_extract_archives () { $opt_debug my_gentop="$1"; shift my_oldlibs=${1+"$@"} my_oldobjs="" my_xlib="" my_xabs="" my_xdir="" for my_xlib in $my_oldlibs; do # Extract the objects. case $my_xlib in [\\/]* | [A-Za-z]:[\\/]*) my_xabs="$my_xlib" ;; *) my_xabs=`pwd`"/$my_xlib" ;; esac func_basename "$my_xlib" my_xlib="$func_basename_result" my_xlib_u=$my_xlib while :; do case " $extracted_archives " in *" $my_xlib_u "*) func_arith $extracted_serial + 1 extracted_serial=$func_arith_result my_xlib_u=lt$extracted_serial-$my_xlib ;; *) break ;; esac done extracted_archives="$extracted_archives $my_xlib_u" my_xdir="$my_gentop/$my_xlib_u" func_mkdir_p "$my_xdir" case $host in *-darwin*) func_verbose "Extracting $my_xabs" # Do not bother doing anything if just a dry run $opt_dry_run || { darwin_orig_dir=`pwd` cd $my_xdir || exit $? darwin_archive=$my_xabs darwin_curdir=`pwd` darwin_base_archive=`basename "$darwin_archive"` darwin_arches=`$LIPO -info "$darwin_archive" 2>/dev/null | $GREP Architectures 2>/dev/null || true` if test -n "$darwin_arches"; then darwin_arches=`$ECHO "$darwin_arches" | $SED -e 's/.*are://'` darwin_arch= func_verbose "$darwin_base_archive has multiple architectures $darwin_arches" for darwin_arch in $darwin_arches ; do func_mkdir_p "unfat-$$/${darwin_base_archive}-${darwin_arch}" $LIPO -thin $darwin_arch -output "unfat-$$/${darwin_base_archive}-${darwin_arch}/${darwin_base_archive}" "${darwin_archive}" cd "unfat-$$/${darwin_base_archive}-${darwin_arch}" func_extract_an_archive "`pwd`" "${darwin_base_archive}" cd "$darwin_curdir" $RM "unfat-$$/${darwin_base_archive}-${darwin_arch}/${darwin_base_archive}" done # $darwin_arches ## Okay now we've a bunch of thin objects, gotta fatten them up :) darwin_filelist=`find unfat-$$ -type f -name \*.o -print -o -name \*.lo -print | $SED -e "$basename" | sort -u` darwin_file= darwin_files= for darwin_file in $darwin_filelist; do darwin_files=`find unfat-$$ -name $darwin_file -print | sort | $NL2SP` $LIPO -create -output "$darwin_file" $darwin_files done # $darwin_filelist $RM -rf unfat-$$ cd "$darwin_orig_dir" else cd $darwin_orig_dir func_extract_an_archive "$my_xdir" "$my_xabs" fi # $darwin_arches } # !$opt_dry_run ;; *) func_extract_an_archive "$my_xdir" "$my_xabs" ;; esac my_oldobjs="$my_oldobjs "`find $my_xdir -name \*.$objext -print -o -name \*.lo -print | sort | $NL2SP` done func_extract_archives_result="$my_oldobjs" } # func_emit_wrapper [arg=no] # # Emit a libtool wrapper script on stdout. # Don't directly open a file because we may want to # incorporate the script contents within a cygwin/mingw # wrapper executable. Must ONLY be called from within # func_mode_link because it depends on a number of variables # set therein. # # ARG is the value that the WRAPPER_SCRIPT_BELONGS_IN_OBJDIR # variable will take. If 'yes', then the emitted script # will assume that the directory in which it is stored is # the $objdir directory. This is a cygwin/mingw-specific # behavior. func_emit_wrapper () { func_emit_wrapper_arg1=${1-no} $ECHO "\ #! $SHELL # $output - temporary wrapper script for $objdir/$outputname # Generated by $PROGRAM (GNU $PACKAGE$TIMESTAMP) $VERSION # # The $output program cannot be directly executed until all the libtool # libraries that it depends on are installed. # # This wrapper script should never be moved out of the build directory. # If it is, it will not operate correctly. # Sed substitution that helps us do robust quoting. It backslashifies # metacharacters that are still active within double-quoted strings. sed_quote_subst='$sed_quote_subst' # Be Bourne compatible if test -n \"\${ZSH_VERSION+set}\" && (emulate sh) >/dev/null 2>&1; then emulate sh NULLCMD=: # Zsh 3.x and 4.x performs word splitting on \${1+\"\$@\"}, which # is contrary to our usage. Disable this feature. alias -g '\${1+\"\$@\"}'='\"\$@\"' setopt NO_GLOB_SUBST else case \`(set -o) 2>/dev/null\` in *posix*) set -o posix;; esac fi BIN_SH=xpg4; export BIN_SH # for Tru64 DUALCASE=1; export DUALCASE # for MKS sh # The HP-UX ksh and POSIX shell print the target directory to stdout # if CDPATH is set. (unset CDPATH) >/dev/null 2>&1 && unset CDPATH relink_command=\"$relink_command\" # This environment variable determines our operation mode. if test \"\$libtool_install_magic\" = \"$magic\"; then # install mode needs the following variables: generated_by_libtool_version='$macro_version' notinst_deplibs='$notinst_deplibs' else # When we are sourced in execute mode, \$file and \$ECHO are already set. if test \"\$libtool_execute_magic\" != \"$magic\"; then file=\"\$0\"" qECHO=`$ECHO "$ECHO" | $SED "$sed_quote_subst"` $ECHO "\ # A function that is used when there is no print builtin or printf. func_fallback_echo () { eval 'cat <<_LTECHO_EOF \$1 _LTECHO_EOF' } ECHO=\"$qECHO\" fi # Very basic option parsing. These options are (a) specific to # the libtool wrapper, (b) are identical between the wrapper # /script/ and the wrapper /executable/ which is used only on # windows platforms, and (c) all begin with the string "--lt-" # (application programs are unlikely to have options which match # this pattern). # # There are only two supported options: --lt-debug and # --lt-dump-script. There is, deliberately, no --lt-help. # # The first argument to this parsing function should be the # script's $0 value, followed by "$@". lt_option_debug= func_parse_lt_options () { lt_script_arg0=\$0 shift for lt_opt do case \"\$lt_opt\" in --lt-debug) lt_option_debug=1 ;; --lt-dump-script) lt_dump_D=\`\$ECHO \"X\$lt_script_arg0\" | $SED -e 's/^X//' -e 's%/[^/]*$%%'\` test \"X\$lt_dump_D\" = \"X\$lt_script_arg0\" && lt_dump_D=. lt_dump_F=\`\$ECHO \"X\$lt_script_arg0\" | $SED -e 's/^X//' -e 's%^.*/%%'\` cat \"\$lt_dump_D/\$lt_dump_F\" exit 0 ;; --lt-*) \$ECHO \"Unrecognized --lt- option: '\$lt_opt'\" 1>&2 exit 1 ;; esac done # Print the debug banner immediately: if test -n \"\$lt_option_debug\"; then echo \"${outputname}:${output}:\${LINENO}: libtool wrapper (GNU $PACKAGE$TIMESTAMP) $VERSION\" 1>&2 fi } # Used when --lt-debug. Prints its arguments to stdout # (redirection is the responsibility of the caller) func_lt_dump_args () { lt_dump_args_N=1; for lt_arg do \$ECHO \"${outputname}:${output}:\${LINENO}: newargv[\$lt_dump_args_N]: \$lt_arg\" lt_dump_args_N=\`expr \$lt_dump_args_N + 1\` done } # Core function for launching the target application func_exec_program_core () { " case $host in # Backslashes separate directories on plain windows *-*-mingw | *-*-os2* | *-cegcc*) $ECHO "\ if test -n \"\$lt_option_debug\"; then \$ECHO \"${outputname}:${output}:\${LINENO}: newargv[0]: \$progdir\\\\\$program\" 1>&2 func_lt_dump_args \${1+\"\$@\"} 1>&2 fi exec \"\$progdir\\\\\$program\" \${1+\"\$@\"} " ;; *) $ECHO "\ if test -n \"\$lt_option_debug\"; then \$ECHO \"${outputname}:${output}:\${LINENO}: newargv[0]: \$progdir/\$program\" 1>&2 func_lt_dump_args \${1+\"\$@\"} 1>&2 fi exec \"\$progdir/\$program\" \${1+\"\$@\"} " ;; esac $ECHO "\ \$ECHO \"\$0: cannot exec \$program \$*\" 1>&2 exit 1 } # A function to encapsulate launching the target application # Strips options in the --lt-* namespace from \$@ and # launches target application with the remaining arguments. func_exec_program () { case \" \$* \" in *\\ --lt-*) for lt_wr_arg do case \$lt_wr_arg in --lt-*) ;; *) set x \"\$@\" \"\$lt_wr_arg\"; shift;; esac shift done ;; esac func_exec_program_core \${1+\"\$@\"} } # Parse options func_parse_lt_options \"\$0\" \${1+\"\$@\"} # Find the directory that this script lives in. thisdir=\`\$ECHO \"\$file\" | $SED 's%/[^/]*$%%'\` test \"x\$thisdir\" = \"x\$file\" && thisdir=. # Follow symbolic links until we get to the real thisdir. file=\`ls -ld \"\$file\" | $SED -n 's/.*-> //p'\` while test -n \"\$file\"; do destdir=\`\$ECHO \"\$file\" | $SED 's%/[^/]*\$%%'\` # If there was a directory component, then change thisdir. if test \"x\$destdir\" != \"x\$file\"; then case \"\$destdir\" in [\\\\/]* | [A-Za-z]:[\\\\/]*) thisdir=\"\$destdir\" ;; *) thisdir=\"\$thisdir/\$destdir\" ;; esac fi file=\`\$ECHO \"\$file\" | $SED 's%^.*/%%'\` file=\`ls -ld \"\$thisdir/\$file\" | $SED -n 's/.*-> //p'\` done # Usually 'no', except on cygwin/mingw when embedded into # the cwrapper. WRAPPER_SCRIPT_BELONGS_IN_OBJDIR=$func_emit_wrapper_arg1 if test \"\$WRAPPER_SCRIPT_BELONGS_IN_OBJDIR\" = \"yes\"; then # special case for '.' if test \"\$thisdir\" = \".\"; then thisdir=\`pwd\` fi # remove .libs from thisdir case \"\$thisdir\" in *[\\\\/]$objdir ) thisdir=\`\$ECHO \"\$thisdir\" | $SED 's%[\\\\/][^\\\\/]*$%%'\` ;; $objdir ) thisdir=. ;; esac fi # Try to get the absolute directory name. absdir=\`cd \"\$thisdir\" && pwd\` test -n \"\$absdir\" && thisdir=\"\$absdir\" " if test "$fast_install" = yes; then $ECHO "\ program=lt-'$outputname'$exeext progdir=\"\$thisdir/$objdir\" if test ! -f \"\$progdir/\$program\" || { file=\`ls -1dt \"\$progdir/\$program\" \"\$progdir/../\$program\" 2>/dev/null | ${SED} 1q\`; \\ test \"X\$file\" != \"X\$progdir/\$program\"; }; then file=\"\$\$-\$program\" if test ! -d \"\$progdir\"; then $MKDIR \"\$progdir\" else $RM \"\$progdir/\$file\" fi" $ECHO "\ # relink executable if necessary if test -n \"\$relink_command\"; then if relink_command_output=\`eval \$relink_command 2>&1\`; then : else $ECHO \"\$relink_command_output\" >&2 $RM \"\$progdir/\$file\" exit 1 fi fi $MV \"\$progdir/\$file\" \"\$progdir/\$program\" 2>/dev/null || { $RM \"\$progdir/\$program\"; $MV \"\$progdir/\$file\" \"\$progdir/\$program\"; } $RM \"\$progdir/\$file\" fi" else $ECHO "\ program='$outputname' progdir=\"\$thisdir/$objdir\" " fi $ECHO "\ if test -f \"\$progdir/\$program\"; then" # fixup the dll searchpath if we need to. # # Fix the DLL searchpath if we need to. Do this before prepending # to shlibpath, because on Windows, both are PATH and uninstalled # libraries must come first. if test -n "$dllsearchpath"; then $ECHO "\ # Add the dll search path components to the executable PATH PATH=$dllsearchpath:\$PATH " fi # Export our shlibpath_var if we have one. if test "$shlibpath_overrides_runpath" = yes && test -n "$shlibpath_var" && test -n "$temp_rpath"; then $ECHO "\ # Add our own library path to $shlibpath_var $shlibpath_var=\"$temp_rpath\$$shlibpath_var\" # Some systems cannot cope with colon-terminated $shlibpath_var # The second colon is a workaround for a bug in BeOS R4 sed $shlibpath_var=\`\$ECHO \"\$$shlibpath_var\" | $SED 's/::*\$//'\` export $shlibpath_var " fi $ECHO "\ if test \"\$libtool_execute_magic\" != \"$magic\"; then # Run the actual program with our arguments. func_exec_program \${1+\"\$@\"} fi else # The program doesn't exist. \$ECHO \"\$0: error: \\\`\$progdir/\$program' does not exist\" 1>&2 \$ECHO \"This script is just a wrapper for \$program.\" 1>&2 \$ECHO \"See the $PACKAGE documentation for more information.\" 1>&2 exit 1 fi fi\ " } # func_emit_cwrapperexe_src # emit the source code for a wrapper executable on stdout # Must ONLY be called from within func_mode_link because # it depends on a number of variable set therein. func_emit_cwrapperexe_src () { cat < #include #ifdef _MSC_VER # include # include # include #else # include # include # ifdef __CYGWIN__ # include # endif #endif #include #include #include #include #include #include #include #include /* declarations of non-ANSI functions */ #if defined(__MINGW32__) # ifdef __STRICT_ANSI__ int _putenv (const char *); # endif #elif defined(__CYGWIN__) # ifdef __STRICT_ANSI__ char *realpath (const char *, char *); int putenv (char *); int setenv (const char *, const char *, int); # endif /* #elif defined (other platforms) ... */ #endif /* portability defines, excluding path handling macros */ #if defined(_MSC_VER) # define setmode _setmode # define stat _stat # define chmod _chmod # define getcwd _getcwd # define putenv _putenv # define S_IXUSR _S_IEXEC # ifndef _INTPTR_T_DEFINED # define _INTPTR_T_DEFINED # define intptr_t int # endif #elif defined(__MINGW32__) # define setmode _setmode # define stat _stat # define chmod _chmod # define getcwd _getcwd # define putenv _putenv #elif defined(__CYGWIN__) # define HAVE_SETENV # define FOPEN_WB "wb" /* #elif defined (other platforms) ... */ #endif #if defined(PATH_MAX) # define LT_PATHMAX PATH_MAX #elif defined(MAXPATHLEN) # define LT_PATHMAX MAXPATHLEN #else # define LT_PATHMAX 1024 #endif #ifndef S_IXOTH # define S_IXOTH 0 #endif #ifndef S_IXGRP # define S_IXGRP 0 #endif /* path handling portability macros */ #ifndef DIR_SEPARATOR # define DIR_SEPARATOR '/' # define PATH_SEPARATOR ':' #endif #if defined (_WIN32) || defined (__MSDOS__) || defined (__DJGPP__) || \ defined (__OS2__) # define HAVE_DOS_BASED_FILE_SYSTEM # define FOPEN_WB "wb" # ifndef DIR_SEPARATOR_2 # define DIR_SEPARATOR_2 '\\' # endif # ifndef PATH_SEPARATOR_2 # define PATH_SEPARATOR_2 ';' # endif #endif #ifndef DIR_SEPARATOR_2 # define IS_DIR_SEPARATOR(ch) ((ch) == DIR_SEPARATOR) #else /* DIR_SEPARATOR_2 */ # define IS_DIR_SEPARATOR(ch) \ (((ch) == DIR_SEPARATOR) || ((ch) == DIR_SEPARATOR_2)) #endif /* DIR_SEPARATOR_2 */ #ifndef PATH_SEPARATOR_2 # define IS_PATH_SEPARATOR(ch) ((ch) == PATH_SEPARATOR) #else /* PATH_SEPARATOR_2 */ # define IS_PATH_SEPARATOR(ch) ((ch) == PATH_SEPARATOR_2) #endif /* PATH_SEPARATOR_2 */ #ifndef FOPEN_WB # define FOPEN_WB "w" #endif #ifndef _O_BINARY # define _O_BINARY 0 #endif #define XMALLOC(type, num) ((type *) xmalloc ((num) * sizeof(type))) #define XFREE(stale) do { \ if (stale) { free ((void *) stale); stale = 0; } \ } while (0) #if defined(LT_DEBUGWRAPPER) static int lt_debug = 1; #else static int lt_debug = 0; #endif const char *program_name = "libtool-wrapper"; /* in case xstrdup fails */ void *xmalloc (size_t num); char *xstrdup (const char *string); const char *base_name (const char *name); char *find_executable (const char *wrapper); char *chase_symlinks (const char *pathspec); int make_executable (const char *path); int check_executable (const char *path); char *strendzap (char *str, const char *pat); void lt_debugprintf (const char *file, int line, const char *fmt, ...); void lt_fatal (const char *file, int line, const char *message, ...); static const char *nonnull (const char *s); static const char *nonempty (const char *s); void lt_setenv (const char *name, const char *value); char *lt_extend_str (const char *orig_value, const char *add, int to_end); void lt_update_exe_path (const char *name, const char *value); void lt_update_lib_path (const char *name, const char *value); char **prepare_spawn (char **argv); void lt_dump_script (FILE *f); EOF cat <= 0) && (st.st_mode & (S_IXUSR | S_IXGRP | S_IXOTH))) return 1; else return 0; } int make_executable (const char *path) { int rval = 0; struct stat st; lt_debugprintf (__FILE__, __LINE__, "(make_executable): %s\n", nonempty (path)); if ((!path) || (!*path)) return 0; if (stat (path, &st) >= 0) { rval = chmod (path, st.st_mode | S_IXOTH | S_IXGRP | S_IXUSR); } return rval; } /* Searches for the full path of the wrapper. Returns newly allocated full path name if found, NULL otherwise Does not chase symlinks, even on platforms that support them. */ char * find_executable (const char *wrapper) { int has_slash = 0; const char *p; const char *p_next; /* static buffer for getcwd */ char tmp[LT_PATHMAX + 1]; int tmp_len; char *concat_name; lt_debugprintf (__FILE__, __LINE__, "(find_executable): %s\n", nonempty (wrapper)); if ((wrapper == NULL) || (*wrapper == '\0')) return NULL; /* Absolute path? */ #if defined (HAVE_DOS_BASED_FILE_SYSTEM) if (isalpha ((unsigned char) wrapper[0]) && wrapper[1] == ':') { concat_name = xstrdup (wrapper); if (check_executable (concat_name)) return concat_name; XFREE (concat_name); } else { #endif if (IS_DIR_SEPARATOR (wrapper[0])) { concat_name = xstrdup (wrapper); if (check_executable (concat_name)) return concat_name; XFREE (concat_name); } #if defined (HAVE_DOS_BASED_FILE_SYSTEM) } #endif for (p = wrapper; *p; p++) if (*p == '/') { has_slash = 1; break; } if (!has_slash) { /* no slashes; search PATH */ const char *path = getenv ("PATH"); if (path != NULL) { for (p = path; *p; p = p_next) { const char *q; size_t p_len; for (q = p; *q; q++) if (IS_PATH_SEPARATOR (*q)) break; p_len = q - p; p_next = (*q == '\0' ? q : q + 1); if (p_len == 0) { /* empty path: current directory */ if (getcwd (tmp, LT_PATHMAX) == NULL) lt_fatal (__FILE__, __LINE__, "getcwd failed: %s", nonnull (strerror (errno))); tmp_len = strlen (tmp); concat_name = XMALLOC (char, tmp_len + 1 + strlen (wrapper) + 1); memcpy (concat_name, tmp, tmp_len); concat_name[tmp_len] = '/'; strcpy (concat_name + tmp_len + 1, wrapper); } else { concat_name = XMALLOC (char, p_len + 1 + strlen (wrapper) + 1); memcpy (concat_name, p, p_len); concat_name[p_len] = '/'; strcpy (concat_name + p_len + 1, wrapper); } if (check_executable (concat_name)) return concat_name; XFREE (concat_name); } } /* not found in PATH; assume curdir */ } /* Relative path | not found in path: prepend cwd */ if (getcwd (tmp, LT_PATHMAX) == NULL) lt_fatal (__FILE__, __LINE__, "getcwd failed: %s", nonnull (strerror (errno))); tmp_len = strlen (tmp); concat_name = XMALLOC (char, tmp_len + 1 + strlen (wrapper) + 1); memcpy (concat_name, tmp, tmp_len); concat_name[tmp_len] = '/'; strcpy (concat_name + tmp_len + 1, wrapper); if (check_executable (concat_name)) return concat_name; XFREE (concat_name); return NULL; } char * chase_symlinks (const char *pathspec) { #ifndef S_ISLNK return xstrdup (pathspec); #else char buf[LT_PATHMAX]; struct stat s; char *tmp_pathspec = xstrdup (pathspec); char *p; int has_symlinks = 0; while (strlen (tmp_pathspec) && !has_symlinks) { lt_debugprintf (__FILE__, __LINE__, "checking path component for symlinks: %s\n", tmp_pathspec); if (lstat (tmp_pathspec, &s) == 0) { if (S_ISLNK (s.st_mode) != 0) { has_symlinks = 1; break; } /* search backwards for last DIR_SEPARATOR */ p = tmp_pathspec + strlen (tmp_pathspec) - 1; while ((p > tmp_pathspec) && (!IS_DIR_SEPARATOR (*p))) p--; if ((p == tmp_pathspec) && (!IS_DIR_SEPARATOR (*p))) { /* no more DIR_SEPARATORS left */ break; } *p = '\0'; } else { lt_fatal (__FILE__, __LINE__, "error accessing file \"%s\": %s", tmp_pathspec, nonnull (strerror (errno))); } } XFREE (tmp_pathspec); if (!has_symlinks) { return xstrdup (pathspec); } tmp_pathspec = realpath (pathspec, buf); if (tmp_pathspec == 0) { lt_fatal (__FILE__, __LINE__, "could not follow symlinks for %s", pathspec); } return xstrdup (tmp_pathspec); #endif } char * strendzap (char *str, const char *pat) { size_t len, patlen; assert (str != NULL); assert (pat != NULL); len = strlen (str); patlen = strlen (pat); if (patlen <= len) { str += len - patlen; if (strcmp (str, pat) == 0) *str = '\0'; } return str; } void lt_debugprintf (const char *file, int line, const char *fmt, ...) { va_list args; if (lt_debug) { (void) fprintf (stderr, "%s:%s:%d: ", program_name, file, line); va_start (args, fmt); (void) vfprintf (stderr, fmt, args); va_end (args); } } static void lt_error_core (int exit_status, const char *file, int line, const char *mode, const char *message, va_list ap) { fprintf (stderr, "%s:%s:%d: %s: ", program_name, file, line, mode); vfprintf (stderr, message, ap); fprintf (stderr, ".\n"); if (exit_status >= 0) exit (exit_status); } void lt_fatal (const char *file, int line, const char *message, ...) { va_list ap; va_start (ap, message); lt_error_core (EXIT_FAILURE, file, line, "FATAL", message, ap); va_end (ap); } static const char * nonnull (const char *s) { return s ? s : "(null)"; } static const char * nonempty (const char *s) { return (s && !*s) ? "(empty)" : nonnull (s); } void lt_setenv (const char *name, const char *value) { lt_debugprintf (__FILE__, __LINE__, "(lt_setenv) setting '%s' to '%s'\n", nonnull (name), nonnull (value)); { #ifdef HAVE_SETENV /* always make a copy, for consistency with !HAVE_SETENV */ char *str = xstrdup (value); setenv (name, str, 1); #else int len = strlen (name) + 1 + strlen (value) + 1; char *str = XMALLOC (char, len); sprintf (str, "%s=%s", name, value); if (putenv (str) != EXIT_SUCCESS) { XFREE (str); } #endif } } char * lt_extend_str (const char *orig_value, const char *add, int to_end) { char *new_value; if (orig_value && *orig_value) { int orig_value_len = strlen (orig_value); int add_len = strlen (add); new_value = XMALLOC (char, add_len + orig_value_len + 1); if (to_end) { strcpy (new_value, orig_value); strcpy (new_value + orig_value_len, add); } else { strcpy (new_value, add); strcpy (new_value + add_len, orig_value); } } else { new_value = xstrdup (add); } return new_value; } void lt_update_exe_path (const char *name, const char *value) { lt_debugprintf (__FILE__, __LINE__, "(lt_update_exe_path) modifying '%s' by prepending '%s'\n", nonnull (name), nonnull (value)); if (name && *name && value && *value) { char *new_value = lt_extend_str (getenv (name), value, 0); /* some systems can't cope with a ':'-terminated path #' */ int len = strlen (new_value); while (((len = strlen (new_value)) > 0) && IS_PATH_SEPARATOR (new_value[len-1])) { new_value[len-1] = '\0'; } lt_setenv (name, new_value); XFREE (new_value); } } void lt_update_lib_path (const char *name, const char *value) { lt_debugprintf (__FILE__, __LINE__, "(lt_update_lib_path) modifying '%s' by prepending '%s'\n", nonnull (name), nonnull (value)); if (name && *name && value && *value) { char *new_value = lt_extend_str (getenv (name), value, 0); lt_setenv (name, new_value); XFREE (new_value); } } EOF case $host_os in mingw*) cat <<"EOF" /* Prepares an argument vector before calling spawn(). Note that spawn() does not by itself call the command interpreter (getenv ("COMSPEC") != NULL ? getenv ("COMSPEC") : ({ OSVERSIONINFO v; v.dwOSVersionInfoSize = sizeof(OSVERSIONINFO); GetVersionEx(&v); v.dwPlatformId == VER_PLATFORM_WIN32_NT; }) ? "cmd.exe" : "command.com"). Instead it simply concatenates the arguments, separated by ' ', and calls CreateProcess(). We must quote the arguments since Win32 CreateProcess() interprets characters like ' ', '\t', '\\', '"' (but not '<' and '>') in a special way: - Space and tab are interpreted as delimiters. They are not treated as delimiters if they are surrounded by double quotes: "...". - Unescaped double quotes are removed from the input. Their only effect is that within double quotes, space and tab are treated like normal characters. - Backslashes not followed by double quotes are not special. - But 2*n+1 backslashes followed by a double quote become n backslashes followed by a double quote (n >= 0): \" -> " \\\" -> \" \\\\\" -> \\" */ #define SHELL_SPECIAL_CHARS "\"\\ \001\002\003\004\005\006\007\010\011\012\013\014\015\016\017\020\021\022\023\024\025\026\027\030\031\032\033\034\035\036\037" #define SHELL_SPACE_CHARS " \001\002\003\004\005\006\007\010\011\012\013\014\015\016\017\020\021\022\023\024\025\026\027\030\031\032\033\034\035\036\037" char ** prepare_spawn (char **argv) { size_t argc; char **new_argv; size_t i; /* Count number of arguments. */ for (argc = 0; argv[argc] != NULL; argc++) ; /* Allocate new argument vector. */ new_argv = XMALLOC (char *, argc + 1); /* Put quoted arguments into the new argument vector. */ for (i = 0; i < argc; i++) { const char *string = argv[i]; if (string[0] == '\0') new_argv[i] = xstrdup ("\"\""); else if (strpbrk (string, SHELL_SPECIAL_CHARS) != NULL) { int quote_around = (strpbrk (string, SHELL_SPACE_CHARS) != NULL); size_t length; unsigned int backslashes; const char *s; char *quoted_string; char *p; length = 0; backslashes = 0; if (quote_around) length++; for (s = string; *s != '\0'; s++) { char c = *s; if (c == '"') length += backslashes + 1; length++; if (c == '\\') backslashes++; else backslashes = 0; } if (quote_around) length += backslashes + 1; quoted_string = XMALLOC (char, length + 1); p = quoted_string; backslashes = 0; if (quote_around) *p++ = '"'; for (s = string; *s != '\0'; s++) { char c = *s; if (c == '"') { unsigned int j; for (j = backslashes + 1; j > 0; j--) *p++ = '\\'; } *p++ = c; if (c == '\\') backslashes++; else backslashes = 0; } if (quote_around) { unsigned int j; for (j = backslashes; j > 0; j--) *p++ = '\\'; *p++ = '"'; } *p = '\0'; new_argv[i] = quoted_string; } else new_argv[i] = (char *) string; } new_argv[argc] = NULL; return new_argv; } EOF ;; esac cat <<"EOF" void lt_dump_script (FILE* f) { EOF func_emit_wrapper yes | $SED -n -e ' s/^\(.\{79\}\)\(..*\)/\1\ \2/ h s/\([\\"]\)/\\\1/g s/$/\\n/ s/\([^\n]*\).*/ fputs ("\1", f);/p g D' cat <<"EOF" } EOF } # end: func_emit_cwrapperexe_src # func_win32_import_lib_p ARG # True if ARG is an import lib, as indicated by $file_magic_cmd func_win32_import_lib_p () { $opt_debug case `eval $file_magic_cmd \"\$1\" 2>/dev/null | $SED -e 10q` in *import*) : ;; *) false ;; esac } # func_mode_link arg... func_mode_link () { $opt_debug case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-os2* | *-cegcc*) # It is impossible to link a dll without this setting, and # we shouldn't force the makefile maintainer to figure out # which system we are compiling for in order to pass an extra # flag for every libtool invocation. # allow_undefined=no # FIXME: Unfortunately, there are problems with the above when trying # to make a dll which has undefined symbols, in which case not # even a static library is built. For now, we need to specify # -no-undefined on the libtool link line when we can be certain # that all symbols are satisfied, otherwise we get a static library. allow_undefined=yes ;; *) allow_undefined=yes ;; esac libtool_args=$nonopt base_compile="$nonopt $@" compile_command=$nonopt finalize_command=$nonopt compile_rpath= finalize_rpath= compile_shlibpath= finalize_shlibpath= convenience= old_convenience= deplibs= old_deplibs= compiler_flags= linker_flags= dllsearchpath= lib_search_path=`pwd` inst_prefix_dir= new_inherited_linker_flags= avoid_version=no bindir= dlfiles= dlprefiles= dlself=no export_dynamic=no export_symbols= export_symbols_regex= generated= libobjs= ltlibs= module=no no_install=no objs= non_pic_objects= precious_files_regex= prefer_static_libs=no preload=no prev= prevarg= release= rpath= xrpath= perm_rpath= temp_rpath= thread_safe=no vinfo= vinfo_number=no weak_libs= single_module="${wl}-single_module" func_infer_tag $base_compile # We need to know -static, to get the right output filenames. for arg do case $arg in -shared) test "$build_libtool_libs" != yes && \ func_fatal_configuration "can not build a shared library" build_old_libs=no break ;; -all-static | -static | -static-libtool-libs) case $arg in -all-static) if test "$build_libtool_libs" = yes && test -z "$link_static_flag"; then func_warning "complete static linking is impossible in this configuration" fi if test -n "$link_static_flag"; then dlopen_self=$dlopen_self_static fi prefer_static_libs=yes ;; -static) if test -z "$pic_flag" && test -n "$link_static_flag"; then dlopen_self=$dlopen_self_static fi prefer_static_libs=built ;; -static-libtool-libs) if test -z "$pic_flag" && test -n "$link_static_flag"; then dlopen_self=$dlopen_self_static fi prefer_static_libs=yes ;; esac build_libtool_libs=no build_old_libs=yes break ;; esac done # See if our shared archives depend on static archives. test -n "$old_archive_from_new_cmds" && build_old_libs=yes # Go through the arguments, transforming them on the way. while test "$#" -gt 0; do arg="$1" shift func_quote_for_eval "$arg" qarg=$func_quote_for_eval_unquoted_result func_append libtool_args " $func_quote_for_eval_result" # If the previous option needs an argument, assign it. if test -n "$prev"; then case $prev in output) func_append compile_command " @OUTPUT@" func_append finalize_command " @OUTPUT@" ;; esac case $prev in bindir) bindir="$arg" prev= continue ;; dlfiles|dlprefiles) if test "$preload" = no; then # Add the symbol object into the linking commands. func_append compile_command " @SYMFILE@" func_append finalize_command " @SYMFILE@" preload=yes fi case $arg in *.la | *.lo) ;; # We handle these cases below. force) if test "$dlself" = no; then dlself=needless export_dynamic=yes fi prev= continue ;; self) if test "$prev" = dlprefiles; then dlself=yes elif test "$prev" = dlfiles && test "$dlopen_self" != yes; then dlself=yes else dlself=needless export_dynamic=yes fi prev= continue ;; *) if test "$prev" = dlfiles; then func_append dlfiles " $arg" else func_append dlprefiles " $arg" fi prev= continue ;; esac ;; expsyms) export_symbols="$arg" test -f "$arg" \ || func_fatal_error "symbol file \`$arg' does not exist" prev= continue ;; expsyms_regex) export_symbols_regex="$arg" prev= continue ;; framework) case $host in *-*-darwin*) case "$deplibs " in *" $qarg.ltframework "*) ;; *) func_append deplibs " $qarg.ltframework" # this is fixed later ;; esac ;; esac prev= continue ;; inst_prefix) inst_prefix_dir="$arg" prev= continue ;; objectlist) if test -f "$arg"; then save_arg=$arg moreargs= for fil in `cat "$save_arg"` do # func_append moreargs " $fil" arg=$fil # A libtool-controlled object. # Check to see that this really is a libtool object. if func_lalib_unsafe_p "$arg"; then pic_object= non_pic_object= # Read the .lo file func_source "$arg" if test -z "$pic_object" || test -z "$non_pic_object" || test "$pic_object" = none && test "$non_pic_object" = none; then func_fatal_error "cannot find name of object for \`$arg'" fi # Extract subdirectory from the argument. func_dirname "$arg" "/" "" xdir="$func_dirname_result" if test "$pic_object" != none; then # Prepend the subdirectory the object is found in. pic_object="$xdir$pic_object" if test "$prev" = dlfiles; then if test "$build_libtool_libs" = yes && test "$dlopen_support" = yes; then func_append dlfiles " $pic_object" prev= continue else # If libtool objects are unsupported, then we need to preload. prev=dlprefiles fi fi # CHECK ME: I think I busted this. -Ossama if test "$prev" = dlprefiles; then # Preload the old-style object. func_append dlprefiles " $pic_object" prev= fi # A PIC object. func_append libobjs " $pic_object" arg="$pic_object" fi # Non-PIC object. if test "$non_pic_object" != none; then # Prepend the subdirectory the object is found in. non_pic_object="$xdir$non_pic_object" # A standard non-PIC object func_append non_pic_objects " $non_pic_object" if test -z "$pic_object" || test "$pic_object" = none ; then arg="$non_pic_object" fi else # If the PIC object exists, use it instead. # $xdir was prepended to $pic_object above. non_pic_object="$pic_object" func_append non_pic_objects " $non_pic_object" fi else # Only an error if not doing a dry-run. if $opt_dry_run; then # Extract subdirectory from the argument. func_dirname "$arg" "/" "" xdir="$func_dirname_result" func_lo2o "$arg" pic_object=$xdir$objdir/$func_lo2o_result non_pic_object=$xdir$func_lo2o_result func_append libobjs " $pic_object" func_append non_pic_objects " $non_pic_object" else func_fatal_error "\`$arg' is not a valid libtool object" fi fi done else func_fatal_error "link input file \`$arg' does not exist" fi arg=$save_arg prev= continue ;; precious_regex) precious_files_regex="$arg" prev= continue ;; release) release="-$arg" prev= continue ;; rpath | xrpath) # We need an absolute path. case $arg in [\\/]* | [A-Za-z]:[\\/]*) ;; *) func_fatal_error "only absolute run-paths are allowed" ;; esac if test "$prev" = rpath; then case "$rpath " in *" $arg "*) ;; *) func_append rpath " $arg" ;; esac else case "$xrpath " in *" $arg "*) ;; *) func_append xrpath " $arg" ;; esac fi prev= continue ;; shrext) shrext_cmds="$arg" prev= continue ;; weak) func_append weak_libs " $arg" prev= continue ;; xcclinker) func_append linker_flags " $qarg" func_append compiler_flags " $qarg" prev= func_append compile_command " $qarg" func_append finalize_command " $qarg" continue ;; xcompiler) func_append compiler_flags " $qarg" prev= func_append compile_command " $qarg" func_append finalize_command " $qarg" continue ;; xlinker) func_append linker_flags " $qarg" func_append compiler_flags " $wl$qarg" prev= func_append compile_command " $wl$qarg" func_append finalize_command " $wl$qarg" continue ;; *) eval "$prev=\"\$arg\"" prev= continue ;; esac fi # test -n "$prev" prevarg="$arg" case $arg in -all-static) if test -n "$link_static_flag"; then # See comment for -static flag below, for more details. func_append compile_command " $link_static_flag" func_append finalize_command " $link_static_flag" fi continue ;; -allow-undefined) # FIXME: remove this flag sometime in the future. func_fatal_error "\`-allow-undefined' must not be used because it is the default" ;; -avoid-version) avoid_version=yes continue ;; -bindir) prev=bindir continue ;; -dlopen) prev=dlfiles continue ;; -dlpreopen) prev=dlprefiles continue ;; -export-dynamic) export_dynamic=yes continue ;; -export-symbols | -export-symbols-regex) if test -n "$export_symbols" || test -n "$export_symbols_regex"; then func_fatal_error "more than one -exported-symbols argument is not allowed" fi if test "X$arg" = "X-export-symbols"; then prev=expsyms else prev=expsyms_regex fi continue ;; -framework) prev=framework continue ;; -inst-prefix-dir) prev=inst_prefix continue ;; # The native IRIX linker understands -LANG:*, -LIST:* and -LNO:* # so, if we see these flags be careful not to treat them like -L -L[A-Z][A-Z]*:*) case $with_gcc/$host in no/*-*-irix* | /*-*-irix*) func_append compile_command " $arg" func_append finalize_command " $arg" ;; esac continue ;; -L*) func_stripname "-L" '' "$arg" if test -z "$func_stripname_result"; then if test "$#" -gt 0; then func_fatal_error "require no space between \`-L' and \`$1'" else func_fatal_error "need path for \`-L' option" fi fi func_resolve_sysroot "$func_stripname_result" dir=$func_resolve_sysroot_result # We need an absolute path. case $dir in [\\/]* | [A-Za-z]:[\\/]*) ;; *) absdir=`cd "$dir" && pwd` test -z "$absdir" && \ func_fatal_error "cannot determine absolute directory name of \`$dir'" dir="$absdir" ;; esac case "$deplibs " in *" -L$dir "* | *" $arg "*) # Will only happen for absolute or sysroot arguments ;; *) # Preserve sysroot, but never include relative directories case $dir in [\\/]* | [A-Za-z]:[\\/]* | =*) func_append deplibs " $arg" ;; *) func_append deplibs " -L$dir" ;; esac func_append lib_search_path " $dir" ;; esac case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-os2* | *-cegcc*) testbindir=`$ECHO "$dir" | $SED 's*/lib$*/bin*'` case :$dllsearchpath: in *":$dir:"*) ;; ::) dllsearchpath=$dir;; *) func_append dllsearchpath ":$dir";; esac case :$dllsearchpath: in *":$testbindir:"*) ;; ::) dllsearchpath=$testbindir;; *) func_append dllsearchpath ":$testbindir";; esac ;; esac continue ;; -l*) if test "X$arg" = "X-lc" || test "X$arg" = "X-lm"; then case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-beos* | *-cegcc* | *-*-haiku*) # These systems don't actually have a C or math library (as such) continue ;; *-*-os2*) # These systems don't actually have a C library (as such) test "X$arg" = "X-lc" && continue ;; *-*-openbsd* | *-*-freebsd* | *-*-dragonfly*) # Do not include libc due to us having libc/libc_r. test "X$arg" = "X-lc" && continue ;; *-*-rhapsody* | *-*-darwin1.[012]) # Rhapsody C and math libraries are in the System framework func_append deplibs " System.ltframework" continue ;; *-*-sco3.2v5* | *-*-sco5v6*) # Causes problems with __ctype test "X$arg" = "X-lc" && continue ;; *-*-sysv4.2uw2* | *-*-sysv5* | *-*-unixware* | *-*-OpenUNIX*) # Compiler inserts libc in the correct place for threads to work test "X$arg" = "X-lc" && continue ;; esac elif test "X$arg" = "X-lc_r"; then case $host in *-*-openbsd* | *-*-freebsd* | *-*-dragonfly*) # Do not include libc_r directly, use -pthread flag. continue ;; esac fi func_append deplibs " $arg" continue ;; -module) module=yes continue ;; # Tru64 UNIX uses -model [arg] to determine the layout of C++ # classes, name mangling, and exception handling. # Darwin uses the -arch flag to determine output architecture. -model|-arch|-isysroot|--sysroot) func_append compiler_flags " $arg" func_append compile_command " $arg" func_append finalize_command " $arg" prev=xcompiler continue ;; -mt|-mthreads|-kthread|-Kthread|-pthread|-pthreads|--thread-safe \ |-threads|-fopenmp|-openmp|-mp|-xopenmp|-omp|-qsmp=*) func_append compiler_flags " $arg" func_append compile_command " $arg" func_append finalize_command " $arg" case "$new_inherited_linker_flags " in *" $arg "*) ;; * ) func_append new_inherited_linker_flags " $arg" ;; esac continue ;; -multi_module) single_module="${wl}-multi_module" continue ;; -no-fast-install) fast_install=no continue ;; -no-install) case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-os2* | *-*-darwin* | *-cegcc*) # The PATH hackery in wrapper scripts is required on Windows # and Darwin in order for the loader to find any dlls it needs. func_warning "\`-no-install' is ignored for $host" func_warning "assuming \`-no-fast-install' instead" fast_install=no ;; *) no_install=yes ;; esac continue ;; -no-undefined) allow_undefined=no continue ;; -objectlist) prev=objectlist continue ;; -o) prev=output ;; -precious-files-regex) prev=precious_regex continue ;; -release) prev=release continue ;; -rpath) prev=rpath continue ;; -R) prev=xrpath continue ;; -R*) func_stripname '-R' '' "$arg" dir=$func_stripname_result # We need an absolute path. case $dir in [\\/]* | [A-Za-z]:[\\/]*) ;; =*) func_stripname '=' '' "$dir" dir=$lt_sysroot$func_stripname_result ;; *) func_fatal_error "only absolute run-paths are allowed" ;; esac case "$xrpath " in *" $dir "*) ;; *) func_append xrpath " $dir" ;; esac continue ;; -shared) # The effects of -shared are defined in a previous loop. continue ;; -shrext) prev=shrext continue ;; -static | -static-libtool-libs) # The effects of -static are defined in a previous loop. # We used to do the same as -all-static on platforms that # didn't have a PIC flag, but the assumption that the effects # would be equivalent was wrong. It would break on at least # Digital Unix and AIX. continue ;; -thread-safe) thread_safe=yes continue ;; -version-info) prev=vinfo continue ;; -version-number) prev=vinfo vinfo_number=yes continue ;; -weak) prev=weak continue ;; -Wc,*) func_stripname '-Wc,' '' "$arg" args=$func_stripname_result arg= save_ifs="$IFS"; IFS=',' for flag in $args; do IFS="$save_ifs" func_quote_for_eval "$flag" func_append arg " $func_quote_for_eval_result" func_append compiler_flags " $func_quote_for_eval_result" done IFS="$save_ifs" func_stripname ' ' '' "$arg" arg=$func_stripname_result ;; -Wl,*) func_stripname '-Wl,' '' "$arg" args=$func_stripname_result arg= save_ifs="$IFS"; IFS=',' for flag in $args; do IFS="$save_ifs" func_quote_for_eval "$flag" func_append arg " $wl$func_quote_for_eval_result" func_append compiler_flags " $wl$func_quote_for_eval_result" func_append linker_flags " $func_quote_for_eval_result" done IFS="$save_ifs" func_stripname ' ' '' "$arg" arg=$func_stripname_result ;; -Xcompiler) prev=xcompiler continue ;; -Xlinker) prev=xlinker continue ;; -XCClinker) prev=xcclinker continue ;; # -msg_* for osf cc -msg_*) func_quote_for_eval "$arg" arg="$func_quote_for_eval_result" ;; # Flags to be passed through unchanged, with rationale: # -64, -mips[0-9] enable 64-bit mode for the SGI compiler # -r[0-9][0-9]* specify processor for the SGI compiler # -xarch=*, -xtarget=* enable 64-bit mode for the Sun compiler # +DA*, +DD* enable 64-bit mode for the HP compiler # -q* compiler args for the IBM compiler # -m*, -t[45]*, -txscale* architecture-specific flags for GCC # -F/path path to uninstalled frameworks, gcc on darwin # -p, -pg, --coverage, -fprofile-* profiling flags for GCC # @file GCC response files # -tp=* Portland pgcc target processor selection # --sysroot=* for sysroot support # -O*, -flto*, -fwhopr*, -fuse-linker-plugin GCC link-time optimization -64|-mips[0-9]|-r[0-9][0-9]*|-xarch=*|-xtarget=*|+DA*|+DD*|-q*|-m*| \ -t[45]*|-txscale*|-p|-pg|--coverage|-fprofile-*|-F*|@*|-tp=*|--sysroot=*| \ -O*|-flto*|-fwhopr*|-fuse-linker-plugin) func_quote_for_eval "$arg" arg="$func_quote_for_eval_result" func_append compile_command " $arg" func_append finalize_command " $arg" func_append compiler_flags " $arg" continue ;; # Some other compiler flag. -* | +*) func_quote_for_eval "$arg" arg="$func_quote_for_eval_result" ;; *.$objext) # A standard object. func_append objs " $arg" ;; *.lo) # A libtool-controlled object. # Check to see that this really is a libtool object. if func_lalib_unsafe_p "$arg"; then pic_object= non_pic_object= # Read the .lo file func_source "$arg" if test -z "$pic_object" || test -z "$non_pic_object" || test "$pic_object" = none && test "$non_pic_object" = none; then func_fatal_error "cannot find name of object for \`$arg'" fi # Extract subdirectory from the argument. func_dirname "$arg" "/" "" xdir="$func_dirname_result" if test "$pic_object" != none; then # Prepend the subdirectory the object is found in. pic_object="$xdir$pic_object" if test "$prev" = dlfiles; then if test "$build_libtool_libs" = yes && test "$dlopen_support" = yes; then func_append dlfiles " $pic_object" prev= continue else # If libtool objects are unsupported, then we need to preload. prev=dlprefiles fi fi # CHECK ME: I think I busted this. -Ossama if test "$prev" = dlprefiles; then # Preload the old-style object. func_append dlprefiles " $pic_object" prev= fi # A PIC object. func_append libobjs " $pic_object" arg="$pic_object" fi # Non-PIC object. if test "$non_pic_object" != none; then # Prepend the subdirectory the object is found in. non_pic_object="$xdir$non_pic_object" # A standard non-PIC object func_append non_pic_objects " $non_pic_object" if test -z "$pic_object" || test "$pic_object" = none ; then arg="$non_pic_object" fi else # If the PIC object exists, use it instead. # $xdir was prepended to $pic_object above. non_pic_object="$pic_object" func_append non_pic_objects " $non_pic_object" fi else # Only an error if not doing a dry-run. if $opt_dry_run; then # Extract subdirectory from the argument. func_dirname "$arg" "/" "" xdir="$func_dirname_result" func_lo2o "$arg" pic_object=$xdir$objdir/$func_lo2o_result non_pic_object=$xdir$func_lo2o_result func_append libobjs " $pic_object" func_append non_pic_objects " $non_pic_object" else func_fatal_error "\`$arg' is not a valid libtool object" fi fi ;; *.$libext) # An archive. func_append deplibs " $arg" func_append old_deplibs " $arg" continue ;; *.la) # A libtool-controlled library. func_resolve_sysroot "$arg" if test "$prev" = dlfiles; then # This library was specified with -dlopen. func_append dlfiles " $func_resolve_sysroot_result" prev= elif test "$prev" = dlprefiles; then # The library was specified with -dlpreopen. func_append dlprefiles " $func_resolve_sysroot_result" prev= else func_append deplibs " $func_resolve_sysroot_result" fi continue ;; # Some other compiler argument. *) # Unknown arguments in both finalize_command and compile_command need # to be aesthetically quoted because they are evaled later. func_quote_for_eval "$arg" arg="$func_quote_for_eval_result" ;; esac # arg # Now actually substitute the argument into the commands. if test -n "$arg"; then func_append compile_command " $arg" func_append finalize_command " $arg" fi done # argument parsing loop test -n "$prev" && \ func_fatal_help "the \`$prevarg' option requires an argument" if test "$export_dynamic" = yes && test -n "$export_dynamic_flag_spec"; then eval arg=\"$export_dynamic_flag_spec\" func_append compile_command " $arg" func_append finalize_command " $arg" fi oldlibs= # calculate the name of the file, without its directory func_basename "$output" outputname="$func_basename_result" libobjs_save="$libobjs" if test -n "$shlibpath_var"; then # get the directories listed in $shlibpath_var eval shlib_search_path=\`\$ECHO \"\${$shlibpath_var}\" \| \$SED \'s/:/ /g\'\` else shlib_search_path= fi eval sys_lib_search_path=\"$sys_lib_search_path_spec\" eval sys_lib_dlsearch_path=\"$sys_lib_dlsearch_path_spec\" func_dirname "$output" "/" "" output_objdir="$func_dirname_result$objdir" func_to_tool_file "$output_objdir/" tool_output_objdir=$func_to_tool_file_result # Create the object directory. func_mkdir_p "$output_objdir" # Determine the type of output case $output in "") func_fatal_help "you must specify an output file" ;; *.$libext) linkmode=oldlib ;; *.lo | *.$objext) linkmode=obj ;; *.la) linkmode=lib ;; *) linkmode=prog ;; # Anything else should be a program. esac specialdeplibs= libs= # Find all interdependent deplibs by searching for libraries # that are linked more than once (e.g. -la -lb -la) for deplib in $deplibs; do if $opt_preserve_dup_deps ; then case "$libs " in *" $deplib "*) func_append specialdeplibs " $deplib" ;; esac fi func_append libs " $deplib" done if test "$linkmode" = lib; then libs="$predeps $libs $compiler_lib_search_path $postdeps" # Compute libraries that are listed more than once in $predeps # $postdeps and mark them as special (i.e., whose duplicates are # not to be eliminated). pre_post_deps= if $opt_duplicate_compiler_generated_deps; then for pre_post_dep in $predeps $postdeps; do case "$pre_post_deps " in *" $pre_post_dep "*) func_append specialdeplibs " $pre_post_deps" ;; esac func_append pre_post_deps " $pre_post_dep" done fi pre_post_deps= fi deplibs= newdependency_libs= newlib_search_path= need_relink=no # whether we're linking any uninstalled libtool libraries notinst_deplibs= # not-installed libtool libraries notinst_path= # paths that contain not-installed libtool libraries case $linkmode in lib) passes="conv dlpreopen link" for file in $dlfiles $dlprefiles; do case $file in *.la) ;; *) func_fatal_help "libraries can \`-dlopen' only libtool libraries: $file" ;; esac done ;; prog) compile_deplibs= finalize_deplibs= alldeplibs=no newdlfiles= newdlprefiles= passes="conv scan dlopen dlpreopen link" ;; *) passes="conv" ;; esac for pass in $passes; do # The preopen pass in lib mode reverses $deplibs; put it back here # so that -L comes before libs that need it for instance... if test "$linkmode,$pass" = "lib,link"; then ## FIXME: Find the place where the list is rebuilt in the wrong ## order, and fix it there properly tmp_deplibs= for deplib in $deplibs; do tmp_deplibs="$deplib $tmp_deplibs" done deplibs="$tmp_deplibs" fi if test "$linkmode,$pass" = "lib,link" || test "$linkmode,$pass" = "prog,scan"; then libs="$deplibs" deplibs= fi if test "$linkmode" = prog; then case $pass in dlopen) libs="$dlfiles" ;; dlpreopen) libs="$dlprefiles" ;; link) libs="$deplibs %DEPLIBS% $dependency_libs" ;; esac fi if test "$linkmode,$pass" = "lib,dlpreopen"; then # Collect and forward deplibs of preopened libtool libs for lib in $dlprefiles; do # Ignore non-libtool-libs dependency_libs= func_resolve_sysroot "$lib" case $lib in *.la) func_source "$func_resolve_sysroot_result" ;; esac # Collect preopened libtool deplibs, except any this library # has declared as weak libs for deplib in $dependency_libs; do func_basename "$deplib" deplib_base=$func_basename_result case " $weak_libs " in *" $deplib_base "*) ;; *) func_append deplibs " $deplib" ;; esac done done libs="$dlprefiles" fi if test "$pass" = dlopen; then # Collect dlpreopened libraries save_deplibs="$deplibs" deplibs= fi for deplib in $libs; do lib= found=no case $deplib in -mt|-mthreads|-kthread|-Kthread|-pthread|-pthreads|--thread-safe \ |-threads|-fopenmp|-openmp|-mp|-xopenmp|-omp|-qsmp=*) if test "$linkmode,$pass" = "prog,link"; then compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" else func_append compiler_flags " $deplib" if test "$linkmode" = lib ; then case "$new_inherited_linker_flags " in *" $deplib "*) ;; * ) func_append new_inherited_linker_flags " $deplib" ;; esac fi fi continue ;; -l*) if test "$linkmode" != lib && test "$linkmode" != prog; then func_warning "\`-l' is ignored for archives/objects" continue fi func_stripname '-l' '' "$deplib" name=$func_stripname_result if test "$linkmode" = lib; then searchdirs="$newlib_search_path $lib_search_path $compiler_lib_search_dirs $sys_lib_search_path $shlib_search_path" else searchdirs="$newlib_search_path $lib_search_path $sys_lib_search_path $shlib_search_path" fi for searchdir in $searchdirs; do for search_ext in .la $std_shrext .so .a; do # Search the libtool library lib="$searchdir/lib${name}${search_ext}" if test -f "$lib"; then if test "$search_ext" = ".la"; then found=yes else found=no fi break 2 fi done done if test "$found" != yes; then # deplib doesn't seem to be a libtool library if test "$linkmode,$pass" = "prog,link"; then compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" else deplibs="$deplib $deplibs" test "$linkmode" = lib && newdependency_libs="$deplib $newdependency_libs" fi continue else # deplib is a libtool library # If $allow_libtool_libs_with_static_runtimes && $deplib is a stdlib, # We need to do some special things here, and not later. if test "X$allow_libtool_libs_with_static_runtimes" = "Xyes" ; then case " $predeps $postdeps " in *" $deplib "*) if func_lalib_p "$lib"; then library_names= old_library= func_source "$lib" for l in $old_library $library_names; do ll="$l" done if test "X$ll" = "X$old_library" ; then # only static version available found=no func_dirname "$lib" "" "." ladir="$func_dirname_result" lib=$ladir/$old_library if test "$linkmode,$pass" = "prog,link"; then compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" else deplibs="$deplib $deplibs" test "$linkmode" = lib && newdependency_libs="$deplib $newdependency_libs" fi continue fi fi ;; *) ;; esac fi fi ;; # -l *.ltframework) if test "$linkmode,$pass" = "prog,link"; then compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" else deplibs="$deplib $deplibs" if test "$linkmode" = lib ; then case "$new_inherited_linker_flags " in *" $deplib "*) ;; * ) func_append new_inherited_linker_flags " $deplib" ;; esac fi fi continue ;; -L*) case $linkmode in lib) deplibs="$deplib $deplibs" test "$pass" = conv && continue newdependency_libs="$deplib $newdependency_libs" func_stripname '-L' '' "$deplib" func_resolve_sysroot "$func_stripname_result" func_append newlib_search_path " $func_resolve_sysroot_result" ;; prog) if test "$pass" = conv; then deplibs="$deplib $deplibs" continue fi if test "$pass" = scan; then deplibs="$deplib $deplibs" else compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" fi func_stripname '-L' '' "$deplib" func_resolve_sysroot "$func_stripname_result" func_append newlib_search_path " $func_resolve_sysroot_result" ;; *) func_warning "\`-L' is ignored for archives/objects" ;; esac # linkmode continue ;; # -L -R*) if test "$pass" = link; then func_stripname '-R' '' "$deplib" func_resolve_sysroot "$func_stripname_result" dir=$func_resolve_sysroot_result # Make sure the xrpath contains only unique directories. case "$xrpath " in *" $dir "*) ;; *) func_append xrpath " $dir" ;; esac fi deplibs="$deplib $deplibs" continue ;; *.la) func_resolve_sysroot "$deplib" lib=$func_resolve_sysroot_result ;; *.$libext) if test "$pass" = conv; then deplibs="$deplib $deplibs" continue fi case $linkmode in lib) # Linking convenience modules into shared libraries is allowed, # but linking other static libraries is non-portable. case " $dlpreconveniencelibs " in *" $deplib "*) ;; *) valid_a_lib=no case $deplibs_check_method in match_pattern*) set dummy $deplibs_check_method; shift match_pattern_regex=`expr "$deplibs_check_method" : "$1 \(.*\)"` if eval "\$ECHO \"$deplib\"" 2>/dev/null | $SED 10q \ | $EGREP "$match_pattern_regex" > /dev/null; then valid_a_lib=yes fi ;; pass_all) valid_a_lib=yes ;; esac if test "$valid_a_lib" != yes; then echo $ECHO "*** Warning: Trying to link with static lib archive $deplib." echo "*** I have the capability to make that library automatically link in when" echo "*** you link to this library. But I can only do this if you have a" echo "*** shared version of the library, which you do not appear to have" echo "*** because the file extensions .$libext of this argument makes me believe" echo "*** that it is just a static archive that I should not use here." else echo $ECHO "*** Warning: Linking the shared library $output against the" $ECHO "*** static library $deplib is not portable!" deplibs="$deplib $deplibs" fi ;; esac continue ;; prog) if test "$pass" != link; then deplibs="$deplib $deplibs" else compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" fi continue ;; esac # linkmode ;; # *.$libext *.lo | *.$objext) if test "$pass" = conv; then deplibs="$deplib $deplibs" elif test "$linkmode" = prog; then if test "$pass" = dlpreopen || test "$dlopen_support" != yes || test "$build_libtool_libs" = no; then # If there is no dlopen support or we're linking statically, # we need to preload. func_append newdlprefiles " $deplib" compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" else func_append newdlfiles " $deplib" fi fi continue ;; %DEPLIBS%) alldeplibs=yes continue ;; esac # case $deplib if test "$found" = yes || test -f "$lib"; then : else func_fatal_error "cannot find the library \`$lib' or unhandled argument \`$deplib'" fi # Check to see that this really is a libtool archive. func_lalib_unsafe_p "$lib" \ || func_fatal_error "\`$lib' is not a valid libtool archive" func_dirname "$lib" "" "." ladir="$func_dirname_result" dlname= dlopen= dlpreopen= libdir= library_names= old_library= inherited_linker_flags= # If the library was installed with an old release of libtool, # it will not redefine variables installed, or shouldnotlink installed=yes shouldnotlink=no avoidtemprpath= # Read the .la file func_source "$lib" # Convert "-framework foo" to "foo.ltframework" if test -n "$inherited_linker_flags"; then tmp_inherited_linker_flags=`$ECHO "$inherited_linker_flags" | $SED 's/-framework \([^ $]*\)/\1.ltframework/g'` for tmp_inherited_linker_flag in $tmp_inherited_linker_flags; do case " $new_inherited_linker_flags " in *" $tmp_inherited_linker_flag "*) ;; *) func_append new_inherited_linker_flags " $tmp_inherited_linker_flag";; esac done fi dependency_libs=`$ECHO " $dependency_libs" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` if test "$linkmode,$pass" = "lib,link" || test "$linkmode,$pass" = "prog,scan" || { test "$linkmode" != prog && test "$linkmode" != lib; }; then test -n "$dlopen" && func_append dlfiles " $dlopen" test -n "$dlpreopen" && func_append dlprefiles " $dlpreopen" fi if test "$pass" = conv; then # Only check for convenience libraries deplibs="$lib $deplibs" if test -z "$libdir"; then if test -z "$old_library"; then func_fatal_error "cannot find name of link library for \`$lib'" fi # It is a libtool convenience library, so add in its objects. func_append convenience " $ladir/$objdir/$old_library" func_append old_convenience " $ladir/$objdir/$old_library" elif test "$linkmode" != prog && test "$linkmode" != lib; then func_fatal_error "\`$lib' is not a convenience library" fi tmp_libs= for deplib in $dependency_libs; do deplibs="$deplib $deplibs" if $opt_preserve_dup_deps ; then case "$tmp_libs " in *" $deplib "*) func_append specialdeplibs " $deplib" ;; esac fi func_append tmp_libs " $deplib" done continue fi # $pass = conv # Get the name of the library we link against. linklib= if test -n "$old_library" && { test "$prefer_static_libs" = yes || test "$prefer_static_libs,$installed" = "built,no"; }; then linklib=$old_library else for l in $old_library $library_names; do linklib="$l" done fi if test -z "$linklib"; then func_fatal_error "cannot find name of link library for \`$lib'" fi # This library was specified with -dlopen. if test "$pass" = dlopen; then if test -z "$libdir"; then func_fatal_error "cannot -dlopen a convenience library: \`$lib'" fi if test -z "$dlname" || test "$dlopen_support" != yes || test "$build_libtool_libs" = no; then # If there is no dlname, no dlopen support or we're linking # statically, we need to preload. We also need to preload any # dependent libraries so libltdl's deplib preloader doesn't # bomb out in the load deplibs phase. func_append dlprefiles " $lib $dependency_libs" else func_append newdlfiles " $lib" fi continue fi # $pass = dlopen # We need an absolute path. case $ladir in [\\/]* | [A-Za-z]:[\\/]*) abs_ladir="$ladir" ;; *) abs_ladir=`cd "$ladir" && pwd` if test -z "$abs_ladir"; then func_warning "cannot determine absolute directory name of \`$ladir'" func_warning "passing it literally to the linker, although it might fail" abs_ladir="$ladir" fi ;; esac func_basename "$lib" laname="$func_basename_result" # Find the relevant object directory and library name. if test "X$installed" = Xyes; then if test ! -f "$lt_sysroot$libdir/$linklib" && test -f "$abs_ladir/$linklib"; then func_warning "library \`$lib' was moved." dir="$ladir" absdir="$abs_ladir" libdir="$abs_ladir" else dir="$lt_sysroot$libdir" absdir="$lt_sysroot$libdir" fi test "X$hardcode_automatic" = Xyes && avoidtemprpath=yes else if test ! -f "$ladir/$objdir/$linklib" && test -f "$abs_ladir/$linklib"; then dir="$ladir" absdir="$abs_ladir" # Remove this search path later func_append notinst_path " $abs_ladir" else dir="$ladir/$objdir" absdir="$abs_ladir/$objdir" # Remove this search path later func_append notinst_path " $abs_ladir" fi fi # $installed = yes func_stripname 'lib' '.la' "$laname" name=$func_stripname_result # This library was specified with -dlpreopen. if test "$pass" = dlpreopen; then if test -z "$libdir" && test "$linkmode" = prog; then func_fatal_error "only libraries may -dlpreopen a convenience library: \`$lib'" fi case "$host" in # special handling for platforms with PE-DLLs. *cygwin* | *mingw* | *cegcc* ) # Linker will automatically link against shared library if both # static and shared are present. Therefore, ensure we extract # symbols from the import library if a shared library is present # (otherwise, the dlopen module name will be incorrect). We do # this by putting the import library name into $newdlprefiles. # We recover the dlopen module name by 'saving' the la file # name in a special purpose variable, and (later) extracting the # dlname from the la file. if test -n "$dlname"; then func_tr_sh "$dir/$linklib" eval "libfile_$func_tr_sh_result=\$abs_ladir/\$laname" func_append newdlprefiles " $dir/$linklib" else func_append newdlprefiles " $dir/$old_library" # Keep a list of preopened convenience libraries to check # that they are being used correctly in the link pass. test -z "$libdir" && \ func_append dlpreconveniencelibs " $dir/$old_library" fi ;; * ) # Prefer using a static library (so that no silly _DYNAMIC symbols # are required to link). if test -n "$old_library"; then func_append newdlprefiles " $dir/$old_library" # Keep a list of preopened convenience libraries to check # that they are being used correctly in the link pass. test -z "$libdir" && \ func_append dlpreconveniencelibs " $dir/$old_library" # Otherwise, use the dlname, so that lt_dlopen finds it. elif test -n "$dlname"; then func_append newdlprefiles " $dir/$dlname" else func_append newdlprefiles " $dir/$linklib" fi ;; esac fi # $pass = dlpreopen if test -z "$libdir"; then # Link the convenience library if test "$linkmode" = lib; then deplibs="$dir/$old_library $deplibs" elif test "$linkmode,$pass" = "prog,link"; then compile_deplibs="$dir/$old_library $compile_deplibs" finalize_deplibs="$dir/$old_library $finalize_deplibs" else deplibs="$lib $deplibs" # used for prog,scan pass fi continue fi if test "$linkmode" = prog && test "$pass" != link; then func_append newlib_search_path " $ladir" deplibs="$lib $deplibs" linkalldeplibs=no if test "$link_all_deplibs" != no || test -z "$library_names" || test "$build_libtool_libs" = no; then linkalldeplibs=yes fi tmp_libs= for deplib in $dependency_libs; do case $deplib in -L*) func_stripname '-L' '' "$deplib" func_resolve_sysroot "$func_stripname_result" func_append newlib_search_path " $func_resolve_sysroot_result" ;; esac # Need to link against all dependency_libs? if test "$linkalldeplibs" = yes; then deplibs="$deplib $deplibs" else # Need to hardcode shared library paths # or/and link against static libraries newdependency_libs="$deplib $newdependency_libs" fi if $opt_preserve_dup_deps ; then case "$tmp_libs " in *" $deplib "*) func_append specialdeplibs " $deplib" ;; esac fi func_append tmp_libs " $deplib" done # for deplib continue fi # $linkmode = prog... if test "$linkmode,$pass" = "prog,link"; then if test -n "$library_names" && { { test "$prefer_static_libs" = no || test "$prefer_static_libs,$installed" = "built,yes"; } || test -z "$old_library"; }; then # We need to hardcode the library path if test -n "$shlibpath_var" && test -z "$avoidtemprpath" ; then # Make sure the rpath contains only unique directories. case "$temp_rpath:" in *"$absdir:"*) ;; *) func_append temp_rpath "$absdir:" ;; esac fi # Hardcode the library path. # Skip directories that are in the system default run-time # search path. case " $sys_lib_dlsearch_path " in *" $absdir "*) ;; *) case "$compile_rpath " in *" $absdir "*) ;; *) func_append compile_rpath " $absdir" ;; esac ;; esac case " $sys_lib_dlsearch_path " in *" $libdir "*) ;; *) case "$finalize_rpath " in *" $libdir "*) ;; *) func_append finalize_rpath " $libdir" ;; esac ;; esac fi # $linkmode,$pass = prog,link... if test "$alldeplibs" = yes && { test "$deplibs_check_method" = pass_all || { test "$build_libtool_libs" = yes && test -n "$library_names"; }; }; then # We only need to search for static libraries continue fi fi link_static=no # Whether the deplib will be linked statically use_static_libs=$prefer_static_libs if test "$use_static_libs" = built && test "$installed" = yes; then use_static_libs=no fi if test -n "$library_names" && { test "$use_static_libs" = no || test -z "$old_library"; }; then case $host in *cygwin* | *mingw* | *cegcc*) # No point in relinking DLLs because paths are not encoded func_append notinst_deplibs " $lib" need_relink=no ;; *) if test "$installed" = no; then func_append notinst_deplibs " $lib" need_relink=yes fi ;; esac # This is a shared library # Warn about portability, can't link against -module's on some # systems (darwin). Don't bleat about dlopened modules though! dlopenmodule="" for dlpremoduletest in $dlprefiles; do if test "X$dlpremoduletest" = "X$lib"; then dlopenmodule="$dlpremoduletest" break fi done if test -z "$dlopenmodule" && test "$shouldnotlink" = yes && test "$pass" = link; then echo if test "$linkmode" = prog; then $ECHO "*** Warning: Linking the executable $output against the loadable module" else $ECHO "*** Warning: Linking the shared library $output against the loadable module" fi $ECHO "*** $linklib is not portable!" fi if test "$linkmode" = lib && test "$hardcode_into_libs" = yes; then # Hardcode the library path. # Skip directories that are in the system default run-time # search path. case " $sys_lib_dlsearch_path " in *" $absdir "*) ;; *) case "$compile_rpath " in *" $absdir "*) ;; *) func_append compile_rpath " $absdir" ;; esac ;; esac case " $sys_lib_dlsearch_path " in *" $libdir "*) ;; *) case "$finalize_rpath " in *" $libdir "*) ;; *) func_append finalize_rpath " $libdir" ;; esac ;; esac fi if test -n "$old_archive_from_expsyms_cmds"; then # figure out the soname set dummy $library_names shift realname="$1" shift libname=`eval "\\$ECHO \"$libname_spec\""` # use dlname if we got it. it's perfectly good, no? if test -n "$dlname"; then soname="$dlname" elif test -n "$soname_spec"; then # bleh windows case $host in *cygwin* | mingw* | *cegcc*) func_arith $current - $age major=$func_arith_result versuffix="-$major" ;; esac eval soname=\"$soname_spec\" else soname="$realname" fi # Make a new name for the extract_expsyms_cmds to use soroot="$soname" func_basename "$soroot" soname="$func_basename_result" func_stripname 'lib' '.dll' "$soname" newlib=libimp-$func_stripname_result.a # If the library has no export list, then create one now if test -f "$output_objdir/$soname-def"; then : else func_verbose "extracting exported symbol list from \`$soname'" func_execute_cmds "$extract_expsyms_cmds" 'exit $?' fi # Create $newlib if test -f "$output_objdir/$newlib"; then :; else func_verbose "generating import library for \`$soname'" func_execute_cmds "$old_archive_from_expsyms_cmds" 'exit $?' fi # make sure the library variables are pointing to the new library dir=$output_objdir linklib=$newlib fi # test -n "$old_archive_from_expsyms_cmds" if test "$linkmode" = prog || test "$opt_mode" != relink; then add_shlibpath= add_dir= add= lib_linked=yes case $hardcode_action in immediate | unsupported) if test "$hardcode_direct" = no; then add="$dir/$linklib" case $host in *-*-sco3.2v5.0.[024]*) add_dir="-L$dir" ;; *-*-sysv4*uw2*) add_dir="-L$dir" ;; *-*-sysv5OpenUNIX* | *-*-sysv5UnixWare7.[01].[10]* | \ *-*-unixware7*) add_dir="-L$dir" ;; *-*-darwin* ) # if the lib is a (non-dlopened) module then we can not # link against it, someone is ignoring the earlier warnings if /usr/bin/file -L $add 2> /dev/null | $GREP ": [^:]* bundle" >/dev/null ; then if test "X$dlopenmodule" != "X$lib"; then $ECHO "*** Warning: lib $linklib is a module, not a shared library" if test -z "$old_library" ; then echo echo "*** And there doesn't seem to be a static archive available" echo "*** The link will probably fail, sorry" else add="$dir/$old_library" fi elif test -n "$old_library"; then add="$dir/$old_library" fi fi esac elif test "$hardcode_minus_L" = no; then case $host in *-*-sunos*) add_shlibpath="$dir" ;; esac add_dir="-L$dir" add="-l$name" elif test "$hardcode_shlibpath_var" = no; then add_shlibpath="$dir" add="-l$name" else lib_linked=no fi ;; relink) if test "$hardcode_direct" = yes && test "$hardcode_direct_absolute" = no; then add="$dir/$linklib" elif test "$hardcode_minus_L" = yes; then add_dir="-L$absdir" # Try looking first in the location we're being installed to. if test -n "$inst_prefix_dir"; then case $libdir in [\\/]*) func_append add_dir " -L$inst_prefix_dir$libdir" ;; esac fi add="-l$name" elif test "$hardcode_shlibpath_var" = yes; then add_shlibpath="$dir" add="-l$name" else lib_linked=no fi ;; *) lib_linked=no ;; esac if test "$lib_linked" != yes; then func_fatal_configuration "unsupported hardcode properties" fi if test -n "$add_shlibpath"; then case :$compile_shlibpath: in *":$add_shlibpath:"*) ;; *) func_append compile_shlibpath "$add_shlibpath:" ;; esac fi if test "$linkmode" = prog; then test -n "$add_dir" && compile_deplibs="$add_dir $compile_deplibs" test -n "$add" && compile_deplibs="$add $compile_deplibs" else test -n "$add_dir" && deplibs="$add_dir $deplibs" test -n "$add" && deplibs="$add $deplibs" if test "$hardcode_direct" != yes && test "$hardcode_minus_L" != yes && test "$hardcode_shlibpath_var" = yes; then case :$finalize_shlibpath: in *":$libdir:"*) ;; *) func_append finalize_shlibpath "$libdir:" ;; esac fi fi fi if test "$linkmode" = prog || test "$opt_mode" = relink; then add_shlibpath= add_dir= add= # Finalize command for both is simple: just hardcode it. if test "$hardcode_direct" = yes && test "$hardcode_direct_absolute" = no; then add="$libdir/$linklib" elif test "$hardcode_minus_L" = yes; then add_dir="-L$libdir" add="-l$name" elif test "$hardcode_shlibpath_var" = yes; then case :$finalize_shlibpath: in *":$libdir:"*) ;; *) func_append finalize_shlibpath "$libdir:" ;; esac add="-l$name" elif test "$hardcode_automatic" = yes; then if test -n "$inst_prefix_dir" && test -f "$inst_prefix_dir$libdir/$linklib" ; then add="$inst_prefix_dir$libdir/$linklib" else add="$libdir/$linklib" fi else # We cannot seem to hardcode it, guess we'll fake it. add_dir="-L$libdir" # Try looking first in the location we're being installed to. if test -n "$inst_prefix_dir"; then case $libdir in [\\/]*) func_append add_dir " -L$inst_prefix_dir$libdir" ;; esac fi add="-l$name" fi if test "$linkmode" = prog; then test -n "$add_dir" && finalize_deplibs="$add_dir $finalize_deplibs" test -n "$add" && finalize_deplibs="$add $finalize_deplibs" else test -n "$add_dir" && deplibs="$add_dir $deplibs" test -n "$add" && deplibs="$add $deplibs" fi fi elif test "$linkmode" = prog; then # Here we assume that one of hardcode_direct or hardcode_minus_L # is not unsupported. This is valid on all known static and # shared platforms. if test "$hardcode_direct" != unsupported; then test -n "$old_library" && linklib="$old_library" compile_deplibs="$dir/$linklib $compile_deplibs" finalize_deplibs="$dir/$linklib $finalize_deplibs" else compile_deplibs="-l$name -L$dir $compile_deplibs" finalize_deplibs="-l$name -L$dir $finalize_deplibs" fi elif test "$build_libtool_libs" = yes; then # Not a shared library if test "$deplibs_check_method" != pass_all; then # We're trying link a shared library against a static one # but the system doesn't support it. # Just print a warning and add the library to dependency_libs so # that the program can be linked against the static library. echo $ECHO "*** Warning: This system can not link to static lib archive $lib." echo "*** I have the capability to make that library automatically link in when" echo "*** you link to this library. But I can only do this if you have a" echo "*** shared version of the library, which you do not appear to have." if test "$module" = yes; then echo "*** But as you try to build a module library, libtool will still create " echo "*** a static module, that should work as long as the dlopening application" echo "*** is linked with the -dlopen flag to resolve symbols at runtime." if test -z "$global_symbol_pipe"; then echo echo "*** However, this would only work if libtool was able to extract symbol" echo "*** lists from a program, using \`nm' or equivalent, but libtool could" echo "*** not find such a program. So, this module is probably useless." echo "*** \`nm' from GNU binutils and a full rebuild may help." fi if test "$build_old_libs" = no; then build_libtool_libs=module build_old_libs=yes else build_libtool_libs=no fi fi else deplibs="$dir/$old_library $deplibs" link_static=yes fi fi # link shared/static library? if test "$linkmode" = lib; then if test -n "$dependency_libs" && { test "$hardcode_into_libs" != yes || test "$build_old_libs" = yes || test "$link_static" = yes; }; then # Extract -R from dependency_libs temp_deplibs= for libdir in $dependency_libs; do case $libdir in -R*) func_stripname '-R' '' "$libdir" temp_xrpath=$func_stripname_result case " $xrpath " in *" $temp_xrpath "*) ;; *) func_append xrpath " $temp_xrpath";; esac;; *) func_append temp_deplibs " $libdir";; esac done dependency_libs="$temp_deplibs" fi func_append newlib_search_path " $absdir" # Link against this library test "$link_static" = no && newdependency_libs="$abs_ladir/$laname $newdependency_libs" # ... and its dependency_libs tmp_libs= for deplib in $dependency_libs; do newdependency_libs="$deplib $newdependency_libs" case $deplib in -L*) func_stripname '-L' '' "$deplib" func_resolve_sysroot "$func_stripname_result";; *) func_resolve_sysroot "$deplib" ;; esac if $opt_preserve_dup_deps ; then case "$tmp_libs " in *" $func_resolve_sysroot_result "*) func_append specialdeplibs " $func_resolve_sysroot_result" ;; esac fi func_append tmp_libs " $func_resolve_sysroot_result" done if test "$link_all_deplibs" != no; then # Add the search paths of all dependency libraries for deplib in $dependency_libs; do path= case $deplib in -L*) path="$deplib" ;; *.la) func_resolve_sysroot "$deplib" deplib=$func_resolve_sysroot_result func_dirname "$deplib" "" "." dir=$func_dirname_result # We need an absolute path. case $dir in [\\/]* | [A-Za-z]:[\\/]*) absdir="$dir" ;; *) absdir=`cd "$dir" && pwd` if test -z "$absdir"; then func_warning "cannot determine absolute directory name of \`$dir'" absdir="$dir" fi ;; esac if $GREP "^installed=no" $deplib > /dev/null; then case $host in *-*-darwin*) depdepl= eval deplibrary_names=`${SED} -n -e 's/^library_names=\(.*\)$/\1/p' $deplib` if test -n "$deplibrary_names" ; then for tmp in $deplibrary_names ; do depdepl=$tmp done if test -f "$absdir/$objdir/$depdepl" ; then depdepl="$absdir/$objdir/$depdepl" darwin_install_name=`${OTOOL} -L $depdepl | awk '{if (NR == 2) {print $1;exit}}'` if test -z "$darwin_install_name"; then darwin_install_name=`${OTOOL64} -L $depdepl | awk '{if (NR == 2) {print $1;exit}}'` fi func_append compiler_flags " ${wl}-dylib_file ${wl}${darwin_install_name}:${depdepl}" func_append linker_flags " -dylib_file ${darwin_install_name}:${depdepl}" path= fi fi ;; *) path="-L$absdir/$objdir" ;; esac else eval libdir=`${SED} -n -e 's/^libdir=\(.*\)$/\1/p' $deplib` test -z "$libdir" && \ func_fatal_error "\`$deplib' is not a valid libtool archive" test "$absdir" != "$libdir" && \ func_warning "\`$deplib' seems to be moved" path="-L$absdir" fi ;; esac case " $deplibs " in *" $path "*) ;; *) deplibs="$path $deplibs" ;; esac done fi # link_all_deplibs != no fi # linkmode = lib done # for deplib in $libs if test "$pass" = link; then if test "$linkmode" = "prog"; then compile_deplibs="$new_inherited_linker_flags $compile_deplibs" finalize_deplibs="$new_inherited_linker_flags $finalize_deplibs" else compiler_flags="$compiler_flags "`$ECHO " $new_inherited_linker_flags" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` fi fi dependency_libs="$newdependency_libs" if test "$pass" = dlpreopen; then # Link the dlpreopened libraries before other libraries for deplib in $save_deplibs; do deplibs="$deplib $deplibs" done fi if test "$pass" != dlopen; then if test "$pass" != conv; then # Make sure lib_search_path contains only unique directories. lib_search_path= for dir in $newlib_search_path; do case "$lib_search_path " in *" $dir "*) ;; *) func_append lib_search_path " $dir" ;; esac done newlib_search_path= fi if test "$linkmode,$pass" != "prog,link"; then vars="deplibs" else vars="compile_deplibs finalize_deplibs" fi for var in $vars dependency_libs; do # Add libraries to $var in reverse order eval tmp_libs=\"\$$var\" new_libs= for deplib in $tmp_libs; do # FIXME: Pedantically, this is the right thing to do, so # that some nasty dependency loop isn't accidentally # broken: #new_libs="$deplib $new_libs" # Pragmatically, this seems to cause very few problems in # practice: case $deplib in -L*) new_libs="$deplib $new_libs" ;; -R*) ;; *) # And here is the reason: when a library appears more # than once as an explicit dependence of a library, or # is implicitly linked in more than once by the # compiler, it is considered special, and multiple # occurrences thereof are not removed. Compare this # with having the same library being listed as a # dependency of multiple other libraries: in this case, # we know (pedantically, we assume) the library does not # need to be listed more than once, so we keep only the # last copy. This is not always right, but it is rare # enough that we require users that really mean to play # such unportable linking tricks to link the library # using -Wl,-lname, so that libtool does not consider it # for duplicate removal. case " $specialdeplibs " in *" $deplib "*) new_libs="$deplib $new_libs" ;; *) case " $new_libs " in *" $deplib "*) ;; *) new_libs="$deplib $new_libs" ;; esac ;; esac ;; esac done tmp_libs= for deplib in $new_libs; do case $deplib in -L*) case " $tmp_libs " in *" $deplib "*) ;; *) func_append tmp_libs " $deplib" ;; esac ;; *) func_append tmp_libs " $deplib" ;; esac done eval $var=\"$tmp_libs\" done # for var fi # Last step: remove runtime libs from dependency_libs # (they stay in deplibs) tmp_libs= for i in $dependency_libs ; do case " $predeps $postdeps $compiler_lib_search_path " in *" $i "*) i="" ;; esac if test -n "$i" ; then func_append tmp_libs " $i" fi done dependency_libs=$tmp_libs done # for pass if test "$linkmode" = prog; then dlfiles="$newdlfiles" fi if test "$linkmode" = prog || test "$linkmode" = lib; then dlprefiles="$newdlprefiles" fi case $linkmode in oldlib) if test -n "$dlfiles$dlprefiles" || test "$dlself" != no; then func_warning "\`-dlopen' is ignored for archives" fi case " $deplibs" in *\ -l* | *\ -L*) func_warning "\`-l' and \`-L' are ignored for archives" ;; esac test -n "$rpath" && \ func_warning "\`-rpath' is ignored for archives" test -n "$xrpath" && \ func_warning "\`-R' is ignored for archives" test -n "$vinfo" && \ func_warning "\`-version-info/-version-number' is ignored for archives" test -n "$release" && \ func_warning "\`-release' is ignored for archives" test -n "$export_symbols$export_symbols_regex" && \ func_warning "\`-export-symbols' is ignored for archives" # Now set the variables for building old libraries. build_libtool_libs=no oldlibs="$output" func_append objs "$old_deplibs" ;; lib) # Make sure we only generate libraries of the form `libNAME.la'. case $outputname in lib*) func_stripname 'lib' '.la' "$outputname" name=$func_stripname_result eval shared_ext=\"$shrext_cmds\" eval libname=\"$libname_spec\" ;; *) test "$module" = no && \ func_fatal_help "libtool library \`$output' must begin with \`lib'" if test "$need_lib_prefix" != no; then # Add the "lib" prefix for modules if required func_stripname '' '.la' "$outputname" name=$func_stripname_result eval shared_ext=\"$shrext_cmds\" eval libname=\"$libname_spec\" else func_stripname '' '.la' "$outputname" libname=$func_stripname_result fi ;; esac if test -n "$objs"; then if test "$deplibs_check_method" != pass_all; then func_fatal_error "cannot build libtool library \`$output' from non-libtool objects on this host:$objs" else echo $ECHO "*** Warning: Linking the shared library $output against the non-libtool" $ECHO "*** objects $objs is not portable!" func_append libobjs " $objs" fi fi test "$dlself" != no && \ func_warning "\`-dlopen self' is ignored for libtool libraries" set dummy $rpath shift test "$#" -gt 1 && \ func_warning "ignoring multiple \`-rpath's for a libtool library" install_libdir="$1" oldlibs= if test -z "$rpath"; then if test "$build_libtool_libs" = yes; then # Building a libtool convenience library. # Some compilers have problems with a `.al' extension so # convenience libraries should have the same extension an # archive normally would. oldlibs="$output_objdir/$libname.$libext $oldlibs" build_libtool_libs=convenience build_old_libs=yes fi test -n "$vinfo" && \ func_warning "\`-version-info/-version-number' is ignored for convenience libraries" test -n "$release" && \ func_warning "\`-release' is ignored for convenience libraries" else # Parse the version information argument. save_ifs="$IFS"; IFS=':' set dummy $vinfo 0 0 0 shift IFS="$save_ifs" test -n "$7" && \ func_fatal_help "too many parameters to \`-version-info'" # convert absolute version numbers to libtool ages # this retains compatibility with .la files and attempts # to make the code below a bit more comprehensible case $vinfo_number in yes) number_major="$1" number_minor="$2" number_revision="$3" # # There are really only two kinds -- those that # use the current revision as the major version # and those that subtract age and use age as # a minor version. But, then there is irix # which has an extra 1 added just for fun # case $version_type in # correct linux to gnu/linux during the next big refactor darwin|linux|osf|windows|none) func_arith $number_major + $number_minor current=$func_arith_result age="$number_minor" revision="$number_revision" ;; freebsd-aout|freebsd-elf|qnx|sunos) current="$number_major" revision="$number_minor" age="0" ;; irix|nonstopux) func_arith $number_major + $number_minor current=$func_arith_result age="$number_minor" revision="$number_minor" lt_irix_increment=no ;; esac ;; no) current="$1" revision="$2" age="$3" ;; esac # Check that each of the things are valid numbers. case $current in 0|[1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9]|[1-9][0-9][0-9][0-9][0-9]) ;; *) func_error "CURRENT \`$current' must be a nonnegative integer" func_fatal_error "\`$vinfo' is not valid version information" ;; esac case $revision in 0|[1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9]|[1-9][0-9][0-9][0-9][0-9]) ;; *) func_error "REVISION \`$revision' must be a nonnegative integer" func_fatal_error "\`$vinfo' is not valid version information" ;; esac case $age in 0|[1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9]|[1-9][0-9][0-9][0-9][0-9]) ;; *) func_error "AGE \`$age' must be a nonnegative integer" func_fatal_error "\`$vinfo' is not valid version information" ;; esac if test "$age" -gt "$current"; then func_error "AGE \`$age' is greater than the current interface number \`$current'" func_fatal_error "\`$vinfo' is not valid version information" fi # Calculate the version variables. major= versuffix= verstring= case $version_type in none) ;; darwin) # Like Linux, but with the current version available in # verstring for coding it into the library header func_arith $current - $age major=.$func_arith_result versuffix="$major.$age.$revision" # Darwin ld doesn't like 0 for these options... func_arith $current + 1 minor_current=$func_arith_result xlcverstring="${wl}-compatibility_version ${wl}$minor_current ${wl}-current_version ${wl}$minor_current.$revision" verstring="-compatibility_version $minor_current -current_version $minor_current.$revision" ;; freebsd-aout) major=".$current" versuffix=".$current.$revision"; ;; freebsd-elf) major=".$current" versuffix=".$current" ;; irix | nonstopux) if test "X$lt_irix_increment" = "Xno"; then func_arith $current - $age else func_arith $current - $age + 1 fi major=$func_arith_result case $version_type in nonstopux) verstring_prefix=nonstopux ;; *) verstring_prefix=sgi ;; esac verstring="$verstring_prefix$major.$revision" # Add in all the interfaces that we are compatible with. loop=$revision while test "$loop" -ne 0; do func_arith $revision - $loop iface=$func_arith_result func_arith $loop - 1 loop=$func_arith_result verstring="$verstring_prefix$major.$iface:$verstring" done # Before this point, $major must not contain `.'. major=.$major versuffix="$major.$revision" ;; linux) # correct to gnu/linux during the next big refactor func_arith $current - $age major=.$func_arith_result versuffix="$major.$age.$revision" ;; osf) func_arith $current - $age major=.$func_arith_result versuffix=".$current.$age.$revision" verstring="$current.$age.$revision" # Add in all the interfaces that we are compatible with. loop=$age while test "$loop" -ne 0; do func_arith $current - $loop iface=$func_arith_result func_arith $loop - 1 loop=$func_arith_result verstring="$verstring:${iface}.0" done # Make executables depend on our current version. func_append verstring ":${current}.0" ;; qnx) major=".$current" versuffix=".$current" ;; sunos) major=".$current" versuffix=".$current.$revision" ;; windows) # Use '-' rather than '.', since we only want one # extension on DOS 8.3 filesystems. func_arith $current - $age major=$func_arith_result versuffix="-$major" ;; *) func_fatal_configuration "unknown library version type \`$version_type'" ;; esac # Clear the version info if we defaulted, and they specified a release. if test -z "$vinfo" && test -n "$release"; then major= case $version_type in darwin) # we can't check for "0.0" in archive_cmds due to quoting # problems, so we reset it completely verstring= ;; *) verstring="0.0" ;; esac if test "$need_version" = no; then versuffix= else versuffix=".0.0" fi fi # Remove version info from name if versioning should be avoided if test "$avoid_version" = yes && test "$need_version" = no; then major= versuffix= verstring="" fi # Check to see if the archive will have undefined symbols. if test "$allow_undefined" = yes; then if test "$allow_undefined_flag" = unsupported; then func_warning "undefined symbols not allowed in $host shared libraries" build_libtool_libs=no build_old_libs=yes fi else # Don't allow undefined symbols. allow_undefined_flag="$no_undefined_flag" fi fi func_generate_dlsyms "$libname" "$libname" "yes" func_append libobjs " $symfileobj" test "X$libobjs" = "X " && libobjs= if test "$opt_mode" != relink; then # Remove our outputs, but don't remove object files since they # may have been created when compiling PIC objects. removelist= tempremovelist=`$ECHO "$output_objdir/*"` for p in $tempremovelist; do case $p in *.$objext | *.gcno) ;; $output_objdir/$outputname | $output_objdir/$libname.* | $output_objdir/${libname}${release}.*) if test "X$precious_files_regex" != "X"; then if $ECHO "$p" | $EGREP -e "$precious_files_regex" >/dev/null 2>&1 then continue fi fi func_append removelist " $p" ;; *) ;; esac done test -n "$removelist" && \ func_show_eval "${RM}r \$removelist" fi # Now set the variables for building old libraries. if test "$build_old_libs" = yes && test "$build_libtool_libs" != convenience ; then func_append oldlibs " $output_objdir/$libname.$libext" # Transform .lo files to .o files. oldobjs="$objs "`$ECHO "$libobjs" | $SP2NL | $SED "/\.${libext}$/d; $lo2o" | $NL2SP` fi # Eliminate all temporary directories. #for path in $notinst_path; do # lib_search_path=`$ECHO "$lib_search_path " | $SED "s% $path % %g"` # deplibs=`$ECHO "$deplibs " | $SED "s% -L$path % %g"` # dependency_libs=`$ECHO "$dependency_libs " | $SED "s% -L$path % %g"` #done if test -n "$xrpath"; then # If the user specified any rpath flags, then add them. temp_xrpath= for libdir in $xrpath; do func_replace_sysroot "$libdir" func_append temp_xrpath " -R$func_replace_sysroot_result" case "$finalize_rpath " in *" $libdir "*) ;; *) func_append finalize_rpath " $libdir" ;; esac done if test "$hardcode_into_libs" != yes || test "$build_old_libs" = yes; then dependency_libs="$temp_xrpath $dependency_libs" fi fi # Make sure dlfiles contains only unique files that won't be dlpreopened old_dlfiles="$dlfiles" dlfiles= for lib in $old_dlfiles; do case " $dlprefiles $dlfiles " in *" $lib "*) ;; *) func_append dlfiles " $lib" ;; esac done # Make sure dlprefiles contains only unique files old_dlprefiles="$dlprefiles" dlprefiles= for lib in $old_dlprefiles; do case "$dlprefiles " in *" $lib "*) ;; *) func_append dlprefiles " $lib" ;; esac done if test "$build_libtool_libs" = yes; then if test -n "$rpath"; then case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-os2* | *-*-beos* | *-cegcc* | *-*-haiku*) # these systems don't actually have a c library (as such)! ;; *-*-rhapsody* | *-*-darwin1.[012]) # Rhapsody C library is in the System framework func_append deplibs " System.ltframework" ;; *-*-netbsd*) # Don't link with libc until the a.out ld.so is fixed. ;; *-*-openbsd* | *-*-freebsd* | *-*-dragonfly*) # Do not include libc due to us having libc/libc_r. ;; *-*-sco3.2v5* | *-*-sco5v6*) # Causes problems with __ctype ;; *-*-sysv4.2uw2* | *-*-sysv5* | *-*-unixware* | *-*-OpenUNIX*) # Compiler inserts libc in the correct place for threads to work ;; *) # Add libc to deplibs on all other systems if necessary. if test "$build_libtool_need_lc" = "yes"; then func_append deplibs " -lc" fi ;; esac fi # Transform deplibs into only deplibs that can be linked in shared. name_save=$name libname_save=$libname release_save=$release versuffix_save=$versuffix major_save=$major # I'm not sure if I'm treating the release correctly. I think # release should show up in the -l (ie -lgmp5) so we don't want to # add it in twice. Is that correct? release="" versuffix="" major="" newdeplibs= droppeddeps=no case $deplibs_check_method in pass_all) # Don't check for shared/static. Everything works. # This might be a little naive. We might want to check # whether the library exists or not. But this is on # osf3 & osf4 and I'm not really sure... Just # implementing what was already the behavior. newdeplibs=$deplibs ;; test_compile) # This code stresses the "libraries are programs" paradigm to its # limits. Maybe even breaks it. We compile a program, linking it # against the deplibs as a proxy for the library. Then we can check # whether they linked in statically or dynamically with ldd. $opt_dry_run || $RM conftest.c cat > conftest.c </dev/null` $nocaseglob else potential_libs=`ls $i/$libnameglob[.-]* 2>/dev/null` fi for potent_lib in $potential_libs; do # Follow soft links. if ls -lLd "$potent_lib" 2>/dev/null | $GREP " -> " >/dev/null; then continue fi # The statement above tries to avoid entering an # endless loop below, in case of cyclic links. # We might still enter an endless loop, since a link # loop can be closed while we follow links, # but so what? potlib="$potent_lib" while test -h "$potlib" 2>/dev/null; do potliblink=`ls -ld $potlib | ${SED} 's/.* -> //'` case $potliblink in [\\/]* | [A-Za-z]:[\\/]*) potlib="$potliblink";; *) potlib=`$ECHO "$potlib" | $SED 's,[^/]*$,,'`"$potliblink";; esac done if eval $file_magic_cmd \"\$potlib\" 2>/dev/null | $SED -e 10q | $EGREP "$file_magic_regex" > /dev/null; then func_append newdeplibs " $a_deplib" a_deplib="" break 2 fi done done fi if test -n "$a_deplib" ; then droppeddeps=yes echo $ECHO "*** Warning: linker path does not have real file for library $a_deplib." echo "*** I have the capability to make that library automatically link in when" echo "*** you link to this library. But I can only do this if you have a" echo "*** shared version of the library, which you do not appear to have" echo "*** because I did check the linker path looking for a file starting" if test -z "$potlib" ; then $ECHO "*** with $libname but no candidates were found. (...for file magic test)" else $ECHO "*** with $libname and none of the candidates passed a file format test" $ECHO "*** using a file magic. 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But I can only do this if you have a" echo "*** shared version of the library, which you do not appear to have" echo "*** because I did check the linker path looking for a file starting" if test -z "$potlib" ; then $ECHO "*** with $libname but no candidates were found. (...for regex pattern test)" else $ECHO "*** with $libname and none of the candidates passed a file format test" $ECHO "*** using a regex pattern. Last file checked: $potlib" fi fi ;; *) # Add a -L argument. func_append newdeplibs " $a_deplib" ;; esac done # Gone through all deplibs. ;; none | unknown | *) newdeplibs="" tmp_deplibs=`$ECHO " $deplibs" | $SED 's/ -lc$//; s/ -[LR][^ ]*//g'` if test "X$allow_libtool_libs_with_static_runtimes" = "Xyes" ; then for i in $predeps $postdeps ; do # can't use Xsed below, because $i might contain '/' tmp_deplibs=`$ECHO " $tmp_deplibs" | $SED "s,$i,,"` done fi case $tmp_deplibs in *[!\ \ ]*) echo if test "X$deplibs_check_method" = "Xnone"; then echo "*** Warning: inter-library dependencies are not supported in this platform." else echo "*** Warning: inter-library dependencies are not known to be supported." fi echo "*** All declared inter-library dependencies are being dropped." droppeddeps=yes ;; esac ;; esac versuffix=$versuffix_save major=$major_save release=$release_save libname=$libname_save name=$name_save case $host in *-*-rhapsody* | *-*-darwin1.[012]) # On Rhapsody replace the C library with the System framework newdeplibs=`$ECHO " $newdeplibs" | $SED 's/ -lc / System.ltframework /'` ;; esac if test "$droppeddeps" = yes; then if test "$module" = yes; then echo echo "*** Warning: libtool could not satisfy all declared inter-library" $ECHO "*** dependencies of module $libname. Therefore, libtool will create" echo "*** a static module, that should work as long as the dlopening" echo "*** application is linked with the -dlopen flag." if test -z "$global_symbol_pipe"; then echo echo "*** However, this would only work if libtool was able to extract symbol" echo "*** lists from a program, using \`nm' or equivalent, but libtool could" echo "*** not find such a program. So, this module is probably useless." echo "*** \`nm' from GNU binutils and a full rebuild may help." fi if test "$build_old_libs" = no; then oldlibs="$output_objdir/$libname.$libext" build_libtool_libs=module build_old_libs=yes else build_libtool_libs=no fi else echo "*** The inter-library dependencies that have been dropped here will be" echo "*** automatically added whenever a program is linked with this library" echo "*** or is declared to -dlopen it." if test "$allow_undefined" = no; then echo echo "*** Since this library must not contain undefined symbols," echo "*** because either the platform does not support them or" echo "*** it was explicitly requested with -no-undefined," echo "*** libtool will only create a static version of it." if test "$build_old_libs" = no; then oldlibs="$output_objdir/$libname.$libext" build_libtool_libs=module build_old_libs=yes else build_libtool_libs=no fi fi fi fi # Done checking deplibs! deplibs=$newdeplibs fi # Time to change all our "foo.ltframework" stuff back to "-framework foo" case $host in *-*-darwin*) newdeplibs=`$ECHO " $newdeplibs" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` new_inherited_linker_flags=`$ECHO " $new_inherited_linker_flags" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` deplibs=`$ECHO " $deplibs" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` ;; esac # move library search paths that coincide with paths to not yet # installed libraries to the beginning of the library search list new_libs= for path in $notinst_path; do case " $new_libs " in *" -L$path/$objdir "*) ;; *) case " $deplibs " in *" -L$path/$objdir "*) func_append new_libs " -L$path/$objdir" ;; esac ;; esac done for deplib in $deplibs; do case $deplib in -L*) case " $new_libs " in *" $deplib "*) ;; *) func_append new_libs " $deplib" ;; esac ;; *) func_append new_libs " $deplib" ;; esac done deplibs="$new_libs" # All the library-specific variables (install_libdir is set above). library_names= old_library= dlname= # Test again, we may have decided not to build it any more if test "$build_libtool_libs" = yes; then # Remove ${wl} instances when linking with ld. # FIXME: should test the right _cmds variable. case $archive_cmds in *\$LD\ *) wl= ;; esac if test "$hardcode_into_libs" = yes; then # Hardcode the library paths hardcode_libdirs= dep_rpath= rpath="$finalize_rpath" test "$opt_mode" != relink && rpath="$compile_rpath$rpath" for libdir in $rpath; do if test -n "$hardcode_libdir_flag_spec"; then if test -n "$hardcode_libdir_separator"; then func_replace_sysroot "$libdir" libdir=$func_replace_sysroot_result if test -z "$hardcode_libdirs"; then hardcode_libdirs="$libdir" else # Just accumulate the unique libdirs. case $hardcode_libdir_separator$hardcode_libdirs$hardcode_libdir_separator in *"$hardcode_libdir_separator$libdir$hardcode_libdir_separator"*) ;; *) func_append hardcode_libdirs "$hardcode_libdir_separator$libdir" ;; esac fi else eval flag=\"$hardcode_libdir_flag_spec\" func_append dep_rpath " $flag" fi elif test -n "$runpath_var"; then case "$perm_rpath " in *" $libdir "*) ;; *) func_append perm_rpath " $libdir" ;; esac fi done # Substitute the hardcoded libdirs into the rpath. if test -n "$hardcode_libdir_separator" && test -n "$hardcode_libdirs"; then libdir="$hardcode_libdirs" eval "dep_rpath=\"$hardcode_libdir_flag_spec\"" fi if test -n "$runpath_var" && test -n "$perm_rpath"; then # We should set the runpath_var. rpath= for dir in $perm_rpath; do func_append rpath "$dir:" done eval "$runpath_var='$rpath\$$runpath_var'; export $runpath_var" fi test -n "$dep_rpath" && deplibs="$dep_rpath $deplibs" fi shlibpath="$finalize_shlibpath" test "$opt_mode" != relink && shlibpath="$compile_shlibpath$shlibpath" if test -n "$shlibpath"; then eval "$shlibpath_var='$shlibpath\$$shlibpath_var'; export $shlibpath_var" fi # Get the real and link names of the library. eval shared_ext=\"$shrext_cmds\" eval library_names=\"$library_names_spec\" set dummy $library_names shift realname="$1" shift if test -n "$soname_spec"; then eval soname=\"$soname_spec\" else soname="$realname" fi if test -z "$dlname"; then dlname=$soname fi lib="$output_objdir/$realname" linknames= for link do func_append linknames " $link" done # Use standard objects if they are pic test -z "$pic_flag" && libobjs=`$ECHO "$libobjs" | $SP2NL | $SED "$lo2o" | $NL2SP` test "X$libobjs" = "X " && libobjs= delfiles= if test -n "$export_symbols" && test -n "$include_expsyms"; then $opt_dry_run || cp "$export_symbols" "$output_objdir/$libname.uexp" export_symbols="$output_objdir/$libname.uexp" func_append delfiles " $export_symbols" fi orig_export_symbols= case $host_os in cygwin* | mingw* | cegcc*) if test -n "$export_symbols" && test -z "$export_symbols_regex"; then # exporting using user supplied symfile if test "x`$SED 1q $export_symbols`" != xEXPORTS; then # and it's NOT already a .def file. Must figure out # which of the given symbols are data symbols and tag # them as such. So, trigger use of export_symbols_cmds. # export_symbols gets reassigned inside the "prepare # the list of exported symbols" if statement, so the # include_expsyms logic still works. orig_export_symbols="$export_symbols" export_symbols= always_export_symbols=yes fi fi ;; esac # Prepare the list of exported symbols if test -z "$export_symbols"; then if test "$always_export_symbols" = yes || test -n "$export_symbols_regex"; then func_verbose "generating symbol list for \`$libname.la'" export_symbols="$output_objdir/$libname.exp" $opt_dry_run || $RM $export_symbols cmds=$export_symbols_cmds save_ifs="$IFS"; IFS='~' for cmd1 in $cmds; do IFS="$save_ifs" # Take the normal branch if the nm_file_list_spec branch # doesn't work or if tool conversion is not needed. case $nm_file_list_spec~$to_tool_file_cmd in *~func_convert_file_noop | *~func_convert_file_msys_to_w32 | ~*) try_normal_branch=yes eval cmd=\"$cmd1\" func_len " $cmd" len=$func_len_result ;; *) try_normal_branch=no ;; esac if test "$try_normal_branch" = yes \ && { test "$len" -lt "$max_cmd_len" \ || test "$max_cmd_len" -le -1; } then func_show_eval "$cmd" 'exit $?' skipped_export=false elif test -n "$nm_file_list_spec"; then func_basename "$output" output_la=$func_basename_result save_libobjs=$libobjs save_output=$output output=${output_objdir}/${output_la}.nm func_to_tool_file "$output" libobjs=$nm_file_list_spec$func_to_tool_file_result func_append delfiles " $output" func_verbose "creating $NM input file list: $output" for obj in $save_libobjs; do func_to_tool_file "$obj" $ECHO "$func_to_tool_file_result" done > "$output" eval cmd=\"$cmd1\" func_show_eval "$cmd" 'exit $?' output=$save_output libobjs=$save_libobjs skipped_export=false else # The command line is too long to execute in one step. func_verbose "using reloadable object file for export list..." skipped_export=: # Break out early, otherwise skipped_export may be # set to false by a later but shorter cmd. break fi done IFS="$save_ifs" if test -n "$export_symbols_regex" && test "X$skipped_export" != "X:"; then func_show_eval '$EGREP -e "$export_symbols_regex" "$export_symbols" > "${export_symbols}T"' func_show_eval '$MV "${export_symbols}T" "$export_symbols"' fi fi fi if test -n "$export_symbols" && test -n "$include_expsyms"; then tmp_export_symbols="$export_symbols" test -n "$orig_export_symbols" && tmp_export_symbols="$orig_export_symbols" $opt_dry_run || eval '$ECHO "$include_expsyms" | $SP2NL >> "$tmp_export_symbols"' fi if test "X$skipped_export" != "X:" && test -n "$orig_export_symbols"; then # The given exports_symbols file has to be filtered, so filter it. func_verbose "filter symbol list for \`$libname.la' to tag DATA exports" # FIXME: $output_objdir/$libname.filter potentially contains lots of # 's' commands which not all seds can handle. GNU sed should be fine # though. Also, the filter scales superlinearly with the number of # global variables. join(1) would be nice here, but unfortunately # isn't a blessed tool. $opt_dry_run || $SED -e '/[ ,]DATA/!d;s,\(.*\)\([ \,].*\),s|^\1$|\1\2|,' < $export_symbols > $output_objdir/$libname.filter func_append delfiles " $export_symbols $output_objdir/$libname.filter" export_symbols=$output_objdir/$libname.def $opt_dry_run || $SED -f $output_objdir/$libname.filter < $orig_export_symbols > $export_symbols fi tmp_deplibs= for test_deplib in $deplibs; do case " $convenience " in *" $test_deplib "*) ;; *) func_append tmp_deplibs " $test_deplib" ;; esac done deplibs="$tmp_deplibs" if test -n "$convenience"; then if test -n "$whole_archive_flag_spec" && test "$compiler_needs_object" = yes && test -z "$libobjs"; then # extract the archives, so we have objects to list. # TODO: could optimize this to just extract one archive. whole_archive_flag_spec= fi if test -n "$whole_archive_flag_spec"; then save_libobjs=$libobjs eval libobjs=\"\$libobjs $whole_archive_flag_spec\" test "X$libobjs" = "X " && libobjs= else gentop="$output_objdir/${outputname}x" func_append generated " $gentop" func_extract_archives $gentop $convenience func_append libobjs " $func_extract_archives_result" test "X$libobjs" = "X " && libobjs= fi fi if test "$thread_safe" = yes && test -n "$thread_safe_flag_spec"; then eval flag=\"$thread_safe_flag_spec\" func_append linker_flags " $flag" fi # Make a backup of the uninstalled library when relinking if test "$opt_mode" = relink; then $opt_dry_run || eval '(cd $output_objdir && $RM ${realname}U && $MV $realname ${realname}U)' || exit $? fi # Do each of the archive commands. if test "$module" = yes && test -n "$module_cmds" ; then if test -n "$export_symbols" && test -n "$module_expsym_cmds"; then eval test_cmds=\"$module_expsym_cmds\" cmds=$module_expsym_cmds else eval test_cmds=\"$module_cmds\" cmds=$module_cmds fi else if test -n "$export_symbols" && test -n "$archive_expsym_cmds"; then eval test_cmds=\"$archive_expsym_cmds\" cmds=$archive_expsym_cmds else eval test_cmds=\"$archive_cmds\" cmds=$archive_cmds fi fi if test "X$skipped_export" != "X:" && func_len " $test_cmds" && len=$func_len_result && test "$len" -lt "$max_cmd_len" || test "$max_cmd_len" -le -1; then : else # The command line is too long to link in one step, link piecewise # or, if using GNU ld and skipped_export is not :, use a linker # script. # Save the value of $output and $libobjs because we want to # use them later. If we have whole_archive_flag_spec, we # want to use save_libobjs as it was before # whole_archive_flag_spec was expanded, because we can't # assume the linker understands whole_archive_flag_spec. # This may have to be revisited, in case too many # convenience libraries get linked in and end up exceeding # the spec. if test -z "$convenience" || test -z "$whole_archive_flag_spec"; then save_libobjs=$libobjs fi save_output=$output func_basename "$output" output_la=$func_basename_result # Clear the reloadable object creation command queue and # initialize k to one. test_cmds= concat_cmds= objlist= last_robj= k=1 if test -n "$save_libobjs" && test "X$skipped_export" != "X:" && test "$with_gnu_ld" = yes; then output=${output_objdir}/${output_la}.lnkscript func_verbose "creating GNU ld script: $output" echo 'INPUT (' > $output for obj in $save_libobjs do func_to_tool_file "$obj" $ECHO "$func_to_tool_file_result" >> $output done echo ')' >> $output func_append delfiles " $output" func_to_tool_file "$output" output=$func_to_tool_file_result elif test -n "$save_libobjs" && test "X$skipped_export" != "X:" && test "X$file_list_spec" != X; then output=${output_objdir}/${output_la}.lnk func_verbose "creating linker input file list: $output" : > $output set x $save_libobjs shift firstobj= if test "$compiler_needs_object" = yes; then firstobj="$1 " shift fi for obj do func_to_tool_file "$obj" $ECHO "$func_to_tool_file_result" >> $output done func_append delfiles " $output" func_to_tool_file "$output" output=$firstobj\"$file_list_spec$func_to_tool_file_result\" else if test -n "$save_libobjs"; then func_verbose "creating reloadable object files..." output=$output_objdir/$output_la-${k}.$objext eval test_cmds=\"$reload_cmds\" func_len " $test_cmds" len0=$func_len_result len=$len0 # Loop over the list of objects to be linked. for obj in $save_libobjs do func_len " $obj" func_arith $len + $func_len_result len=$func_arith_result if test "X$objlist" = X || test "$len" -lt "$max_cmd_len"; then func_append objlist " $obj" else # The command $test_cmds is almost too long, add a # command to the queue. if test "$k" -eq 1 ; then # The first file doesn't have a previous command to add. reload_objs=$objlist eval concat_cmds=\"$reload_cmds\" else # All subsequent reloadable object files will link in # the last one created. reload_objs="$objlist $last_robj" eval concat_cmds=\"\$concat_cmds~$reload_cmds~\$RM $last_robj\" fi last_robj=$output_objdir/$output_la-${k}.$objext func_arith $k + 1 k=$func_arith_result output=$output_objdir/$output_la-${k}.$objext objlist=" $obj" func_len " $last_robj" func_arith $len0 + $func_len_result len=$func_arith_result fi done # Handle the remaining objects by creating one last # reloadable object file. All subsequent reloadable object # files will link in the last one created. test -z "$concat_cmds" || concat_cmds=$concat_cmds~ reload_objs="$objlist $last_robj" eval concat_cmds=\"\${concat_cmds}$reload_cmds\" if test -n "$last_robj"; then eval concat_cmds=\"\${concat_cmds}~\$RM $last_robj\" fi func_append delfiles " $output" else output= fi if ${skipped_export-false}; then func_verbose "generating symbol list for \`$libname.la'" export_symbols="$output_objdir/$libname.exp" $opt_dry_run || $RM $export_symbols libobjs=$output # Append the command to create the export file. test -z "$concat_cmds" || concat_cmds=$concat_cmds~ eval concat_cmds=\"\$concat_cmds$export_symbols_cmds\" if test -n "$last_robj"; then eval concat_cmds=\"\$concat_cmds~\$RM $last_robj\" fi fi test -n "$save_libobjs" && func_verbose "creating a temporary reloadable object file: $output" # Loop through the commands generated above and execute them. save_ifs="$IFS"; IFS='~' for cmd in $concat_cmds; do IFS="$save_ifs" $opt_silent || { func_quote_for_expand "$cmd" eval "func_echo $func_quote_for_expand_result" } $opt_dry_run || eval "$cmd" || { lt_exit=$? # Restore the uninstalled library and exit if test "$opt_mode" = relink; then ( cd "$output_objdir" && \ $RM "${realname}T" && \ $MV "${realname}U" "$realname" ) fi exit $lt_exit } done IFS="$save_ifs" if test -n "$export_symbols_regex" && ${skipped_export-false}; then func_show_eval '$EGREP -e "$export_symbols_regex" "$export_symbols" > "${export_symbols}T"' func_show_eval '$MV "${export_symbols}T" "$export_symbols"' fi fi if ${skipped_export-false}; then if test -n "$export_symbols" && test -n "$include_expsyms"; then tmp_export_symbols="$export_symbols" test -n "$orig_export_symbols" && tmp_export_symbols="$orig_export_symbols" $opt_dry_run || eval '$ECHO "$include_expsyms" | $SP2NL >> "$tmp_export_symbols"' fi if test -n "$orig_export_symbols"; then # The given exports_symbols file has to be filtered, so filter it. func_verbose "filter symbol list for \`$libname.la' to tag DATA exports" # FIXME: $output_objdir/$libname.filter potentially contains lots of # 's' commands which not all seds can handle. GNU sed should be fine # though. Also, the filter scales superlinearly with the number of # global variables. join(1) would be nice here, but unfortunately # isn't a blessed tool. $opt_dry_run || $SED -e '/[ ,]DATA/!d;s,\(.*\)\([ \,].*\),s|^\1$|\1\2|,' < $export_symbols > $output_objdir/$libname.filter func_append delfiles " $export_symbols $output_objdir/$libname.filter" export_symbols=$output_objdir/$libname.def $opt_dry_run || $SED -f $output_objdir/$libname.filter < $orig_export_symbols > $export_symbols fi fi libobjs=$output # Restore the value of output. output=$save_output if test -n "$convenience" && test -n "$whole_archive_flag_spec"; then eval libobjs=\"\$libobjs $whole_archive_flag_spec\" test "X$libobjs" = "X " && libobjs= fi # Expand the library linking commands again to reset the # value of $libobjs for piecewise linking. # Do each of the archive commands. if test "$module" = yes && test -n "$module_cmds" ; then if test -n "$export_symbols" && test -n "$module_expsym_cmds"; then cmds=$module_expsym_cmds else cmds=$module_cmds fi else if test -n "$export_symbols" && test -n "$archive_expsym_cmds"; then cmds=$archive_expsym_cmds else cmds=$archive_cmds fi fi fi if test -n "$delfiles"; then # Append the command to remove temporary files to $cmds. eval cmds=\"\$cmds~\$RM $delfiles\" fi # Add any objects from preloaded convenience libraries if test -n "$dlprefiles"; then gentop="$output_objdir/${outputname}x" func_append generated " $gentop" func_extract_archives $gentop $dlprefiles func_append libobjs " $func_extract_archives_result" test "X$libobjs" = "X " && libobjs= fi save_ifs="$IFS"; IFS='~' for cmd in $cmds; do IFS="$save_ifs" eval cmd=\"$cmd\" $opt_silent || { func_quote_for_expand "$cmd" eval "func_echo $func_quote_for_expand_result" } $opt_dry_run || eval "$cmd" || { lt_exit=$? # Restore the uninstalled library and exit if test "$opt_mode" = relink; then ( cd "$output_objdir" && \ $RM "${realname}T" && \ $MV "${realname}U" "$realname" ) fi exit $lt_exit } done IFS="$save_ifs" # Restore the uninstalled library and exit if test "$opt_mode" = relink; then $opt_dry_run || eval '(cd $output_objdir && $RM ${realname}T && $MV $realname ${realname}T && $MV ${realname}U $realname)' || exit $? if test -n "$convenience"; then if test -z "$whole_archive_flag_spec"; then func_show_eval '${RM}r "$gentop"' fi fi exit $EXIT_SUCCESS fi # Create links to the real library. for linkname in $linknames; do if test "$realname" != "$linkname"; then func_show_eval '(cd "$output_objdir" && $RM "$linkname" && $LN_S "$realname" "$linkname")' 'exit $?' fi done # If -module or -export-dynamic was specified, set the dlname. if test "$module" = yes || test "$export_dynamic" = yes; then # On all known operating systems, these are identical. dlname="$soname" fi fi ;; obj) if test -n "$dlfiles$dlprefiles" || test "$dlself" != no; then func_warning "\`-dlopen' is ignored for objects" fi case " $deplibs" in *\ -l* | *\ -L*) func_warning "\`-l' and \`-L' are ignored for objects" ;; esac test -n "$rpath" && \ func_warning "\`-rpath' is ignored for objects" test -n "$xrpath" && \ func_warning "\`-R' is ignored for objects" test -n "$vinfo" && \ func_warning "\`-version-info' is ignored for objects" test -n "$release" && \ func_warning "\`-release' is ignored for objects" case $output in *.lo) test -n "$objs$old_deplibs" && \ func_fatal_error "cannot build library object \`$output' from non-libtool objects" libobj=$output func_lo2o "$libobj" obj=$func_lo2o_result ;; *) libobj= obj="$output" ;; esac # Delete the old objects. $opt_dry_run || $RM $obj $libobj # Objects from convenience libraries. This assumes # single-version convenience libraries. Whenever we create # different ones for PIC/non-PIC, this we'll have to duplicate # the extraction. reload_conv_objs= gentop= # reload_cmds runs $LD directly, so let us get rid of # -Wl from whole_archive_flag_spec and hope we can get by with # turning comma into space.. wl= if test -n "$convenience"; then if test -n "$whole_archive_flag_spec"; then eval tmp_whole_archive_flags=\"$whole_archive_flag_spec\" reload_conv_objs=$reload_objs\ `$ECHO "$tmp_whole_archive_flags" | $SED 's|,| |g'` else gentop="$output_objdir/${obj}x" func_append generated " $gentop" func_extract_archives $gentop $convenience reload_conv_objs="$reload_objs $func_extract_archives_result" fi fi # If we're not building shared, we need to use non_pic_objs test "$build_libtool_libs" != yes && libobjs="$non_pic_objects" # Create the old-style object. reload_objs="$objs$old_deplibs "`$ECHO "$libobjs" | $SP2NL | $SED "/\.${libext}$/d; /\.lib$/d; $lo2o" | $NL2SP`" $reload_conv_objs" ### testsuite: skip nested quoting test output="$obj" func_execute_cmds "$reload_cmds" 'exit $?' # Exit if we aren't doing a library object file. if test -z "$libobj"; then if test -n "$gentop"; then func_show_eval '${RM}r "$gentop"' fi exit $EXIT_SUCCESS fi if test "$build_libtool_libs" != yes; then if test -n "$gentop"; then func_show_eval '${RM}r "$gentop"' fi # Create an invalid libtool object if no PIC, so that we don't # accidentally link it into a program. # $show "echo timestamp > $libobj" # $opt_dry_run || eval "echo timestamp > $libobj" || exit $? exit $EXIT_SUCCESS fi if test -n "$pic_flag" || test "$pic_mode" != default; then # Only do commands if we really have different PIC objects. reload_objs="$libobjs $reload_conv_objs" output="$libobj" func_execute_cmds "$reload_cmds" 'exit $?' fi if test -n "$gentop"; then func_show_eval '${RM}r "$gentop"' fi exit $EXIT_SUCCESS ;; prog) case $host in *cygwin*) func_stripname '' '.exe' "$output" output=$func_stripname_result.exe;; esac test -n "$vinfo" && \ func_warning "\`-version-info' is ignored for programs" test -n "$release" && \ func_warning "\`-release' is ignored for programs" test "$preload" = yes \ && test "$dlopen_support" = unknown \ && test "$dlopen_self" = unknown \ && test "$dlopen_self_static" = unknown && \ func_warning "\`LT_INIT([dlopen])' not used. Assuming no dlopen support." case $host in *-*-rhapsody* | *-*-darwin1.[012]) # On Rhapsody replace the C library is the System framework compile_deplibs=`$ECHO " $compile_deplibs" | $SED 's/ -lc / System.ltframework /'` finalize_deplibs=`$ECHO " $finalize_deplibs" | $SED 's/ -lc / System.ltframework /'` ;; esac case $host in *-*-darwin*) # Don't allow lazy linking, it breaks C++ global constructors # But is supposedly fixed on 10.4 or later (yay!). if test "$tagname" = CXX ; then case ${MACOSX_DEPLOYMENT_TARGET-10.0} in 10.[0123]) func_append compile_command " ${wl}-bind_at_load" func_append finalize_command " ${wl}-bind_at_load" ;; esac fi # Time to change all our "foo.ltframework" stuff back to "-framework foo" compile_deplibs=`$ECHO " $compile_deplibs" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` finalize_deplibs=`$ECHO " $finalize_deplibs" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` ;; esac # move library search paths that coincide with paths to not yet # installed libraries to the beginning of the library search list new_libs= for path in $notinst_path; do case " $new_libs " in *" -L$path/$objdir "*) ;; *) case " $compile_deplibs " in *" -L$path/$objdir "*) func_append new_libs " -L$path/$objdir" ;; esac ;; esac done for deplib in $compile_deplibs; do case $deplib in -L*) case " $new_libs " in *" $deplib "*) ;; *) func_append new_libs " $deplib" ;; esac ;; *) func_append new_libs " $deplib" ;; esac done compile_deplibs="$new_libs" func_append compile_command " $compile_deplibs" func_append finalize_command " $finalize_deplibs" if test -n "$rpath$xrpath"; then # If the user specified any rpath flags, then add them. for libdir in $rpath $xrpath; do # This is the magic to use -rpath. case "$finalize_rpath " in *" $libdir "*) ;; *) func_append finalize_rpath " $libdir" ;; esac done fi # Now hardcode the library paths rpath= hardcode_libdirs= for libdir in $compile_rpath $finalize_rpath; do if test -n "$hardcode_libdir_flag_spec"; then if test -n "$hardcode_libdir_separator"; then if test -z "$hardcode_libdirs"; then hardcode_libdirs="$libdir" else # Just accumulate the unique libdirs. case $hardcode_libdir_separator$hardcode_libdirs$hardcode_libdir_separator in *"$hardcode_libdir_separator$libdir$hardcode_libdir_separator"*) ;; *) func_append hardcode_libdirs "$hardcode_libdir_separator$libdir" ;; esac fi else eval flag=\"$hardcode_libdir_flag_spec\" func_append rpath " $flag" fi elif test -n "$runpath_var"; then case "$perm_rpath " in *" $libdir "*) ;; *) func_append perm_rpath " $libdir" ;; esac fi case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-os2* | *-cegcc*) testbindir=`${ECHO} "$libdir" | ${SED} -e 's*/lib$*/bin*'` case :$dllsearchpath: in *":$libdir:"*) ;; ::) dllsearchpath=$libdir;; *) func_append dllsearchpath ":$libdir";; esac case :$dllsearchpath: in *":$testbindir:"*) ;; ::) dllsearchpath=$testbindir;; *) func_append dllsearchpath ":$testbindir";; esac ;; esac done # Substitute the hardcoded libdirs into the rpath. if test -n "$hardcode_libdir_separator" && test -n "$hardcode_libdirs"; then libdir="$hardcode_libdirs" eval rpath=\" $hardcode_libdir_flag_spec\" fi compile_rpath="$rpath" rpath= hardcode_libdirs= for libdir in $finalize_rpath; do if test -n "$hardcode_libdir_flag_spec"; then if test -n "$hardcode_libdir_separator"; then if test -z "$hardcode_libdirs"; then hardcode_libdirs="$libdir" else # Just accumulate the unique libdirs. case $hardcode_libdir_separator$hardcode_libdirs$hardcode_libdir_separator in *"$hardcode_libdir_separator$libdir$hardcode_libdir_separator"*) ;; *) func_append hardcode_libdirs "$hardcode_libdir_separator$libdir" ;; esac fi else eval flag=\"$hardcode_libdir_flag_spec\" func_append rpath " $flag" fi elif test -n "$runpath_var"; then case "$finalize_perm_rpath " in *" $libdir "*) ;; *) func_append finalize_perm_rpath " $libdir" ;; esac fi done # Substitute the hardcoded libdirs into the rpath. if test -n "$hardcode_libdir_separator" && test -n "$hardcode_libdirs"; then libdir="$hardcode_libdirs" eval rpath=\" $hardcode_libdir_flag_spec\" fi finalize_rpath="$rpath" if test -n "$libobjs" && test "$build_old_libs" = yes; then # Transform all the library objects into standard objects. compile_command=`$ECHO "$compile_command" | $SP2NL | $SED "$lo2o" | $NL2SP` finalize_command=`$ECHO "$finalize_command" | $SP2NL | $SED "$lo2o" | $NL2SP` fi func_generate_dlsyms "$outputname" "@PROGRAM@" "no" # template prelinking step if test -n "$prelink_cmds"; then func_execute_cmds "$prelink_cmds" 'exit $?' fi wrappers_required=yes case $host in *cegcc* | *mingw32ce*) # Disable wrappers for cegcc and mingw32ce hosts, we are cross compiling anyway. wrappers_required=no ;; *cygwin* | *mingw* ) if test "$build_libtool_libs" != yes; then wrappers_required=no fi ;; *) if test "$need_relink" = no || test "$build_libtool_libs" != yes; then wrappers_required=no fi ;; esac if test "$wrappers_required" = no; then # Replace the output file specification. compile_command=`$ECHO "$compile_command" | $SED 's%@OUTPUT@%'"$output"'%g'` link_command="$compile_command$compile_rpath" # We have no uninstalled library dependencies, so finalize right now. exit_status=0 func_show_eval "$link_command" 'exit_status=$?' if test -n "$postlink_cmds"; then func_to_tool_file "$output" postlink_cmds=`func_echo_all "$postlink_cmds" | $SED -e 's%@OUTPUT@%'"$output"'%g' -e 's%@TOOL_OUTPUT@%'"$func_to_tool_file_result"'%g'` func_execute_cmds "$postlink_cmds" 'exit $?' fi # Delete the generated files. if test -f "$output_objdir/${outputname}S.${objext}"; then func_show_eval '$RM "$output_objdir/${outputname}S.${objext}"' fi exit $exit_status fi if test -n "$compile_shlibpath$finalize_shlibpath"; then compile_command="$shlibpath_var=\"$compile_shlibpath$finalize_shlibpath\$$shlibpath_var\" $compile_command" fi if test -n "$finalize_shlibpath"; then finalize_command="$shlibpath_var=\"$finalize_shlibpath\$$shlibpath_var\" $finalize_command" fi compile_var= finalize_var= if test -n "$runpath_var"; then if test -n "$perm_rpath"; then # We should set the runpath_var. rpath= for dir in $perm_rpath; do func_append rpath "$dir:" done compile_var="$runpath_var=\"$rpath\$$runpath_var\" " fi if test -n "$finalize_perm_rpath"; then # We should set the runpath_var. rpath= for dir in $finalize_perm_rpath; do func_append rpath "$dir:" done finalize_var="$runpath_var=\"$rpath\$$runpath_var\" " fi fi if test "$no_install" = yes; then # We don't need to create a wrapper script. link_command="$compile_var$compile_command$compile_rpath" # Replace the output file specification. link_command=`$ECHO "$link_command" | $SED 's%@OUTPUT@%'"$output"'%g'` # Delete the old output file. $opt_dry_run || $RM $output # Link the executable and exit func_show_eval "$link_command" 'exit $?' if test -n "$postlink_cmds"; then func_to_tool_file "$output" postlink_cmds=`func_echo_all "$postlink_cmds" | $SED -e 's%@OUTPUT@%'"$output"'%g' -e 's%@TOOL_OUTPUT@%'"$func_to_tool_file_result"'%g'` func_execute_cmds "$postlink_cmds" 'exit $?' fi exit $EXIT_SUCCESS fi if test "$hardcode_action" = relink; then # Fast installation is not supported link_command="$compile_var$compile_command$compile_rpath" relink_command="$finalize_var$finalize_command$finalize_rpath" func_warning "this platform does not like uninstalled shared libraries" func_warning "\`$output' will be relinked during installation" else if test "$fast_install" != no; then link_command="$finalize_var$compile_command$finalize_rpath" if test "$fast_install" = yes; then relink_command=`$ECHO "$compile_var$compile_command$compile_rpath" | $SED 's%@OUTPUT@%\$progdir/\$file%g'` else # fast_install is set to needless relink_command= fi else link_command="$compile_var$compile_command$compile_rpath" relink_command="$finalize_var$finalize_command$finalize_rpath" fi fi # Replace the output file specification. link_command=`$ECHO "$link_command" | $SED 's%@OUTPUT@%'"$output_objdir/$outputname"'%g'` # Delete the old output files. $opt_dry_run || $RM $output $output_objdir/$outputname $output_objdir/lt-$outputname func_show_eval "$link_command" 'exit $?' if test -n "$postlink_cmds"; then func_to_tool_file "$output_objdir/$outputname" postlink_cmds=`func_echo_all "$postlink_cmds" | $SED -e 's%@OUTPUT@%'"$output_objdir/$outputname"'%g' -e 's%@TOOL_OUTPUT@%'"$func_to_tool_file_result"'%g'` func_execute_cmds "$postlink_cmds" 'exit $?' fi # Now create the wrapper script. func_verbose "creating $output" # Quote the relink command for shipping. if test -n "$relink_command"; then # Preserve any variables that may affect compiler behavior for var in $variables_saved_for_relink; do if eval test -z \"\${$var+set}\"; then relink_command="{ test -z \"\${$var+set}\" || $lt_unset $var || { $var=; export $var; }; }; $relink_command" elif eval var_value=\$$var; test -z "$var_value"; then relink_command="$var=; export $var; $relink_command" else func_quote_for_eval "$var_value" relink_command="$var=$func_quote_for_eval_result; export $var; $relink_command" fi done relink_command="(cd `pwd`; $relink_command)" relink_command=`$ECHO "$relink_command" | $SED "$sed_quote_subst"` fi # Only actually do things if not in dry run mode. $opt_dry_run || { # win32 will think the script is a binary if it has # a .exe suffix, so we strip it off here. case $output in *.exe) func_stripname '' '.exe' "$output" output=$func_stripname_result ;; esac # test for cygwin because mv fails w/o .exe extensions case $host in *cygwin*) exeext=.exe func_stripname '' '.exe' "$outputname" outputname=$func_stripname_result ;; *) exeext= ;; esac case $host in *cygwin* | *mingw* ) func_dirname_and_basename "$output" "" "." output_name=$func_basename_result output_path=$func_dirname_result cwrappersource="$output_path/$objdir/lt-$output_name.c" cwrapper="$output_path/$output_name.exe" $RM $cwrappersource $cwrapper trap "$RM $cwrappersource $cwrapper; exit $EXIT_FAILURE" 1 2 15 func_emit_cwrapperexe_src > $cwrappersource # The wrapper executable is built using the $host compiler, # because it contains $host paths and files. If cross- # compiling, it, like the target executable, must be # executed on the $host or under an emulation environment. $opt_dry_run || { $LTCC $LTCFLAGS -o $cwrapper $cwrappersource $STRIP $cwrapper } # Now, create the wrapper script for func_source use: func_ltwrapper_scriptname $cwrapper $RM $func_ltwrapper_scriptname_result trap "$RM $func_ltwrapper_scriptname_result; exit $EXIT_FAILURE" 1 2 15 $opt_dry_run || { # note: this script will not be executed, so do not chmod. if test "x$build" = "x$host" ; then $cwrapper --lt-dump-script > $func_ltwrapper_scriptname_result else func_emit_wrapper no > $func_ltwrapper_scriptname_result fi } ;; * ) $RM $output trap "$RM $output; exit $EXIT_FAILURE" 1 2 15 func_emit_wrapper no > $output chmod +x $output ;; esac } exit $EXIT_SUCCESS ;; esac # See if we need to build an old-fashioned archive. for oldlib in $oldlibs; do if test "$build_libtool_libs" = convenience; then oldobjs="$libobjs_save $symfileobj" addlibs="$convenience" build_libtool_libs=no else if test "$build_libtool_libs" = module; then oldobjs="$libobjs_save" build_libtool_libs=no else oldobjs="$old_deplibs $non_pic_objects" if test "$preload" = yes && test -f "$symfileobj"; then func_append oldobjs " $symfileobj" fi fi addlibs="$old_convenience" fi if test -n "$addlibs"; then gentop="$output_objdir/${outputname}x" func_append generated " $gentop" func_extract_archives $gentop $addlibs func_append oldobjs " $func_extract_archives_result" fi # Do each command in the archive commands. if test -n "$old_archive_from_new_cmds" && test "$build_libtool_libs" = yes; then cmds=$old_archive_from_new_cmds else # Add any objects from preloaded convenience libraries if test -n "$dlprefiles"; then gentop="$output_objdir/${outputname}x" func_append generated " $gentop" func_extract_archives $gentop $dlprefiles func_append oldobjs " $func_extract_archives_result" fi # POSIX demands no paths to be encoded in archives. We have # to avoid creating archives with duplicate basenames if we # might have to extract them afterwards, e.g., when creating a # static archive out of a convenience library, or when linking # the entirety of a libtool archive into another (currently # not supported by libtool). if (for obj in $oldobjs do func_basename "$obj" $ECHO "$func_basename_result" done | sort | sort -uc >/dev/null 2>&1); then : else echo "copying selected object files to avoid basename conflicts..." gentop="$output_objdir/${outputname}x" func_append generated " $gentop" func_mkdir_p "$gentop" save_oldobjs=$oldobjs oldobjs= counter=1 for obj in $save_oldobjs do func_basename "$obj" objbase="$func_basename_result" case " $oldobjs " in " ") oldobjs=$obj ;; *[\ /]"$objbase "*) while :; do # Make sure we don't pick an alternate name that also # overlaps. newobj=lt$counter-$objbase func_arith $counter + 1 counter=$func_arith_result case " $oldobjs " in *[\ /]"$newobj "*) ;; *) if test ! -f "$gentop/$newobj"; then break; fi ;; esac done func_show_eval "ln $obj $gentop/$newobj || cp $obj $gentop/$newobj" func_append oldobjs " $gentop/$newobj" ;; *) func_append oldobjs " $obj" ;; esac done fi func_to_tool_file "$oldlib" func_convert_file_msys_to_w32 tool_oldlib=$func_to_tool_file_result eval cmds=\"$old_archive_cmds\" func_len " $cmds" len=$func_len_result if test "$len" -lt "$max_cmd_len" || test "$max_cmd_len" -le -1; then cmds=$old_archive_cmds elif test -n "$archiver_list_spec"; then func_verbose "using command file archive linking..." for obj in $oldobjs do func_to_tool_file "$obj" $ECHO "$func_to_tool_file_result" done > $output_objdir/$libname.libcmd func_to_tool_file "$output_objdir/$libname.libcmd" oldobjs=" $archiver_list_spec$func_to_tool_file_result" cmds=$old_archive_cmds else # the command line is too long to link in one step, link in parts func_verbose "using piecewise archive linking..." save_RANLIB=$RANLIB RANLIB=: objlist= concat_cmds= save_oldobjs=$oldobjs oldobjs= # Is there a better way of finding the last object in the list? for obj in $save_oldobjs do last_oldobj=$obj done eval test_cmds=\"$old_archive_cmds\" func_len " $test_cmds" len0=$func_len_result len=$len0 for obj in $save_oldobjs do func_len " $obj" func_arith $len + $func_len_result len=$func_arith_result func_append objlist " $obj" if test "$len" -lt "$max_cmd_len"; then : else # the above command should be used before it gets too long oldobjs=$objlist if test "$obj" = "$last_oldobj" ; then RANLIB=$save_RANLIB fi test -z "$concat_cmds" || concat_cmds=$concat_cmds~ eval concat_cmds=\"\${concat_cmds}$old_archive_cmds\" objlist= len=$len0 fi done RANLIB=$save_RANLIB oldobjs=$objlist if test "X$oldobjs" = "X" ; then eval cmds=\"\$concat_cmds\" else eval cmds=\"\$concat_cmds~\$old_archive_cmds\" fi fi fi func_execute_cmds "$cmds" 'exit $?' done test -n "$generated" && \ func_show_eval "${RM}r$generated" # Now create the libtool archive. case $output in *.la) old_library= test "$build_old_libs" = yes && old_library="$libname.$libext" func_verbose "creating $output" # Preserve any variables that may affect compiler behavior for var in $variables_saved_for_relink; do if eval test -z \"\${$var+set}\"; then relink_command="{ test -z \"\${$var+set}\" || $lt_unset $var || { $var=; export $var; }; }; $relink_command" elif eval var_value=\$$var; test -z "$var_value"; then relink_command="$var=; export $var; $relink_command" else func_quote_for_eval "$var_value" relink_command="$var=$func_quote_for_eval_result; export $var; $relink_command" fi done # Quote the link command for shipping. relink_command="(cd `pwd`; $SHELL $progpath $preserve_args --mode=relink $libtool_args @inst_prefix_dir@)" relink_command=`$ECHO "$relink_command" | $SED "$sed_quote_subst"` if test "$hardcode_automatic" = yes ; then relink_command= fi # Only create the output if not a dry run. $opt_dry_run || { for installed in no yes; do if test "$installed" = yes; then if test -z "$install_libdir"; then break fi output="$output_objdir/$outputname"i # Replace all uninstalled libtool libraries with the installed ones newdependency_libs= for deplib in $dependency_libs; do case $deplib in *.la) func_basename "$deplib" name="$func_basename_result" func_resolve_sysroot "$deplib" eval libdir=`${SED} -n -e 's/^libdir=\(.*\)$/\1/p' $func_resolve_sysroot_result` test -z "$libdir" && \ func_fatal_error "\`$deplib' is not a valid libtool archive" func_append newdependency_libs " ${lt_sysroot:+=}$libdir/$name" ;; -L*) func_stripname -L '' "$deplib" func_replace_sysroot "$func_stripname_result" func_append newdependency_libs " -L$func_replace_sysroot_result" ;; -R*) func_stripname -R '' "$deplib" func_replace_sysroot "$func_stripname_result" func_append newdependency_libs " -R$func_replace_sysroot_result" ;; *) func_append newdependency_libs " $deplib" ;; esac done dependency_libs="$newdependency_libs" newdlfiles= for lib in $dlfiles; do case $lib in *.la) func_basename "$lib" name="$func_basename_result" eval libdir=`${SED} -n -e 's/^libdir=\(.*\)$/\1/p' $lib` test -z "$libdir" && \ func_fatal_error "\`$lib' is not a valid libtool archive" func_append newdlfiles " ${lt_sysroot:+=}$libdir/$name" ;; *) func_append newdlfiles " $lib" ;; esac done dlfiles="$newdlfiles" newdlprefiles= for lib in $dlprefiles; do case $lib in *.la) # Only pass preopened files to the pseudo-archive (for # eventual linking with the app. that links it) if we # didn't already link the preopened objects directly into # the library: func_basename "$lib" name="$func_basename_result" eval libdir=`${SED} -n -e 's/^libdir=\(.*\)$/\1/p' $lib` test -z "$libdir" && \ func_fatal_error "\`$lib' is not a valid libtool archive" func_append newdlprefiles " ${lt_sysroot:+=}$libdir/$name" ;; esac done dlprefiles="$newdlprefiles" else newdlfiles= for lib in $dlfiles; do case $lib in [\\/]* | [A-Za-z]:[\\/]*) abs="$lib" ;; *) abs=`pwd`"/$lib" ;; esac func_append newdlfiles " $abs" done dlfiles="$newdlfiles" newdlprefiles= for lib in $dlprefiles; do case $lib in [\\/]* | [A-Za-z]:[\\/]*) abs="$lib" ;; *) abs=`pwd`"/$lib" ;; esac func_append newdlprefiles " $abs" done dlprefiles="$newdlprefiles" fi $RM $output # place dlname in correct position for cygwin # In fact, it would be nice if we could use this code for all target # systems that can't hard-code library paths into their executables # and that have no shared library path variable independent of PATH, # but it turns out we can't easily determine that from inspecting # libtool variables, so we have to hard-code the OSs to which it # applies here; at the moment, that means platforms that use the PE # object format with DLL files. See the long comment at the top of # tests/bindir.at for full details. tdlname=$dlname case $host,$output,$installed,$module,$dlname in *cygwin*,*lai,yes,no,*.dll | *mingw*,*lai,yes,no,*.dll | *cegcc*,*lai,yes,no,*.dll) # If a -bindir argument was supplied, place the dll there. if test "x$bindir" != x ; then func_relative_path "$install_libdir" "$bindir" tdlname=$func_relative_path_result$dlname else # Otherwise fall back on heuristic. tdlname=../bin/$dlname fi ;; esac $ECHO > $output "\ # $outputname - a libtool library file # Generated by $PROGRAM (GNU $PACKAGE$TIMESTAMP) $VERSION # # Please DO NOT delete this file! # It is necessary for linking the library. # The name that we can dlopen(3). dlname='$tdlname' # Names of this library. library_names='$library_names' # The name of the static archive. old_library='$old_library' # Linker flags that can not go in dependency_libs. inherited_linker_flags='$new_inherited_linker_flags' # Libraries that this one depends upon. dependency_libs='$dependency_libs' # Names of additional weak libraries provided by this library weak_library_names='$weak_libs' # Version information for $libname. current=$current age=$age revision=$revision # Is this an already installed library? installed=$installed # Should we warn about portability when linking against -modules? shouldnotlink=$module # Files to dlopen/dlpreopen dlopen='$dlfiles' dlpreopen='$dlprefiles' # Directory that this library needs to be installed in: libdir='$install_libdir'" if test "$installed" = no && test "$need_relink" = yes; then $ECHO >> $output "\ relink_command=\"$relink_command\"" fi done } # Do a symbolic link so that the libtool archive can be found in # LD_LIBRARY_PATH before the program is installed. func_show_eval '( cd "$output_objdir" && $RM "$outputname" && $LN_S "../$outputname" "$outputname" )' 'exit $?' ;; esac exit $EXIT_SUCCESS } { test "$opt_mode" = link || test "$opt_mode" = relink; } && func_mode_link ${1+"$@"} # func_mode_uninstall arg... func_mode_uninstall () { $opt_debug RM="$nonopt" files= rmforce= exit_status=0 # This variable tells wrapper scripts just to set variables rather # than running their programs. libtool_install_magic="$magic" for arg do case $arg in -f) func_append RM " $arg"; rmforce=yes ;; -*) func_append RM " $arg" ;; *) func_append files " $arg" ;; esac done test -z "$RM" && \ func_fatal_help "you must specify an RM program" rmdirs= for file in $files; do func_dirname "$file" "" "." dir="$func_dirname_result" if test "X$dir" = X.; then odir="$objdir" else odir="$dir/$objdir" fi func_basename "$file" name="$func_basename_result" test "$opt_mode" = uninstall && odir="$dir" # Remember odir for removal later, being careful to avoid duplicates if test "$opt_mode" = clean; then case " $rmdirs " in *" $odir "*) ;; *) func_append rmdirs " $odir" ;; esac fi # Don't error if the file doesn't exist and rm -f was used. if { test -L "$file"; } >/dev/null 2>&1 || { test -h "$file"; } >/dev/null 2>&1 || test -f "$file"; then : elif test -d "$file"; then exit_status=1 continue elif test "$rmforce" = yes; then continue fi rmfiles="$file" case $name in *.la) # Possibly a libtool archive, so verify it. if func_lalib_p "$file"; then func_source $dir/$name # Delete the libtool libraries and symlinks. for n in $library_names; do func_append rmfiles " $odir/$n" done test -n "$old_library" && func_append rmfiles " $odir/$old_library" case "$opt_mode" in clean) case " $library_names " in *" $dlname "*) ;; *) test -n "$dlname" && func_append rmfiles " $odir/$dlname" ;; esac test -n "$libdir" && func_append rmfiles " $odir/$name $odir/${name}i" ;; uninstall) if test -n "$library_names"; then # Do each command in the postuninstall commands. func_execute_cmds "$postuninstall_cmds" 'test "$rmforce" = yes || exit_status=1' fi if test -n "$old_library"; then # Do each command in the old_postuninstall commands. func_execute_cmds "$old_postuninstall_cmds" 'test "$rmforce" = yes || exit_status=1' fi # FIXME: should reinstall the best remaining shared library. ;; esac fi ;; *.lo) # Possibly a libtool object, so verify it. if func_lalib_p "$file"; then # Read the .lo file func_source $dir/$name # Add PIC object to the list of files to remove. if test -n "$pic_object" && test "$pic_object" != none; then func_append rmfiles " $dir/$pic_object" fi # Add non-PIC object to the list of files to remove. if test -n "$non_pic_object" && test "$non_pic_object" != none; then func_append rmfiles " $dir/$non_pic_object" fi fi ;; *) if test "$opt_mode" = clean ; then noexename=$name case $file in *.exe) func_stripname '' '.exe' "$file" file=$func_stripname_result func_stripname '' '.exe' "$name" noexename=$func_stripname_result # $file with .exe has already been added to rmfiles, # add $file without .exe func_append rmfiles " $file" ;; esac # Do a test to see if this is a libtool program. if func_ltwrapper_p "$file"; then if func_ltwrapper_executable_p "$file"; then func_ltwrapper_scriptname "$file" relink_command= func_source $func_ltwrapper_scriptname_result func_append rmfiles " $func_ltwrapper_scriptname_result" else relink_command= func_source $dir/$noexename fi # note $name still contains .exe if it was in $file originally # as does the version of $file that was added into $rmfiles func_append rmfiles " $odir/$name $odir/${name}S.${objext}" if test "$fast_install" = yes && test -n "$relink_command"; then func_append rmfiles " $odir/lt-$name" fi if test "X$noexename" != "X$name" ; then func_append rmfiles " $odir/lt-${noexename}.c" fi fi fi ;; esac func_show_eval "$RM $rmfiles" 'exit_status=1' done # Try to remove the ${objdir}s in the directories where we deleted files for dir in $rmdirs; do if test -d "$dir"; then func_show_eval "rmdir $dir >/dev/null 2>&1" fi done exit $exit_status } { test "$opt_mode" = uninstall || test "$opt_mode" = clean; } && func_mode_uninstall ${1+"$@"} test -z "$opt_mode" && { help="$generic_help" func_fatal_help "you must specify a MODE" } test -z "$exec_cmd" && \ func_fatal_help "invalid operation mode \`$opt_mode'" if test -n "$exec_cmd"; then eval exec "$exec_cmd" exit $EXIT_FAILURE fi exit $exit_status # The TAGs below are defined such that we never get into a situation # in which we disable both kinds of libraries. Given conflicting # choices, we go for a static library, that is the most portable, # since we can't tell whether shared libraries were disabled because # the user asked for that or because the platform doesn't support # them. This is particularly important on AIX, because we don't # support having both static and shared libraries enabled at the same # time on that platform, so we default to a shared-only configuration. # If a disable-shared tag is given, we'll fallback to a static-only # configuration. But we'll never go from static-only to shared-only. # ### BEGIN LIBTOOL TAG CONFIG: disable-shared build_libtool_libs=no build_old_libs=yes # ### END LIBTOOL TAG CONFIG: disable-shared # ### BEGIN LIBTOOL TAG CONFIG: disable-static build_old_libs=`case $build_libtool_libs in yes) echo no;; *) echo yes;; esac` # ### END LIBTOOL TAG CONFIG: disable-static # Local Variables: # mode:shell-script # sh-indentation:2 # End: # vi:sw=2 nloptr/src/nlopt_src/INSTALL0000644000176000001440000003660513263734241015462 0ustar ripleyusersInstallation Instructions ************************* Copyright (C) 1994-1996, 1999-2002, 2004-2013 Free Software Foundation, Inc. Copying and distribution of this file, with or without modification, are permitted in any medium without royalty provided the copyright notice and this notice are preserved. This file is offered as-is, without warranty of any kind. Basic Installation ================== Briefly, the shell commands `./configure; make; make install' should configure, build, and install this package. The following more-detailed instructions are generic; see the `README' file for instructions specific to this package. Some packages provide this `INSTALL' file but do not implement all of the features documented below. The lack of an optional feature in a given package is not necessarily a bug. More recommendations for GNU packages can be found in *note Makefile Conventions: (standards)Makefile Conventions. The `configure' shell script attempts to guess correct values for various system-dependent variables used during compilation. It uses those values to create a `Makefile' in each directory of the package. It may also create one or more `.h' files containing system-dependent definitions. Finally, it creates a shell script `config.status' that you can run in the future to recreate the current configuration, and a file `config.log' containing compiler output (useful mainly for debugging `configure'). It can also use an optional file (typically called `config.cache' and enabled with `--cache-file=config.cache' or simply `-C') that saves the results of its tests to speed up reconfiguring. Caching is disabled by default to prevent problems with accidental use of stale cache files. If you need to do unusual things to compile the package, please try to figure out how `configure' could check whether to do them, and mail diffs or instructions to the address given in the `README' so they can be considered for the next release. If you are using the cache, and at some point `config.cache' contains results you don't want to keep, you may remove or edit it. The file `configure.ac' (or `configure.in') is used to create `configure' by a program called `autoconf'. You need `configure.ac' if you want to change it or regenerate `configure' using a newer version of `autoconf'. The simplest way to compile this package is: 1. `cd' to the directory containing the package's source code and type `./configure' to configure the package for your system. Running `configure' might take a while. While running, it prints some messages telling which features it is checking for. 2. Type `make' to compile the package. 3. Optionally, type `make check' to run any self-tests that come with the package, generally using the just-built uninstalled binaries. 4. Type `make install' to install the programs and any data files and documentation. When installing into a prefix owned by root, it is recommended that the package be configured and built as a regular user, and only the `make install' phase executed with root privileges. 5. Optionally, type `make installcheck' to repeat any self-tests, but this time using the binaries in their final installed location. This target does not install anything. Running this target as a regular user, particularly if the prior `make install' required root privileges, verifies that the installation completed correctly. 6. You can remove the program binaries and object files from the source code directory by typing `make clean'. To also remove the files that `configure' created (so you can compile the package for a different kind of computer), type `make distclean'. There is also a `make maintainer-clean' target, but that is intended mainly for the package's developers. If you use it, you may have to get all sorts of other programs in order to regenerate files that came with the distribution. 7. Often, you can also type `make uninstall' to remove the installed files again. In practice, not all packages have tested that uninstallation works correctly, even though it is required by the GNU Coding Standards. 8. Some packages, particularly those that use Automake, provide `make distcheck', which can by used by developers to test that all other targets like `make install' and `make uninstall' work correctly. This target is generally not run by end users. Compilers and Options ===================== Some systems require unusual options for compilation or linking that the `configure' script does not know about. Run `./configure --help' for details on some of the pertinent environment variables. You can give `configure' initial values for configuration parameters by setting variables in the command line or in the environment. Here is an example: ./configure CC=c99 CFLAGS=-g LIBS=-lposix *Note Defining Variables::, for more details. Compiling For Multiple Architectures ==================================== You can compile the package for more than one kind of computer at the same time, by placing the object files for each architecture in their own directory. To do this, you can use GNU `make'. `cd' to the directory where you want the object files and executables to go and run the `configure' script. `configure' automatically checks for the source code in the directory that `configure' is in and in `..'. This is known as a "VPATH" build. With a non-GNU `make', it is safer to compile the package for one architecture at a time in the source code directory. After you have installed the package for one architecture, use `make distclean' before reconfiguring for another architecture. On MacOS X 10.5 and later systems, you can create libraries and executables that work on multiple system types--known as "fat" or "universal" binaries--by specifying multiple `-arch' options to the compiler but only a single `-arch' option to the preprocessor. Like this: ./configure CC="gcc -arch i386 -arch x86_64 -arch ppc -arch ppc64" \ CXX="g++ -arch i386 -arch x86_64 -arch ppc -arch ppc64" \ CPP="gcc -E" CXXCPP="g++ -E" This is not guaranteed to produce working output in all cases, you may have to build one architecture at a time and combine the results using the `lipo' tool if you have problems. Installation Names ================== By default, `make install' installs the package's commands under `/usr/local/bin', include files under `/usr/local/include', etc. You can specify an installation prefix other than `/usr/local' by giving `configure' the option `--prefix=PREFIX', where PREFIX must be an absolute file name. You can specify separate installation prefixes for architecture-specific files and architecture-independent files. If you pass the option `--exec-prefix=PREFIX' to `configure', the package uses PREFIX as the prefix for installing programs and libraries. Documentation and other data files still use the regular prefix. In addition, if you use an unusual directory layout you can give options like `--bindir=DIR' to specify different values for particular kinds of files. Run `configure --help' for a list of the directories you can set and what kinds of files go in them. In general, the default for these options is expressed in terms of `${prefix}', so that specifying just `--prefix' will affect all of the other directory specifications that were not explicitly provided. The most portable way to affect installation locations is to pass the correct locations to `configure'; however, many packages provide one or both of the following shortcuts of passing variable assignments to the `make install' command line to change installation locations without having to reconfigure or recompile. The first method involves providing an override variable for each affected directory. For example, `make install prefix=/alternate/directory' will choose an alternate location for all directory configuration variables that were expressed in terms of `${prefix}'. Any directories that were specified during `configure', but not in terms of `${prefix}', must each be overridden at install time for the entire installation to be relocated. The approach of makefile variable overrides for each directory variable is required by the GNU Coding Standards, and ideally causes no recompilation. However, some platforms have known limitations with the semantics of shared libraries that end up requiring recompilation when using this method, particularly noticeable in packages that use GNU Libtool. The second method involves providing the `DESTDIR' variable. For example, `make install DESTDIR=/alternate/directory' will prepend `/alternate/directory' before all installation names. The approach of `DESTDIR' overrides is not required by the GNU Coding Standards, and does not work on platforms that have drive letters. On the other hand, it does better at avoiding recompilation issues, and works well even when some directory options were not specified in terms of `${prefix}' at `configure' time. Optional Features ================= If the package supports it, you can cause programs to be installed with an extra prefix or suffix on their names by giving `configure' the option `--program-prefix=PREFIX' or `--program-suffix=SUFFIX'. Some packages pay attention to `--enable-FEATURE' options to `configure', where FEATURE indicates an optional part of the package. They may also pay attention to `--with-PACKAGE' options, where PACKAGE is something like `gnu-as' or `x' (for the X Window System). The `README' should mention any `--enable-' and `--with-' options that the package recognizes. 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NLopt 2.4.1 (19 November 2013) * Use cdecl calling convention instead of stdcall on Win32, to simplify shared-library usage and for consistency with Win64. NLopt 2.4 (2 November 2013) * New genetic algorithm ESCH, thanks to Carlos Henrique da Silva Santos. * Fix swig dir for VPATH builds, thanks to Sandro Vitenti for the bug report. * Use python-config program in the configure script to find the include directories for Python, if possible (may be overridden by PYTHON_CONFIG environment variable). * Bugfix in copy constructor for C++ and Python interfaces. * Bugfix in return value for setting min/max objective. * Handle negative rescalings in COBYLA and BOBYQA. * Plugin installation honors the configure --prefix, if any (thanks to xantares@github). * Various compilation fixes for compatibility with recent software. NLopt 2.3.1 (16 September 2012) * Bug fix: COBLYA should return ROUNDOFF_LIMITED rather than XTOL_REACHED if the trust-region radius underflows to zero; thanks to David Liu. * Bug fix: incorrect return value from set min/max objective. * Handle case of negative rescalings (from negative dx) in COBYLA and BOBYQA; thanks to Alexander Law for the bug report NLopt 2.3 (20 July 2012) * In Matlab/Octave interface, make returning NaN from the objective equivalent to nlopt_force_stop; thanks to Norman Violet for the suggestion. * Added CCSA-quadratic (NLOPT_LD_CCSAQ), similar to MMA. * Added interface for supplying a preconditioner (approximate Hessian); currently only supported in CCSAQ. * When adding mconstraints, allow tol==NULL as synonym for zero tolerances. * Added missing NLOPT_LD_SLSQP constant in Matlab/Octave. * Lower tolerance for dual optimization in MMA/CCSAQ; thanks to Christophe Leruste for the problem report * Fixed bug in timer, thanks to William Vaughn for the patch. * Bug fix to convergence test in sbplx; thanks to Douglas Bates. NLopt 2.2.4 (9 June 2011) * Fixed linking problem for C++ and Python shared libraries on MacOS X; thanks to Volker Lorrmann for the bug report. NLopt 2.2.3 (8 June 2011) * Fixed additional re-entrancy problem in Luksan algorithms missed in the previous version; thanks to Gert Wollny for the bug report. * Fixed set/get vector_storage in Fortran interface for Fortran compilers with all-uppercase linkage. NLopt 2.2.2 (26 May 2011) * Added set_vector_storage API to modify the memory usage and the subspace dimension for low-storage quasi-Newton methods. * Fixed missing support for maxtime stopping criteria in Luksan and ORIG_DIRECT algorithms; thanks to Jurgen Werner for the bug report. * Fixed algorithms to support the case where the lower and upper bounds are equal for some variables (which effectively eliminates those variables from optimization). * Added missing xtol check to SLSQP, which caused erroneous ROUNDOFF_LIMITED error codes to be returned; thanks to Alexander Riess for the bug report. * Fixed slight overcounting of function evaluations for maxeval check in SLSQP. * Fixed deprecated API to support xtol_abs == NULL for backward compatibility (thanks to Francesco Biscani for the bug report). * Made Luksan algorithms (e.g. LBFGS and other quasi-Newton methods) re-entrant; thanks to Gert Wollny for the bug report. (Fixes Gentoo bug #368685.) * Fixed bug in DIRECT (not ORIG_DIRECT), where a typo caused suboptimal convergence in some cases; thanks to Sinisa Hristov for the bug report. NLopt 2.2.1 (6 September 2010) * If you compile nlopt.h with the NLOPT_DLL_EXPORT symbol #defined, it now uses the dllexport directive (under Windows), useful for compiling an NLopt DLL under Microsoft compilers; thanks to Benoit Scherrer for the suggestion. * Handle case where copysign function is missing, e.g. on Windows; thanks to Benoit Scherrer for the bug report. * Remove C99-style mixed declaration and code in a couple files, so that code compiles in C89; thanks to Benoit Scherrer for the bug report. * Removed a few compiler warnings under Microsoft compilers; thanks to Benoit Scherrer for the bug report. * Export nlopt_get_algorithm_name function on Windows; thanks to Ofek Shilon for the bug report. * Don't use dllimport directive with lcc on Windows (which doesn't support it); thanks to Laurent Vanbeylen for the bug report. * Update Nodedal README directory to indicate that Nocedal's LBFGS code is now available under the GPL, and therefore may be distributed with a future NLopt version (although Luksan's LBFGS code already works well). * Bug fix in set/get_xtol_abs; thanks to David Rivest-Henault for the report. NLopt 2.2 (15 July 2010) * Added SLSQP algorithm for gradient-based local optimization with nonlinear constraints via sequential quadratic programming, based on the implementation by Dieter Kraft that was adapted for SciPy. * Modified BOBYQA and COBYLA algorithms to support unequal initial step sizes in different directions; thanks to Tom Fiddaman for pointing out the need for this in the case where different directions have very different scales. * Added Python module docstring; thanks to Sebastian Walter for the suggestion. * Added GUILE_INSTALL_DIR variable to allow the user to change the Guile installation directory. * Added Fortran interface for vector-valued constraints. * Throw correct exceptions in Python for the add_*constraint functions; thanks to Dmitrey Kroshko for the bug report. * Support forced stop and exceptions in ORIG_DIRECT algorithm. * Remove arbitrary 1e20 upper bound on function values from ORIG_DIRECT code. * Bugfix in C++ interface (and some other language front-ends) when deallocating the nlopt_opt object in cases like MLSL where local_optimizer is used; thanks to Jurgen Werner for the bug report. NLopt 2.1.2 (8 July 2010) * The Python mconstraint (vector-valued constraint) functions now pass a 2-dimensional array for the gradient argument, rather than a flattened 1d array. * Improved handling of exceptions and forced stops for constrained optimization, making sure that no constraints are evaluated after the stop. * Return an NLOPT_INVALID_ARGS error if more than n equality constraints are added in an n-dimensional problem. * Fix bug that could cause spurious NLOPT_INVALID_ARGS errors when adding constraints under rare circumstances. * Eliminate a few small memory leaks that could occur under error conditions. NLopt 2.1.1 (7 July 2010) * More robust configure check for Python include directories, via distutils.sysconfig.get_python_inc() and numpy.get_include(). Thanks to Nathaniel Smith for the tip. * Bug fix in Guile interface: added missing prefix to nlopt-version-major etcetera. NLopt 2.1 (6 July 2010) * New vector-valued constraint feature; thanks to Dmitrey Kroshko of OpenOpt for the suggestion. * COBYLA now accepts equality constraints. * Guard against multiple inclusion in nlopt.hpp; thanks to Saul Thurrowgood for the suggestion. NLopt 2.0.2 (17 June 2010) * Fixed compilation failure in Microsoft Visual Studio, due to incorrect usage of __stdcall keyword; thanks to Dave Katz for the bug report. NLopt 2.0.1 (16 June 2010) * Bug fix in Fortran API (for nlo_get_ functions returning arrays). * Fixed buggy compilation with MinGW. NLopt 2.0 (15 June 2010) * New C API, that works by creating an nlopt_opt "object" and then calling functions to set the optimization parameters -- much more extensible than the old API (which is preserved for backwards compatibility). (Updated Fortran, Matlab, and GNU Octave wrappers as well.) * C++ nlopt.hpp wrapper around C API, allowing namespaces, object constructors/destructors, std::vector, and exceptions to be exploited. * New nlopt wrappers callable from Python and GNU Guile, generated with the help of SWIG. * New 'man nlopt' manual page documenting new API. * New AUGLAG algorithm(s) implementing an augmented-Lagrangian method proposed by Birgin and Martinez (2008), which supports nonlinear equality and inequality constraints "wrapped around" other local/global optimization methods. * Added API for nonlinear equality constraints (currently only supported by AUGLAG and ISRES algorithms). * Support inequality constraints directly in ORIG_DIRECT algorithms (no need to return NaN when constraint is violated). * Inequality/equality constraints now have optional tolerances that are used as conditions in stopping criteria. * Pseudo-randomize simplex steps in COBYLA algorithm, improving robustness by avoiding accidentally taking steps that don't improve conditioning (which seems to happen sometimes with active bound constraints). The algorithm remains deterministic (a deterministic seed is used), however. * Allow COBYLA to increase the trust-region radius if the predicted improvement was approximately right and the simplex is OK, following a suggestion in the SAS manual for PROC NLP that seems to improve convergence speed. * Added nlopt_force_stop function to force a (graceful) halt to the optimization, and corresponding NLOPT_FORCED_STOP return code. * Improved thread-safety in random-number generation: thread-local storage is used for random-number state, on compilers that support it (e.g. gcc, Intel, Microsoft), to make the generation thread-safe. In this case, the random-number seed must be set per-thread. * Return an error in global-search algorithms if the domain is not finite. * Use stdcall convention on Windows; thanks to Alan Young for the suggestion. * Added missing absolute-tolerance criteria in Luksan algorithms; thanks to Greg Nicholas for the bug report. * Fixed compilation under C++, and use C++ compiler for everything in --with-cxx mode; thanks to Greg Nicholas for the bug report. * In MMA, only stop at minf_max/stopval if the point is feasible. * Fix Matlab mex file to not include unnecessary nlopt-util.h file, simplifying Windows compilation. NLopt 1.2 (18 November 2009) * Added Powell's BOBYQA algorithm for box-constrained optimization without derivatives, an improvement on NEWUOA. * Added ISRES genetic algorithm, supporting nonlinearly constrained global optimization. * New functions nlopt_{set/get}_stochastic_population to provide optional greater control over the random "population" sizes in stochastic algorithms (although it still has a sensible default). * Bug fix: remove extraneous text accidentally included in nlopt.f Fortran include file. * Bug fix: configure script now correctly handles Matlab installation when MEX_INSTALL_DIR is specified manually by the user. NLopt 1.1 (12 November 2009) * configure script detects whether --enable-shared is required in order to compile Matlab and Octave plugins (as is the case on x86_64), and disables compilation of those plugins if --enable-shared is not used. * Added --without-octave and --without-matlab configure options to disable Octave and Matlab plugins, respectively. * Modified COBYLA algorithm to have better support for bound constraints. * Added new NLOPT_ROUNDOFF_LIMITED failure code to indicate cases in which optimization breaks down due to roundoff errors, in which case it is possible that useful results were obtained. * Experimental support for nonlinear equality constraints via augmented-Lagrangian method. * Support for compiling under Windows (ideally with MinGW) as a DLL, although you have to manually add #define NLOPT_DLL to nlopt.h *after* installing (after compiling NLopt). * Added several checks for roundoff-related breakdown to NEWUOA code. * When only a relative error tolerance is specified, no longer fails to halt when exact convergence to zero is obtained. * Workaround for incompatible qsort_r functions in BSD and GNU libc by always using my own version; thanks to Wendy Vandoolaeghe and Philippe Preux for the bug report and explanation. * Workaround for gcc 3.4.x conflict with HUGE_VAL definition in Solaris (gcc bug 19933). * Better identification of Matlab-plugin installation directory. * Fixed identification of Octave-plugin installation directory for recent Octave versions. NLopt 1.0.1 (13 Nov. 2008) * Allow user to override Matlab-plugin installation directory with MEX_INSTALL_DIR. * Bug fix in my DIRECT code that prevented convergence (DIRECT-L unaffected). * MLSL needs a nonzero default ftol_rel and/or xtol_rel to ensure that its local searches terminate; use roughly machine precision as defaults. 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It is designed as as simple, unified interface and packaging of several free/open-source nonlinear optimization libraries. The latest release and a complete manual may be found at the NLopt home page: http://ab-initio.mit.edu/nlopt It is compiled and installed with the standard GNU autoconf/automake commands: ./configure make make install See `./configure --help` or the `INSTALL` file for other options. To build the latest development sources: git clone git://github.com/stevengj/nlopt cd nlopt sh autogen.sh make (To build from git, you will need GNU autoconf, automake, and libtool installed, along with SWIG and Unix tools such as m4, perl, and sed.) Once it is installed, `#include ` in your C/C++ programs and link it with `-lnlopt -lm`. You may need to use the C++ compiler to link in order to include the C++ libraries (which are used internally by NLopt, even though it has a C API). The minimization function, `nlopt_minimize`, is described in the [man page](http://en.wikipedia.org/wiki/Man_page) (`api/nlopt_minimize.3`, which is installed by `make install`. See also the manual on our web page. There are also interfaces for Fortran, Python, Matlab, Octave, OCaml, GNU Guile, GNU R, Lua, and Julia. Interfaces for other languages may be added in the future. nloptr/src/nlopt_src/cobyla/0000755000176000001440000000000013263734242015671 5ustar ripleyusersnloptr/src/nlopt_src/cobyla/COPYRIGHT0000644000176000001440000000227513263734242017172 0ustar ripleyusersCopyright (c) 1992, Michael J. D. Powell (M.J.D.Powell@damtp.cam.ac.uk) Copyright (c) 2004, Jean-Sebastien Roy (js@jeannot.org) Copyright (c) 2008, Steven G. 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D. Powell (M.J.D.Powell@damtp.cam.ac.uk) * Copyright (c) 2004, Jean-Sebastien Roy (js@jeannot.org) * * Permission is hereby granted, free of charge, to any person obtaining a * copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be included * in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS * OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ /* * This software is a C version of COBYLA2, a contrained optimization by linear * approximation package developed by Michael J. D. Powell in Fortran. * * The original source code can be found at : * http://plato.la.asu.edu/topics/problems/nlores.html */ /* $Jeannot: cobyla.h,v 1.10 2004/04/18 09:51:37 js Exp $ */ #ifndef _COBYLA_ #define _COBYLA_ #include "nlopt.h" #include "nlopt-util.h" #ifdef __cplusplus extern "C" { #endif /* __cplusplus */ /* NLopt-style interface function */ nlopt_result cobyla_minimize(unsigned n, nlopt_func f, void *f_data, unsigned m, nlopt_constraint *fc, unsigned p, nlopt_constraint *h, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, nlopt_stopping *stop, const double *dx); #ifdef __cplusplus } #endif #endif /* _COBYLA_ */ nloptr/src/nlopt_src/cobyla/README0000644000176000001440000000403313263734242016551 0ustar ripleyusersThis code implements COBYLA (Constrained Optimization BY Linear Approximations) algorithm derivative free optimization with nonlinear inequality constraints by M. J. D. Powell, described by: M. J. D. Powell, "A direct search optimization method that models the objective and constraint functions by linear interpolation," in Advances in Optimization and Numerical Analysis, eds. S. Gomez and J.-P. Hennart (Kluwer Academic: Dordrecht, 1994), p. 51-67. and reviewed in: M. J. D. Powell, "Direct search algorithms for optimization calculations," Acta Numerica 7, 287-336 (1998). It constructs successive linear approximations of the objective function and constraints via a simplex of n+1 points (in n dimensions), and optimizes these approximations in a trust region at each step. The original code itself was written in Fortran by Powell, and apparently released without restrictions (like several of his other programs), and was converted to C in 2004 by Jean-Sebastien Roy (js@jeannot.org) for the SciPy project. The C version was released under the attached license (basically the MIT license) at: http://www.jeannot.org/~js/code/index.en.html#COBYLA It was incorporated into NLopt in 2008 by S. G. Johnson, and kept under the same MIT license. In incorporating it into NLopt, SGJ adapted it to include the NLopt stopping conditions (the original code provided an x tolerance and a maximum number of function evaluations only). The original COBYLA did not have explicit support for bound constraints; these are included as linear constraints along with any other nonlinear constraints. This is mostly fine---linear constraints are handled exactly by COBYLA's linear approximations. However, occasionally COBYLA takes a "simplex" step, either to create the initial simplex or to fix a degenerate simplex, and these steps could violate the bound constraints. SGJ modified COBYLA to explicitly honor the bound constraints in these cases, so that the objective/constraints are never evaluated outside of the bound constraints, without slowing convergence. nloptr/src/nlopt_src/cobyla/cobyla.c0000644000176000001440000015422213355070635017315 0ustar ripleyusers/* cobyla : contrained optimization by linear approximation */ /* * Copyright (c) 1992, Michael J. D. Powell (M.J.D.Powell@damtp.cam.ac.uk) * Copyright (c) 2004, Jean-Sebastien Roy (js@jeannot.org) * * Permission is hereby granted, free of charge, to any person obtaining a * copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be included * in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS * OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ /* * This software is a C version of COBYLA2, a contrained optimization by linear * approximation package developed by Michael J. D. Powell in Fortran. * * The original source code can be found at : * http://plato.la.asu.edu/topics/problems/nlores.html */ static char const rcsid[] = "@(#) $Jeannot: cobyla.c,v 1.11 2004/04/18 09:51:36 js Exp $"; #include #include #include #include "cobyla.h" /* SGJ, 2008: modified COBYLA code to take explicit account of bound constraints. Since bound constraints are linear, these should already be handled exactly when COBYLA optimizes it linear model. However, they were not handled properly when COBYLA created its initial simplex, or when COBYLA updated unacceptable simplices. Since NLopt guarantees that the objective will not be evaluated outside the bound constraints, this required us to handle such points by putting a slope discontinuity into the objective & constraints (below), which slows convergence considerably for smooth functions. Instead, handling them explicitly prevents this problem */ #define ENFORCE_BOUNDS 1 #define MIN2(a,b) ((a) <= (b) ? (a) : (b)) #define MAX2(a,b) ((a) >= (b) ? (a) : (b)) #define U(n) ((unsigned) (n)) /**************************************************************************/ /* SGJ, 2008: NLopt-style interface function: */ typedef struct { nlopt_func f; void *f_data; unsigned m_orig; nlopt_constraint *fc; unsigned p; nlopt_constraint *h; double *xtmp; double *lb, *ub; double *con_tol, *scale; nlopt_stopping *stop; } func_wrap_state; static int func_wrap(int ni, int mi, double *x, double *f, double *con, func_wrap_state *s) { unsigned n = U(ni); unsigned i, j, k; double *xtmp = s->xtmp; const double *lb = s->lb, *ub = s->ub; (void) mi; /* unused */ /* in nlopt, we guarante that the function is never evaluated outside the lb and ub bounds, so we need force this with xtmp ... note that this leads to discontinuity in the first derivative, which slows convergence if we don't enable the ENFORCE_BOUNDS feature above. */ for (j = 0; j < n; ++j) { if (x[j] < lb[j]) xtmp[j] = lb[j]; else if (x[j] > ub[j]) xtmp[j] = ub[j]; else xtmp[j] = x[j]; } nlopt_unscale(n, s->scale, xtmp, xtmp); *f = s->f(n, xtmp, NULL, s->f_data); if (nlopt_stop_forced(s->stop)) return 1; i = 0; for (j = 0; j < s->m_orig; ++j) { nlopt_eval_constraint(con + i, NULL, s->fc+j, n, xtmp); if (nlopt_stop_forced(s->stop)) return 1; for (k = 0; k < s->fc[j].m; ++k) con[i + k] = -con[i + k]; i += s->fc[j].m; } for (j = 0; j < s->p; ++j) { nlopt_eval_constraint(con + i, NULL, s->h+j, n, xtmp); if (nlopt_stop_forced(s->stop)) return 1; for (k = 0; k < s->h[j].m; ++k) con[(i + s->h[j].m) + k] = -con[i + k]; i += 2 * s->h[j].m; } for (j = 0; j < n; ++j) { if (!nlopt_isinf(lb[j])) con[i++] = x[j] - lb[j]; if (!nlopt_isinf(ub[j])) con[i++] = ub[j] - x[j]; } return 0; } /* * Verbosity level */ typedef enum { COBYLA_MSG_NONE = 0, /* No messages */ COBYLA_MSG_EXIT = 1, /* Exit reasons */ COBYLA_MSG_ITER = 2, /* Rho and Sigma changes */ COBYLA_MSG_INFO = 3 /* Informational messages */ } cobyla_message; /* * A function as required by cobyla * state is a void pointer provided to the function at each call * * n : the number of variables * m : the number of constraints * x : on input, then vector of variables (should not be modified) * f : on output, the value of the function * con : on output, the value of the constraints (vector of size m) * state : on input, the value of the state variable as provided to cobyla * * COBYLA will try to make all the values of the constraints positive. * So if you want to input a constraint j such as x[i] <= MAX, set: * con[j] = MAX - x[i] * The function must returns 0 if no error occurs or 1 to immediately end the * minimization. * */ typedef int cobyla_function(int n, int m, double *x, double *f, double *con, func_wrap_state *state); /* * cobyla : minimize a function subject to constraints * * n : number of variables (>=0) * m : number of constraints (>=0) * x : on input, initial estimate ; on output, the solution * minf : on output, minimum objective function * rhobeg : a reasonable initial change to the variables * stop : the NLopt stopping criteria * lb, ub : lower and upper bounds on x * message : see the cobyla_message enum * calcfc : the function to minimize (see cobyla_function) * state : used by function (see cobyla_function) * * The cobyla function returns the usual nlopt_result codes. * */ extern nlopt_result cobyla(int n, int m, double *x, double *minf, double rhobeg, double rhoend, nlopt_stopping *stop, const double *lb, const double *ub, int message, cobyla_function *calcfc, func_wrap_state *state); nlopt_result cobyla_minimize(unsigned n, nlopt_func f, void *f_data, unsigned m, nlopt_constraint *fc, unsigned p, nlopt_constraint *h, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, nlopt_stopping *stop, const double *dx) { unsigned i, j; func_wrap_state s; nlopt_result ret; double rhobeg, rhoend; s.f = f; s.f_data = f_data; s.m_orig = m; s.fc = fc; s.p = p; s.h = h; s.stop = stop; s.lb = s.ub = s.xtmp = s.con_tol = s.scale = NULL; s.scale = nlopt_compute_rescaling(n, dx); if (!s.scale) { ret = NLOPT_OUT_OF_MEMORY; goto done; } s.lb = nlopt_new_rescaled(n, s.scale, lb); if (!s.lb) { ret = NLOPT_OUT_OF_MEMORY; goto done; } s.ub = nlopt_new_rescaled(n, s.scale, ub); if (!s.ub) { ret = NLOPT_OUT_OF_MEMORY; goto done; } nlopt_reorder_bounds(n, s.lb, s.ub); s.xtmp = (double *) malloc(sizeof(double) * n); if (!s.xtmp) { ret = NLOPT_OUT_OF_MEMORY; goto done; } /* SGJ, 2008: compute rhoend from NLopt stop info */ rhobeg = fabs(dx[0] / s.scale[0]); rhoend = stop->xtol_rel * (rhobeg); for (j = 0; j < n; ++j) if (rhoend < stop->xtol_abs[j] / fabs(s.scale[j])) rhoend = stop->xtol_abs[j] / fabs(s.scale[j]); /* each equality constraint gives two inequality constraints */ m = nlopt_count_constraints(m, fc) + 2 * nlopt_count_constraints(p, h); /* add constraints for lower/upper bounds (if any) */ for (j = 0; j < n; ++j) { if (!nlopt_isinf(lb[j])) ++m; if (!nlopt_isinf(ub[j])) ++m; } s.con_tol = (double *) malloc(sizeof(double) * m); if (m && !s.con_tol) { ret = NLOPT_OUT_OF_MEMORY; goto done; } for (j = 0; j < m; ++j) s.con_tol[j] = 0; for (j = i = 0; i < s.m_orig; ++i) { unsigned ji = j, jnext = j + fc[i].m; for (; j < jnext; ++j) s.con_tol[j] = fc[i].tol[j - ji]; } for (i = 0; i < s.p; ++i) { unsigned ji = j, jnext = j + h[i].m; for (; j < jnext; ++j) s.con_tol[j] = h[i].tol[j - ji]; ji = j; jnext = j + h[i].m; for (; j < jnext; ++j) s.con_tol[j] = h[i].tol[j - ji]; } nlopt_rescale(n, s.scale, x, x); ret = cobyla((int) n, (int) m, x, minf, rhobeg, rhoend, stop, s.lb, s.ub, COBYLA_MSG_NONE, func_wrap, &s); nlopt_unscale(n, s.scale, x, x); /* make sure e.g. rounding errors didn't push us slightly out of bounds */ for (j = 0; j < n; ++j) { if (x[j] < lb[j]) x[j] = lb[j]; if (x[j] > ub[j]) x[j] = ub[j]; } done: free(s.con_tol); free(s.xtmp); free(s.ub); free(s.lb); free(s.scale); return ret; } /**************************************************************************/ /* SGJ, 2010: I find that it seems to increase robustness of the algorithm if, on "simplex" steps (which are intended to improve the linear independence of the simplex, not improve the objective), we multiply the steps by pseudorandom numbers in [0,1). Intuitively, pseudorandom steps are likely to avoid any accidental dependency in the simplex vertices. However, since I still want COBYLA to be a deterministic algorithm, and I don't care all that much about the quality of the randomness here, I implement a simple linear congruential generator rather than use nlopt_urand, and set the initial seed deterministically. */ #if defined(HAVE_STDINT_H) # include #endif #ifndef HAVE_UINT32_T # if SIZEOF_UNSIGNED_LONG == 4 typedef unsigned long uint32_t; # elif SIZEOF_UNSIGNED_INT == 4 typedef unsigned int uint32_t; # else # error No 32-bit unsigned integer type # endif #endif /* a simple linear congruential generator */ static uint32_t lcg_rand(uint32_t *seed) { return (*seed = *seed * 1103515245 + 12345); } static double lcg_urand(uint32_t *seed, double a, double b) { return a + lcg_rand(seed) * (b - a) / ((uint32_t) -1); } /**************************************************************************/ static nlopt_result cobylb(int *n, int *m, int *mpp, double *x, double *minf, double *rhobeg, double rhoend, nlopt_stopping *stop, const double *lb, const double *ub, int *iprint, double *con, double *sim, double *simi, double *datmat, double *a, double *vsig, double *veta, double *sigbar, double *dx, double *w, int *iact, cobyla_function *calcfc, func_wrap_state *state); static nlopt_result trstlp(int *n, int *m, double *a, double *b, double *rho, double *dx, int *ifull, int *iact, double *z__, double *zdota, double *vmultc, double *sdirn, double *dxnew, double *vmultd); /* ------------------------------------------------------------------------ */ nlopt_result cobyla(int n, int m, double *x, double *minf, double rhobeg, double rhoend, nlopt_stopping *stop, const double *lb, const double *ub, int iprint, cobyla_function *calcfc, func_wrap_state *state) { int icon, isim, isigb, idatm, iveta, isimi, ivsig, iwork, ia, idx, mpp; int *iact; double *w; nlopt_result rc; /* * This subroutine minimizes an objective function F(X) subject to M * inequality constraints on X, where X is a vector of variables that has * N components. The algorithm employs linear approximations to the * objective and constraint functions, the approximations being formed by * linear interpolation at N+1 points in the space of the variables. * We regard these interpolation points as vertices of a simplex. The * parameter RHO controls the size of the simplex and it is reduced * automatically from RHOBEG to RHOEND. For each RHO the subroutine tries * to achieve a good vector of variables for the current size, and then * RHO is reduced until the value RHOEND is reached. Therefore RHOBEG and * RHOEND should be set to reasonable initial changes to and the required * accuracy in the variables respectively, but this accuracy should be * viewed as a subject for experimentation because it is not guaranteed. * The subroutine has an advantage over many of its competitors, however, * which is that it treats each constraint individually when calculating * a change to the variables, instead of lumping the constraints together * into a single penalty function. The name of the subroutine is derived * from the phrase Constrained Optimization BY Linear Approximations. * * The user must set the values of N, M, RHOBEG and RHOEND, and must * provide an initial vector of variables in X. Further, the value of * IPRINT should be set to 0, 1, 2 or 3, which controls the amount of * printing during the calculation. Specifically, there is no output if * IPRINT=0 and there is output only at the end of the calculation if * IPRINT=1. Otherwise each new value of RHO and SIGMA is printed. * Further, the vector of variables and some function information are * given either when RHO is reduced or when each new value of F(X) is * computed in the cases IPRINT=2 or IPRINT=3 respectively. Here SIGMA * is a penalty parameter, it being assumed that a change to X is an * improvement if it reduces the merit function * F(X)+SIGMA*MAX(0.0,-C1(X),-C2(X),...,-CM(X)), * where C1,C2,...,CM denote the constraint functions that should become * nonnegative eventually, at least to the precision of RHOEND. In the * printed output the displayed term that is multiplied by SIGMA is * called MAXCV, which stands for 'MAXimum Constraint Violation'. The * argument MAXFUN is an int variable that must be set by the user to a * limit on the number of calls of CALCFC, the purpose of this routine being * given below. The value of MAXFUN will be altered to the number of calls * of CALCFC that are made. The arguments W and IACT provide real and * int arrays that are used as working space. Their lengths must be at * least N*(3*N+2*M+11)+4*M+6 and M+1 respectively. * * In order to define the objective and constraint functions, we require * a subroutine that has the name and arguments * SUBROUTINE CALCFC (N,M,X,F,CON) * DIMENSION X(*),CON(*) . * The values of N and M are fixed and have been defined already, while * X is now the current vector of variables. The subroutine should return * the objective and constraint functions at X in F and CON(1),CON(2), * ...,CON(M). Note that we are trying to adjust X so that F(X) is as * small as possible subject to the constraint functions being nonnegative. * * Partition the working space array W to provide the storage that is needed * for the main calculation. */ stop->nevals = 0; if (n == 0) { if (iprint>=1) fprintf(stderr, "cobyla: N==0.\n"); return NLOPT_SUCCESS; } if (n < 0 || m < 0) { if (iprint>=1) fprintf(stderr, "cobyla: N<0 or M<0.\n"); return NLOPT_INVALID_ARGS; } /* workspace allocation */ w = (double*) malloc(U(n*(3*n+2*m+11)+4*m+6)*sizeof(*w)); if (w == NULL) { if (iprint>=1) fprintf(stderr, "cobyla: memory allocation error.\n"); return NLOPT_OUT_OF_MEMORY; } iact = (int*)malloc(U(m+1)*sizeof(*iact)); if (iact == NULL) { if (iprint>=1) fprintf(stderr, "cobyla: memory allocation error.\n"); free(w); return NLOPT_OUT_OF_MEMORY; } /* Parameter adjustments */ --iact; --w; --x; --lb; --ub; /* Function Body */ mpp = m + 2; icon = 1; isim = icon + mpp; isimi = isim + n * n + n; idatm = isimi + n * n; ia = idatm + n * mpp + mpp; ivsig = ia + m * n + n; iveta = ivsig + n; isigb = iveta + n; idx = isigb + n; iwork = idx + n; rc = cobylb(&n, &m, &mpp, &x[1], minf, &rhobeg, rhoend, stop, &lb[1], &ub[1], &iprint, &w[icon], &w[isim], &w[isimi], &w[idatm], &w[ia], &w[ivsig], &w[iveta], &w[isigb], &w[idx], &w[iwork], &iact[1], calcfc, state); /* Parameter adjustments (reverse) */ ++iact; ++w; free(w); free(iact); return rc; } /* cobyla */ /* ------------------------------------------------------------------------- */ static nlopt_result cobylb(int *n, int *m, int *mpp, double *x, double *minf, double *rhobeg, double rhoend, nlopt_stopping *stop, const double *lb, const double *ub, int *iprint, double *con, double *sim, double *simi, double *datmat, double *a, double *vsig, double *veta, double *sigbar, double *dx, double *w, int *iact, cobyla_function *calcfc, func_wrap_state *state) { /* System generated locals */ int sim_dim1, sim_offset, simi_dim1, simi_offset, datmat_dim1, datmat_offset, a_dim1, a_offset, i__1, i__2, i__3; double d__1, d__2; /* Local variables */ double alpha, delta, denom, tempa, barmu; double beta, cmin = 0.0, cmax = 0.0; double cvmaxm, dxsign, prerem = 0.0; double edgmax, pareta, prerec = 0.0, phimin, parsig = 0.0; double gamma_; double phi, rho, sum = 0.0; double ratio, vmold, parmu, error, vmnew; double resmax, cvmaxp; double resnew, trured; double temp, wsig, f; double weta; int i__, j, k, l; int idxnew; int iflag = 0; int iptemp; int isdirn, izdota; int ivmc; int ivmd; int mp, np, iz, ibrnch; int nbest, ifull, iptem, jdrop; nlopt_result rc = NLOPT_SUCCESS; uint32_t seed = (uint32_t) (*n + *m); /* arbitrary deterministic LCG seed */ int feasible; /* SGJ, 2008: added code to keep track of minimum feasible function val */ *minf = HUGE_VAL; /* Set the initial values of some parameters. The last column of SIM holds */ /* the optimal vertex of the current simplex, and the preceding N columns */ /* hold the displacements from the optimal vertex to the other vertices. */ /* Further, SIMI holds the inverse of the matrix that is contained in the */ /* first N columns of SIM. */ /* Parameter adjustments */ a_dim1 = *n; a_offset = 1 + a_dim1 * 1; a -= a_offset; simi_dim1 = *n; simi_offset = 1 + simi_dim1 * 1; simi -= simi_offset; sim_dim1 = *n; sim_offset = 1 + sim_dim1 * 1; sim -= sim_offset; datmat_dim1 = *mpp; datmat_offset = 1 + datmat_dim1 * 1; datmat -= datmat_offset; --x; --con; --vsig; --veta; --sigbar; --dx; --w; --iact; --lb; --ub; /* Function Body */ iptem = MIN2(*n,4); iptemp = iptem + 1; np = *n + 1; mp = *m + 1; alpha = .25; beta = 2.1; gamma_ = .5; delta = 1.1; rho = *rhobeg; parmu = 0.; if (*iprint >= 2) { fprintf(stderr, "cobyla: the initial value of RHO is %12.6E and PARMU is set to zero.\n", rho); } temp = 1. / rho; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { double rhocur; sim[i__ + np * sim_dim1] = x[i__]; i__2 = *n; for (j = 1; j <= i__2; ++j) { sim[i__ + j * sim_dim1] = 0.; simi[i__ + j * simi_dim1] = 0.; } rhocur = rho; #if ENFORCE_BOUNDS /* SGJ: make sure step rhocur stays inside [lb,ub] */ if (x[i__] + rhocur > ub[i__]) { if (x[i__] - rhocur >= lb[i__]) rhocur = -rhocur; else if (ub[i__] - x[i__] > x[i__] - lb[i__]) rhocur = 0.5 * (ub[i__] - x[i__]); else rhocur = 0.5 * (x[i__] - lb[i__]); } #endif sim[i__ + i__ * sim_dim1] = rhocur; simi[i__ + i__ * simi_dim1] = 1.0 / rhocur; } jdrop = np; ibrnch = 0; /* Make the next call of the user-supplied subroutine CALCFC. These */ /* instructions are also used for calling CALCFC during the iterations of */ /* the algorithm. */ /* SGJ comment: why the hell couldn't he just use a damn subroutine? #*&!%*@ Fortran-66 spaghetti code */ L40: if (nlopt_stop_forced(stop)) rc = NLOPT_FORCED_STOP; else if (stop->nevals > 0) { if (nlopt_stop_evals(stop)) rc = NLOPT_MAXEVAL_REACHED; else if (nlopt_stop_time(stop)) rc = NLOPT_MAXTIME_REACHED; } if (rc != NLOPT_SUCCESS) goto L600; stop->nevals++; if (calcfc(*n, *m, &x[1], &f, &con[1], state)) { if (*iprint >= 1) { fprintf(stderr, "cobyla: user requested end of minimization.\n"); } rc = NLOPT_FORCED_STOP; goto L600; } resmax = 0.; feasible = 1; /* SGJ, 2010 */ if (*m > 0) { i__1 = *m; for (k = 1; k <= i__1; ++k) { d__1 = resmax, d__2 = -con[k]; resmax = MAX2(d__1,d__2); if (d__2 > state->con_tol[k-1]) feasible = 0; /* SGJ, 2010 */ } } /* SGJ, 2008: check for minf_max reached by feasible point */ if (f < stop->minf_max && feasible) { rc = NLOPT_MINF_MAX_REACHED; goto L620; /* not L600 because we want to use current x, f, resmax */ } if (stop->nevals == *iprint - 1 || *iprint == 3) { fprintf(stderr, "cobyla: NFVALS = %4d, F =%13.6E, MAXCV =%13.6E\n", stop->nevals, f, resmax); i__1 = iptem; fprintf(stderr, "cobyla: X ="); for (i__ = 1; i__ <= i__1; ++i__) { if (i__>1) fprintf(stderr, " "); fprintf(stderr, "%13.6E", x[i__]); } if (iptem < *n) { i__1 = *n; for (i__ = iptemp; i__ <= i__1; ++i__) { if (!((i__-1) % 4)) fprintf(stderr, "\ncobyla: "); fprintf(stderr, "%15.6E", x[i__]); } } fprintf(stderr, "\n"); } con[mp] = f; con[*mpp] = resmax; if (ibrnch == 1) { goto L440; } /* Set the recently calculated function values in a column of DATMAT. This */ /* array has a column for each vertex of the current simplex, the entries of */ /* each column being the values of the constraint functions (if any) */ /* followed by the objective function and the greatest constraint violation */ /* at the vertex. */ i__1 = *mpp; for (k = 1; k <= i__1; ++k) { datmat[k + jdrop * datmat_dim1] = con[k]; } if (stop->nevals > np) { goto L130; } /* Exchange the new vertex of the initial simplex with the optimal vertex if */ /* necessary. Then, if the initial simplex is not complete, pick its next */ /* vertex and calculate the function values there. */ if (jdrop <= *n) { if (datmat[mp + np * datmat_dim1] <= f) { x[jdrop] = sim[jdrop + np * sim_dim1]; } else { /* improvement in function val */ double rhocur = x[jdrop] - sim[jdrop + np * sim_dim1]; /* SGJ: use rhocur instead of rho. In original code, rhocur == rho always, but I want to change this to ensure that simplex points fall within [lb,ub]. */ sim[jdrop + np * sim_dim1] = x[jdrop]; i__1 = *mpp; for (k = 1; k <= i__1; ++k) { datmat[k + jdrop * datmat_dim1] = datmat[k + np * datmat_dim1] ; datmat[k + np * datmat_dim1] = con[k]; } i__1 = jdrop; for (k = 1; k <= i__1; ++k) { sim[jdrop + k * sim_dim1] = -rhocur; temp = 0.f; i__2 = jdrop; for (i__ = k; i__ <= i__2; ++i__) { temp -= simi[i__ + k * simi_dim1]; } simi[jdrop + k * simi_dim1] = temp; } } } if (stop->nevals <= *n) { /* evaluating initial simplex */ jdrop = stop->nevals; /* SGJ: was += rho, but using sim[jdrop,jdrop] enforces consistency if we change the stepsize above to stay in [lb,ub]. */ x[jdrop] += sim[jdrop + jdrop * sim_dim1]; goto L40; } L130: ibrnch = 1; /* Identify the optimal vertex of the current simplex. */ L140: phimin = datmat[mp + np * datmat_dim1] + parmu * datmat[*mpp + np * datmat_dim1]; nbest = np; i__1 = *n; for (j = 1; j <= i__1; ++j) { temp = datmat[mp + j * datmat_dim1] + parmu * datmat[*mpp + j * datmat_dim1]; if (temp < phimin) { nbest = j; phimin = temp; } else if (temp == phimin && parmu == 0.) { if (datmat[*mpp + j * datmat_dim1] < datmat[*mpp + nbest * datmat_dim1]) { nbest = j; } } } /* Switch the best vertex into pole position if it is not there already, */ /* and also update SIM, SIMI and DATMAT. */ if (nbest <= *n) { i__1 = *mpp; for (i__ = 1; i__ <= i__1; ++i__) { temp = datmat[i__ + np * datmat_dim1]; datmat[i__ + np * datmat_dim1] = datmat[i__ + nbest * datmat_dim1] ; datmat[i__ + nbest * datmat_dim1] = temp; } i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp = sim[i__ + nbest * sim_dim1]; sim[i__ + nbest * sim_dim1] = 0.; sim[i__ + np * sim_dim1] += temp; tempa = 0.; i__2 = *n; for (k = 1; k <= i__2; ++k) { sim[i__ + k * sim_dim1] -= temp; tempa -= simi[k + i__ * simi_dim1]; } simi[nbest + i__ * simi_dim1] = tempa; } } /* Make an error return if SIGI is a poor approximation to the inverse of */ /* the leading N by N submatrix of SIG. */ error = 0.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { i__2 = *n; for (j = 1; j <= i__2; ++j) { temp = 0.; if (i__ == j) { temp += -1.; } i__3 = *n; for (k = 1; k <= i__3; ++k) if (sim[k + j * sim_dim1] != 0) { temp += simi[i__ + k * simi_dim1] * sim[k + j * sim_dim1]; } d__1 = error, d__2 = fabs(temp); error = MAX2(d__1,d__2); } } if (error > .1) { if (*iprint >= 1) { fprintf(stderr, "cobyla: rounding errors are becoming damaging.\n"); } rc = NLOPT_ROUNDOFF_LIMITED; goto L600; } /* Calculate the coefficients of the linear approximations to the objective */ /* and constraint functions, placing minus the objective function gradient */ /* after the constraint gradients in the array A. The vector W is used for */ /* working space. */ i__2 = mp; for (k = 1; k <= i__2; ++k) { con[k] = -datmat[k + np * datmat_dim1]; i__1 = *n; for (j = 1; j <= i__1; ++j) { w[j] = datmat[k + j * datmat_dim1] + con[k]; } i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp = 0.; i__3 = *n; for (j = 1; j <= i__3; ++j) { temp += w[j] * simi[j + i__ * simi_dim1]; } if (k == mp) { temp = -temp; } a[i__ + k * a_dim1] = temp; } } /* Calculate the values of sigma and eta, and set IFLAG=0 if the current */ /* simplex is not acceptable. */ iflag = 1; parsig = alpha * rho; pareta = beta * rho; i__1 = *n; for (j = 1; j <= i__1; ++j) { wsig = 0.; weta = 0.; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { d__1 = simi[j + i__ * simi_dim1]; wsig += d__1 * d__1; d__1 = sim[i__ + j * sim_dim1]; weta += d__1 * d__1; } vsig[j] = 1. / sqrt(wsig); veta[j] = sqrt(weta); if (vsig[j] < parsig || veta[j] > pareta) { iflag = 0; } } /* If a new vertex is needed to improve acceptability, then decide which */ /* vertex to drop from the simplex. */ if (ibrnch == 1 || iflag == 1) { goto L370; } jdrop = 0; temp = pareta; i__1 = *n; for (j = 1; j <= i__1; ++j) { if (veta[j] > temp) { jdrop = j; temp = veta[j]; } } if (jdrop == 0) { i__1 = *n; for (j = 1; j <= i__1; ++j) { if (vsig[j] < temp) { jdrop = j; temp = vsig[j]; } } } /* Calculate the step to the new vertex and its sign. */ temp = gamma_ * rho * vsig[jdrop]; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { dx[i__] = temp * simi[jdrop + i__ * simi_dim1]; } cvmaxp = 0.; cvmaxm = 0.; i__1 = mp; for (k = 1; k <= i__1; ++k) { sum = 0.; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { sum += a[i__ + k * a_dim1] * dx[i__]; } if (k < mp) { temp = datmat[k + np * datmat_dim1]; d__1 = cvmaxp, d__2 = -sum - temp; cvmaxp = MAX2(d__1,d__2); d__1 = cvmaxm, d__2 = sum - temp; cvmaxm = MAX2(d__1,d__2); } } dxsign = 1.; if (parmu * (cvmaxp - cvmaxm) > sum + sum) { dxsign = -1.; } /* Update the elements of SIM and SIMI, and set the next X. */ temp = 0.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* SGJ, 2010: pseudo-randomize simplex steps (see LCG comments above) */ dx[i__] = dxsign * dx[i__] * lcg_urand(&seed, 0.01, 1); /* SGJ: make sure dx step says in [lb,ub] */ #if ENFORCE_BOUNDS { double xi = sim[i__ + np * sim_dim1]; fixdx: if (xi + dx[i__] > ub[i__]) dx[i__] = -dx[i__]; if (xi + dx[i__] < lb[i__]) { if (xi - dx[i__] <= ub[i__]) dx[i__] = -dx[i__]; else { /* try again with halved step */ dx[i__] *= 0.5; goto fixdx; } } } #endif sim[i__ + jdrop * sim_dim1] = dx[i__]; temp += simi[jdrop + i__ * simi_dim1] * dx[i__]; } i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { simi[jdrop + i__ * simi_dim1] /= temp; } i__1 = *n; for (j = 1; j <= i__1; ++j) { if (j != jdrop) { temp = 0.; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { temp += simi[j + i__ * simi_dim1] * dx[i__]; } i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { simi[j + i__ * simi_dim1] -= temp * simi[jdrop + i__ * simi_dim1]; } } x[j] = sim[j + np * sim_dim1] + dx[j]; } goto L40; /* Calculate DX=x(*)-x(0). Branch if the length of DX is less than 0.5*RHO. */ L370: iz = 1; izdota = iz + *n * *n; ivmc = izdota + *n; isdirn = ivmc + mp; idxnew = isdirn + *n; ivmd = idxnew + *n; rc = trstlp(n, m, &a[a_offset], &con[1], &rho, &dx[1], &ifull, &iact[1], &w[ iz], &w[izdota], &w[ivmc], &w[isdirn], &w[idxnew], &w[ivmd]); if (rc != NLOPT_SUCCESS) goto L600; #if ENFORCE_BOUNDS /* SGJ: since the bound constraints are linear, we should never get a dx that lies outside the [lb,ub] constraints here, but we'll enforce this anyway just to be paranoid */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { double xi = sim[i__ + np * sim_dim1]; if (xi + dx[i__] > ub[i__]) dx[i__] = ub[i__] - xi; if (xi + dx[i__] < lb[i__]) dx[i__] = xi - lb[i__]; } #endif if (ifull == 0) { temp = 0.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { d__1 = dx[i__]; temp += d__1 * d__1; } if (temp < rho * .25 * rho) { ibrnch = 1; goto L550; } } /* Predict the change to F and the new maximum constraint violation if the */ /* variables are altered from x(0) to x(0)+DX. */ resnew = 0.; con[mp] = 0.; i__1 = mp; for (k = 1; k <= i__1; ++k) { sum = con[k]; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { sum -= a[i__ + k * a_dim1] * dx[i__]; } if (k < mp) { resnew = MAX2(resnew,sum); } } /* Increase PARMU if necessary and branch back if this change alters the */ /* optimal vertex. Otherwise PREREM and PREREC will be set to the predicted */ /* reductions in the merit function and the maximum constraint violation */ /* respectively. */ barmu = 0.; prerec = datmat[*mpp + np * datmat_dim1] - resnew; if (prerec > 0.) { barmu = sum / prerec; } if (parmu < barmu * 1.5) { parmu = barmu * 2.; if (*iprint >= 2) { fprintf(stderr, "cobyla: increase in PARMU to %12.6E\n", parmu); } phi = datmat[mp + np * datmat_dim1] + parmu * datmat[*mpp + np * datmat_dim1]; i__1 = *n; for (j = 1; j <= i__1; ++j) { temp = datmat[mp + j * datmat_dim1] + parmu * datmat[*mpp + j * datmat_dim1]; if (temp < phi) { goto L140; } if (temp == phi && parmu == 0.f) { if (datmat[*mpp + j * datmat_dim1] < datmat[*mpp + np * datmat_dim1]) { goto L140; } } } } prerem = parmu * prerec - sum; /* Calculate the constraint and objective functions at x(*). Then find the */ /* actual reduction in the merit function. */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { x[i__] = sim[i__ + np * sim_dim1] + dx[i__]; } ibrnch = 1; goto L40; L440: vmold = datmat[mp + np * datmat_dim1] + parmu * datmat[*mpp + np * datmat_dim1]; vmnew = f + parmu * resmax; trured = vmold - vmnew; if (parmu == 0. && f == datmat[mp + np * datmat_dim1]) { prerem = prerec; trured = datmat[*mpp + np * datmat_dim1] - resmax; } /* Begin the operations that decide whether x(*) should replace one of the */ /* vertices of the current simplex, the change being mandatory if TRURED is */ /* positive. Firstly, JDROP is set to the index of the vertex that is to be */ /* replaced. */ ratio = 0.; if (trured <= 0.f) { ratio = 1.f; } jdrop = 0; i__1 = *n; for (j = 1; j <= i__1; ++j) { temp = 0.; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { temp += simi[j + i__ * simi_dim1] * dx[i__]; } temp = fabs(temp); if (temp > ratio) { jdrop = j; ratio = temp; } sigbar[j] = temp * vsig[j]; } /* Calculate the value of ell. */ edgmax = delta * rho; l = 0; i__1 = *n; for (j = 1; j <= i__1; ++j) { if (sigbar[j] >= parsig || sigbar[j] >= vsig[j]) { temp = veta[j]; if (trured > 0.) { temp = 0.; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { d__1 = dx[i__] - sim[i__ + j * sim_dim1]; temp += d__1 * d__1; } temp = sqrt(temp); } if (temp > edgmax) { l = j; edgmax = temp; } } } if (l > 0) { jdrop = l; } if (jdrop == 0) { goto L550; } /* Revise the simplex by updating the elements of SIM, SIMI and DATMAT. */ temp = 0.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { sim[i__ + jdrop * sim_dim1] = dx[i__]; temp += simi[jdrop + i__ * simi_dim1] * dx[i__]; } i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { simi[jdrop + i__ * simi_dim1] /= temp; } i__1 = *n; for (j = 1; j <= i__1; ++j) { if (j != jdrop) { temp = 0.; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { temp += simi[j + i__ * simi_dim1] * dx[i__]; } i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { simi[j + i__ * simi_dim1] -= temp * simi[jdrop + i__ * simi_dim1]; } } } i__1 = *mpp; for (k = 1; k <= i__1; ++k) { datmat[k + jdrop * datmat_dim1] = con[k]; } /* Branch back for further iterations with the current RHO. */ if (trured > 0. && trured >= prerem * .1) { /* SGJ, 2010: following a suggestion in the SAS manual (which mentions a similar modification to COBYLA, although they didn't publish their source code), increase rho if predicted reduction is sufficiently close to the actual reduction. Otherwise, COBLYA seems to easily get stuck making very small steps. Also require iflag != 0 (i.e., acceptable simplex), again following SAS suggestion (otherwise I get convergence failure in some cases.) */ if (trured >= prerem * 0.9 && trured <= prerem * 1.1 && iflag) { rho *= 2.0; } goto L140; } L550: if (iflag == 0) { ibrnch = 0; goto L140; } /* SGJ, 2008: convergence tests for function vals; note that current best val is stored in datmat[mp + np * datmat_dim1], or in f if ifull == 1, and previous best is in *minf. This seems like a sensible place to put the convergence test, as it is the same place that Powell checks the x tolerance (rho > rhoend). */ { double fbest = ifull == 1 ? f : datmat[mp + np * datmat_dim1]; if (fbest < *minf && nlopt_stop_ftol(stop, fbest, *minf)) { rc = NLOPT_FTOL_REACHED; goto L600; } *minf = fbest; } /* Otherwise reduce RHO if it is not at its least value and reset PARMU. */ if (rho > rhoend) { rho *= .5; if (rho <= rhoend * 1.5) { rho = rhoend; } if (parmu > 0.) { denom = 0.; i__1 = mp; for (k = 1; k <= i__1; ++k) { cmin = datmat[k + np * datmat_dim1]; cmax = cmin; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { d__1 = cmin, d__2 = datmat[k + i__ * datmat_dim1]; cmin = MIN2(d__1,d__2); d__1 = cmax, d__2 = datmat[k + i__ * datmat_dim1]; cmax = MAX2(d__1,d__2); } if (k <= *m && cmin < cmax * .5) { temp = MAX2(cmax,0.) - cmin; if (denom <= 0.) { denom = temp; } else { denom = MIN2(denom,temp); } } } if (denom == 0.) { parmu = 0.; } else if (cmax - cmin < parmu * denom) { parmu = (cmax - cmin) / denom; } } if (*iprint >= 2) { fprintf(stderr, "cobyla: reduction in RHO to %12.6E and PARMU =%13.6E\n", rho, parmu); } if (*iprint == 2) { fprintf(stderr, "cobyla: NFVALS = %4d, F =%13.6E, MAXCV =%13.6E\n", stop->nevals, datmat[mp + np * datmat_dim1], datmat[*mpp + np * datmat_dim1]); fprintf(stderr, "cobyla: X ="); i__1 = iptem; for (i__ = 1; i__ <= i__1; ++i__) { if (i__>1) fprintf(stderr, " "); fprintf(stderr, "%13.6E", sim[i__ + np * sim_dim1]); } if (iptem < *n) { i__1 = *n; for (i__ = iptemp; i__ <= i__1; ++i__) { if (!((i__-1) % 4)) fprintf(stderr, "\ncobyla: "); fprintf(stderr, "%15.6E", x[i__]); } } fprintf(stderr, "\n"); } goto L140; } else /* rho <= rhoend */ rc = rhoend > 0 ? NLOPT_XTOL_REACHED : NLOPT_ROUNDOFF_LIMITED; /* Return the best calculated values of the variables. */ if (*iprint >= 1) { fprintf(stderr, "cobyla: normal return.\n"); } if (ifull == 1) { goto L620; } L600: i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { x[i__] = sim[i__ + np * sim_dim1]; } f = datmat[mp + np * datmat_dim1]; resmax = datmat[*mpp + np * datmat_dim1]; L620: *minf = f; if (*iprint >= 1) { fprintf(stderr, "cobyla: NFVALS = %4d, F =%13.6E, MAXCV =%13.6E\n", stop->nevals, f, resmax); i__1 = iptem; fprintf(stderr, "cobyla: X ="); for (i__ = 1; i__ <= i__1; ++i__) { if (i__>1) fprintf(stderr, " "); fprintf(stderr, "%13.6E", x[i__]); } if (iptem < *n) { i__1 = *n; for (i__ = iptemp; i__ <= i__1; ++i__) { if (!((i__-1) % 4)) fprintf(stderr, "\ncobyla: "); fprintf(stderr, "%15.6E", x[i__]); } } fprintf(stderr, "\n"); } return rc; } /* cobylb */ /* ------------------------------------------------------------------------- */ static nlopt_result trstlp(int *n, int *m, double *a, double *b, double *rho, double *dx, int *ifull, int *iact, double *z__, double *zdota, double *vmultc, double *sdirn, double *dxnew, double *vmultd) { /* System generated locals */ int a_dim1, a_offset, z_dim1, z_offset, i__1, i__2; double d__1, d__2; /* Local variables */ double alpha, tempa; double beta; double optnew, stpful, sum, tot, acca, accb; double ratio, vsave, zdotv, zdotw, dd; double sd; double sp, ss, resold = 0.0, zdvabs, zdwabs, sumabs, resmax, optold; double spabs; double temp, step; int icount; int iout, i__, j, k; int isave; int kk; int kl, kp, kw; int nact, icon = 0, mcon; int nactx = 0; /* This subroutine calculates an N-component vector DX by applying the */ /* following two stages. In the first stage, DX is set to the shortest */ /* vector that minimizes the greatest violation of the constraints */ /* A(1,K)*DX(1)+A(2,K)*DX(2)+...+A(N,K)*DX(N) .GE. B(K), K=2,3,...,M, */ /* subject to the Euclidean length of DX being at most RHO. If its length is */ /* strictly less than RHO, then we use the resultant freedom in DX to */ /* minimize the objective function */ /* -A(1,M+1)*DX(1)-A(2,M+1)*DX(2)-...-A(N,M+1)*DX(N) */ /* subject to no increase in any greatest constraint violation. This */ /* notation allows the gradient of the objective function to be regarded as */ /* the gradient of a constraint. Therefore the two stages are distinguished */ /* by MCON .EQ. M and MCON .GT. M respectively. It is possible that a */ /* degeneracy may prevent DX from attaining the target length RHO. Then the */ /* value IFULL=0 would be set, but usually IFULL=1 on return. */ /* In general NACT is the number of constraints in the active set and */ /* IACT(1),...,IACT(NACT) are their indices, while the remainder of IACT */ /* contains a permutation of the remaining constraint indices. Further, Z is */ /* an orthogonal matrix whose first NACT columns can be regarded as the */ /* result of Gram-Schmidt applied to the active constraint gradients. For */ /* J=1,2,...,NACT, the number ZDOTA(J) is the scalar product of the J-th */ /* column of Z with the gradient of the J-th active constraint. DX is the */ /* current vector of variables and here the residuals of the active */ /* constraints should be zero. Further, the active constraints have */ /* nonnegative Lagrange multipliers that are held at the beginning of */ /* VMULTC. The remainder of this vector holds the residuals of the inactive */ /* constraints at DX, the ordering of the components of VMULTC being in */ /* agreement with the permutation of the indices of the constraints that is */ /* in IACT. All these residuals are nonnegative, which is achieved by the */ /* shift RESMAX that makes the least residual zero. */ /* Initialize Z and some other variables. The value of RESMAX will be */ /* appropriate to DX=0, while ICON will be the index of a most violated */ /* constraint if RESMAX is positive. Usually during the first stage the */ /* vector SDIRN gives a search direction that reduces all the active */ /* constraint violations by one simultaneously. */ /* Parameter adjustments */ z_dim1 = *n; z_offset = 1 + z_dim1 * 1; z__ -= z_offset; a_dim1 = *n; a_offset = 1 + a_dim1 * 1; a -= a_offset; --b; --dx; --iact; --zdota; --vmultc; --sdirn; --dxnew; --vmultd; /* Function Body */ *ifull = 1; mcon = *m; nact = 0; resmax = 0.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { i__2 = *n; for (j = 1; j <= i__2; ++j) { z__[i__ + j * z_dim1] = 0.; } z__[i__ + i__ * z_dim1] = 1.; dx[i__] = 0.; } if (*m >= 1) { i__1 = *m; for (k = 1; k <= i__1; ++k) { if (b[k] > resmax) { resmax = b[k]; icon = k; } } i__1 = *m; for (k = 1; k <= i__1; ++k) { iact[k] = k; vmultc[k] = resmax - b[k]; } } if (resmax == 0.) { goto L480; } i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { sdirn[i__] = 0.; } /* End the current stage of the calculation if 3 consecutive iterations */ /* have either failed to reduce the best calculated value of the objective */ /* function or to increase the number of active constraints since the best */ /* value was calculated. This strategy prevents cycling, but there is a */ /* remote possibility that it will cause premature termination. */ L60: optold = 0.; icount = 0; L70: if (mcon == *m) { optnew = resmax; } else { optnew = 0.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { optnew -= dx[i__] * a[i__ + mcon * a_dim1]; } } if (icount == 0 || optnew < optold) { optold = optnew; nactx = nact; icount = 3; } else if (nact > nactx) { nactx = nact; icount = 3; } else { --icount; if (icount == 0) { goto L490; } } /* If ICON exceeds NACT, then we add the constraint with index IACT(ICON) to */ /* the active set. Apply Givens rotations so that the last N-NACT-1 columns */ /* of Z are orthogonal to the gradient of the new constraint, a scalar */ /* product being set to zero if its nonzero value could be due to computer */ /* rounding errors. The array DXNEW is used for working space. */ if (icon <= nact) { goto L260; } kk = iact[icon]; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { dxnew[i__] = a[i__ + kk * a_dim1]; } tot = 0.; k = *n; L100: if (k > nact) { sp = 0.; spabs = 0.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp = z__[i__ + k * z_dim1] * dxnew[i__]; sp += temp; spabs += fabs(temp); } acca = spabs + fabs(sp) * .1; accb = spabs + fabs(sp) * .2; if (spabs >= acca || acca >= accb) { sp = 0.; } if (tot == 0.) { tot = sp; } else { kp = k + 1; temp = sqrt(sp * sp + tot * tot); alpha = sp / temp; beta = tot / temp; tot = temp; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp = alpha * z__[i__ + k * z_dim1] + beta * z__[i__ + kp * z_dim1]; z__[i__ + kp * z_dim1] = alpha * z__[i__ + kp * z_dim1] - beta * z__[i__ + k * z_dim1]; z__[i__ + k * z_dim1] = temp; } } --k; goto L100; } /* Add the new constraint if this can be done without a deletion from the */ /* active set. */ if (tot != 0.) { ++nact; zdota[nact] = tot; vmultc[icon] = vmultc[nact]; vmultc[nact] = 0.; goto L210; } /* The next instruction is reached if a deletion has to be made from the */ /* active set in order to make room for the new active constraint, because */ /* the new constraint gradient is a linear combination of the gradients of */ /* the old active constraints. Set the elements of VMULTD to the multipliers */ /* of the linear combination. Further, set IOUT to the index of the */ /* constraint to be deleted, but branch if no suitable index can be found. */ ratio = -1.; k = nact; L130: zdotv = 0.; zdvabs = 0.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp = z__[i__ + k * z_dim1] * dxnew[i__]; zdotv += temp; zdvabs += fabs(temp); } acca = zdvabs + fabs(zdotv) * .1; accb = zdvabs + fabs(zdotv) * .2; if (zdvabs < acca && acca < accb) { temp = zdotv / zdota[k]; if (temp > 0. && iact[k] <= *m) { tempa = vmultc[k] / temp; if (ratio < 0. || tempa < ratio) { ratio = tempa; iout = k; } } if (k >= 2) { kw = iact[k]; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { dxnew[i__] -= temp * a[i__ + kw * a_dim1]; } } vmultd[k] = temp; } else { vmultd[k] = 0.; } --k; if (k > 0) { goto L130; } if (ratio < 0.) { goto L490; } /* Revise the Lagrange multipliers and reorder the active constraints so */ /* that the one to be replaced is at the end of the list. Also calculate the */ /* new value of ZDOTA(NACT) and branch if it is not acceptable. */ i__1 = nact; for (k = 1; k <= i__1; ++k) { d__1 = 0., d__2 = vmultc[k] - ratio * vmultd[k]; vmultc[k] = MAX2(d__1,d__2); } if (icon < nact) { isave = iact[icon]; vsave = vmultc[icon]; k = icon; L170: kp = k + 1; kw = iact[kp]; sp = 0.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { sp += z__[i__ + k * z_dim1] * a[i__ + kw * a_dim1]; } d__1 = zdota[kp]; temp = sqrt(sp * sp + d__1 * d__1); alpha = zdota[kp] / temp; beta = sp / temp; zdota[kp] = alpha * zdota[k]; zdota[k] = temp; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp = alpha * z__[i__ + kp * z_dim1] + beta * z__[i__ + k * z_dim1]; z__[i__ + kp * z_dim1] = alpha * z__[i__ + k * z_dim1] - beta * z__[i__ + kp * z_dim1]; z__[i__ + k * z_dim1] = temp; } iact[k] = kw; vmultc[k] = vmultc[kp]; k = kp; if (k < nact) { goto L170; } iact[k] = isave; vmultc[k] = vsave; } temp = 0.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp += z__[i__ + nact * z_dim1] * a[i__ + kk * a_dim1]; } if (temp == 0.) { goto L490; } zdota[nact] = temp; vmultc[icon] = 0.; vmultc[nact] = ratio; /* Update IACT and ensure that the objective function continues to be */ /* treated as the last active constraint when MCON>M. */ L210: iact[icon] = iact[nact]; iact[nact] = kk; if (mcon > *m && kk != mcon) { k = nact - 1; sp = 0.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { sp += z__[i__ + k * z_dim1] * a[i__ + kk * a_dim1]; } d__1 = zdota[nact]; temp = sqrt(sp * sp + d__1 * d__1); alpha = zdota[nact] / temp; beta = sp / temp; zdota[nact] = alpha * zdota[k]; zdota[k] = temp; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp = alpha * z__[i__ + nact * z_dim1] + beta * z__[i__ + k * z_dim1]; z__[i__ + nact * z_dim1] = alpha * z__[i__ + k * z_dim1] - beta * z__[i__ + nact * z_dim1]; z__[i__ + k * z_dim1] = temp; } iact[nact] = iact[k]; iact[k] = kk; temp = vmultc[k]; vmultc[k] = vmultc[nact]; vmultc[nact] = temp; } /* If stage one is in progress, then set SDIRN to the direction of the next */ /* change to the current vector of variables. */ if (mcon > *m) { goto L320; } kk = iact[nact]; temp = 0.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp += sdirn[i__] * a[i__ + kk * a_dim1]; } temp += -1.; temp /= zdota[nact]; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { sdirn[i__] -= temp * z__[i__ + nact * z_dim1]; } goto L340; /* Delete the constraint that has the index IACT(ICON) from the active set. */ L260: if (icon < nact) { isave = iact[icon]; vsave = vmultc[icon]; k = icon; L270: kp = k + 1; kk = iact[kp]; sp = 0.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { sp += z__[i__ + k * z_dim1] * a[i__ + kk * a_dim1]; } d__1 = zdota[kp]; temp = sqrt(sp * sp + d__1 * d__1); alpha = zdota[kp] / temp; beta = sp / temp; zdota[kp] = alpha * zdota[k]; zdota[k] = temp; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp = alpha * z__[i__ + kp * z_dim1] + beta * z__[i__ + k * z_dim1]; z__[i__ + kp * z_dim1] = alpha * z__[i__ + k * z_dim1] - beta * z__[i__ + kp * z_dim1]; z__[i__ + k * z_dim1] = temp; } iact[k] = kk; vmultc[k] = vmultc[kp]; k = kp; if (k < nact) { goto L270; } iact[k] = isave; vmultc[k] = vsave; } --nact; /* If stage one is in progress, then set SDIRN to the direction of the next */ /* change to the current vector of variables. */ if (mcon > *m) { goto L320; } temp = 0.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp += sdirn[i__] * z__[i__ + (nact + 1) * z_dim1]; } i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { sdirn[i__] -= temp * z__[i__ + (nact + 1) * z_dim1]; } goto L340; /* Pick the next search direction of stage two. */ L320: temp = 1. / zdota[nact]; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { sdirn[i__] = temp * z__[i__ + nact * z_dim1]; } /* Calculate the step to the boundary of the trust region or take the step */ /* that reduces RESMAX to zero. The two statements below that include the */ /* factor 1.0E-6 prevent some harmless underflows that occurred in a test */ /* calculation. Further, we skip the step if it could be zero within a */ /* reasonable tolerance for computer rounding errors. */ L340: dd = *rho * *rho; sd = 0.; ss = 0.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if ((d__1 = dx[i__], fabs(d__1)) >= *rho * 1e-6f) { d__2 = dx[i__]; dd -= d__2 * d__2; } sd += dx[i__] * sdirn[i__]; d__1 = sdirn[i__]; ss += d__1 * d__1; } if (dd <= 0.) { goto L490; } temp = sqrt(ss * dd); if (fabs(sd) >= temp * 1e-6f) { temp = sqrt(ss * dd + sd * sd); } stpful = dd / (temp + sd); step = stpful; if (mcon == *m) { acca = step + resmax * .1; accb = step + resmax * .2; if (step >= acca || acca >= accb) { goto L480; } step = MIN2(step,resmax); } /* SGJ, 2010: check for error here */ if (nlopt_isinf(step)) return NLOPT_ROUNDOFF_LIMITED; /* Set DXNEW to the new variables if STEP is the steplength, and reduce */ /* RESMAX to the corresponding maximum residual if stage one is being done. */ /* Because DXNEW will be changed during the calculation of some Lagrange */ /* multipliers, it will be restored to the following value later. */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { dxnew[i__] = dx[i__] + step * sdirn[i__]; } if (mcon == *m) { resold = resmax; resmax = 0.; i__1 = nact; for (k = 1; k <= i__1; ++k) { kk = iact[k]; temp = b[kk]; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { temp -= a[i__ + kk * a_dim1] * dxnew[i__]; } resmax = MAX2(resmax,temp); } } /* Set VMULTD to the VMULTC vector that would occur if DX became DXNEW. A */ /* device is included to force VMULTD(K)=0.0 if deviations from this value */ /* can be attributed to computer rounding errors. First calculate the new */ /* Lagrange multipliers. */ k = nact; L390: zdotw = 0.; zdwabs = 0.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp = z__[i__ + k * z_dim1] * dxnew[i__]; zdotw += temp; zdwabs += fabs(temp); } acca = zdwabs + fabs(zdotw) * .1; accb = zdwabs + fabs(zdotw) * .2; if (zdwabs >= acca || acca >= accb) { zdotw = 0.; } vmultd[k] = zdotw / zdota[k]; if (k >= 2) { kk = iact[k]; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { dxnew[i__] -= vmultd[k] * a[i__ + kk * a_dim1]; } --k; goto L390; } if (mcon > *m) { d__1 = 0., d__2 = vmultd[nact]; vmultd[nact] = MAX2(d__1,d__2); } /* Complete VMULTC by finding the new constraint residuals. */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { dxnew[i__] = dx[i__] + step * sdirn[i__]; } if (mcon > nact) { kl = nact + 1; i__1 = mcon; for (k = kl; k <= i__1; ++k) { kk = iact[k]; sum = resmax - b[kk]; sumabs = resmax + (d__1 = b[kk], fabs(d__1)); i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { temp = a[i__ + kk * a_dim1] * dxnew[i__]; sum += temp; sumabs += fabs(temp); } acca = sumabs + fabs(sum) * .1f; accb = sumabs + fabs(sum) * .2f; if (sumabs >= acca || acca >= accb) { sum = 0.f; } vmultd[k] = sum; } } /* Calculate the fraction of the step from DX to DXNEW that will be taken. */ ratio = 1.; icon = 0; i__1 = mcon; for (k = 1; k <= i__1; ++k) { if (vmultd[k] < 0.) { temp = vmultc[k] / (vmultc[k] - vmultd[k]); if (temp < ratio) { ratio = temp; icon = k; } } } /* Update DX, VMULTC and RESMAX. */ temp = 1. - ratio; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { dx[i__] = temp * dx[i__] + ratio * dxnew[i__]; } i__1 = mcon; for (k = 1; k <= i__1; ++k) { d__1 = 0., d__2 = temp * vmultc[k] + ratio * vmultd[k]; vmultc[k] = MAX2(d__1,d__2); } if (mcon == *m) { resmax = resold + ratio * (resmax - resold); } /* If the full step is not acceptable then begin another iteration. */ /* Otherwise switch to stage two or end the calculation. */ if (icon > 0) { goto L70; } if (step == stpful) { goto L500; } L480: mcon = *m + 1; icon = mcon; iact[mcon] = mcon; vmultc[mcon] = 0.; goto L60; /* We employ any freedom that may be available to reduce the objective */ /* function before returning a DX whose length is less than RHO. */ L490: if (mcon == *m) { goto L480; } *ifull = 0; L500: return NLOPT_SUCCESS; } /* trstlp */ nloptr/src/nlopt_src/cobyla/README.orig0000644000176000001440000000546413263734242017521 0ustar ripleyusers# COBYLA : constrained optimization by linear approximation # Version 1.1 # Copyright (c) 1992, Michael J. D. Powell (M.J.D.Powell@damtp.cam.ac.uk) # Copyright (c) 2004, J.S. Roy (js@jeannot.org) # See the LICENSE file for copyright information. # $Jeannot: README,v 1.7 2004/04/18 14:04:20 js Exp $ This software is a C version of COBYLA2, a contrained optimization by linear approximation package developed by Michael J. D. Powell in Fortran. The original source code can be found at : http://plato.la.asu.edu/topics/problems/nlores.html Reference article for the method: Powell, J.M.D. (1992), "A Direct Search Optimization Method that Models the Objective and Constraint Functions by Linear Interpolation", DAMTP/NA5, Cambridge, England. This package was initially built by J.S. Roy to ease integration into SciPy. See: http://www.scipy.org/ Many thanks to Michael J. D. Powell for allowing this to happen ! This software, a derivative free non-linear optimizer, aims at minimizing the value of a nonlinear function subject to nonlinear constraints. It requires to be able to evaluate the function and the value of the constraints. COBYLA will try to make all the values of the constraints positive. So if you want to input a constraint j such as variable x[i] <= MAX, set: constraint[j] = MAX - x[i] See the comments in cobyla.c for more details. This software has been converted from the Fortran into C and provides the following modifications : - reentrancy, no global variables or functions ; - ability to pass a pointer to the function to be optimized (to provide access to constants) ; - ability to end the minimization at any time ; And other small changes. The last version (and other software) is avalaible at the URL : http://www.jeannot.org/~js/code/index.en.html A Python interface module is also provided. Contents : - cobyla.c : Source - cobyla.h : Header, and API documentation - LICENSE : License and copyright information - HISTORY : Release history - README : This file - example.c : A simple example - Makefile : Make file used to build the examples - moduleCobyla.c : the source of the python module - cobyla.py : the python module wrapper - example.py : an example for the python module - setup.py : the python installer Use is described in cobyla.h. For more information, see the example. The example can be built and executed by doing : make test You may need to adjust the Makefile before building cobyla. To install the module in the current directory, use: python setup.py build_ext --inplace To test it, execute: python cobyla.py To install it globaly, use: python setup.py install If you make use of this software, or if you make modifications to it (for a specific platform for example), you are encouraged to contact the author of this Fortran to C conversion at the following email : js@jeannot.org Thanks ! nloptr/src/nlopt_src/m4/0000755000176000001440000000000013263734242014740 5ustar ripleyusersnloptr/src/nlopt_src/m4/ax_c_threadlocal.m40000644000176000001440000000347613263734242020470 0ustar ripleyusersdnl @synopsis AX_C_THREADLOCAL([ACTION-IF-FOUND[, ACTION-IF-NOT-FOUND]]) dnl @summary determine C keyword for threadlocal storage dnl dnl This macro tries to discover a C keyword to declare variables dnl as having thread-local storage. Most commonly, this is either dnl __thread [gcc] or __declspec(thread) [Windows]. dnl dnl On success, it #defines the THREADLOCAL preprocessor symbol to dnl the appropriate keyword. You would then use it in C code as, e.g.: dnl THREADLOCAL int myvariable; dnl dnl ACTION-IF-FOUND is a list of shell commands to run if an thread-local dnl keyword is found, and ACTION-IF-NOT-FOUND is a list of commands dnl to run it if one is not found. If ACTION-IF-FOUND is not specified, dnl the default action does nothing. dnl dnl @version 2010-05-28 dnl @license GPLWithACException dnl @author Steven G. Johnson AC_DEFUN([AX_C_THREADLOCAL], [AC_ARG_WITH(threadlocal, [AC_HELP_STRING([--without-threadlocal], [no thread-local storage keyword])], with_ax_c_threadlocal=$withval, with_ax_c_threadlocal=yes) AC_CACHE_CHECK([for C thread-local keyword], ax_cv_c_threadlocal, [if test "x$with_ax_c_threadlocal" = xno; then ax_cv_c_threadlocal=disabled else ax_cv_c_threadlocal=unsupported AC_LANG_SAVE AC_LANG_C for ax_kw in __thread "__declspec(thread)"; do AC_TRY_COMPILE([], [static $ax_kw int x = 0;], [ax_cv_c_threadlocal=$ax_kw; break]) done AC_LANG_RESTORE fi ]) ax_kw="$ax_cv_c_threadlocal" if test "x$ax_kw" = xunsupported; then ax_kw=""; fi if test "x$ax_kw" = xdisabled; then ax_kw=""; fi AC_DEFINE_UNQUOTED(THREADLOCAL, $ax_kw, [Define to C thread-local keyword, or to nothing if this is not supported in your compiler.]) if test "$ax_cv_c_threadlocal" = unsupported; then m4_default([$2],:) else m4_default([$1],:) fi ]) nloptr/src/nlopt_src/README0000644000176000001440000000264213263734241015303 0ustar ripleyusersNLopt is a library for nonlinear local and global optimization, for functions with and without gradient information. It is designed as as simple, unified interface and packaging of several free/open-source nonlinear optimization libraries. The latest release and a complete manual may be found at the NLopt home page: http://ab-initio.mit.edu/nlopt It is compiled and installed with the standard GNU autoconf/automake commands: ./configure make make install See `./configure --help` or the `INSTALL` file for other options. To build the latest development sources: git clone git://github.com/stevengj/nlopt cd nlopt sh autogen.sh make (To build from git, you will need GNU autoconf, automake, and libtool installed, along with SWIG and Unix tools such as m4, perl, and sed.) Once it is installed, `#include ` in your C/C++ programs and link it with `-lnlopt -lm`. You may need to use the C++ compiler to link in order to include the C++ libraries (which are used internally by NLopt, even though it has a C API). The minimization function, `nlopt_minimize`, is described in the [man page](http://en.wikipedia.org/wiki/Man_page) (`api/nlopt_minimize.3`, which is installed by `make install`. See also the manual on our web page. There are also interfaces for Fortran, Python, Matlab, Octave, OCaml, GNU Guile, GNU R, Lua, and Julia. Interfaces for other languages may be added in the future. nloptr/src/nlopt_src/octave/0000755000176000001440000000000013263734242015701 5ustar ripleyusersnloptr/src/nlopt_src/octave/NLOPT_LN_PRAXIS.m0000644000176000001440000000023113263734242020426 0ustar ripleyusers% NLOPT_LN_PRAXIS: Principal-axis, praxis (local, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_LN_PRAXIS val = 12; nloptr/src/nlopt_src/octave/NLOPT_GN_DIRECT.m0000644000176000001440000000021113263734242020363 0ustar ripleyusers% NLOPT_GN_DIRECT: DIRECT (global, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_GN_DIRECT val = 0; nloptr/src/nlopt_src/octave/nlopt_minimize_constrained.m0000644000176000001440000001206613263734242023512 0ustar ripleyusers% Usage: [xopt, fmin, retcode] = nlopt_minimize_constrained % (algorithm, f, f_data, % fc, fc_data, lb, ub, % xinit, stop) % % Minimizes a nonlinear multivariable function f(x, f_data{:}), subject % to nonlinear constraints described by fc and fc_data (see below), where % x is a row vector, returning the optimal x found (xopt) along with % the minimum function value (fmin = f(xopt)) and a return code (retcode). % A variety of local and global optimization algorithms can be used, % as specified by the algorithm parameter described below. lb and ub % are row vectors giving the upper and lower bounds on x, xinit is % a row vector giving the initial guess for x, and stop is a struct % containing termination conditions (see below). % % This function is a front-end for the external routine % nlopt_minimize_constrained in the free NLopt nonlinear-optimization % library, which is a wrapper around a number of free/open-source % optimization subroutines. More details can be found on the NLopt % web page (ab-initio.mit.edu/nlopt) and also under % 'man nlopt_minimize_constrained' on Unix. % % f should be a handle (@) to a function of the form: % % [val, gradient] = f(x, ...) % % where x is a row vector, val is the function value f(x), and gradient % is a row vector giving the gradient of the function with respect to x. % The gradient is only used for gradient-based optimization algorithms; % some of the algorithms (below) are derivative-free and only require % f to return val (its value). f can take additional arguments (...) % which are passed via the argument f_data: f_data is a cell array % of the additional arguments to pass to f. (Recall that cell arrays % are specified by curly brackets { ... }. For example, pass f_data={} % for functions that require no additional arguments.) % % A few of the algorithms (below) support nonlinear constraints, % in particular NLOPT_LD_MMA and NLOPT_LN_COBYLA. These (if any) % are specified by fc and fc_data. fc is a cell array of % function handles, and fc_data is a cell array of cell arrays of the % corresponding arguments. Both must have the same length m, the % number of nonlinear constraints. That is, fc{i} is a handle % to a function of the form: % % [val, gradient] = fc(x, ...) % % (where the gradient is only used for gradient-based algorithms), % and the ... arguments are given by fc_data{i}{:}. % % If you have no nonlinear constraints, i.e. fc = fc_data = {}, then % it is equivalent to calling the the nlopt_minimize() function, % which omits the fc and fc_data arguments. % % stop describes the termination criteria, and is a struct with a % number of optional fields: % stop.ftol_rel = fractional tolerance on function value % stop.ftol_abs = absolute tolerance on function value % stop.xtol_rel = fractional tolerance on x % stop.xtol_abs = row vector of absolute tolerances on x components % stop.fmin_max = stop when f < fmin_max is found % stop.maxeval = maximum number of function evaluations % stop.maxtime = maximum run time in seconds % stop.verbose = > 0 indicates verbose output % Minimization stops when any one of these conditions is met; any % condition that is omitted from stop will be ignored. WARNING: % not all algorithms interpret the stopping criteria in exactly the % same way, and in any case ftol/xtol specify only a crude estimate % for the accuracy of the minimum function value/x. % % The algorithm should be one of the following constants (name and % interpretation are the same as for the C function). Names with % _G*_ are global optimization, and names with _L*_ are local % optimization. Names with _*N_ are derivative-free, while names % with _*D_ are gradient-based algorithms. Algorithms: % % NLOPT_GD_MLSL_LDS, NLOPT_GD_MLSL, NLOPT_GD_STOGO, NLOPT_GD_STOGO_RAND, % NLOPT_GN_CRS2_LM, NLOPT_GN_DIRECT_L, NLOPT_GN_DIRECT_L_NOSCAL, % NLOPT_GN_DIRECT_L_RAND, NLOPT_GN_DIRECT_L_RAND_NOSCAL, NLOPT_GN_DIRECT, % NLOPT_GN_DIRECT_NOSCAL, NLOPT_GN_ISRES, NLOPT_GN_MLSL_LDS, NLOPT_GN_MLSL, % NLOPT_GN_ORIG_DIRECT_L, NLOPT_GN_ORIG_DIRECT, NLOPT_LD_AUGLAG_EQ, % NLOPT_LD_AUGLAG, NLOPT_LD_LBFGS, NLOPT_LD_LBFGS_NOCEDAL, NLOPT_LD_MMA, % NLOPT_LD_TNEWTON, NLOPT_LD_TNEWTON_PRECOND, % NLOPT_LD_TNEWTON_PRECOND_RESTART, NLOPT_LD_TNEWTON_RESTART, % NLOPT_LD_VAR1, NLOPT_LD_VAR2, NLOPT_LN_AUGLAG_EQ, NLOPT_LN_AUGLAG, % NLOPT_LN_BOBYQA, NLOPT_LN_COBYLA, NLOPT_LN_NELDERMEAD, % NLOPT_LN_NEWUOA_BOUND, NLOPT_LN_NEWUOA, NLOPT_LN_PRAXIS, NLOPT_LN_SBPLX % % For more information on individual algorithms, see their individual % help pages (e.g. "help NLOPT_LN_SBPLX"). function [xopt, fmin, retcode] = nlopt_minimize_constrained(algorithm, f, f_data, fc, fc_data, lb, ub, xinit, stop) opt = stop; if (isfield(stop, 'minf_max')) opt.stopval = stop.minf_max; end opt.algorithm = algorithm; opt.min_objective = @(x) f(x, f_data{:}); opt.lower_bounds = lb; opt.upper_bounds = ub; for i = 1:length(fc) opt.fc{i} = @(x) fc{i}(x, fc_data{i}{:}); end [xopt, fmin, retcode] = nlopt_optimize(opt, xinit); nloptr/src/nlopt_src/octave/NLOPT_GN_MLSL.m0000644000176000001440000000025113263734242020164 0ustar ripleyusers% NLOPT_GN_MLSL: Multi-level single-linkage (MLSL), random (global, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_GN_MLSL val = 20; nloptr/src/nlopt_src/octave/NLOPT_GN_CRS2_LM.m0000644000176000001440000000027113263734242020520 0ustar ripleyusers% NLOPT_GN_CRS2_LM: Controlled random search (CRS2) with local mutation (global, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_GN_CRS2_LM val = 19; nloptr/src/nlopt_src/octave/NLOPT_GD_MLSL_LDS.m0000644000176000001440000000026413263734242020660 0ustar ripleyusers% NLOPT_GD_MLSL_LDS: Multi-level single-linkage (MLSL), quasi-random (global, derivative) % % See nlopt_minimize for more information. function val = NLOPT_GD_MLSL_LDS val = 23; nloptr/src/nlopt_src/octave/NLOPT_GN_DIRECT_L_RAND_NOSCAL.m0000644000176000001440000000027313263734242022511 0ustar ripleyusers% NLOPT_GN_DIRECT_L_RAND_NOSCAL: Unscaled Randomized DIRECT-L (global, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_GN_DIRECT_L_RAND_NOSCAL val = 5; nloptr/src/nlopt_src/octave/NLOPT_G_MLSL_LDS.m0000644000176000001440000000027313263734242020554 0ustar ripleyusers% NLOPT_G_MLSL_LDS: Multi-level single-linkage (MLSL), quasi-random (global, needs sub-algorithm) % % See nlopt_minimize for more information. function val = NLOPT_G_MLSL_LDS val = 39; nloptr/src/nlopt_src/octave/NLOPT_LN_AUGLAG_EQ.m0000644000176000001440000000027513263734242020755 0ustar ripleyusers% NLOPT_LN_AUGLAG_EQ: Augmented Lagrangian method for equality constraints (local, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_LN_AUGLAG_EQ val = 32; nloptr/src/nlopt_src/octave/Makefile.am0000644000176000001440000000535313263734242017743 0ustar ripleyusersAM_CPPFLAGS = -I$(top_srcdir)/api -I$(top_srcdir)/util MFILES = NLOPT_GN_DIRECT.m NLOPT_GN_DIRECT_L.m NLOPT_GN_DIRECT_L_RAND.m NLOPT_GN_DIRECT_NOSCAL.m NLOPT_GN_DIRECT_L_NOSCAL.m NLOPT_GN_DIRECT_L_RAND_NOSCAL.m NLOPT_GN_ORIG_DIRECT.m NLOPT_GN_ORIG_DIRECT_L.m NLOPT_GD_STOGO.m NLOPT_GD_STOGO_RAND.m NLOPT_LD_LBFGS_NOCEDAL.m NLOPT_LD_LBFGS.m NLOPT_LN_PRAXIS.m NLOPT_LD_VAR1.m NLOPT_LD_VAR2.m NLOPT_LD_TNEWTON.m NLOPT_LD_TNEWTON_RESTART.m NLOPT_LD_TNEWTON_PRECOND.m NLOPT_LD_TNEWTON_PRECOND_RESTART.m NLOPT_GN_CRS2_LM.m NLOPT_GN_MLSL.m NLOPT_GD_MLSL.m NLOPT_GN_MLSL_LDS.m NLOPT_GD_MLSL_LDS.m NLOPT_LD_MMA.m NLOPT_LN_COBYLA.m NLOPT_LN_NEWUOA.m NLOPT_LN_NEWUOA_BOUND.m NLOPT_LN_NELDERMEAD.m NLOPT_LN_SBPLX.m NLOPT_LN_AUGLAG.m NLOPT_LD_AUGLAG.m NLOPT_LN_AUGLAG_EQ.m NLOPT_LD_AUGLAG_EQ.m NLOPT_LN_BOBYQA.m NLOPT_GN_ISRES.m NLOPT_AUGLAG.m NLOPT_AUGLAG_EQ.m NLOPT_G_MLSL.m NLOPT_G_MLSL_LDS.m NLOPT_LD_SLSQP.m NLOPT_LD_CCSAQ.m NLOPT_GN_ESCH.m ####################################################################### # dummy C program to fool automake into making sure that CPPFLAGS etc. # are all defined noinst_PROGRAMS = dummy dummy_SOURCES = dummy.c ####################################################################### octdir = $(OCT_INSTALL_DIR) mdir = $(M_INSTALL_DIR) if WITH_OCTAVE oct_DATA = nlopt_optimize.oct m_DATA = $(MFILES) nlopt_minimize.m nlopt_minimize_constrained.m nlopt_optimize.m endif nlopt_optimize.oct: nlopt_optimize-oct.cc nlopt_optimize_usage.h $(top_builddir)/.libs/libnlopt@NLOPT_SUFFIX@.la $(MKOCTFILE) -o $@ $(DEFS) $(DEFAULT_INCLUDES) $(INCLUDES) $(AM_CPPFLAGS) $(srcdir)/nlopt_optimize-oct.cc $(LDFLAGS) -L$(top_builddir)/.libs -lnlopt@NLOPT_SUFFIX@ BUILT_SOURCES = nlopt_optimize_usage.h MAINTAINERCLEANFILES = $(BUILT_SOURCES) nlopt_optimize_usage.h: $(srcdir)/nlopt_optimize.m echo "#define NLOPT_OPTIMIZE_USAGE \\" > $@ sed 's/\"/\\"/g' $(srcdir)/nlopt_optimize.m | sed 's,^% ,\",;s,^%,\",;s,$$,\\n\" \\,' >> $@ echo "" >> $@ ####################################################################### mexdir = $(MEX_INSTALL_DIR) if WITH_MATLAB mex_DATA = nlopt_optimize.$(MEXSUFF) $(MFILES) nlopt_minimize.m nlopt_minimize_constrained.m nlopt_optimize.m endif nlopt_optimize.$(MEXSUFF): nlopt_optimize-mex.c $(top_builddir)/.libs/libnlopt@NLOPT_SUFFIX@.la $(MEX) -output nlopt_optimize -O $(DEFS) $(DEFAULT_INCLUDES) $(INCLUDES) $(AM_CPPFLAGS) $(srcdir)/nlopt_optimize-mex.c $(LDFLAGS) -L$(top_builddir)/.libs -lnlopt@NLOPT_SUFFIX@ ####################################################################### EXTRA_DIST = nlopt_optimize-oct.cc nlopt_optimize-mex.c $(MFILES) nlopt_minimize.m nlopt_minimize_constrained.m nlopt_optimize.m nlopt_optimize_usage.h CLEANFILES = nlopt_optimize.oct nlopt_optimize.h nlopt_optimize.$(MEXSUFF) nlopt_optimize-oct.o nloptr/src/nlopt_src/octave/NLOPT_LD_VAR2.m0000644000176000001440000000025013263734242020121 0ustar ripleyusers% NLOPT_LD_VAR2: Limited-memory variable-metric, rank 2 (local, derivative-based) % % See nlopt_minimize for more information. function val = NLOPT_LD_VAR2 val = 14; nloptr/src/nlopt_src/octave/NLOPT_LN_NEWUOA.m0000644000176000001440000000027113263734242020422 0ustar ripleyusers% NLOPT_LN_NEWUOA: NEWUOA unconstrained optimization via quadratic models (local, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_LN_NEWUOA val = 26; nloptr/src/nlopt_src/octave/NLOPT_LN_NELDERMEAD.m0000644000176000001440000000025013263734242021021 0ustar ripleyusers% NLOPT_LN_NELDERMEAD: Nelder-Mead simplex algorithm (local, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_LN_NELDERMEAD val = 28; nloptr/src/nlopt_src/octave/NLOPT_LD_LBFGS_NOCEDAL.m0000644000176000001440000000027013263734242021373 0ustar ripleyusers% NLOPT_LD_LBFGS_NOCEDAL: original NON-FREE L-BFGS code by Nocedal et al. (NOT COMPILED) % % See nlopt_minimize for more information. function val = NLOPT_LD_LBFGS_NOCEDAL val = 10; nloptr/src/nlopt_src/octave/NLOPT_LD_SLSQP.m0000644000176000001440000000024413263734242020314 0ustar ripleyusers% NLOPT_LD_SLSQP: Sequential Quadratic Programming (SQP) (local, derivative) % % See nlopt_minimize for more information. function val = NLOPT_LD_SLSQP val = 40; nloptr/src/nlopt_src/octave/nlopt_optimize.m0000644000176000001440000002126413263734242021140 0ustar ripleyusers% Usage: [xopt, fopt, retcode] = nlopt_optimize(opt, xinit) % % Optimizes (minimizes or maximizes) a nonlinear function under % nonlinear constraints from the starting guess xinit, where the % objective, constraints, stopping criteria, and other options are % specified in the structure opt described below. A variety of local % and global optimization algorithms can be used, as specified by the % opt.algorithm parameter described below. Returns the optimum % function value fopt, the location xopt of the optimum, and a % return code retcode described below (> 0 on success). % % The dimension (n) of the problem, i.e. the number of design variables, % is specified implicitly via the length of xinit. % % This function is a front-end for the external routine nlopt_optimize % in the free NLopt nonlinear-optimization library, which is a wrapper % around a number of free/open-source optimization subroutines. More % details can be found on the NLopt web page (ab-initio.mit.edu/nlopt) % and also under 'man nlopt_minimize' on Unix. % % OBJECTIVE FUNCTION: % % The objective function f is specified via opt.min_objective or % opt.max_objective for minimization or maximization, respectively. % opt.min/max_objective should be a handle (@) to a function of the form: % % [val, gradient] = f(x) % % where x is a row vector, val is the function value f(x), and gradient % is a row vector giving the gradient of the function with respect to x. % The gradient is only used for gradient-based optimization algorithms; % some of the algorithms (below) are derivative-free and only require % f to return val (its value). % % BOUND CONSTRAINTS: % % Lower and/or upper bounds for the design variables x are specified % via opt.lower_bounds and/or opt.upper_bounds, respectively: these % are vectors (of the same length as xinit, above) giving the bounds % in each component. An unbounded component may be specified by a % lower/upper bound of -inf/+inf, respectively. If opt.lower_bounds % and/or opt.upper_bounds are not specified, the default bounds are % -inf/+inf (i.e. unbounded), respectively. % % NONLINEAR CONSTRAINTS: % % Several of the algorithms in NLopt (MMA, COBYLA, and ORIG_DIRECT) also % support arbitrary nonlinear inequality constraints, and some also allow % nonlinear equality constraints (ISRES and AUGLAG). For these % algorithms, you can specify as many nonlinear constraints as you wish. % (The default is no nonlinear constraints.) % % Inequality constraints of the form fc{i}(x) <= 0 are specified via opt.fc, % which is a cell array of function handles (@) of the same form as % the objective function above (i.e., returning the value and optionally % the gradient of the constraint function fc{i}, where the gradient is % only needed for gradient-based algorithms). % % Equality constraints of the form h{i}(x) = 0 are specified via opt.h, % which is a cell array of function handles (@) of the same form as % the objective function above (i.e., returning the value and optionally % the gradient of the constraint function h{i}, where the gradient is % only needed for gradient-based algorithms). % % For both inequality and equality constraints, you can supply a % "tolerance" for each constraint: this tolerance is used for convergence % tests only, and a point x is considered feasible for purposes of % convergence if the constraint is violated by the given tolerance. % The tolerances are specified via opt.fc_tol and opt.h_tol, respectively, % which must be vectors of the same length as opt.fc and opt.h, so % that opt.fc_tol(i) is the tolerance for opt.fc{i} and opt.h_tol(i) % is the tolerance for opt.h{i}. These tolerances default to zero; a % small nonzero tolerance is recommended, however, especially for h_tol. % % ALGORITHMS % % The optimization algorithm must be specified via opt.algorithm. % % The algorithm should be one of the following constants (name and % interpretation are the same as for the C language interface). Names % with _G*_ are global optimization, and names with _L*_ are local % optimization. Names with _*N_ are derivative-free, while names % with _*D_ are gradient-based algorithms. Algorithms: % % NLOPT_GD_MLSL_LDS, NLOPT_GD_MLSL, NLOPT_GD_STOGO, NLOPT_GD_STOGO_RAND, % NLOPT_GN_CRS2_LM, NLOPT_GN_DIRECT_L, NLOPT_GN_DIRECT_L_NOSCAL, % NLOPT_GN_DIRECT_L_RAND, NLOPT_GN_DIRECT_L_RAND_NOSCAL, NLOPT_GN_DIRECT, % NLOPT_GN_DIRECT_NOSCAL, NLOPT_GN_ISRES, NLOPT_GN_MLSL_LDS, NLOPT_GN_MLSL, % NLOPT_GN_ORIG_DIRECT_L, NLOPT_GN_ORIG_DIRECT, NLOPT_LD_AUGLAG_EQ, % NLOPT_LD_AUGLAG, NLOPT_LD_LBFGS, NLOPT_LD_LBFGS_NOCEDAL, NLOPT_LD_MMA, % NLOPT_LD_TNEWTON, NLOPT_LD_TNEWTON_PRECOND, % NLOPT_LD_TNEWTON_PRECOND_RESTART, NLOPT_LD_TNEWTON_RESTART, % NLOPT_LD_VAR1, NLOPT_LD_VAR2, NLOPT_LN_AUGLAG_EQ, NLOPT_LN_AUGLAG, % NLOPT_LN_BOBYQA, NLOPT_LN_COBYLA, NLOPT_LN_NELDERMEAD, % NLOPT_LN_NEWUOA_BOUND, NLOPT_LN_NEWUOA, NLOPT_LN_PRAXIS, NLOPT_LN_SBPLX % % For more information on individual algorithms, see their individual % help pages (e.g. "help NLOPT_LN_SBPLX"). % % STOPPING CRITERIA: % % Multiple stopping criteria can be specified by setting one or more of % the following fields of opt. The optimization halts whenever any % one of the given criteria is satisfied. % % opt.stopval: Stop when an objective value of at least stopval is found. % That is, stop minimizing when a value <= stopval is found, or stop % maximizing when a value >= stopval is found. % % opt.ftol_rel: Relative tolerance on function value, to stop when % an optimization step (or an estimate of the optimum) changes % the function value by less than opt.ftol_rel multiplied by % the absolute value of the function. % % opt.ftol_abs: Absolute tolerance on function value, to stop when % an optimization step (or an estimate of the optimum) changes % the function value by less than opt.ftol_abs. % % opt.xtol_rel: Relative tolerance on function value, to stop when % an optimization step (or an estimate of the optimum) changes % every component of x by less than opt.xtol_rel multiplied by % the absolute value of that component of x. % % opt.xtol_abs: Absolute tolerance on function value, to stop when % an optimization step (or an estimate of the optimum) changes % every component of x by less than that component of opt.xtol_abs % -- should be a vector of same length as x. % % opt.maxeval: Maximum number of function evaluations. % % opt.maxtime: Maximum runtime (in seconds) for the optimization. % % RETURN CODE: % % The retcode result is positive upon successful completion, and % negative for an error. The specific values are: % % generic success code: +1 % stopval reached: +2 % ftol reached: +3 % xtol reached: +4 % maxeval reached: +5 % maxtime reached: +6 % generic failure code: -1 % invalid arguments: -2 % out of memory: -3 % roundoff-limited: -4 % % LOCAL OPTIMIZER: % % Some of the algorithms, especially MLSL and AUGLAG, use a different % optimization algorithm as a subroutine, typically for local optimization. % By default, they use MMA or COBYLA for gradient-based or derivative-free % searching, respectively. However, you can change this by specifying % opt.local_optimizer: this is a structure with the same types of fields as opt % (stopping criteria, algorithm, etcetera). The objective function % and nonlinear constraint parameters of opt.local_optimizer are ignored. % % INITIAL STEP SIZE: % % For derivative-free local-optimization algorithms, the optimizer must % somehow decide on some initial step size to perturb x by when it begins % the optimization. This step size should be big enough that the value % of the objective changes significantly, but not too big if you want to % find the local optimum nearest to x. By default, NLopt chooses this % initial step size heuristically from the bounds, tolerances, and other % information, but this may not always be the best choice. % % You can modify the initial step by setting opt.initial_step, which % is a vector of the same length as x containing the (nonzero) initial % step size for each component of x. % % STOCHASTIC POPULATION: % % Several of the stochastic search algorithms (e.g., CRS, MLSL, and % ISRES) start by generating some initial "population" of random points % x. 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uninstall-octDATA nlopt_optimize.oct: nlopt_optimize-oct.cc nlopt_optimize_usage.h $(top_builddir)/.libs/libnlopt@NLOPT_SUFFIX@.la $(MKOCTFILE) -o $@ $(DEFS) $(DEFAULT_INCLUDES) $(INCLUDES) $(AM_CPPFLAGS) $(srcdir)/nlopt_optimize-oct.cc $(LDFLAGS) -L$(top_builddir)/.libs -lnlopt@NLOPT_SUFFIX@ nlopt_optimize_usage.h: $(srcdir)/nlopt_optimize.m echo "#define NLOPT_OPTIMIZE_USAGE \\" > $@ sed 's/\"/\\"/g' $(srcdir)/nlopt_optimize.m | sed 's,^% ,\",;s,^%,\",;s,$$,\\n\" \\,' >> $@ echo "" >> $@ nlopt_optimize.$(MEXSUFF): nlopt_optimize-mex.c $(top_builddir)/.libs/libnlopt@NLOPT_SUFFIX@.la $(MEX) -output nlopt_optimize -O $(DEFS) $(DEFAULT_INCLUDES) $(INCLUDES) $(AM_CPPFLAGS) $(srcdir)/nlopt_optimize-mex.c $(LDFLAGS) -L$(top_builddir)/.libs -lnlopt@NLOPT_SUFFIX@ # Tell versions [3.59,3.63) of GNU make to not export all variables. # Otherwise a system limit (for SysV at least) may be exceeded. .NOEXPORT: nloptr/src/nlopt_src/octave/NLOPT_LN_SBPLX.m0000644000176000001440000000030413263734242020311 0ustar ripleyusers% NLOPT_LN_SBPLX: Sbplx variant of Nelder-Mead (re-implementation of Rowan's Subplex) (local, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_LN_SBPLX val = 29; nloptr/src/nlopt_src/octave/NLOPT_LN_AUGLAG.m0000644000176000001440000000023613263734242020365 0ustar ripleyusers% NLOPT_LN_AUGLAG: Augmented Lagrangian method (local, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_LN_AUGLAG val = 30; nloptr/src/nlopt_src/octave/NLOPT_LD_VAR1.m0000644000176000001440000000025013263734242020120 0ustar ripleyusers% NLOPT_LD_VAR1: Limited-memory variable-metric, rank 1 (local, derivative-based) % % See nlopt_minimize for more information. function val = NLOPT_LD_VAR1 val = 13; nloptr/src/nlopt_src/octave/NLOPT_G_MLSL.m0000644000176000001440000000025513263734242020052 0ustar ripleyusers% NLOPT_G_MLSL: Multi-level single-linkage (MLSL), random (global, needs sub-algorithm) % % See nlopt_minimize for more information. function val = NLOPT_G_MLSL val = 38; nloptr/src/nlopt_src/octave/NLOPT_GN_ORIG_DIRECT_L.m0000644000176000001440000000025213263734242021463 0ustar ripleyusers% NLOPT_GN_ORIG_DIRECT_L: Original DIRECT-L version (global, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_GN_ORIG_DIRECT_L val = 7; nloptr/src/nlopt_src/octave/NLOPT_GD_STOGO.m0000644000176000001440000000021113263734242020272 0ustar ripleyusers% NLOPT_GD_STOGO: StoGO (global, derivative-based) % % See nlopt_minimize for more information. function val = NLOPT_GD_STOGO val = 8; nloptr/src/nlopt_src/octave/NLOPT_GD_MLSL.m0000644000176000001440000000024613263734242020156 0ustar ripleyusers% NLOPT_GD_MLSL: Multi-level single-linkage (MLSL), random (global, derivative) % % See nlopt_minimize for more information. function val = NLOPT_GD_MLSL val = 21; nloptr/src/nlopt_src/octave/NLOPT_GN_DIRECT_L_NOSCAL.m0000644000176000001440000000024613263734242021705 0ustar ripleyusers% NLOPT_GN_DIRECT_L_NOSCAL: Unscaled DIRECT-L (global, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_GN_DIRECT_L_NOSCAL val = 4; nloptr/src/nlopt_src/octave/NLOPT_GN_ISRES.m0000644000176000001440000000025513263734242020306 0ustar ripleyusers% NLOPT_GN_ISRES: ISRES evolutionary constrained optimization (global, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_GN_ISRES val = 35; nloptr/src/nlopt_src/octave/nlopt_optimize-oct.cc0000644000176000001440000002420613355070635022054 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include #include #include #include #include #include "nlopt.h" #include "nlopt_optimize_usage.h" static int struct_val_default(Octave_map &m, const std::string& k, int dflt) { if (m.contains(k)) { if (m.contents(k).length() == 1 && (m.contents(k))(0).is_real_scalar()) return (m.contents(k))(0).int_value(); } return dflt; } static double struct_val_default(Octave_map &m, const std::string& k, double dflt) { if (m.contains(k)) { if (m.contents(k).length() == 1 && (m.contents(k))(0).is_real_scalar()) return (m.contents(k))(0).double_value(); } return dflt; } static Matrix struct_val_default(Octave_map &m, const std::string& k, Matrix &dflt) { if (m.contains(k)) { if ((m.contents(k)).length() == 1) { if ((m.contents(k))(0).is_real_scalar()) return Matrix(1, dflt.length(), (m.contents(k))(0).double_value()); else if ((m.contents(k))(0).is_real_matrix()) return (m.contents(k))(0).matrix_value(); } } return dflt; } typedef struct { octave_function *f; int neval, verbose; nlopt_opt opt; } user_function_data; static double user_function(unsigned n, const double *x, double *gradient, /* NULL if not needed */ void *data_) { user_function_data *data = (user_function_data *) data_; octave_value_list args(1, 0); Matrix xm(1,n); for (unsigned i = 0; i < n; ++i) xm(i) = x[i]; args(0) = xm; octave_value_list res = data->f->do_multi_index_op(gradient ? 2 : 1, args); if (res.length() < (gradient ? 2 : 1)) gripe_user_supplied_eval("nlopt_optimize"); else if (!res(0).is_real_scalar() || (gradient && !res(1).is_real_matrix() && !(n == 1 && res(1).is_real_scalar()))) gripe_user_returned_invalid("nlopt_optimize"); else { if (gradient) { if (n == 1 && res(1).is_real_scalar()) gradient[0] = res(1).double_value(); else { Matrix grad = res(1).matrix_value(); for (unsigned i = 0; i < n; ++i) gradient[i] = grad(i); } } data->neval++; if (data->verbose) printf("nlopt_optimize eval #%d: %g\n", data->neval, res(0).double_value()); double f = res(0).double_value(); if (f != f /* isnan(f) */) nlopt_force_stop(data->opt); return f; } return 0; } static double user_function1(unsigned n, const double *x, double *gradient, /* NULL if not needed */ void *data_) { octave_function *f = (octave_function *) data_; octave_value_list args(1, 0); Matrix xm(1,n); for (unsigned i = 0; i < n; ++i) xm(i) = x[i]; args(0) = xm; octave_value_list res = f->do_multi_index_op(gradient ? 2 : 1, args); if (res.length() < (gradient ? 2 : 1)) gripe_user_supplied_eval("nlopt_optimize"); else if (!res(0).is_real_scalar() || (gradient && !res(1).is_real_matrix() && !(n == 1 && res(1).is_real_scalar()))) gripe_user_returned_invalid("nlopt_optimize"); else { if (gradient) { if (n == 1 && res(1).is_real_scalar()) gradient[0] = res(1).double_value(); else { Matrix grad = res(1).matrix_value(); for (unsigned i = 0; i < n; ++i) gradient[i] = grad(i); } } return res(0).double_value(); } return 0; } #define CHECK1(cond, msg) if (!(cond)) { fprintf(stderr, msg "\n\n"); nlopt_destroy(opt); nlopt_destroy(local_opt); return NULL; } nlopt_opt make_opt(Octave_map &opts, int n) { nlopt_opt opt = NULL, local_opt = NULL; nlopt_algorithm algorithm = nlopt_algorithm(struct_val_default(opts, "algorithm", NLOPT_NUM_ALGORITHMS)); CHECK1(((int)algorithm) >= 0 && algorithm < NLOPT_NUM_ALGORITHMS, "invalid opt.algorithm"); opt = nlopt_create(algorithm, n); CHECK1(opt, "nlopt: out of memory"); Matrix m_inf(1, n, -HUGE_VAL); Matrix lb = struct_val_default(opts, "lower_bounds", m_inf); CHECK1(n == lb.length(), "wrong length of opt.lower_bounds"); CHECK1(nlopt_set_lower_bounds(opt, lb.data()) > 0, "nlopt: out of memory"); Matrix p_inf(1, n, +HUGE_VAL); Matrix ub = struct_val_default(opts, "upper_bounds", p_inf); CHECK1(n == ub.length(), "wrong length of opt.upper_bounds"); CHECK1(nlopt_set_upper_bounds(opt, ub.data()) > 0, "nlopt: out of memory"); nlopt_set_stopval(opt, struct_val_default(opts, "stopval", -HUGE_VAL)); nlopt_set_ftol_rel(opt, struct_val_default(opts, "ftol_rel", 0.0)); nlopt_set_ftol_abs(opt, struct_val_default(opts, "ftol_abs", 0.0)); nlopt_set_xtol_rel(opt, struct_val_default(opts, "xtol_rel", 0.0)); { Matrix zeros(1, n, 0.0); Matrix xtol_abs = struct_val_default(opts, "xtol_abs", zeros); CHECK1(n == xtol_abs.length(), "stop.xtol_abs must have same length as x"); CHECK1(nlopt_set_xtol_abs(opt, xtol_abs.data())>0, "nlopt: out of memory"); } nlopt_set_maxeval(opt, struct_val_default(opts, "maxeval", 0) < 0 ? 0 : struct_val_default(opts, "maxeval", 0)); nlopt_set_maxtime(opt, struct_val_default(opts, "maxtime", 0.0)); nlopt_set_population(opt, struct_val_default(opts, "population", 0)); nlopt_set_vector_storage(opt, struct_val_default(opts, "vector_storage", 0)); if (opts.contains("initial_step")) { Matrix zeros(1, n, 0.0); Matrix initial_step = struct_val_default(opts, "initial_step", zeros); CHECK1(n == initial_step.length(), "stop.initial_step must have same length as x"); CHECK1(nlopt_set_initial_step(opt, initial_step.data()) > 0, "nlopt: out of memory"); } if (opts.contains("local_optimizer")) { CHECK1(opts.contents("local_optimizer").length() == 1 && (opts.contents("local_optimizer"))(0).is_map(), "opt.local_optimizer must be a structure"); Octave_map local_opts = (opts.contents("local_optimizer"))(0).map_value(); CHECK1((local_opt = make_opt(local_opts, n)), "error initializing local optimizer"); nlopt_set_local_optimizer(opt, local_opt); nlopt_destroy(local_opt); local_opt = NULL; } return opt; } #define CHECK(cond, msg) if (!(cond)) { fprintf(stderr, msg "\n\n"); nlopt_destroy(opt); return retval; } DEFUN_DLD(nlopt_optimize, args, nargout, NLOPT_OPTIMIZE_USAGE) { octave_value_list retval; double A; nlopt_opt opt = NULL; CHECK(args.length() == 2 && nargout <= 3, "wrong number of args"); CHECK(args(0).is_map(), "opt must be structure") Octave_map opts = args(0).map_value(); CHECK(args(1).is_real_matrix() || args(1).is_real_scalar(), "x must be real vector"); Matrix x = args(1).is_real_scalar() ? Matrix(1, 1, args(1).double_value()) : args(1).matrix_value(); int n = x.length(); CHECK((opt = make_opt(opts, n)), "error initializing nlopt options"); user_function_data d; d.neval = 0; d.verbose = struct_val_default(opts, "verbose", 0); d.opt = opt; if (opts.contains("min_objective")) { CHECK(opts.contents("min_objective").length() == 1 && (opts.contents("min_objective"))(0).is_function_handle(), "opt.min_objective must be a function"); d.f = (opts.contents("min_objective"))(0).function_value(); nlopt_set_min_objective(opt, user_function, &d); } else if (opts.contains("max_objective")) { CHECK(opts.contents("max_objective").length() == 1 && (opts.contents("max_objective"))(0).is_function_handle(), "opt.max_objective must be a function"); d.f = (opts.contents("max_objective"))(0).function_value(); nlopt_set_max_objective(opt, user_function, &d); } else { CHECK(0,"either opt.min_objective or opt.max_objective must exist"); } if (opts.contains("fc") && opts.contents("fc").length() == 1) { CHECK((opts.contents("fc"))(0).is_cell(), "opt.fc must be cell array"); Cell fc = (opts.contents("fc"))(0).cell_value(); Matrix zeros(1, fc.length(), 0.0); Matrix fc_tol = struct_val_default(opts, "fc_tol", zeros); CHECK(fc_tol.length() == fc.length(), "opt.fc must have same length as opt.fc_tol"); for (int i = 0; i < fc.length(); ++i) { CHECK(fc(i).is_function() || fc(i).is_function_handle(), "opt.fc must be a cell array of function handles"); CHECK(nlopt_add_inequality_constraint(opt, user_function1, fc(i).function_value(), fc_tol(i)) > 0, "nlopt error adding inequality constraint"); } } if (opts.contains("h") && opts.contents("h").length() == 1) { CHECK((opts.contents("h"))(0).is_cell(), "opt.h must be cell array"); Cell h = (opts.contents("h"))(0).cell_value(); Matrix zeros(1, h.length(), 0.0); Matrix h_tol = struct_val_default(opts, "h_tol", zeros); CHECK(h_tol.length() == h.length(), "opt.h must have same length as opt.h_tol"); for (int i = 0; i < h.length(); ++i) { CHECK(h(i).is_function() || h(i).is_function_handle(), "opt.h must be a cell array of function handles"); CHECK(nlopt_add_equality_constraint(opt, user_function1, h(i).function_value(), h_tol(i)) > 0, "nlopt error adding equality constraint"); } } double opt_f; nlopt_result ret = nlopt_optimize(opt, x.fortran_vec(), &opt_f); retval(0) = x; if (nargout > 1) retval(1) = opt_f; if (nargout > 2) retval(2) = int(ret); nlopt_destroy(opt); return retval; } nloptr/src/nlopt_src/octave/NLOPT_LN_BOBYQA.m0000644000176000001440000000027513263734242020405 0ustar ripleyusers% NLOPT_LN_BOBYQA: BOBYQA bound-constrained optimization via quadratic models (local, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_LN_BOBYQA val = 34; nloptr/src/nlopt_src/octave/NLOPT_LD_MMA.m0000644000176000001440000000023313263734242020022 0ustar ripleyusers% NLOPT_LD_MMA: Method of Moving Asymptotes (MMA) (local, derivative) % % See nlopt_minimize for more information. function val = NLOPT_LD_MMA val = 24; nloptr/src/nlopt_src/octave/NLOPT_LD_TNEWTON_RESTART.m0000644000176000001440000000027013263734242021753 0ustar ripleyusers% NLOPT_LD_TNEWTON_RESTART: Truncated Newton with restarting (local, derivative-based) % % See nlopt_minimize for more information. function val = NLOPT_LD_TNEWTON_RESTART val = 16; nloptr/src/nlopt_src/octave/nlopt_minimize.m0000644000176000001440000000761213263734242021122 0ustar ripleyusers% Usage: [xopt, fmin, retcode] = nlopt_minimize(algorithm, f, f_data, lb, ub, % xinit, stop) % % Minimizes a nonlinear multivariable function f(x, f_data{:}), where % x is a row vector, returning the optimal x found (xopt) along with % the minimum function value (fmin = f(xopt)) and a return code (retcode). % A variety of local and global optimization algorithms can be used, % as specified by the algorithm parameter described below. lb and ub % are row vectors giving the upper and lower bounds on x, xinit is % a row vector giving the initial guess for x, and stop is a struct % containing termination conditions (see below). % % This function is a front-end for the external routine nlopt_minimize % in the free NLopt nonlinear-optimization library, which is a wrapper % around a number of free/open-source optimization subroutines. More % details can be found on the NLopt web page (ab-initio.mit.edu/nlopt) % and also under 'man nlopt_minimize' on Unix. % % f should be a handle (@) to a function of the form: % % [val, gradient] = f(x, ...) % % where x is a row vector, val is the function value f(x), and gradient % is a row vector giving the gradient of the function with respect to x. % The gradient is only used for gradient-based optimization algorithms; % some of the algorithms (below) are derivative-free and only require % f to return val (its value). f can take additional arguments (...) % which are passed via the argument f_data: f_data is a cell array % of the additional arguments to pass to f. (Recall that cell arrays % are specified by curly brackets { ... }. For example, pass f_data={} % for functions that require no additional arguments.) % % stop describes the termination criteria, and is a struct with a % number of optional fields: % stop.ftol_rel = fractional tolerance on function value % stop.ftol_abs = absolute tolerance on function value % stop.xtol_rel = fractional tolerance on x % stop.xtol_abs = row vector of absolute tolerances on x components % stop.fmin_max = stop when f < fmin_max is found % stop.maxeval = maximum number of function evaluations % stop.maxtime = maximum run time in seconds % stop.verbose = > 0 indicates verbose output % Minimization stops when any one of these conditions is met; any % condition that is omitted from stop will be ignored. WARNING: % not all algorithms interpret the stopping criteria in exactly the % same way, and in any case ftol/xtol specify only a crude estimate % for the accuracy of the minimum function value/x. % % The algorithm should be one of the following constants (name and % interpretation are the same as for the C function). Names with % _G*_ are global optimization, and names with _L*_ are local % optimization. Names with _*N_ are derivative-free, while names % with _*D_ are gradient-based algorithms. Algorithms: % % NLOPT_GD_MLSL_LDS, NLOPT_GD_MLSL, NLOPT_GD_STOGO, NLOPT_GD_STOGO_RAND, % NLOPT_GN_CRS2_LM, NLOPT_GN_DIRECT_L, NLOPT_GN_DIRECT_L_NOSCAL, % NLOPT_GN_DIRECT_L_RAND, NLOPT_GN_DIRECT_L_RAND_NOSCAL, NLOPT_GN_DIRECT, % NLOPT_GN_DIRECT_NOSCAL, NLOPT_GN_ISRES, NLOPT_GN_MLSL_LDS, NLOPT_GN_MLSL, % NLOPT_GN_ORIG_DIRECT_L, NLOPT_GN_ORIG_DIRECT, NLOPT_LD_AUGLAG_EQ, % NLOPT_LD_AUGLAG, NLOPT_LD_LBFGS, NLOPT_LD_LBFGS_NOCEDAL, NLOPT_LD_MMA, % NLOPT_LD_TNEWTON, NLOPT_LD_TNEWTON_PRECOND, % NLOPT_LD_TNEWTON_PRECOND_RESTART, NLOPT_LD_TNEWTON_RESTART, % NLOPT_LD_VAR1, NLOPT_LD_VAR2, NLOPT_LN_AUGLAG_EQ, NLOPT_LN_AUGLAG, % NLOPT_LN_BOBYQA, NLOPT_LN_COBYLA, NLOPT_LN_NELDERMEAD, % NLOPT_LN_NEWUOA_BOUND, NLOPT_LN_NEWUOA, NLOPT_LN_PRAXIS, NLOPT_LN_SBPLX % % For more information on individual algorithms, see their individual % help pages (e.g. "help NLOPT_LN_SBPLX"). function [xopt, fmin, retcode] = nlopt_minimize(algorithm, f, f_data, lb, ub, xinit, stop) [xopt, fmin, retcode] = nlopt_minimize_constrained(algorithm, f, f_data, {}, {}, lb, ub, xinit, stop); nloptr/src/nlopt_src/octave/NLOPT_LD_TNEWTON_PRECOND_RESTART.m0000644000176000001440000000032713263734242023130 0ustar ripleyusers% NLOPT_LD_TNEWTON_PRECOND_RESTART: Preconditioned truncated Newton with restarting (local, derivative-based) % % See nlopt_minimize for more information. function val = NLOPT_LD_TNEWTON_PRECOND_RESTART val = 18; nloptr/src/nlopt_src/octave/NLOPT_GN_DIRECT_L.m0000644000176000001440000000021713263734242020644 0ustar ripleyusers% NLOPT_GN_DIRECT_L: DIRECT-L (global, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_GN_DIRECT_L val = 1; nloptr/src/nlopt_src/octave/nlopt_optimize-mex.c0000644000176000001440000003021513355070635021712 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ /* Matlab MEX interface to NLopt, and in particular to nlopt_optimize */ #include #include #include #include #include #include "nlopt.h" #define CHECK0(cond, msg) if (!(cond)) mexErrMsgTxt(msg); static double struct_val_default(const mxArray *s, const char *name, double dflt) { mxArray *val = mxGetField(s, 0, name); if (val) { CHECK0(mxIsNumeric(val) && !mxIsComplex(val) && mxGetM(val) * mxGetN(val) == 1, "opt fields, other than xtol_abs, must be real scalars"); return mxGetScalar(val); } return dflt; } static double *struct_arrval(const mxArray *s, const char *name, unsigned n, double *dflt) { mxArray *val = mxGetField(s, 0, name); if (val) { CHECK0(mxIsNumeric(val) && !mxIsComplex(val) && mxGetM(val) * mxGetN(val) == n, "opt vector field is not of length n"); return mxGetPr(val); } return dflt; } static mxArray *struct_funcval(const mxArray *s, const char *name) { mxArray *val = mxGetField(s, 0, name); if (val) { CHECK0(mxIsChar(val) || mxIsFunctionHandle(val), "opt function field is not a function handle/name"); return val; } return NULL; } static double *fill(double *arr, unsigned n, double val) { unsigned i; for (i = 0; i < n; ++i) arr[i] = val; return arr; } #define FLEN 128 /* max length of user function name */ #define MAXRHS 3 /* max nrhs for user function */ typedef struct user_function_data_s { char f[FLEN]; mxArray *plhs[2]; mxArray *prhs[MAXRHS]; int xrhs, nrhs; int verbose, neval; struct user_function_data_s *dpre; nlopt_opt opt; } user_function_data; static double user_function(unsigned n, const double *x, double *gradient, /* NULL if not needed */ void *d_) { user_function_data *d = (user_function_data *) d_; double f; d->plhs[0] = d->plhs[1] = NULL; memcpy(mxGetPr(d->prhs[d->xrhs]), x, n * sizeof(double)); CHECK0(0 == mexCallMATLAB(gradient ? 2 : 1, d->plhs, d->nrhs, d->prhs, d->f), "error calling user function"); CHECK0(mxIsNumeric(d->plhs[0]) && !mxIsComplex(d->plhs[0]) && mxGetM(d->plhs[0]) * mxGetN(d->plhs[0]) == 1, "user function must return real scalar"); f = mxGetScalar(d->plhs[0]); mxDestroyArray(d->plhs[0]); if (gradient) { CHECK0(mxIsDouble(d->plhs[1]) && !mxIsComplex(d->plhs[1]) && (mxGetM(d->plhs[1]) == 1 || mxGetN(d->plhs[1]) == 1) && mxGetM(d->plhs[1]) * mxGetN(d->plhs[1]) == n, "gradient vector from user function is the wrong size"); memcpy(gradient, mxGetPr(d->plhs[1]), n * sizeof(double)); mxDestroyArray(d->plhs[1]); } d->neval++; if (d->verbose) mexPrintf("nlopt_optimize eval #%d: %g\n", d->neval, f); if (mxIsNaN(f)) nlopt_force_stop(d->opt); return f; } static void user_pre(unsigned n, const double *x, const double *v, double *vpre, void *d_) { user_function_data *d = ((user_function_data *) d_)->dpre; d->plhs[0] = d->plhs[1] = NULL; memcpy(mxGetPr(d->prhs[d->xrhs]), x, n * sizeof(double)); memcpy(mxGetPr(d->prhs[d->xrhs + 1]), v, n * sizeof(double)); CHECK0(0 == mexCallMATLAB(1, d->plhs, d->nrhs, d->prhs, d->f), "error calling user function"); CHECK0(mxIsDouble(d->plhs[0]) && !mxIsComplex(d->plhs[0]) && (mxGetM(d->plhs[0]) == 1 || mxGetN(d->plhs[0]) == 1) && mxGetM(d->plhs[0]) * mxGetN(d->plhs[0]) == n, "vpre vector from user function is the wrong size"); memcpy(vpre, mxGetPr(d->plhs[0]), n * sizeof(double)); mxDestroyArray(d->plhs[0]); d->neval++; if (d->verbose) mexPrintf("nlopt_optimize precond eval #%d\n", d->neval); } #define CHECK1(cond, msg) if (!(cond)) { mxFree(tmp); nlopt_destroy(opt); nlopt_destroy(local_opt); mexWarnMsgTxt(msg); return NULL; }; nlopt_opt make_opt(const mxArray *opts, unsigned n) { nlopt_opt opt = NULL, local_opt = NULL; nlopt_algorithm algorithm; double *tmp = NULL; unsigned i; algorithm = (nlopt_algorithm) struct_val_default(opts, "algorithm", NLOPT_NUM_ALGORITHMS); CHECK1(((int)algorithm) >= 0 && algorithm < NLOPT_NUM_ALGORITHMS, "invalid opt.algorithm"); tmp = (double *) mxCalloc(n, sizeof(double)); opt = nlopt_create(algorithm, n); CHECK1(opt, "nlopt: out of memory"); nlopt_set_lower_bounds(opt, struct_arrval(opts, "lower_bounds", n, fill(tmp, n, -HUGE_VAL))); nlopt_set_upper_bounds(opt, struct_arrval(opts, "upper_bounds", n, fill(tmp, n, +HUGE_VAL))); nlopt_set_stopval(opt, struct_val_default(opts, "stopval", -HUGE_VAL)); nlopt_set_ftol_rel(opt, struct_val_default(opts, "ftol_rel", 0.0)); nlopt_set_ftol_abs(opt, struct_val_default(opts, "ftol_abs", 0.0)); nlopt_set_xtol_rel(opt, struct_val_default(opts, "xtol_rel", 0.0)); nlopt_set_xtol_abs(opt, struct_arrval(opts, "xtol_abs", n, fill(tmp, n, 0.0))); nlopt_set_maxeval(opt, struct_val_default(opts, "maxeval", 0.0) < 0 ? 0 : struct_val_default(opts, "maxeval", 0.0)); nlopt_set_maxtime(opt, struct_val_default(opts, "maxtime", 0.0)); nlopt_set_population(opt, struct_val_default(opts, "population", 0)); nlopt_set_vector_storage(opt, struct_val_default(opts, "vector_storage", 0)); if (struct_arrval(opts, "initial_step", n, NULL)) nlopt_set_initial_step(opt, struct_arrval(opts, "initial_step", n, NULL)); if (mxGetField(opts, 0, "local_optimizer")) { const mxArray *local_opts = mxGetField(opts, 0, "local_optimizer"); CHECK1(mxIsStruct(local_opts), "opt.local_optimizer must be a structure"); CHECK1(local_opt = make_opt(local_opts, n), "error initializing local optimizer"); nlopt_set_local_optimizer(opt, local_opt); nlopt_destroy(local_opt); local_opt = NULL; } mxFree(tmp); return opt; } #define CHECK(cond, msg) if (!(cond)) { mxFree(dh); mxFree(dfc); nlopt_destroy(opt); mexErrMsgTxt(msg); } void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) { unsigned n; double *x, *x0, opt_f; nlopt_result ret; mxArray *x_mx, *mx; user_function_data d, dpre, *dfc = NULL, *dh = NULL; nlopt_opt opt = NULL; CHECK(nrhs == 2 && nlhs <= 3, "wrong number of arguments"); /* options = prhs[0] */ CHECK(mxIsStruct(prhs[0]), "opt must be a struct"); /* x0 = prhs[1] */ CHECK(mxIsDouble(prhs[1]) && !mxIsComplex(prhs[1]) && (mxGetM(prhs[1]) == 1 || mxGetN(prhs[1]) == 1), "x must be real row or column vector"); n = mxGetM(prhs[1]) * mxGetN(prhs[1]), x0 = mxGetPr(prhs[1]); CHECK(opt = make_opt(prhs[0], n), "error initializing nlopt options"); d.neval = 0; d.verbose = (int) struct_val_default(prhs[0], "verbose", 0); d.opt = opt; /* function f = prhs[1] */ mx = struct_funcval(prhs[0], "min_objective"); if (!mx) mx = struct_funcval(prhs[0], "max_objective"); CHECK(mx, "either opt.min_objective or opt.max_objective must exist"); if (mxIsChar(mx)) { CHECK(mxGetString(mx, d.f, FLEN) == 0, "error reading function name string (too long?)"); d.nrhs = 1; d.xrhs = 0; } else { d.prhs[0] = mx; strcpy(d.f, "feval"); d.nrhs = 2; d.xrhs = 1; } d.prhs[d.xrhs] = mxCreateDoubleMatrix(1, n, mxREAL); if ((mx = struct_funcval(prhs[0], "pre"))) { CHECK(mxIsChar(mx) || mxIsFunctionHandle(mx), "pre must contain function handles or function names"); if (mxIsChar(mx)) { CHECK(mxGetString(mx, dpre.f, FLEN) == 0, "error reading function name string (too long?)"); dpre.nrhs = 2; dpre.xrhs = 0; } else { dpre.prhs[0] = mx; strcpy(dpre.f, "feval"); dpre.nrhs = 3; dpre.xrhs = 1; } dpre.verbose = d.verbose > 2; dpre.opt = opt; dpre.neval = 0; dpre.prhs[dpre.xrhs] = d.prhs[d.xrhs]; dpre.prhs[d.xrhs+1] = mxCreateDoubleMatrix(1, n, mxREAL); d.dpre = &dpre; if (struct_funcval(prhs[0], "min_objective")) nlopt_set_precond_min_objective(opt, user_function,user_pre,&d); else nlopt_set_precond_max_objective(opt, user_function,user_pre,&d); } else { dpre.nrhs = 0; if (struct_funcval(prhs[0], "min_objective")) nlopt_set_min_objective(opt, user_function, &d); else nlopt_set_max_objective(opt, user_function, &d); } if ((mx = mxGetField(prhs[0], 0, "fc"))) { int j, m; double *fc_tol; CHECK(mxIsCell(mx), "fc must be a Cell array"); m = mxGetM(mx) * mxGetN(mx);; dfc = (user_function_data *) mxCalloc(m, sizeof(user_function_data)); fc_tol = struct_arrval(prhs[0], "fc_tol", m, NULL); for (j = 0; j < m; ++j) { mxArray *fc = mxGetCell(mx, j); CHECK(mxIsChar(fc) || mxIsFunctionHandle(fc), "fc must contain function handles or function names"); if (mxIsChar(fc)) { CHECK(mxGetString(fc, dfc[j].f, FLEN) == 0, "error reading function name string (too long?)"); dfc[j].nrhs = 1; dfc[j].xrhs = 0; } else { dfc[j].prhs[0] = fc; strcpy(dfc[j].f, "feval"); dfc[j].nrhs = 2; dfc[j].xrhs = 1; } dfc[j].verbose = d.verbose > 1; dfc[j].opt = opt; dfc[j].neval = 0; dfc[j].prhs[dfc[j].xrhs] = d.prhs[d.xrhs]; CHECK(nlopt_add_inequality_constraint(opt, user_function, dfc + j, fc_tol ? fc_tol[j] : 0) > 0, "nlopt error adding inequality constraint"); } } if ((mx = mxGetField(prhs[0], 0, "h"))) { int j, m; double *h_tol; CHECK(mxIsCell(mx), "h must be a Cell array"); m = mxGetM(mx) * mxGetN(mx);; dh = (user_function_data *) mxCalloc(m, sizeof(user_function_data)); h_tol = struct_arrval(prhs[0], "h_tol", m, NULL); for (j = 0; j < m; ++j) { mxArray *h = mxGetCell(mx, j); CHECK(mxIsChar(h) || mxIsFunctionHandle(h), "h must contain function handles or function names"); if (mxIsChar(h)) { CHECK(mxGetString(h, dh[j].f, FLEN) == 0, "error reading function name string (too long?)"); dh[j].nrhs = 1; dh[j].xrhs = 0; } else { dh[j].prhs[0] = h; strcpy(dh[j].f, "feval"); dh[j].nrhs = 2; dh[j].xrhs = 1; } dh[j].verbose = d.verbose > 1; dh[j].opt = opt; dh[j].neval = 0; dh[j].prhs[dh[j].xrhs] = d.prhs[d.xrhs]; CHECK(nlopt_add_equality_constraint(opt, user_function, dh + j, h_tol ? h_tol[j] : 0) > 0, "nlopt error adding equality constraint"); } } x_mx = mxCreateDoubleMatrix(mxGetM(prhs[1]), mxGetN(prhs[1]), mxREAL); x = mxGetPr(x_mx); memcpy(x, x0, sizeof(double) * n); ret = nlopt_optimize(opt, x, &opt_f); mxFree(dh); mxFree(dfc); mxDestroyArray(d.prhs[d.xrhs]); if (dpre.nrhs > 0) mxDestroyArray(dpre.prhs[d.xrhs+1]); nlopt_destroy(opt); plhs[0] = x_mx; if (nlhs > 1) { plhs[1] = mxCreateDoubleMatrix(1, 1, mxREAL); *(mxGetPr(plhs[1])) = opt_f; } if (nlhs > 2) { plhs[2] = mxCreateDoubleMatrix(1, 1, mxREAL); *(mxGetPr(plhs[2])) = (int) ret; } } nloptr/src/nlopt_src/octave/dummy.c0000644000176000001440000000011213355070635017173 0ustar ripleyusers#include int main(void) { printf("Hello world.\n"); return 0; } nloptr/src/nlopt_src/octave/NLOPT_GN_DIRECT_L_RAND.m0000644000176000001440000000024413263734242021450 0ustar ripleyusers% NLOPT_GN_DIRECT_L_RAND: Randomized DIRECT-L (global, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_GN_DIRECT_L_RAND val = 2; nloptr/src/nlopt_src/octave/NLOPT_GN_ESCH.m0000644000176000001440000000020213263734242020133 0ustar ripleyusers% NLOPT_GN_ESCH: ESCH evolutionary strategy % % See nlopt_minimize for more information. function val = NLOPT_GN_ESCH val = 42; nloptr/src/nlopt_src/octave/NLOPT_LD_LBFGS.m0000644000176000001440000000024013263734242020243 0ustar ripleyusers% NLOPT_LD_LBFGS: Limited-memory BFGS (L-BFGS) (local, derivative-based) % % See nlopt_minimize for more information. function val = NLOPT_LD_LBFGS val = 11; nloptr/src/nlopt_src/octave/NLOPT_AUGLAG_EQ.m0000644000176000001440000000026613263734242020364 0ustar ripleyusers% NLOPT_AUGLAG_EQ: Augmented Lagrangian method for equality constraints (needs sub-algorithm) % % See nlopt_minimize for more information. function val = NLOPT_AUGLAG_EQ val = 37; nloptr/src/nlopt_src/octave/NLOPT_LN_COBYLA.m0000644000176000001440000000027513263734242020401 0ustar ripleyusers% NLOPT_LN_COBYLA: COBYLA (Constrained Optimization BY Linear Approximations) (local, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_LN_COBYLA val = 25; nloptr/src/nlopt_src/octave/NLOPT_LD_TNEWTON.m0000644000176000001440000000023013263734242020543 0ustar ripleyusers% NLOPT_LD_TNEWTON: Truncated Newton (local, derivative-based) % % See nlopt_minimize for more information. function val = NLOPT_LD_TNEWTON val = 15; nloptr/src/nlopt_src/octave/NLOPT_LD_AUGLAG.m0000644000176000001440000000023313263734242020350 0ustar ripleyusers% NLOPT_LD_AUGLAG: Augmented Lagrangian method (local, derivative) % % See nlopt_minimize for more information. function val = NLOPT_LD_AUGLAG val = 31; nloptr/src/nlopt_src/octave/NLOPT_LD_TNEWTON_PRECOND.m0000644000176000001440000000026713263734242021727 0ustar ripleyusers% NLOPT_LD_TNEWTON_PRECOND: Preconditioned truncated Newton (local, derivative-based) % % See nlopt_minimize for more information. function val = NLOPT_LD_TNEWTON_PRECOND val = 17; nloptr/src/nlopt_src/octave/NLOPT_GN_MLSL_LDS.m0000644000176000001440000000026713263734242020675 0ustar ripleyusers% NLOPT_GN_MLSL_LDS: Multi-level single-linkage (MLSL), quasi-random (global, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_GN_MLSL_LDS val = 22; nloptr/src/nlopt_src/octave/NLOPT_LN_NEWUOA_BOUND.m0000644000176000001440000000031713263734242021352 0ustar ripleyusers% NLOPT_LN_NEWUOA_BOUND: Bound-constrained optimization via NEWUOA-based quadratic models (local, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_LN_NEWUOA_BOUND val = 27; nloptr/src/nlopt_src/octave/NLOPT_LD_AUGLAG_EQ.m0000644000176000001440000000027213263734242020740 0ustar ripleyusers% NLOPT_LD_AUGLAG_EQ: Augmented Lagrangian method for equality constraints (local, derivative) % % See nlopt_minimize for more information. function val = NLOPT_LD_AUGLAG_EQ val = 33; nloptr/src/nlopt_src/octave/NLOPT_AUGLAG.m0000644000176000001440000000022713263734242017774 0ustar ripleyusers% NLOPT_AUGLAG: Augmented Lagrangian method (needs sub-algorithm) % % See nlopt_minimize for more information. function val = NLOPT_AUGLAG val = 36; nloptr/src/nlopt_src/octave/NLOPT_GN_DIRECT_NOSCAL.m0000644000176000001440000000024013263734242021424 0ustar ripleyusers% NLOPT_GN_DIRECT_NOSCAL: Unscaled DIRECT (global, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_GN_DIRECT_NOSCAL val = 3; nloptr/src/nlopt_src/octave/NLOPT_LD_CCSAQ.m0000644000176000001440000000032613263734242020245 0ustar ripleyusers% NLOPT_LD_CCSAQ: CCSA (Conservative Convex Separable Approximations) with simple quadratic approximations (local, derivative) % % See nlopt_minimize for more information. function val = NLOPT_LD_CCSAQ val = 41; nloptr/src/nlopt_src/octave/NLOPT_GD_STOGO_RAND.m0000644000176000001440000000025213263734242021103 0ustar ripleyusers% NLOPT_GD_STOGO_RAND: StoGO with randomized search (global, derivative-based) % % See nlopt_minimize for more information. function val = NLOPT_GD_STOGO_RAND val = 9; nloptr/src/nlopt_src/octave/NLOPT_GN_ORIG_DIRECT.m0000644000176000001440000000024413263734242021211 0ustar ripleyusers% NLOPT_GN_ORIG_DIRECT: Original DIRECT version (global, no-derivative) % % See nlopt_minimize for more information. function val = NLOPT_GN_ORIG_DIRECT val = 6; nloptr/src/nlopt_src/config.guess0000644000176000001440000013036113263734242016741 0ustar ripleyusers#! /bin/sh # Attempt to guess a canonical system name. # Copyright 1992-2013 Free Software Foundation, Inc. timestamp='2013-06-10' # This file is free software; 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LIBC=gnu eval $set_cc_for_build cat <<-EOF > $dummy.c #include #if defined(__UCLIBC__) LIBC=uclibc #elif defined(__dietlibc__) LIBC=dietlibc #else LIBC=gnu #endif EOF eval `$CC_FOR_BUILD -E $dummy.c 2>/dev/null | grep '^LIBC'` ;; esac # Note: order is significant - the case branches are not exclusive. case "${UNAME_MACHINE}:${UNAME_SYSTEM}:${UNAME_RELEASE}:${UNAME_VERSION}" in *:NetBSD:*:*) # NetBSD (nbsd) targets should (where applicable) match one or # more of the tuples: *-*-netbsdelf*, *-*-netbsdaout*, # *-*-netbsdecoff* and *-*-netbsd*. For targets that recently # switched to ELF, *-*-netbsd* would select the old # object file format. This provides both forward # compatibility and a consistent mechanism for selecting the # object file format. # # Note: NetBSD doesn't particularly care about the vendor # portion of the name. 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I hope that # covers most systems running today. This code pipes the CPU # types through head -n 1, so we only detect the type of CPU 0. ALPHA_CPU_TYPE=`/usr/sbin/psrinfo -v | sed -n -e 's/^ The alpha \(.*\) processor.*$/\1/p' | head -n 1` case "$ALPHA_CPU_TYPE" in "EV4 (21064)") UNAME_MACHINE="alpha" ;; "EV4.5 (21064)") UNAME_MACHINE="alpha" ;; "LCA4 (21066/21068)") UNAME_MACHINE="alpha" ;; "EV5 (21164)") UNAME_MACHINE="alphaev5" ;; "EV5.6 (21164A)") UNAME_MACHINE="alphaev56" ;; "EV5.6 (21164PC)") UNAME_MACHINE="alphapca56" ;; "EV5.7 (21164PC)") UNAME_MACHINE="alphapca57" ;; "EV6 (21264)") UNAME_MACHINE="alphaev6" ;; "EV6.7 (21264A)") UNAME_MACHINE="alphaev67" ;; "EV6.8CB (21264C)") UNAME_MACHINE="alphaev68" ;; "EV6.8AL (21264B)") UNAME_MACHINE="alphaev68" ;; "EV6.8CX (21264D)") UNAME_MACHINE="alphaev68" ;; "EV6.9A (21264/EV69A)") UNAME_MACHINE="alphaev69" ;; "EV7 (21364)") UNAME_MACHINE="alphaev7" ;; "EV7.9 (21364A)") UNAME_MACHINE="alphaev79" ;; esac # A Pn.n version is a patched version. # A Vn.n version is a released version. # A Tn.n version is a released field test version. # A Xn.n version is an unreleased experimental baselevel. # 1.2 uses "1.2" for uname -r. echo ${UNAME_MACHINE}-dec-osf`echo ${UNAME_RELEASE} | sed -e 's/^[PVTX]//' | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz'` # Reset EXIT trap before exiting to avoid spurious non-zero exit code. exitcode=$? trap '' 0 exit $exitcode ;; Alpha\ *:Windows_NT*:*) # How do we know it's Interix rather than the generic POSIX subsystem? # Should we change UNAME_MACHINE based on the output of uname instead # of the specific Alpha model? echo alpha-pc-interix exit ;; 21064:Windows_NT:50:3) echo alpha-dec-winnt3.5 exit ;; Amiga*:UNIX_System_V:4.0:*) echo m68k-unknown-sysv4 exit ;; *:[Aa]miga[Oo][Ss]:*:*) echo ${UNAME_MACHINE}-unknown-amigaos exit ;; *:[Mm]orph[Oo][Ss]:*:*) echo ${UNAME_MACHINE}-unknown-morphos exit ;; *:OS/390:*:*) echo i370-ibm-openedition exit ;; *:z/VM:*:*) echo s390-ibm-zvmoe exit ;; *:OS400:*:*) echo powerpc-ibm-os400 exit ;; arm:RISC*:1.[012]*:*|arm:riscix:1.[012]*:*) echo arm-acorn-riscix${UNAME_RELEASE} exit ;; arm*:riscos:*:*|arm*:RISCOS:*:*) echo arm-unknown-riscos exit ;; SR2?01:HI-UX/MPP:*:* | SR8000:HI-UX/MPP:*:*) echo hppa1.1-hitachi-hiuxmpp exit ;; Pyramid*:OSx*:*:* | MIS*:OSx*:*:* | MIS*:SMP_DC-OSx*:*:*) # akee@wpdis03.wpafb.af.mil (Earle F. Ake) contributed MIS and NILE. if test "`(/bin/universe) 2>/dev/null`" = att ; then echo pyramid-pyramid-sysv3 else echo pyramid-pyramid-bsd fi exit ;; NILE*:*:*:dcosx) echo pyramid-pyramid-svr4 exit ;; DRS?6000:unix:4.0:6*) echo sparc-icl-nx6 exit ;; DRS?6000:UNIX_SV:4.2*:7* | DRS?6000:isis:4.2*:7*) case `/usr/bin/uname -p` in sparc) echo sparc-icl-nx7; exit ;; esac ;; s390x:SunOS:*:*) echo ${UNAME_MACHINE}-ibm-solaris2`echo ${UNAME_RELEASE}|sed -e 's/[^.]*//'` exit ;; sun4H:SunOS:5.*:*) echo sparc-hal-solaris2`echo ${UNAME_RELEASE}|sed -e 's/[^.]*//'` exit ;; sun4*:SunOS:5.*:* | tadpole*:SunOS:5.*:*) echo sparc-sun-solaris2`echo ${UNAME_RELEASE}|sed -e 's/[^.]*//'` exit ;; i86pc:AuroraUX:5.*:* | i86xen:AuroraUX:5.*:*) echo i386-pc-auroraux${UNAME_RELEASE} exit ;; i86pc:SunOS:5.*:* | i86xen:SunOS:5.*:*) eval $set_cc_for_build SUN_ARCH="i386" # If there is a compiler, see if it is configured for 64-bit objects. # Note that the Sun cc does not turn __LP64__ into 1 like gcc does. # This test works for both compilers. if [ "$CC_FOR_BUILD" != 'no_compiler_found' ]; then if (echo '#ifdef __amd64'; echo IS_64BIT_ARCH; echo '#endif') | \ (CCOPTS= $CC_FOR_BUILD -E - 2>/dev/null) | \ grep IS_64BIT_ARCH >/dev/null then SUN_ARCH="x86_64" fi fi echo ${SUN_ARCH}-pc-solaris2`echo ${UNAME_RELEASE}|sed -e 's/[^.]*//'` exit ;; sun4*:SunOS:6*:*) # According to config.sub, this is the proper way to canonicalize # SunOS6. Hard to guess exactly what SunOS6 will be like, but # it's likely to be more like Solaris than SunOS4. echo sparc-sun-solaris3`echo ${UNAME_RELEASE}|sed -e 's/[^.]*//'` exit ;; sun4*:SunOS:*:*) case "`/usr/bin/arch -k`" in Series*|S4*) UNAME_RELEASE=`uname -v` ;; esac # Japanese Language versions have a version number like `4.1.3-JL'. echo sparc-sun-sunos`echo ${UNAME_RELEASE}|sed -e 's/-/_/'` exit ;; sun3*:SunOS:*:*) echo m68k-sun-sunos${UNAME_RELEASE} exit ;; sun*:*:4.2BSD:*) UNAME_RELEASE=`(sed 1q /etc/motd | awk '{print substr($5,1,3)}') 2>/dev/null` test "x${UNAME_RELEASE}" = "x" && UNAME_RELEASE=3 case "`/bin/arch`" in sun3) echo m68k-sun-sunos${UNAME_RELEASE} ;; sun4) echo sparc-sun-sunos${UNAME_RELEASE} ;; esac exit ;; aushp:SunOS:*:*) echo sparc-auspex-sunos${UNAME_RELEASE} exit ;; # The situation for MiNT is a little confusing. The machine name # can be virtually everything (everything which is not # "atarist" or "atariste" at least should have a processor # > m68000). The system name ranges from "MiNT" over "FreeMiNT" # to the lowercase version "mint" (or "freemint"). Finally # the system name "TOS" denotes a system which is actually not # MiNT. But MiNT is downward compatible to TOS, so this should # be no problem. atarist[e]:*MiNT:*:* | atarist[e]:*mint:*:* | atarist[e]:*TOS:*:*) echo m68k-atari-mint${UNAME_RELEASE} exit ;; atari*:*MiNT:*:* | atari*:*mint:*:* | atarist[e]:*TOS:*:*) echo m68k-atari-mint${UNAME_RELEASE} exit ;; *falcon*:*MiNT:*:* | *falcon*:*mint:*:* | *falcon*:*TOS:*:*) echo m68k-atari-mint${UNAME_RELEASE} exit ;; milan*:*MiNT:*:* | milan*:*mint:*:* | *milan*:*TOS:*:*) echo m68k-milan-mint${UNAME_RELEASE} exit ;; hades*:*MiNT:*:* | hades*:*mint:*:* | *hades*:*TOS:*:*) echo m68k-hades-mint${UNAME_RELEASE} exit ;; *:*MiNT:*:* | *:*mint:*:* | *:*TOS:*:*) echo m68k-unknown-mint${UNAME_RELEASE} exit ;; m68k:machten:*:*) echo m68k-apple-machten${UNAME_RELEASE} exit ;; powerpc:machten:*:*) echo powerpc-apple-machten${UNAME_RELEASE} exit ;; RISC*:Mach:*:*) echo mips-dec-mach_bsd4.3 exit ;; RISC*:ULTRIX:*:*) echo mips-dec-ultrix${UNAME_RELEASE} exit ;; VAX*:ULTRIX*:*:*) echo vax-dec-ultrix${UNAME_RELEASE} exit ;; 2020:CLIX:*:* | 2430:CLIX:*:*) echo clipper-intergraph-clix${UNAME_RELEASE} exit ;; mips:*:*:UMIPS | mips:*:*:RISCos) eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #ifdef __cplusplus #include /* for printf() prototype */ int main (int argc, char *argv[]) { #else int main (argc, argv) int argc; char *argv[]; { #endif #if defined (host_mips) && defined (MIPSEB) #if defined (SYSTYPE_SYSV) printf ("mips-mips-riscos%ssysv\n", argv[1]); exit (0); #endif #if defined (SYSTYPE_SVR4) printf ("mips-mips-riscos%ssvr4\n", argv[1]); exit (0); #endif #if defined (SYSTYPE_BSD43) || defined(SYSTYPE_BSD) printf ("mips-mips-riscos%sbsd\n", argv[1]); exit (0); #endif #endif exit (-1); } EOF $CC_FOR_BUILD -o $dummy $dummy.c && dummyarg=`echo "${UNAME_RELEASE}" | sed -n 's/\([0-9]*\).*/\1/p'` && SYSTEM_NAME=`$dummy $dummyarg` && { echo "$SYSTEM_NAME"; exit; } echo mips-mips-riscos${UNAME_RELEASE} exit ;; Motorola:PowerMAX_OS:*:*) echo powerpc-motorola-powermax exit ;; Motorola:*:4.3:PL8-*) echo powerpc-harris-powermax exit ;; Night_Hawk:*:*:PowerMAX_OS | Synergy:PowerMAX_OS:*:*) echo powerpc-harris-powermax exit ;; Night_Hawk:Power_UNIX:*:*) echo powerpc-harris-powerunix exit ;; m88k:CX/UX:7*:*) echo m88k-harris-cxux7 exit ;; m88k:*:4*:R4*) echo m88k-motorola-sysv4 exit ;; m88k:*:3*:R3*) echo m88k-motorola-sysv3 exit ;; AViiON:dgux:*:*) # DG/UX returns AViiON for all architectures UNAME_PROCESSOR=`/usr/bin/uname -p` if [ $UNAME_PROCESSOR = mc88100 ] || [ $UNAME_PROCESSOR = mc88110 ] then if [ ${TARGET_BINARY_INTERFACE}x = m88kdguxelfx ] || \ [ ${TARGET_BINARY_INTERFACE}x = x ] then echo m88k-dg-dgux${UNAME_RELEASE} else echo m88k-dg-dguxbcs${UNAME_RELEASE} fi else echo i586-dg-dgux${UNAME_RELEASE} fi exit ;; M88*:DolphinOS:*:*) # DolphinOS (SVR3) echo m88k-dolphin-sysv3 exit ;; M88*:*:R3*:*) # Delta 88k system running SVR3 echo m88k-motorola-sysv3 exit ;; XD88*:*:*:*) # Tektronix XD88 system running UTekV (SVR3) echo m88k-tektronix-sysv3 exit ;; Tek43[0-9][0-9]:UTek:*:*) # Tektronix 4300 system running UTek (BSD) echo m68k-tektronix-bsd exit ;; *:IRIX*:*:*) echo mips-sgi-irix`echo ${UNAME_RELEASE}|sed -e 's/-/_/g'` exit ;; ????????:AIX?:[12].1:2) # AIX 2.2.1 or AIX 2.1.1 is RT/PC AIX. echo romp-ibm-aix # uname -m gives an 8 hex-code CPU id exit ;; # Note that: echo "'`uname -s`'" gives 'AIX ' i*86:AIX:*:*) echo i386-ibm-aix exit ;; ia64:AIX:*:*) if [ -x /usr/bin/oslevel ] ; then IBM_REV=`/usr/bin/oslevel` else IBM_REV=${UNAME_VERSION}.${UNAME_RELEASE} fi echo ${UNAME_MACHINE}-ibm-aix${IBM_REV} exit ;; *:AIX:2:3) if grep bos325 /usr/include/stdio.h >/dev/null 2>&1; then eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #include main() { if (!__power_pc()) exit(1); puts("powerpc-ibm-aix3.2.5"); exit(0); } EOF if $CC_FOR_BUILD -o $dummy $dummy.c && SYSTEM_NAME=`$dummy` then echo "$SYSTEM_NAME" else echo rs6000-ibm-aix3.2.5 fi elif grep bos324 /usr/include/stdio.h >/dev/null 2>&1; then echo rs6000-ibm-aix3.2.4 else echo rs6000-ibm-aix3.2 fi exit ;; *:AIX:*:[4567]) IBM_CPU_ID=`/usr/sbin/lsdev -C -c processor -S available | sed 1q | awk '{ print $1 }'` if /usr/sbin/lsattr -El ${IBM_CPU_ID} | grep ' POWER' >/dev/null 2>&1; then IBM_ARCH=rs6000 else IBM_ARCH=powerpc fi if [ -x /usr/bin/oslevel ] ; then IBM_REV=`/usr/bin/oslevel` else IBM_REV=${UNAME_VERSION}.${UNAME_RELEASE} fi echo ${IBM_ARCH}-ibm-aix${IBM_REV} exit ;; *:AIX:*:*) echo rs6000-ibm-aix exit ;; ibmrt:4.4BSD:*|romp-ibm:BSD:*) echo romp-ibm-bsd4.4 exit ;; ibmrt:*BSD:*|romp-ibm:BSD:*) # covers RT/PC BSD and echo romp-ibm-bsd${UNAME_RELEASE} # 4.3 with uname added to exit ;; # report: romp-ibm BSD 4.3 *:BOSX:*:*) echo rs6000-bull-bosx exit ;; DPX/2?00:B.O.S.:*:*) echo m68k-bull-sysv3 exit ;; 9000/[34]??:4.3bsd:1.*:*) echo m68k-hp-bsd exit ;; hp300:4.4BSD:*:* | 9000/[34]??:4.3bsd:2.*:*) echo m68k-hp-bsd4.4 exit ;; 9000/[34678]??:HP-UX:*:*) HPUX_REV=`echo ${UNAME_RELEASE}|sed -e 's/[^.]*.[0B]*//'` case "${UNAME_MACHINE}" in 9000/31? ) HP_ARCH=m68000 ;; 9000/[34]?? ) HP_ARCH=m68k ;; 9000/[678][0-9][0-9]) if [ -x /usr/bin/getconf ]; then sc_cpu_version=`/usr/bin/getconf SC_CPU_VERSION 2>/dev/null` sc_kernel_bits=`/usr/bin/getconf SC_KERNEL_BITS 2>/dev/null` case "${sc_cpu_version}" in 523) HP_ARCH="hppa1.0" ;; # CPU_PA_RISC1_0 528) HP_ARCH="hppa1.1" ;; # CPU_PA_RISC1_1 532) # CPU_PA_RISC2_0 case "${sc_kernel_bits}" in 32) HP_ARCH="hppa2.0n" ;; 64) HP_ARCH="hppa2.0w" ;; '') HP_ARCH="hppa2.0" ;; # HP-UX 10.20 esac ;; esac fi if [ "${HP_ARCH}" = "" ]; then eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #define _HPUX_SOURCE #include #include int main () { #if defined(_SC_KERNEL_BITS) long bits = sysconf(_SC_KERNEL_BITS); #endif long cpu = sysconf (_SC_CPU_VERSION); switch (cpu) { case CPU_PA_RISC1_0: puts ("hppa1.0"); break; case CPU_PA_RISC1_1: puts ("hppa1.1"); break; case CPU_PA_RISC2_0: #if defined(_SC_KERNEL_BITS) switch (bits) { case 64: puts ("hppa2.0w"); break; case 32: puts ("hppa2.0n"); break; default: puts ("hppa2.0"); break; } break; #else /* !defined(_SC_KERNEL_BITS) */ puts ("hppa2.0"); break; #endif default: puts ("hppa1.0"); break; } exit (0); } EOF (CCOPTS= $CC_FOR_BUILD -o $dummy $dummy.c 2>/dev/null) && HP_ARCH=`$dummy` test -z "$HP_ARCH" && HP_ARCH=hppa fi ;; esac if [ ${HP_ARCH} = "hppa2.0w" ] then eval $set_cc_for_build # hppa2.0w-hp-hpux* has a 64-bit kernel and a compiler generating # 32-bit code. hppa64-hp-hpux* has the same kernel and a compiler # generating 64-bit code. GNU and HP use different nomenclature: # # $ CC_FOR_BUILD=cc ./config.guess # => hppa2.0w-hp-hpux11.23 # $ CC_FOR_BUILD="cc +DA2.0w" ./config.guess # => hppa64-hp-hpux11.23 if echo __LP64__ | (CCOPTS= $CC_FOR_BUILD -E - 2>/dev/null) | grep -q __LP64__ then HP_ARCH="hppa2.0w" else HP_ARCH="hppa64" fi fi echo ${HP_ARCH}-hp-hpux${HPUX_REV} exit ;; ia64:HP-UX:*:*) HPUX_REV=`echo ${UNAME_RELEASE}|sed -e 's/[^.]*.[0B]*//'` echo ia64-hp-hpux${HPUX_REV} exit ;; 3050*:HI-UX:*:*) eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #include int main () { long cpu = sysconf (_SC_CPU_VERSION); /* The order matters, because CPU_IS_HP_MC68K erroneously returns true for CPU_PA_RISC1_0. CPU_IS_PA_RISC returns correct results, however. */ if (CPU_IS_PA_RISC (cpu)) { switch (cpu) { case CPU_PA_RISC1_0: puts ("hppa1.0-hitachi-hiuxwe2"); break; case CPU_PA_RISC1_1: puts ("hppa1.1-hitachi-hiuxwe2"); break; case CPU_PA_RISC2_0: puts ("hppa2.0-hitachi-hiuxwe2"); break; default: puts ("hppa-hitachi-hiuxwe2"); break; } } else if (CPU_IS_HP_MC68K (cpu)) puts ("m68k-hitachi-hiuxwe2"); else puts ("unknown-hitachi-hiuxwe2"); exit (0); } EOF $CC_FOR_BUILD -o $dummy $dummy.c && SYSTEM_NAME=`$dummy` && { echo "$SYSTEM_NAME"; exit; } echo unknown-hitachi-hiuxwe2 exit ;; 9000/7??:4.3bsd:*:* | 9000/8?[79]:4.3bsd:*:* ) echo hppa1.1-hp-bsd exit ;; 9000/8??:4.3bsd:*:*) echo hppa1.0-hp-bsd exit ;; *9??*:MPE/iX:*:* | *3000*:MPE/iX:*:*) echo hppa1.0-hp-mpeix exit ;; hp7??:OSF1:*:* | hp8?[79]:OSF1:*:* ) echo hppa1.1-hp-osf exit ;; hp8??:OSF1:*:*) echo hppa1.0-hp-osf exit ;; i*86:OSF1:*:*) if [ -x /usr/sbin/sysversion ] ; then echo ${UNAME_MACHINE}-unknown-osf1mk else echo ${UNAME_MACHINE}-unknown-osf1 fi exit ;; parisc*:Lites*:*:*) echo hppa1.1-hp-lites exit ;; C1*:ConvexOS:*:* | convex:ConvexOS:C1*:*) echo c1-convex-bsd exit ;; C2*:ConvexOS:*:* | convex:ConvexOS:C2*:*) if getsysinfo -f scalar_acc then echo c32-convex-bsd else echo c2-convex-bsd fi exit ;; C34*:ConvexOS:*:* | convex:ConvexOS:C34*:*) echo c34-convex-bsd exit ;; C38*:ConvexOS:*:* | convex:ConvexOS:C38*:*) echo c38-convex-bsd exit ;; C4*:ConvexOS:*:* | convex:ConvexOS:C4*:*) echo c4-convex-bsd exit ;; CRAY*Y-MP:*:*:*) echo ymp-cray-unicos${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; CRAY*[A-Z]90:*:*:*) echo ${UNAME_MACHINE}-cray-unicos${UNAME_RELEASE} \ | sed -e 's/CRAY.*\([A-Z]90\)/\1/' \ -e y/ABCDEFGHIJKLMNOPQRSTUVWXYZ/abcdefghijklmnopqrstuvwxyz/ \ -e 's/\.[^.]*$/.X/' exit ;; CRAY*TS:*:*:*) echo t90-cray-unicos${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; CRAY*T3E:*:*:*) echo alphaev5-cray-unicosmk${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; CRAY*SV1:*:*:*) echo sv1-cray-unicos${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; *:UNICOS/mp:*:*) echo craynv-cray-unicosmp${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; F30[01]:UNIX_System_V:*:* | F700:UNIX_System_V:*:*) FUJITSU_PROC=`uname -m | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz'` FUJITSU_SYS=`uname -p | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz' | sed -e 's/\///'` FUJITSU_REL=`echo ${UNAME_RELEASE} | sed -e 's/ /_/'` echo "${FUJITSU_PROC}-fujitsu-${FUJITSU_SYS}${FUJITSU_REL}" exit ;; 5000:UNIX_System_V:4.*:*) FUJITSU_SYS=`uname -p | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz' | sed -e 's/\///'` FUJITSU_REL=`echo ${UNAME_RELEASE} | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz' | sed -e 's/ /_/'` echo "sparc-fujitsu-${FUJITSU_SYS}${FUJITSU_REL}" exit ;; i*86:BSD/386:*:* | i*86:BSD/OS:*:* | *:Ascend\ Embedded/OS:*:*) echo ${UNAME_MACHINE}-pc-bsdi${UNAME_RELEASE} exit ;; sparc*:BSD/OS:*:*) echo sparc-unknown-bsdi${UNAME_RELEASE} exit ;; *:BSD/OS:*:*) echo ${UNAME_MACHINE}-unknown-bsdi${UNAME_RELEASE} exit ;; *:FreeBSD:*:*) UNAME_PROCESSOR=`/usr/bin/uname -p` case ${UNAME_PROCESSOR} in amd64) echo x86_64-unknown-freebsd`echo ${UNAME_RELEASE}|sed -e 's/[-(].*//'` ;; *) echo ${UNAME_PROCESSOR}-unknown-freebsd`echo ${UNAME_RELEASE}|sed -e 's/[-(].*//'` ;; esac exit ;; i*:CYGWIN*:*) echo ${UNAME_MACHINE}-pc-cygwin exit ;; *:MINGW64*:*) echo ${UNAME_MACHINE}-pc-mingw64 exit ;; *:MINGW*:*) echo ${UNAME_MACHINE}-pc-mingw32 exit ;; i*:MSYS*:*) echo ${UNAME_MACHINE}-pc-msys exit ;; i*:windows32*:*) # uname -m includes "-pc" on this system. echo ${UNAME_MACHINE}-mingw32 exit ;; i*:PW*:*) echo ${UNAME_MACHINE}-pc-pw32 exit ;; *:Interix*:*) case ${UNAME_MACHINE} in x86) echo i586-pc-interix${UNAME_RELEASE} exit ;; authenticamd | genuineintel | EM64T) echo x86_64-unknown-interix${UNAME_RELEASE} exit ;; IA64) echo ia64-unknown-interix${UNAME_RELEASE} exit ;; esac ;; [345]86:Windows_95:* | [345]86:Windows_98:* | [345]86:Windows_NT:*) echo i${UNAME_MACHINE}-pc-mks exit ;; 8664:Windows_NT:*) echo x86_64-pc-mks exit ;; i*:Windows_NT*:* | Pentium*:Windows_NT*:*) # How do we know it's Interix rather than the generic POSIX subsystem? # It also conflicts with pre-2.0 versions of AT&T UWIN. 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EV56) UNAME_MACHINE=alphaev56 ;; PCA56) UNAME_MACHINE=alphapca56 ;; PCA57) UNAME_MACHINE=alphapca56 ;; EV6) UNAME_MACHINE=alphaev6 ;; EV67) UNAME_MACHINE=alphaev67 ;; EV68*) UNAME_MACHINE=alphaev68 ;; esac objdump --private-headers /bin/sh | grep -q ld.so.1 if test "$?" = 0 ; then LIBC="gnulibc1" ; fi echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; arc:Linux:*:* | arceb:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; arm*:Linux:*:*) eval $set_cc_for_build if echo __ARM_EABI__ | $CC_FOR_BUILD -E - 2>/dev/null \ | grep -q __ARM_EABI__ then echo ${UNAME_MACHINE}-unknown-linux-${LIBC} else if echo __ARM_PCS_VFP | $CC_FOR_BUILD -E - 2>/dev/null \ | grep -q __ARM_PCS_VFP then echo ${UNAME_MACHINE}-unknown-linux-${LIBC}eabi else echo ${UNAME_MACHINE}-unknown-linux-${LIBC}eabihf fi fi exit ;; avr32*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; cris:Linux:*:*) echo ${UNAME_MACHINE}-axis-linux-${LIBC} exit ;; crisv32:Linux:*:*) echo ${UNAME_MACHINE}-axis-linux-${LIBC} exit ;; frv:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; hexagon:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; i*86:Linux:*:*) echo ${UNAME_MACHINE}-pc-linux-${LIBC} exit ;; ia64:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; m32r*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; m68*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; mips:Linux:*:* | mips64:Linux:*:*) eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #undef CPU #undef ${UNAME_MACHINE} #undef ${UNAME_MACHINE}el #if defined(__MIPSEL__) || defined(__MIPSEL) || defined(_MIPSEL) || defined(MIPSEL) CPU=${UNAME_MACHINE}el #else #if defined(__MIPSEB__) || defined(__MIPSEB) || defined(_MIPSEB) || defined(MIPSEB) CPU=${UNAME_MACHINE} #else CPU= #endif #endif EOF eval `$CC_FOR_BUILD -E $dummy.c 2>/dev/null | grep '^CPU'` test x"${CPU}" != x && { echo "${CPU}-unknown-linux-${LIBC}"; exit; } ;; or1k:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; or32:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; padre:Linux:*:*) echo sparc-unknown-linux-${LIBC} exit ;; parisc64:Linux:*:* | hppa64:Linux:*:*) echo hppa64-unknown-linux-${LIBC} exit ;; parisc:Linux:*:* | hppa:Linux:*:*) # Look for CPU level case `grep '^cpu[^a-z]*:' /proc/cpuinfo 2>/dev/null | cut -d' ' -f2` in PA7*) echo hppa1.1-unknown-linux-${LIBC} ;; PA8*) echo hppa2.0-unknown-linux-${LIBC} ;; *) echo hppa-unknown-linux-${LIBC} ;; esac exit ;; ppc64:Linux:*:*) echo powerpc64-unknown-linux-${LIBC} exit ;; ppc:Linux:*:*) echo powerpc-unknown-linux-${LIBC} exit ;; ppc64le:Linux:*:*) echo powerpc64le-unknown-linux-${LIBC} exit ;; ppcle:Linux:*:*) echo powerpcle-unknown-linux-${LIBC} exit ;; s390:Linux:*:* | s390x:Linux:*:*) echo ${UNAME_MACHINE}-ibm-linux-${LIBC} exit ;; sh64*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; sh*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; sparc:Linux:*:* | sparc64:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; tile*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; vax:Linux:*:*) echo ${UNAME_MACHINE}-dec-linux-${LIBC} exit ;; x86_64:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; xtensa*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-${LIBC} exit ;; i*86:DYNIX/ptx:4*:*) # ptx 4.0 does uname -s correctly, with DYNIX/ptx in there. # earlier versions are messed up and put the nodename in both # sysname and nodename. echo i386-sequent-sysv4 exit ;; i*86:UNIX_SV:4.2MP:2.*) # Unixware is an offshoot of SVR4, but it has its own version # number series starting with 2... # I am not positive that other SVR4 systems won't match this, # I just have to hope. -- rms. # Use sysv4.2uw... so that sysv4* matches it. echo ${UNAME_MACHINE}-pc-sysv4.2uw${UNAME_VERSION} exit ;; i*86:OS/2:*:*) # If we were able to find `uname', then EMX Unix compatibility # is probably installed. echo ${UNAME_MACHINE}-pc-os2-emx exit ;; i*86:XTS-300:*:STOP) echo ${UNAME_MACHINE}-unknown-stop exit ;; i*86:atheos:*:*) echo ${UNAME_MACHINE}-unknown-atheos exit ;; i*86:syllable:*:*) echo ${UNAME_MACHINE}-pc-syllable exit ;; i*86:LynxOS:2.*:* | i*86:LynxOS:3.[01]*:* | i*86:LynxOS:4.[02]*:*) echo i386-unknown-lynxos${UNAME_RELEASE} exit ;; i*86:*DOS:*:*) echo ${UNAME_MACHINE}-pc-msdosdjgpp exit ;; i*86:*:4.*:* | i*86:SYSTEM_V:4.*:*) UNAME_REL=`echo ${UNAME_RELEASE} | sed 's/\/MP$//'` if grep Novell /usr/include/link.h >/dev/null 2>/dev/null; then echo ${UNAME_MACHINE}-univel-sysv${UNAME_REL} else echo ${UNAME_MACHINE}-pc-sysv${UNAME_REL} fi exit ;; i*86:*:5:[678]*) # UnixWare 7.x, OpenUNIX and OpenServer 6. case `/bin/uname -X | grep "^Machine"` in *486*) UNAME_MACHINE=i486 ;; *Pentium) UNAME_MACHINE=i586 ;; *Pent*|*Celeron) UNAME_MACHINE=i686 ;; esac echo ${UNAME_MACHINE}-unknown-sysv${UNAME_RELEASE}${UNAME_SYSTEM}${UNAME_VERSION} exit ;; i*86:*:3.2:*) if test -f /usr/options/cb.name; then UNAME_REL=`sed -n 's/.*Version //p' /dev/null >/dev/null ; then UNAME_REL=`(/bin/uname -X|grep Release|sed -e 's/.*= //')` (/bin/uname -X|grep i80486 >/dev/null) && UNAME_MACHINE=i486 (/bin/uname -X|grep '^Machine.*Pentium' >/dev/null) \ && UNAME_MACHINE=i586 (/bin/uname -X|grep '^Machine.*Pent *II' >/dev/null) \ && UNAME_MACHINE=i686 (/bin/uname -X|grep '^Machine.*Pentium Pro' >/dev/null) \ && UNAME_MACHINE=i686 echo ${UNAME_MACHINE}-pc-sco$UNAME_REL else echo ${UNAME_MACHINE}-pc-sysv32 fi exit ;; pc:*:*:*) # Left here for compatibility: # uname -m prints for DJGPP always 'pc', but it prints nothing about # the processor, so we play safe by assuming i586. # Note: whatever this is, it MUST be the same as what config.sub # prints for the "djgpp" host, or else GDB configury will decide that # this is a cross-build. echo i586-pc-msdosdjgpp exit ;; Intel:Mach:3*:*) echo i386-pc-mach3 exit ;; paragon:*:*:*) echo i860-intel-osf1 exit ;; i860:*:4.*:*) # i860-SVR4 if grep Stardent /usr/include/sys/uadmin.h >/dev/null 2>&1 ; then echo i860-stardent-sysv${UNAME_RELEASE} # Stardent Vistra i860-SVR4 else # Add other i860-SVR4 vendors below as they are discovered. echo i860-unknown-sysv${UNAME_RELEASE} # Unknown i860-SVR4 fi exit ;; mini*:CTIX:SYS*5:*) # "miniframe" echo m68010-convergent-sysv exit ;; mc68k:UNIX:SYSTEM5:3.51m) echo m68k-convergent-sysv exit ;; M680?0:D-NIX:5.3:*) echo m68k-diab-dnix exit ;; M68*:*:R3V[5678]*:*) test -r /sysV68 && { echo 'm68k-motorola-sysv'; exit; } ;; 3[345]??:*:4.0:3.0 | 3[34]??A:*:4.0:3.0 | 3[34]??,*:*:4.0:3.0 | 3[34]??/*:*:4.0:3.0 | 4400:*:4.0:3.0 | 4850:*:4.0:3.0 | SKA40:*:4.0:3.0 | SDS2:*:4.0:3.0 | SHG2:*:4.0:3.0 | S7501*:*:4.0:3.0) OS_REL='' test -r /etc/.relid \ && OS_REL=.`sed -n 's/[^ ]* [^ ]* \([0-9][0-9]\).*/\1/p' < /etc/.relid` /bin/uname -p 2>/dev/null | grep 86 >/dev/null \ && { echo i486-ncr-sysv4.3${OS_REL}; exit; } /bin/uname -p 2>/dev/null | /bin/grep entium >/dev/null \ && { echo i586-ncr-sysv4.3${OS_REL}; exit; } ;; 3[34]??:*:4.0:* | 3[34]??,*:*:4.0:*) /bin/uname -p 2>/dev/null | grep 86 >/dev/null \ && { echo i486-ncr-sysv4; exit; } ;; NCR*:*:4.2:* | MPRAS*:*:4.2:*) OS_REL='.3' test -r /etc/.relid \ && OS_REL=.`sed -n 's/[^ ]* [^ ]* \([0-9][0-9]\).*/\1/p' < /etc/.relid` /bin/uname -p 2>/dev/null | grep 86 >/dev/null \ && { echo i486-ncr-sysv4.3${OS_REL}; exit; } /bin/uname -p 2>/dev/null | /bin/grep entium >/dev/null \ && { echo i586-ncr-sysv4.3${OS_REL}; exit; } /bin/uname -p 2>/dev/null | /bin/grep pteron >/dev/null \ && { echo i586-ncr-sysv4.3${OS_REL}; exit; } ;; m68*:LynxOS:2.*:* | m68*:LynxOS:3.0*:*) echo m68k-unknown-lynxos${UNAME_RELEASE} exit ;; mc68030:UNIX_System_V:4.*:*) echo m68k-atari-sysv4 exit ;; TSUNAMI:LynxOS:2.*:*) echo sparc-unknown-lynxos${UNAME_RELEASE} exit ;; rs6000:LynxOS:2.*:*) echo rs6000-unknown-lynxos${UNAME_RELEASE} exit ;; PowerPC:LynxOS:2.*:* | PowerPC:LynxOS:3.[01]*:* | PowerPC:LynxOS:4.[02]*:*) echo powerpc-unknown-lynxos${UNAME_RELEASE} exit ;; SM[BE]S:UNIX_SV:*:*) echo mips-dde-sysv${UNAME_RELEASE} exit ;; RM*:ReliantUNIX-*:*:*) echo mips-sni-sysv4 exit ;; RM*:SINIX-*:*:*) echo mips-sni-sysv4 exit ;; *:SINIX-*:*:*) if uname -p 2>/dev/null >/dev/null ; then UNAME_MACHINE=`(uname -p) 2>/dev/null` echo ${UNAME_MACHINE}-sni-sysv4 else echo ns32k-sni-sysv fi exit ;; PENTIUM:*:4.0*:*) # Unisys `ClearPath HMP IX 4000' SVR4/MP effort # says echo i586-unisys-sysv4 exit ;; *:UNIX_System_V:4*:FTX*) # From Gerald Hewes . # How about differentiating between stratus architectures? -djm echo hppa1.1-stratus-sysv4 exit ;; *:*:*:FTX*) # From seanf@swdc.stratus.com. echo i860-stratus-sysv4 exit ;; i*86:VOS:*:*) # From Paul.Green@stratus.com. echo ${UNAME_MACHINE}-stratus-vos exit ;; *:VOS:*:*) # From Paul.Green@stratus.com. echo hppa1.1-stratus-vos exit ;; mc68*:A/UX:*:*) echo m68k-apple-aux${UNAME_RELEASE} exit ;; news*:NEWS-OS:6*:*) echo mips-sony-newsos6 exit ;; R[34]000:*System_V*:*:* | R4000:UNIX_SYSV:*:* | R*000:UNIX_SV:*:*) if [ -d /usr/nec ]; then echo mips-nec-sysv${UNAME_RELEASE} else echo mips-unknown-sysv${UNAME_RELEASE} fi exit ;; BeBox:BeOS:*:*) # BeOS running on hardware made by Be, PPC only. echo powerpc-be-beos exit ;; BeMac:BeOS:*:*) # BeOS running on Mac or Mac clone, PPC only. echo powerpc-apple-beos exit ;; BePC:BeOS:*:*) # BeOS running on Intel PC compatible. echo i586-pc-beos exit ;; BePC:Haiku:*:*) # Haiku running on Intel PC compatible. echo i586-pc-haiku exit ;; x86_64:Haiku:*:*) echo x86_64-unknown-haiku exit ;; SX-4:SUPER-UX:*:*) echo sx4-nec-superux${UNAME_RELEASE} exit ;; SX-5:SUPER-UX:*:*) echo sx5-nec-superux${UNAME_RELEASE} exit ;; SX-6:SUPER-UX:*:*) echo sx6-nec-superux${UNAME_RELEASE} exit ;; SX-7:SUPER-UX:*:*) echo sx7-nec-superux${UNAME_RELEASE} exit ;; SX-8:SUPER-UX:*:*) echo sx8-nec-superux${UNAME_RELEASE} exit ;; SX-8R:SUPER-UX:*:*) echo sx8r-nec-superux${UNAME_RELEASE} exit ;; Power*:Rhapsody:*:*) echo powerpc-apple-rhapsody${UNAME_RELEASE} exit ;; *:Rhapsody:*:*) echo ${UNAME_MACHINE}-apple-rhapsody${UNAME_RELEASE} exit ;; *:Darwin:*:*) UNAME_PROCESSOR=`uname -p` || UNAME_PROCESSOR=unknown eval $set_cc_for_build if test "$UNAME_PROCESSOR" = unknown ; then UNAME_PROCESSOR=powerpc fi if [ "$CC_FOR_BUILD" != 'no_compiler_found' ]; then if (echo '#ifdef __LP64__'; echo IS_64BIT_ARCH; echo '#endif') | \ (CCOPTS= $CC_FOR_BUILD -E - 2>/dev/null) | \ grep IS_64BIT_ARCH >/dev/null then case $UNAME_PROCESSOR in i386) UNAME_PROCESSOR=x86_64 ;; powerpc) UNAME_PROCESSOR=powerpc64 ;; esac fi fi echo ${UNAME_PROCESSOR}-apple-darwin${UNAME_RELEASE} exit ;; *:procnto*:*:* | *:QNX:[0123456789]*:*) UNAME_PROCESSOR=`uname -p` if test "$UNAME_PROCESSOR" = "x86"; then UNAME_PROCESSOR=i386 UNAME_MACHINE=pc fi echo ${UNAME_PROCESSOR}-${UNAME_MACHINE}-nto-qnx${UNAME_RELEASE} exit ;; *:QNX:*:4*) echo i386-pc-qnx exit ;; NEO-?:NONSTOP_KERNEL:*:*) echo neo-tandem-nsk${UNAME_RELEASE} exit ;; NSE-*:NONSTOP_KERNEL:*:*) echo nse-tandem-nsk${UNAME_RELEASE} exit ;; NSR-?:NONSTOP_KERNEL:*:*) echo nsr-tandem-nsk${UNAME_RELEASE} exit ;; *:NonStop-UX:*:*) echo mips-compaq-nonstopux exit ;; 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Since this code contains no copyright statements and is dated prior to 1977, under US copyright law it is in the public domain (not copyrighted). Converted to C via f2c and cleaned up by Steven G. Johnson (stevenj@alum.mit.edu). C version is licensed under the MIT license (which is in the same spirit as public domain, but disclaims warranty and is clearer legally). nloptr/src/nlopt_src/praxis/praxis.c0000644000176000001440000007627313355070635017420 0ustar ripleyusers/* See README */ #include #include #include #include "nlopt-util.h" #include "praxis.h" /* Common Block Declarations */ struct global_s { double fx, ldt, dmin__; int nf, nl; }; struct q_s { double *v; /* size n x n */ double *q0, *q1, *t_flin; /* size n */ double qa, qb, qc, qd0, qd1, qf1; double fbest, *xbest; /* size n */ nlopt_stopping *stop; }; /* Table of constant values */ static int pow_ii(int x, int n) /* compute x^n, n >= 0 */ { int p = 1; while (n > 0) { if (n & 1) { n--; p *= x; } else { n >>= 1; x *= x; } } return p; } static void minfit_(int m, int n, double machep, double *tol, double *ab, double *q, double *ework); static nlopt_result min_(int n, int j, int nits, double *d2, double *x1, double *f1, int fk, praxis_func f, void *f_data, double *x, double *t_old, double machep, double *h__, struct global_s *global_1, struct q_s *q_1); static double flin_(int n, int j, double *l, praxis_func f, void *f_data, double *x, int *nf, struct q_s *q_1, nlopt_result *ret); static void sort_(int m, int n, double *d__, double *v); static void quad_(int n, praxis_func f, void *f_data, double *x, double *t_old, double machep, double *h__, struct global_s *global_1, struct q_s *q_1); nlopt_result praxis_(double t0, double machep, double h0, int n, double *x, praxis_func f, void *f_data, nlopt_stopping *stop, double *minf) { /* System generated locals */ int i__1, i__2, i__3; nlopt_result ret = NLOPT_SUCCESS; double d__1; /* Global */ struct global_s global_1; struct q_s q_1; /* Local variables */ double *d__, *y, *z__, *e_minfit, *prev_xbest; /* size n */ double prev_fbest; double h__; int i__, j, k; double s, t_old, f1; int k2; double m2, m4, t2_old, df, dn; int kl, ii; double sf; int kt; double sl; int im1, km1; double dni, lds; int ktm; double scbd; int illc; int klmk; double ldfac, large, small, value; double vlarge; double vsmall; double *work; /* LAST MODIFIED 3/1/73 */ /* Modified August 2007 by S. G. Johnson: after conversion to C via f2c and some manual cleanup, eliminating write statements, I additionally: - modified the routine to use NLopt stop criteria - allocate arrays dynamically to allow n > 20 - return the NLopt return code, where the min. function value is now given by the parameter minf */ /* PRAXIS RETURNS THE MINIMUM OF THE FUNCTION F(N,X) OF N VARIABLES */ /* USING THE PRINCIPAL AXIS METHOD. THE GRADIENT OF THE FUNCTION IS */ /* NOT REQUIRED. */ /* FOR A DESCRIPTION OF THE ALGORITHM, SEE CHAPTER SEVEN OF */ /* "ALGORITHMS FOR FINDING ZEROS AND EXTREMA OF FUNCTIONS WITHOUT */ /* CALCULATING DERIVATIVES" BY RICHARD P BRENT. */ /* THE PARAMETERS ARE: */ /* T0 IS A TOLERANCE. PRAXIS ATTEMPTS TO RETURN PRAXIS=F(X) */ /* SUCH THAT IF X0 IS THE TRUE LOCAL MINIMUM NEAR X, THEN */ /* NORM(X-X0) < T0 + SQUAREROOT(MACHEP)*NORM(X). */ /* MACHEP IS THE MACHINE PRECISION, THE SMALLEST NUMBER SUCH THAT */ /* 1 + MACHEP > 1. MACHEP SHOULD BE 16.**-13 (ABOUT */ /* 2.22D-16) FOR REAL*8 ARITHMETIC ON THE IBM 360. */ /* H0 IS THE MAXIMUM STEP SIZE. H0 SHOULD BE SET TO ABOUT THE */ /* MAXIMUM DISTANCE FROM THE INITIAL GUESS TO THE MINIMUM. */ /* (IF H0 IS SET TOO LARGE OR TOO SMALL, THE INITIAL RATE OF */ /* CONVERGENCE MAY BE SLOW.) */ /* N (AT LEAST TWO) IS THE NUMBER OF VARIABLES UPON WHICH */ /* THE FUNCTION DEPENDS. */ /* X IS AN ARRAY CONTAINING ON ENTRY A GUESS OF THE POINT OF */ /* MINIMUM, ON RETURN THE ESTIMATED POINT OF MINIMUM. */ /* F(N,X) IS THE FUNCTION TO BE MINIMIZED. F SHOULD BE A REAL*8 */ /* FUNCTION DECLARED EXTERNAL IN THE CALLING PROGRAM. */ /* THE APPROXIMATING QUADRATIC FORM IS */ /* Q(X') = F(N,X) + (1/2) * (X'-X)-TRANSPOSE * A * (X'-X) */ /* WHERE X IS THE BEST ESTIMATE OF THE MINIMUM AND A IS */ /* INVERSE(V-TRANSPOSE) * D * INVERSE(V) */ /* (V(*,*) IS THE MATRIX OF SEARCH DIRECTIONS; D(*) IS THE ARRAY */ /* OF SECOND DIFFERENCES). IF F HAS CONTINUOUS SECOND DERIVATIVES */ /* NEAR X0, A WILL TEND TO THE HESSIAN OF F AT X0 AS X APPROACHES X0. */ /* IT IS ASSUMED THAT ON FLOATING-POINT UNDERFLOW THE RESULT IS SET */ /* TO ZERO. */ /* THE USER SHOULD OBSERVE THE COMMENT ON HEURISTIC NUMBERS AFTER */ /* THE INITIALIZATION OF MACHINE DEPENDENT NUMBERS. */ /* .....IF N>20 OR IF N<20 AND YOU NEED MORE SPACE, CHANGE '20' TO THE */ /* LARGEST VALUE OF N IN THE NEXT CARD, IN THE CARD 'IDIM=20', AND */ /* IN THE DIMENSION STATEMENTS IN SUBROUTINES MINFIT,MIN,FLIN,QUAD. */ /* ...changed by S. G. Johnson, 2007, to use malloc */ /* .....INITIALIZATION..... */ /* MACHINE DEPENDENT NUMBERS: */ /* Parameter adjustments */ --x; /* Function Body */ small = machep * machep; vsmall = small * small; large = 1. / small; vlarge = 1. / vsmall; m2 = sqrt(machep); m4 = sqrt(m2); /* new: dynamic allocation of temporary arrays */ work = (double *) malloc(sizeof(double) * (n*n + n*9)); if (!work) return NLOPT_OUT_OF_MEMORY; q_1.v = work; q_1.q0 = q_1.v + n*n; q_1.q1 = q_1.q0 + n; q_1.t_flin = q_1.q1 + n; q_1.xbest = q_1.t_flin + n; d__ = q_1.xbest + n; y = d__ + n; z__ = y + n; e_minfit = y + n; prev_xbest = e_minfit + n; /* HEURISTIC NUMBERS: */ /* IF THE AXES MAY BE BADLY SCALED (WHICH IS TO BE AVOIDED IF */ /* POSSIBLE), THEN SET SCBD=10. OTHERWISE SET SCBD=1. */ /* IF THE PROBLEM IS KNOWN TO BE ILL-CONDITIONED, SET ILLC=TRUE. */ /* OTHERWISE SET ILLC=FALSE. */ /* KTM IS THE NUMBER OF ITERATIONS WITHOUT IMPROVEMENT BEFORE THE */ /* ALGORITHM TERMINATES. KTM=4 IS VERY CAUTIOUS; USUALLY KTM=1 */ /* IS SATISFACTORY. */ scbd = 1.; illc = 0 /* false */; ktm = 1; ldfac = .01; if (illc) { ldfac = .1; } kt = 0; global_1.nl = 0; global_1.nf = 1; prev_fbest = q_1.fbest = global_1.fx = f(n, &x[1], f_data); memcpy(q_1.xbest, &x[1], n*sizeof(double)); memcpy(prev_xbest, &x[1], n*sizeof(double)); stop->nevals++; q_1.stop = stop; q_1.qf1 = global_1.fx; if (t0 > 0) t_old = small + t0; else { t_old = 0; for (i__ = 0; i__ < n; ++i__) if (stop->xtol_abs[i__] > t_old) t_old = stop->xtol_abs[i__]; t_old += small; } t2_old = t_old; global_1.dmin__ = small; h__ = h0; if (h__ < t_old * 100) { h__ = t_old * 100; } global_1.ldt = h__; /* .....THE FIRST SET OF SEARCH DIRECTIONS V IS THE IDENTITY MATRIX..... */ i__1 = n; for (i__ = 1; i__ <= i__1; ++i__) { i__2 = n; for (j = 1; j <= i__2; ++j) { /* L10: */ q_1.v[i__ + j * n - (n+1)] = 0.; } /* L20: */ q_1.v[i__ + i__ * n - (n+1)] = 1.; } d__[0] = 0.; q_1.qd0 = 0.; i__1 = n; for (i__ = 1; i__ <= i__1; ++i__) { q_1.q0[i__ - 1] = x[i__]; /* L30: */ q_1.q1[i__ - 1] = x[i__]; } /* .....THE MAIN LOOP STARTS HERE..... */ L40: sf = d__[0]; d__[0] = 0.; s = 0.; /* .....MINIMIZE ALONG THE FIRST DIRECTION V(*,1). */ /* FX MUST BE PASSED TO MIN BY VALUE. */ value = global_1.fx; ret = min_(n, 1, 2, d__, &s, &value, 0, f,f_data, &x[1], &t_old, machep, &h__, &global_1, &q_1); if (ret != NLOPT_SUCCESS) goto done; if (s > 0.) { goto L50; } i__1 = n; for (i__ = 1; i__ <= i__1; ++i__) { /* L45: */ q_1.v[i__ - 1] = -q_1.v[i__ - 1]; } L50: if (sf > d__[0] * .9 && sf * .9 < d__[0]) { goto L70; } i__1 = n; for (i__ = 2; i__ <= i__1; ++i__) { /* L60: */ d__[i__ - 1] = 0.; } /* .....THE INNER LOOP STARTS HERE..... */ L70: i__1 = n; for (k = 2; k <= i__1; ++k) { i__2 = n; for (i__ = 1; i__ <= i__2; ++i__) { /* L75: */ y[i__ - 1] = x[i__]; } sf = global_1.fx; if (kt > 0) { illc = 1 /* true */; } L80: kl = k; df = 0.; /* .....A RANDOM STEP FOLLOWS (TO AVOID RESOLUTION VALLEYS). */ /* PRAXIS ASSUMES THAT RANDOM RETURNS A RANDOM NUMBER UNIFORMLY */ /* DISTRIBUTED IN (0,1). */ if (! illc) { goto L95; } i__2 = n; for (i__ = 1; i__ <= i__2; ++i__) { s = (global_1.ldt * .1 + t2_old * pow_ii(10, kt)) * nlopt_urand(-.5,.5); /* was: (random_(n) - .5); */ z__[i__ - 1] = s; i__3 = n; for (j = 1; j <= i__3; ++j) { /* L85: */ x[j] += s * q_1.v[j + i__ * n - (n+1)]; } /* L90: */ } global_1.fx = (*f)(n, &x[1], f_data); ++global_1.nf; /* .....MINIMIZE ALONG THE "NON-CONJUGATE" DIRECTIONS V(*,K),...,V(*,N) */ L95: i__2 = n; for (k2 = k; k2 <= i__2; ++k2) { sl = global_1.fx; s = 0.; value = global_1.fx; ret = min_(n, k2, 2, &d__[k2 - 1], &s, &value, 0, f,f_data, & x[1], &t_old, machep, &h__, &global_1, &q_1); if (ret != NLOPT_SUCCESS) goto done; if (illc) { goto L97; } s = sl - global_1.fx; goto L99; L97: /* Computing 2nd power */ d__1 = s + z__[k2 - 1]; s = d__[k2 - 1] * (d__1 * d__1); L99: if (df > s) { goto L105; } df = s; kl = k2; L105: ; } if (illc || df >= (d__1 = machep * 100 * global_1.fx, fabs(d__1))) { goto L110; } /* .....IF THERE WAS NOT MUCH IMPROVEMENT ON THE FIRST TRY, SET */ /* ILLC=TRUE AND START THE INNER LOOP AGAIN..... */ illc = 1 /* true */; goto L80; L110: /* .....MINIMIZE ALONG THE "CONJUGATE" DIRECTIONS V(*,1),...,V(*,K-1) */ km1 = k - 1; i__2 = km1; for (k2 = 1; k2 <= i__2; ++k2) { s = 0.; value = global_1.fx; ret = min_(n, k2, 2, &d__[k2 - 1], &s, &value, 0, f,f_data, & x[1], &t_old, machep, &h__, &global_1, &q_1); if (ret != NLOPT_SUCCESS) goto done; /* L120: */ } f1 = global_1.fx; global_1.fx = sf; lds = 0.; i__2 = n; for (i__ = 1; i__ <= i__2; ++i__) { sl = x[i__]; x[i__] = y[i__ - 1]; sl -= y[i__ - 1]; y[i__ - 1] = sl; /* L130: */ lds += sl * sl; } lds = sqrt(lds); if (lds <= small) { goto L160; } /* .....DISCARD DIRECTION V(*,KL). */ /* IF NO RANDOM STEP WAS TAKEN, V(*,KL) IS THE "NON-CONJUGATE" */ /* DIRECTION ALONG WHICH THE GREATEST IMPROVEMENT WAS MADE..... */ klmk = kl - k; if (klmk < 1) { goto L141; } i__2 = klmk; for (ii = 1; ii <= i__2; ++ii) { i__ = kl - ii; i__3 = n; for (j = 1; j <= i__3; ++j) { /* L135: */ q_1.v[j + (i__ + 1) * n - (n+1)] = q_1.v[j + i__ * n - (n+1)]; } /* L140: */ d__[i__] = d__[i__ - 1]; } L141: d__[k - 1] = 0.; i__2 = n; for (i__ = 1; i__ <= i__2; ++i__) { /* L145: */ q_1.v[i__ + k * n - (n+1)] = y[i__ - 1] / lds; } /* .....MINIMIZE ALONG THE NEW "CONJUGATE" DIRECTION V(*,K), WHICH IS */ /* THE NORMALIZED VECTOR: (NEW X) - (0LD X)..... */ value = f1; ret = min_(n, k, 4, &d__[k - 1], &lds, &value, 1, f,f_data, &x[1], &t_old, machep, &h__, &global_1, &q_1); if (ret != NLOPT_SUCCESS) goto done; if (lds > 0.) { goto L160; } lds = -lds; i__2 = n; for (i__ = 1; i__ <= i__2; ++i__) { /* L150: */ q_1.v[i__ + k * n - (n+1)] = -q_1.v[i__ + k * n - (n+1)]; } L160: global_1.ldt = ldfac * global_1.ldt; if (global_1.ldt < lds) { global_1.ldt = lds; } t2_old = 0.; i__2 = n; for (i__ = 1; i__ <= i__2; ++i__) { /* L165: */ /* Computing 2nd power */ d__1 = x[i__]; t2_old += d__1 * d__1; } t2_old = m2 * sqrt(t2_old) + t_old; /* .....SEE WHETHER THE LENGTH OF THE STEP TAKEN SINCE STARTING THE */ /* INNER LOOP EXCEEDS HALF THE TOLERANCE..... */ if (global_1.ldt > t2_old * .5f && !nlopt_stop_f(stop, q_1.fbest, prev_fbest) && !nlopt_stop_x(stop, q_1.xbest, prev_xbest)) { kt = -1; } ++kt; if (kt > ktm) { if (nlopt_stop_f(stop, q_1.fbest, prev_fbest)) ret = NLOPT_FTOL_REACHED; else if (nlopt_stop_x(stop, q_1.xbest, prev_xbest)) ret = NLOPT_XTOL_REACHED; goto done; } prev_fbest = q_1.fbest; memcpy(prev_xbest, q_1.xbest, n * sizeof(double)); /* L170: */ } /* .....THE INNER LOOP ENDS HERE. */ /* TRY QUADRATIC EXTRAPOLATION IN CASE WE ARE IN A CURVED VALLEY. */ /* L171: */ quad_(n, f,f_data, &x[1], &t_old, machep, &h__, &global_1, &q_1); dn = 0.; i__1 = n; for (i__ = 1; i__ <= i__1; ++i__) { d__[i__ - 1] = 1. / sqrt(d__[i__ - 1]); if (dn < d__[i__ - 1]) { dn = d__[i__ - 1]; } /* L175: */ } i__1 = n; for (j = 1; j <= i__1; ++j) { s = d__[j - 1] / dn; i__2 = n; for (i__ = 1; i__ <= i__2; ++i__) { /* L180: */ q_1.v[i__ + j * n - (n+1)] = s * q_1.v[i__ + j * n - (n+1)]; } } /* .....SCALE THE AXES TO TRY TO REDUCE THE CONDITION NUMBER..... */ if (scbd <= 1.) { goto L200; } s = vlarge; i__2 = n; for (i__ = 1; i__ <= i__2; ++i__) { sl = 0.; i__1 = n; for (j = 1; j <= i__1; ++j) { /* L182: */ sl += q_1.v[i__ + j * n - (n+1)] * q_1.v[i__ + j * n - (n+1)]; } z__[i__ - 1] = sqrt(sl); if (z__[i__ - 1] < m4) { z__[i__ - 1] = m4; } if (s > z__[i__ - 1]) { s = z__[i__ - 1]; } /* L185: */ } i__2 = n; for (i__ = 1; i__ <= i__2; ++i__) { sl = s / z__[i__ - 1]; z__[i__ - 1] = 1. / sl; if (z__[i__ - 1] <= scbd) { goto L189; } sl = 1. / scbd; z__[i__ - 1] = scbd; L189: i__1 = n; for (j = 1; j <= i__1; ++j) { /* L190: */ q_1.v[i__ + j * n - (n+1)] = sl * q_1.v[i__ + j * n - (n+1)]; } /* L195: */ } /* .....CALCULATE A NEW SET OF ORTHOGONAL DIRECTIONS BEFORE REPEATING */ /* THE MAIN LOOP. */ /* FIRST TRANSPOSE V FOR MINFIT: */ L200: i__2 = n; for (i__ = 2; i__ <= i__2; ++i__) { im1 = i__ - 1; i__1 = im1; for (j = 1; j <= i__1; ++j) { s = q_1.v[i__ + j * n - (n+1)]; q_1.v[i__ + j * n - (n+1)] = q_1.v[j + i__ * n - (n+1)]; /* L210: */ q_1.v[j + i__ * n - (n+1)] = s; } /* L220: */ } /* .....CALL MINFIT TO FIND THE SINGULAR VALUE DECOMPOSITION OF V. */ /* THIS GIVES THE PRINCIPAL VALUES AND PRINCIPAL DIRECTIONS OF THE */ /* APPROXIMATING QUADRATIC FORM WITHOUT SQUARING THE CONDITION */ /* NUMBER..... */ minfit_(n, n, machep, &vsmall, q_1.v, d__, e_minfit); /* .....UNSCALE THE AXES..... */ if (scbd <= 1.) { goto L250; } i__2 = n; for (i__ = 1; i__ <= i__2; ++i__) { s = z__[i__ - 1]; i__1 = n; for (j = 1; j <= i__1; ++j) { /* L225: */ q_1.v[i__ + j * n - (n+1)] = s * q_1.v[i__ + j * n - (n+1)]; } /* L230: */ } i__2 = n; for (i__ = 1; i__ <= i__2; ++i__) { s = 0.; i__1 = n; for (j = 1; j <= i__1; ++j) { /* L235: */ /* Computing 2nd power */ d__1 = q_1.v[j + i__ * n - (n+1)]; s += d__1 * d__1; } s = sqrt(s); d__[i__ - 1] = s * d__[i__ - 1]; s = 1 / s; i__1 = n; for (j = 1; j <= i__1; ++j) { /* L240: */ q_1.v[j + i__ * n - (n+1)] = s * q_1.v[j + i__ * n - (n+1)]; } /* L245: */ } L250: i__2 = n; for (i__ = 1; i__ <= i__2; ++i__) { dni = dn * d__[i__ - 1]; if (dni > large) { goto L265; } if (dni < small) { goto L260; } d__[i__ - 1] = 1 / (dni * dni); goto L270; L260: d__[i__ - 1] = vlarge; goto L270; L265: d__[i__ - 1] = vsmall; L270: ; } /* .....SORT THE EIGENVALUES AND EIGENVECTORS..... */ sort_(n, n, d__, q_1.v); global_1.dmin__ = d__[n - 1]; if (global_1.dmin__ < small) { global_1.dmin__ = small; } illc = 0 /* false */; if (m2 * d__[0] > global_1.dmin__) { illc = 1 /* true */; } /* .....THE MAIN LOOP ENDS HERE..... */ goto L40; /* .....RETURN..... */ done: if (ret != NLOPT_OUT_OF_MEMORY) { *minf = q_1.fbest; memcpy(&x[1], q_1.xbest, n * sizeof(double)); } free(work); return ret; } /* praxis_ */ static void minfit_(int m, int n, double machep, double *tol, double *ab, double *q, double *ework) { /* System generated locals */ int ab_dim1, ab_offset, i__1, i__2, i__3; double d__1, d__2; /* Local variables */ double *e; /* size n */ double c__, f, g, h__; int i__, j, k, l; double s, x, y, z__; int l2, ii, kk, kt, ll2, lp1; double eps, temp; e = ework; /* ...AN IMPROVED VERSION OF MINFIT (SEE GOLUB AND REINSCH, 1969) */ /* RESTRICTED TO M=N,P=0. */ /* THE SINGULAR VALUES OF THE ARRAY AB ARE RETURNED IN Q AND AB IS */ /* OVERWRITTEN WITH THE ORTHOGONAL MATRIX V SUCH THAT U.DIAG(Q) = AB.V, */ /* WHERE U IS ANOTHER ORTHOGONAL MATRIX. */ /* ...HOUSEHOLDER'S REDUCTION TO BIDIAGONAL FORM... */ /* Parameter adjustments */ --q; ab_dim1 = m; ab_offset = 1 + ab_dim1; ab -= ab_offset; /* Function Body */ if (n == 1) { goto L200; } eps = machep; g = 0.; x = 0.; i__1 = n; for (i__ = 1; i__ <= i__1; ++i__) { e[i__ - 1] = g; s = 0.; l = i__ + 1; i__2 = n; for (j = i__; j <= i__2; ++j) { /* L1: */ /* Computing 2nd power */ d__1 = ab[j + i__ * ab_dim1]; s += d__1 * d__1; } g = 0.; if (s < *tol) { goto L4; } f = ab[i__ + i__ * ab_dim1]; g = sqrt(s); if (f >= 0.) { g = -g; } h__ = f * g - s; ab[i__ + i__ * ab_dim1] = f - g; if (l > n) { goto L4; } i__2 = n; for (j = l; j <= i__2; ++j) { f = 0.; i__3 = n; for (k = i__; k <= i__3; ++k) { /* L2: */ f += ab[k + i__ * ab_dim1] * ab[k + j * ab_dim1]; } f /= h__; i__3 = n; for (k = i__; k <= i__3; ++k) { /* L3: */ ab[k + j * ab_dim1] += f * ab[k + i__ * ab_dim1]; } } L4: q[i__] = g; s = 0.; if (i__ == n) { goto L6; } i__3 = n; for (j = l; j <= i__3; ++j) { /* L5: */ s += ab[i__ + j * ab_dim1] * ab[i__ + j * ab_dim1]; } L6: g = 0.; if (s < *tol) { goto L10; } if (i__ == n) { goto L16; } f = ab[i__ + (i__ + 1) * ab_dim1]; L16: g = sqrt(s); if (f >= 0.) { g = -g; } h__ = f * g - s; if (i__ == n) { goto L10; } ab[i__ + (i__ + 1) * ab_dim1] = f - g; i__3 = n; for (j = l; j <= i__3; ++j) { /* L7: */ e[j - 1] = ab[i__ + j * ab_dim1] / h__; } i__3 = n; for (j = l; j <= i__3; ++j) { s = 0.; i__2 = n; for (k = l; k <= i__2; ++k) { /* L8: */ s += ab[j + k * ab_dim1] * ab[i__ + k * ab_dim1]; } i__2 = n; for (k = l; k <= i__2; ++k) { /* L9: */ ab[j + k * ab_dim1] += s * e[k - 1]; } } L10: y = (d__1 = q[i__], fabs(d__1)) + (d__2 = e[i__ - 1], fabs(d__2)); /* L11: */ if (y > x) { x = y; } } /* ...ACCUMULATION OF RIGHT-HAND TRANSFORMATIONS... */ ab[n + n * ab_dim1] = 1.; g = e[n - 1]; l = n; i__1 = n; for (ii = 2; ii <= i__1; ++ii) { i__ = n - ii + 1; if (g == 0.) { goto L23; } h__ = ab[i__ + (i__ + 1) * ab_dim1] * g; i__2 = n; for (j = l; j <= i__2; ++j) { /* L20: */ ab[j + i__ * ab_dim1] = ab[i__ + j * ab_dim1] / h__; } i__2 = n; for (j = l; j <= i__2; ++j) { s = 0.; i__3 = n; for (k = l; k <= i__3; ++k) { /* L21: */ s += ab[i__ + k * ab_dim1] * ab[k + j * ab_dim1]; } i__3 = n; for (k = l; k <= i__3; ++k) { /* L22: */ ab[k + j * ab_dim1] += s * ab[k + i__ * ab_dim1]; } } L23: i__3 = n; for (j = l; j <= i__3; ++j) { ab[i__ + j * ab_dim1] = 0.; /* L24: */ ab[j + i__ * ab_dim1] = 0.; } ab[i__ + i__ * ab_dim1] = 1.; g = e[i__ - 1]; /* L25: */ l = i__; } /* ...DIAGONALIZATION OF THE BIDIAGONAL FORM... */ /* L100: */ eps *= x; i__1 = n; for (kk = 1; kk <= i__1; ++kk) { k = n - kk + 1; kt = 0; L101: ++kt; if (kt <= 30) { goto L102; } e[k - 1] = 0.; /* fprintf(stderr, "QR failed\n"); */ L102: i__3 = k; for (ll2 = 1; ll2 <= i__3; ++ll2) { l2 = k - ll2 + 1; l = l2; if ((d__1 = e[l - 1], fabs(d__1)) <= eps) { goto L120; } if (l == 1) { goto L103; } if ((d__1 = q[l - 1], fabs(d__1)) <= eps) { goto L110; } L103: ; } /* ...CANCELLATION OF E(L) IF L>1... */ L110: c__ = 0.; s = 1.; i__3 = k; for (i__ = l; i__ <= i__3; ++i__) { f = s * e[i__ - 1]; e[i__ - 1] = c__ * e[i__ - 1]; if (fabs(f) <= eps) { goto L120; } g = q[i__]; /* ...Q(I) = H = DSQRT(G*G + F*F)... */ if (fabs(f) < fabs(g)) { goto L113; } if (f != 0.) { goto L112; } else { goto L111; } L111: h__ = 0.; goto L114; L112: /* Computing 2nd power */ d__1 = g / f; h__ = fabs(f) * sqrt(d__1 * d__1 + 1); goto L114; L113: /* Computing 2nd power */ d__1 = f / g; h__ = fabs(g) * sqrt(d__1 * d__1 + 1); L114: q[i__] = h__; if (h__ != 0.) { goto L115; } g = 1.; h__ = 1.; L115: c__ = g / h__; /* L116: */ s = -f / h__; } /* ...TEST FOR CONVERGENCE... */ L120: z__ = q[k]; if (l == k) { goto L140; } /* ...SHIFT FROM BOTTOM 2*2 MINOR... */ x = q[l]; y = q[k - 1]; g = e[k - 2]; h__ = e[k - 1]; f = ((y - z__) * (y + z__) + (g - h__) * (g + h__)) / (h__ * 2 * y); g = sqrt(f * f + 1.); temp = f - g; if (f >= 0.) { temp = f + g; } f = ((x - z__) * (x + z__) + h__ * (y / temp - h__)) / x; /* ...NEXT QR TRANSFORMATION... */ c__ = 1.; s = 1.; lp1 = l + 1; if (lp1 > k) { goto L133; } i__3 = k; for (i__ = lp1; i__ <= i__3; ++i__) { g = e[i__ - 1]; y = q[i__]; h__ = s * g; g *= c__; if (fabs(f) < fabs(h__)) { goto L123; } if (f != 0.) { goto L122; } else { goto L121; } L121: z__ = 0.; goto L124; L122: /* Computing 2nd power */ d__1 = h__ / f; z__ = fabs(f) * sqrt(d__1 * d__1 + 1); goto L124; L123: /* Computing 2nd power */ d__1 = f / h__; z__ = fabs(h__) * sqrt(d__1 * d__1 + 1); L124: e[i__ - 2] = z__; if (z__ != 0.) { goto L125; } f = 1.; z__ = 1.; L125: c__ = f / z__; s = h__ / z__; f = x * c__ + g * s; g = -x * s + g * c__; h__ = y * s; y *= c__; i__2 = n; for (j = 1; j <= i__2; ++j) { x = ab[j + (i__ - 1) * ab_dim1]; z__ = ab[j + i__ * ab_dim1]; ab[j + (i__ - 1) * ab_dim1] = x * c__ + z__ * s; /* L126: */ ab[j + i__ * ab_dim1] = -x * s + z__ * c__; } if (fabs(f) < fabs(h__)) { goto L129; } if (f != 0.) { goto L128; } else { goto L127; } L127: z__ = 0.; goto L130; L128: /* Computing 2nd power */ d__1 = h__ / f; z__ = fabs(f) * sqrt(d__1 * d__1 + 1); goto L130; L129: /* Computing 2nd power */ d__1 = f / h__; z__ = fabs(h__) * sqrt(d__1 * d__1 + 1); L130: q[i__ - 1] = z__; if (z__ != 0.) { goto L131; } f = 1.; z__ = 1.; L131: c__ = f / z__; s = h__ / z__; f = c__ * g + s * y; /* L132: */ x = -s * g + c__ * y; } L133: e[l - 1] = 0.; e[k - 1] = f; q[k] = x; goto L101; /* ...CONVERGENCE: Q(K) IS MADE NON-NEGATIVE... */ L140: if (z__ >= 0.) { goto L150; } q[k] = -z__; i__3 = n; for (j = 1; j <= i__3; ++j) { /* L141: */ ab[j + k * ab_dim1] = -ab[j + k * ab_dim1]; } L150: ; } return; L200: q[1] = ab[ab_dim1 + 1]; ab[ab_dim1 + 1] = 1.; } /* minfit_ */ static nlopt_result min_(int n, int j, int nits, double * d2, double *x1, double *f1, int fk, praxis_func f, void *f_data, double * x, double *t_old, double machep, double *h__, struct global_s *global_1, struct q_s *q_1) { /* System generated locals */ int i__1; double d__1, d__2; /* Local variables */ int i__, k; double s, d1, f0, f2, m2, m4, t2, x2, fm; int dz; double xm, sf1, sx1; double temp, small; nlopt_result ret = NLOPT_SUCCESS; /* ...THE SUBROUTINE MIN MINIMIZES F FROM X IN THE DIRECTION V(*,J) UNLESS */ /* J IS LESS THAN 1, WHEN A QUADRATIC SEARCH IS MADE IN THE PLANE */ /* DEFINED BY Q0,Q1,X. */ /* D2 IS EITHER ZERO OR AN APPROXIMATION TO HALF F". */ /* ON ENTRY, X1 IS AN ESTIMATE OF THE DISTANCE FROM X TO THE MINIMUM */ /* ALONG V(*,J) (OR, IF J=0, A CURVE). ON RETURN, X1 IS THE DISTANCE */ /* FOUND. */ /* IF FK=.TRUE., THEN F1 IS FLIN(X1). OTHERWISE X1 AND F1 ARE IGNORED */ /* ON ENTRY UNLESS FINAL FX IS GREATER THAN F1. */ /* NITS CONTROLS THE NUMBER OF TIMES AN ATTEMPT WILL BE MADE TO HALVE */ /* THE INTERVAL. */ /* Parameter adjustments */ --x; /* Function Body */ /* Computing 2nd power */ d__1 = machep; small = d__1 * d__1; m2 = sqrt(machep); m4 = sqrt(m2); sf1 = *f1; sx1 = *x1; k = 0; xm = 0.; fm = global_1->fx; f0 = global_1->fx; dz = *d2 < machep; /* ...FIND THE STEP SIZE... */ s = 0.; i__1 = n; for (i__ = 1; i__ <= i__1; ++i__) { /* L1: */ /* Computing 2nd power */ d__1 = x[i__]; s += d__1 * d__1; } s = sqrt(s); temp = *d2; if (dz) { temp = global_1->dmin__; } t2 = m4 * sqrt(fabs(global_1->fx) / temp + s * global_1->ldt) + m2 * global_1->ldt; s = m4 * s + *t_old; if (dz && t2 > s) { t2 = s; } t2 = t2 > small ? t2 : small; /* Computing MIN */ d__1 = t2, d__2 = *h__ * .01; t2 = d__1 < d__2 ? d__1 : d__2; if (! (fk) || *f1 > fm) { goto L2; } xm = *x1; fm = *f1; L2: if (fk && fabs(*x1) >= t2) { goto L3; } temp = 1.; if (*x1 < 0.) { temp = -1.; } *x1 = temp * t2; *f1 = flin_(n, j, x1, f,f_data, &x[1], &global_1->nf, q_1, &ret); if (ret != NLOPT_SUCCESS) return ret; L3: if (*f1 > fm) { goto L4; } xm = *x1; fm = *f1; L4: if (! dz) { goto L6; } /* ...EVALUATE FLIN AT ANOTHER POINT AND ESTIMATE THE SECOND DERIVATIVE... */ x2 = -(*x1); if (f0 >= *f1) { x2 = *x1 * 2.; } f2 = flin_(n, j, &x2, f,f_data, &x[1], &global_1->nf, q_1, &ret); if (ret != NLOPT_SUCCESS) return ret; if (f2 > fm) { goto L5; } xm = x2; fm = f2; L5: *d2 = (x2 * (*f1 - f0) - *x1 * (f2 - f0)) / (*x1 * x2 * (*x1 - x2)); /* ...ESTIMATE THE FIRST DERIVATIVE AT 0... */ L6: d1 = (*f1 - f0) / *x1 - *x1 * *d2; dz = 1 /* true */; /* ...PREDICT THE MINIMUM... */ if (*d2 > small) { goto L7; } x2 = *h__; if (d1 >= 0.) { x2 = -x2; } goto L8; L7: x2 = d1 * -.5 / *d2; L8: if (fabs(x2) <= *h__) { goto L11; } if (x2 <= 0.) { goto L9; } else { goto L10; } L9: x2 = -(*h__); goto L11; L10: x2 = *h__; /* ...EVALUATE F AT THE PREDICTED MINIMUM... */ L11: f2 = flin_(n, j, &x2, f,f_data, &x[1], &global_1->nf, q_1, &ret); if (ret != NLOPT_SUCCESS) return ret; if (k >= nits || f2 <= f0) { goto L12; } /* ...NO SUCCESS, SO TRY AGAIN... */ ++k; if (f0 < *f1 && *x1 * x2 > 0.) { goto L4; } x2 *= .5; goto L11; /* ...INCREMENT THE ONE-DIMENSIONAL SEARCH COUNTER... */ L12: ++global_1->nl; if (f2 <= fm) { goto L13; } x2 = xm; goto L14; L13: fm = f2; /* ...GET A NEW ESTIMATE OF THE SECOND DERIVATIVE... */ L14: if ((d__1 = x2 * (x2 - *x1), fabs(d__1)) <= small) { goto L15; } *d2 = (x2 * (*f1 - f0) - *x1 * (fm - f0)) / (*x1 * x2 * (*x1 - x2)); goto L16; L15: if (k > 0) { *d2 = 0.; } L16: if (*d2 <= small) { *d2 = small; } *x1 = x2; global_1->fx = fm; if (sf1 >= global_1->fx) { goto L17; } global_1->fx = sf1; *x1 = sx1; /* ...UPDATE X FOR LINEAR BUT NOT PARABOLIC SEARCH... */ L17: if (j == 0) { return NLOPT_SUCCESS; } i__1 = n; for (i__ = 1; i__ <= i__1; ++i__) { /* L18: */ x[i__] += *x1 * q_1->v[i__ + j * n - (n+1)]; } return NLOPT_SUCCESS; } /* min_ */ static double flin_(int n, int j, double *l, praxis_func f, void *f_data, double *x, int *nf, struct q_s *q_1, nlopt_result *ret) { /* System generated locals */ int i__1; double ret_val; /* Local variables */ nlopt_stopping *stop = q_1->stop; int i__; double *t; /* size n */ t = q_1->t_flin; /* ...FLIN IS THE FUNCTION OF ONE REAL VARIABLE L THAT IS MINIMIZED */ /* BY THE SUBROUTINE MIN... */ /* Parameter adjustments */ --x; /* Function Body */ if (j == 0) { goto L2; } /* ...THE SEARCH IS LINEAR... */ i__1 = n; for (i__ = 1; i__ <= i__1; ++i__) { /* L1: */ t[i__ - 1] = x[i__] + *l * q_1->v[i__ + j * n - (n+1)]; } goto L4; /* ...THE SEARCH IS ALONG A PARABOLIC SPACE CURVE... */ L2: q_1->qa = *l * (*l - q_1->qd1) / (q_1->qd0 * (q_1->qd0 + q_1->qd1)); q_1->qb = (*l + q_1->qd0) * (q_1->qd1 - *l) / (q_1->qd0 * q_1->qd1); q_1->qc = *l * (*l + q_1->qd0) / (q_1->qd1 * (q_1->qd0 + q_1->qd1)); i__1 = n; for (i__ = 1; i__ <= i__1; ++i__) { /* L3: */ t[i__ - 1] = q_1->qa * q_1->q0[i__ - 1] + q_1->qb * x[i__] + q_1->qc * q_1->q1[i__ - 1]; } /* ...THE FUNCTION EVALUATION COUNTER NF IS INCREMENTED... */ L4: ++(*nf); ret_val = f(n, t, f_data); stop->nevals++; if (ret_val < q_1->fbest) { q_1->fbest = ret_val; memcpy(q_1->xbest, t, n * sizeof(double)); } if (nlopt_stop_forced(stop)) *ret = NLOPT_FORCED_STOP; else if (nlopt_stop_evals(stop)) *ret = NLOPT_MAXEVAL_REACHED; else if (nlopt_stop_time(stop)) *ret = NLOPT_MAXTIME_REACHED; else if (ret_val <= stop->minf_max) *ret = NLOPT_MINF_MAX_REACHED; return ret_val; } /* flin_ */ static void sort_(int m, int n, double *d__, double *v) { /* System generated locals */ int v_dim1, v_offset, i__1, i__2; /* Local variables */ int i__, j, k; double s; int ip1, nm1; /* ...SORTS THE ELEMENTS OF D(N) INTO DESCENDING ORDER AND MOVES THE */ /* CORRESPONDING COLUMNS OF V(N,N). */ /* M IS THE ROW DIMENSION OF V AS DECLARED IN THE CALLING PROGRAM. */ /* Parameter adjustments */ v_dim1 = m; v_offset = 1 + v_dim1; v -= v_offset; --d__; /* Function Body */ if (n == 1) { return; } nm1 = n - 1; i__1 = nm1; for (i__ = 1; i__ <= i__1; ++i__) { k = i__; s = d__[i__]; ip1 = i__ + 1; i__2 = n; for (j = ip1; j <= i__2; ++j) { if (d__[j] <= s) { goto L1; } k = j; s = d__[j]; L1: ; } if (k <= i__) { goto L3; } d__[k] = d__[i__]; d__[i__] = s; i__2 = n; for (j = 1; j <= i__2; ++j) { s = v[j + i__ * v_dim1]; v[j + i__ * v_dim1] = v[j + k * v_dim1]; /* L2: */ v[j + k * v_dim1] = s; } L3: ; } } /* sort_ */ static void quad_(int n, praxis_func f, void *f_data, double *x, double *t_old, double machep, double *h__, struct global_s *global_1, struct q_s *q_1) { /* System generated locals */ int i__1; double d__1; /* Local variables */ int i__; double l, s; double value; /* ...QUAD LOOKS FOR THE MINIMUM OF F ALONG A CURVE DEFINED BY Q0,Q1,X... */ /* Parameter adjustments */ --x; /* Function Body */ s = global_1->fx; global_1->fx = q_1->qf1; q_1->qf1 = s; q_1->qd1 = 0.; i__1 = n; for (i__ = 1; i__ <= i__1; ++i__) { s = x[i__]; l = q_1->q1[i__ - 1]; x[i__] = l; q_1->q1[i__ - 1] = s; /* L1: */ /* Computing 2nd power */ d__1 = s - l; q_1->qd1 += d__1 * d__1; } q_1->qd1 = sqrt(q_1->qd1); l = q_1->qd1; s = 0.; if (q_1->qd0 <= 0. || q_1->qd1 <= 0. || global_1->nl < n * 3 * n) { goto L2; } value = q_1->qf1; min_(n, 0, 2, &s, &l, &value, 1, f,f_data, &x[1], t_old, machep, h__, global_1, q_1); q_1->qa = l * (l - q_1->qd1) / (q_1->qd0 * (q_1->qd0 + q_1->qd1)); q_1->qb = (l + q_1->qd0) * (q_1->qd1 - l) / (q_1->qd0 * q_1->qd1); q_1->qc = l * (l + q_1->qd0) / (q_1->qd1 * (q_1->qd0 + q_1->qd1)); goto L3; L2: global_1->fx = q_1->qf1; q_1->qa = 0.; q_1->qb = q_1->qa; q_1->qc = 1.; L3: q_1->qd0 = q_1->qd1; i__1 = n; for (i__ = 1; i__ <= i__1; ++i__) { s = q_1->q0[i__ - 1]; q_1->q0[i__ - 1] = x[i__]; /* L4: */ x[i__] = q_1->qa * s + q_1->qb * x[i__] + q_1->qc * q_1->q1[i__ - 1]; } } /* quad_ */ nloptr/src/nlopt_src/praxis/praxis.h0000644000176000001440000000070413355070635017407 0ustar ripleyusers#ifndef PRAXIS_H #define PRAXIS_H #include "nlopt-util.h" #include "nlopt.h" #ifdef __cplusplus extern "C" { #endif /* __cplusplus */ typedef double (*praxis_func)(int n, const double *x, void *f_data); 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" >&6; } if ${lt_cv_nm_interface+:} false; then : $as_echo_n "(cached) " >&6 else lt_cv_nm_interface="BSD nm" echo "int some_variable = 0;" > conftest.$ac_ext (eval echo "\"\$as_me:$LINENO: $ac_compile\"" >&5) (eval "$ac_compile" 2>conftest.err) cat conftest.err >&5 (eval echo "\"\$as_me:$LINENO: $NM \\\"conftest.$ac_objext\\\"\"" >&5) (eval "$NM \"conftest.$ac_objext\"" 2>conftest.err > conftest.out) cat conftest.err >&5 (eval echo "\"\$as_me:$LINENO: output\"" >&5) cat conftest.out >&5 if $GREP 'External.*some_variable' conftest.out > /dev/null; then lt_cv_nm_interface="MS dumpbin" fi rm -f conftest* fi { $as_echo "$as_me:${as_lineno-$LINENO}: result: $lt_cv_nm_interface" >&5 $as_echo "$lt_cv_nm_interface" >&6; } # find the maximum length of command line arguments { $as_echo "$as_me:${as_lineno-$LINENO}: checking the maximum length of command line arguments" >&5 $as_echo_n "checking the maximum length of command line arguments... " >&6; } if ${lt_cv_sys_max_cmd_len+:} false; then : $as_echo_n "(cached) " >&6 else i=0 teststring="ABCD" case $build_os in msdosdjgpp*) # On DJGPP, this test can blow up pretty badly due to problems in libc # (any single argument exceeding 2000 bytes causes a buffer overrun # during glob expansion). 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" >&6; } if ${ac_cv_prog_OBJDUMP+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$OBJDUMP"; then ac_cv_prog_OBJDUMP="$OBJDUMP" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_OBJDUMP="${ac_tool_prefix}objdump" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi OBJDUMP=$ac_cv_prog_OBJDUMP if test -n "$OBJDUMP"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $OBJDUMP" >&5 $as_echo "$OBJDUMP" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi fi if test -z "$ac_cv_prog_OBJDUMP"; then ac_ct_OBJDUMP=$OBJDUMP # Extract the first word of "objdump", so it can be a program name with args. set dummy objdump; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_ac_ct_OBJDUMP+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$ac_ct_OBJDUMP"; then ac_cv_prog_ac_ct_OBJDUMP="$ac_ct_OBJDUMP" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_OBJDUMP="objdump" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi ac_ct_OBJDUMP=$ac_cv_prog_ac_ct_OBJDUMP if test -n "$ac_ct_OBJDUMP"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $ac_ct_OBJDUMP" >&5 $as_echo "$ac_ct_OBJDUMP" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi if test "x$ac_ct_OBJDUMP" = x; then OBJDUMP="false" else case $cross_compiling:$ac_tool_warned in yes:) { $as_echo "$as_me:${as_lineno-$LINENO}: WARNING: using cross tools not prefixed with host triplet" >&5 $as_echo "$as_me: WARNING: using cross tools not prefixed with host triplet" >&2;} ac_tool_warned=yes ;; esac OBJDUMP=$ac_ct_OBJDUMP fi else OBJDUMP="$ac_cv_prog_OBJDUMP" fi test -z "$OBJDUMP" && OBJDUMP=objdump { $as_echo "$as_me:${as_lineno-$LINENO}: checking how to recognize dependent libraries" >&5 $as_echo_n "checking how to recognize dependent libraries... " >&6; } if ${lt_cv_deplibs_check_method+:} false; then : $as_echo_n "(cached) " >&6 else lt_cv_file_magic_cmd='$MAGIC_CMD' lt_cv_file_magic_test_file= lt_cv_deplibs_check_method='unknown' # Need to set the preceding variable on all platforms that support # interlibrary dependencies. # 'none' -- dependencies not supported. # `unknown' -- same as none, but documents that we really don't know. # 'pass_all' -- all dependencies passed with no checks. # 'test_compile' -- check by making test program. # 'file_magic [[regex]]' -- check by looking for files in library path # which responds to the $file_magic_cmd with a given extended regex. # If you have `file' or equivalent on your system and you're not sure # whether `pass_all' will *always* work, you probably want this one. case $host_os in aix[4-9]*) lt_cv_deplibs_check_method=pass_all ;; beos*) lt_cv_deplibs_check_method=pass_all ;; bsdi[45]*) lt_cv_deplibs_check_method='file_magic ELF [0-9][0-9]*-bit [ML]SB (shared object|dynamic lib)' lt_cv_file_magic_cmd='/usr/bin/file -L' lt_cv_file_magic_test_file=/shlib/libc.so ;; cygwin*) # func_win32_libid is a shell function defined in ltmain.sh lt_cv_deplibs_check_method='file_magic ^x86 archive import|^x86 DLL' lt_cv_file_magic_cmd='func_win32_libid' ;; mingw* | pw32*) # Base MSYS/MinGW do not provide the 'file' command needed by # func_win32_libid shell function, so use a weaker test based on 'objdump', # unless we find 'file', for example because we are cross-compiling. # func_win32_libid assumes BSD nm, so disallow it if using MS dumpbin. if ( test "$lt_cv_nm_interface" = "BSD nm" && file / ) >/dev/null 2>&1; then lt_cv_deplibs_check_method='file_magic ^x86 archive import|^x86 DLL' lt_cv_file_magic_cmd='func_win32_libid' else # Keep this pattern in sync with the one in func_win32_libid. lt_cv_deplibs_check_method='file_magic file format (pei*-i386(.*architecture: i386)?|pe-arm-wince|pe-x86-64)' lt_cv_file_magic_cmd='$OBJDUMP -f' fi ;; cegcc*) # use the weaker test based on 'objdump'. See mingw*. lt_cv_deplibs_check_method='file_magic file format pe-arm-.*little(.*architecture: arm)?' lt_cv_file_magic_cmd='$OBJDUMP -f' ;; darwin* | rhapsody*) lt_cv_deplibs_check_method=pass_all ;; freebsd* | dragonfly*) if echo __ELF__ | $CC -E - | $GREP __ELF__ > /dev/null; then case $host_cpu in i*86 ) # Not sure whether the presence of OpenBSD here was a mistake. # Let's accept both of them until this is cleared up. lt_cv_deplibs_check_method='file_magic (FreeBSD|OpenBSD|DragonFly)/i[3-9]86 (compact )?demand paged shared library' lt_cv_file_magic_cmd=/usr/bin/file lt_cv_file_magic_test_file=`echo /usr/lib/libc.so.*` ;; esac else lt_cv_deplibs_check_method=pass_all fi ;; gnu*) lt_cv_deplibs_check_method=pass_all ;; haiku*) lt_cv_deplibs_check_method=pass_all ;; hpux10.20* | hpux11*) lt_cv_file_magic_cmd=/usr/bin/file case $host_cpu in ia64*) lt_cv_deplibs_check_method='file_magic (s[0-9][0-9][0-9]|ELF-[0-9][0-9]) shared object file - IA64' lt_cv_file_magic_test_file=/usr/lib/hpux32/libc.so ;; hppa*64*) lt_cv_deplibs_check_method='file_magic (s[0-9][0-9][0-9]|ELF[ -][0-9][0-9])(-bit)?( [LM]SB)? shared object( file)?[, -]* PA-RISC [0-9]\.[0-9]' lt_cv_file_magic_test_file=/usr/lib/pa20_64/libc.sl ;; *) lt_cv_deplibs_check_method='file_magic (s[0-9][0-9][0-9]|PA-RISC[0-9]\.[0-9]) shared library' lt_cv_file_magic_test_file=/usr/lib/libc.sl ;; esac ;; interix[3-9]*) # PIC code is broken on Interix 3.x, that's why |\.a not |_pic\.a here lt_cv_deplibs_check_method='match_pattern /lib[^/]+(\.so|\.a)$' ;; irix5* | irix6* | nonstopux*) case $LD in *-32|*"-32 ") libmagic=32-bit;; *-n32|*"-n32 ") libmagic=N32;; *-64|*"-64 ") libmagic=64-bit;; *) libmagic=never-match;; esac lt_cv_deplibs_check_method=pass_all ;; # This must be glibc/ELF. linux* | k*bsd*-gnu | kopensolaris*-gnu) lt_cv_deplibs_check_method=pass_all ;; netbsd*) if echo __ELF__ | $CC -E - | $GREP __ELF__ > /dev/null; then lt_cv_deplibs_check_method='match_pattern /lib[^/]+(\.so\.[0-9]+\.[0-9]+|_pic\.a)$' else lt_cv_deplibs_check_method='match_pattern /lib[^/]+(\.so|_pic\.a)$' fi ;; newos6*) lt_cv_deplibs_check_method='file_magic ELF [0-9][0-9]*-bit [ML]SB (executable|dynamic lib)' lt_cv_file_magic_cmd=/usr/bin/file lt_cv_file_magic_test_file=/usr/lib/libnls.so ;; *nto* | *qnx*) lt_cv_deplibs_check_method=pass_all ;; openbsd*) if test -z "`echo __ELF__ | $CC -E - | $GREP __ELF__`" || test "$host_os-$host_cpu" = "openbsd2.8-powerpc"; then lt_cv_deplibs_check_method='match_pattern /lib[^/]+(\.so\.[0-9]+\.[0-9]+|\.so|_pic\.a)$' else lt_cv_deplibs_check_method='match_pattern /lib[^/]+(\.so\.[0-9]+\.[0-9]+|_pic\.a)$' fi ;; osf3* | osf4* | osf5*) lt_cv_deplibs_check_method=pass_all ;; rdos*) lt_cv_deplibs_check_method=pass_all ;; solaris*) lt_cv_deplibs_check_method=pass_all ;; sysv5* | sco3.2v5* | sco5v6* | unixware* | OpenUNIX* | sysv4*uw2*) lt_cv_deplibs_check_method=pass_all ;; sysv4 | sysv4.3*) case $host_vendor in motorola) lt_cv_deplibs_check_method='file_magic ELF [0-9][0-9]*-bit [ML]SB (shared object|dynamic lib) M[0-9][0-9]* Version [0-9]' lt_cv_file_magic_test_file=`echo /usr/lib/libc.so*` ;; ncr) lt_cv_deplibs_check_method=pass_all ;; sequent) lt_cv_file_magic_cmd='/bin/file' lt_cv_deplibs_check_method='file_magic ELF [0-9][0-9]*-bit [LM]SB (shared object|dynamic lib )' ;; sni) lt_cv_file_magic_cmd='/bin/file' lt_cv_deplibs_check_method="file_magic ELF [0-9][0-9]*-bit [LM]SB dynamic lib" lt_cv_file_magic_test_file=/lib/libc.so ;; siemens) lt_cv_deplibs_check_method=pass_all ;; pc) lt_cv_deplibs_check_method=pass_all ;; esac ;; tpf*) lt_cv_deplibs_check_method=pass_all ;; esac fi { $as_echo "$as_me:${as_lineno-$LINENO}: result: $lt_cv_deplibs_check_method" >&5 $as_echo "$lt_cv_deplibs_check_method" >&6; } file_magic_glob= want_nocaseglob=no if test "$build" = "$host"; then case $host_os in mingw* | pw32*) if ( shopt | grep nocaseglob ) >/dev/null 2>&1; then want_nocaseglob=yes else file_magic_glob=`echo aAbBcCdDeEfFgGhHiIjJkKlLmMnNoOpPqQrRsStTuUvVwWxXyYzZ | $SED -e "s/\(..\)/s\/[\1]\/[\1]\/g;/g"` fi ;; esac fi file_magic_cmd=$lt_cv_file_magic_cmd deplibs_check_method=$lt_cv_deplibs_check_method test -z "$deplibs_check_method" && deplibs_check_method=unknown if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}dlltool", so it can be a program name with args. set dummy ${ac_tool_prefix}dlltool; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_DLLTOOL+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$DLLTOOL"; then ac_cv_prog_DLLTOOL="$DLLTOOL" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_DLLTOOL="${ac_tool_prefix}dlltool" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi DLLTOOL=$ac_cv_prog_DLLTOOL if test -n "$DLLTOOL"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $DLLTOOL" >&5 $as_echo "$DLLTOOL" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi fi if test -z "$ac_cv_prog_DLLTOOL"; then ac_ct_DLLTOOL=$DLLTOOL # Extract the first word of "dlltool", so it can be a program name with args. set dummy dlltool; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_ac_ct_DLLTOOL+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$ac_ct_DLLTOOL"; then ac_cv_prog_ac_ct_DLLTOOL="$ac_ct_DLLTOOL" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_DLLTOOL="dlltool" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi ac_ct_DLLTOOL=$ac_cv_prog_ac_ct_DLLTOOL if test -n "$ac_ct_DLLTOOL"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $ac_ct_DLLTOOL" >&5 $as_echo "$ac_ct_DLLTOOL" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi if test "x$ac_ct_DLLTOOL" = x; then DLLTOOL="false" else case $cross_compiling:$ac_tool_warned in yes:) { $as_echo "$as_me:${as_lineno-$LINENO}: WARNING: using cross tools not prefixed with host triplet" >&5 $as_echo "$as_me: WARNING: using cross tools not prefixed with host triplet" >&2;} ac_tool_warned=yes ;; esac DLLTOOL=$ac_ct_DLLTOOL fi else DLLTOOL="$ac_cv_prog_DLLTOOL" fi test -z "$DLLTOOL" && DLLTOOL=dlltool { $as_echo "$as_me:${as_lineno-$LINENO}: checking how to associate runtime and link libraries" >&5 $as_echo_n "checking how to associate runtime and link libraries... " >&6; } if ${lt_cv_sharedlib_from_linklib_cmd+:} false; then : $as_echo_n "(cached) " >&6 else lt_cv_sharedlib_from_linklib_cmd='unknown' case $host_os in cygwin* | mingw* | pw32* | cegcc*) # two different shell functions defined in ltmain.sh # decide which to use based on capabilities of $DLLTOOL case `$DLLTOOL --help 2>&1` in *--identify-strict*) lt_cv_sharedlib_from_linklib_cmd=func_cygming_dll_for_implib ;; *) lt_cv_sharedlib_from_linklib_cmd=func_cygming_dll_for_implib_fallback ;; esac ;; *) # fallback: assume linklib IS sharedlib lt_cv_sharedlib_from_linklib_cmd="$ECHO" ;; esac fi { $as_echo "$as_me:${as_lineno-$LINENO}: result: $lt_cv_sharedlib_from_linklib_cmd" >&5 $as_echo "$lt_cv_sharedlib_from_linklib_cmd" >&6; } sharedlib_from_linklib_cmd=$lt_cv_sharedlib_from_linklib_cmd test -z "$sharedlib_from_linklib_cmd" && sharedlib_from_linklib_cmd=$ECHO if test -n "$ac_tool_prefix"; then for ac_prog in ar do # Extract the first word of "$ac_tool_prefix$ac_prog", so it can be a program name with args. set dummy $ac_tool_prefix$ac_prog; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_AR+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$AR"; then ac_cv_prog_AR="$AR" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_AR="$ac_tool_prefix$ac_prog" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi AR=$ac_cv_prog_AR if test -n "$AR"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $AR" >&5 $as_echo "$AR" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi test -n "$AR" && break done fi if test -z "$AR"; then ac_ct_AR=$AR for ac_prog in ar do # Extract the first word of "$ac_prog", so it can be a program name with args. set dummy $ac_prog; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_ac_ct_AR+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$ac_ct_AR"; then ac_cv_prog_ac_ct_AR="$ac_ct_AR" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_AR="$ac_prog" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi ac_ct_AR=$ac_cv_prog_ac_ct_AR if test -n "$ac_ct_AR"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $ac_ct_AR" >&5 $as_echo "$ac_ct_AR" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi test -n "$ac_ct_AR" && break done if test "x$ac_ct_AR" = x; then AR="false" else case $cross_compiling:$ac_tool_warned in yes:) { $as_echo "$as_me:${as_lineno-$LINENO}: WARNING: using cross tools not prefixed with host triplet" >&5 $as_echo "$as_me: WARNING: using cross tools not prefixed with host triplet" >&2;} ac_tool_warned=yes ;; esac AR=$ac_ct_AR fi fi : ${AR=ar} : ${AR_FLAGS=cru} { $as_echo "$as_me:${as_lineno-$LINENO}: checking for archiver @FILE support" >&5 $as_echo_n "checking for archiver @FILE support... " >&6; } if ${lt_cv_ar_at_file+:} false; then : $as_echo_n "(cached) " >&6 else lt_cv_ar_at_file=no cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ int main () { ; return 0; } _ACEOF if ac_fn_c_try_compile "$LINENO"; then : echo conftest.$ac_objext > conftest.lst lt_ar_try='$AR $AR_FLAGS libconftest.a @conftest.lst >&5' { { eval echo "\"\$as_me\":${as_lineno-$LINENO}: \"$lt_ar_try\""; } >&5 (eval $lt_ar_try) 2>&5 ac_status=$? $as_echo "$as_me:${as_lineno-$LINENO}: \$? = $ac_status" >&5 test $ac_status = 0; } if test "$ac_status" -eq 0; then # Ensure the archiver fails upon bogus file names. rm -f conftest.$ac_objext libconftest.a { { eval echo "\"\$as_me\":${as_lineno-$LINENO}: \"$lt_ar_try\""; } >&5 (eval $lt_ar_try) 2>&5 ac_status=$? $as_echo "$as_me:${as_lineno-$LINENO}: \$? = $ac_status" >&5 test $ac_status = 0; } if test "$ac_status" -ne 0; then lt_cv_ar_at_file=@ fi fi rm -f conftest.* libconftest.a fi rm -f core conftest.err conftest.$ac_objext conftest.$ac_ext fi { $as_echo "$as_me:${as_lineno-$LINENO}: result: $lt_cv_ar_at_file" >&5 $as_echo "$lt_cv_ar_at_file" >&6; } if test "x$lt_cv_ar_at_file" = xno; then archiver_list_spec= else archiver_list_spec=$lt_cv_ar_at_file fi if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}strip", so it can be a program name with args. set dummy ${ac_tool_prefix}strip; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_STRIP+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$STRIP"; then ac_cv_prog_STRIP="$STRIP" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_STRIP="${ac_tool_prefix}strip" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi STRIP=$ac_cv_prog_STRIP if test -n "$STRIP"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $STRIP" >&5 $as_echo "$STRIP" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi fi if test -z "$ac_cv_prog_STRIP"; then ac_ct_STRIP=$STRIP # Extract the first word of "strip", so it can be a program name with args. set dummy strip; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_ac_ct_STRIP+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$ac_ct_STRIP"; then ac_cv_prog_ac_ct_STRIP="$ac_ct_STRIP" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_STRIP="strip" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi ac_ct_STRIP=$ac_cv_prog_ac_ct_STRIP if test -n "$ac_ct_STRIP"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $ac_ct_STRIP" >&5 $as_echo "$ac_ct_STRIP" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi if test "x$ac_ct_STRIP" = x; then STRIP=":" else case $cross_compiling:$ac_tool_warned in yes:) { $as_echo "$as_me:${as_lineno-$LINENO}: WARNING: using cross tools not prefixed with host triplet" >&5 $as_echo "$as_me: WARNING: using cross tools not prefixed with host triplet" >&2;} ac_tool_warned=yes ;; esac STRIP=$ac_ct_STRIP fi else STRIP="$ac_cv_prog_STRIP" fi test -z "$STRIP" && STRIP=: if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}ranlib", so it can be a program name with args. set dummy ${ac_tool_prefix}ranlib; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_RANLIB+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$RANLIB"; then ac_cv_prog_RANLIB="$RANLIB" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_RANLIB="${ac_tool_prefix}ranlib" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi RANLIB=$ac_cv_prog_RANLIB if test -n "$RANLIB"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $RANLIB" >&5 $as_echo "$RANLIB" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi fi if test -z "$ac_cv_prog_RANLIB"; then ac_ct_RANLIB=$RANLIB # Extract the first word of "ranlib", so it can be a program name with args. set dummy ranlib; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_ac_ct_RANLIB+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$ac_ct_RANLIB"; then ac_cv_prog_ac_ct_RANLIB="$ac_ct_RANLIB" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_RANLIB="ranlib" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi ac_ct_RANLIB=$ac_cv_prog_ac_ct_RANLIB if test -n "$ac_ct_RANLIB"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $ac_ct_RANLIB" >&5 $as_echo "$ac_ct_RANLIB" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi if test "x$ac_ct_RANLIB" = x; then RANLIB=":" else case $cross_compiling:$ac_tool_warned in yes:) { $as_echo "$as_me:${as_lineno-$LINENO}: WARNING: using cross tools not prefixed with host triplet" >&5 $as_echo "$as_me: WARNING: using cross tools not prefixed with host triplet" >&2;} ac_tool_warned=yes ;; esac RANLIB=$ac_ct_RANLIB fi else RANLIB="$ac_cv_prog_RANLIB" fi test -z "$RANLIB" && RANLIB=: # Determine commands to create old-style static archives. old_archive_cmds='$AR $AR_FLAGS $oldlib$oldobjs' old_postinstall_cmds='chmod 644 $oldlib' old_postuninstall_cmds= if test -n "$RANLIB"; then case $host_os in openbsd*) old_postinstall_cmds="$old_postinstall_cmds~\$RANLIB -t \$tool_oldlib" ;; *) old_postinstall_cmds="$old_postinstall_cmds~\$RANLIB \$tool_oldlib" ;; esac old_archive_cmds="$old_archive_cmds~\$RANLIB \$tool_oldlib" fi case $host_os in darwin*) lock_old_archive_extraction=yes ;; *) lock_old_archive_extraction=no ;; esac # If no C compiler was specified, use CC. LTCC=${LTCC-"$CC"} # If no C compiler flags were specified, use CFLAGS. LTCFLAGS=${LTCFLAGS-"$CFLAGS"} # Allow CC to be a program name with arguments. compiler=$CC # Check for command to grab the raw symbol name followed by C symbol from nm. { $as_echo "$as_me:${as_lineno-$LINENO}: checking command to parse $NM output from $compiler object" >&5 $as_echo_n "checking command to parse $NM output from $compiler object... " >&6; } if ${lt_cv_sys_global_symbol_pipe+:} false; then : $as_echo_n "(cached) " >&6 else # These are sane defaults that work on at least a few old systems. # [They come from Ultrix. What could be older than Ultrix?!! ;)] # Character class describing NM global symbol codes. symcode='[BCDEGRST]' # Regexp to match symbols that can be accessed directly from C. sympat='\([_A-Za-z][_A-Za-z0-9]*\)' # Define system-specific variables. case $host_os in aix*) symcode='[BCDT]' ;; cygwin* | mingw* | pw32* | cegcc*) symcode='[ABCDGISTW]' ;; hpux*) if test "$host_cpu" = ia64; then symcode='[ABCDEGRST]' fi ;; irix* | nonstopux*) symcode='[BCDEGRST]' ;; osf*) symcode='[BCDEGQRST]' ;; solaris*) symcode='[BDRT]' ;; sco3.2v5*) symcode='[DT]' ;; sysv4.2uw2*) symcode='[DT]' ;; sysv5* | sco5v6* | unixware* | OpenUNIX*) symcode='[ABDT]' ;; sysv4) symcode='[DFNSTU]' ;; esac # If we're using GNU nm, then use its standard symbol codes. case `$NM -V 2>&1` in *GNU* | *'with BFD'*) symcode='[ABCDGIRSTW]' ;; esac # Transform an extracted symbol line into a proper C declaration. # Some systems (esp. on ia64) link data and code symbols differently, # so use this general approach. lt_cv_sys_global_symbol_to_cdecl="sed -n -e 's/^T .* \(.*\)$/extern int \1();/p' -e 's/^$symcode* .* \(.*\)$/extern char \1;/p'" # Transform an extracted symbol line into symbol name and symbol address lt_cv_sys_global_symbol_to_c_name_address="sed -n -e 's/^: \([^ ]*\)[ ]*$/ {\\\"\1\\\", (void *) 0},/p' -e 's/^$symcode* \([^ ]*\) \([^ ]*\)$/ {\"\2\", (void *) \&\2},/p'" lt_cv_sys_global_symbol_to_c_name_address_lib_prefix="sed -n -e 's/^: \([^ ]*\)[ ]*$/ {\\\"\1\\\", (void *) 0},/p' -e 's/^$symcode* \([^ ]*\) \(lib[^ ]*\)$/ {\"\2\", (void *) \&\2},/p' -e 's/^$symcode* \([^ ]*\) \([^ ]*\)$/ {\"lib\2\", (void *) \&\2},/p'" # Handle CRLF in mingw tool chain opt_cr= case $build_os in mingw*) opt_cr=`$ECHO 'x\{0,1\}' | tr x '\015'` # option cr in regexp ;; esac # Try without a prefix underscore, then with it. for ac_symprfx in "" "_"; do # Transform symcode, sympat, and symprfx into a raw symbol and a C symbol. symxfrm="\\1 $ac_symprfx\\2 \\2" # Write the raw and C identifiers. if test "$lt_cv_nm_interface" = "MS dumpbin"; then # Fake it for dumpbin and say T for any non-static function # and D for any global variable. # Also find C++ and __fastcall symbols from MSVC++, # which start with @ or ?. lt_cv_sys_global_symbol_pipe="$AWK '"\ " {last_section=section; section=\$ 3};"\ " /^COFF SYMBOL TABLE/{for(i in hide) delete hide[i]};"\ " /Section length .*#relocs.*(pick any)/{hide[last_section]=1};"\ " \$ 0!~/External *\|/{next};"\ " / 0+ UNDEF /{next}; / UNDEF \([^|]\)*()/{next};"\ " {if(hide[section]) next};"\ " {f=0}; \$ 0~/\(\).*\|/{f=1}; {printf f ? \"T \" : \"D \"};"\ " {split(\$ 0, a, /\||\r/); split(a[2], s)};"\ " s[1]~/^[@?]/{print s[1], s[1]; next};"\ " s[1]~prfx {split(s[1],t,\"@\"); print t[1], substr(t[1],length(prfx))}"\ " ' prfx=^$ac_symprfx" else lt_cv_sys_global_symbol_pipe="sed -n -e 's/^.*[ ]\($symcode$symcode*\)[ ][ ]*$ac_symprfx$sympat$opt_cr$/$symxfrm/p'" fi lt_cv_sys_global_symbol_pipe="$lt_cv_sys_global_symbol_pipe | sed '/ __gnu_lto/d'" # Check to see that the pipe works correctly. pipe_works=no rm -f conftest* cat > conftest.$ac_ext <<_LT_EOF #ifdef __cplusplus extern "C" { #endif char nm_test_var; void nm_test_func(void); void nm_test_func(void){} #ifdef __cplusplus } #endif int main(){nm_test_var='a';nm_test_func();return(0);} _LT_EOF if { { eval echo "\"\$as_me\":${as_lineno-$LINENO}: \"$ac_compile\""; } >&5 (eval $ac_compile) 2>&5 ac_status=$? $as_echo "$as_me:${as_lineno-$LINENO}: \$? = $ac_status" >&5 test $ac_status = 0; }; then # Now try to grab the symbols. nlist=conftest.nm if { { eval echo "\"\$as_me\":${as_lineno-$LINENO}: \"$NM conftest.$ac_objext \| "$lt_cv_sys_global_symbol_pipe" \> $nlist\""; } >&5 (eval $NM conftest.$ac_objext \| "$lt_cv_sys_global_symbol_pipe" \> $nlist) 2>&5 ac_status=$? $as_echo "$as_me:${as_lineno-$LINENO}: \$? = $ac_status" >&5 test $ac_status = 0; } && test -s "$nlist"; then # Try sorting and uniquifying the output. if sort "$nlist" | uniq > "$nlist"T; then mv -f "$nlist"T "$nlist" else rm -f "$nlist"T fi # Make sure that we snagged all the symbols we need. if $GREP ' nm_test_var$' "$nlist" >/dev/null; then if $GREP ' nm_test_func$' "$nlist" >/dev/null; then cat <<_LT_EOF > conftest.$ac_ext /* Keep this code in sync between libtool.m4, ltmain, lt_system.h, and tests. */ #if defined(_WIN32) || defined(__CYGWIN__) || defined(_WIN32_WCE) /* DATA imports from DLLs on WIN32 con't be const, because runtime relocations are performed -- see ld's documentation on pseudo-relocs. */ # define LT_DLSYM_CONST #elif defined(__osf__) /* This system does not cope well with relocations in const data. */ # define LT_DLSYM_CONST #else # define LT_DLSYM_CONST const #endif #ifdef __cplusplus extern "C" { #endif _LT_EOF # Now generate the symbol file. eval "$lt_cv_sys_global_symbol_to_cdecl"' < "$nlist" | $GREP -v main >> conftest.$ac_ext' cat <<_LT_EOF >> conftest.$ac_ext /* The mapping between symbol names and symbols. */ LT_DLSYM_CONST struct { const char *name; void *address; } lt__PROGRAM__LTX_preloaded_symbols[] = { { "@PROGRAM@", (void *) 0 }, _LT_EOF $SED "s/^$symcode$symcode* \(.*\) \(.*\)$/ {\"\2\", (void *) \&\2},/" < "$nlist" | $GREP -v main >> conftest.$ac_ext cat <<\_LT_EOF >> conftest.$ac_ext {0, (void *) 0} }; /* This works around a problem in FreeBSD linker */ #ifdef FREEBSD_WORKAROUND static const void *lt_preloaded_setup() { return lt__PROGRAM__LTX_preloaded_symbols; } #endif #ifdef __cplusplus } #endif _LT_EOF # Now try linking the two files. mv conftest.$ac_objext conftstm.$ac_objext lt_globsym_save_LIBS=$LIBS lt_globsym_save_CFLAGS=$CFLAGS LIBS="conftstm.$ac_objext" CFLAGS="$CFLAGS$lt_prog_compiler_no_builtin_flag" if { { eval echo "\"\$as_me\":${as_lineno-$LINENO}: \"$ac_link\""; } >&5 (eval $ac_link) 2>&5 ac_status=$? $as_echo "$as_me:${as_lineno-$LINENO}: \$? = $ac_status" >&5 test $ac_status = 0; } && test -s conftest${ac_exeext}; then pipe_works=yes fi LIBS=$lt_globsym_save_LIBS CFLAGS=$lt_globsym_save_CFLAGS else echo "cannot find nm_test_func in $nlist" >&5 fi else echo "cannot find nm_test_var in $nlist" >&5 fi else echo "cannot run $lt_cv_sys_global_symbol_pipe" >&5 fi else echo "$progname: failed program was:" >&5 cat conftest.$ac_ext >&5 fi rm -rf conftest* conftst* # Do not use the global_symbol_pipe unless it works. if test "$pipe_works" = yes; then break else lt_cv_sys_global_symbol_pipe= fi done fi if test -z "$lt_cv_sys_global_symbol_pipe"; then lt_cv_sys_global_symbol_to_cdecl= fi if test -z "$lt_cv_sys_global_symbol_pipe$lt_cv_sys_global_symbol_to_cdecl"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: failed" >&5 $as_echo "failed" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: ok" >&5 $as_echo "ok" >&6; } fi # Response file support. if test "$lt_cv_nm_interface" = "MS dumpbin"; then nm_file_list_spec='@' elif $NM --help 2>/dev/null | grep '[@]FILE' >/dev/null; then nm_file_list_spec='@' fi { $as_echo "$as_me:${as_lineno-$LINENO}: checking for sysroot" >&5 $as_echo_n "checking for sysroot... " >&6; } # Check whether --with-sysroot was given. if test "${with_sysroot+set}" = set; then : withval=$with_sysroot; else with_sysroot=no fi lt_sysroot= case ${with_sysroot} in #( yes) if test "$GCC" = yes; then lt_sysroot=`$CC --print-sysroot 2>/dev/null` fi ;; #( /*) lt_sysroot=`echo "$with_sysroot" | sed -e "$sed_quote_subst"` ;; #( no|'') ;; #( *) { $as_echo "$as_me:${as_lineno-$LINENO}: result: ${with_sysroot}" >&5 $as_echo "${with_sysroot}" >&6; } as_fn_error $? "The sysroot must be an absolute path." "$LINENO" 5 ;; esac { $as_echo "$as_me:${as_lineno-$LINENO}: result: ${lt_sysroot:-no}" >&5 $as_echo "${lt_sysroot:-no}" >&6; } # Check whether --enable-libtool-lock was given. if test "${enable_libtool_lock+set}" = set; then : enableval=$enable_libtool_lock; fi test "x$enable_libtool_lock" != xno && enable_libtool_lock=yes # Some flags need to be propagated to the compiler or linker for good # libtool support. case $host in ia64-*-hpux*) # Find out which ABI we are using. echo 'int i;' > conftest.$ac_ext if { { eval echo "\"\$as_me\":${as_lineno-$LINENO}: \"$ac_compile\""; } >&5 (eval $ac_compile) 2>&5 ac_status=$? $as_echo "$as_me:${as_lineno-$LINENO}: \$? = $ac_status" >&5 test $ac_status = 0; }; then case `/usr/bin/file conftest.$ac_objext` in *ELF-32*) HPUX_IA64_MODE="32" ;; *ELF-64*) HPUX_IA64_MODE="64" ;; esac fi rm -rf conftest* ;; *-*-irix6*) # Find out which ABI we are using. echo '#line '$LINENO' "configure"' > conftest.$ac_ext if { { eval echo "\"\$as_me\":${as_lineno-$LINENO}: \"$ac_compile\""; } >&5 (eval $ac_compile) 2>&5 ac_status=$? $as_echo "$as_me:${as_lineno-$LINENO}: \$? = $ac_status" >&5 test $ac_status = 0; }; then if test "$lt_cv_prog_gnu_ld" = yes; then case `/usr/bin/file conftest.$ac_objext` in *32-bit*) LD="${LD-ld} -melf32bsmip" ;; *N32*) LD="${LD-ld} -melf32bmipn32" ;; *64-bit*) LD="${LD-ld} -melf64bmip" ;; esac else case `/usr/bin/file conftest.$ac_objext` in *32-bit*) LD="${LD-ld} -32" ;; *N32*) LD="${LD-ld} -n32" ;; *64-bit*) LD="${LD-ld} -64" ;; esac fi fi rm -rf conftest* ;; x86_64-*kfreebsd*-gnu|x86_64-*linux*|ppc*-*linux*|powerpc*-*linux*| \ s390*-*linux*|s390*-*tpf*|sparc*-*linux*) # Find out which ABI we are using. echo 'int i;' > conftest.$ac_ext if { { eval echo "\"\$as_me\":${as_lineno-$LINENO}: \"$ac_compile\""; } >&5 (eval $ac_compile) 2>&5 ac_status=$? $as_echo "$as_me:${as_lineno-$LINENO}: \$? = $ac_status" >&5 test $ac_status = 0; }; then case `/usr/bin/file conftest.o` in *32-bit*) case $host in x86_64-*kfreebsd*-gnu) LD="${LD-ld} -m elf_i386_fbsd" ;; x86_64-*linux*) LD="${LD-ld} -m elf_i386" ;; ppc64-*linux*|powerpc64-*linux*) LD="${LD-ld} -m elf32ppclinux" ;; s390x-*linux*) LD="${LD-ld} -m elf_s390" ;; sparc64-*linux*) LD="${LD-ld} -m elf32_sparc" ;; esac ;; *64-bit*) case $host in x86_64-*kfreebsd*-gnu) LD="${LD-ld} -m elf_x86_64_fbsd" ;; x86_64-*linux*) LD="${LD-ld} -m elf_x86_64" ;; ppc*-*linux*|powerpc*-*linux*) LD="${LD-ld} -m elf64ppc" ;; s390*-*linux*|s390*-*tpf*) LD="${LD-ld} -m elf64_s390" ;; sparc*-*linux*) LD="${LD-ld} -m elf64_sparc" ;; esac ;; esac fi rm -rf conftest* ;; *-*-sco3.2v5*) # On SCO OpenServer 5, we need -belf to get full-featured binaries. SAVE_CFLAGS="$CFLAGS" CFLAGS="$CFLAGS -belf" { $as_echo "$as_me:${as_lineno-$LINENO}: checking whether the C compiler needs -belf" >&5 $as_echo_n "checking whether the C compiler needs -belf... " >&6; } if ${lt_cv_cc_needs_belf+:} false; then : $as_echo_n "(cached) " >&6 else ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ int main () { ; return 0; } _ACEOF if ac_fn_c_try_link "$LINENO"; then : lt_cv_cc_needs_belf=yes else lt_cv_cc_needs_belf=no fi rm -f core conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu fi { $as_echo "$as_me:${as_lineno-$LINENO}: result: $lt_cv_cc_needs_belf" >&5 $as_echo "$lt_cv_cc_needs_belf" >&6; } if test x"$lt_cv_cc_needs_belf" != x"yes"; then # this is probably gcc 2.8.0, egcs 1.0 or newer; no need for -belf CFLAGS="$SAVE_CFLAGS" fi ;; *-*solaris*) # Find out which ABI we are using. echo 'int i;' > conftest.$ac_ext if { { eval echo "\"\$as_me\":${as_lineno-$LINENO}: \"$ac_compile\""; } >&5 (eval $ac_compile) 2>&5 ac_status=$? $as_echo "$as_me:${as_lineno-$LINENO}: \$? = $ac_status" >&5 test $ac_status = 0; }; then case `/usr/bin/file conftest.o` in *64-bit*) case $lt_cv_prog_gnu_ld in yes*) case $host in i?86-*-solaris*) LD="${LD-ld} -m elf_x86_64" ;; sparc*-*-solaris*) LD="${LD-ld} -m elf64_sparc" ;; esac # GNU ld 2.21 introduced _sol2 emulations. Use them if available. if ${LD-ld} -V | grep _sol2 >/dev/null 2>&1; then LD="${LD-ld}_sol2" fi ;; *) if ${LD-ld} -64 -r -o conftest2.o conftest.o >/dev/null 2>&1; then LD="${LD-ld} -64" fi ;; esac ;; esac fi rm -rf conftest* ;; esac need_locks="$enable_libtool_lock" if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}mt", so it can be a program name with args. set dummy ${ac_tool_prefix}mt; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_MANIFEST_TOOL+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$MANIFEST_TOOL"; then ac_cv_prog_MANIFEST_TOOL="$MANIFEST_TOOL" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_MANIFEST_TOOL="${ac_tool_prefix}mt" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi MANIFEST_TOOL=$ac_cv_prog_MANIFEST_TOOL if test -n "$MANIFEST_TOOL"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $MANIFEST_TOOL" >&5 $as_echo "$MANIFEST_TOOL" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi fi if test -z "$ac_cv_prog_MANIFEST_TOOL"; then ac_ct_MANIFEST_TOOL=$MANIFEST_TOOL # Extract the first word of "mt", so it can be a program name with args. set dummy mt; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_ac_ct_MANIFEST_TOOL+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$ac_ct_MANIFEST_TOOL"; then ac_cv_prog_ac_ct_MANIFEST_TOOL="$ac_ct_MANIFEST_TOOL" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_MANIFEST_TOOL="mt" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi ac_ct_MANIFEST_TOOL=$ac_cv_prog_ac_ct_MANIFEST_TOOL if test -n "$ac_ct_MANIFEST_TOOL"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $ac_ct_MANIFEST_TOOL" >&5 $as_echo "$ac_ct_MANIFEST_TOOL" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi if test "x$ac_ct_MANIFEST_TOOL" = x; then MANIFEST_TOOL=":" else case $cross_compiling:$ac_tool_warned in yes:) { $as_echo "$as_me:${as_lineno-$LINENO}: WARNING: using cross tools not prefixed with host triplet" >&5 $as_echo "$as_me: WARNING: using cross tools not prefixed with host triplet" >&2;} ac_tool_warned=yes ;; esac MANIFEST_TOOL=$ac_ct_MANIFEST_TOOL fi else MANIFEST_TOOL="$ac_cv_prog_MANIFEST_TOOL" fi test -z "$MANIFEST_TOOL" && MANIFEST_TOOL=mt { $as_echo "$as_me:${as_lineno-$LINENO}: checking if $MANIFEST_TOOL is a manifest tool" >&5 $as_echo_n "checking if $MANIFEST_TOOL is a manifest tool... " >&6; } if ${lt_cv_path_mainfest_tool+:} false; then : $as_echo_n "(cached) " >&6 else lt_cv_path_mainfest_tool=no echo "$as_me:$LINENO: $MANIFEST_TOOL '-?'" >&5 $MANIFEST_TOOL '-?' 2>conftest.err > conftest.out cat conftest.err >&5 if $GREP 'Manifest Tool' conftest.out > /dev/null; then lt_cv_path_mainfest_tool=yes fi rm -f conftest* fi { $as_echo "$as_me:${as_lineno-$LINENO}: result: $lt_cv_path_mainfest_tool" >&5 $as_echo "$lt_cv_path_mainfest_tool" >&6; } if test "x$lt_cv_path_mainfest_tool" != xyes; then MANIFEST_TOOL=: fi case $host_os in rhapsody* | darwin*) if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}dsymutil", so it can be a program name with args. set dummy ${ac_tool_prefix}dsymutil; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_DSYMUTIL+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$DSYMUTIL"; then ac_cv_prog_DSYMUTIL="$DSYMUTIL" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_DSYMUTIL="${ac_tool_prefix}dsymutil" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi DSYMUTIL=$ac_cv_prog_DSYMUTIL if test -n "$DSYMUTIL"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $DSYMUTIL" >&5 $as_echo "$DSYMUTIL" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi fi if test -z "$ac_cv_prog_DSYMUTIL"; then ac_ct_DSYMUTIL=$DSYMUTIL # Extract the first word of "dsymutil", so it can be a program name with args. set dummy dsymutil; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_ac_ct_DSYMUTIL+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$ac_ct_DSYMUTIL"; then ac_cv_prog_ac_ct_DSYMUTIL="$ac_ct_DSYMUTIL" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_DSYMUTIL="dsymutil" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi ac_ct_DSYMUTIL=$ac_cv_prog_ac_ct_DSYMUTIL if test -n "$ac_ct_DSYMUTIL"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $ac_ct_DSYMUTIL" >&5 $as_echo "$ac_ct_DSYMUTIL" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi if test "x$ac_ct_DSYMUTIL" = x; then DSYMUTIL=":" else case $cross_compiling:$ac_tool_warned in yes:) { $as_echo "$as_me:${as_lineno-$LINENO}: WARNING: using cross tools not prefixed with host triplet" >&5 $as_echo "$as_me: WARNING: using cross tools not prefixed with host triplet" >&2;} ac_tool_warned=yes ;; esac DSYMUTIL=$ac_ct_DSYMUTIL fi else DSYMUTIL="$ac_cv_prog_DSYMUTIL" fi if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}nmedit", so it can be a program name with args. set dummy ${ac_tool_prefix}nmedit; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_NMEDIT+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$NMEDIT"; then ac_cv_prog_NMEDIT="$NMEDIT" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_NMEDIT="${ac_tool_prefix}nmedit" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi NMEDIT=$ac_cv_prog_NMEDIT if test -n "$NMEDIT"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $NMEDIT" >&5 $as_echo "$NMEDIT" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi fi if test -z "$ac_cv_prog_NMEDIT"; then ac_ct_NMEDIT=$NMEDIT # Extract the first word of "nmedit", so it can be a program name with args. set dummy nmedit; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_ac_ct_NMEDIT+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$ac_ct_NMEDIT"; then ac_cv_prog_ac_ct_NMEDIT="$ac_ct_NMEDIT" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_NMEDIT="nmedit" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi ac_ct_NMEDIT=$ac_cv_prog_ac_ct_NMEDIT if test -n "$ac_ct_NMEDIT"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $ac_ct_NMEDIT" >&5 $as_echo "$ac_ct_NMEDIT" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi if test "x$ac_ct_NMEDIT" = x; then NMEDIT=":" else case $cross_compiling:$ac_tool_warned in yes:) { $as_echo "$as_me:${as_lineno-$LINENO}: WARNING: using cross tools not prefixed with host triplet" >&5 $as_echo "$as_me: WARNING: using cross tools not prefixed with host triplet" >&2;} ac_tool_warned=yes ;; esac NMEDIT=$ac_ct_NMEDIT fi else NMEDIT="$ac_cv_prog_NMEDIT" fi if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}lipo", so it can be a program name with args. set dummy ${ac_tool_prefix}lipo; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_LIPO+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$LIPO"; then ac_cv_prog_LIPO="$LIPO" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_LIPO="${ac_tool_prefix}lipo" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi LIPO=$ac_cv_prog_LIPO if test -n "$LIPO"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $LIPO" >&5 $as_echo "$LIPO" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi fi if test -z "$ac_cv_prog_LIPO"; then ac_ct_LIPO=$LIPO # Extract the first word of "lipo", so it can be a program name with args. set dummy lipo; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_ac_ct_LIPO+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$ac_ct_LIPO"; then ac_cv_prog_ac_ct_LIPO="$ac_ct_LIPO" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_LIPO="lipo" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi ac_ct_LIPO=$ac_cv_prog_ac_ct_LIPO if test -n "$ac_ct_LIPO"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $ac_ct_LIPO" >&5 $as_echo "$ac_ct_LIPO" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi if test "x$ac_ct_LIPO" = x; then LIPO=":" else case $cross_compiling:$ac_tool_warned in yes:) { $as_echo "$as_me:${as_lineno-$LINENO}: WARNING: using cross tools not prefixed with host triplet" >&5 $as_echo "$as_me: WARNING: using cross tools not prefixed with host triplet" >&2;} ac_tool_warned=yes ;; esac LIPO=$ac_ct_LIPO fi else LIPO="$ac_cv_prog_LIPO" fi if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}otool", so it can be a program name with args. set dummy ${ac_tool_prefix}otool; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_OTOOL+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$OTOOL"; then ac_cv_prog_OTOOL="$OTOOL" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_OTOOL="${ac_tool_prefix}otool" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi OTOOL=$ac_cv_prog_OTOOL if test -n "$OTOOL"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $OTOOL" >&5 $as_echo "$OTOOL" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi fi if test -z "$ac_cv_prog_OTOOL"; then ac_ct_OTOOL=$OTOOL # Extract the first word of "otool", so it can be a program name with args. set dummy otool; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_ac_ct_OTOOL+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$ac_ct_OTOOL"; then ac_cv_prog_ac_ct_OTOOL="$ac_ct_OTOOL" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_OTOOL="otool" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi ac_ct_OTOOL=$ac_cv_prog_ac_ct_OTOOL if test -n "$ac_ct_OTOOL"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $ac_ct_OTOOL" >&5 $as_echo "$ac_ct_OTOOL" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi if test "x$ac_ct_OTOOL" = x; then OTOOL=":" else case $cross_compiling:$ac_tool_warned in yes:) { $as_echo "$as_me:${as_lineno-$LINENO}: WARNING: using cross tools not prefixed with host triplet" >&5 $as_echo "$as_me: WARNING: using cross tools not prefixed with host triplet" >&2;} ac_tool_warned=yes ;; esac OTOOL=$ac_ct_OTOOL fi else OTOOL="$ac_cv_prog_OTOOL" fi if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}otool64", so it can be a program name with args. set dummy ${ac_tool_prefix}otool64; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_OTOOL64+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$OTOOL64"; then ac_cv_prog_OTOOL64="$OTOOL64" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_OTOOL64="${ac_tool_prefix}otool64" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi OTOOL64=$ac_cv_prog_OTOOL64 if test -n "$OTOOL64"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $OTOOL64" >&5 $as_echo "$OTOOL64" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi fi if test -z "$ac_cv_prog_OTOOL64"; then ac_ct_OTOOL64=$OTOOL64 # Extract the first word of "otool64", so it can be a program name with args. set dummy otool64; ac_word=$2 { $as_echo "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 $as_echo_n "checking for $ac_word... " >&6; } if ${ac_cv_prog_ac_ct_OTOOL64+:} false; then : $as_echo_n "(cached) " >&6 else if test -n "$ac_ct_OTOOL64"; then ac_cv_prog_ac_ct_OTOOL64="$ac_ct_OTOOL64" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_OTOOL64="otool64" $as_echo "$as_me:${as_lineno-$LINENO}: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi fi ac_ct_OTOOL64=$ac_cv_prog_ac_ct_OTOOL64 if test -n "$ac_ct_OTOOL64"; then { $as_echo "$as_me:${as_lineno-$LINENO}: result: $ac_ct_OTOOL64" >&5 $as_echo "$ac_ct_OTOOL64" >&6; } else { $as_echo "$as_me:${as_lineno-$LINENO}: result: no" >&5 $as_echo "no" >&6; } fi if test "x$ac_ct_OTOOL64" = x; then OTOOL64=":" else case $cross_compiling:$ac_tool_warned in yes:) { $as_echo "$as_me:${as_lineno-$LINENO}: WARNING: using cross tools not prefixed with host triplet" >&5 $as_echo "$as_me: WARNING: using cross tools not prefixed with host triplet" >&2;} ac_tool_warned=yes ;; esac OTOOL64=$ac_ct_OTOOL64 fi else OTOOL64="$ac_cv_prog_OTOOL64" fi { $as_echo "$as_me:${as_lineno-$LINENO}: checking for -single_module linker flag" >&5 $as_echo_n "checking for -single_module linker flag... " >&6; } if ${lt_cv_apple_cc_single_mod+:} false; then : $as_echo_n "(cached) " >&6 else lt_cv_apple_cc_single_mod=no if test -z "${LT_MULTI_MODULE}"; then # By default we will add the -single_module flag. You can override # by either setting the environment variable LT_MULTI_MODULE # non-empty at configure time, or by adding -multi_module to the # link flags. rm -rf libconftest.dylib* echo "int foo(void){return 1;}" > conftest.c echo "$LTCC $LTCFLAGS $LDFLAGS -o libconftest.dylib \ -dynamiclib -Wl,-single_module conftest.c" >&5 $LTCC $LTCFLAGS $LDFLAGS -o libconftest.dylib \ -dynamiclib -Wl,-single_module conftest.c 2>conftest.err _lt_result=$? # If there is a non-empty error log, and "single_module" # appears in it, assume the flag caused a linker warning if test -s conftest.err && $GREP single_module conftest.err; then cat conftest.err >&5 # Otherwise, if the output was created with a 0 exit code from # the compiler, it worked. elif test -f libconftest.dylib && test $_lt_result -eq 0; then lt_cv_apple_cc_single_mod=yes else cat conftest.err >&5 fi rm -rf libconftest.dylib* rm -f conftest.* fi fi { $as_echo "$as_me:${as_lineno-$LINENO}: result: $lt_cv_apple_cc_single_mod" >&5 $as_echo "$lt_cv_apple_cc_single_mod" >&6; } { $as_echo "$as_me:${as_lineno-$LINENO}: checking for -exported_symbols_list linker flag" >&5 $as_echo_n "checking for -exported_symbols_list linker flag... 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Therefore, libtool *** is disabling shared libraries support. We urge you to upgrade GNU *** binutils to release 2.9.1 or newer. Another option is to modify *** your PATH or compiler configuration so that the native linker is *** used, and then restart. _LT_EOF elif $LD --help 2>&1 | $GREP ': supported targets:.* elf' > /dev/null; then archive_cmds='$CC -shared $pic_flag $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' archive_expsym_cmds='$CC -shared $pic_flag $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname ${wl}-retain-symbols-file $wl$export_symbols -o $lib' else ld_shlibs=no fi ;; sysv5* | sco3.2v5* | sco5v6* | unixware* | OpenUNIX*) case `$LD -v 2>&1` in *\ [01].* | *\ 2.[0-9].* | *\ 2.1[0-5].*) ld_shlibs=no cat <<_LT_EOF 1>&2 *** Warning: Releases of the GNU linker prior to 2.16.91.0.3 can not *** reliably create shared libraries on SCO systems. Therefore, libtool *** is disabling shared libraries support. We urge you to upgrade GNU *** binutils to release 2.16.91.0.3 or newer. Another option is to modify *** your PATH or compiler configuration so that the native linker is *** used, and then restart. _LT_EOF ;; *) # For security reasons, it is highly recommended that you always # use absolute paths for naming shared libraries, and exclude the # DT_RUNPATH tag from executables and libraries. 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then lt_cv_aix_libpath_=`dump -HX64 conftest$ac_exeext 2>/dev/null | $SED -n -e "$lt_aix_libpath_sed"` fi fi rm -f core conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext if test -z "$lt_cv_aix_libpath_"; then lt_cv_aix_libpath_="/usr/lib:/lib" fi fi aix_libpath=$lt_cv_aix_libpath_ fi hardcode_libdir_flag_spec='${wl}-blibpath:$libdir:'"$aix_libpath" archive_expsym_cmds='$CC -o $output_objdir/$soname $libobjs $deplibs '"\${wl}$no_entry_flag"' $compiler_flags `if test "x${allow_undefined_flag}" != "x"; then func_echo_all "${wl}${allow_undefined_flag}"; else :; fi` '"\${wl}$exp_sym_flag:\$export_symbols $shared_flag" else if test "$host_cpu" = ia64; then hardcode_libdir_flag_spec='${wl}-R $libdir:/usr/lib:/lib' allow_undefined_flag="-z nodefs" archive_expsym_cmds="\$CC $shared_flag"' -o $output_objdir/$soname $libobjs $deplibs '"\${wl}$no_entry_flag"' $compiler_flags ${wl}${allow_undefined_flag} '"\${wl}$exp_sym_flag:\$export_symbols" else # Determine the default libpath from the value encoded in an # empty executable. if test "${lt_cv_aix_libpath+set}" = set; 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then # We only use this code for GNU lds that support --whole-archive. whole_archive_flag_spec='${wl}--whole-archive$convenience ${wl}--no-whole-archive' else # Exported symbols can be pulled into shared objects from archives whole_archive_flag_spec='$convenience' fi archive_cmds_need_lc=yes # This is similar to how AIX traditionally builds its shared libraries. archive_expsym_cmds="\$CC $shared_flag"' -o $output_objdir/$soname $libobjs $deplibs ${wl}-bnoentry $compiler_flags ${wl}-bE:$export_symbols${allow_undefined_flag}~$AR $AR_FLAGS $output_objdir/$libname$release.a $output_objdir/$soname' fi fi ;; amigaos*) case $host_cpu in powerpc) # see comment about AmigaOS4 .so support archive_cmds='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' archive_expsym_cmds='' ;; m68k) archive_cmds='$RM $output_objdir/a2ixlibrary.data~$ECHO "#define NAME $libname" > $output_objdir/a2ixlibrary.data~$ECHO "#define LIBRARY_ID 1" >> $output_objdir/a2ixlibrary.data~$ECHO "#define VERSION $major" >> $output_objdir/a2ixlibrary.data~$ECHO "#define REVISION $revision" >> $output_objdir/a2ixlibrary.data~$AR $AR_FLAGS $lib $libobjs~$RANLIB $lib~(cd $output_objdir && a2ixlibrary -32)' hardcode_libdir_flag_spec='-L$libdir' hardcode_minus_L=yes ;; esac ;; bsdi[45]*) export_dynamic_flag_spec=-rdynamic ;; cygwin* | mingw* | pw32* | cegcc*) # When not using gcc, we currently assume that we are using # Microsoft Visual C++. # hardcode_libdir_flag_spec is actually meaningless, as there is # no search path for DLLs. case $cc_basename in cl*) # Native MSVC hardcode_libdir_flag_spec=' ' allow_undefined_flag=unsupported always_export_symbols=yes file_list_spec='@' # Tell ltmain to make .lib files, not .a files. libext=lib # Tell ltmain to make .dll files, not .so files. shrext_cmds=".dll" # FIXME: Setting linknames here is a bad hack. archive_cmds='$CC -o $output_objdir/$soname $libobjs $compiler_flags $deplibs -Wl,-dll~linknames=' archive_expsym_cmds='if test "x`$SED 1q $export_symbols`" = xEXPORTS; then sed -n -e 's/\\\\\\\(.*\\\\\\\)/-link\\\ -EXPORT:\\\\\\\1/' -e '1\\\!p' < $export_symbols > $output_objdir/$soname.exp; else sed -e 's/\\\\\\\(.*\\\\\\\)/-link\\\ -EXPORT:\\\\\\\1/' < $export_symbols > $output_objdir/$soname.exp; fi~ $CC -o $tool_output_objdir$soname $libobjs $compiler_flags $deplibs "@$tool_output_objdir$soname.exp" -Wl,-DLL,-IMPLIB:"$tool_output_objdir$libname.dll.lib"~ linknames=' # The linker will not automatically build a static lib if we build a DLL. # _LT_TAGVAR(old_archive_from_new_cmds, )='true' enable_shared_with_static_runtimes=yes exclude_expsyms='_NULL_IMPORT_DESCRIPTOR|_IMPORT_DESCRIPTOR_.*' export_symbols_cmds='$NM $libobjs $convenience | $global_symbol_pipe | $SED -e '\''/^[BCDGRS][ ]/s/.*[ ]\([^ ]*\)/\1,DATA/'\'' | $SED -e '\''/^[AITW][ ]/s/.*[ ]//'\'' | sort | uniq > $export_symbols' # Don't use ranlib old_postinstall_cmds='chmod 644 $oldlib' postlink_cmds='lt_outputfile="@OUTPUT@"~ lt_tool_outputfile="@TOOL_OUTPUT@"~ case $lt_outputfile in *.exe|*.EXE) ;; *) lt_outputfile="$lt_outputfile.exe" lt_tool_outputfile="$lt_tool_outputfile.exe" ;; esac~ if test "$MANIFEST_TOOL" != ":" && test -f "$lt_outputfile.manifest"; then $MANIFEST_TOOL -manifest "$lt_tool_outputfile.manifest" -outputresource:"$lt_tool_outputfile" || exit 1; $RM "$lt_outputfile.manifest"; fi' ;; *) # Assume MSVC wrapper hardcode_libdir_flag_spec=' ' allow_undefined_flag=unsupported # Tell ltmain to make .lib files, not .a files. libext=lib # Tell ltmain to make .dll files, not .so files. shrext_cmds=".dll" # FIXME: Setting linknames here is a bad hack. archive_cmds='$CC -o $lib $libobjs $compiler_flags `func_echo_all "$deplibs" | $SED '\''s/ -lc$//'\''` -link -dll~linknames=' # The linker will automatically build a .lib file if we build a DLL. old_archive_from_new_cmds='true' # FIXME: Should let the user specify the lib program. old_archive_cmds='lib -OUT:$oldlib$oldobjs$old_deplibs' enable_shared_with_static_runtimes=yes ;; esac ;; darwin* | rhapsody*) archive_cmds_need_lc=no hardcode_direct=no hardcode_automatic=yes hardcode_shlibpath_var=unsupported if test "$lt_cv_ld_force_load" = "yes"; then whole_archive_flag_spec='`for conv in $convenience\"\"; do test -n \"$conv\" && new_convenience=\"$new_convenience ${wl}-force_load,$conv\"; done; func_echo_all \"$new_convenience\"`' else whole_archive_flag_spec='' fi link_all_deplibs=yes allow_undefined_flag="$_lt_dar_allow_undefined" case $cc_basename in ifort*) _lt_dar_can_shared=yes ;; *) _lt_dar_can_shared=$GCC ;; esac if test "$_lt_dar_can_shared" = "yes"; then output_verbose_link_cmd=func_echo_all archive_cmds="\$CC -dynamiclib \$allow_undefined_flag -o \$lib \$libobjs \$deplibs \$compiler_flags -install_name \$rpath/\$soname \$verstring $_lt_dar_single_mod${_lt_dsymutil}" module_cmds="\$CC \$allow_undefined_flag -o \$lib -bundle \$libobjs \$deplibs \$compiler_flags${_lt_dsymutil}" archive_expsym_cmds="sed 's,^,_,' < \$export_symbols > \$output_objdir/\${libname}-symbols.expsym~\$CC -dynamiclib \$allow_undefined_flag -o \$lib \$libobjs \$deplibs \$compiler_flags -install_name \$rpath/\$soname \$verstring ${_lt_dar_single_mod}${_lt_dar_export_syms}${_lt_dsymutil}" module_expsym_cmds="sed -e 's,^,_,' < \$export_symbols > \$output_objdir/\${libname}-symbols.expsym~\$CC \$allow_undefined_flag -o \$lib -bundle \$libobjs \$deplibs \$compiler_flags${_lt_dar_export_syms}${_lt_dsymutil}" else ld_shlibs=no fi ;; dgux*) archive_cmds='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_libdir_flag_spec='-L$libdir' hardcode_shlibpath_var=no ;; # FreeBSD 2.2.[012] allows us to include c++rt0.o to get C++ constructor # support. Future versions do this automatically, but an explicit c++rt0.o # does not break anything, and helps significantly (at the cost of a little # extra space). freebsd2.2*) archive_cmds='$LD -Bshareable -o $lib $libobjs $deplibs $linker_flags /usr/lib/c++rt0.o' hardcode_libdir_flag_spec='-R$libdir' hardcode_direct=yes hardcode_shlibpath_var=no ;; # Unfortunately, older versions of FreeBSD 2 do not have this feature. freebsd2.*) archive_cmds='$LD -Bshareable -o $lib $libobjs $deplibs $linker_flags' hardcode_direct=yes hardcode_minus_L=yes hardcode_shlibpath_var=no ;; # FreeBSD 3 and greater uses gcc -shared to do shared libraries. freebsd* | dragonfly*) archive_cmds='$CC -shared $pic_flag -o $lib $libobjs $deplibs $compiler_flags' hardcode_libdir_flag_spec='-R$libdir' hardcode_direct=yes hardcode_shlibpath_var=no ;; hpux9*) if test "$GCC" = yes; then archive_cmds='$RM $output_objdir/$soname~$CC -shared $pic_flag ${wl}+b ${wl}$install_libdir -o $output_objdir/$soname $libobjs $deplibs $compiler_flags~test $output_objdir/$soname = $lib || mv $output_objdir/$soname $lib' else archive_cmds='$RM $output_objdir/$soname~$LD -b +b $install_libdir -o $output_objdir/$soname $libobjs $deplibs $linker_flags~test $output_objdir/$soname = $lib || mv $output_objdir/$soname $lib' fi hardcode_libdir_flag_spec='${wl}+b ${wl}$libdir' hardcode_libdir_separator=: hardcode_direct=yes # hardcode_minus_L: Not really in the search PATH, # but as the default location of the library. hardcode_minus_L=yes export_dynamic_flag_spec='${wl}-E' ;; hpux10*) if test "$GCC" = yes && test "$with_gnu_ld" = no; then archive_cmds='$CC -shared $pic_flag ${wl}+h ${wl}$soname ${wl}+b ${wl}$install_libdir -o $lib $libobjs $deplibs $compiler_flags' else archive_cmds='$LD -b +h $soname +b $install_libdir -o $lib $libobjs $deplibs $linker_flags' fi if test "$with_gnu_ld" = no; then hardcode_libdir_flag_spec='${wl}+b ${wl}$libdir' hardcode_libdir_separator=: hardcode_direct=yes hardcode_direct_absolute=yes export_dynamic_flag_spec='${wl}-E' # hardcode_minus_L: Not really in the search PATH, # but as the default location of the library. hardcode_minus_L=yes fi ;; hpux11*) if test "$GCC" = yes && test "$with_gnu_ld" = no; then case $host_cpu in hppa*64*) archive_cmds='$CC -shared ${wl}+h ${wl}$soname -o $lib $libobjs $deplibs $compiler_flags' ;; ia64*) archive_cmds='$CC -shared $pic_flag ${wl}+h ${wl}$soname ${wl}+nodefaultrpath -o $lib $libobjs $deplibs $compiler_flags' ;; *) archive_cmds='$CC -shared $pic_flag ${wl}+h ${wl}$soname ${wl}+b ${wl}$install_libdir -o $lib $libobjs $deplibs $compiler_flags' ;; esac else case $host_cpu in hppa*64*) archive_cmds='$CC -b ${wl}+h ${wl}$soname -o $lib $libobjs $deplibs $compiler_flags' ;; ia64*) archive_cmds='$CC -b ${wl}+h ${wl}$soname ${wl}+nodefaultrpath -o $lib $libobjs $deplibs $compiler_flags' ;; *) # Older versions of the 11.00 compiler do not understand -b yet # (HP92453-01 A.11.01.20 doesn't, HP92453-01 B.11.X.35175-35176.GP does) { $as_echo "$as_me:${as_lineno-$LINENO}: checking if $CC understands -b" >&5 $as_echo_n "checking if $CC understands -b... " >&6; } if ${lt_cv_prog_compiler__b+:} false; then : $as_echo_n "(cached) " >&6 else lt_cv_prog_compiler__b=no save_LDFLAGS="$LDFLAGS" LDFLAGS="$LDFLAGS -b" echo "$lt_simple_link_test_code" > conftest.$ac_ext if (eval $ac_link 2>conftest.err) && test -s conftest$ac_exeext; then # The linker can only warn and ignore the option if not recognized # So say no if there are warnings if test -s conftest.err; then # Append any errors to the config.log. cat conftest.err 1>&5 $ECHO "$_lt_linker_boilerplate" | $SED '/^$/d' > conftest.exp $SED '/^$/d; /^ *+/d' conftest.err >conftest.er2 if diff conftest.exp conftest.er2 >/dev/null; then lt_cv_prog_compiler__b=yes fi else lt_cv_prog_compiler__b=yes fi fi $RM -r conftest* LDFLAGS="$save_LDFLAGS" fi { $as_echo "$as_me:${as_lineno-$LINENO}: result: $lt_cv_prog_compiler__b" >&5 $as_echo "$lt_cv_prog_compiler__b" >&6; } if test x"$lt_cv_prog_compiler__b" = xyes; then archive_cmds='$CC -b ${wl}+h ${wl}$soname ${wl}+b ${wl}$install_libdir -o $lib $libobjs $deplibs $compiler_flags' else archive_cmds='$LD -b +h $soname +b $install_libdir -o $lib $libobjs $deplibs $linker_flags' fi ;; esac fi if test "$with_gnu_ld" = no; then hardcode_libdir_flag_spec='${wl}+b ${wl}$libdir' hardcode_libdir_separator=: case $host_cpu in hppa*64*|ia64*) hardcode_direct=no hardcode_shlibpath_var=no ;; *) hardcode_direct=yes hardcode_direct_absolute=yes export_dynamic_flag_spec='${wl}-E' # hardcode_minus_L: Not really in the search PATH, # but as the default location of the library. hardcode_minus_L=yes ;; esac fi ;; irix5* | irix6* | nonstopux*) if test "$GCC" = yes; then archive_cmds='$CC -shared $pic_flag $libobjs $deplibs $compiler_flags ${wl}-soname ${wl}$soname `test -n "$verstring" && func_echo_all "${wl}-set_version ${wl}$verstring"` ${wl}-update_registry ${wl}${output_objdir}/so_locations -o $lib' # Try to use the -exported_symbol ld option, if it does not # work, assume that -exports_file does not work either and # implicitly export all symbols. # This should be the same for all languages, so no per-tag cache variable. { $as_echo "$as_me:${as_lineno-$LINENO}: checking whether the $host_os linker accepts -exported_symbol" >&5 $as_echo_n "checking whether the $host_os linker accepts -exported_symbol... " >&6; } if ${lt_cv_irix_exported_symbol+:} false; then : $as_echo_n "(cached) " >&6 else save_LDFLAGS="$LDFLAGS" LDFLAGS="$LDFLAGS -shared ${wl}-exported_symbol ${wl}foo ${wl}-update_registry ${wl}/dev/null" cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ int foo (void) { return 0; } _ACEOF if ac_fn_c_try_link "$LINENO"; then : lt_cv_irix_exported_symbol=yes else lt_cv_irix_exported_symbol=no fi rm -f core conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext LDFLAGS="$save_LDFLAGS" fi { $as_echo "$as_me:${as_lineno-$LINENO}: result: $lt_cv_irix_exported_symbol" >&5 $as_echo "$lt_cv_irix_exported_symbol" >&6; } if test "$lt_cv_irix_exported_symbol" = yes; then archive_expsym_cmds='$CC -shared $pic_flag $libobjs $deplibs $compiler_flags ${wl}-soname ${wl}$soname `test -n "$verstring" && func_echo_all "${wl}-set_version ${wl}$verstring"` ${wl}-update_registry ${wl}${output_objdir}/so_locations ${wl}-exports_file ${wl}$export_symbols -o $lib' fi else archive_cmds='$CC -shared $libobjs $deplibs $compiler_flags -soname $soname `test -n "$verstring" && func_echo_all "-set_version $verstring"` -update_registry ${output_objdir}/so_locations -o $lib' archive_expsym_cmds='$CC -shared $libobjs $deplibs $compiler_flags -soname $soname `test -n "$verstring" && func_echo_all "-set_version $verstring"` -update_registry ${output_objdir}/so_locations -exports_file $export_symbols -o $lib' fi archive_cmds_need_lc='no' hardcode_libdir_flag_spec='${wl}-rpath ${wl}$libdir' hardcode_libdir_separator=: inherit_rpath=yes link_all_deplibs=yes ;; netbsd*) if echo __ELF__ | $CC -E - | $GREP __ELF__ >/dev/null; then archive_cmds='$LD -Bshareable -o $lib $libobjs $deplibs $linker_flags' # a.out else archive_cmds='$LD -shared -o $lib $libobjs $deplibs $linker_flags' # ELF fi hardcode_libdir_flag_spec='-R$libdir' hardcode_direct=yes hardcode_shlibpath_var=no ;; newsos6) archive_cmds='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_direct=yes hardcode_libdir_flag_spec='${wl}-rpath ${wl}$libdir' hardcode_libdir_separator=: hardcode_shlibpath_var=no ;; *nto* | *qnx*) ;; openbsd*) if test -f /usr/libexec/ld.so; then hardcode_direct=yes hardcode_shlibpath_var=no hardcode_direct_absolute=yes if test -z "`echo __ELF__ | $CC -E - | $GREP __ELF__`" || test "$host_os-$host_cpu" = "openbsd2.8-powerpc"; then archive_cmds='$CC -shared $pic_flag -o $lib $libobjs $deplibs $compiler_flags' archive_expsym_cmds='$CC -shared $pic_flag -o $lib $libobjs $deplibs $compiler_flags ${wl}-retain-symbols-file,$export_symbols' hardcode_libdir_flag_spec='${wl}-rpath,$libdir' export_dynamic_flag_spec='${wl}-E' else case $host_os in openbsd[01].* | openbsd2.[0-7] | openbsd2.[0-7].*) archive_cmds='$LD -Bshareable -o $lib $libobjs $deplibs $linker_flags' hardcode_libdir_flag_spec='-R$libdir' ;; *) archive_cmds='$CC -shared $pic_flag -o $lib $libobjs $deplibs $compiler_flags' hardcode_libdir_flag_spec='${wl}-rpath,$libdir' ;; esac fi else ld_shlibs=no fi ;; os2*) hardcode_libdir_flag_spec='-L$libdir' hardcode_minus_L=yes allow_undefined_flag=unsupported archive_cmds='$ECHO "LIBRARY $libname INITINSTANCE" > $output_objdir/$libname.def~$ECHO "DESCRIPTION \"$libname\"" >> $output_objdir/$libname.def~echo DATA >> $output_objdir/$libname.def~echo " SINGLE NONSHARED" >> $output_objdir/$libname.def~echo EXPORTS >> $output_objdir/$libname.def~emxexp $libobjs >> $output_objdir/$libname.def~$CC -Zdll -Zcrtdll -o $lib $libobjs $deplibs $compiler_flags $output_objdir/$libname.def' old_archive_from_new_cmds='emximp -o $output_objdir/$libname.a $output_objdir/$libname.def' ;; osf3*) if test "$GCC" = yes; then allow_undefined_flag=' ${wl}-expect_unresolved ${wl}\*' archive_cmds='$CC -shared${allow_undefined_flag} $libobjs $deplibs $compiler_flags ${wl}-soname ${wl}$soname `test -n "$verstring" && func_echo_all "${wl}-set_version ${wl}$verstring"` ${wl}-update_registry ${wl}${output_objdir}/so_locations -o $lib' else allow_undefined_flag=' -expect_unresolved \*' archive_cmds='$CC -shared${allow_undefined_flag} $libobjs $deplibs $compiler_flags -soname $soname `test -n "$verstring" && func_echo_all "-set_version $verstring"` -update_registry ${output_objdir}/so_locations -o $lib' fi archive_cmds_need_lc='no' hardcode_libdir_flag_spec='${wl}-rpath ${wl}$libdir' hardcode_libdir_separator=: ;; osf4* | osf5*) # as osf3* with the addition of -msym flag if test "$GCC" = yes; then allow_undefined_flag=' ${wl}-expect_unresolved ${wl}\*' archive_cmds='$CC -shared${allow_undefined_flag} $pic_flag $libobjs $deplibs $compiler_flags ${wl}-msym ${wl}-soname ${wl}$soname `test -n "$verstring" && func_echo_all "${wl}-set_version ${wl}$verstring"` ${wl}-update_registry ${wl}${output_objdir}/so_locations -o $lib' hardcode_libdir_flag_spec='${wl}-rpath ${wl}$libdir' else allow_undefined_flag=' -expect_unresolved \*' archive_cmds='$CC -shared${allow_undefined_flag} $libobjs $deplibs $compiler_flags -msym -soname $soname `test -n "$verstring" && func_echo_all "-set_version $verstring"` -update_registry ${output_objdir}/so_locations -o $lib' archive_expsym_cmds='for i in `cat $export_symbols`; do printf "%s %s\\n" -exported_symbol "\$i" >> $lib.exp; done; printf "%s\\n" "-hidden">> $lib.exp~ $CC -shared${allow_undefined_flag} ${wl}-input ${wl}$lib.exp $compiler_flags $libobjs $deplibs -soname $soname `test -n "$verstring" && $ECHO "-set_version $verstring"` -update_registry ${output_objdir}/so_locations -o $lib~$RM $lib.exp' # Both c and cxx compiler support -rpath directly hardcode_libdir_flag_spec='-rpath $libdir' fi archive_cmds_need_lc='no' hardcode_libdir_separator=: ;; solaris*) no_undefined_flag=' -z defs' if test "$GCC" = yes; then wlarc='${wl}' archive_cmds='$CC -shared $pic_flag ${wl}-z ${wl}text ${wl}-h ${wl}$soname -o $lib $libobjs $deplibs $compiler_flags' archive_expsym_cmds='echo "{ global:" > $lib.exp~cat $export_symbols | $SED -e "s/\(.*\)/\1;/" >> $lib.exp~echo "local: *; };" >> $lib.exp~ $CC -shared $pic_flag ${wl}-z ${wl}text ${wl}-M ${wl}$lib.exp ${wl}-h ${wl}$soname -o $lib $libobjs $deplibs $compiler_flags~$RM $lib.exp' else case `$CC -V 2>&1` in *"Compilers 5.0"*) wlarc='' archive_cmds='$LD -G${allow_undefined_flag} -h $soname -o $lib $libobjs $deplibs $linker_flags' archive_expsym_cmds='echo "{ global:" > $lib.exp~cat $export_symbols | $SED -e "s/\(.*\)/\1;/" >> $lib.exp~echo "local: *; };" >> $lib.exp~ $LD -G${allow_undefined_flag} -M $lib.exp -h $soname -o $lib $libobjs $deplibs $linker_flags~$RM $lib.exp' ;; *) wlarc='${wl}' archive_cmds='$CC -G${allow_undefined_flag} -h $soname -o $lib $libobjs $deplibs $compiler_flags' archive_expsym_cmds='echo "{ global:" > $lib.exp~cat $export_symbols | $SED -e "s/\(.*\)/\1;/" >> $lib.exp~echo "local: *; };" >> $lib.exp~ $CC -G${allow_undefined_flag} -M $lib.exp -h $soname -o $lib $libobjs $deplibs $compiler_flags~$RM $lib.exp' ;; esac fi hardcode_libdir_flag_spec='-R$libdir' hardcode_shlibpath_var=no case $host_os in solaris2.[0-5] | solaris2.[0-5].*) ;; *) # The compiler driver will combine and reorder linker options, # but understands `-z linker_flag'. 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then sys_lib_search_path_spec="/usr/lib/hpux32 /usr/local/lib/hpux32 /usr/local/lib" else sys_lib_search_path_spec="/usr/lib/hpux64 /usr/local/lib/hpux64" fi sys_lib_dlsearch_path_spec=$sys_lib_search_path_spec ;; hppa*64*) shrext_cmds='.sl' hardcode_into_libs=yes dynamic_linker="$host_os dld.sl" shlibpath_var=LD_LIBRARY_PATH # How should we handle SHLIB_PATH shlibpath_overrides_runpath=yes # Unless +noenvvar is specified. library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' sys_lib_search_path_spec="/usr/lib/pa20_64 /usr/ccs/lib/pa20_64" sys_lib_dlsearch_path_spec=$sys_lib_search_path_spec ;; *) shrext_cmds='.sl' dynamic_linker="$host_os dld.sl" shlibpath_var=SHLIB_PATH shlibpath_overrides_runpath=no # +s is required to enable SHLIB_PATH library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' ;; esac # HP-UX runs *really* slowly unless shared libraries are mode 555, ... postinstall_cmds='chmod 555 $lib' # or fails outright, so override atomically: install_override_mode=555 ;; interix[3-9]*) version_type=linux # correct to gnu/linux during the next big refactor need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major ${libname}${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' dynamic_linker='Interix 3.x ld.so.1 (PE, like ELF)' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=no hardcode_into_libs=yes ;; irix5* | irix6* | nonstopux*) case $host_os in nonstopux*) version_type=nonstopux ;; *) if test "$lt_cv_prog_gnu_ld" = yes; then version_type=linux # correct to gnu/linux during the next big refactor else version_type=irix fi ;; esac need_lib_prefix=no need_version=no soname_spec='${libname}${release}${shared_ext}$major' library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major ${libname}${release}${shared_ext} $libname${shared_ext}' case $host_os in irix5* | nonstopux*) libsuff= shlibsuff= ;; *) case $LD in # libtool.m4 will add one of these switches to LD *-32|*"-32 "|*-melf32bsmip|*"-melf32bsmip ") libsuff= shlibsuff= libmagic=32-bit;; *-n32|*"-n32 "|*-melf32bmipn32|*"-melf32bmipn32 ") libsuff=32 shlibsuff=N32 libmagic=N32;; *-64|*"-64 "|*-melf64bmip|*"-melf64bmip ") libsuff=64 shlibsuff=64 libmagic=64-bit;; *) libsuff= shlibsuff= libmagic=never-match;; esac ;; esac shlibpath_var=LD_LIBRARY${shlibsuff}_PATH shlibpath_overrides_runpath=no sys_lib_search_path_spec="/usr/lib${libsuff} /lib${libsuff} /usr/local/lib${libsuff}" sys_lib_dlsearch_path_spec="/usr/lib${libsuff} /lib${libsuff}" hardcode_into_libs=yes ;; # No shared lib support for Linux oldld, aout, or coff. linux*oldld* | linux*aout* | linux*coff*) dynamic_linker=no ;; # This must be glibc/ELF. linux* | k*bsd*-gnu | kopensolaris*-gnu) version_type=linux # correct to gnu/linux during the next big refactor need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' finish_cmds='PATH="\$PATH:/sbin" ldconfig -n $libdir' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=no # Some binutils ld are patched to set DT_RUNPATH if ${lt_cv_shlibpath_overrides_runpath+:} false; then : $as_echo_n "(cached) " >&6 else lt_cv_shlibpath_overrides_runpath=no save_LDFLAGS=$LDFLAGS save_libdir=$libdir eval "libdir=/foo; wl=\"$lt_prog_compiler_wl\"; \ LDFLAGS=\"\$LDFLAGS $hardcode_libdir_flag_spec\"" cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ int main () { ; return 0; } _ACEOF if ac_fn_c_try_link "$LINENO"; then : if ($OBJDUMP -p conftest$ac_exeext) 2>/dev/null | grep "RUNPATH.*$libdir" >/dev/null; then : lt_cv_shlibpath_overrides_runpath=yes fi fi rm -f core conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext LDFLAGS=$save_LDFLAGS libdir=$save_libdir fi shlibpath_overrides_runpath=$lt_cv_shlibpath_overrides_runpath # This implies no fast_install, which is unacceptable. # Some rework will be needed to allow for fast_install # before this can be enabled. hardcode_into_libs=yes # Append ld.so.conf contents to the search path if test -f /etc/ld.so.conf; then lt_ld_extra=`awk '/^include / { system(sprintf("cd /etc; cat %s 2>/dev/null", \$2)); skip = 1; } { if (!skip) print \$0; skip = 0; }' < /etc/ld.so.conf | $SED -e 's/#.*//;/^[ ]*hwcap[ ]/d;s/[:, ]/ /g;s/=[^=]*$//;s/=[^= ]* / /g;s/"//g;/^$/d' | tr '\n' ' '` sys_lib_dlsearch_path_spec="/lib /usr/lib $lt_ld_extra" fi # We used to test for /lib/ld.so.1 and disable shared libraries on # powerpc, because MkLinux only supported shared libraries with the # GNU dynamic linker. Since this was broken with cross compilers, # most powerpc-linux boxes support dynamic linking these days and # people can always --disable-shared, the test was removed, and we # assume the GNU/Linux dynamic linker is in use. dynamic_linker='GNU/Linux ld.so' ;; netbsd*) version_type=sunos need_lib_prefix=no need_version=no if echo __ELF__ | $CC -E - | $GREP __ELF__ >/dev/null; then library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${shared_ext}$versuffix' finish_cmds='PATH="\$PATH:/sbin" ldconfig -m $libdir' dynamic_linker='NetBSD (a.out) ld.so' else library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major ${libname}${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' dynamic_linker='NetBSD ld.elf_so' fi shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=yes hardcode_into_libs=yes ;; newsos6) version_type=linux # correct to gnu/linux during the next big refactor library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=yes ;; *nto* | *qnx*) version_type=qnx need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=no hardcode_into_libs=yes dynamic_linker='ldqnx.so' ;; openbsd*) version_type=sunos sys_lib_dlsearch_path_spec="/usr/lib" need_lib_prefix=no # Some older versions of OpenBSD (3.3 at least) *do* need versioned libs. case $host_os in openbsd3.3 | openbsd3.3.*) need_version=yes ;; *) need_version=no ;; esac library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${shared_ext}$versuffix' finish_cmds='PATH="\$PATH:/sbin" ldconfig -m $libdir' shlibpath_var=LD_LIBRARY_PATH if test -z "`echo __ELF__ | $CC -E - | $GREP __ELF__`" || test "$host_os-$host_cpu" = "openbsd2.8-powerpc"; then case $host_os in openbsd2.[89] | openbsd2.[89].*) shlibpath_overrides_runpath=no ;; *) shlibpath_overrides_runpath=yes ;; esac else shlibpath_overrides_runpath=yes fi ;; os2*) libname_spec='$name' shrext_cmds=".dll" need_lib_prefix=no library_names_spec='$libname${shared_ext} $libname.a' dynamic_linker='OS/2 ld.exe' shlibpath_var=LIBPATH ;; osf3* | osf4* | osf5*) version_type=osf need_lib_prefix=no need_version=no soname_spec='${libname}${release}${shared_ext}$major' library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' shlibpath_var=LD_LIBRARY_PATH sys_lib_search_path_spec="/usr/shlib /usr/ccs/lib /usr/lib/cmplrs/cc /usr/lib /usr/local/lib /var/shlib" sys_lib_dlsearch_path_spec="$sys_lib_search_path_spec" ;; rdos*) dynamic_linker=no ;; solaris*) version_type=linux # correct to gnu/linux during the next big refactor need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=yes hardcode_into_libs=yes # ldd complains unless libraries are executable postinstall_cmds='chmod +x $lib' ;; sunos4*) version_type=sunos library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${shared_ext}$versuffix' finish_cmds='PATH="\$PATH:/usr/etc" ldconfig $libdir' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=yes if test "$with_gnu_ld" = yes; then need_lib_prefix=no fi need_version=yes ;; sysv4 | sysv4.3*) version_type=linux # correct to gnu/linux during the next big refactor library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' shlibpath_var=LD_LIBRARY_PATH case $host_vendor in sni) shlibpath_overrides_runpath=no need_lib_prefix=no runpath_var=LD_RUN_PATH ;; siemens) need_lib_prefix=no ;; motorola) need_lib_prefix=no need_version=no shlibpath_overrides_runpath=no sys_lib_search_path_spec='/lib /usr/lib /usr/ccs/lib' ;; esac ;; sysv4*MP*) if test -d /usr/nec ;then version_type=linux # correct to gnu/linux during the next big refactor library_names_spec='$libname${shared_ext}.$versuffix $libname${shared_ext}.$major $libname${shared_ext}' soname_spec='$libname${shared_ext}.$major' shlibpath_var=LD_LIBRARY_PATH fi ;; sysv5* | sco3.2v5* | sco5v6* | unixware* | OpenUNIX* | sysv4*uw2*) version_type=freebsd-elf need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext} $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=yes hardcode_into_libs=yes if test "$with_gnu_ld" = yes; then sys_lib_search_path_spec='/usr/local/lib /usr/gnu/lib /usr/ccs/lib /usr/lib /lib' else sys_lib_search_path_spec='/usr/ccs/lib /usr/lib' case $host_os in sco3.2v5*) sys_lib_search_path_spec="$sys_lib_search_path_spec /lib" ;; esac fi sys_lib_dlsearch_path_spec='/usr/lib' ;; tpf*) # TPF is a cross-target only. Preferred cross-host = GNU/Linux. version_type=linux # correct to gnu/linux during the next big refactor need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=no hardcode_into_libs=yes ;; uts4*) version_type=linux # correct to gnu/linux during the next big refactor library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' shlibpath_var=LD_LIBRARY_PATH ;; *) dynamic_linker=no ;; esac { $as_echo "$as_me:${as_lineno-$LINENO}: result: $dynamic_linker" >&5 $as_echo "$dynamic_linker" >&6; } test "$dynamic_linker" = no && can_build_shared=no variables_saved_for_relink="PATH $shlibpath_var $runpath_var" if test "$GCC" = yes; then variables_saved_for_relink="$variables_saved_for_relink GCC_EXEC_PREFIX COMPILER_PATH LIBRARY_PATH" fi if test "${lt_cv_sys_lib_search_path_spec+set}" = set; then sys_lib_search_path_spec="$lt_cv_sys_lib_search_path_spec" fi if test "${lt_cv_sys_lib_dlsearch_path_spec+set}" = set; then sys_lib_dlsearch_path_spec="$lt_cv_sys_lib_dlsearch_path_spec" fi { $as_echo "$as_me:${as_lineno-$LINENO}: checking how to hardcode library paths into programs" >&5 $as_echo_n "checking how to hardcode library paths into programs... " >&6; } hardcode_action= if test -n "$hardcode_libdir_flag_spec" || test -n "$runpath_var" || test "X$hardcode_automatic" = "Xyes" ; then # We can hardcode non-existent directories. if test "$hardcode_direct" != no && # If the only mechanism to avoid hardcoding is shlibpath_var, we # have to relink, otherwise we might link with an installed library # when we should be linking with a yet-to-be-installed one ## test "$_LT_TAGVAR(hardcode_shlibpath_var, )" != no && test "$hardcode_minus_L" != no; then # Linking always hardcodes the temporary library directory. hardcode_action=relink else # We can link without hardcoding, and we can hardcode nonexisting dirs. hardcode_action=immediate fi else # We cannot hardcode anything, or else we can only hardcode existing # directories. hardcode_action=unsupported fi { $as_echo "$as_me:${as_lineno-$LINENO}: result: $hardcode_action" >&5 $as_echo "$hardcode_action" >&6; } if test "$hardcode_action" = relink || test "$inherit_rpath" = yes; then # Fast installation is not supported enable_fast_install=no elif test "$shlibpath_overrides_runpath" = yes || test "$enable_shared" = no; then # Fast installation is not necessary enable_fast_install=needless fi if test "x$enable_dlopen" != xyes; then enable_dlopen=unknown enable_dlopen_self=unknown enable_dlopen_self_static=unknown else lt_cv_dlopen=no lt_cv_dlopen_libs= case $host_os in beos*) lt_cv_dlopen="load_add_on" lt_cv_dlopen_libs= lt_cv_dlopen_self=yes ;; mingw* | pw32* | cegcc*) lt_cv_dlopen="LoadLibrary" lt_cv_dlopen_libs= ;; cygwin*) lt_cv_dlopen="dlopen" lt_cv_dlopen_libs= ;; darwin*) # if libdl is installed we need to link against it { $as_echo "$as_me:${as_lineno-$LINENO}: checking for dlopen in -ldl" >&5 $as_echo_n "checking for dlopen in -ldl... " >&6; } if ${ac_cv_lib_dl_dlopen+:} false; then : $as_echo_n "(cached) " >&6 else ac_check_lib_save_LIBS=$LIBS LIBS="-ldl $LIBS" cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ /* Override any GCC internal prototype to avoid an error. Use char because int might match the return type of a GCC builtin and then its argument prototype would still apply. */ #ifdef __cplusplus extern "C" #endif char dlopen (); int main () { return dlopen (); ; return 0; } _ACEOF if ac_fn_c_try_link "$LINENO"; then : ac_cv_lib_dl_dlopen=yes else ac_cv_lib_dl_dlopen=no fi rm -f core conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext LIBS=$ac_check_lib_save_LIBS fi { $as_echo "$as_me:${as_lineno-$LINENO}: result: $ac_cv_lib_dl_dlopen" >&5 $as_echo "$ac_cv_lib_dl_dlopen" >&6; } if test "x$ac_cv_lib_dl_dlopen" = xyes; then : lt_cv_dlopen="dlopen" lt_cv_dlopen_libs="-ldl" else lt_cv_dlopen="dyld" lt_cv_dlopen_libs= lt_cv_dlopen_self=yes fi ;; *) ac_fn_c_check_func "$LINENO" "shl_load" "ac_cv_func_shl_load" if test "x$ac_cv_func_shl_load" = xyes; then : lt_cv_dlopen="shl_load" else { $as_echo "$as_me:${as_lineno-$LINENO}: checking for shl_load in -ldld" >&5 $as_echo_n "checking for shl_load in -ldld... " >&6; } if ${ac_cv_lib_dld_shl_load+:} false; then : $as_echo_n "(cached) " >&6 else ac_check_lib_save_LIBS=$LIBS LIBS="-ldld $LIBS" cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ /* Override any GCC internal prototype to avoid an error. Use char because int might match the return type of a GCC builtin and then its argument prototype would still apply. */ #ifdef __cplusplus extern "C" #endif char shl_load (); int main () { return shl_load (); ; return 0; } _ACEOF if ac_fn_c_try_link "$LINENO"; then : ac_cv_lib_dld_shl_load=yes else ac_cv_lib_dld_shl_load=no fi rm -f core conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext LIBS=$ac_check_lib_save_LIBS fi { $as_echo "$as_me:${as_lineno-$LINENO}: result: $ac_cv_lib_dld_shl_load" >&5 $as_echo "$ac_cv_lib_dld_shl_load" >&6; } if test "x$ac_cv_lib_dld_shl_load" = xyes; then : lt_cv_dlopen="shl_load" lt_cv_dlopen_libs="-ldld" else ac_fn_c_check_func "$LINENO" "dlopen" "ac_cv_func_dlopen" if test "x$ac_cv_func_dlopen" = xyes; then : lt_cv_dlopen="dlopen" else { $as_echo "$as_me:${as_lineno-$LINENO}: checking for dlopen in -ldl" >&5 $as_echo_n "checking for dlopen in -ldl... " >&6; } if ${ac_cv_lib_dl_dlopen+:} false; then : $as_echo_n "(cached) " >&6 else ac_check_lib_save_LIBS=$LIBS LIBS="-ldl $LIBS" cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ /* Override any GCC internal prototype to avoid an error. Use char because int might match the return type of a GCC builtin and then its argument prototype would still apply. */ #ifdef __cplusplus extern "C" #endif char dlopen (); int main () { return dlopen (); ; return 0; } _ACEOF if ac_fn_c_try_link "$LINENO"; then : ac_cv_lib_dl_dlopen=yes else ac_cv_lib_dl_dlopen=no fi rm -f core conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext LIBS=$ac_check_lib_save_LIBS fi { $as_echo "$as_me:${as_lineno-$LINENO}: result: $ac_cv_lib_dl_dlopen" >&5 $as_echo "$ac_cv_lib_dl_dlopen" >&6; } if test "x$ac_cv_lib_dl_dlopen" = xyes; then : lt_cv_dlopen="dlopen" lt_cv_dlopen_libs="-ldl" else { $as_echo "$as_me:${as_lineno-$LINENO}: checking for dlopen in -lsvld" >&5 $as_echo_n "checking for dlopen in -lsvld... " >&6; } if ${ac_cv_lib_svld_dlopen+:} false; then : $as_echo_n "(cached) " >&6 else ac_check_lib_save_LIBS=$LIBS LIBS="-lsvld $LIBS" cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ /* Override any GCC internal prototype to avoid an error. Use char because int might match the return type of a GCC builtin and then its argument prototype would still apply. */ #ifdef __cplusplus extern "C" #endif char dlopen (); int main () { return dlopen (); ; return 0; } _ACEOF if ac_fn_c_try_link "$LINENO"; then : ac_cv_lib_svld_dlopen=yes else ac_cv_lib_svld_dlopen=no fi rm -f core conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext LIBS=$ac_check_lib_save_LIBS fi { $as_echo "$as_me:${as_lineno-$LINENO}: result: $ac_cv_lib_svld_dlopen" >&5 $as_echo "$ac_cv_lib_svld_dlopen" >&6; } if test "x$ac_cv_lib_svld_dlopen" = xyes; then : lt_cv_dlopen="dlopen" lt_cv_dlopen_libs="-lsvld" else { $as_echo "$as_me:${as_lineno-$LINENO}: checking for dld_link in -ldld" >&5 $as_echo_n "checking for dld_link in -ldld... " >&6; } if ${ac_cv_lib_dld_dld_link+:} false; then : $as_echo_n "(cached) " >&6 else ac_check_lib_save_LIBS=$LIBS LIBS="-ldld $LIBS" cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ /* Override any GCC internal prototype to avoid an error. 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then sys_lib_search_path_spec="/usr/lib/hpux32 /usr/local/lib/hpux32 /usr/local/lib" else sys_lib_search_path_spec="/usr/lib/hpux64 /usr/local/lib/hpux64" fi sys_lib_dlsearch_path_spec=$sys_lib_search_path_spec ;; hppa*64*) shrext_cmds='.sl' hardcode_into_libs=yes dynamic_linker="$host_os dld.sl" shlibpath_var=LD_LIBRARY_PATH # How should we handle SHLIB_PATH shlibpath_overrides_runpath=yes # Unless +noenvvar is specified. library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' sys_lib_search_path_spec="/usr/lib/pa20_64 /usr/ccs/lib/pa20_64" sys_lib_dlsearch_path_spec=$sys_lib_search_path_spec ;; *) shrext_cmds='.sl' dynamic_linker="$host_os dld.sl" shlibpath_var=SHLIB_PATH shlibpath_overrides_runpath=no # +s is required to enable SHLIB_PATH library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' ;; esac # HP-UX runs *really* slowly unless shared libraries are mode 555, ... postinstall_cmds='chmod 555 $lib' # or fails outright, so override atomically: install_override_mode=555 ;; interix[3-9]*) version_type=linux # correct to gnu/linux during the next big refactor need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major ${libname}${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' dynamic_linker='Interix 3.x ld.so.1 (PE, like ELF)' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=no hardcode_into_libs=yes ;; irix5* | irix6* | nonstopux*) case $host_os in nonstopux*) version_type=nonstopux ;; *) if test "$lt_cv_prog_gnu_ld" = yes; then version_type=linux # correct to gnu/linux during the next big refactor else version_type=irix fi ;; esac need_lib_prefix=no need_version=no soname_spec='${libname}${release}${shared_ext}$major' library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major ${libname}${release}${shared_ext} $libname${shared_ext}' case $host_os in irix5* | nonstopux*) libsuff= shlibsuff= ;; *) case $LD in # libtool.m4 will add one of these switches to LD *-32|*"-32 "|*-melf32bsmip|*"-melf32bsmip ") libsuff= shlibsuff= libmagic=32-bit;; *-n32|*"-n32 "|*-melf32bmipn32|*"-melf32bmipn32 ") libsuff=32 shlibsuff=N32 libmagic=N32;; *-64|*"-64 "|*-melf64bmip|*"-melf64bmip ") libsuff=64 shlibsuff=64 libmagic=64-bit;; *) libsuff= shlibsuff= libmagic=never-match;; esac ;; esac shlibpath_var=LD_LIBRARY${shlibsuff}_PATH shlibpath_overrides_runpath=no sys_lib_search_path_spec="/usr/lib${libsuff} /lib${libsuff} /usr/local/lib${libsuff}" sys_lib_dlsearch_path_spec="/usr/lib${libsuff} /lib${libsuff}" hardcode_into_libs=yes ;; # No shared lib support for Linux oldld, aout, or coff. linux*oldld* | linux*aout* | linux*coff*) dynamic_linker=no ;; # This must be glibc/ELF. linux* | k*bsd*-gnu | kopensolaris*-gnu) version_type=linux # correct to gnu/linux during the next big refactor need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' finish_cmds='PATH="\$PATH:/sbin" ldconfig -n $libdir' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=no # Some binutils ld are patched to set DT_RUNPATH if ${lt_cv_shlibpath_overrides_runpath+:} false; then : $as_echo_n "(cached) " >&6 else lt_cv_shlibpath_overrides_runpath=no save_LDFLAGS=$LDFLAGS save_libdir=$libdir eval "libdir=/foo; wl=\"$lt_prog_compiler_wl_CXX\"; \ LDFLAGS=\"\$LDFLAGS $hardcode_libdir_flag_spec_CXX\"" cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ int main () { ; return 0; } _ACEOF if ac_fn_cxx_try_link "$LINENO"; then : if ($OBJDUMP -p conftest$ac_exeext) 2>/dev/null | grep "RUNPATH.*$libdir" >/dev/null; then : lt_cv_shlibpath_overrides_runpath=yes fi fi rm -f core conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext LDFLAGS=$save_LDFLAGS libdir=$save_libdir fi shlibpath_overrides_runpath=$lt_cv_shlibpath_overrides_runpath # This implies no fast_install, which is unacceptable. # Some rework will be needed to allow for fast_install # before this can be enabled. hardcode_into_libs=yes # Append ld.so.conf contents to the search path if test -f /etc/ld.so.conf; then lt_ld_extra=`awk '/^include / { system(sprintf("cd /etc; cat %s 2>/dev/null", \$2)); 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esac else shlibpath_overrides_runpath=yes fi ;; os2*) libname_spec='$name' shrext_cmds=".dll" need_lib_prefix=no library_names_spec='$libname${shared_ext} $libname.a' dynamic_linker='OS/2 ld.exe' shlibpath_var=LIBPATH ;; osf3* | osf4* | osf5*) version_type=osf need_lib_prefix=no need_version=no soname_spec='${libname}${release}${shared_ext}$major' library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' shlibpath_var=LD_LIBRARY_PATH sys_lib_search_path_spec="/usr/shlib /usr/ccs/lib /usr/lib/cmplrs/cc /usr/lib /usr/local/lib /var/shlib" sys_lib_dlsearch_path_spec="$sys_lib_search_path_spec" ;; rdos*) dynamic_linker=no ;; solaris*) version_type=linux # correct to gnu/linux during the next big refactor need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=yes hardcode_into_libs=yes # ldd complains unless libraries are executable postinstall_cmds='chmod +x $lib' ;; 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See: A. H. G. Rinnooy Kan and G. T. Timmer, "Stochastic global optimization methods," Mathematical Programming, vol. 39, p. 27-78 (1987). [ actually 2 papers -- part I: clustering methods (p. 27), then part II: multilevel methods (p. 57) ] and also: Sergei Kucherenko and Yury Sytsko, "Application of deterministic low-discrepancy sequences in global optimization," Computational Optimization and Applications, vol. 30, p. 297-318 (2005). It is under the same MIT license as the rest of my code in NLopt (see ../COPYRIGHT). Steven G. Johnson September 2007 nloptr/src/nlopt_src/mlsl/mlsl.c0000644000176000001440000003520013355070635016503 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ /* Multi-Level Single-Linkage (MLSL) algorithm for global optimization by random local optimizations (a multistart algorithm with "clustering" to avoid repeated detection of the same local minimum), modified to optionally use a Sobol' low-discrepancy sequence (LDS) instead of pseudorandom numbers. See: A. H. G. Rinnooy Kan and G. T. Timmer, "Stochastic global optimization methods," Mathematical Programming, vol. 39, p. 27-78 (1987). [ actually 2 papers -- part I: clustering methods (p. 27), then part II: multilevel methods (p. 57) ] and also: Sergei Kucherenko and Yury Sytsko, "Application of deterministic low-discrepancy sequences in global optimization," Computational Optimization and Applications, vol. 30, p. 297-318 (2005). Compared to the above papers, I made a couple other modifications to the algorithm besides the use of a LDS. 1) first, the original algorithm suggests discarding points within a *fixed* distance of the boundaries or of previous local minima; I changed this to a distance that decreases with, iteration, proportional to the same threshold radius used for clustering. (In the case of the boundaries, I make the proportionality constant rather small as well.) 2) Kan and Timmer suggest using fancy "spiral-search" algorithms to quickly locate nearest-neighbor points, reducing the overall time for N sample points from O(N^2) to O(N log N) However, recent papers seem to show that such schemes (kd trees, etcetera) become inefficient for high-dimensional spaces (d > 20), and finding a better approach seems to be an open question. Therefore, since I am mainly interested in MLSL for high-dimensional problems (in low dimensions we have DIRECT etc.), I punted on this question for now. In practice, O(N^2) (which does not count the #points evaluated in local searches) does not seem too bad if the objective function is expensive. */ #include #include #include #include "redblack.h" #include "mlsl.h" /* data structure for each random/quasirandom point in the population */ typedef struct { double f; /* function value at x */ int minimized; /* if we have already minimized starting from x */ double closest_pt_d; /* distance^2 to closest pt with smaller f */ double closest_lm_d; /* distance^2 to closest lm with smaller f*/ double x[1]; /* array of length n (K&R struct hack) */ } pt; /* all of the data used by the various mlsl routines...it's not clear in hindsight that we need to put all of this in a data structure since most of the work occurs in a single routine, but it doesn't hurt us */ typedef struct { int n; /* # dimensions */ const double *lb, *ub; nlopt_stopping *stop; /* stopping criteria */ nlopt_func f; void *f_data; rb_tree pts; /* tree of points (k == pt), sorted by f */ rb_tree lms; /* tree of local minimizers, sorted by function value (k = array of length d+1, [0] = f, [1..d] = x) */ nlopt_sobol s; /* sobol data for LDS point generation, or NULL to use pseudo-random numbers */ double R_prefactor, dlm, dbound, gamma; /* parameters of MLSL */ int N; /* number of pts to add per iteration */ } mlsl_data; /* comparison routines to sort the red-black trees by function value */ static int pt_compare(rb_key p1_, rb_key p2_) { pt *p1 = (pt *) p1_; pt *p2 = (pt *) p2_; if (p1->f < p2->f) return -1; if (p1->f > p2->f) return +1; return 0; } static int lm_compare(double *k1, double *k2) { if (*k1 < *k2) return -1; if (*k1 > *k2) return +1; return 0; } /* Euclidean distance |x1 - x2|^2 */ static double distance2(int n, const double *x1, const double *x2) { int i; double d = 0.; for (i = 0; i < n; ++i) { double dx = x1[i] - x2[i]; d += dx * dx; } return d; } /* find the closest pt to p with a smaller function value; this function is called when p is first added to our tree */ static void find_closest_pt(int n, rb_tree *pts, pt *p) { rb_node *node = rb_tree_find_lt(pts, (rb_key) p); double closest_d = HUGE_VAL; while (node) { double d = distance2(n, p->x, ((pt *) node->k)->x); if (d < closest_d) closest_d = d; node = rb_tree_pred(node); } p->closest_pt_d = closest_d; } /* find the closest local minimizer (lm) to p with a smaller function value; this function is called when p is first added to our tree */ static void find_closest_lm(int n, rb_tree *lms, pt *p) { rb_node *node = rb_tree_find_lt(lms, &p->f); double closest_d = HUGE_VAL; while (node) { double d = distance2(n, p->x, node->k+1); if (d < closest_d) closest_d = d; node = rb_tree_pred(node); } p->closest_lm_d = closest_d; } /* given newpt, which presumably has just been added to our tree, update all pts with a greater function value in case newpt is closer to them than their previous closest_pt ... we can ignore already-minimized points since we never do local minimization from the same point twice */ static void pts_update_newpt(int n, rb_tree *pts, pt *newpt) { rb_node *node = rb_tree_find_gt(pts, (rb_key) newpt); while (node) { pt *p = (pt *) node->k; if (!p->minimized) { double d = distance2(n, newpt->x, p->x); if (d < p->closest_pt_d) p->closest_pt_d = d; } node = rb_tree_succ(node); } } /* given newlm, which presumably has just been added to our tree, update all pts with a greater function value in case newlm is closer to them than their previous closest_lm ... we can ignore already-minimized points since we never do local minimization from the same point twice */ static void pts_update_newlm(int n, rb_tree *pts, double *newlm) { pt tmp_pt; rb_node *node; tmp_pt.f = newlm[0]; node = rb_tree_find_gt(pts, (rb_key) &tmp_pt); while (node) { pt *p = (pt *) node->k; if (!p->minimized) { double d = distance2(n, newlm+1, p->x); if (d < p->closest_lm_d) p->closest_lm_d = d; } node = rb_tree_succ(node); } } static int is_potential_minimizer(mlsl_data *mlsl, pt *p, double dpt_min, double dlm_min, double dbound_min) { int i, n = mlsl->n; const double *lb = mlsl->lb; const double *ub = mlsl->ub; const double *x = p->x; if (p->minimized) return 0; if (p->closest_pt_d <= dpt_min*dpt_min) return 0; if (p->closest_lm_d <= dlm_min*dlm_min) return 0; for (i = 0; i < n; ++i) if ((x[i] - lb[i] <= dbound_min || ub[i] - x[i] <= dbound_min) && ub[i] - lb[i] > dbound_min) return 0; return 1; } #define K2PI (6.2831853071795864769252867665590057683943388) /* compute Gamma(1 + n/2)^{1/n} ... this is a constant factor used in MLSL as part of the minimum-distance cutoff*/ static double gam(int n) { /* use Stirling's approximation: Gamma(1 + z) ~ sqrt(2*pi*z) * z^z / exp(z) ... this is more than accurate enough for our purposes (error in gam < 15% for d=1, < 4% for d=2, < 2% for d=3, ...) and avoids overflow problems because we can combine it with the ^{1/n} exponent */ double z = n/2; return sqrt(pow(K2PI * z, 1.0/n) * z) * exp(-0.5); } static pt *alloc_pt(int n) { pt *p = (pt *) malloc(sizeof(pt) + (n-1) * sizeof(double)); if (p) { p->minimized = 0; p->closest_pt_d = HUGE_VAL; p->closest_lm_d = HUGE_VAL; } return p; } /* wrapper around objective function that increments evaluation count */ static double fcount(unsigned n, const double *x, double *grad, void *p_) { mlsl_data *p = (mlsl_data *) p_; p->stop->nevals++; return p->f(n, x, grad, p->f_data); } static void get_minf(mlsl_data *d, double *minf, double *x) { rb_node *node = rb_tree_min(&d->pts); if (node) { *minf = ((pt *) node->k)->f; memcpy(x, ((pt *) node->k)->x, sizeof(double) * d->n); } node = rb_tree_min(&d->lms); if (node && node->k[0] < *minf) { *minf = node->k[0]; memcpy(x, node->k + 1, sizeof(double) * d->n); } } #define MIN(a,b) ((a) < (b) ? (a) : (b)) #define MLSL_SIGMA 2. /* MLSL sigma parameter, using value from the papers */ #define MLSL_GAMMA 0.3 /* MLSL gamma parameter (FIXME: what should it be?) */ nlopt_result mlsl_minimize(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, nlopt_stopping *stop, nlopt_opt local_opt, int Nsamples, /* #samples/iteration (0=default) */ int lds) /* random or low-discrepancy seq. (lds) */ { nlopt_result ret = NLOPT_SUCCESS; mlsl_data d; int i; pt *p; if (!Nsamples) d.N = 4; /* FIXME: what is good number of samples per iteration? */ else d.N = Nsamples; if (d.N < 1) return NLOPT_INVALID_ARGS; d.n = n; d.lb = lb; d.ub = ub; d.stop = stop; d.f = f; d.f_data = f_data; rb_tree_init(&d.pts, pt_compare); rb_tree_init(&d.lms, lm_compare); d.s = lds ? nlopt_sobol_create((unsigned) n) : NULL; nlopt_set_min_objective(local_opt, fcount, &d); nlopt_set_lower_bounds(local_opt, lb); nlopt_set_upper_bounds(local_opt, ub); nlopt_set_stopval(local_opt, stop->minf_max); d.gamma = MLSL_GAMMA; d.R_prefactor = sqrt(2./K2PI) * pow(gam(n) * MLSL_SIGMA, 1.0/n); for (i = 0; i < n; ++i) d.R_prefactor *= pow(ub[i] - lb[i], 1.0/n); /* MLSL also suggests setting some minimum distance from points to previous local minimiza and to the boundaries; I don't know how to choose this as a fixed number, so I set it proportional to R; see also the comments at top. dlm and dbound are the proportionality constants. */ d.dlm = 1.0; /* min distance/R to local minima (FIXME: good value?) */ d.dbound = 1e-6; /* min distance/R to ub/lb boundaries (good value?) */ p = alloc_pt(n); if (!p) { ret = NLOPT_OUT_OF_MEMORY; goto done; } /* FIXME: how many sobol points to skip, if any? */ nlopt_sobol_skip(d.s, (unsigned) (10*n+d.N), p->x); memcpy(p->x, x, n * sizeof(double)); p->f = f(n, x, NULL, f_data); stop->nevals++; if (!rb_tree_insert(&d.pts, (rb_key) p)) { free(p); ret = NLOPT_OUT_OF_MEMORY; } if (nlopt_stop_forced(stop)) ret = NLOPT_FORCED_STOP; else if (nlopt_stop_evals(stop)) ret = NLOPT_MAXEVAL_REACHED; else if (nlopt_stop_time(stop)) ret = NLOPT_MAXTIME_REACHED; else if (p->f < stop->minf_max) ret = NLOPT_MINF_MAX_REACHED; while (ret == NLOPT_SUCCESS) { rb_node *node; double R; get_minf(&d, minf, x); /* sampling phase: add random/quasi-random points */ for (i = 0; i < d.N && ret == NLOPT_SUCCESS; ++i) { p = alloc_pt(n); if (!p) { ret = NLOPT_OUT_OF_MEMORY; goto done; } if (d.s) nlopt_sobol_next(d.s, p->x, lb, ub); else { /* use random points instead of LDS */ int j; for (j = 0; j < n; ++j) p->x[j] = nlopt_urand(lb[j],ub[j]); } p->f = f(n, p->x, NULL, f_data); stop->nevals++; if (!rb_tree_insert(&d.pts, (rb_key) p)) { free(p); ret = NLOPT_OUT_OF_MEMORY; } if (nlopt_stop_forced(stop)) ret = NLOPT_FORCED_STOP; else if (nlopt_stop_evals(stop)) ret = NLOPT_MAXEVAL_REACHED; else if (nlopt_stop_time(stop)) ret = NLOPT_MAXTIME_REACHED; else if (p->f < stop->minf_max) ret = NLOPT_MINF_MAX_REACHED; else { find_closest_pt(n, &d.pts, p); find_closest_lm(n, &d.lms, p); pts_update_newpt(n, &d.pts, p); } } /* distance threshhold parameter R in MLSL */ R = d.R_prefactor * pow(log((double) d.pts.N) / d.pts.N, 1.0 / n); /* local search phase: do local opt. for promising points */ node = rb_tree_min(&d.pts); for (i = (int) (ceil(d.gamma * d.pts.N) + 0.5); node && i > 0 && ret == NLOPT_SUCCESS; --i) { p = (pt *) node->k; if (is_potential_minimizer(&d, p, R, d.dlm*R, d.dbound*R)) { nlopt_result lret; double *lm; double t = nlopt_seconds(); if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; break; } if (nlopt_stop_evals(stop)) { ret = NLOPT_MAXEVAL_REACHED; break; } if (stop->maxtime > 0 && t - stop->start >= stop->maxtime) { ret = NLOPT_MAXTIME_REACHED; break; } lm = (double *) malloc(sizeof(double) * (n+1)); if (!lm) { ret = NLOPT_OUT_OF_MEMORY; goto done; } memcpy(lm+1, p->x, sizeof(double) * n); lret = nlopt_optimize_limited(local_opt, lm+1, lm, stop->maxeval - stop->nevals, stop->maxtime - (t - stop->start)); p->minimized = 1; if (lret < 0) { free(lm); ret = lret; goto done; } if (!rb_tree_insert(&d.lms, lm)) { free(lm); ret = NLOPT_OUT_OF_MEMORY; } else if (nlopt_stop_forced(stop)) ret = NLOPT_FORCED_STOP; else if (*lm < stop->minf_max) ret = NLOPT_MINF_MAX_REACHED; else if (nlopt_stop_evals(stop)) ret = NLOPT_MAXEVAL_REACHED; else if (nlopt_stop_time(stop)) ret = NLOPT_MAXTIME_REACHED; else pts_update_newlm(n, &d.pts, lm); } /* TODO: additional stopping criteria based e.g. on improvement in function values, etc? */ node = rb_tree_succ(node); } } get_minf(&d, minf, x); done: nlopt_sobol_destroy(d.s); rb_tree_destroy_with_keys(&d.lms); rb_tree_destroy_with_keys(&d.pts); return ret; } nloptr/src/nlopt_src/isres/0000755000176000001440000000000013263734242015545 5ustar ripleyusersnloptr/src/nlopt_src/isres/isres.h0000644000176000001440000000330513355070635017045 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #ifndef ISRES_H #define ISRES_H #include "nlopt.h" #include "nlopt-util.h" #ifdef __cplusplus extern "C" { #endif /* __cplusplus */ nlopt_result isres_minimize(int n, nlopt_func f, void *f_data, int m, nlopt_constraint *fc, /* fc <= 0 */ int p, nlopt_constraint *h, /* h == 0 */ const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, nlopt_stopping *stop, int population); /* init. population */ #ifdef __cplusplus } /* extern "C" */ #endif /* __cplusplus */ #endif nloptr/src/nlopt_src/isres/Makefile.am0000644000176000001440000000022413263734242017577 0ustar ripleyusersAM_CPPFLAGS = -I$(top_srcdir)/util -I$(top_srcdir)/api noinst_LTLIBRARIES = libisres.la libisres_la_SOURCES = isres.c isres.h EXTRA_DIST = README nloptr/src/nlopt_src/isres/isres.c0000644000176000001440000002423513355070635017045 0ustar ripleyusers/* Copyright (c) 2010 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include #include #include #include "isres.h" /* Improved Stochastic Ranking Evolution Strategy (ISRES) algorithm for nonlinearly-constrained global optimization, based on the method described in: Thomas Philip Runarsson and Xin Yao, "Search biases in constrained evolutionary optimization," IEEE Trans. on Systems, Man, and Cybernetics Part C: Applications and Reviews, vol. 35 (no. 2), pp. 233-243 (2005). It is a refinement of an earlier method described in: Thomas P. Runarsson and Xin Yao, "Stochastic ranking for constrained evolutionary optimization," IEEE Trans. Evolutionary Computation, vol. 4 (no. 3), pp. 284-294 (2000). This is an independent implementation by S. G. Johnson (2009) based on the papers above. Runarsson also has his own Matlab implemention available from his web page: http://www3.hi.is/~tpr */ static int key_compare(void *keys_, const void *a_, const void *b_) { const double *keys = (const double *) keys_; const int *a = (const int *) a_; const int *b = (const int *) b_; return keys[*a] < keys[*b] ? -1 : (keys[*a] > keys[*b] ? +1 : 0); } static unsigned imax2(unsigned a, unsigned b) { return (a > b ? a : b); } nlopt_result isres_minimize(int n, nlopt_func f, void *f_data, int m, nlopt_constraint *fc, /* fc <= 0 */ int p, nlopt_constraint *h, /* h == 0 */ const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, nlopt_stopping *stop, int population) /* pop. size (= 0 for default) */ { const double ALPHA = 0.2; /* smoothing factor from paper */ const double GAMMA = 0.85; /* step-reduction factor from paper */ const double PHI = 1.0; /* expected rate of convergence, from paper */ const double PF = 0.45; /* fitness probability, from paper */ const double SURVIVOR = 1.0/7.0; /* survivor fraction, from paper */ int survivors; nlopt_result ret = NLOPT_SUCCESS; double *sigmas = 0, *xs; /* population-by-n arrays (row-major) */ double *fval; /* population array of function vals */ double *penalty; /* population array of penalty vals */ double *x0; int *irank = 0; int k, i, j, c; int mp = m + p; double minf_penalty = HUGE_VAL, minf_gpenalty = HUGE_VAL; double taup, tau; double *results = 0; /* scratch space for mconstraint results */ unsigned ires; *minf = HUGE_VAL; if (!population) population = 20 * (n + 1); if (population < 1) return NLOPT_INVALID_ARGS; survivors = ceil(population * SURVIVOR); taup = PHI / sqrt((double) 2*n); tau = PHI / sqrt((double) 2*sqrt((double) n)); /* we don't handle unbounded search regions */ for (j = 0; j < n; ++j) if (nlopt_isinf(lb[j]) || nlopt_isinf(ub[j])) return NLOPT_INVALID_ARGS; ires = imax2(nlopt_max_constraint_dim(m, fc), nlopt_max_constraint_dim(p, h)); results = (double *) malloc(ires * sizeof(double)); if (ires > 0 && !results) return NLOPT_OUT_OF_MEMORY; sigmas = (double*) malloc(sizeof(double) * (population*n*2 + population + population + n)); if (!sigmas) { free(results); return NLOPT_OUT_OF_MEMORY; } xs = sigmas + population*n; fval = xs + population*n; penalty = fval + population; x0 = penalty + population; irank = (int*) malloc(sizeof(int) * population); if (!irank) { ret = NLOPT_OUT_OF_MEMORY; goto done; } for (k = 0; k < population; ++k) { for (j = 0; j < n; ++j) { sigmas[k*n+j] = (ub[j] - lb[j]) / sqrt((double) n); xs[k*n+j] = nlopt_urand(lb[j], ub[j]); } } memcpy(xs, x, sizeof(double) * n); /* use input x for xs_0 */ while (1) { /* each loop body = one generation */ int all_feasible = 1; /* evaluate f and constraint violations for whole population */ for (k = 0; k < population; ++k) { int feasible = 1; double gpenalty; stop->nevals++; fval[k] = f(n, xs + k*n, NULL, f_data); if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } penalty[k] = 0; for (c = 0; c < m; ++c) { /* inequality constraints */ nlopt_eval_constraint(results, NULL, fc + c, n, xs + k*n); if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } for (ires = 0; ires < fc[c].m; ++ires) { double gval = results[ires]; if (gval > fc[c].tol[ires]) feasible = 0; if (gval < 0) gval = 0; penalty[k] += gval*gval; } } gpenalty = penalty[k]; for (c = m; c < mp; ++c) { /* equality constraints */ nlopt_eval_constraint(results, NULL, h + (c-m), n, xs + k*n); if (nlopt_stop_forced(stop)) { ret = NLOPT_FORCED_STOP; goto done; } for (ires = 0; ires < h[c-m].m; ++ires) { double hval = results[ires]; if (fabs(hval) > h[c-m].tol[ires]) feasible = 0; penalty[k] += hval*hval; } } if (penalty[k] > 0) all_feasible = 0; /* convergence criteria (FIXME: improve?) */ /* FIXME: with equality constraints, how do we decide which solution is the "best" so far? ... need some total order on the solutions? */ if ((penalty[k] <= minf_penalty || feasible) && (fval[k] <= *minf || minf_gpenalty > 0) && ((feasible ? 0 : penalty[k]) != minf_penalty || fval[k] != *minf)) { if (fval[k] < stop->minf_max && feasible) ret = NLOPT_MINF_MAX_REACHED; else if (!nlopt_isinf(*minf)) { if (nlopt_stop_f(stop, fval[k], *minf) && nlopt_stop_f(stop, feasible ? 0 : penalty[k], minf_penalty)) ret = NLOPT_FTOL_REACHED; else if (nlopt_stop_x(stop, xs+k*n, x)) ret = NLOPT_XTOL_REACHED; } memcpy(x, xs+k*n, sizeof(double)*n); *minf = fval[k]; minf_penalty = feasible ? 0 : penalty[k]; minf_gpenalty = feasible ? 0 : gpenalty; if (ret != NLOPT_SUCCESS) goto done; } if (nlopt_stop_forced(stop)) ret = NLOPT_FORCED_STOP; else if (nlopt_stop_evals(stop)) ret = NLOPT_MAXEVAL_REACHED; else if (nlopt_stop_time(stop)) ret = NLOPT_MAXTIME_REACHED; if (ret != NLOPT_SUCCESS) goto done; } /* "selection" step: rank the population */ for (k = 0; k < population; ++k) irank[k] = k; if (all_feasible) /* special case: rank by objective function */ nlopt_qsort_r(irank, population, sizeof(int), fval,key_compare); else { /* Runarsson & Yao's stochastic ranking of the population */ for (i = 0; i < population; ++i) { int swapped = 0; for (j = 0; j < population-1; ++j) { double u = nlopt_urand(0,1); if (u < PF || (penalty[irank[j]] == 0 && penalty[irank[j+1]] == 0)) { if (fval[irank[j]] > fval[irank[j+1]]) { int irankj = irank[j]; irank[j] = irank[j+1]; irank[j+1] = irankj; swapped = 1; } } else if (penalty[irank[j]] > penalty[irank[j+1]]) { int irankj = irank[j]; irank[j] = irank[j+1]; irank[j+1] = irankj; swapped = 1; } } if (!swapped) break; } } /* evolve the population: differential evolution for the best survivors, and standard mutation of the best survivors for the rest: */ for (k = survivors; k < population; ++k) { /* standard mutation */ double taup_rand = taup * nlopt_nrand(0,1); int rk = irank[k], ri; i = k % survivors; ri = irank[i]; for (j = 0; j < n; ++j) { double sigmamax = (ub[j] - lb[j]) / sqrt((double) n); sigmas[rk*n+j] = sigmas[ri*n+j] * exp(taup_rand + tau*nlopt_nrand(0,1)); if (sigmas[rk*n+j] > sigmamax) sigmas[rk*n+j] = sigmamax; do { xs[rk*n+j] = xs[ri*n+j] + sigmas[rk*n+j] * nlopt_nrand(0,1); } while (xs[rk*n+j] < lb[j] || xs[rk*n+j] > ub[j]); sigmas[rk*n+j] = sigmas[ri*n+j] + ALPHA*(sigmas[rk*n+j] - sigmas[ri*n+j]); } } memcpy(x0, xs, n * sizeof(double)); for (k = 0; k < survivors; ++k) { /* differential variation */ double taup_rand = taup * nlopt_nrand(0,1); int rk = irank[k]; for (j = 0; j < n; ++j) { double xi = xs[rk*n+j]; if (k+1 < survivors) xs[rk*n+j] += GAMMA * (x0[j] - xs[(k+1)*n+j]); if (k+1 == survivors || xs[rk*n+j] < lb[j] || xs[rk*n+j] > ub[j]) { /* standard mutation for last survivor and for any survivor components that are now outside the bounds */ double sigmamax = (ub[j] - lb[j]) / sqrt((double) n); double sigi = sigmas[rk*n+j]; sigmas[rk*n+j] *= exp(taup_rand + tau*nlopt_nrand(0,1)); if (sigmas[rk*n+j] > sigmamax) sigmas[rk*n+j] = sigmamax; do { xs[rk*n+j] = xi + sigmas[rk*n+j] * nlopt_nrand(0,1); } while (xs[rk*n+j] < lb[j] || xs[rk*n+j] > ub[j]); sigmas[rk*n+j] = sigi + ALPHA * (sigmas[rk*n+j] - sigi); } } } } done: if (irank) free(irank); if (sigmas) free(sigmas); if (results) free(results); return ret; } nloptr/src/nlopt_src/isres/Makefile.in0000644000176000001440000004346413355070635017626 0ustar ripleyusers# Makefile.in generated by automake 1.14 from Makefile.am. # @configure_input@ # Copyright (C) 1994-2013 Free Software Foundation, Inc. # This Makefile.in is free software; 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It is a refinement of an earlier method described in: Thomas P. Runarsson and Xin Yao, "Stochastic ranking for constrained evolutionary optimization," IEEE Trans. Evolutionary Computation, vol. 4 (no. 3), pp. 284-294 (2000). This is an independent implementation by S. G. Johnson (2009) based on the papers above. Runarsson also has his own Matlab implemention available from his web page: http://www3.hi.is/~tpr It is under the same MIT license as the rest of my code in NLopt (see ../COPYRIGHT). Steven G. Johnson November 2009 nloptr/src/nlopt_src/esch/0000755000176000001440000000000013263734242015342 5ustar ripleyusersnloptr/src/nlopt_src/esch/COPYRIGHT0000644000176000001440000000217113263734242016636 0ustar ripleyusers/* Copyright (c) 2008-2013 Carlos Henrique da Silva Santos * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ nloptr/src/nlopt_src/esch/Makefile.am0000644000176000001440000000023313263734242017374 0ustar ripleyusersAM_CPPFLAGS = -I$(top_srcdir)/util -I$(top_srcdir)/api noinst_LTLIBRARIES = libesch.la libesch_la_SOURCES = esch.c esch.h EXTRA_DIST = README COPYRIGHT nloptr/src/nlopt_src/esch/Makefile.in0000644000176000001440000004345213355070635017420 0ustar ripleyusers# Makefile.in generated by automake 1.14 from Makefile.am. # @configure_input@ # Copyright (C) 1994-2013 Free Software Foundation, Inc. # This Makefile.in is free software; the Free Software Foundation # gives unlimited permission to copy and/or distribute it, # with or without modifications, as long as this notice is preserved. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY, to the extent permitted by law; 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H. da Silva Santos, M. S. Gonçalves, and H. E. Hernandez-Figueroa, "Designing Novel Photonic Devices by Bio-Inspired Computing," IEEE Photonics Technology Letters 22(15), pp. 1177-1179 (2010). C. H. da Silva Santos, "Parallel and Bio-Inspired Computing Applied to Analyze Microwave and Photonic Metamaterial Strucutures," University of Campinas, (2010) http://www.bibliotecadigital.unicamp.br/document/?code=000767537&opt=4&lg=en_US The algorithms are adapted from ideas described in: H.-G. Beyer and H.-P. Schwefel. Evolution Strategies: A Comprehensive Introduction. Journal Natural Computing, 1(1):3–52, 2002. Ingo Rechenberg (1971): Evolutionsstrategie – Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (PhD thesis). Reprinted by Fromman-Holzboog (1973). It is distributed under the "MIT license" given in the attached COPYRIGHT file (similar to the rest of NLopt), and was supportedfinancially by the São Paulo Science Foundation (FAPESP - Fundação de Amparo à Pesquisa do Estado de São Paulo) under the grant 2012/14553-9. Carlos Henrique da Silva Santos January 2013 nloptr/src/nlopt_src/esch/esch.h0000644000176000001440000000346713355070635016450 0ustar ripleyusers/* Copyright (c) 2008-2013 Carlos Henrique da Silva Santos * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #ifndef ES_POP_H #define ES_POP_H 1 #include "nlopt-util.h" #ifdef __cplusplus extern "C" { #endif /* __cplusplus */ nlopt_result chevolutionarystrategy( unsigned, /* Number of input parameters */ nlopt_func, /* Recursive Objective Funtion Call */ void *, /* Data to Objective Function */ const double*, /* Lower bound values */ const double*, /* Upper bound values */ double*, /* in: initial guess, out: minimizer */ double*, nlopt_stopping*, /* nlopt stop condition */ unsigned, /* Number of Parents */ unsigned); /* Number of Offsprings */ #ifdef __cplusplus } /* extern "C" */ #endif /* __cplusplus */ #endif /* ES_POP_H */ nloptr/src/nlopt_src/esch/esch.c0000644000176000001440000002364313355070635016441 0ustar ripleyusers/* Copyright (c) 2008-2013 Carlos Henrique da Silva Santos * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include #include #include "esch.h" // --------------------------------------------------------------------------- // Cauchy random number distribution static double randcauchy(const double params[7]) { /* double min, double max, double mi, double t, double band */ double na_unif, cauchy_mit, limit_inf, limit_sup; double valor; double min = params[1], max = params[2], mi = params[3], t = params[4], band = params[5]; limit_inf = mi - (band*0.5); limit_sup = mi + (band*0.5); do { na_unif = nlopt_urand(0,1); // ran2(0,1); cauchy_mit = t*tan((na_unif-0.5)*3.14159265358979323846) + mi; } while ( (cauchy_mitlimit_sup) ); if (cauchy_mit < 0) cauchy_mit = -cauchy_mit; else cauchy_mit = cauchy_mit + (band*0.5); valor = cauchy_mit/band; valor = min+(max-min)*valor; return valor; } // --------------------------------------------------------------------------- // main Individual representation type typedef struct IndividualStructure { double * parameters; double fitness; } Individual; static int CompareIndividuals(void *unused, const void *a_, const void *b_) { // (void) unused; const Individual *a = (const Individual *) a_; const Individual *b = (const Individual *) b_; return a->fitness < b->fitness ? -1 : (a->fitness > b->fitness ? +1 : 0); } nlopt_result chevolutionarystrategy( unsigned nparameters, /* Number of input parameters */ nlopt_func f, /* Recursive Objective Funtion Call */ void * data_f, /* Data to Objective Function */ const double* lb, /* Lower bound values */ const double* ub, /* Upper bound values */ double* x, /*in: initial guess, out: minimizer */ double* minf, nlopt_stopping* stop, /* nlopt stop condition */ unsigned np, /* Number of Parents */ unsigned no) { /* Number of Offsprings */ // variables from nlopt nlopt_result ret = NLOPT_SUCCESS; double vetor[8]; unsigned i, id, item; int parent1, parent2; unsigned crosspoint; // crossover parameteres int contmutation, totalmutation; // mutation parameters int idoffmutation, paramoffmutation; // mutation parameters Individual * esparents; // Parents population Individual * esoffsprings; // Offsprings population Individual * estotal;// copy containing Parents and Offsprings pops /* It is interesting to maintain the parents and offsprings * populations stablished and sorted; when the final iterations * is achieved, they are ranked and updated. */ // ------------------------------- // controling the population size // ------------------------------- if (!np) np = 40; if (!no) no = 60; if ((np < 1)||(no<1)) return NLOPT_INVALID_ARGS; esparents = (Individual*) malloc(sizeof(Individual) * np); esoffsprings = (Individual*) malloc(sizeof(Individual) * no); estotal = (Individual*) malloc(sizeof(Individual) * (np+no)); if ((!esparents)||(!esoffsprings)||(!estotal)) { free(esparents); free(esoffsprings); free(estotal); return NLOPT_OUT_OF_MEMORY; } for (id=0; id < np; id++) esparents[id].parameters = NULL; for (id=0; id < no; id++) esoffsprings[id].parameters = NULL; // From here the population is initialized /* we don't handle unbounded search regions; this check is unnecessary since it is performed in nlopt_optimize. for (j = 0; j < nparameters; ++j) if (nlopt_isinf(low[j]) || nlopt_isinf(up[j])) return NLOPT_INVALID_ARGS; */ // main vector of parameters to randcauchy vetor[0] = 4; // ignored vetor[3] = 0; vetor[4] = 1; vetor[5] = 10; vetor[6] = 1; vetor[7] = 0; // ignored // ------------------------------------ // Initializing parents population // ------------------------------------ for (id=0; id < np; id++) { esparents[id].parameters = (double*) malloc(sizeof(double) * nparameters); if (!esparents[id].parameters) { ret = NLOPT_OUT_OF_MEMORY; goto done; } for (item=0; itemnevals++; if (*minf > esparents[id].fitness) { *minf = esparents[id].fitness; memcpy(x, esparents[id].parameters, nparameters * sizeof(double)); } if (nlopt_stop_forced(stop)) ret = NLOPT_FORCED_STOP; else if (*minf < stop->minf_max) ret = NLOPT_MINF_MAX_REACHED; else if (nlopt_stop_evals(stop)) ret = NLOPT_MAXEVAL_REACHED; else if (nlopt_stop_time(stop)) ret = NLOPT_MAXTIME_REACHED; if (ret != NLOPT_SUCCESS) goto done; } // ------------------------------------ // Main Loop - Generations // ------------------------------------ while (1) { // ------------------------------------ // Crossover // ------------------------------------ for (id=0; id < no; id++) { parent1 = nlopt_iurand((int) np); parent2 = nlopt_iurand((int) np); crosspoint = (unsigned) nlopt_iurand((int) nparameters); for (item=0; item < crosspoint; item++) esoffsprings[id].parameters[item] = esparents[parent1].parameters[item]; for (item=crosspoint; item < nparameters; item++) esoffsprings[id].parameters[item] = esparents[parent2].parameters[item]; } // ------------------------------------ // Gaussian Mutation // ------------------------------------ totalmutation = (int) ((no * nparameters) / 10); if (totalmutation < 1) totalmutation = 1; for (contmutation=0; contmutation < totalmutation; contmutation++) { idoffmutation = nlopt_iurand((int) no); paramoffmutation = nlopt_iurand((int) nparameters); vetor[1] = lb[paramoffmutation]; vetor[2] = ub[paramoffmutation]; vetor[7] = vetor[7]+contmutation; esoffsprings[idoffmutation].parameters[paramoffmutation] = randcauchy(vetor); } // ------------------------------------ // Offsprings fitness evaluation // ------------------------------------ for (id=0; id < no; id++){ //esoffsprings[id].fitness = (double)fitness(esoffsprings[id].parameters, nparameters,fittype); esoffsprings[id].fitness = f(nparameters, esoffsprings[id].parameters, NULL, data_f); estotal[id+np].fitness = esoffsprings[id].fitness; stop->nevals++; if (*minf > esoffsprings[id].fitness) { *minf = esoffsprings[id].fitness; memcpy(x, esoffsprings[id].parameters, nparameters * sizeof(double)); } if (nlopt_stop_forced(stop)) ret = NLOPT_FORCED_STOP; else if (*minf < stop->minf_max) ret = NLOPT_MINF_MAX_REACHED; else if (nlopt_stop_evals(stop)) ret = NLOPT_MAXEVAL_REACHED; else if (nlopt_stop_time(stop)) ret = NLOPT_MAXTIME_REACHED; if (ret != NLOPT_SUCCESS) goto done; } // ------------------------------------ // Individual selection // ------------------------------------ // all the individuals are copied to one vector to easily identify best solutions for (i=0; i < np; i++) estotal[i] = esparents[i]; for (i=0; i < no; i++) estotal[np+i] = esoffsprings[i]; // Sorting nlopt_qsort_r(estotal, no+np, sizeof(Individual), NULL, CompareIndividuals); // copy after sorting: for (i=0; i < no+np; i++) { if (i #include "nlopt-util.h" /* utility routines to implement the various stopping criteria */ static int relstop(double vold, double vnew, double reltol, double abstol) { if (nlopt_isinf(vold)) return 0; return(fabs(vnew - vold) < abstol || fabs(vnew - vold) < reltol * (fabs(vnew) + fabs(vold)) * 0.5 || (reltol > 0 && vnew == vold)); /* catch vnew == vold == 0 */ } int nlopt_stop_ftol(const nlopt_stopping *s, double f, double oldf) { return (relstop(oldf, f, s->ftol_rel, s->ftol_abs)); } int nlopt_stop_f(const nlopt_stopping *s, double f, double oldf) { return (f <= s->minf_max || nlopt_stop_ftol(s, f, oldf)); } int nlopt_stop_x(const nlopt_stopping *s, const double *x, const double *oldx) { unsigned i; for (i = 0; i < s->n; ++i) if (!relstop(oldx[i], x[i], s->xtol_rel, s->xtol_abs[i])) return 0; return 1; } int nlopt_stop_dx(const nlopt_stopping *s, const double *x, const double *dx) { unsigned i; for (i = 0; i < s->n; ++i) if (!relstop(x[i] - dx[i], x[i], s->xtol_rel, s->xtol_abs[i])) return 0; return 1; } static double sc(double x, double smin, double smax) { return smin + x * (smax - smin); } /* some of the algorithms rescale x to a unit hypercube, so we need to scale back before we can compare to the tolerances */ int nlopt_stop_xs(const nlopt_stopping *s, const double *xs, const double *oldxs, const double *scale_min, const double *scale_max) { unsigned i; for (i = 0; i < s->n; ++i) if (relstop(sc(oldxs[i], scale_min[i], scale_max[i]), sc(xs[i], scale_min[i], scale_max[i]), s->xtol_rel, s->xtol_abs[i])) return 1; return 0; } int nlopt_stop_evals(const nlopt_stopping *s) { return (s->maxeval > 0 && s->nevals >= s->maxeval); } int nlopt_stop_time_(double start, double maxtime) { return (maxtime > 0 && nlopt_seconds() - start >= maxtime); } int nlopt_stop_time(const nlopt_stopping *s) { return nlopt_stop_time_(s->start, s->maxtime); } int nlopt_stop_evalstime(const nlopt_stopping *stop) { return nlopt_stop_evals(stop) || nlopt_stop_time(stop); } int nlopt_stop_forced(const nlopt_stopping *stop) { return stop->force_stop && *(stop->force_stop); } unsigned nlopt_count_constraints(unsigned p, const nlopt_constraint *c) { unsigned i, count = 0; for (i = 0; i < p; ++i) count += c[i].m; return count; } unsigned nlopt_max_constraint_dim(unsigned p, const nlopt_constraint *c) { unsigned i, max_dim = 0; for (i = 0; i < p; ++i) if (c[i].m > max_dim) max_dim = c[i].m; return max_dim; } void nlopt_eval_constraint(double *result, double *grad, const nlopt_constraint *c, unsigned n, const double *x) { if (c->f) result[0] = c->f(n, x, grad, c->f_data); else c->mf(c->m, result, n, x, grad, c->f_data); } nloptr/src/nlopt_src/util/timer.c0000644000176000001440000000601613355070635016665 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include "nlopt-util.h" #if TIME_WITH_SYS_TIME # include # include #else # if HAVE_SYS_TIME_H # include # else # include # endif #endif #if defined(_WIN32) || defined(__WIN32__) # include #endif /* return time in seconds since some arbitrary point in the past */ double nlopt_seconds(void) { static THREADLOCAL int start_inited = 0; /* whether start time has been initialized */ #if defined(HAVE_GETTIMEOFDAY) static THREADLOCAL struct timeval start; struct timeval tv; if (!start_inited) { start_inited = 1; gettimeofday(&start, NULL); } gettimeofday(&tv, NULL); return (tv.tv_sec - start.tv_sec) + 1.e-6 * (tv.tv_usec - start.tv_usec); #elif defined(HAVE_TIME) return time(NULL); #elif defined(_WIN32) || defined(__WIN32__) static THREADLOCAL ULONGLONG start; FILETIME ft; if (!start_inited) { start_inited = 1; GetSystemTimeAsFileTime(&ft); start = (((ULONGLONG) ft.dwHighDateTime) << 32) + ft.dwLowDateTime; } GetSystemTimeAsFileTime(&ft); return 100e-9 * (((((ULONGLONG) ft.dwHighDateTime) << 32) + ft.dwLowDateTime) - start); #else /* use clock() as a fallback... this is somewhat annoying because clock() may wrap around with a fairly short period */ static THREADLOCAL clock_t start; if (!start_inited) { start_inited = 1; start = clock(); } return (clock() - start) * 1.0 / CLOCKS_PER_SEC; #endif } /* number based on time for use as random seed */ unsigned long nlopt_time_seed(void) { #if defined(HAVE_GETTIMEOFDAY) struct timeval tv; gettimeofday(&tv, NULL); return (tv.tv_sec ^ tv.tv_usec); #elif defined(HAVE_TIME) return time(NULL); #elif defined(_WIN32) || defined(__WIN32__) FILETIME ft; GetSystemTimeAsFileTime(&ft); return ft.dwHighDateTime ^ ft.dwLowDateTime; #else return clock(); #endif } nloptr/src/nlopt_src/util/redblack.c0000644000176000001440000002420313355070635017312 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ /* simple implementation of red-black trees optimized for use with DIRECT */ #include #include #include "redblack.h" /* it is convenient to use an explicit node for NULL nodes ... we need to be careful never to change this node indirectly via one of our pointers! */ rb_node nil = {&nil, &nil, &nil, 0, BLACK}; #define NIL (&nil) void rb_tree_init(rb_tree *t, rb_compare compare) { t->compare = compare; t->root = NIL; t->N = 0; } static void destroy(rb_node *n) { if (n != NIL) { destroy(n->l); destroy(n->r); free(n); } } void rb_tree_destroy(rb_tree *t) { destroy(t->root); t->root = NIL; } void rb_tree_destroy_with_keys(rb_tree *t) { rb_node *n = rb_tree_min(t); while (n) { free(n->k); n->k = NULL; n = rb_tree_succ(n); } rb_tree_destroy(t); } static void rotate_left(rb_node *p, rb_tree *t) { rb_node *n = p->r; /* must be non-NIL */ p->r = n->l; n->l = p; if (p->p != NIL) { if (p == p->p->l) p->p->l = n; else p->p->r = n; } else t->root = n; n->p = p->p; p->p = n; if (p->r != NIL) p->r->p = p; } static void rotate_right(rb_node *p, rb_tree *t) { rb_node *n = p->l; /* must be non-NIL */ p->l = n->r; n->r = p; if (p->p != NIL) { if (p == p->p->l) p->p->l = n; else p->p->r = n; } else t->root = n; n->p = p->p; p->p = n; if (p->l != NIL) p->l->p = p; } static void insert_node(rb_tree *t, rb_node *n) { rb_compare compare = t->compare; rb_key k = n->k; rb_node *p = t->root; n->c = RED; n->p = n->l = n->r = NIL; t->N++; if (p == NIL) { t->root = n; n->c = BLACK; return; } /* insert (RED) node into tree */ while (1) { if (compare(k, p->k) <= 0) { /* k <= p->k */ if (p->l != NIL) p = p->l; else { p->l = n; n->p = p; break; } } else { if (p->r != NIL) p = p->r; else { p->r = n; n->p = p; break; } } } fixtree: if (n->p->c == RED) { /* red cannot have red child */ rb_node *u = p == p->p->l ? p->p->r : p->p->l; if (u != NIL && u->c == RED) { p->c = u->c = BLACK; n = p->p; if ((p = n->p) != NIL) { n->c = RED; goto fixtree; } } else { if (n == p->r && p == p->p->l) { rotate_left(p, t); p = n; n = n->l; } else if (n == p->l && p == p->p->r) { rotate_right(p, t); p = n; n = n->r; } p->c = BLACK; p->p->c = RED; if (n == p->l && p == p->p->l) rotate_right(p->p, t); else if (n == p->r && p == p->p->r) rotate_left(p->p, t); } } } rb_node *rb_tree_insert(rb_tree *t, rb_key k) { rb_node *n = (rb_node *) malloc(sizeof(rb_node)); if (!n) return NULL; n->k = k; insert_node(t, n); return n; } static int check_node(rb_node *n, int *nblack, rb_tree *t) { int nbl, nbr; rb_compare compare = t->compare; if (n == NIL) { *nblack = 0; return 1; } if (n->r != NIL && n->r->p != n) return 0; if (n->r != NIL && compare(n->r->k, n->k) < 0) return 0; if (n->l != NIL && n->l->p != n) return 0; if (n->l != NIL && compare(n->l->k, n->k) > 0) return 0; if (n->c == RED) { if (n->r != NIL && n->r->c == RED) return 0; if (n->l != NIL && n->l->c == RED) return 0; } if (!(check_node(n->r, &nbl, t) && check_node(n->l, &nbr, t))) return 0; if (nbl != nbr) return 0; *nblack = nbl + (n->c == BLACK); return 1; } int rb_tree_check(rb_tree *t) { int nblack; if (nil.c != BLACK) return 0; if (nil.p != NIL || nil.r != NIL || nil.l != NIL) return 0; if (t->root == NIL) return 1; if (t->root->c != BLACK) return 0; return check_node(t->root, &nblack, t); } rb_node *rb_tree_find(rb_tree *t, rb_key k) { rb_compare compare = t->compare; rb_node *p = t->root; while (p != NIL) { int comp = compare(k, p->k); if (!comp) return p; p = comp <= 0 ? p->l : p->r; } return NULL; } /* find greatest point in subtree p that is <= k */ static rb_node *find_le(rb_node *p, rb_key k, rb_tree *t) { rb_compare compare = t->compare; while (p != NIL) { if (compare(p->k, k) <= 0) { /* p->k <= k */ rb_node *r = find_le(p->r, k, t); if (r) return r; else return p; } else /* p->k > k */ p = p->l; } return NULL; /* k < everything in subtree */ } /* find greatest point in t <= k */ rb_node *rb_tree_find_le(rb_tree *t, rb_key k) { return find_le(t->root, k, t); } /* find greatest point in subtree p that is < k */ static rb_node *find_lt(rb_node *p, rb_key k, rb_tree *t) { rb_compare compare = t->compare; while (p != NIL) { if (compare(p->k, k) < 0) { /* p->k < k */ rb_node *r = find_lt(p->r, k, t); if (r) return r; else return p; } else /* p->k >= k */ p = p->l; } return NULL; /* k <= everything in subtree */ } /* find greatest point in t < k */ rb_node *rb_tree_find_lt(rb_tree *t, rb_key k) { return find_lt(t->root, k, t); } /* find least point in subtree p that is > k */ static rb_node *find_gt(rb_node *p, rb_key k, rb_tree *t) { rb_compare compare = t->compare; while (p != NIL) { if (compare(p->k, k) > 0) { /* p->k > k */ rb_node *l = find_gt(p->l, k, t); if (l) return l; else return p; } else /* p->k <= k */ p = p->r; } return NULL; /* k >= everything in subtree */ } /* find least point in t > k */ rb_node *rb_tree_find_gt(rb_tree *t, rb_key k) { return find_gt(t->root, k, t); } rb_node *rb_tree_min(rb_tree *t) { rb_node *n = t->root; while (n != NIL && n->l != NIL) n = n->l; return(n == NIL ? NULL : n); } rb_node *rb_tree_max(rb_tree *t) { rb_node *n = t->root; while (n != NIL && n->r != NIL) n = n->r; return(n == NIL ? NULL : n); } rb_node *rb_tree_succ(rb_node *n) { if (!n) return NULL; if (n->r == NIL) { rb_node *prev; do { prev = n; n = n->p; } while (prev == n->r && n != NIL); return n == NIL ? NULL : n; } else { n = n->r; while (n->l != NIL) n = n->l; return n; } } rb_node *rb_tree_pred(rb_node *n) { if (!n) return NULL; if (n->l == NIL) { rb_node *prev; do { prev = n; n = n->p; } while (prev == n->l && n != NIL); return n == NIL ? NULL : n; } else { n = n->l; while (n->r != NIL) n = n->r; return n; } } rb_node *rb_tree_remove(rb_tree *t, rb_node *n) { rb_key k = n->k; rb_node *m, *mp; if (n->l != NIL && n->r != NIL) { rb_node *lmax = n->l; while (lmax->r != NIL) lmax = lmax->r; n->k = lmax->k; n = lmax; } m = n->l != NIL ? n->l : n->r; if (n->p != NIL) { if (n->p->r == n) n->p->r = m; else n->p->l = m; } else t->root = m; mp = n->p; if (m != NIL) m->p = mp; if (n->c == BLACK) { if (m->c == RED) m->c = BLACK; else { deleteblack: if (mp != NIL) { rb_node *s = m == mp->l ? mp->r : mp->l; if (s->c == RED) { mp->c = RED; s->c = BLACK; if (m == mp->l) rotate_left(mp, t); else rotate_right(mp, t); s = m == mp->l ? mp->r : mp->l; } if (mp->c == BLACK && s->c == BLACK && s->l->c == BLACK && s->r->c == BLACK) { if (s != NIL) s->c = RED; m = mp; mp = m->p; goto deleteblack; } else if (mp->c == RED && s->c == BLACK && s->l->c == BLACK && s->r->c == BLACK) { if (s != NIL) s->c = RED; mp->c = BLACK; } else { if (m == mp->l && s->c == BLACK && s->l->c == RED && s->r->c == BLACK) { s->c = RED; s->l->c = BLACK; rotate_right(s, t); s = m == mp->l ? mp->r : mp->l; } else if (m == mp->r && s->c == BLACK && s->r->c == RED && s->l->c == BLACK) { s->c = RED; s->r->c = BLACK; rotate_left(s, t); s = m == mp->l ? mp->r : mp->l; } s->c = mp->c; mp->c = BLACK; if (m == mp->l) { s->r->c = BLACK; rotate_left(mp, t); } else { s->l->c = BLACK; rotate_right(mp, t); } } } } } t->N--; n->k = k; /* n may have changed during remove */ return n; /* the node that was deleted may be different from initial n */ } rb_node *rb_tree_resort(rb_tree *t, rb_node *n) { n = rb_tree_remove(t, n); insert_node(t, n); return n; } /* shift all key pointers by kshift ... this is useful when the keys are pointers into another array, that has been resized with realloc */ static void shift_keys(rb_node *n, ptrdiff_t kshift) /* assumes n != NIL */ { n->k += kshift; if (n->l != NIL) shift_keys(n->l, kshift); if (n->r != NIL) shift_keys(n->r, kshift); } void rb_tree_shift_keys(rb_tree *t, ptrdiff_t kshift) { if (t->root != NIL) shift_keys(t->root, kshift); } nloptr/src/nlopt_src/util/Makefile.am0000644000176000001440000000045313263734242017433 0ustar ripleyusersAM_CPPFLAGS = -I$(top_srcdir)/api noinst_LTLIBRARIES = libutil.la libutil_la_SOURCES = mt19937ar.c sobolseq.c soboldata.h timer.c stop.c nlopt-util.h redblack.c redblack.h qsort_r.c rescale.c noinst_PROGRAMS = redblack_test redblack_test_SOURCES = redblack_test.c redblack_test_LDADD = libutil.la nloptr/src/nlopt_src/util/sobolseq.c0000644000176000001440000002240613355070635017375 0ustar ripleyusers/* Copyright (c) 2007 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ /* Generation of Sobol sequences in up to 1111 dimensions, based on the algorithms described in: P. Bratley and B. L. Fox, Algorithm 659, ACM Trans. Math. Soft. 14 (1), 88-100 (1988), as modified by: S. Joe and F. Y. Kuo, ACM Trans. Math. Soft 29 (1), 49-57 (2003). Note that the code below was written without even looking at the Fortran code from the TOMS paper, which is only semi-free (being under the restrictive ACM copyright terms). Then I went to the Fortran code and took out the table of primitive polynomials and starting direction #'s ... since this is just a table of numbers generated by a deterministic algorithm, it is not copyrightable. (Obviously, the format of these tables then necessitated some slight modifications to the code.) For the test integral of Joe and Kuo (see the main() program below), I get exactly the same results for integrals up to 1111 dimensions compared to the table of published numbers (to the 5 published significant digits). This is not to say that the authors above should not be credited for their clear description of the algorithm (and their tabulation of the critical numbers). Please cite them. Just that I needed a free/open-source implementation. */ #include #include #include "nlopt-util.h" #if defined(HAVE_STDINT_H) # include #endif #ifndef HAVE_UINT32_T # if SIZEOF_UNSIGNED_LONG == 4 typedef unsigned long uint32_t; # elif SIZEOF_UNSIGNED_INT == 4 typedef unsigned int uint32_t; # else # error No 32-bit unsigned integer type # endif #endif typedef struct nlopt_soboldata_s { unsigned sdim; /* dimension of sequence being generated */ uint32_t *mdata; /* array of length 32 * sdim */ uint32_t *m[32]; /* more convenient pointers to mdata, of direction #s */ uint32_t *x; /* previous x = x_n, array of length sdim */ unsigned *b; /* position of fixed point in x[i] is after bit b[i] */ uint32_t n; /* number of x's generated so far */ } soboldata; /* Return position (0, 1, ...) of rightmost (least-significant) zero bit in n. * * This code uses a 32-bit version of algorithm to find the rightmost * one bit in Knuth, _The Art of Computer Programming_, volume 4A * (draft fascicle), section 7.1.3, "Bitwise tricks and * techniques." * * Assumes n has a zero bit, i.e. n < 2^32 - 1. * */ static unsigned rightzero32(uint32_t n) { #if defined(__GNUC__) && \ ((__GNUC__ == 3 && __GNUC_MINOR__ >= 4) || __GNUC__ > 3) return __builtin_ctz(~n); /* gcc builtin for version >= 3.4 */ #else const uint32_t a = 0x05f66a47; /* magic number, found by brute force */ static const unsigned decode[32] = {0,1,2,26,23,3,15,27,24,21,19,4,12,16,28,6,31,25,22,14,20,18,11,5,30,13,17,10,29,9,8,7}; n = ~n; /* change to rightmost-one problem */ n = a * (n & (-n)); /* store in n to make sure mult. is 32 bits */ return decode[n >> 27]; #endif } /* generate the next term x_{n+1} in the Sobol sequence, as an array x[sdim] of numbers in (0,1). Returns 1 on success, 0 on failure (if too many #'s generated) */ static int sobol_gen(soboldata *sd, double *x) { unsigned c, b, i, sdim; if (sd->n == 4294967295U) return 0; /* n == 2^32 - 1 ... we would need to switch to a 64-bit version to generate more terms. */ c = rightzero32(sd->n++); sdim = sd->sdim; for (i = 0; i < sdim; ++i) { b = sd->b[i]; if (b >= c) { sd->x[i] ^= sd->m[c][i] << (b - c); x[i] = ((double) (sd->x[i])) / (1U << (b+1)); } else { sd->x[i] = (sd->x[i] << (c - b)) ^ sd->m[c][i]; sd->b[i] = c; x[i] = ((double) (sd->x[i])) / (1U << (c+1)); } } return 1; } #include "soboldata.h" static int sobol_init(soboldata *sd, unsigned sdim) { unsigned i,j; if (!sdim || sdim > MAXDIM) return 0; sd->mdata = (uint32_t *) malloc(sizeof(uint32_t) * (sdim * 32)); if (!sd->mdata) return 0; for (j = 0; j < 32; ++j) { sd->m[j] = sd->mdata + j * sdim; sd->m[j][0] = 1; /* special-case Sobol sequence */ } for (i = 1; i < sdim; ++i) { uint32_t a = sobol_a[i-1]; unsigned d = 0, k; while (a) { ++d; a >>= 1; } d--; /* d is now degree of poly */ /* set initial values of m from table */ for (j = 0; j < d; ++j) sd->m[j][i] = sobol_minit[j][i-1]; /* fill in remaining values using recurrence */ for (j = d; j < 32; ++j) { a = sobol_a[i-1]; sd->m[j][i] = sd->m[j - d][i]; for (k = 0; k < d; ++k) { sd->m[j][i] ^= ((a & 1) * sd->m[j-d+k][i]) << (d-k); a >>= 1; } } } sd->x = (uint32_t *) malloc(sizeof(uint32_t) * sdim); if (!sd->x) { free(sd->mdata); return 0; } sd->b = (unsigned *) malloc(sizeof(unsigned) * sdim); if (!sd->b) { free(sd->x); free(sd->mdata); return 0; } for (i = 0; i < sdim; ++i) { sd->x[i] = 0; sd->b[i] = 0; } sd->n = 0; sd->sdim = sdim; return 1; } static void sobol_destroy(soboldata *sd) { free(sd->mdata); free(sd->x); free(sd->b); } /************************************************************************/ /* NLopt API to Sobol sequence creation, which hides soboldata structure behind an opaque pointer */ nlopt_sobol nlopt_sobol_create(unsigned sdim) { nlopt_sobol s = (nlopt_sobol) malloc(sizeof(soboldata)); if (!s) return NULL; if (!sobol_init(s, sdim)) { free(s); return NULL; } return s; } extern void nlopt_sobol_destroy(nlopt_sobol s) { if (s) { sobol_destroy(s); free(s); } } /* next vector x[sdim] in Sobol sequence, with each x[i] in (0,1) */ void nlopt_sobol_next01(nlopt_sobol s, double *x) { if (!sobol_gen(s, x)) { /* fall back on pseudo random numbers in the unlikely event that we exceed 2^32-1 points */ unsigned i; for (i = 0; i < s->sdim; ++i) x[i] = nlopt_urand(0.0,1.0); } } /* next vector in Sobol sequence, scaled to (lb[i], ub[i]) interval */ void nlopt_sobol_next(nlopt_sobol s, double *x, const double *lb, const double *ub) { unsigned i, sdim; nlopt_sobol_next01(s, x); for (sdim = s->sdim, i = 0; i < sdim; ++i) x[i] = lb[i] + (ub[i] - lb[i]) * x[i]; } /* if we know in advance how many points (n) we want to compute, then adopt the suggestion of the Joe and Kuo paper, which in turn is taken from Acworth et al (1998), of skipping a number of points equal to the largest power of 2 smaller than n */ void nlopt_sobol_skip(nlopt_sobol s, unsigned n, double *x) { if (s) { unsigned k = 1; while (k*2 < n) k *= 2; while (k-- > 0) sobol_gen(s, x); } } /************************************************************************/ /* compile with -DSOBOLSEQ_TEST for test program */ #ifdef SOBOLSEQ_TEST #include #include /* test integrand from Joe and Kuo paper ... integrates to 1 */ static double testfunc(unsigned n, const double *x) { double f = 1; unsigned j; for (j = 1; j <= n; ++j) { double cj = pow((double) j, 0.3333333333333333333); f *= (fabs(4*x[j-1] - 2) + cj) / (1 + cj); } return f; } int main(int argc, char **argv) { unsigned n, j, i, sdim; static double x[MAXDIM]; double testint_sobol = 0, testint_rand = 0; nlopt_sobol s; if (argc < 3) { fprintf(stderr, "Usage: %s \n", argv[0]); return 1; } nlopt_init_genrand(time(NULL)); sdim = atoi(argv[1]); s = nlopt_sobol_create(sdim); n = atoi(argv[2]); nlopt_sobol_skip(s, n, x); for (j = 1; j <= n; ++j) { nlopt_sobol_next01(s, x); testint_sobol += testfunc(sdim, x); if (j < 100) { printf("x[%d]: %g", j, x[0]); for (i = 1; i < sdim; ++i) printf(", %g", x[i]); printf("\n"); } for (i = 0; i < sdim; ++i) x[i] = nlopt_urand(0.,1.); testint_rand += testfunc(sdim, x); } nlopt_sobol_destroy(s); printf("Test integral = %g using Sobol, %g using pseudorandom.\n", testint_sobol / n, testint_rand / n); printf(" error = %g using Sobol, %g using pseudorandom.\n", testint_sobol / n - 1, testint_rand / n - 1); return 0; } #endif nloptr/src/nlopt_src/util/Makefile.in0000644000176000001440000004642213355070635017453 0ustar ripleyusers# Makefile.in generated by automake 1.14 from Makefile.am. # @configure_input@ # Copyright (C) 1994-2013 Free Software Foundation, Inc. # This Makefile.in is free software; the Free Software Foundation # gives unlimited permission to copy and/or distribute it, # with or without modifications, as long as this notice is preserved. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY, to the extent permitted by law; without # even the implied warranty of MERCHANTABILITY or FITNESS FOR A # PARTICULAR PURPOSE. @SET_MAKE@ VPATH = @srcdir@ am__is_gnu_make = test -n '$(MAKEFILE_LIST)' && test -n '$(MAKELEVEL)' am__make_running_with_option = \ case $${target_option-} in \ ?) ;; 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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #ifndef REDBLACK_H #define REDBLACK_H #include /* for ptrdiff_t */ #ifdef __cplusplus extern "C" { #endif /* __cplusplus */ typedef double *rb_key; /* key type ... double* is convenient for us, but of course this could be cast to anything desired (although void* would look more generic) */ typedef enum { RED, BLACK } rb_color; typedef struct rb_node_s { struct rb_node_s *p, *r, *l; /* parent, right, left */ rb_key k; /* key (and data) */ rb_color c; } rb_node; typedef int (*rb_compare)(rb_key k1, rb_key k2); typedef struct { rb_compare compare; rb_node *root; int N; /* number of nodes */ } rb_tree; extern void rb_tree_init(rb_tree *t, rb_compare compare); extern void rb_tree_destroy(rb_tree *t); extern void rb_tree_destroy_with_keys(rb_tree *t); extern rb_node *rb_tree_insert(rb_tree *t, rb_key k); extern int rb_tree_check(rb_tree *t); extern rb_node *rb_tree_find(rb_tree *t, rb_key k); extern rb_node *rb_tree_find_le(rb_tree *t, rb_key k); extern rb_node *rb_tree_find_lt(rb_tree *t, rb_key k); extern rb_node *rb_tree_find_gt(rb_tree *t, rb_key k); extern rb_node *rb_tree_resort(rb_tree *t, rb_node *n); extern rb_node *rb_tree_min(rb_tree *t); extern rb_node *rb_tree_max(rb_tree *t); extern rb_node *rb_tree_succ(rb_node *n); extern rb_node *rb_tree_pred(rb_node *n); extern void rb_tree_shift_keys(rb_tree *t, ptrdiff_t kshift); /* To change a key, use rb_tree_find+resort. Removing a node currently wastes memory unless you change the allocation scheme in redblack.c */ extern rb_node *rb_tree_remove(rb_tree *t, rb_node *n); #ifdef __cplusplus } /* extern "C" */ #endif /* __cplusplus */ #endif nloptr/src/nlopt_src/util/nlopt-util.h0000644000176000001440000001214113355070635017655 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #ifndef NLOPT_UTIL_H #define NLOPT_UTIL_H #include #include #include "config.h" #include "nlopt.h" /* workaround for Solaris + gcc 3.4.x bug (see configure.ac) */ #if defined(__GNUC__) && defined(REPLACEMENT_HUGE_VAL) # undef HUGE_VAL # define HUGE_VAL REPLACEMENT_HUGE_VAL #endif #ifndef HAVE_COPYSIGN /* not quite right for y == -0, but good enough for us */ # define copysign(x, y) ((y) < 0 ? -fabs(x) : fabs(x)) #endif #ifdef __cplusplus extern "C" { #endif /* __cplusplus */ int nlopt_isinf(double x); /* re-entrant qsort */ extern void nlopt_qsort_r(void *base_, size_t nmemb, size_t size, void *thunk, int (*compar)(void *, const void *, const void *)); /* seconds timer */ extern double nlopt_seconds(void); extern unsigned long nlopt_time_seed(void); /* pseudorandom number generation by Mersenne twister algorithm */ extern void nlopt_init_genrand(unsigned long s); extern double nlopt_urand(double a, double b); extern int nlopt_iurand(int n); extern double nlopt_nrand(double mean, double stddev); /* Sobol' low-discrepancy-sequence generation */ typedef struct nlopt_soboldata_s *nlopt_sobol; extern nlopt_sobol nlopt_sobol_create(unsigned sdim); extern void nlopt_sobol_destroy(nlopt_sobol s); extern void nlopt_sobol_next01(nlopt_sobol s, double *x); extern void nlopt_sobol_next(nlopt_sobol s, double *x, const double *lb, const double *ub); extern void nlopt_sobol_skip(nlopt_sobol s, unsigned n, double *x); /* stopping criteria */ typedef struct { unsigned n; double minf_max; double ftol_rel; double ftol_abs; double xtol_rel; const double *xtol_abs; int nevals, maxeval; double maxtime, start; int *force_stop; } nlopt_stopping; extern int nlopt_stop_f(const nlopt_stopping *stop, double f, double oldf); extern int nlopt_stop_ftol(const nlopt_stopping *stop, double f, double oldf); extern int nlopt_stop_x(const nlopt_stopping *stop, const double *x, const double *oldx); extern int nlopt_stop_dx(const nlopt_stopping *stop, const double *x, const double *dx); extern int nlopt_stop_xs(const nlopt_stopping *stop, const double *xs, const double *oldxs, const double *scale_min, const double *scale_max); extern int nlopt_stop_evals(const nlopt_stopping *stop); extern int nlopt_stop_time_(double start, double maxtime); extern int nlopt_stop_time(const nlopt_stopping *stop); extern int nlopt_stop_evalstime(const nlopt_stopping *stop); extern int nlopt_stop_forced(const nlopt_stopping *stop); /* for local optimizations, temporarily setting eval/time limits */ extern nlopt_result nlopt_optimize_limited(nlopt_opt opt, double *x, double *minf, int maxevals, double maxtime); /* data structure for nonlinear inequality or equality constraint (f <= 0 or f = 0, respectively). tol (>= 0) is a tolerance that is used for stopping criteria -- the point is considered "feasible" for purposes of stopping if the constraint is violated by at most tol. */ typedef struct { unsigned m; /* dimensional of constraint: mf maps R^n -> R^m */ nlopt_func f; /* one-dimensional constraint, requires m == 1 */ nlopt_mfunc mf; nlopt_precond pre; /* preconditioner for f (NULL if none or if mf) */ void *f_data; double *tol; } nlopt_constraint; extern unsigned nlopt_count_constraints(unsigned p, const nlopt_constraint *c); extern unsigned nlopt_max_constraint_dim(unsigned p, const nlopt_constraint *c); extern void nlopt_eval_constraint(double *result, double *grad, const nlopt_constraint *c, unsigned n, const double *x); /* rescale.c: */ double *nlopt_compute_rescaling(unsigned n, const double *dx); double *nlopt_new_rescaled(unsigned n, const double *s, const double *x); void nlopt_rescale(unsigned n, const double *s, const double *x, double *xs); void nlopt_unscale(unsigned n, const double *s, const double *x, double *xs); void nlopt_reorder_bounds(unsigned n, double *lb, double *ub); #ifdef __cplusplus } /* extern "C" */ #endif /* __cplusplus */ #endif nloptr/src/nlopt_src/util/redblack_test.c0000644000176000001440000001320713355070635020353 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include #include #include #include #include "redblack.h" #include "config.h" /* workaround for Solaris + gcc 3.4.x bug (see configure.ac) */ #if defined(__GNUC__) && defined(REPLACEMENT_HUGE_VAL) # undef HUGE_VAL # define HUGE_VAL REPLACEMENT_HUGE_VAL #endif static int comp(rb_key k1, rb_key k2) { if (*k1 < *k2) return -1; if (*k1 > *k2) return +1; return 0; } int main(int argc, char **argv) { int N, M; int *k; double kd; rb_tree t; rb_node *n; int i, j; if (argc < 2) { fprintf(stderr, "Usage: redblack_test Ntest [rand seed]\n"); return 1; } N = atoi(argv[1]); k = (int *) malloc(N * sizeof(int)); rb_tree_init(&t, comp); srand((unsigned) (argc > 2 ? atoi(argv[2]) : time(NULL))); for (i = 0; i < N; ++i) { double *newk = (double *) malloc(sizeof(double)); *newk = (k[i] = rand() % N); if (!rb_tree_insert(&t, newk)) { fprintf(stderr, "error in rb_tree_insert\n"); return 1; } if (!rb_tree_check(&t)) { fprintf(stderr, "rb_tree_check_failed after insert!\n"); return 1; } } if (t.N != N) { fprintf(stderr, "incorrect N (%d) in tree (vs. %d)\n", t.N, N); return 1; } for (i = 0; i < N; ++i) { kd = k[i]; if (!rb_tree_find(&t, &kd)) { fprintf(stderr, "rb_tree_find lost %d!\n", k[i]); return 1; } } n = rb_tree_min(&t); for (i = 0; i < N; ++i) { if (!n) { fprintf(stderr, "not enough successors %d\n!", i); return 1; } printf("%d: %g\n", i, n->k[0]); n = rb_tree_succ(n); } if (n) { fprintf(stderr, "too many successors!\n"); return 1; } n = rb_tree_max(&t); for (i = 0; i < N; ++i) { if (!n) { fprintf(stderr, "not enough predecessors %d\n!", i); return 1; } printf("%d: %g\n", i, n->k[0]); n = rb_tree_pred(n); } if (n) { fprintf(stderr, "too many predecessors!\n"); return 1; } for (M = N; M > 0; --M) { int knew = rand() % N; /* random new key */ j = rand() % M; /* random original key to replace */ for (i = 0; i < N; ++i) if (k[i] >= 0) if (j-- == 0) break; if (i >= N) abort(); kd = k[i]; if (!(n = rb_tree_find(&t, &kd))) { fprintf(stderr, "rb_tree_find lost %d!\n", k[i]); return 1; } n->k[0] = knew; if (!rb_tree_resort(&t, n)) { fprintf(stderr, "error in rb_tree_resort\n"); return 1; } if (!rb_tree_check(&t)) { fprintf(stderr, "rb_tree_check_failed after change %d!\n", N - M + 1); return 1; } k[i] = -1 - knew; } if (t.N != N) { fprintf(stderr, "incorrect N (%d) in tree (vs. %d)\n", t.N, N); return 1; } for (i = 0; i < N; ++i) k[i] = -1 - k[i]; /* undo negation above */ for (i = 0; i < N; ++i) { rb_node *le, *gt; kd = 0.01 * (rand() % (N * 150) - N*25); le = rb_tree_find_le(&t, &kd); gt = rb_tree_find_gt(&t, &kd); n = rb_tree_min(&t); double lek = le ? le->k[0] : -HUGE_VAL; double gtk = gt ? gt->k[0] : +HUGE_VAL; printf("%g <= %g < %g\n", lek, kd, gtk); if (n->k[0] > kd) { if (le) { fprintf(stderr, "found invalid le %g for %g\n", lek, kd); return 1; } if (gt != n) { fprintf(stderr, "gt is not first node for k=%g\n", kd); return 1; } } else { rb_node *succ = n; do { n = succ; succ = rb_tree_succ(n); } while (succ && succ->k[0] <= kd); if (n != le) { fprintf(stderr, "rb_tree_find_le gave wrong result for k=%g\n",kd); return 1; } if (succ != gt) { fprintf(stderr, "rb_tree_find_gt gave wrong result for k=%g\n",kd); return 1; } } } for (M = N; M > 0; --M) { j = rand() % M; for (i = 0; i < N; ++i) if (k[i] >= 0) if (j-- == 0) break; if (i >= N) abort(); kd = k[i]; if (!(n = rb_tree_find(&t, &kd))) { fprintf(stderr, "rb_tree_find lost %d!\n", k[i]); return 1; } n = rb_tree_remove(&t, n); free(n->k); free(n); if (!rb_tree_check(&t)) { fprintf(stderr, "rb_tree_check_failed after remove!\n"); return 1; } k[i] = -1 - k[i]; } if (t.N != 0) { fprintf(stderr, "nonzero N (%d) in tree at end\n", t.N); return 1; } rb_tree_destroy(&t); free(k); printf("SUCCESS.\n"); return 0; } nloptr/src/nlopt_src/util/mt19937ar.c0000644000176000001440000001750613355070635017133 0ustar ripleyusers/* A C-program for MT19937, with initialization improved 2002/1/26. Coded by Takuji Nishimura and Makoto Matsumoto. Before using, initialize the state by using init_genrand(seed) or init_by_array(init_key, key_length). Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura, All rights reserved. Modified 2007 by Steven G. Johnson for use with NLopt (to avoid namespace pollution, use uint32_t instead of unsigned long, and add the urand function). Modified 2009 to add normal-distributed random numbers. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. The names of its contributors may not be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Any feedback is very welcome. http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html email: m-mat @ math.sci.hiroshima-u.ac.jp (remove space) */ #include "nlopt-util.h" #if defined(HAVE_STDINT_H) # include #endif #ifndef HAVE_UINT32_T # if SIZEOF_UNSIGNED_LONG == 4 typedef unsigned long uint32_t; # elif SIZEOF_UNSIGNED_INT == 4 typedef unsigned int uint32_t; # else # error No 32-bit unsigned integer type # endif #endif /* Period parameters */ #define N 624 #define M 397 #define MATRIX_A 0x9908b0dfUL /* constant vector a */ #define UPPER_MASK 0x80000000UL /* most significant w-r bits */ #define LOWER_MASK 0x7fffffffUL /* least significant r bits */ /* SGJ 2010: make RNG thread-safe by declaring the RNG state as thread-local storage, at least for GCC, MSVC, and Intel C++ */ static THREADLOCAL uint32_t mt[N]; /* the array for the state vector */ static THREADLOCAL int mti=N+1; /* mti==N+1 means mt[N] is not initialized */ /* initializes mt[N] with a seed */ void nlopt_init_genrand(unsigned long s) { mt[0]= s & 0xffffffffUL; for (mti=1; mti> 30)) + mti); /* See Knuth TAOCP Vol2. 3rd Ed. P.106 for multiplier. */ /* In the previous versions, MSBs of the seed affect */ /* only MSBs of the array mt[]. */ /* 2002/01/09 modified by Makoto Matsumoto */ mt[mti] &= 0xffffffffUL; /* for >32 bit machines */ } } /* generates a random number on [0,0xffffffff]-interval */ static uint32_t nlopt_genrand_int32(void) { uint32_t y; static uint32_t mag01[2]={0x0UL, MATRIX_A}; /* mag01[x] = x * MATRIX_A for x=0,1 */ if (mti >= N) { /* generate N words at one time */ int kk; if (mti == N+1) /* if init_genrand() has not been called, */ nlopt_init_genrand(5489UL); /* a default initial seed is used */ for (kk=0;kk> 1) ^ mag01[y & 0x1UL]; } for (;kk> 1) ^ mag01[y & 0x1UL]; } y = (mt[N-1]&UPPER_MASK)|(mt[0]&LOWER_MASK); mt[N-1] = mt[M-1] ^ (y >> 1) ^ mag01[y & 0x1UL]; mti = 0; } y = mt[mti++]; /* Tempering */ y ^= (y >> 11); y ^= (y << 7) & 0x9d2c5680UL; y ^= (y << 15) & 0xefc60000UL; y ^= (y >> 18); return y; } #if 0 /* not used in NLopt */ /* initialize by an array with array-length */ /* init_key is the array for initializing keys */ /* key_length is its length */ /* slight change for C++, 2004/2/26 */ static void nlopt_init_by_array(uint32_t init_key[], int key_length) { int i, j, k; nlopt_init_genrand(19650218UL); i=1; j=0; k = (N>key_length ? N : key_length); for (; k; k--) { mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * 1664525UL)) + init_key[j] + j; /* non linear */ mt[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */ i++; j++; if (i>=N) { mt[0] = mt[N-1]; i=1; } if (j>=key_length) j=0; } for (k=N-1; k; k--) { mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * 1566083941UL)) - i; /* non linear */ mt[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */ i++; if (i>=N) { mt[0] = mt[N-1]; i=1; } } mt[0] = 0x80000000UL; /* MSB is 1; assuring non-zero initial array */ } /* generates a random number on [0,0x7fffffff]-interval */ static long nlopt_genrand_int31(void) { return (long)(nlopt_genrand_int32()>>1); } /* generates a random number on [0,1]-real-interval */ static double nlopt_genrand_real1(void) { return nlopt_genrand_int32()*(1.0/4294967295.0); /* divided by 2^32-1 */ } /* generates a random number on [0,1)-real-interval */ static double nlopt_genrand_real2(void) { return nlopt_genrand_int32()*(1.0/4294967296.0); /* divided by 2^32 */ } /* generates a random number on (0,1)-real-interval */ static double nlopt_genrand_real3(void) { return (((double)nlopt_genrand_int32()) + 0.5)*(1.0/4294967296.0); /* divided by 2^32 */ } #endif /* generates a random number on [0,1) with 53-bit resolution*/ static double nlopt_genrand_res53(void) { uint32_t a=nlopt_genrand_int32()>>5, b=nlopt_genrand_int32()>>6; return(a*67108864.0+b)*(1.0/9007199254740992.0); } /* These real versions are due to Isaku Wada, 2002/01/09 added */ /* generate uniform random number in [a,b) with 53-bit resolution, added by SGJ */ double nlopt_urand(double a, double b) { return(a + (b - a) * nlopt_genrand_res53()); } /* generate a uniform random number in [0,n), added by SGJ */ int nlopt_iurand(int n) { return(nlopt_genrand_int32() % n); } /* normal-distributed random numbers with the given mean and std. deviation, added by SGJ */ double nlopt_nrand(double mean, double stddev) { // Box-Muller algorithm to generate Gaussian from uniform // see Knuth vol II algorithm P, sec. 3.4.1 double v1, v2, s; do { v1 = nlopt_urand(-1, 1); v2 = nlopt_urand(-1, 1); s = v1*v1 + v2*v2; } while (s >= 1.0); if (s == 0) { return mean; } else { return mean + v1 * sqrt(-2 * log(s) / s) * stddev; } } #if 0 #include int main(void) { int i; uint32_t init[4]={0x123, 0x234, 0x345, 0x456}, length=4; init_by_array(init, length); printf("1000 outputs of nlopt_genrand_int32()\n"); for (i=0; i<1000; i++) { printf("%10lu ", nlopt_genrand_int32()); if (i%5==4) printf("\n"); } printf("\n1000 outputs of genrand_real2()\n"); for (i=0; i<1000; i++) { printf("%10.8f ", genrand_real2()); if (i%5==4) printf("\n"); } return 0; } #endif nloptr/src/nlopt_src/util/rescale.c0000644000176000001440000000567713355070635017177 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include #include "nlopt-util.h" /* Return a new array of length n (> 0) that gives a rescaling factor for each dimension, or NULL if out of memory, with dx being the array of nonzero initial steps in each dimension. */ double *nlopt_compute_rescaling(unsigned n, const double *dx) { double *s = (double *) malloc(sizeof(double) * n); unsigned i; if (!s) return NULL; for (i = 0; i < n; ++i) s[i] = 1.0; /* default: no rescaling */ if (n == 1) return s; for (i = 1; i < n && dx[i] == dx[i-1]; ++i) ; if (i < n) { /* unequal initial steps, rescale to make equal to dx[0] */ for (i = 1; i < n; ++i) s[i] = dx[i] / dx[0]; } return s; } void nlopt_rescale(unsigned n, const double *s, const double *x, double *xs) { unsigned i; if (!s) { for (i = 0; i < n;++i) xs[i] = x[i]; } else { for (i = 0; i < n;++i) xs[i] = x[i] / s[i]; } } void nlopt_unscale(unsigned n, const double *s, const double *x, double *xs) { unsigned i; if (!s) { for (i = 0; i < n;++i) xs[i] = x[i]; } else { for (i = 0; i < n;++i) xs[i] = x[i] * s[i]; } } /* return a new array of length n equal to the original array x divided by the scale factors s, or NULL on a memory error */ double *nlopt_new_rescaled(unsigned n, const double *s, const double *x) { double *xs = (double *) malloc(sizeof(double) * n); if (!xs) return NULL; nlopt_rescale(n, s, x, xs); return xs; } /* since rescaling can flip the signs of the x components and the bounds, we may have to re-order the bounds in order to ensure that they remain in the correct order */ void nlopt_reorder_bounds(unsigned n, double *lb, double *ub) { unsigned i; for (i = 0; i < n; ++i) if (lb[i] > ub[i]) { double t = lb[i]; lb[i] = ub[i]; ub[i] = t; } } nloptr/src/nlopt_src/util/qsort_r.c0000644000176000001440000001012013355070635017225 0ustar ripleyusersextern "C" { /* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include "nlopt-util.h" /* Simple replacement for the BSD qsort_r function (re-entrant sorting), if it is not available. (glibc 2.8 included a qsort_r function as well, but totally *%&$#-ed things up by gratuitously changing the argument order, in such a way as to allow code using the BSD ordering to compile but die a flaming death at runtime. Damn them all to Hell, I'll just use my own implementation.) (Actually, with glibc 2.3.6 on my Intel Core Duo, my implementation below seems to be significantly faster than qsort. Go figure.) */ #ifndef HAVE_QSORT_R_damn_it_use_my_own /* swap size bytes between a_ and b_ */ static void swap(void *a_, void *b_, size_t size) { if (a_ == b_) return; { size_t i, nlong = size / sizeof(long); long *a = (long *) a_, *b = (long *) b_; for (i = 0; i < nlong; ++i) { long c = a[i]; a[i] = b[i]; b[i] = c; } a_ = (void*) (a + nlong); b_ = (void*) (b + nlong); } { size_t i; char *a = (char *) a_, *b = (char *) b_; size = size % sizeof(long); for (i = 0; i < size; ++i) { char c = a[i]; a[i] = b[i]; b[i] = c; } } } #endif /* HAVE_QSORT_R */ void nlopt_qsort_r(void *base_, size_t nmemb, size_t size, void *thunk, int (*compar)(void *, const void *, const void *)) { #ifdef HAVE_QSORT_R_damn_it_use_my_own /* Even if we could detect glibc vs. BSD by appropriate macrology, there is no way to make the calls compatible without writing a wrapper for the compar function...screw this. */ qsort_r(base_, nmemb, size, thunk, compar); #else char *base = (char *) base_; if (nmemb < 10) { /* use O(nmemb^2) algorithm for small enough nmemb */ size_t i, j; for (i = 0; i+1 < nmemb; ++i) for (j = i+1; j < nmemb; ++j) if (compar(thunk, base+i*size, base+j*size) > 0) swap(base+i*size, base+j*size, size); } else { size_t i, pivot, npart; /* pick median of first/middle/last elements as pivot */ { const char *a = base, *b = base + (nmemb/2)*size, *c = base + (nmemb-1)*size; pivot = compar(thunk,a,b) < 0 ? (compar(thunk,b,c) < 0 ? nmemb/2 : (compar(thunk,a,c) < 0 ? nmemb-1 : 0)) : (compar(thunk,a,c) < 0 ? 0 : (compar(thunk,b,c) < 0 ? nmemb-1 : nmemb/2)); } /* partition array */ swap(base + pivot*size, base + (nmemb-1) * size, size); pivot = (nmemb - 1) * size; for (i = npart = 0; i < nmemb-1; ++i) if (compar(thunk, base+i*size, base+pivot) <= 0) swap(base+i*size, base+(npart++)*size, size); swap(base+npart*size, base+pivot, size); /* recursive sort of two partitions */ nlopt_qsort_r(base, npart, size, thunk, compar); npart++; /* don't need to sort pivot */ nlopt_qsort_r(base+npart*size, nmemb-npart, size, thunk, compar); } #endif /* !HAVE_QSORT_R */ } } nloptr/src/nlopt_src/util/soboldata.h0000644000176000001440000015732213355070635017531 0ustar ripleyusers/* Copyright (c) 2007 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #ifndef SOBOLSEQ_H #define SOBOLSEQ_H /* Data on the primitive binary polynomials (a) and the corresponding starting values m, for Sobol sequences in up to 1111 dimensions, taken from: P. Bratley and B. L. Fox, Algorithm 659, ACM Trans. Math. Soft. 14 (1), 88-100 (1988), as modified by: S. Joe and F. Y. Kuo, ACM Trans. Math. Soft 29 (1), 49-57 (2003). */ #define MAXDIM 1111 #define MAXDEG 12 /* successive primitive binary-coefficient polynomials p(z) = a_0 + a_1 z + a_2 z^2 + ... a_31 z^31, where a_i is the i-th bit of sobol_a[j] for the j-th polynomial. */ static const uint32_t sobol_a[MAXDIM-1] = { 3,7,11,13,19,25,37,59,47,61,55,41,67,97,91, 109,103,115,131,193,137,145,143,241,157,185,167,229,171,213, 191,253,203,211,239,247,285,369,299,301,333,351,355,357,361, 391,397,425,451,463,487,501,529,539,545,557,563,601,607,617, 623,631,637,647,661,675,677,687,695,701,719,721,731,757,761, 787,789,799,803,817,827,847,859,865,875,877,883,895,901,911, 949,953,967,971,973,981,985,995,1001,1019,1033,1051,1063, 1069,1125,1135,1153,1163,1221,1239,1255,1267,1279,1293,1305, 1315,1329,1341,1347,1367,1387,1413,1423,1431,1441,1479,1509, 1527,1531,1555,1557,1573,1591,1603,1615,1627,1657,1663,1673, 1717,1729,1747,1759,1789,1815,1821,1825,1849,1863,1869,1877, 1881,1891,1917,1933,1939,1969,2011,2035,2041,2053,2071,2091, 2093,2119,2147,2149,2161,2171,2189,2197,2207,2217,2225,2255, 2257,2273,2279,2283,2293,2317,2323,2341,2345,2363,2365,2373, 2377,2385,2395,2419,2421,2431,2435,2447,2475,2477,2489,2503, 2521,2533,2551,2561,2567,2579,2581,2601,2633,2657,2669, 2681,2687,2693,2705,2717,2727,2731,2739, 2741,2773,2783,2793,2799,2801,2811,2819,2825,2833,2867,2879, 2881,2891,2905,2911,2917,2927,2941,2951,2955,2963,2965,2991, 2999,3005,3017,3035,3037,3047,3053,3083,3085,3097,3103,3159, 3169,3179,3187,3205,3209,3223,3227,3229,3251,3263,3271,3277, 3283,3285,3299,3305,3319,3331,3343,3357,3367,3373,3393,3399, 3413,3417,3427,3439,3441,3475,3487,3497,3515,3517,3529,3543, 3547,3553,3559,3573,3589,3613,3617,3623,3627,3635,3641,3655, 3659,3669,3679,3697,3707,3709,3713,3731,3743,3747,3771,3791, 3805,3827,3833,3851,3865,3889,3895,3933,3947,3949,3957,3971, 3985,3991,3995,4007,4013,4021,4045,4051,4069,4073,4179,4201, 4219,4221,4249,4305,4331,4359,4383,4387,4411,4431,4439,4449, 4459,4485,4531,4569,4575,4621,4663,4669,4711,4723,4735,4793, 4801,4811,4879,4893,4897,4921,4927,4941,4977,5017,5027,5033, 5127,5169,5175,5199,5213,5223,5237,5287,5293,5331,5391,5405, 5453,5523,5573,5591,5597,5611,5641,5703,5717,5721,5797,5821, 5909,5913, 5955,5957,6005,6025,6061,6067,6079,6081, 6231,6237,6289,6295,6329,6383,6427,6453,6465,6501,6523,6539, 6577,6589,6601,6607,6631,6683,6699,6707,6761,6795,6865,6881, 6901,6923,6931,6943,6999,7057,7079,7103,7105,7123,7173,7185, 7191,7207,7245,7303,7327,7333,7355,7365,7369,7375,7411,7431, 7459,7491,7505,7515,7541,7557,7561,7701,7705,7727,7749,7761, 7783,7795,7823,7907,7953,7963,7975,8049,8089,8123,8125,8137, 8219,8231,8245,8275,8293,8303,8331,8333,8351,8357,8367,8379, 8381,8387,8393,8417,8435,8461,8469,8489,8495,8507,8515,8551, 8555,8569,8585,8599,8605,8639,8641,8647,8653,8671,8675,8689, 8699,8729,8741,8759,8765,8771,8795,8797,8825,8831,8841,8855, 8859,8883,8895,8909,8943,8951,8955,8965,8999,9003,9031,9045, 9049,9071,9073,9085,9095,9101,9109,9123,9129,9137,9143,9147, 9185,9197,9209,9227,9235,9247,9253,9257,9277,9297,9303,9313, 9325,9343,9347,9371,9373,9397,9407,9409,9415,9419,9443,9481, 9495,9501,9505,9517,9529,9555,9557,9571,9585,9591,9607,9611, 9621,9625, 9631,9647,9661,9669,9679,9687,9707,9731, 9733,9745,9773,9791,9803,9811,9817,9833,9847,9851,9863,9875, 9881,9905,9911,9917,9923,9963,9973,10003,10025,10043,10063, 10071,10077,10091,10099,10105,10115,10129,10145,10169,10183, 10187,10207,10223,10225,10247,10265,10271,10275,10289,10299, 10301,10309,10343,10357,10373,10411,10413,10431,10445,10453, 10463,10467,10473,10491,10505,10511,10513,10523,10539,10549, 10559,10561,10571,10581,10615,10621,10625,10643,10655,10671, 10679,10685,10691,10711,10739,10741,10755,10767,10781,10785, 10803,10805,10829,10857,10863,10865,10875,10877,10917,10921, 10929,10949,10967,10971,10987,10995,11009,11029,11043,11045, 11055,11063,11075,11081,11117,11135,11141,11159,11163,11181, 11187,11225,11237,11261,11279,11297,11307,11309,11327,11329, 11341,11377,11403,11405,11413,11427,11439,11453,11461,11473, 11479,11489,11495,11499,11533,11545,11561,11567,11575,11579, 11589,11611,11623,11637,11657,11663,11687,11691,11701,11747, 11761,11773,11783,11795,11797,11817,11849,11855,11867,11869, 11873,11883,11919, 11921,11927,11933,11947,11955,11961, 11999,12027,12029,12037,12041,12049,12055,12095,12097,12107, 12109,12121,12127,12133,12137,12181,12197,12207,12209,12239, 12253,12263,12269,12277,12287,12295,12309,12313,12335,12361, 12367,12391,12409,12415,12433,12449,12469,12479,12481,12499, 12505,12517,12527,12549,12559,12597,12615,12621,12639,12643, 12657,12667,12707,12713,12727,12741,12745,12763,12769,12779, 12781,12787,12799,12809,12815,12829,12839,12857,12875,12883, 12889,12901,12929,12947,12953,12959,12969,12983,12987,12995, 13015,13019,13031,13063,13077,13103,13137,13149,13173,13207, 13211,13227,13241,13249,13255,13269,13283,13285,13303,13307, 13321,13339,13351,13377,13389,13407,13417,13431,13435,13447, 13459,13465,13477,13501,13513,13531,13543,13561,13581,13599, 13605,13617,13623,13637,13647,13661,13677,13683,13695,13725, 13729,13753,13773,13781,13785,13795,13801,13807,13825,13835, 13855,13861,13871,13883,13897,13905,13915,13939,13941,13969, 13979,13981,13997,14027,14035,14037,14051,14063,14085,14095, 14107,14113,14125,14137,14145, 14151,14163,14193,14199,14219,14229, 14233,14243,14277,14287,14289,14295,14301,14305,14323,14339, 14341,14359,14365,14375,14387,14411,14425,14441,14449,14499, 14513,14523,14537,14543,14561,14579,14585,14593,14599,14603, 14611,14641,14671,14695,14701,14723,14725,14743,14753,14759, 14765,14795,14797,14803,14831,14839,14845,14855,14889,14895, 14909,14929,14941,14945,14951,14963,14965,14985,15033,15039, 15053,15059,15061,15071,15077,15081,15099,15121,15147,15149, 15157,15167,15187,15193,15203,15205,15215,15217,15223,15243, 15257,15269,15273,15287,15291,15313,15335,15347,15359,15373, 15379,15381,15391,15395,15397,15419,15439,15453,15469,15491, 15503,15517,15527,15531,15545,15559,15593,15611,15613,15619, 15639,15643,15649,15661,15667,15669,15681,15693,15717,15721, 15741,15745,15765,15793,15799,15811,15825,15835,15847,15851, 15865,15877,15881,15887,15899,15915,15935,15937,15955,15973, 15977,16011,16035,16061,16069,16087,16093,16097,16121,16141, 16153,16159,16165,16183,16189,16195,16197,16201,16209,16215, 16225,16259,16265,16273,16299, 16309,16355,16375,16381, }; /* starting direction #'s m[i] = sobol_minit[i][j] for i=0..d of the * degree-d primitive polynomial sobol_a[j]. */ static const uint32_t sobol_minit[MAXDEG+1][MAXDIM-1] = { /* [0][*] */ { 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 }, /* [1][*] */ { 0, 1,3,1,3,1,3,3,1,3,1,3,1,3,1,1,3,1,3,1,3, 1,3,3,1,1,1,3,1,3,1,3,3,1,3,1,1,1,3,1,3,1,1,1,3,3,1,3,3,1,1, 3,3,1,3,3,3,1,3,1,3,1,1,3,3,1,1,1,1,3,1,1,3,1,1,1,3,3,1,3,3, 1,3,3,3,1,3,3,3,1,3,3,1,3,3,3,1,3,1,3,1,1,3,3,1,3,3,1,1,1,3, 3,1,3,3,1,3,1,1,3,3,3,1,1,1,3,1,1,3,1,1,3,3,1,3,1,3,3,3,3,1, 1,1,3,3,1,1,3,1,1,1,1,1,1,3,1,3,1,1,1,3,1,3,1,3,3,3,1,1,3,3, 1,3,1,3,1,1,3,1,3,1,3,1,3,1,1,1,3,3,1,3,3,1,3,1,1,1,3,1,3,1, 1,3,1,1,3,3,1,1,3,3,3,1,3,3,3,1,3,1,3,1,1,1,3,1,1,1,3,1,1,1, 1,1,3,3,3,1,1,1,1,3,3,3,1,3,3,1,1,1,1,3,1,1,3,1,3,3,1,1,3,3, 1,1,1,1,3,1,3,3,1,3,3,1,1,1,3,3,3,1,3,3,1,3,3,1,3,1,3,3,3,1, 3,1,1,3,1,3,1,1,1,3,3,3,1,1,3,1,3,1,1,1,1,1,1,3,1,1,3,1,3,3, 1,1,1,1,3,1,3,1,3,1,1,1,1,3,3,1,1,1,1,1,3,3,3,1,1,3,3,3,3,3, 1,3,3,1,3,3,3,3,1,1,1,1,1,1,3,1,1,3,1,1,1,3,1,1,1,3,3,3,1,3, 1,1,3,3,3,1,3,3,1,3,1,3,3,1,3,3,3,1,1, 3,3,1,3,1,3,1,1,1,3,3,3,3,1,3,1,1,3,1, 3,1,1,1,3,1,3,1,3,1,3,3,3,3,3,3,3,3,1,3,3,3,3,3,1,3,1,3,3,3, 1,3,1,3,1,3,3,1,3,3,3,3,3,3,3,3,3,1,1,1,1,1,1,3,3,1,1,3,3,1, 1,1,3,3,1,1,3,3,3,3,1,1,3,1,3,3,1,3,3,1,1,1,3,3,3,1,1,3,3,3, 3,3,1,1,1,3,1,3,3,1,3,3,3,3,1,1,3,1,1,3,1,3,1,3,1,3,3,1,1,3, 3,1,3,3,1,3,3,1,1,3,1,3,3,1,1,3,1,3,1,3,1,1,3,3,1,1,1,3,3,1, 3,1,1,3,3,1,1,3,1,3,1,1,1,1,1,3,1,1,1,1,3,1,3,1,1,3,3,1,1,3, 1,3,1,3,3,3,1,3,3,3,1,1,3,3,3,1,1,1,1,3,1,3,1,3,1,1,3,3,1,1, 1,3,3,1,3,1,3,1,1,1,1,1,1,3,1,3,3,1,3,3,3,1,3,1,1,3,3,1,1,3, 3,1,1,1,3,1,3,3,1,1,3,1,1,3,1,3,1,1,1,3,3,3,3,1,1,3,3,1,1,1, 1,3,1,1,3,3,3,1,1,3,3,1,3,3,1,1,3,3,3,3,3,3,3,1,3,3,1,3,1,3, 1,1,3,3,1,1,1,3,1,3,3,1,3,3,1,3,1,1,3,3,3,1,1,1,3,1,1,1,3,3, 3,1,3,3,1,3,1,1,3,3,3,1,3,3,1,1,1,3,1,3,3,3,3,3,3,3,3,1,3,3, 1,3,1,1,3,3,3,1,3,3,3,3,3,1,3,3,3,1,1,1, 3,3,1,3,3,1,3,1,3,1,3,1,3,3,3,3,3,3, 1,1,3,1,3,1,1,1,1,1,3,1,1,1,3,1,3,1,1,3,3,3,1,3,1,3,1,1,3,1, 3,3,1,3,1,3,3,1,3,3,1,3,3,3,3,3,3,1,3,1,1,3,3,3,1,1,3,3,3,3, 3,3,3,1,3,3,3,3,1,3,1,3,3,3,1,3,1,3,1,1,1,3,3,1,3,1,1,3,3,1, 3,1,1,1,1,3,1,3,1,1,3,1,3,1,3,3,3,3,3,3,1,3,3,3,3,1,3,3,1,3, 3,3,3,3,1,1,1,1,3,3,3,1,3,3,1,1,3,3,1,1,3,3,1,3,1,1,3,1,3,3, 3,3,3,1,3,1,1,3,3,3,3,1,3,1,1,3,3,3,3,3,3,1,1,3,1,3,1,1,3,1, 1,1,1,3,3,1,1,3,1,1,1,3,1,3,1,1,3,3,1,3,1,1,3,3,3,3,3,1,3,1, 1,1,3,1,1,1,3,1,1,3,1,3,3,3,3,3,1,1,1,3,3,3,3,1,3,3,3,3,1,1, 3,3,3,1,3,1,1,3,3,1,3,3,1,1,1,1,1,3,1,1,3,3,1,1,1,3,1,1,3,3, 1,3,3,3,3,3,3,3,3,1,1,3,3,1,1,3,1,3,3,3,3,3,1}, /* [2][*] */ { 0,0, 7,5,1,3,3,7,5,5,7,7,1,3,3,7,5,1,1,5,3,7, 1,7,5,1,3,7,7,1,1,1,5,7,7,5,1,3,3,7,5,5,5,3,3,3,1,1,5,1,1,5, 3,3,3,3,1,3,7,5,7,3,7,1,3,3,5,1,3,5,5,7,7,7,1,1,3,3,1,1,5,1, 5,7,5,1,7,5,3,3,1,5,7,1,7,5,1,7,3,1,7,1,7,3,3,5,7,3,3,5,1,3, 3,1,3,5,1,3,3,3,7,1,1,7,3,1,3,7,5,5,7,5,5,3,1,3,3,3,1,3,3,7, 3,3,1,7,5,1,7,7,5,7,5,1,3,1,7,3,7,3,5,7,3,1,3,3,3,1,5,7,3,3, 7,7,7,5,3,1,7,1,3,7,5,3,3,3,7,1,1,3,1,5,7,1,3,5,3,5,3,3,7,5, 5,3,3,1,3,7,7,7,1,5,7,1,3,1,1,7,1,3,1,7,1,5,3,5,3,1,1,5,5,3, 3,5,7,1,5,3,7,7,3,5,3,3,1,7,3,1,3,5,7,1,3,7,1,5,1,3,1,5,3,1, 7,1,5,5,5,3,7,1,1,7,3,1,1,7,5,7,5,7,7,3,7,1,3,7,7,3,5,1,1,7, 1,5,5,5,1,5,1,7,5,5,7,1,1,7,1,7,7,1,1,3,3,3,7,7,5,3,7,3,1,3, 7,5,3,3,5,7,1,1,5,5,7,7,1,1,1,1,5,5,5,7,5,7,1,1,3,5,1,3,3,7, 3,7,5,3,5,3,1,7,1,7,7,1,1,7,7,7,5,5,1,1,7,5,5,7,5,1,1,5,5,5, 5,5,5,1,3,1,5,7,3,3,5,7,3,7,1,7,7,1,3, 5,1,5,5,3,7,3,7,7,5,7,5,7,1,1,5,3,5,1, 5,3,7,1,5,7,7,3,5,1,3,5,1,5,3,3,3,7,3,5,1,3,7,7,3,7,5,3,3,1, 7,5,1,1,3,7,1,7,1,7,3,7,3,5,7,3,5,3,1,1,1,5,7,7,3,3,1,1,1,5, 5,7,3,1,1,3,3,7,3,3,5,1,3,7,3,3,7,3,5,7,5,7,7,3,3,5,1,3,5,3, 1,3,5,1,1,3,7,7,1,5,1,3,7,3,7,3,5,1,7,1,1,3,5,3,7,1,5,5,1,1, 3,1,3,3,7,1,7,3,1,7,3,1,7,3,5,3,5,7,3,3,3,5,1,7,7,1,3,1,3,7, 7,1,3,7,3,1,5,3,1,1,1,5,3,3,7,1,5,3,5,1,3,1,3,1,5,7,7,1,1,5, 3,1,5,1,1,7,7,3,5,5,1,7,1,5,1,1,3,1,5,7,5,7,7,1,5,1,1,3,5,1, 5,5,3,1,3,1,5,5,3,3,3,3,1,1,3,1,3,5,5,7,5,5,7,5,7,1,3,7,7,3, 5,5,7,5,5,3,3,3,1,7,1,5,5,5,3,3,5,1,3,1,3,3,3,7,1,7,7,3,7,1, 1,5,7,1,7,1,7,7,1,3,7,5,1,3,5,5,5,1,1,7,1,7,1,7,7,3,1,1,5,1, 5,1,5,3,5,5,5,5,5,3,3,7,3,3,5,5,3,7,1,5,7,5,1,5,5,3,5,5,7,5, 3,5,5,5,1,5,5,5,5,1,3,5,3,1,7,5,5,7,1,5,3,3,1,5,3,7,1,7,5,1, 1,3,1,1,7,1,5,5,3,7,3,7,5,3,1,1,3,1,3,5, 5,7,5,3,7,7,7,3,7,3,7,1,3,1,7,7,1,7, 3,7,3,7,3,7,3,5,1,1,7,3,1,5,5,7,1,5,5,5,7,1,5,5,1,5,5,3,1,3, 1,7,3,1,3,5,7,7,7,1,1,7,3,1,5,5,5,1,1,1,1,1,5,3,5,1,3,5,3,1, 1,1,1,3,7,3,7,5,7,1,5,5,7,5,3,3,7,5,3,1,1,3,1,3,1,1,3,7,1,7, 1,1,5,1,7,5,3,7,3,5,3,1,1,5,5,1,7,7,3,7,3,7,1,5,1,5,3,7,3,5, 7,7,7,3,3,1,1,5,5,3,7,1,1,1,3,5,3,1,1,3,3,7,5,1,1,3,7,1,5,7, 3,7,5,5,7,3,5,3,1,5,3,1,1,7,5,1,7,3,7,5,1,7,1,7,7,1,1,7,1,5, 5,1,1,7,5,7,1,5,3,5,3,3,7,1,5,1,1,5,5,3,3,7,5,5,1,1,1,3,1,5, 7,7,1,7,5,7,3,7,3,1,3,7,3,1,5,5,3,5,1,3,5,5,5,1,1,7,7,1,5,5, 1,3,5,1,5,3,5,3,3,7,5,7,3,7,3,1,3,7,7,3,3,1,1,3,3,3,3,3,5,5, 3,3,3,1,3,5,7,7,1,5,7,3,7,1,1,3,5,7,5,3,3,3}, /* [3][*] */ { 0,0,0,0, 1,7,9,13,11,1,3,7,9,5,13,13,11,3,15,5,3, 15,7,9,13,9,1,11,7,5,15,1,15,11,5,11,1,7,9,7,7,1,15,15,15,13, 3,3,15,5,9,7,13,3,7,5,11,9,1,9,1,5,7,13,9,9,1,7,3,5,1,11,11, 13,7,7,9,9,1,1,3,9,15,1,5,13,1,9,9,9,9,9,13,11,3,5,11,11,13, 5,3,15,1,11,11,7,13,15,11,13,9,11,15,15,13,3,15,7,9,11,13,11, 9,9,5,13,9,1,13,7,7,7,7,7,5,9,7,13,11,9,11,15,3,13,11,1,11,3, 3,9,11,1,7,1,15,15,3,1,9,1,7,13,11,3,13,11,7,3,3,5,13,11,5, 11,1,3,9,7,15,7,5,13,7,9,13,15,13,9,7,15,7,9,5,11,11,13,13,9, 3,5,13,9,11,15,11,7,1,7,13,3,13,3,13,9,15,7,13,13,3,13,15,15, 11,9,13,9,15,1,1,15,11,11,7,1,11,13,9,13,3,5,11,13,9,9,13,1, 11,15,13,3,13,7,15,1,15,3,3,11,7,13,7,7,9,7,5,15,9,5,5,7,15, 13,15,5,15,5,3,1,11,7,1,5,7,9,3,11,1,15,1,3,15,11,13,5,13,1, 7,1,15,7,5,1,1,15,13,11,11,13,5,11,7,9,7,1,5,3,9,5,5,11,5,1, 7,1,11,7,9,13,15,13,3,1,11,13,15,1,1,11,9,13,3,13,11,15,13,9, 9,9,5,5,5,5,1,15,5,9, 11,7,15,5,3,13,5,3,11,5,1,11,13,9,11, 3,7,13,15,1,7,11,1,13,1,15,1,9,7,3,9,11,1,9,13,13,3,11,7,9,1, 7,15,9,1,5,13,5,11,3,9,15,11,13,5,1,7,7,5,13,7,7,9,5,11,11,1, 1,15,3,13,9,13,9,9,11,5,5,13,15,3,9,15,3,11,11,15,15,3,11,15, 15,3,1,3,1,3,3,1,3,13,1,11,5,15,7,15,9,1,7,1,9,11,15,1,13,9, 13,11,7,3,7,3,13,7,9,7,7,3,3,9,9,7,5,11,13,13,7,7,15,9,5,5,3, 3,13,3,9,3,1,11,1,3,11,15,11,11,11,9,13,7,9,15,9,11,1,3,3,9, 7,15,13,13,7,15,9,13,9,15,13,15,9,13,1,11,7,11,3,13,5,1,7,15, 3,13,7,13,13,11,3,5,3,13,11,9,9,3,11,11,7,9,13,11,7,15,13,7, 5,3,1,5,15,15,3,11,1,7,3,15,11,5,5,3,5,5,1,15,5,1,5,3,7,5,11, 3,13,9,13,15,5,3,5,9,5,3,11,1,13,9,15,3,5,11,9,1,3,15,9,9,9, 11,7,5,13,1,15,3,13,9,13,5,1,5,1,13,13,7,7,1,9,5,11,9,11,13, 3,15,15,13,15,7,5,7,9,7,9,9,9,11,9,3,11,15,13,13,5,9,15,1,1, 9,5,13,3,13,15,3,1,3,11,13,1,15,9,9,3,1,9,1,9,1,13,11,15,7, 11,15,13,15,1,9,9,7, 3,5,11,7,3,9,5,15,7,5,3,13,7,1,1,9, 15,15,15,11,3,5,15,13,7,15,15,11,11,9,5,15,9,7,3,13,1,1,5,1, 3,1,7,1,1,5,1,11,11,9,9,5,13,7,7,7,1,1,9,9,11,11,15,7,5,5,3, 11,1,3,7,13,7,7,7,3,15,15,11,9,3,9,3,15,13,5,3,3,3,5,9,15,9, 9,1,5,9,9,15,5,15,7,9,1,9,9,5,11,5,15,15,11,7,7,7,1,1,11,11, 13,15,3,13,5,1,7,1,11,3,13,15,3,5,3,5,7,3,9,9,5,1,7,11,9,3,5, 11,13,13,13,9,15,5,7,1,15,11,9,15,15,13,13,13,1,11,9,15,9,5, 15,5,7,3,11,3,15,7,13,11,7,3,7,13,5,13,15,5,13,9,1,15,11,5,5, 1,11,3,3,7,1,9,7,15,9,9,3,11,15,7,1,3,1,1,1,9,1,5,15,15,7,5, 5,7,9,7,15,13,13,11,1,9,11,1,13,1,7,15,15,5,5,1,11,3,9,11,9, 9,9,1,9,3,5,15,1,1,9,7,3,3,1,9,9,11,9,9,13,13,3,13,11,13,5,1, 5,5,9,9,3,13,13,9,15,9,11,7,11,9,13,9,1,15,9,7,7,1,7,9,9,15, 1,11,1,13,13,15,9,13,7,15,3,9,3,1,13,7,5,9,3,1,7,1,1,13,3,3, 11,1,7,13,15,15,5,7,13,13,15,11,13,1,13,13,3,9,15,15,11,15,9, 15,1,13,15,1,1,5, 11,5,1,11,11,5,3,9,1,3,5,13,9,7,7,1, 9,9,15,7,5,5,15,13,9,7,13,3,13,11,13,7,9,13,13,13,15,9,5,5,3, 3,3,1,3,15}, /* [4][*] */ { 0,0,0,0,0,0, 9,3,27,15,29,21,23,19,11,25,7,13,17,1, 25,29,3,31,11,5,23,27,19,21,5,1,17,13,7,15,9,31,25,3,5,23,7, 3,17,23,3,3,21,25,25,23,11,19,3,11,31,7,9,5,17,23,17,17,25, 13,11,31,27,19,17,23,7,5,11,19,19,7,13,21,21,7,9,11,1,5,21, 11,13,25,9,7,7,27,15,25,15,21,17,19,19,21,5,11,3,5,29,31,29, 5,5,1,31,27,11,13,1,3,7,11,7,3,23,13,31,17,1,27,11,25,1,23, 29,17,25,7,25,27,17,13,17,23,5,17,5,13,11,21,5,11,5,9,31,19, 17,9,9,27,21,15,15,1,1,29,5,31,11,17,23,19,21,25,15,11,5,5,1, 19,19,19,7,13,21,17,17,25,23,19,23,15,13,5,19,25,9,7,3,21,17, 25,1,27,25,27,25,9,13,3,17,25,23,9,25,9,13,17,17,3,15,7,7,29, 3,19,29,29,19,29,13,15,25,27,1,3,9,9,13,31,29,31,5,15,29,1, 19,5,9,19,5,15,3,5,7,15,17,17,23,11,9,23,19,3,17,1,27,9,9,17, 13,25,29,23,29,11,31,25,21,29,19,27,31,3,5,3,3,13,21,9,29,3, 17,11,11,9,21,19,7,17,31,25,1,27,5,15,27,29,29,29,25,27,25,3, 21,17,25,13,15,17,13,23,9,3,11,7,9,9,7,17,7,1, 27,1,9,5,31,21,25,25,21,11,1,23,19,27, 15,3,5,23,9,25,7,29,11,9,13,5,11,1,3,31,27,3,17,27,11,13,15, 29,15,1,15,23,25,13,21,15,3,29,29,5,25,17,11,7,15,5,21,7,31, 13,11,23,5,7,23,27,21,29,15,7,27,27,19,7,15,27,27,19,19,9,15, 1,3,29,29,5,27,31,9,1,7,3,19,19,29,9,3,21,31,29,25,1,3,9,27, 5,27,25,21,11,29,31,27,21,29,17,9,17,13,11,25,15,21,11,19,31, 3,19,5,3,3,9,13,13,3,29,7,5,9,23,13,21,23,21,31,11,7,7,3,23, 1,23,5,9,17,21,1,17,29,7,5,17,13,25,17,9,19,9,5,7,21,19,13,9, 7,3,9,3,15,31,29,29,25,13,9,21,9,31,7,15,5,31,7,15,27,25,19, 9,9,25,25,23,1,9,7,11,15,19,15,27,17,11,11,31,13,25,25,9,7, 13,29,19,5,19,31,25,13,25,15,5,9,29,31,9,29,27,25,27,11,17,5, 17,3,23,15,9,9,17,17,31,11,19,25,13,23,15,25,21,31,19,3,11, 25,7,15,19,7,5,3,13,13,1,23,5,25,11,25,15,13,21,11,23,29,5, 17,27,9,19,15,5,29,23,19,1,27,3,23,21,19,27,11,17,13,27,11, 31,23,5,9,21,31,29,11,21,17,15,7,15,7,9,21,27,25, 29,11,3,21,13,23,19,27,17,29,25,17,9, 1,19,23,5,23,1,17,17,13,27,23,7,7,11,13,17,13,11,21,13,23,1, 27,13,9,7,1,27,29,5,13,25,21,3,31,15,13,3,19,13,1,27,15,17,1, 3,13,13,13,31,29,27,7,7,21,29,15,17,17,21,19,17,3,15,5,27,27, 3,31,31,7,21,3,13,11,17,27,25,1,9,7,29,27,21,23,13,25,29,15, 17,29,9,15,3,21,15,17,17,31,9,9,23,19,25,3,1,11,27,29,1,31, 29,25,29,1,23,29,25,13,3,31,25,5,5,11,3,21,9,23,7,11,23,11,1, 1,3,23,25,23,1,23,3,27,9,27,3,23,25,19,29,29,13,27,5,9,29,29, 13,17,3,23,19,7,13,3,19,23,5,29,29,13,13,5,19,5,17,9,11,11, 29,27,23,19,17,25,13,1,13,3,11,1,17,29,1,13,17,9,17,21,1,11, 1,1,25,5,7,29,29,19,19,1,29,13,3,1,31,15,13,3,1,11,19,5,29, 13,29,23,3,1,31,13,19,17,5,5,1,29,23,3,19,25,19,27,9,27,13, 15,29,23,13,25,25,17,19,17,15,27,3,25,17,27,3,27,31,23,13,31, 11,15,7,21,19,27,19,21,29,7,31,13,9,9,7,21,13,11,9,11,29,19, 11,19,21,5,29,13,7,19,19,27,23,31,1,27,21,7,3,7,11, 23,13,29,11,31,19,1,5,5,11,5,3,27,5, 7,11,31,1,27,31,31,23,5,21,27,9,25,3,15,19,1,19,9,5,25,21,15, 25,29,15,21,11,19,15,3,7,13,11,25,17,1,5,31,13,29,23,9,5,29, 7,17,27,7,17,31,9,31,9,9,7,21,3,3,3,9,11,21,11,31,9,25,5,1, 31,13,29,9,29,1,11,19,7,27,13,31,7,31,7,25,23,21,29,11,11,13, 11,27,1,23,31,21,23,21,19,31,5,31,25,25,19,17,11,25,7,13,1, 29,17,23,15,7,29,17,13,3,17}, /* [5][*] */ { 0,0,0,0,0,0,0,0,0,0,0,0, 37,33,7,5,11,39,63,59,17,15,23,29,3,21, 13,31,25,9,49,33,19,29,11,19,27,15,25,63,55,17,63,49,19,41, 59,3,57,33,49,53,57,57,39,21,7,53,9,55,15,59,19,49,31,3,39,5, 5,41,9,19,9,57,25,1,15,51,11,19,61,53,29,19,11,9,21,19,43,13, 13,41,25,31,9,11,19,5,53,37,7,51,45,7,7,61,23,45,7,59,41,1, 29,61,37,27,47,15,31,35,31,17,51,13,25,45,5,5,33,39,5,47,29, 35,47,63,45,37,47,59,21,59,33,51,9,27,13,25,43,3,17,21,59,61, 27,47,57,11,17,39,1,63,21,59,17,13,31,3,31,7,9,27,37,23,31,9, 45,43,31,63,21,39,51,27,7,53,11,1,59,39,23,49,23,7,55,59,3, 19,35,13,9,13,15,23,9,7,43,55,3,19,9,27,33,27,49,23,47,19,7, 11,55,27,35,5,5,55,35,37,9,33,29,47,25,11,47,53,61,59,3,53, 47,5,19,59,5,47,23,45,53,3,49,61,47,39,29,17,57,5,17,31,23, 41,39,5,27,7,29,29,33,31,41,31,29,17,29,29,9,9,31,27,53,35,5, 61,1,49,13,57,29,5,21,43,25,57,49,37,27,11,61,37,49,5,63,63, 3,45,37,63,21,21,19,27,59,21,45,23,13,15,3,43,63,39,19, 63,31,41,41,15,43,63,53,1,63,31,7,17, 11,61,31,51,37,29,59,25,63,59,47,15,27,19,29,45,35,55,39,19, 43,21,19,13,17,51,37,5,33,35,49,25,45,1,63,47,9,63,15,25,25, 15,41,13,3,19,51,49,37,25,49,13,53,47,23,35,29,33,21,35,23,3, 43,31,63,9,1,61,43,3,11,55,11,35,1,63,35,49,19,45,9,57,51,1, 47,41,9,11,37,19,55,23,55,55,13,7,47,37,11,43,17,3,25,19,55, 59,37,33,43,1,5,21,5,63,49,61,21,51,15,19,43,47,17,9,53,45, 11,51,25,11,25,47,47,1,43,29,17,31,15,59,27,63,11,41,51,29,7, 27,63,31,43,3,29,39,3,59,59,1,53,63,23,63,47,51,23,61,39,47, 21,39,15,3,9,57,61,39,37,21,51,1,23,43,27,25,11,13,21,43,7, 11,33,55,1,37,35,27,61,39,5,19,61,61,57,59,21,59,61,57,25,55, 27,31,41,33,63,19,57,35,13,63,35,17,11,11,49,41,55,5,45,17, 35,5,31,31,37,17,45,51,1,39,49,55,19,41,13,5,51,5,49,1,21,13, 17,59,51,11,3,61,1,33,37,33,61,25,27,59,7,49,13,63,3,33,3,15, 9,13,35,39,11,59,59,1,57,11,5,57,13,31,13,11,55,45,9,55,55, 19,25,41,23,45,29,63,59,27,39,21,37,7, 61,49,35,39,9,29,7,25,23,57,5,19,15,33,49,37,25,17,45,29,15, 25,3,3,49,11,39,15,19,57,39,15,11,3,57,31,55,61,19,5,41,35, 59,61,39,41,53,53,63,31,9,59,13,35,55,41,49,5,41,25,27,43,5, 5,43,5,5,17,5,15,27,29,17,9,3,55,31,1,45,45,13,57,17,3,61,15, 49,15,47,9,37,45,9,51,61,21,33,11,21,63,63,47,57,61,49,9,59, 19,29,21,23,55,23,43,41,57,9,39,27,41,35,61,29,57,63,21,31, 59,35,49,3,49,47,49,33,21,19,21,35,11,17,37,23,59,13,37,35, 55,57,1,29,45,11,1,15,9,33,19,53,43,39,23,7,13,13,1,19,41,55, 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2177,2641,3279,389,2009,525,4085,3299,987,2409,813,2683,373, 2695,3775,2375,1119,2791,223,325,587,1379,2877,2867,3793,655, 831,3425,1663,1681,2657,1865,3943,2977,1979,2271,3247,1267, 1747,811,159,429,2001,1195,3065,553,1499,3529,1081,2877,3077, 845,1793,2409,3995,2559,4081,1195,2955,1117,1409,785,287, 1521,1607,85,3055,3123,2533,2329,3477,799,3683,3715,337,3139, 3311,431,3511,2299,365,2941,3067,1331,1081,1097,2853,2299, 495,1745,749,3819,619,1059,3559,183,3743,723,949,3501,733, 2599,3983,3961,911,1899,985,2493,1795,653,157,433,2361,3093, 3119,3679,2367,1701,1445,1321,2397,1241,3305,3985,2349,4067, 3805,3073,2837,1567,3783,451,2441,1181,487,543,1201,3735, 2517,733,1535,2175,3613,3019}, /* [12][*] */ 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3357,2073,4395,4925,2643,7215,5817,1199,1597,1619,7535,4833, 609,4797,8171,6847,793,6757,8165,3371,2431,5235,4739,7703, 7223,6525,5891,5605,4433,3533,5267,5125,5037,225,6717,1121, 5741,2013,4327,4839,569,5227,7677,4315,2391,5551,859,3627, 6377,3903,4311,6527,7573,4905,7731,1909,1555,3279,1949,1887, 6675,5509,2033,5473,3539,5033,5935,6095,4761,1771,1271,1717, 4415,5083,6277,3147,7695,2461,4783,4539,5833,5583,651,1419, 2605,5511,3913,5795,2333,2329,4431,3725,6069,2699,7055,6879, 1017,3121,2547,4603,2385,6915,6103,5669,7833,2001,4287,6619, 955,2761,5711,6291,3415,3909,2841,5627,4939,7671,6059,6275, 6517,1931,4583,7301,1267,7509,1435,2169,6939,3515,2985,2787, 2123,1969,3307,353,4359,7059,5273,5873,6657,6765,6229,3179, 1583,6237,2155,371,273,7491,3309,6805,3015,6831,7819,713, 4747,3935,4109,1311,709,3089,7059,4247,2989,1509,4919,1841, 3045,3821,6929,4655,1333,6429,6649,2131,5265,1051,261,8057, 3379,2179,1993,5655,3063,6381, 3587,7417,1579,1541,2107,5085,2873, 6141,955,3537,2157,841,1999,1465,5171,5651,1535,7235,4349, 1263,1453,1005,6893,2919,1947,1635,3963,397,969,4569,655, 6737,2995,7235,7713,973,4821,2377,1673,1,6541} }; #endif nloptr/src/nlopt_src/swig/0000755000176000001440000000000013263734242015371 5ustar ripleyusersnloptr/src/nlopt_src/swig/Makefile.am0000644000176000001440000000442013263734242017425 0ustar ripleyusersSWIG_SRC = nlopt.i nlopt-exceptions.i nlopt-enum-renames.i EXTRA_DIST = $(SWIG_SRC) nlopt-guile.i nlopt-python.i nlopt.scm.in nlopt.py numpy.i BUILT_SOURCES = nlopt-guile.cpp nlopt-python.cpp nlopt-enum-renames.i nlopt.scm.in HDR = $(top_srcdir)/api/nlopt.hpp ############################################################################## # Guile wrapper libnlopt@NLOPT_SUFFIX@_guile_la_SOURCES = nlopt-guile.cpp libnlopt@NLOPT_SUFFIX@_guile_la_LIBADD = ../libnlopt@NLOPT_SUFFIX@.la libnlopt@NLOPT_SUFFIX@_guile_la_LDFLAGS = -version-info @SHARED_VERSION_INFO@ libnlopt@NLOPT_SUFFIX@_guile_la_CPPFLAGS = $(GUILE_CPPFLAGS) -I$(top_srcdir)/api guiledir = $(GUILE_INSTALL_DIR) if WITH_GUILE guile_DATA = nlopt.scm guilelib = libnlopt@NLOPT_SUFFIX@_guile.la endif ############################################################################## # Python wrapper _nlopt_la_SOURCES = nlopt-python.cpp _nlopt_la_LIBADD = ../libnlopt@NLOPT_SUFFIX@.la _nlopt_la_LDFLAGS = -module -version-info @SHARED_VERSION_INFO@ _nlopt_la_CPPFLAGS = $(PYTHON_INCLUDES) -I$(top_srcdir)/api if WITH_PYTHON python_PYTHON = nlopt.py pyexec_LTLIBRARIES = _nlopt.la endif ############################################################################## lib_LTLIBRARIES = $(guilelib) ############################################################################## # Generate wrappers via SWIG: if MAINTAINER_MODE nlopt-guile.cpp nlopt.scm.in: $(SWIG_SRC) nlopt-guile.i $(HDR) swig -I$(top_srcdir)/api -outdir $(builddir) -c++ -guile -scmstub -o $@ $(srcdir)/nlopt.i rm -f nlopt.scm.in mv nlopt.scm nlopt.scm.in nlopt-python.cpp nlopt.py: $(SWIG_SRC) nlopt-python.i numpy.i $(HDR) swig -I$(top_srcdir)/api -outdir $(builddir) -c++ -python -o $@ $(srcdir)/nlopt.i nlopt-enum-renames.i: $(top_srcdir)/api/nlopt.h (echo "// AUTOMATICALLY GENERATED -- DO NOT EDIT"; egrep 'NLOPT_[LG][DN]|NLOPT_AUGLAG|NLOPT_G_MLSL|NLOPT_NUM_ALGORITHMS' $(top_srcdir)/api/nlopt.h | sed 's/NLOPT_//g' |tr -d ' ' |tr '/' ',' |tr '=' ',' |cut -d, -f1 |while read name; do echo "%rename(NLOPT_$$name) nlopt::$$name;"; done; egrep 'NLOPT_[A-Z_]* =' $(top_srcdir)/api/nlopt.h | egrep -v 'NLOPT_[LG][DN]|NLOPT_AUGLAG|NLOPT_G_MLSL' | sed 's/NLOPT_//g' |tr -d ' ' |cut -d'=' -f1 | while read name; do echo "%rename(NLOPT_$$name) nlopt::$$name;"; done) > $@ endif nloptr/src/nlopt_src/swig/nlopt-python.i0000644000176000001440000001442313263734242020222 0ustar ripleyusers// -*- C++ -*- ////////////////////////////////////////////////////////////////////////////// // Converting NLopt/C++ exceptions to Python exceptions %{ #define ExceptionSubclass(EXCNAME, EXCDOC) \ static PyTypeObject MyExc_ ## EXCNAME = { \ PyVarObject_HEAD_INIT(NULL, 0) \ "nlopt." # EXCNAME, \ sizeof(PyBaseExceptionObject), \ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \ Py_TPFLAGS_DEFAULT, \ PyDoc_STR(EXCDOC) \ }; \ static void init_ ## EXCNAME(PyObject *m) { \ MyExc_ ## EXCNAME .tp_base = (PyTypeObject *) PyExc_Exception; \ PyType_Ready(&MyExc_ ## EXCNAME); \ Py_INCREF(&MyExc_ ## EXCNAME); \ PyModule_AddObject(m, # EXCNAME, (PyObject *) &MyExc_ ## EXCNAME); \ } ExceptionSubclass(ForcedStop, "Python version of nlopt::forced_stop exception.") ExceptionSubclass(RoundoffLimited, "Python version of nlopt::roundoff_limited exception.") %} %init %{ init_ForcedStop(m); init_RoundoffLimited(m); %} %pythoncode %{ ForcedStop = _nlopt.ForcedStop RoundoffLimited = _nlopt.RoundoffLimited __version__ = str(_nlopt.version_major())+'.'+str(_nlopt.version_minor())+'.'+str(_nlopt.version_bugfix()) %} %typemap(throws) std::bad_alloc %{ PyErr_SetString(PyExc_MemoryError, ($1).what()); SWIG_fail; %} %typemap(throws) nlopt::forced_stop %{ if (!PyErr_Occurred()) PyErr_SetString((PyObject*)&MyExc_ForcedStop, "NLopt forced stop"); SWIG_fail; %} %typemap(throws) nlopt::roundoff_limited %{ PyErr_SetString((PyObject*)&MyExc_RoundoffLimited, "NLopt roundoff-limited"); SWIG_fail; %} ////////////////////////////////////////////////////////////////////////////// %{ #define SWIG_FILE_WITH_INIT %} %include "numpy.i" %init %{ import_array(); %} %numpy_typemaps(double, NPY_DOUBLE, unsigned) ////////////////////////////////////////////////////////////////////////////// // numpy.i does not include maps for std::vector, so I add them here, // taking advantage of the conversion functions provided by numpy.i // Typemap for input arguments of type const std::vector & %typecheck(SWIG_TYPECHECK_POINTER, fragment="NumPy_Macros") const std::vector & { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") const std::vector & (PyArrayObject* array=NULL, int is_new_object=0, std::vector arrayv) { npy_intp size[1] = { -1 }; array = obj_to_array_allow_conversion($input, NPY_DOUBLE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; arrayv = std::vector(array_size(array,0)); $1 = &arrayv; { double *arr_data = (double *) array_data(array); int arr_i, arr_s = array_stride(array,0) / sizeof(double); int arr_sz = array_size(array,0); for (arr_i = 0; arr_i < arr_sz; ++arr_i) arrayv[arr_i] = arr_data[arr_i * arr_s]; } } %typemap(freearg) const std::vector & { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } // Typemap for return values of type std::vector %typemap(out, fragment="NumPy_Fragments") std::vector { npy_intp sz = $1.size(); $result = PyArray_SimpleNew(1, &sz, NPY_DOUBLE); std::memcpy(array_data($result), $1.empty() ? NULL : &$1[0], sizeof(double) * sz); } ////////////////////////////////////////////////////////////////////////////// // Wrapper for objective function callbacks %{ static void *free_pyfunc(void *p) { Py_DECREF((PyObject*) p); return p; } static void *dup_pyfunc(void *p) { Py_INCREF((PyObject*) p); return p; } #if NPY_API_VERSION < 0x00000007 # define NPY_ARRAY_C_CONTIGUOUS NPY_C_CONTIGUOUS # define NPY_ARRAY_ALIGNED NPY_ALIGNED #endif static double func_python(unsigned n, const double *x, double *grad, void *f) { npy_intp sz = npy_intp(n), sz0 = 0, stride1 = sizeof(double); PyObject *xpy = PyArray_New(&PyArray_Type, 1, &sz, NPY_DOUBLE, &stride1, const_cast(x), // not NPY_WRITEABLE 0, NPY_ARRAY_C_CONTIGUOUS | NPY_ARRAY_ALIGNED, NULL); PyObject *gradpy = grad ? 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'/' ',' |tr '=' ',' |cut -d, -f1 |while read name; do echo "%rename(NLOPT_$$name) nlopt::$$name;"; done; egrep 'NLOPT_[A-Z_]* =' $(top_srcdir)/api/nlopt.h | egrep -v 'NLOPT_[LG][DN]|NLOPT_AUGLAG|NLOPT_G_MLSL' | sed 's/NLOPT_//g' |tr -d ' ' |cut -d'=' -f1 | while read name; do echo "%rename(NLOPT_$$name) nlopt::$$name;"; done) > $@ # Tell versions [3.59,3.63) of GNU make to not export all variables. # Otherwise a system limit (for SysV at least) may be exceeded. .NOEXPORT: nloptr/src/nlopt_src/swig/nlopt.scm.in0000644000176000001440000001673713263734242017654 0ustar ripleyusers;;; This file was automatically generated by SWIG (http://www.swig.org). ;;; Version 2.0.12 ;;; ;;; Do not make changes to this file unless you know what you are doing--modify ;;; the SWIG interface file instead. (define-module (nlopt)) (load-extension "libnlopt@NLOPT_SUFFIX@_guile.so" "SWIG_init") (set! NLOPT-GN-DIRECT (NLOPT-GN-DIRECT)) (set! NLOPT-GN-DIRECT-L (NLOPT-GN-DIRECT-L)) (set! NLOPT-GN-DIRECT-L-RAND (NLOPT-GN-DIRECT-L-RAND)) (set! NLOPT-GN-DIRECT-NOSCAL (NLOPT-GN-DIRECT-NOSCAL)) (set! NLOPT-GN-DIRECT-L-NOSCAL (NLOPT-GN-DIRECT-L-NOSCAL)) (set! NLOPT-GN-DIRECT-L-RAND-NOSCAL (NLOPT-GN-DIRECT-L-RAND-NOSCAL)) (set! NLOPT-GN-ORIG-DIRECT (NLOPT-GN-ORIG-DIRECT)) (set! NLOPT-GN-ORIG-DIRECT-L (NLOPT-GN-ORIG-DIRECT-L)) (set! NLOPT-GD-STOGO (NLOPT-GD-STOGO)) (set! NLOPT-GD-STOGO-RAND (NLOPT-GD-STOGO-RAND)) (set! NLOPT-LD-LBFGS-NOCEDAL (NLOPT-LD-LBFGS-NOCEDAL)) (set! NLOPT-LD-LBFGS (NLOPT-LD-LBFGS)) (set! NLOPT-LN-PRAXIS (NLOPT-LN-PRAXIS)) (set! NLOPT-LD-VAR1 (NLOPT-LD-VAR1)) (set! NLOPT-LD-VAR2 (NLOPT-LD-VAR2)) (set! NLOPT-LD-TNEWTON (NLOPT-LD-TNEWTON)) (set! NLOPT-LD-TNEWTON-RESTART (NLOPT-LD-TNEWTON-RESTART)) (set! NLOPT-LD-TNEWTON-PRECOND (NLOPT-LD-TNEWTON-PRECOND)) (set! NLOPT-LD-TNEWTON-PRECOND-RESTART (NLOPT-LD-TNEWTON-PRECOND-RESTART)) (set! NLOPT-GN-CRS2-LM (NLOPT-GN-CRS2-LM)) (set! NLOPT-GN-MLSL (NLOPT-GN-MLSL)) (set! NLOPT-GD-MLSL (NLOPT-GD-MLSL)) (set! NLOPT-GN-MLSL-LDS (NLOPT-GN-MLSL-LDS)) (set! NLOPT-GD-MLSL-LDS (NLOPT-GD-MLSL-LDS)) (set! NLOPT-LD-MMA (NLOPT-LD-MMA)) (set! NLOPT-LN-COBYLA (NLOPT-LN-COBYLA)) (set! NLOPT-LN-NEWUOA (NLOPT-LN-NEWUOA)) (set! NLOPT-LN-NEWUOA-BOUND (NLOPT-LN-NEWUOA-BOUND)) (set! NLOPT-LN-NELDERMEAD (NLOPT-LN-NELDERMEAD)) (set! NLOPT-LN-SBPLX (NLOPT-LN-SBPLX)) (set! NLOPT-LN-AUGLAG (NLOPT-LN-AUGLAG)) (set! NLOPT-LD-AUGLAG (NLOPT-LD-AUGLAG)) (set! NLOPT-LN-AUGLAG-EQ (NLOPT-LN-AUGLAG-EQ)) (set! NLOPT-LD-AUGLAG-EQ (NLOPT-LD-AUGLAG-EQ)) (set! NLOPT-LN-BOBYQA (NLOPT-LN-BOBYQA)) (set! NLOPT-GN-ISRES (NLOPT-GN-ISRES)) (set! NLOPT-AUGLAG (NLOPT-AUGLAG)) (set! NLOPT-AUGLAG-EQ (NLOPT-AUGLAG-EQ)) (set! NLOPT-G-MLSL (NLOPT-G-MLSL)) (set! NLOPT-G-MLSL-LDS (NLOPT-G-MLSL-LDS)) (set! NLOPT-LD-SLSQP (NLOPT-LD-SLSQP)) (set! NLOPT-LD-CCSAQ (NLOPT-LD-CCSAQ)) (set! NLOPT-GN-ESCH (NLOPT-GN-ESCH)) (set! NLOPT-NUM-ALGORITHMS (NLOPT-NUM-ALGORITHMS)) (set! NLOPT-FAILURE (NLOPT-FAILURE)) (set! NLOPT-INVALID-ARGS (NLOPT-INVALID-ARGS)) (set! NLOPT-OUT-OF-MEMORY (NLOPT-OUT-OF-MEMORY)) (set! NLOPT-ROUNDOFF-LIMITED (NLOPT-ROUNDOFF-LIMITED)) (set! NLOPT-FORCED-STOP (NLOPT-FORCED-STOP)) (set! NLOPT-SUCCESS (NLOPT-SUCCESS)) (set! NLOPT-STOPVAL-REACHED (NLOPT-STOPVAL-REACHED)) (set! NLOPT-FTOL-REACHED (NLOPT-FTOL-REACHED)) (set! NLOPT-XTOL-REACHED (NLOPT-XTOL-REACHED)) (set! NLOPT-MAXEVAL-REACHED (NLOPT-MAXEVAL-REACHED)) (set! NLOPT-MAXTIME-REACHED (NLOPT-MAXTIME-REACHED)) (export new-nlopt-doublevector new-nlopt-doublevector new-nlopt-doublevector new-nlopt-doublevector nlopt-doublevector-length nlopt-doublevector-empty? nlopt-doublevector-clear! nlopt-doublevector-push! nlopt-doublevector-pop! nlopt-doublevector-ref nlopt-doublevector-set! delete-nlopt-doublevector nlopt-get-initial-step NLOPT-GN-DIRECT NLOPT-GN-DIRECT-L NLOPT-GN-DIRECT-L-RAND NLOPT-GN-DIRECT-NOSCAL NLOPT-GN-DIRECT-L-NOSCAL NLOPT-GN-DIRECT-L-RAND-NOSCAL NLOPT-GN-ORIG-DIRECT NLOPT-GN-ORIG-DIRECT-L NLOPT-GD-STOGO NLOPT-GD-STOGO-RAND NLOPT-LD-LBFGS-NOCEDAL NLOPT-LD-LBFGS NLOPT-LN-PRAXIS NLOPT-LD-VAR1 NLOPT-LD-VAR2 NLOPT-LD-TNEWTON NLOPT-LD-TNEWTON-RESTART NLOPT-LD-TNEWTON-PRECOND NLOPT-LD-TNEWTON-PRECOND-RESTART NLOPT-GN-CRS2-LM NLOPT-GN-MLSL NLOPT-GD-MLSL NLOPT-GN-MLSL-LDS NLOPT-GD-MLSL-LDS NLOPT-LD-MMA NLOPT-LN-COBYLA NLOPT-LN-NEWUOA NLOPT-LN-NEWUOA-BOUND NLOPT-LN-NELDERMEAD NLOPT-LN-SBPLX NLOPT-LN-AUGLAG NLOPT-LD-AUGLAG NLOPT-LN-AUGLAG-EQ NLOPT-LD-AUGLAG-EQ NLOPT-LN-BOBYQA NLOPT-GN-ISRES NLOPT-AUGLAG NLOPT-AUGLAG-EQ NLOPT-G-MLSL NLOPT-G-MLSL-LDS NLOPT-LD-SLSQP NLOPT-LD-CCSAQ NLOPT-GN-ESCH NLOPT-NUM-ALGORITHMS NLOPT-FAILURE NLOPT-INVALID-ARGS NLOPT-OUT-OF-MEMORY NLOPT-ROUNDOFF-LIMITED NLOPT-FORCED-STOP NLOPT-SUCCESS NLOPT-STOPVAL-REACHED NLOPT-FTOL-REACHED NLOPT-XTOL-REACHED NLOPT-MAXEVAL-REACHED NLOPT-MAXTIME-REACHED new-nlopt-roundoff-limited delete-nlopt-roundoff-limited new-nlopt-forced-stop delete-nlopt-forced-stop new-nlopt-opt delete-nlopt-opt new-nlopt-opt new-nlopt-opt nlopt-opt-optimize nlopt-opt-optimize nlopt-opt-last-optimize-result nlopt-opt-last-optimum-value nlopt-opt-get-algorithm nlopt-opt-get-algorithm-name nlopt-opt-get-dimension nlopt-opt-set-min-objective nlopt-opt-set-min-objective nlopt-opt-set-max-objective nlopt-opt-set-max-objective nlopt-opt-set-min-objective nlopt-opt-set-max-objective nlopt-opt-remove-inequality-constraints nlopt-opt-add-inequality-constraint nlopt-opt-add-inequality-constraint nlopt-opt-add-inequality-constraint nlopt-opt-add-inequality-constraint nlopt-opt-add-inequality-mconstraint nlopt-opt-remove-equality-constraints nlopt-opt-add-equality-constraint nlopt-opt-add-equality-constraint nlopt-opt-add-equality-constraint nlopt-opt-add-equality-constraint nlopt-opt-add-equality-mconstraint nlopt-opt-add-inequality-constraint nlopt-opt-add-inequality-constraint nlopt-opt-add-equality-constraint nlopt-opt-add-equality-constraint nlopt-opt-add-inequality-mconstraint nlopt-opt-add-equality-mconstraint nlopt-opt-set-lower-bounds nlopt-opt-get-lower-bounds nlopt-opt-get-lower-bounds nlopt-opt-set-lower-bounds nlopt-opt-set-upper-bounds nlopt-opt-get-upper-bounds nlopt-opt-get-upper-bounds nlopt-opt-set-upper-bounds nlopt-opt-get-stopval nlopt-opt-set-stopval nlopt-opt-get-ftol-rel nlopt-opt-set-ftol-rel nlopt-opt-get-ftol-abs nlopt-opt-set-ftol-abs nlopt-opt-get-xtol-rel nlopt-opt-set-xtol-rel nlopt-opt-set-xtol-abs nlopt-opt-get-xtol-abs nlopt-opt-get-xtol-abs nlopt-opt-set-xtol-abs nlopt-opt-get-maxeval nlopt-opt-set-maxeval nlopt-opt-get-maxtime nlopt-opt-set-maxtime nlopt-opt-get-force-stop nlopt-opt-set-force-stop nlopt-opt-force-stop nlopt-opt-set-local-optimizer nlopt-opt-get-population nlopt-opt-set-population nlopt-opt-get-vector-storage nlopt-opt-set-vector-storage nlopt-opt-set-initial-step nlopt-opt-get-initial-step nlopt-opt-get-initial-step nlopt-opt-set-initial-step nlopt-opt-set-default-initial-step nlopt-opt-get-initial-step nlopt-opt-get-initial-step- nlopt-srand nlopt-srand-time nlopt-version nlopt-version-major nlopt-version-minor nlopt-version-bugfix nlopt-algorithm-name) nloptr/src/nlopt_src/swig/nlopt-guile.i0000644000176000001440000000503213263734242020002 0ustar ripleyusers// -*- C++ -*- %{ // work around obsolete stuff used by swig guile #if SCM_MAJOR_VERSION >= 2 # define gh_symbol2scm scm_from_latin1_symbol #else # define gh_symbol2scm scm_str2symbol #endif %} %typemap(throws) std::runtime_error %{ scm_throw(gh_symbol2scm("runtime-error"), scm_list_1(scm_from_locale_string(($1).what()))); %} %typemap(throws) std::bad_alloc %{ scm_throw(gh_symbol2scm("bad-alloc"), scm_list_1(scm_from_locale_string(($1).what()))); %} %typemap(throws) std::invalid_argument %{ scm_throw(gh_symbol2scm("invalid-argument"), scm_list_1(scm_from_locale_string(($1).what()))); %} %typemap(throws) nlopt::forced_stop %{ scm_throw(gh_symbol2scm("forced-stop"), SCM_EOL); %} %typemap(throws) nlopt::roundoff_limited %{ scm_throw(gh_symbol2scm("roundoff-limited"), SCM_EOL); %} %{ // because our f_data pointer to the Scheme function is stored on the // heap, rather than the stack, it may be missed by the Guile garbage // collection and be accidentally freed. Hence, use NLopts munge // feature to prevent this, by incrementing Guile's reference count. static void *free_guilefunc(void *p) { scm_gc_unprotect_object((SCM) p); return p; } static void *dup_guilefunc(void *p) { scm_gc_protect_object((SCM) p); return p; } // func wrapper around Guile function val = f(x, grad) static double func_guile(unsigned n, const double *x, double *grad, void *f) { SCM xscm = scm_c_make_vector(n, SCM_UNSPECIFIED); for (unsigned i = 0; i < n; ++i) SCM_SIMPLE_VECTOR_SET(xscm, i, scm_make_real(x[i])); SCM grad_scm = grad ? scm_c_make_vector(n, SCM_UNSPECIFIED) : SCM_BOOL_F; SCM ret = scm_call_2((SCM) f, xscm, grad_scm); if (!scm_real_p(ret)) throw std::invalid_argument("invalid result passed to nlopt"); if (grad) { for (unsigned i = 0; i < n; ++i) { if (!scm_real_p(ret)) throw std::invalid_argument("invalid gradient passed to nlopt"); grad[i] = scm_to_double(SCM_SIMPLE_VECTOR_REF(grad_scm, i)); } } return scm_to_double(ret); } %} %typemap(in)(nlopt::func f, void *f_data, nlopt_munge md, nlopt_munge mc) { $1 = func_guile; $2 = dup_guilefunc((void*) $input); // input = SCM pointer to Scheme function $3 = free_guilefunc; $4 = dup_guilefunc; } %typecheck(SWIG_TYPECHECK_POINTER)(nlopt::func f, void *f_data, nlopt_munge md, nlopt_munge mc) { $1 = SCM_NFALSEP(scm_procedure_p($input)); } // export constants as variables, rather than as functions returning the value %feature("constasvar", "1"); %scheme %{ (load-extension "libnlopt@NLOPT_SUFFIX@_guile.so" "SWIG_init") %} nloptr/src/nlopt_src/swig/nlopt.i0000644000176000001440000000226413263734242016703 0ustar ripleyusers// -*- C++ -*- %define DOCSTRING "NLopt is a multi-language library for nonlinear optimization (local or global, with or without derivatives, and supporting nonlinear constraints). Complete documentation, including a Python tutorial, can be found at the NLopt web page: http://ab-initio.mit.edu/nlopt" %enddef %module(docstring=DOCSTRING) nlopt %{ #include "nlopt.hpp" %} %include "std_vector.i" namespace std { %template(nlopt_doublevector) vector; }; // prepend "nlopt_" in Guile to substitute for namespace #if defined(SWIGGUILE) %rename(nlopt_opt) nlopt::opt; %rename(nlopt_roundoff_limited) nlopt::roundoff_limited; %rename(nlopt_forced_stop) nlopt::forced_stop; %rename(nlopt_srand) nlopt::srand; %rename(nlopt_srand_time) nlopt::srand_time; %rename(nlopt_version) nlopt::version; %rename(nlopt_version_major) nlopt::version_major; %rename(nlopt_version_minor) nlopt::version_minor; %rename(nlopt_version_bugfix) nlopt::version_bugfix; %rename(nlopt_algorithm_name) nlopt::algorithm_name; %include "nlopt-enum-renames.i" #endif %include "nlopt-exceptions.i" #ifdef SWIGGUILE %include "nlopt-guile.i" #endif #ifdef SWIGPYTHON %include "nlopt-python.i" #endif %include "nlopt.hpp" nloptr/src/nlopt_src/swig/numpy.i0000644000176000001440000031577213263734242016732 0ustar ripleyusers/* -*- C -*- (not really, but good for syntax highlighting) */ #ifdef SWIGPYTHON %{ #ifndef SWIG_FILE_WITH_INIT #define NO_IMPORT_ARRAY #endif #include "stdio.h" #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION #include %} /**********************************************************************/ %fragment("NumPy_Backward_Compatibility", "header") { %#if NPY_API_VERSION < 0x00000007 %#define NPY_ARRAY_DEFAULT NPY_DEFAULT %#define NPY_ARRAY_FARRAY NPY_FARRAY %#define NPY_FORTRANORDER NPY_FORTRAN %#endif } /**********************************************************************/ /* The following code originally appeared in * enthought/kiva/agg/src/numeric.i written by Eric Jones. It was * translated from C++ to C by John Hunter. Bill Spotz has modified * it to fix some minor bugs, upgrade from Numeric to numpy (all * versions), add some comments and functionality, and convert from * direct code insertion to SWIG fragments. */ %fragment("NumPy_Macros", "header") { /* Macros to extract array attributes. */ %#if NPY_API_VERSION < 0x00000007 %#define is_array(a) ((a) && PyArray_Check((PyArrayObject*)a)) %#define array_type(a) (int)(PyArray_TYPE((PyArrayObject*)a)) %#define array_numdims(a) (((PyArrayObject*)a)->nd) %#define array_dimensions(a) (((PyArrayObject*)a)->dimensions) %#define array_size(a,i) (((PyArrayObject*)a)->dimensions[i]) %#define array_strides(a) (((PyArrayObject*)a)->strides) %#define array_stride(a,i) (((PyArrayObject*)a)->strides[i]) %#define array_data(a) (((PyArrayObject*)a)->data) %#define array_descr(a) (((PyArrayObject*)a)->descr) %#define array_flags(a) (((PyArrayObject*)a)->flags) %#define array_enableflags(a,f) (((PyArrayObject*)a)->flags) = f %#else %#define is_array(a) ((a) && PyArray_Check(a)) %#define array_type(a) PyArray_TYPE((PyArrayObject*)a) %#define array_numdims(a) PyArray_NDIM((PyArrayObject*)a) %#define array_dimensions(a) PyArray_DIMS((PyArrayObject*)a) %#define array_strides(a) PyArray_STRIDES((PyArrayObject*)a) %#define array_stride(a,i) PyArray_STRIDE((PyArrayObject*)a,i) %#define array_size(a,i) PyArray_DIM((PyArrayObject*)a,i) %#define array_data(a) PyArray_DATA((PyArrayObject*)a) %#define array_descr(a) PyArray_DESCR((PyArrayObject*)a) %#define array_flags(a) PyArray_FLAGS((PyArrayObject*)a) %#define array_enableflags(a,f) PyArray_ENABLEFLAGS((PyArrayObject*)a,f) %#endif %#define array_is_contiguous(a) (PyArray_ISCONTIGUOUS((PyArrayObject*)a)) %#define array_is_native(a) (PyArray_ISNOTSWAPPED((PyArrayObject*)a)) %#define array_is_fortran(a) (PyArray_ISFORTRAN((PyArrayObject*)a)) } /**********************************************************************/ %fragment("NumPy_Utilities", "header") { /* Given a PyObject, return a string describing its type. */ const char* pytype_string(PyObject* py_obj) { if (py_obj == NULL ) return "C NULL value"; if (py_obj == Py_None ) return "Python None" ; if (PyCallable_Check(py_obj)) return "callable" ; if (PyString_Check( py_obj)) return "string" ; if (PyInt_Check( py_obj)) return "int" ; if (PyFloat_Check( py_obj)) return "float" ; if (PyDict_Check( py_obj)) return "dict" ; if (PyList_Check( py_obj)) return "list" ; if (PyTuple_Check( py_obj)) return "tuple" ; %#if PY_MAJOR_VERSION < 3 if (PyFile_Check( py_obj)) return "file" ; if (PyModule_Check( py_obj)) return "module" ; if (PyInstance_Check(py_obj)) return "instance" ; %#endif return "unkown type"; } /* Given a NumPy typecode, return a string describing the type. */ const char* typecode_string(int typecode) { static const char* type_names[25] = {"bool", "byte", "unsigned byte", "short", "unsigned short", "int", "unsigned int", "long", "unsigned long", "long long", "unsigned long long", "float", "double", "long double", "complex float", "complex double", "complex long double", "object", "string", "unicode", "void", "ntypes", "notype", "char", "unknown"}; return typecode < 24 ? type_names[typecode] : type_names[24]; } /* Make sure input has correct numpy type. This now just calls PyArray_EquivTypenums(). */ int type_match(int actual_type, int desired_type) { return PyArray_EquivTypenums(actual_type, desired_type); } %#ifdef SWIGPY_USE_CAPSULE void free_cap(PyObject * cap) { void* array = (void*) PyCapsule_GetPointer(cap,SWIGPY_CAPSULE_NAME); if (array != NULL) free(array); } %#endif } /**********************************************************************/ %fragment("NumPy_Object_to_Array", "header", fragment="NumPy_Backward_Compatibility", fragment="NumPy_Macros", fragment="NumPy_Utilities") { /* Given a PyObject pointer, cast it to a PyArrayObject pointer if * legal. If not, set the python error string appropriately and * return NULL. */ PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode) { PyArrayObject* ary = NULL; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input), typecode))) { ary = (PyArrayObject*) input; } else if is_array(input) { const char* desired_type = typecode_string(typecode); const char* actual_type = typecode_string(array_type(input)); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. Array of type '%s' given", desired_type, actual_type); ary = NULL; } else { const char* desired_type = typecode_string(typecode); const char* actual_type = pytype_string(input); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. A '%s' was given", desired_type, actual_type); ary = NULL; } return ary; } /* Convert the given PyObject to a NumPy array with the given * typecode. On success, return a valid PyArrayObject* with the * correct type. On failure, the python error string will be set and * the routine returns NULL. */ PyArrayObject* obj_to_array_allow_conversion(PyObject* input, int typecode, int* is_new_object) { PyArrayObject* ary = NULL; PyObject* py_obj; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input),typecode))) { ary = (PyArrayObject*) input; *is_new_object = 0; } else { py_obj = PyArray_FROMANY(input, typecode, 0, 0, NPY_ARRAY_DEFAULT); /* If NULL, PyArray_FromObject will have set python error value.*/ ary = (PyArrayObject*) py_obj; *is_new_object = 1; } return ary; } /* Given a PyArrayObject, check to see if it is contiguous. If so, * return the input pointer and flag it as not a new object. If it is * not contiguous, create a new PyArrayObject using the original data, * flag it as a new object and return the pointer. */ PyArrayObject* make_contiguous(PyArrayObject* ary, int* is_new_object, int min_dims, int max_dims) { PyArrayObject* result; if (array_is_contiguous(ary)) { result = ary; *is_new_object = 0; } else { result = (PyArrayObject*) PyArray_ContiguousFromObject((PyObject*)ary, array_type(ary), min_dims, max_dims); *is_new_object = 1; } return result; } /* Given a PyArrayObject, check to see if it is Fortran-contiguous. * If so, return the input pointer, but do not flag it as not a new * object. If it is not Fortran-contiguous, create a new * PyArrayObject using the original data, flag it as a new object * and return the pointer. */ PyArrayObject* make_fortran(PyArrayObject* ary, int* is_new_object) { PyArrayObject* result; if (array_is_fortran(ary)) { result = ary; *is_new_object = 0; } else { Py_INCREF(array_descr(ary)); result = (PyArrayObject*) PyArray_FromArray(ary, array_descr(ary), NPY_FORTRANORDER); *is_new_object = 1; } return result; } /* Convert a given PyObject to a contiguous PyArrayObject of the * specified type. If the input object is not a contiguous * PyArrayObject, a new one will be created and the new object flag * will be set. */ PyArrayObject* obj_to_array_contiguous_allow_conversion(PyObject* input, int typecode, int* is_new_object) { int is_new1 = 0; int is_new2 = 0; PyArrayObject* ary2; PyArrayObject* ary1 = obj_to_array_allow_conversion(input, typecode, &is_new1); if (ary1) { ary2 = make_contiguous(ary1, &is_new2, 0, 0); if ( is_new1 && is_new2) { Py_DECREF(ary1); } ary1 = ary2; } *is_new_object = is_new1 || is_new2; return ary1; } /* Convert a given PyObject to a Fortran-ordered PyArrayObject of the * specified type. If the input object is not a Fortran-ordered * PyArrayObject, a new one will be created and the new object flag * will be set. */ PyArrayObject* obj_to_array_fortran_allow_conversion(PyObject* input, int typecode, int* is_new_object) { int is_new1 = 0; int is_new2 = 0; PyArrayObject* ary2; PyArrayObject* ary1 = obj_to_array_allow_conversion(input, typecode, &is_new1); if (ary1) { ary2 = make_fortran(ary1, &is_new2); if (is_new1 && is_new2) { Py_DECREF(ary1); } ary1 = ary2; } *is_new_object = is_new1 || is_new2; return ary1; } } /* end fragment */ /**********************************************************************/ %fragment("NumPy_Array_Requirements", "header", fragment="NumPy_Backward_Compatibility", fragment="NumPy_Macros") { /* Test whether a python object is contiguous. If array is * contiguous, return 1. Otherwise, set the python error string and * return 0. */ int require_contiguous(PyArrayObject* ary) { int contiguous = 1; if (!array_is_contiguous(ary)) { PyErr_SetString(PyExc_TypeError, "Array must be contiguous. A non-contiguous array was given"); contiguous = 0; } return contiguous; } /* Require that a numpy array is not byte-swapped. If the array is * not byte-swapped, return 1. Otherwise, set the python error string * and return 0. */ int require_native(PyArrayObject* ary) { int native = 1; if (!array_is_native(ary)) { PyErr_SetString(PyExc_TypeError, "Array must have native byteorder. " "A byte-swapped array was given"); native = 0; } return native; } /* Require the given PyArrayObject to have a specified number of * dimensions. If the array has the specified number of dimensions, * return 1. Otherwise, set the python error string and return 0. */ int require_dimensions(PyArrayObject* ary, int exact_dimensions) { int success = 1; if (array_numdims(ary) != exact_dimensions) { PyErr_Format(PyExc_TypeError, "Array must have %d dimensions. Given array has %d dimensions", exact_dimensions, array_numdims(ary)); success = 0; } return success; } /* Require the given PyArrayObject to have one of a list of specified * number of dimensions. If the array has one of the specified number * of dimensions, return 1. Otherwise, set the python error string * and return 0. */ int require_dimensions_n(PyArrayObject* ary, int* exact_dimensions, int n) { int success = 0; int i; char dims_str[255] = ""; char s[255]; for (i = 0; i < n && !success; i++) { if (array_numdims(ary) == exact_dimensions[i]) { success = 1; } } if (!success) { for (i = 0; i < n-1; i++) { sprintf(s, "%d, ", exact_dimensions[i]); strcat(dims_str,s); } sprintf(s, " or %d", exact_dimensions[n-1]); strcat(dims_str,s); PyErr_Format(PyExc_TypeError, "Array must have %s dimensions. Given array has %d dimensions", dims_str, array_numdims(ary)); } return success; } /* Require the given PyArrayObject to have a specified shape. If the * array has the specified shape, return 1. Otherwise, set the python * error string and return 0. */ int require_size(PyArrayObject* ary, npy_intp* size, int n) { int i; int success = 1; int len; char desired_dims[255] = "["; char s[255]; char actual_dims[255] = "["; for(i=0; i < n;i++) { if (size[i] != -1 && size[i] != array_size(ary,i)) { success = 0; } } if (!success) { for (i = 0; i < n; i++) { if (size[i] == -1) { sprintf(s, "*,"); } else { sprintf(s, "%ld,", (long int)size[i]); } strcat(desired_dims,s); } len = strlen(desired_dims); desired_dims[len-1] = ']'; for (i = 0; i < n; i++) { sprintf(s, "%ld,", (long int)array_size(ary,i)); strcat(actual_dims,s); } len = strlen(actual_dims); actual_dims[len-1] = ']'; PyErr_Format(PyExc_TypeError, "Array must have shape of %s. Given array has shape of %s", desired_dims, actual_dims); } return success; } /* Require the given PyArrayObject to to be Fortran ordered. If the * the PyArrayObject is already Fortran ordered, do nothing. Else, * set the Fortran ordering flag and recompute the strides. */ int require_fortran(PyArrayObject* ary) { int success = 1; int nd = array_numdims(ary); int i; npy_intp * strides = array_strides(ary); if (array_is_fortran(ary)) return success; /* Set the Fortran ordered flag */ array_enableflags(ary,NPY_ARRAY_FARRAY); /* Recompute the strides */ strides[0] = strides[nd-1]; for (i=1; i < nd; ++i) strides[i] = strides[i-1] * array_size(ary,i-1); return success; } } /* Combine all NumPy fragments into one for convenience */ %fragment("NumPy_Fragments", "header", fragment="NumPy_Backward_Compatibility", fragment="NumPy_Macros", fragment="NumPy_Utilities", fragment="NumPy_Object_to_Array", fragment="NumPy_Array_Requirements") { } /* End John Hunter translation (with modifications by Bill Spotz) */ /* %numpy_typemaps() macro * * This macro defines a family of 74 typemaps that allow C arguments * of the form * * 1. (DATA_TYPE IN_ARRAY1[ANY]) * 2. (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) * 3. (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) * * 4. (DATA_TYPE IN_ARRAY2[ANY][ANY]) * 5. (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * 6. (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) * 7. (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * 8. (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) * * 9. (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) * 10. (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * 11. (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * 12. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) * 13. (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * 14. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) * * 15. (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY]) * 16. (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) * 17. (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) * 18. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, , DIM_TYPE DIM4, DATA_TYPE* IN_ARRAY4) * 19. (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) * 20. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_FARRAY4) * * 21. (DATA_TYPE INPLACE_ARRAY1[ANY]) * 22. (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) * 23. (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) * * 24. (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) * 25. (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * 26. (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) * 27. (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * 28. (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) * * 29. (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) * 30. (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * 31. (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * 32. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3) * 33. (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * 34. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3) * * 35. (DATA_TYPE INPLACE_ARRAY4[ANY][ANY][ANY][ANY]) * 36. (DATA_TYPE* INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) * 37. (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) * 38. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_ARRAY4) * 39. (DATA_TYPE* INPLACE_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) * 40. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_FARRAY4) * * 41. (DATA_TYPE ARGOUT_ARRAY1[ANY]) * 42. (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) * 43. (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) * * 44. (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) * * 45. (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) * * 46. (DATA_TYPE ARGOUT_ARRAY4[ANY][ANY][ANY][ANY]) * * 47. (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1) * 48. (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1) * * 49. (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) * 50. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2) * 51. (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) * 52. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2) * * 53. (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) * 54. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) * 55. (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) * 56. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) * * 57. (DATA_TYPE** ARGOUTVIEW_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) * 58. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_ARRAY4) * 59. (DATA_TYPE** ARGOUTVIEW_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) * 60. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_FARRAY4) * * 61. (DATA_TYPE** ARGOUTVIEWM_ARRAY1, DIM_TYPE* DIM1) * 62. (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEWM_ARRAY1) * * 63. (DATA_TYPE** ARGOUTVIEWM_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) * 64. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_ARRAY2) * 65. (DATA_TYPE** ARGOUTVIEWM_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) * 66. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_FARRAY2) * * 67. (DATA_TYPE** ARGOUTVIEWM_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) * 68. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_ARRAY3) * 69. (DATA_TYPE** ARGOUTVIEWM_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) * 70. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_FARRAY3) * * 71. (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) * 72. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_ARRAY4) * 73. (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) * 74. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_FARRAY4) * * where "DATA_TYPE" is any type supported by the NumPy module, and * "DIM_TYPE" is any int-like type suitable for specifying dimensions. * The difference between "ARRAY" typemaps and "FARRAY" typemaps is * that the "FARRAY" typemaps expect Fortran ordering of * multidimensional arrays. In python, the dimensions will not need * to be specified (except for the "DATA_TYPE* ARGOUT_ARRAY1" * typemaps). The IN_ARRAYs can be a numpy array or any sequence that * can be converted to a numpy array of the specified type. The * INPLACE_ARRAYs must be numpy arrays of the appropriate type. The * ARGOUT_ARRAYs will be returned as new numpy arrays of the * appropriate type. * * These typemaps can be applied to existing functions using the * %apply directive. For example: * * %apply (double* IN_ARRAY1, int DIM1) {(double* series, int length)}; * double prod(double* series, int length); * * %apply (int DIM1, int DIM2, double* INPLACE_ARRAY2) * {(int rows, int cols, double* matrix )}; * void floor(int rows, int cols, double* matrix, double f); * * %apply (double IN_ARRAY3[ANY][ANY][ANY]) * {(double tensor[2][2][2] )}; * %apply (double ARGOUT_ARRAY3[ANY][ANY][ANY]) * {(double low[2][2][2] )}; * %apply (double ARGOUT_ARRAY3[ANY][ANY][ANY]) * {(double upp[2][2][2] )}; * void luSplit(double tensor[2][2][2], * double low[2][2][2], * double upp[2][2][2] ); * * or directly with * * double prod(double* IN_ARRAY1, int DIM1); * * void floor(int DIM1, int DIM2, double* INPLACE_ARRAY2, double f); * * void luSplit(double IN_ARRAY3[ANY][ANY][ANY], * double ARGOUT_ARRAY3[ANY][ANY][ANY], * double ARGOUT_ARRAY3[ANY][ANY][ANY]); */ %define %numpy_typemaps(DATA_TYPE, DATA_TYPECODE, DIM_TYPE) /************************/ /* Input Array Typemaps */ /************************/ /* Typemap suite for (DATA_TYPE IN_ARRAY1[ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY1[ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY1[ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[1] = { $1_dim0 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY1[ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[1] = { -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); } %typemap(freearg) (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[1] = {-1}; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE IN_ARRAY2[ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY2[ANY][ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY2[ANY][ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { $1_dim0, $1_dim1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY2[ANY][ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } %typemap(freearg) (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } %typemap(freearg) (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } %typemap(freearg) (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { /* for now, only concerned with lists */ $1 = PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (DATA_TYPE** array=NULL, PyArrayObject** object_array=NULL, int* is_new_object_array=NULL) { npy_intp size[2] = { -1, -1 }; PyArrayObject* temp_array; Py_ssize_t i; int is_new_object; /* length of the list */ $2 = PyList_Size($input); /* the arrays */ array = (DATA_TYPE **)malloc($2*sizeof(DATA_TYPE *)); object_array = (PyArrayObject **)calloc($2,sizeof(PyArrayObject *)); is_new_object_array = (int *)calloc($2,sizeof(int)); if (array == NULL || object_array == NULL || is_new_object_array == NULL) { SWIG_fail; } for (i=0; i<$2; i++) { temp_array = obj_to_array_contiguous_allow_conversion(PySequence_GetItem($input,i), DATA_TYPECODE, &is_new_object); /* the new array must be stored so that it can be destroyed in freearg */ object_array[i] = temp_array; is_new_object_array[i] = is_new_object; if (!temp_array || !require_dimensions(temp_array, 2)) SWIG_fail; /* store the size of the first array in the list, then use that for comparison. */ if (i == 0) { size[0] = array_size(temp_array,0); size[1] = array_size(temp_array,1); } if (!require_size(temp_array, size, 2)) SWIG_fail; array[i] = (DATA_TYPE*) array_data(temp_array); } $1 = (DATA_TYPE**) array; $3 = (DIM_TYPE) size[0]; $4 = (DIM_TYPE) size[1]; } %typemap(freearg) (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { Py_ssize_t i; if (array$argnum!=NULL) free(array$argnum); /*freeing the individual arrays if needed */ if (object_array$argnum!=NULL) { if (is_new_object_array$argnum!=NULL) { for (i=0; i<$2; i++) { if (object_array$argnum[i] != NULL && is_new_object_array$argnum[i]) { Py_DECREF(object_array$argnum[i]); } } free(is_new_object_array$argnum); } free(object_array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* IN_ARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3) | !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } %typemap(freearg) (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* IN_FARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[4] = { $1_dim0, $1_dim1, $1_dim2 , $1_dim3}; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 4) || !require_size(array, size, 4)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3, DIM_TYPE DIM4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[4] = { -1, -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 4) || !require_size(array, size, 4)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); $5 = (DIM_TYPE) array_size(array,3); } %typemap(freearg) (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3, DIM_TYPE DIM4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { /* for now, only concerned with lists */ $1 = PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) (DATA_TYPE** array=NULL, PyArrayObject** object_array=NULL, int* is_new_object_array=NULL) { npy_intp size[3] = { -1, -1, -1 }; PyArrayObject* temp_array; Py_ssize_t i; int is_new_object; /* length of the list */ $2 = PyList_Size($input); /* the arrays */ array = (DATA_TYPE **)malloc($2*sizeof(DATA_TYPE *)); object_array = (PyArrayObject **)calloc($2,sizeof(PyArrayObject *)); is_new_object_array = (int *)calloc($2,sizeof(int)); if (array == NULL || object_array == NULL || is_new_object_array == NULL) { SWIG_fail; } for (i=0; i<$2; i++) { temp_array = obj_to_array_contiguous_allow_conversion(PySequence_GetItem($input,i), DATA_TYPECODE, &is_new_object); /* the new array must be stored so that it can be destroyed in freearg */ object_array[i] = temp_array; is_new_object_array[i] = is_new_object; if (!temp_array || !require_dimensions(temp_array, 3)) SWIG_fail; /* store the size of the first array in the list, then use that for comparison. */ if (i == 0) { size[0] = array_size(temp_array,0); size[1] = array_size(temp_array,1); size[2] = array_size(temp_array,2); } if (!require_size(temp_array, size, 3)) SWIG_fail; array[i] = (DATA_TYPE*) array_data(temp_array); } $1 = (DATA_TYPE**) array; $3 = (DIM_TYPE) size[0]; $4 = (DIM_TYPE) size[1]; $5 = (DIM_TYPE) size[2]; } %typemap(freearg) (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { Py_ssize_t i; if (array$argnum!=NULL) free(array$argnum); /*freeing the individual arrays if needed */ if (object_array$argnum!=NULL) { if (is_new_object_array$argnum!=NULL) { for (i=0; i<$2; i++) { if (object_array$argnum[i] != NULL && is_new_object_array$argnum[i]) { Py_DECREF(object_array$argnum[i]); } } free(is_new_object_array$argnum); } free(object_array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, * DATA_TYPE* IN_ARRAY4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_ARRAY4) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_ARRAY4) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[4] = { -1, -1, -1 , -1}; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 4) || !require_size(array, size, 4)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DIM_TYPE) array_size(array,3); $5 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_ARRAY4) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3, DIM_TYPE DIM4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[4] = { -1, -1, -1, -1 }; array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 4) || !require_size(array, size, 4) | !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); $5 = (DIM_TYPE) array_size(array,3); } %typemap(freearg) (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, * DATA_TYPE* IN_FARRAY4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_FARRAY4) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_FARRAY4) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[4] = { -1, -1, -1 , -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 4) || !require_size(array, size, 4) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DIM_TYPE) array_size(array,3); $5 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_FARRAY4) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /***************************/ /* In-Place Array Typemaps */ /***************************/ /* Typemap suite for (DATA_TYPE INPLACE_ARRAY1[ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY1[ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY1[ANY]) (PyArrayObject* array=NULL) { npy_intp size[1] = { $1_dim0 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,1) || !require_size(array, size, 1) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) (PyArrayObject* array=NULL, int i=1) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,1) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = 1; for (i=0; i < array_numdims(array); ++i) $2 *= array_size(array,i); } /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) (PyArrayObject* array=NULL, int i=0) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,1) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = 1; for (i=0; i < array_numdims(array); ++i) $1 *= array_size(array,i); $2 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) (PyArrayObject* array=NULL) { npy_intp size[2] = { $1_dim0, $1_dim1 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_size(array, size, 2) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) (PyArrayObject* array=NULL) { npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_size(array, size, 3) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } /* Typemap suite for (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (DATA_TYPE** array=NULL, PyArrayObject** object_array=NULL) { npy_intp size[2] = { -1, -1 }; PyArrayObject* temp_array; Py_ssize_t i; /* length of the list */ $2 = PyList_Size($input); /* the arrays */ array = (DATA_TYPE **)malloc($2*sizeof(DATA_TYPE *)); object_array = (PyArrayObject **)calloc($2,sizeof(PyArrayObject *)); if (array == NULL || object_array == NULL) { SWIG_fail; } for (i=0; i<$2; i++) { temp_array = obj_to_array_no_conversion(PySequence_GetItem($input,i), DATA_TYPECODE); /* the new array must be stored so that it can be destroyed in freearg */ object_array[i] = temp_array; if ( !temp_array || !require_dimensions(temp_array, 2) || !require_contiguous(temp_array) || !require_native(temp_array) || !PyArray_EquivTypenums(array_type(temp_array), DATA_TYPECODE) ) SWIG_fail; /* store the size of the first array in the list, then use that for comparison. */ if (i == 0) { size[0] = array_size(temp_array,0); size[1] = array_size(temp_array,1); } if (!require_size(temp_array, size, 2)) SWIG_fail; array[i] = (DATA_TYPE*) array_data(temp_array); } $1 = (DATA_TYPE**) array; $3 = (DIM_TYPE) size[0]; $4 = (DIM_TYPE) size[1]; } %typemap(freearg) (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { if (array$argnum!=NULL) free(array$argnum); if (object_array$argnum!=NULL) free(object_array$argnum); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* INPLACE_ARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* INPLACE_FARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE INPLACE_ARRAY4[ANY][ANY][ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY4[ANY][ANY][ANY][ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY4[ANY][ANY][ANY][ANY]) (PyArrayObject* array=NULL) { npy_intp size[4] = { $1_dim0, $1_dim1, $1_dim2 , $1_dim3 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,4) || !require_size(array, size, 4) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3, DIM_TYPE DIM4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,4) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); $5 = (DIM_TYPE) array_size(array,3); } /* Typemap suite for (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3, DIM_TYPE DIM4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { $1 = PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) (DATA_TYPE** array=NULL, PyArrayObject** object_array=NULL) { npy_intp size[3] = { -1, -1, -1 }; PyArrayObject* temp_array; Py_ssize_t i; /* length of the list */ $2 = PyList_Size($input); /* the arrays */ array = (DATA_TYPE **)malloc($2*sizeof(DATA_TYPE *)); object_array = (PyArrayObject **)calloc($2,sizeof(PyArrayObject *)); if (array == NULL || object_array == NULL) { SWIG_fail; } for (i=0; i<$2; i++) { temp_array = obj_to_array_no_conversion(PySequence_GetItem($input,i), DATA_TYPECODE); /* the new array must be stored so that it can be destroyed in freearg */ object_array[i] = temp_array; if ( !temp_array || !require_dimensions(temp_array, 3) || !require_contiguous(temp_array) || !require_native(temp_array) || !PyArray_EquivTypenums(array_type(temp_array), DATA_TYPECODE) ) SWIG_fail; /* store the size of the first array in the list, then use that for comparison. */ if (i == 0) { size[0] = array_size(temp_array,0); size[1] = array_size(temp_array,1); size[2] = array_size(temp_array,2); } if (!require_size(temp_array, size, 3)) SWIG_fail; array[i] = (DATA_TYPE*) array_data(temp_array); } $1 = (DATA_TYPE**) array; $3 = (DIM_TYPE) size[0]; $4 = (DIM_TYPE) size[1]; $5 = (DIM_TYPE) size[2]; } %typemap(freearg) (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { if (array$argnum!=NULL) free(array$argnum); if (object_array$argnum!=NULL) free(object_array$argnum); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, * DATA_TYPE* INPLACE_ARRAY4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_ARRAY4) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_ARRAY4) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,4) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DIM_TYPE) array_size(array,3); $5 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3, DIM_TYPE DIM4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,4) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); $5 = (DIM_TYPE) array_size(array,3); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* INPLACE_FARRAY4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_FARRAY4) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_FARRAY4) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,4) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DIM_TYPE) array_size(array,3); $5 = (DATA_TYPE*) array_data(array); } /*************************/ /* Argout Array Typemaps */ /*************************/ /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY1[ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY1[ANY]) (PyObject* array = NULL) { npy_intp dims[1] = { $1_dim0 }; array = PyArray_SimpleNew(1, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY1[ANY]) { $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum); } /* Typemap suite for (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) */ %typemap(in,numinputs=1, fragment="NumPy_Fragments") (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) (PyObject* array = NULL) { npy_intp dims[1]; if (!PyInt_Check($input)) { const char* typestring = pytype_string($input); PyErr_Format(PyExc_TypeError, "Int dimension expected. '%s' given.", typestring); SWIG_fail; } $2 = (DIM_TYPE) PyInt_AsLong($input); dims[0] = (npy_intp) $2; array = PyArray_SimpleNew(1, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); } %typemap(argout) (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) { $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum); } /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) */ %typemap(in,numinputs=1, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) (PyObject* array = NULL) { npy_intp dims[1]; if (!PyInt_Check($input)) { const char* typestring = pytype_string($input); PyErr_Format(PyExc_TypeError, "Int dimension expected. '%s' given.", typestring); SWIG_fail; } $1 = (DIM_TYPE) PyInt_AsLong($input); dims[0] = (npy_intp) $1; array = PyArray_SimpleNew(1, dims, DATA_TYPECODE); if (!array) SWIG_fail; $2 = (DATA_TYPE*) array_data(array); } %typemap(argout) (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) { $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum); } /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) (PyObject* array = NULL) { npy_intp dims[2] = { $1_dim0, $1_dim1 }; array = PyArray_SimpleNew(2, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) { $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum); } /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) (PyObject* array = NULL) { npy_intp dims[3] = { $1_dim0, $1_dim1, $1_dim2 }; array = PyArray_SimpleNew(3, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) { $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum); } /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY4[ANY][ANY][ANY][ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY4[ANY][ANY][ANY][ANY]) (PyObject* array = NULL) { npy_intp dims[4] = { $1_dim0, $1_dim1, $1_dim2, $1_dim3 }; array = PyArray_SimpleNew(4, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY4[ANY][ANY][ANY][ANY]) { $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum); } /*****************************/ /* Argoutview Array Typemaps */ /*****************************/ /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim_temp) { $1 = &data_temp; $2 = &dim_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1) { npy_intp dims[1] = { *$2 }; PyObject* obj = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DATA_TYPE** ARGOUTVIEW_ARRAY1) (DIM_TYPE dim_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim_temp; $2 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1) { npy_intp dims[1] = { *$1 }; PyObject* obj = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$2)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) { npy_intp dims[2] = { *$2, *$3 }; PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEW_ARRAY2) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2) { npy_intp dims[2] = { *$1, *$2 }; PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) { npy_intp dims[2] = { *$2, *$3 }; PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEW_FARRAY2) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2) { npy_intp dims[2] = { *$1, *$2 }; PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) { npy_intp dims[3] = { *$2, *$3, *$4 }; PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp = NULL) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) { npy_intp dims[3] = { *$1, *$2, *$3 }; PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$4)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) { npy_intp dims[3] = { *$2, *$3, *$4 }; PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DATA_TYPE** ARGOUTVIEW_FARRAY3) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) { npy_intp dims[3] = { *$1, *$2, *$3 }; PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$4)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; $5 = &dim4_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) { npy_intp dims[4] = { *$2, *$3, *$4 , *$5 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_ARRAY4) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEW_ARRAY4) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &dim4_temp; $5 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_ARRAY4) { npy_intp dims[4] = { *$1, *$2, *$3 , *$4 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_FARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; $5 = &dim4_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DATA_TYPE** ARGOUTVIEW_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) { npy_intp dims[4] = { *$2, *$3, *$4 , *$5 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_FARRAY4) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEW_FARRAY4) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &dim4_temp; $5 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_FARRAY4) { npy_intp dims[4] = { *$1, *$2, *$3 , *$4 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /*************************************/ /* Managed Argoutview Array Typemaps */ /*************************************/ /* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY1, DIM_TYPE* DIM1) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEWM_ARRAY1, DIM_TYPE* DIM1 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim_temp) { $1 = &data_temp; $2 = &dim_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEWM_ARRAY1, DIM_TYPE* DIM1) { npy_intp dims[1] = { *$2 }; PyObject* obj = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEWM_ARRAY1) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DATA_TYPE** ARGOUTVIEWM_ARRAY1) (DIM_TYPE dim_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim_temp; $2 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEWM_ARRAY1) { npy_intp dims[1] = { *$1 }; PyObject* obj = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$2)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEWM_ARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEWM_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) { npy_intp dims[2] = { *$2, *$3 }; PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_ARRAY2) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEWM_ARRAY2) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_ARRAY2) { npy_intp dims[2] = { *$1, *$2 }; PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEWM_FARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DATA_TYPE** ARGOUTVIEWM_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) { npy_intp dims[2] = { *$2, *$3 }; PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_FARRAY2) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEWM_FARRAY2) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_FARRAY2) { npy_intp dims[2] = { *$1, *$2 }; PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEWM_ARRAY3, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEWM_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) { npy_intp dims[3] = { *$2, *$3, *$4 }; PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_ARRAY3) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DATA_TYPE** ARGOUTVIEWM_ARRAY3) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_ARRAY3) { npy_intp dims[3] = { *$1, *$2, *$3 }; PyObject* obj= PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$4)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEWM_FARRAY3, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DATA_TYPE** ARGOUTVIEWM_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) { npy_intp dims[3] = { *$2, *$3, *$4 }; PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || require_fortran(array)) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_FARRAY3) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DATA_TYPE** ARGOUTVIEWM_FARRAY3) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_FARRAY3) { npy_intp dims[3] = { *$1, *$2, *$3 }; PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$4)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || require_fortran(array)) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; $5 = &dim4_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) { npy_intp dims[4] = { *$2, *$3, *$4 , *$5 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_ARRAY4) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEWM_ARRAY4) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &dim4_temp; $5 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_ARRAY4) { npy_intp dims[4] = { *$1, *$2, *$3 , *$4 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; $5 = &dim4_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) { npy_intp dims[4] = { *$2, *$3, *$4 , *$5 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || require_fortran(array)) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_FARRAY4) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEWM_FARRAY4) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &dim4_temp; $5 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_FARRAY4) { npy_intp dims[4] = { *$1, *$2, *$3 , *$4 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || require_fortran(array)) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; $5 = &dim4_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) { npy_intp dims[4] = { *$2, *$3, *$4 , *$5 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_ARRAY4) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEWM_ARRAY4) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &dim4_temp; $5 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_ARRAY4) { npy_intp dims[4] = { *$1, *$2, *$3 , *$4 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; $5 = &dim4_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) { npy_intp dims[4] = { *$2, *$3, *$4 , *$5 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || require_fortran(array)) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_FARRAY4) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEWM_FARRAY4) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &dim4_temp; $5 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_FARRAY4) { npy_intp dims[4] = { *$1, *$2, *$3 , *$4 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || require_fortran(array)) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } %enddef /* %numpy_typemaps() macro */ /* *************************************************************** */ /* Concrete instances of the %numpy_typemaps() macro: Each invocation * below applies all of the typemaps above to the specified data type. */ %numpy_typemaps(signed char , NPY_BYTE , int) %numpy_typemaps(unsigned char , NPY_UBYTE , int) %numpy_typemaps(short , NPY_SHORT , int) %numpy_typemaps(unsigned short , NPY_USHORT , int) %numpy_typemaps(int , NPY_INT , int) %numpy_typemaps(unsigned int , NPY_UINT , int) %numpy_typemaps(long , NPY_LONG , int) %numpy_typemaps(unsigned long , NPY_ULONG , int) %numpy_typemaps(long long , NPY_LONGLONG , int) %numpy_typemaps(unsigned long long, NPY_ULONGLONG, int) %numpy_typemaps(float , NPY_FLOAT , int) %numpy_typemaps(double , NPY_DOUBLE , int) /* *************************************************************** * The follow macro expansion does not work, because C++ bool is 4 * bytes and NPY_BOOL is 1 byte * * %numpy_typemaps(bool, NPY_BOOL, int) */ /* *************************************************************** * On my Mac, I get the following warning for this macro expansion: * 'swig/python detected a memory leak of type 'long double *', no destructor found.' * * %numpy_typemaps(long double, NPY_LONGDOUBLE, int) */ /* *************************************************************** * Swig complains about a syntax error for the following macro * expansions: * * %numpy_typemaps(complex float, NPY_CFLOAT , int) * * %numpy_typemaps(complex double, NPY_CDOUBLE, int) * * %numpy_typemaps(complex long double, NPY_CLONGDOUBLE, int) */ #endif /* SWIGPYTHON */ nloptr/src/nlopt_src/swig/nlopt-exceptions.i0000644000176000001440000001122313263734242021055 0ustar ripleyusers// since exception specifications in C++ are evil, we instead provide // %catches specifications here so that SWIG can generate language-specific // exceptions (at least for exceptions explicitly thrown by NLopt) // // manually doing this stuff is annoying %catches(std::bad_alloc) nlopt::opt::opt(); %catches(std::bad_alloc) nlopt::opt::opt(algorithm a, unsigned n); %catches(std::bad_alloc) nlopt::opt::opt(const opt& f); %catches(std::bad_alloc) nlopt::opt::operator=(opt const& f); %catches(nlopt::roundoff_limited,nlopt::forced_stop,std::runtime_error,std::bad_alloc,std::invalid_argument) nlopt::opt::optimize(std::vector &x, double &opt_f); %catches(nlopt::roundoff_limited,nlopt::forced_stop,std::runtime_error,std::bad_alloc,std::invalid_argument) nlopt::opt::optimize(const std::vector &x0); %catches(std::runtime_error) nlopt::opt::get_algorithm(); %catches(std::runtime_error) nlopt::opt::get_algorithm_name(); %catches(std::runtime_error) nlopt::opt::get_dimension(); %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::set_min_objective(func f, void *f_data); %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::set_min_objective(vfunc vf, void *f_data); %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::set_max_objective(func f, void *f_data); %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::set_max_objective(vfunc vf, void *f_data); %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::set_min_objective(func f, void *f_data, nlopt_munge md, nlopt_munge mc); %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::set_max_objective(func f, void *f_data, nlopt_munge md, nlopt_munge mc); %catches(std::invalid_argument) nlopt::opt::remove_inequality_constraints(); %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::add_inequality_constraint(func f, void *f_data, double tol=0); %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::add_inequality_constraint(vfunc vf, void *f_data, double tol=0); %catches(std::invalid_argument) nlopt::opt::remove_equality_constraints(); %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::add_equality_constraint(func f, void *f_data, double tol=0); %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::add_equality_constraint(vfunc vf, void *f_data, double tol=0); %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::add_inequality_mconstraint(mfunc mf, void *f_data, const std::vector &tol); %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::add_equality_mconstraint(mfunc mf, void *f_data, const std::vector &tol); %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::add_inequality_constraint(func f, void *f_data, nlopt_munge md, nlopt_munge mc, double tol=0); %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::add_equality_constraint(func f, void *f_data, nlopt_munge md, nlopt_munge mc, double tol=0); %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::add_inequality_mconstraint(mfunc mf, void *f_data, nlopt_munge md, nlopt_munge mc, const std::vector &tol); %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::add_equality_mconstraint(mfunc mf, void *f_data, nlopt_munge md, nlopt_munge mc, const std::vector &tol); #define SET_EXCEPT(name, T) %catches(std::invalid_argument) nlopt::opt::set_##name(T val); #define GET_EXCEPT(name) %catches(std::invalid_argument) nlopt::opt::get_##name(); #define SETVEC_EXCEPT(name) %catches(std::invalid_argument) nlopt::opt::set_##name(const std::vector &v); #define GETVEC_EXCEPT(name) %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::get_##name(std::vector &v); #define GETSET_EXCEPT(name, T) GET_EXCEPT(name) SET_EXCEPT(name, T) #define GETSETVEC_EXCEPT(name) GET_EXCEPT(name) SET_EXCEPT(name, double) GETVEC_EXCEPT(name) SETVEC_EXCEPT(name) GETSETVEC_EXCEPT(lower_bounds) GETSETVEC_EXCEPT(upper_bounds) GETSET_EXCEPT(stopval, double) GETSET_EXCEPT(ftol_rel, double) GETSET_EXCEPT(ftol_abs, double) GETSET_EXCEPT(xtol_rel, double) GETSETVEC_EXCEPT(xtol_abs) GETSET_EXCEPT(maxeval, int) GETSET_EXCEPT(maxtime, double) GETSET_EXCEPT(force_stop, int) %catches(std::invalid_argument) nlopt::opt::force_stop(); %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::set_local_optimizer(const opt &lo); GETSET_EXCEPT(population, unsigned) GETSET_EXCEPT(vector_storage, unsigned) GETSETVEC_EXCEPT(initial_step) %catches(std::bad_alloc,std::invalid_argument) nlopt::opt::set_default_initial_step(const std::vector &x); %catches(std::invalid_argument) nlopt::opt::get_initial_step(const std::vector &x, std::vector &dx); 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void *f_data; int has_gradient; int n; const double *lb, *ub, *xmin; double minf; const char *name; } testfunc; #define NTESTFUNCS 22 extern const testfunc testfuncs[NTESTFUNCS]; extern int testfuncs_verbose; extern int testfuncs_counter; #ifdef __cplusplus } /* extern "C" */ #endif /* __cplusplus */ #endif nloptr/src/nlopt_src/test/testfuncs.c0000644000176000001440000004172013355070635017566 0ustar ripleyusers#include #include #include "testfuncs.h" #include "config.h" #define UNUSED(x) (void) x static double sqr(double x) { return x * x; } int testfuncs_verbose = 0; int testfuncs_counter = 0; static double testfuncs_status(unsigned n, const double *x, double f) { ++testfuncs_counter; if (testfuncs_verbose) { unsigned i; printf("f_%d (%g", testfuncs_counter, x[0]); for (i = 1; i < n; ++i) printf(", %g", x[i]); printf(") = %g\n", f); } return f; } #define RETURN(f) return testfuncs_status(n, x, f); #define PI2 6.283185307179586 /* 2*pi */ #define PI3 9.424777960769379 /* 3*pi */ #define PI4 12.5663706143592 /* 4*pi */ /****************************************************************************/ static double rosenbrock_f(unsigned n, const double *x, double *grad, void *data) { double a = x[1] - x[0] * x[0], b = 1 - x[0]; UNUSED(data); if (grad) { grad[0] = -400 * a * x[0] - 2*b; grad[1] = 200 * a; } RETURN(100 * sqr(a) + sqr(b)); } static const double rosenbrock_lb[2] = {-2, -2}; static const double rosenbrock_ub[2] = {2, 2}; static const double rosenbrock_xmin[2] = {1, 1}; /****************************************************************************/ static double mccormic_f(unsigned n, const double *x, double *grad, void *data) { double a = x[0] + x[1], b = x[0] - x[1]; UNUSED(data); if (grad) { grad[0] = cos(a) + 2*b - 1.5; grad[1] = cos(a) - 2*b + 2.5; } RETURN(sin(a) + sqr(b) - 1.5*x[0] + 2.5*x[1] + 1); } static const double mccormic_lb[2] = {-1.5, -3}; static const double mccormic_ub[2] = {4, 4}; static const double mccormic_xmin[2] = {-0.547197553, -1.54719756}; /****************************************************************************/ static double boxbetts_f(unsigned n, const double *x, double *grad, void *data) { unsigned i; double f = 0; UNUSED(data); if (grad) grad[0] = grad[1] = grad[2] = 0; for (i = 1; i <= 10; ++i) { double e0 = exp(-0.1*i*x[0]); double e1 = exp(-0.1*i*x[1]); double e2 = exp(-0.1*i) - exp((double) -i); double g = e0 - e1 - e2 * x[2]; f += sqr(g); if (grad) { grad[0] += (2 * g) * (-0.1*i*e0); grad[1] += (2 * g) * (0.1*i*e1); grad[2] += -(2 * g) * e2; } } RETURN(f); } static const double boxbetts_lb[3] = {0.9,9,0.9}; static const double boxbetts_ub[3] = {1.2,11.2,1.2}; static const double boxbetts_xmin[3] = {1,10,1}; /****************************************************************************/ static double paviani_f(unsigned n, const double *x, double *grad, void *data) { unsigned i; double f = 0, prod = 1; UNUSED(data); if (grad) for (i = 0; i < 10; ++i) grad[i] = 0; for (i = 0; i < 10; ++i) { double ln1 = log(x[i] - 2); double ln2 = log(10 - x[i]); f += sqr(ln1) + sqr(ln2); if (grad) grad[i] += 2 * ln1 / (x[i] - 2) - 2 * ln2 / (10 - x[i]); prod *= x[i]; } f -= (prod = pow(prod, 0.2)); if (grad) for (i = 0; i < 10; ++i) grad[i] -= 0.2 * prod / x[i]; RETURN(f); } static const double paviani_lb[10] = {2.001,2.001,2.001,2.001,2.001,2.001,2.001,2.001,2.001,2.001}; static const double paviani_ub[10] = {9.999,9.999,9.999,9.999,9.999,9.999,9.999,9.999,9.999,9.999}; static const double paviani_xmin[10] = {9.35026583,9.35026583,9.35026583,9.35026583,9.35026583,9.35026583,9.35026583,9.35026583,9.35026583,9.35026583}; /****************************************************************************/ static double grosenbrock_f(unsigned n, const double *x, double *grad, void *data) { unsigned i; double f = 0; UNUSED(data); if (grad) grad[0] = 0; for (i = 0; i < 29; ++i) { double a = x[i+1] - x[i] * x[i], b = 1 - x[i]; if (grad) { grad[i] += -400 * a * x[i] - 2*b; grad[i+1] = 200 * a; } f += 100 * sqr(a) + sqr(b); } RETURN(f); } static const double grosenbrock_lb[30] = {-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30,-30}; static const double grosenbrock_ub[30] = {30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30}; static const double grosenbrock_xmin[30] = {1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1}; /****************************************************************************/ static double goldsteinprice_f(unsigned n, const double *x, double *grad, void *data) { double x0, x1, a1, a12, a2, b1, b12, b2; UNUSED(data); x0 = x[0]; x1 = x[1]; a1 = x0+x1+1; a12 = sqr(a1); a2 = 19 - 14*x0 + 3*x0*x0 - 14*x1 + 6*x0*x1 + 3*x1*x1; b1 = 2*x0-3*x1; b12 = sqr(b1); b2 = 18 - 32*x0 + 12*x0*x0 + 48*x1 - 36*x0*x1 + 27*x1*x1; if (grad) { grad[0] = (1 + a12 * a2) * (2 * b1 * 2 * b2 + b12 * (-32 + 24*x0 - 36*x1)) + (2 * a1 * a2 + a12 * (-14 + 6*x0 + 6*x1)) * (30 + b12 * b2); grad[1] = (1 + a12 * a2) * (2 * b1 * (-3) * b2 + b12 * (48 - 36*x0 + 54 * x1)) + (2 * a1 * a2 + a12 * (-14 + 6*x0 + 6*x1)) * (30 + b12 * b2); } RETURN((1 + a12 * a2) * (30 + b12 * b2)); } static const double goldsteinprice_lb[2] = {-2, -2}; static const double goldsteinprice_ub[2] = {2, 2}; static const double goldsteinprice_xmin[2] = {0, -1}; /****************************************************************************/ static double shekel_f(unsigned n, const double *x, double *grad, void *data) { static const double A[10][4] = { {4,4,4,4}, {1,1,1,1}, {8,8,8,8}, {6,6,6,6}, {3,7,3,7}, {2,9,2,9}, {5,5,3,3}, {8,1,8,1}, {6,2,6,2}, {7,3.6,7,3.6} }; static const double c[10] = {.1,.2,.2,.4,.4,.6,.3,.7,.5,.5}; unsigned i; double f = 0; if (grad) for (i = 0; i < n; ++i) grad[i] = 0; unsigned m = *((unsigned *) data); for (i = 0; i < m; ++i) { double fi = 1.0 / (c[i] + sqr(x[0]-A[i][0]) + sqr(x[1]-A[i][1]) + sqr(x[2]-A[i][2]) + sqr(x[3]-A[i][3])); f -= fi; if (grad) { grad[0] += (2*fi*fi) * (x[0]-A[i][0]); grad[1] += (2*fi*fi) * (x[1]-A[i][1]); grad[2] += (2*fi*fi) * (x[2]-A[i][2]); grad[3] += (2*fi*fi) * (x[3]-A[i][3]); } } RETURN(f); } static unsigned shekel_m[3] = {5,7,10}; static const double shekel_lb[4] = {0,0,0,0}; static const double shekel_ub[4] = {10,10,10,10}; static const double shekel0_xmin[4] = {4.000037154,4.000133276,4.000037154,4.000133276}; static const double shekel1_xmin[4] = {4.000572917,4.000689366,3.999489709,3.999606158}; static const double shekel2_xmin[4] = {4.000746531,4.000592935,3.999663399,3.999509801}; /****************************************************************************/ static double levy_f(unsigned n, const double *x, double *grad, void *data) { UNUSED(data); unsigned i; double a = x[n-1] - 1, b = 1 + sqr(sin(PI2*x[n-1])); double f = sqr(sin(PI3*x[0])) + a * b; if (grad) { for (i = 0; i < n; ++i) grad[i] = 0; grad[0] = 2 * PI3 * sin(PI3*x[0]) * cos(PI3*x[0]); grad[n-1] += b + a * 2 * PI2 * sin(PI2*x[n-1]) * cos(PI2*x[n-1]); } for (i = 0; i < n-1; ++i) { a = x[i] - 1; b = 1 + sqr(sin(PI3*x[i+1])); f += sqr(a) * b; if (grad) { grad[i] += 2 * a * b; grad[i+1] += 2*PI3 * sqr(a) * sin(PI3*x[i+1])*cos(PI3*x[i+1]); } } RETURN(f); } static const double levy_lb[7] = {-5,-5,-5,-5,-5,-5,-5}; static const double levy_ub[7] = {5,5,5,5,5,5,5}; static const double levy_xmin[7] = {1,1,1,1,1,1,-4.75440246}; static const double levy4_lb[4] = {-10,-10,-10,-10}; static const double levy4_ub[4] = {10,10,10,10}; static const double levy4_xmin[4] = {1,1,1,-9.75235596}; /****************************************************************************/ static double griewank_f(unsigned n, const double *x, double *grad, void *data) { unsigned i; double f = 1, p = 1; UNUSED(data); for (i = 0; i < n; ++i) { f += sqr(x[i]) * 0.00025; p *= cos(x[i] / sqrt(i + 1.)); if (grad) grad[i] = x[i] * 0.0005; } f -= p; if (grad) for (i = 0; i < n; ++i) grad[i] += p * tan(x[i] / sqrt(i + 1.)) / sqrt(i + 1.); RETURN(f); } static const double griewank_lb[10] = {-500,-500,-500,-500,-500,-500,-500,-500,-500,-500}; static const double griewank_ub[10] = {600,600,600,600,600,600,600,600,600,600}; static const double griewank_xmin[10] = {0,0,0,0,0,0,0,0,0,0}; /****************************************************************************/ static double sixhumpcamel_f(unsigned n, const double *x, double *grad, void *data) { UNUSED(data); if (grad) { grad[0] = 8*x[0] - 2.1*4*pow(x[0],3.) + 2*pow(x[0],5.) + x[1]; grad[1] = x[0] - 8*x[1] + 16*pow(x[1],3.); } RETURN(4*sqr(x[0]) - 2.1 * pow(x[0],4.) + pow(x[0],6.)/3. + x[0]*x[1] - 4*sqr(x[1]) + 4*pow(x[1],4.)); } static const double sixhumpcamel_lb[2] = {-5,-5}; static const double sixhumpcamel_ub[2] = {5,5}; static const double sixhumpcamel_xmin[2] = {0.08984201317, -0.7126564032}; /****************************************************************************/ static double convexcosh_f(unsigned n, const double *x, double *grad, void *data) { unsigned i; double f = 1; UNUSED(data); for (i = 0; i < n; ++i) f *= cosh((x[i] - i) * (i+1)); if (grad) for (i = 0; i < n; ++i) grad[i] = f * tanh((x[i] - i) * (i+1)) * (i+1); RETURN(f); } static const double convexcosh_lb[10] = {-1,0,0,0,0,0,0,0,0,0}; static const double convexcosh_ub[10] = {2,3,6,7,8,10,11,13,14,16}; static const double convexcosh_xmin[10] = {0,1,2,3,4,5,6,7,8,9}; /****************************************************************************/ static double branin_f(unsigned n, const double *x, double *grad, void *data) { double a = 1 - 2*x[1] + 0.05 * sin(PI4 * x[1]) - x[0]; double b = x[1] - 0.5 * sin(PI2 * x[0]); UNUSED(data); if (grad) { grad[0] = -2*a - cos(PI2 * x[0]) * PI2 * b; grad[1] = 2*a*(0.05 * PI4 * cos(PI4*x[1]) - 2) + 2*b; } RETURN(sqr(a) + sqr(b)); } static const double branin_lb[2] = {-10,-10}; static const double branin_ub[2] = {10,10}; static const double branin_xmin[2] = {1,0}; /****************************************************************************/ static double shubert_f(unsigned n, const double *x, double *grad, void *data) { UNUSED(data); unsigned i, j; double f = 0; for (j = 1; j <= 5; ++j) for (i = 0; i < n; ++i) f -= j * sin((j+1) * x[i] + j); if (grad) { for (i = 0; i < n; ++i) { grad[i] = 0; for (j = 1; j <= 5; ++j) grad[i] -= j * (j+1) * cos((j+1) * x[i] + j); } } RETURN(f); } static const double shubert_lb[2] = {-10,-10}; static const double shubert_ub[2] = {10,10}; static const double shubert_xmin[2] = {-6.774576143, -6.774576143}; /****************************************************************************/ static double hansen_f(unsigned n, const double *x, double *grad, void *data) { unsigned i; double a = 0, b = 0; UNUSED(data); for (i = 1; i <= 5; ++i) a += i * cos((i-1) * x[0] + i); for (i = 1; i <= 5; ++i) b += i * cos((i+1) * x[1] + i); if (grad) { grad[0] = 0; for (i = 1; i <= 5; ++i) grad[0] -= i * (i-1) * sin((i-1) * x[0] + i); grad[0] *= b; grad[1] = 0; for (i = 1; i <= 5; ++i) grad[1] -= i * (i+1) * sin((i+1) * x[1] + i); grad[1] *= a; } RETURN(a*b); } static const double hansen_lb[2] = {-10,-10}; static const double hansen_ub[2] = {10,10}; static const double hansen_xmin[2] = {-1.306707704,-1.425128429}; /****************************************************************************/ static double osc1d_f(unsigned n, const double *x, double *grad, void *data) { double y = *x - 1.23456789; UNUSED(data); if (grad) grad[0] = y*0.02 + sin(y - 2*sin(3*y)) * (1 - 6*cos(3*y)); RETURN(sqr(y*0.1) - cos(y - 2*sin(3*y))); } static const double osc1d_lb[1] = {-5}; static const double osc1d_ub[1] = {5}; static const double osc1d_xmin[1] = {1.23456789}; /****************************************************************************/ static double corner4d_f(unsigned n, const double *x, double *grad, void *data) { UNUSED(data); UNUSED(n); double u = x[0] + x[1] * x[2] * sin(2 * x[3]); double v = x[0] + 2*sin(u); if (grad) { grad[0] = 2*v * (1 + 2*cos(u)); grad[1] = 2*v * 2*cos(u) * x[2] * sin(2*x[3]) + 0.1; grad[2] = 2*v * 2*cos(u) * x[1] * sin(2*x[3]) + 0.1; grad[3] = 2*v * 2*cos(u) * x[1]*x[2] * cos(2*x[3]) * 2 + 0.1; } RETURN(1 + v*v + 0.1*(x[1]+x[2]+x[3])); } static const double corner4d_lb[4] = {0,0,0,0}; static const double corner4d_ub[4] = {1,1,1,1}; static const double corner4d_xmin[4] = {0,0,0,0}; /****************************************************************************/ static double side4d_f(unsigned n, const double *x, double *grad, void *data) { UNUSED(data); UNUSED(n); double x0, x1, x2, x3, d0,d1,d2,d3; const double w0 = 0.1, w1 = 0.2, w2 = 0.3, w3 = 0.4; x0 = +0.4977 * x[0] - 0.3153 * x[1] - 0.5066 * x[2] - 0.4391 * x[3]; x1 = -0.3153 * x[0] + 0.3248 * x[1] - 0.4382 * x[2] - 0.4096 * x[3]; x2 = -0.5066 * x[0] - 0.4382 * x[1] + 0.3807 * x[2] - 0.4543 * x[3]; x3 = -0.4391 * x[0] - 0.4096 * x[1] - 0.4543 * x[2] + 0.5667 * x[3]; d0 = -1. / (x0*x0 + w0*w0); d1 = -1. / (x1*x1 + w1*w1); d2 = -1. / (x2*x2 + w2*w2); d3 = -1. / (x3*x3 + w3*w3); if (grad) { grad[0] = 2 * (x0*d0*d0 * +0.4977 + x1*d1*d1 * -0.3153 + x2*d2*d2 * -0.5066 + x3*d3*d3 * -0.4391); grad[1] = 2 * (x0*d0*d0 * -0.3153 + x1*d1*d1 * +0.3248 + x2*d2*d2 * -0.4382 + x3*d3*d3 * -0.4096); grad[2] = 2 * (x0*d0*d0 * -0.5066 + x1*d1*d1 * -0.4382 + x2*d2*d2 * +0.3807 + x3*d3*d3 * -0.4543); grad[3] = 2 * (x0*d0*d0 * -0.4391 + x1*d1*d1 * -0.4096 + x2*d2*d2 * -0.4543 + x3*d3*d3 * +0.5667); } RETURN(d0 + d1 + d2 + d3); } static const double side4d_lb[4] = {0.1,-1,-1,-1}; static const double side4d_ub[4] = {1,1,1,1}; static const double side4d_xmin[4] = {0.1,0.102971169,0.0760520641,-0.0497098571}; /****************************************************************************/ /****************************************************************************/ const testfunc testfuncs[NTESTFUNCS] = { { rosenbrock_f, NULL, 1, 2, rosenbrock_lb, rosenbrock_ub, rosenbrock_xmin, 0.0, "Rosenbrock function" }, { mccormic_f, NULL, 1, 2, mccormic_lb, mccormic_ub, mccormic_xmin, -1.91322295, "McCormic function" }, { boxbetts_f, NULL, 1, 3, boxbetts_lb, boxbetts_ub, boxbetts_xmin, 0.0, "Box and Betts exponential quadratic sum" }, { paviani_f, NULL, 1, 10, paviani_lb, paviani_ub, paviani_xmin, -45.7784697, "Paviani function" }, { grosenbrock_f, NULL, 1, 30, grosenbrock_lb, grosenbrock_ub, grosenbrock_xmin, 0.0, "Generalized Rosenbrock function" }, { goldsteinprice_f, NULL, 1, 2, goldsteinprice_lb, goldsteinprice_ub, goldsteinprice_xmin, 3.0, "Goldstein and Price function" }, { shekel_f, shekel_m + 0, 1, 4, shekel_lb, shekel_ub, shekel0_xmin, -10.15319968, "Shekel m=5 function" }, { shekel_f, shekel_m + 1, 1, 4, shekel_lb, shekel_ub, shekel1_xmin, -10.40294057, "Shekel m=7 function" }, { shekel_f, shekel_m + 2, 1, 4, shekel_lb, shekel_ub, shekel2_xmin, -10.53640982, "Shekel m=10 function" }, { levy_f, NULL, 1, 4, levy4_lb, levy4_ub, levy4_xmin, -21.50235596, "Levy n=4 function" }, { levy_f, NULL, 1, 5, levy_lb, levy_ub, levy_xmin+2, -11.50440302, "Levy n=5 function" }, { levy_f, NULL, 1, 6, levy_lb, levy_ub, levy_xmin+1, -11.50440302, "Levy n=6 function" }, { levy_f, NULL, 1, 7, levy_lb, levy_ub, levy_xmin, -11.50440302, "Levy n=7 function" }, { griewank_f, NULL, 1, 10, griewank_lb, griewank_ub, griewank_xmin, 0.0, "Griewank function" }, { sixhumpcamel_f, NULL, 1, 2, sixhumpcamel_lb, sixhumpcamel_ub, sixhumpcamel_xmin, -1.031628453, "Six-hump camel back function" }, { convexcosh_f, NULL, 1, 10, convexcosh_lb, convexcosh_ub, convexcosh_xmin, 1.0, "Convex product of cosh functions" }, { branin_f, NULL, 1, 2, branin_lb, branin_ub, branin_xmin, -.0, "Branin function" }, { shubert_f, NULL, 1, 2, shubert_lb, shubert_ub, shubert_xmin, -24.06249888, "Shubert function" }, { hansen_f, NULL, 1, 2, hansen_lb, hansen_ub, hansen_xmin, -176.5417931367, "Hansen function" }, { osc1d_f, NULL, 1, 1, osc1d_lb, osc1d_ub, osc1d_xmin, -1.0, "1d oscillating function with a single minimum" }, { corner4d_f, NULL, 1, 4, corner4d_lb, corner4d_ub, corner4d_xmin, 1.0, "4d function with minimum at corner" }, { side4d_f, NULL, 1, 4, side4d_lb, side4d_ub, side4d_xmin, -141.285020472, "4d function with minimum at side" } }; nloptr/src/nlopt_src/test/test_std.py0000644000176000001440000000117713263734242017610 0ustar ripleyusers#!/usr/bin/env python import nlopt import numpy as np print ('nlopt version='+nlopt.__version__) def f(x, grad): F=x[0] L=x[1] E=x[2] I=x[3] D=F*L**3/(3.*E*I) return D n = 4 opt = nlopt.opt(nlopt.LN_COBYLA, n) opt.set_min_objective(f) lb = np.array([40., 50., 30e3, 1.]) ub = np.array([60., 60., 40e3, 10.]) x = (lb+ub)/2. opt.set_lower_bounds(lb) opt.set_upper_bounds(ub) opt.set_xtol_rel(1e-3) opt.set_ftol_rel(1e-3) xopt = opt.optimize(x) opt_val = opt.last_optimum_value() result = opt.last_optimize_result() print ('opt_result='+str(result)) print ('optimizer='+str(xopt)) print ('opt_val='+str(opt_val)) nloptr/src/nlopt_src/test/testopt.cpp0000644000176000001440000002671213355070635017616 0ustar ripleyusers/* Copyright (c) 2007-2011 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include #include #include #include #include "config.h" #ifdef HAVE_UNISTD_H # include #endif #ifdef HAVE_GETOPT_H # include #endif #define USE_FEENABLEEXCEPT 0 #if USE_FEENABLEEXCEPT # include extern "C" int feenableexcept (int EXCEPTS); #endif #include "nlopt.h" #include "nlopt-util.h" #include "testfuncs.h" static nlopt_algorithm algorithm = NLOPT_GN_DIRECT_L; static double ftol_rel = 0, ftol_abs = 0, xtol_rel = 0, xtol_abs = 0, minf_max_delta = -HUGE_VAL; static int maxeval = 1000, iterations = 1, center_start = 0; static double maxtime = 0.0; static double xinit_tol = -1; static int force_constraints = 0; static int fix_bounds[100] = {0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0}; static void listalgs(FILE *f) { int i; fprintf(f, "Available algorithms:\n"); for (i = 0; i < NLOPT_NUM_ALGORITHMS; ++i) fprintf(f, " %2d: %s\n", i, nlopt_algorithm_name((nlopt_algorithm) i)); } static void listfuncs(FILE *f) { int i; fprintf(f, "Available objective functions:\n"); for (i = 0; i < NTESTFUNCS; ++i) fprintf(f, " %2d: %s (%d dims)\n", i, testfuncs[i].name, testfuncs[i].n); } typedef struct { const double *lb, *ub; nlopt_func f; void *f_data; } bounds_wrap_data; static double bounds_wrap_func(int n, const double *x, double *grad, void *d_) { bounds_wrap_data *d = (bounds_wrap_data *) d_; int i; double b = 0; for (i = 0; i < n; ++i) { if (x[i] < d->lb[i]) { b = d->lb[i]; break; } else if (x[i] > d->ub[i]) { b = d->ub[i]; break; } } if (i < n) fprintf(stderr, "WARNING: bounds violated by x[%d] = %g = %g + %g\n", i, x[i], b, x[i] - b); return d->f(n, x, grad, d->f_data); } static int test_function(int ifunc) { testfunc func; int i, iter; double *x, minf, minf_max, f0, *xtabs, *lb, *ub; nlopt_result ret; double start = nlopt_seconds(); int total_count = 0, max_count = 0, min_count = 1<<30; double total_err = 0, max_err = 0; bounds_wrap_data bw; if (ifunc < 0 || ifunc >= NTESTFUNCS) { fprintf(stderr, "testopt: invalid function %d\n", ifunc); listfuncs(stderr); return 0; } func = testfuncs[ifunc]; x = (double *) malloc(sizeof(double) * func.n * 5); if (!x) { fprintf(stderr, "testopt: Out of memory!\n"); return 0; } lb = x + func.n * 3; ub = lb + func.n; xtabs = x + func.n * 2; bw.lb = lb; bw.ub = ub; bw.f = func.f; bw.f_data = func.f_data; for (i = 0; i < func.n; ++i) xtabs[i] = xtol_abs; minf_max = minf_max_delta > (-HUGE_VAL) ? minf_max_delta + func.minf : (-HUGE_VAL); printf("-----------------------------------------------------------\n"); printf("Optimizing %s (%d dims) using %s algorithm\n", func.name, func.n, nlopt_algorithm_name(algorithm)); printf("lower bounds at lb = ["); for (i = 0; i < func.n; ++i) printf(" %g", func.lb[i]); printf("]\n"); printf("upper bounds at ub = ["); for (i = 0; i < func.n; ++i) printf(" %g", func.ub[i]); printf("]\n"); memcpy(lb, func.lb, func.n * sizeof(double)); memcpy(ub, func.ub, func.n * sizeof(double)); for (i = 0; i < func.n; ++i) if (fix_bounds[i]) { printf("fixing bounds for dim[%d] to xmin[%d]=%g\n", i, i, func.xmin[i]); lb[i] = ub[i] = func.xmin[i]; } if (force_constraints) { for (i = 0; i < func.n; ++i) { if (nlopt_iurand(2) == 0) ub[i] = nlopt_urand(lb[i], func.xmin[i]); else lb[i] = nlopt_urand(func.xmin[i], ub[i]); } printf("adjusted lower bounds at lb = ["); for (i = 0; i < func.n; ++i) printf(" %g", lb[i]); printf("]\n"); printf("adjusted upper bounds at ub = ["); for (i = 0; i < func.n; ++i) printf(" %g", ub[i]); printf("]\n"); } if (fabs(func.f(func.n, func.xmin, 0, func.f_data) - func.minf) > 1e-8) { fprintf(stderr, "BUG: function does not achieve given lower bound!\n"); fprintf(stderr, "f(%g", func.xmin[0]); for (i = 1; i < func.n; ++i) fprintf(stderr, ", %g", func.xmin[i]); fprintf(stderr, ") = %0.16g instead of %0.16g, |diff| = %g\n", func.f(func.n, func.xmin, 0, func.f_data), func.minf, fabs(func.f(func.n, func.xmin, 0, func.f_data) - func.minf)); return 0; } for (iter = 0; iter < iterations; ++iter) { double val; testfuncs_counter = 0; printf("Starting guess x = ["); for (i = 0; i < func.n; ++i) { if (center_start) x[i] = (ub[i] + lb[i]) * 0.5; else if (xinit_tol < 0) { /* random starting point near center of box */ double dx = (ub[i] - lb[i]) * 0.25; double xm = 0.5 * (ub[i] + lb[i]); x[i] = nlopt_urand(xm - dx, xm + dx); } else { x[i] = nlopt_urand(-xinit_tol, xinit_tol) + (1 + nlopt_urand(-xinit_tol, xinit_tol)) * func.xmin[i]; if (x[i] > ub[i]) x[i] = ub[i]; else if (x[i] < lb[i]) x[i] = lb[i]; } printf(" %g", x[i]); } printf("]\n"); f0 = func.f(func.n, x, x + func.n, func.f_data); printf("Starting function value = %g\n", f0); if (iter == 0 && testfuncs_verbose && func.has_gradient) { printf("checking gradient:\n"); for (i = 0; i < func.n; ++i) { double f; x[i] *= 1 + 1e-6; f = func.f(func.n, x, NULL, func.f_data); x[i] /= 1 + 1e-6; printf(" grad[%d] = %g vs. numerical derivative %g\n", i, x[i + func.n], (f - f0) / (x[i] * 1e-6)); } } testfuncs_counter = 0; ret = nlopt_minimize(algorithm, func.n, bounds_wrap_func, &bw, lb, ub, x, &minf, minf_max, ftol_rel, ftol_abs, xtol_rel, xtabs, maxeval, maxtime); printf("finished after %g seconds.\n", nlopt_seconds() - start); printf("return code %d from nlopt_minimize\n", ret); if (ret < 0 && ret != NLOPT_ROUNDOFF_LIMITED && ret != NLOPT_FORCED_STOP) { fprintf(stderr, "testopt: error in nlopt_minimize\n"); free(x); return 0; } printf("Found minimum f = %g after %d evaluations.\n", minf, testfuncs_counter); total_count += testfuncs_counter; if (testfuncs_counter > max_count) max_count = testfuncs_counter; if (testfuncs_counter < min_count) min_count = testfuncs_counter; printf("Minimum at x = ["); for (i = 0; i < func.n; ++i) printf(" %g", x[i]); printf("]\n"); if (func.minf == 0) printf("|f - minf| = %g\n", fabs(minf - func.minf)); else printf("|f - minf| = %g, |f - minf| / |minf| = %e\n", fabs(minf - func.minf), fabs(minf - func.minf) / fabs(func.minf)); total_err += fabs(minf - func.minf); if (fabs(minf - func.minf) > max_err) max_err = fabs(minf - func.minf); printf("vs. global minimum f = %g at x = [", func.minf); for (i = 0; i < func.n; ++i) printf(" %g", func.xmin[i]); printf("]\n"); val = func.f(func.n, x, NULL, func.f_data); if (val != minf) { fprintf(stderr, "Mismatch %g between returned minf=%g and f(x) = %g\n", minf - val, minf, val); free(x); return 0; } } if (iterations > 1) printf("average #evaluations = %g (%d-%d)\naverage |f-minf| = %g, max |f-minf| = %g\n", total_count * 1.0 / iterations, min_count, max_count, total_err / iterations, max_err); free(x); return 1; } static void usage(FILE *f) { fprintf(f, "Usage: testopt [OPTIONS]\n" "Options:\n" " -h : print this help\n" " -L : list available algorithms and objective functions\n" " -v : verbose mode\n" " -a : use optimization algorithm \n" " -o : use objective function \n" " -0 : starting guess within + (1+) * optimum\n" " -b : eliminate given dims by equating bounds\n" " -c : starting guess at center of cell\n" " -C : put optimum outside of bound constraints\n" " -e : use at most evals (default: %d, 0 to disable)\n" " -t : use at most seconds (default: disabled)\n" " -x : relative tolerance on x (default: disabled)\n" " -X : absolute tolerance on x (default: disabled)\n" " -f : relative tolerance on f (default: disabled)\n" " -F : absolute tolerance on f (default: disabled)\n" " -m : stop when minf+ is reached (default: disabled)\n" " -i : iterate optimization times (default: 1)\n" " -r : use random seed for starting guesses\n" , maxeval); } int main(int argc, char **argv) { int c; nlopt_srand_time(); testfuncs_verbose = 0; if (argc <= 1) usage(stdout); #if USE_FEENABLEEXCEPT feenableexcept(FE_INVALID); #endif while ((c = getopt(argc, argv, "hLvCc0:r:a:o:i:e:t:x:X:f:F:m:b:")) != -1) switch (c) { case 'h': usage(stdout); return EXIT_SUCCESS; case 'L': listalgs(stdout); listfuncs(stdout); return EXIT_SUCCESS; case 'v': testfuncs_verbose = 1; break; case 'C': force_constraints = 1; break; case 'r': nlopt_srand((unsigned long) atoi(optarg)); break; case 'a': c = atoi(optarg); if (c < 0 || c >= NLOPT_NUM_ALGORITHMS) { fprintf(stderr, "testopt: invalid algorithm %d\n", c); listalgs(stderr); return EXIT_FAILURE; } algorithm = (nlopt_algorithm) c; break; case 'o': if (!test_function(atoi(optarg))) return EXIT_FAILURE; break; case 'e': maxeval = atoi(optarg); break; case 'i': iterations = atoi(optarg); break; case 't': maxtime = atof(optarg); break; case 'x': xtol_rel = atof(optarg); break; case 'X': xtol_abs = atof(optarg); break; case 'f': ftol_rel = atof(optarg); break; case 'F': ftol_abs = atof(optarg); break; case 'm': minf_max_delta = atof(optarg); break; case 'c': center_start = 1; break; case '0': center_start = 0; xinit_tol = atof(optarg); break; case 'b': { const char *s = optarg; while (s && *s) { int b = atoi(s); if (b < 0 || b >= 100) { fprintf(stderr, "invalid -b argument"); return EXIT_FAILURE; } fix_bounds[b] = 1; s = strchr(s, ','); if (s) ++s; } break; } default: fprintf(stderr, "harminv: invalid argument -%c\n", c); usage(stderr); return EXIT_FAILURE; } return EXIT_SUCCESS; } nloptr/src/nlopt_src/config.sub0000644000176000001440000010530113263734242016400 0ustar ripleyusers#! /bin/sh # Configuration validation subroutine script. # Copyright 1992-2013 Free Software Foundation, Inc. timestamp='2013-04-24' # This file is free software; you can redistribute it and/or modify it # under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, see . # # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that # program. This Exception is an additional permission under section 7 # of the GNU General Public License, version 3 ("GPLv3"). # Please send patches with a ChangeLog entry to config-patches@gnu.org. # # Configuration subroutine to validate and canonicalize a configuration type. # Supply the specified configuration type as an argument. # If it is invalid, we print an error message on stderr and exit with code 1. # Otherwise, we print the canonical config type on stdout and succeed. # You can get the latest version of this script from: # http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD # This file is supposed to be the same for all GNU packages # and recognize all the CPU types, system types and aliases # that are meaningful with *any* GNU software. # Each package is responsible for reporting which valid configurations # it does not support. The user should be able to distinguish # a failure to support a valid configuration from a meaningless # configuration. # The goal of this file is to map all the various variations of a given # machine specification into a single specification in the form: # CPU_TYPE-MANUFACTURER-OPERATING_SYSTEM # or in some cases, the newer four-part form: # CPU_TYPE-MANUFACTURER-KERNEL-OPERATING_SYSTEM # It is wrong to echo any other type of specification. me=`echo "$0" | sed -e 's,.*/,,'` usage="\ Usage: $0 [OPTION] CPU-MFR-OPSYS $0 [OPTION] ALIAS Canonicalize a configuration name. Operation modes: -h, --help print this help, then exit -t, --time-stamp print date of last modification, then exit -v, --version print version number, then exit Report bugs and patches to ." version="\ GNU config.sub ($timestamp) Copyright 1992-2013 Free Software Foundation, Inc. This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE." help=" Try \`$me --help' for more information." # Parse command line while test $# -gt 0 ; do case $1 in --time-stamp | --time* | -t ) echo "$timestamp" ; exit ;; --version | -v ) echo "$version" ; exit ;; --help | --h* | -h ) echo "$usage"; exit ;; -- ) # Stop option processing shift; break ;; - ) # Use stdin as input. break ;; -* ) echo "$me: invalid option $1$help" exit 1 ;; *local*) # First pass through any local machine types. echo $1 exit ;; * ) break ;; esac done case $# in 0) echo "$me: missing argument$help" >&2 exit 1;; 1) ;; *) echo "$me: too many arguments$help" >&2 exit 1;; esac # Separate what the user gave into CPU-COMPANY and OS or KERNEL-OS (if any). # Here we must recognize all the valid KERNEL-OS combinations. maybe_os=`echo $1 | sed 's/^\(.*\)-\([^-]*-[^-]*\)$/\2/'` case $maybe_os in nto-qnx* | linux-gnu* | linux-android* | linux-dietlibc | linux-newlib* | \ linux-musl* | linux-uclibc* | uclinux-uclibc* | uclinux-gnu* | kfreebsd*-gnu* | \ knetbsd*-gnu* | netbsd*-gnu* | \ kopensolaris*-gnu* | \ storm-chaos* | os2-emx* | rtmk-nova*) os=-$maybe_os basic_machine=`echo $1 | sed 's/^\(.*\)-\([^-]*-[^-]*\)$/\1/'` ;; android-linux) os=-linux-android basic_machine=`echo $1 | sed 's/^\(.*\)-\([^-]*-[^-]*\)$/\1/'`-unknown ;; *) basic_machine=`echo $1 | sed 's/-[^-]*$//'` if [ $basic_machine != $1 ] then os=`echo $1 | sed 's/.*-/-/'` else os=; fi ;; esac ### Let's recognize common machines as not being operating systems so ### that things like config.sub decstation-3100 work. We also ### recognize some manufacturers as not being operating systems, so we ### can provide default operating systems below. case $os in -sun*os*) # Prevent following clause from handling this invalid input. ;; -dec* | -mips* | -sequent* | -encore* | -pc532* | -sgi* | -sony* | \ -att* | -7300* | -3300* | -delta* | -motorola* | -sun[234]* | \ -unicom* | -ibm* | -next | -hp | -isi* | -apollo | -altos* | \ -convergent* | -ncr* | -news | -32* | -3600* | -3100* | -hitachi* |\ -c[123]* | -convex* | -sun | -crds | -omron* | -dg | -ultra | -tti* | \ -harris | -dolphin | -highlevel | -gould | -cbm | -ns | -masscomp | \ -apple | -axis | -knuth | -cray | -microblaze*) os= basic_machine=$1 ;; -bluegene*) os=-cnk ;; -sim | -cisco | -oki | -wec | -winbond) os= basic_machine=$1 ;; -scout) ;; -wrs) os=-vxworks basic_machine=$1 ;; -chorusos*) os=-chorusos basic_machine=$1 ;; -chorusrdb) os=-chorusrdb basic_machine=$1 ;; -hiux*) os=-hiuxwe2 ;; -sco6) os=-sco5v6 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco5) os=-sco3.2v5 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco4) os=-sco3.2v4 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco3.2.[4-9]*) os=`echo $os | sed -e 's/sco3.2./sco3.2v/'` basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco3.2v[4-9]*) # Don't forget version if it is 3.2v4 or newer. basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco5v6*) # Don't forget version if it is 3.2v4 or newer. basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco*) os=-sco3.2v2 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -udk*) basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -isc) os=-isc2.2 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -clix*) basic_machine=clipper-intergraph ;; -isc*) basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -lynx*178) os=-lynxos178 ;; -lynx*5) os=-lynxos5 ;; -lynx*) os=-lynxos ;; -ptx*) basic_machine=`echo $1 | sed -e 's/86-.*/86-sequent/'` ;; -windowsnt*) os=`echo $os | sed -e 's/windowsnt/winnt/'` ;; -psos*) os=-psos ;; -mint | -mint[0-9]*) basic_machine=m68k-atari os=-mint ;; esac # Decode aliases for certain CPU-COMPANY combinations. case $basic_machine in # Recognize the basic CPU types without company name. # Some are omitted here because they have special meanings below. 1750a | 580 \ | a29k \ | aarch64 | aarch64_be \ | alpha | alphaev[4-8] | alphaev56 | alphaev6[78] | alphapca5[67] \ | alpha64 | alpha64ev[4-8] | alpha64ev56 | alpha64ev6[78] | alpha64pca5[67] \ | am33_2.0 \ | arc | arceb \ | arm | arm[bl]e | arme[lb] | armv[2-8] | armv[3-8][lb] | armv7[arm] \ | avr | avr32 \ | be32 | be64 \ | bfin \ | c4x | clipper \ | d10v | d30v | dlx | dsp16xx \ | epiphany \ | fido | fr30 | frv \ | h8300 | h8500 | hppa | hppa1.[01] | hppa2.0 | hppa2.0[nw] | hppa64 \ | hexagon \ | i370 | i860 | i960 | ia64 \ | ip2k | iq2000 \ | le32 | le64 \ | lm32 \ | m32c | m32r | m32rle | m68000 | m68k | m88k \ | maxq | mb | microblaze | microblazeel | mcore | mep | metag \ | mips | mipsbe | mipseb | mipsel | mipsle \ | mips16 \ | mips64 | mips64el \ | mips64octeon | mips64octeonel \ | mips64orion | mips64orionel \ | mips64r5900 | mips64r5900el \ | mips64vr | mips64vrel \ | mips64vr4100 | mips64vr4100el \ | mips64vr4300 | mips64vr4300el \ | mips64vr5000 | mips64vr5000el \ | mips64vr5900 | mips64vr5900el \ | mipsisa32 | mipsisa32el \ | mipsisa32r2 | mipsisa32r2el \ | mipsisa64 | mipsisa64el \ | mipsisa64r2 | mipsisa64r2el \ | mipsisa64sb1 | mipsisa64sb1el \ | mipsisa64sr71k | mipsisa64sr71kel \ | mipsr5900 | mipsr5900el \ | mipstx39 | mipstx39el \ | mn10200 | mn10300 \ | moxie \ | mt \ | msp430 \ | nds32 | nds32le | nds32be \ | nios | nios2 | nios2eb | nios2el \ | ns16k | ns32k \ | open8 \ | or1k | or32 \ | pdp10 | pdp11 | pj | pjl \ | powerpc | powerpc64 | powerpc64le | powerpcle \ | pyramid \ | rl78 | rx \ | score \ | sh | sh[1234] | sh[24]a | sh[24]aeb | sh[23]e | sh[34]eb | sheb | shbe | shle | sh[1234]le | sh3ele \ | sh64 | sh64le \ | sparc | sparc64 | sparc64b | sparc64v | sparc86x | sparclet | sparclite \ | sparcv8 | sparcv9 | sparcv9b | sparcv9v \ | spu \ | tahoe | tic4x | tic54x | tic55x | tic6x | tic80 | tron \ | ubicom32 \ | v850 | v850e | v850e1 | v850e2 | v850es | v850e2v3 \ | we32k \ | x86 | xc16x | xstormy16 | xtensa \ | z8k | z80) basic_machine=$basic_machine-unknown ;; c54x) basic_machine=tic54x-unknown ;; c55x) basic_machine=tic55x-unknown ;; c6x) basic_machine=tic6x-unknown ;; m6811 | m68hc11 | m6812 | m68hc12 | m68hcs12x | picochip) basic_machine=$basic_machine-unknown os=-none ;; m88110 | m680[12346]0 | m683?2 | m68360 | m5200 | v70 | w65 | z8k) ;; ms1) basic_machine=mt-unknown ;; strongarm | thumb | xscale) basic_machine=arm-unknown ;; xgate) basic_machine=$basic_machine-unknown os=-none ;; xscaleeb) basic_machine=armeb-unknown ;; xscaleel) basic_machine=armel-unknown ;; # We use `pc' rather than `unknown' # because (1) that's what they normally are, and # (2) the word "unknown" tends to confuse beginning users. i*86 | x86_64) basic_machine=$basic_machine-pc ;; # Object if more than one company name word. *-*-*) echo Invalid configuration \`$1\': machine \`$basic_machine\' not recognized 1>&2 exit 1 ;; # Recognize the basic CPU types with company name. 580-* \ | a29k-* \ | aarch64-* | aarch64_be-* \ | alpha-* | alphaev[4-8]-* | alphaev56-* | alphaev6[78]-* \ | alpha64-* | alpha64ev[4-8]-* | alpha64ev56-* | alpha64ev6[78]-* \ | alphapca5[67]-* | alpha64pca5[67]-* | arc-* | arceb-* \ | arm-* | armbe-* | armle-* | armeb-* | armv*-* \ | avr-* | avr32-* \ | be32-* | be64-* \ | bfin-* | bs2000-* \ | c[123]* | c30-* | [cjt]90-* | c4x-* \ | clipper-* | craynv-* | cydra-* \ | d10v-* | d30v-* | dlx-* \ | elxsi-* \ | f30[01]-* | f700-* | fido-* | fr30-* | frv-* | fx80-* \ | h8300-* | h8500-* \ | hppa-* | hppa1.[01]-* | hppa2.0-* | hppa2.0[nw]-* | hppa64-* \ | hexagon-* \ | i*86-* | i860-* | i960-* | ia64-* \ | ip2k-* | iq2000-* \ | le32-* | le64-* \ | lm32-* \ | m32c-* | m32r-* | m32rle-* \ | m68000-* | m680[012346]0-* | m68360-* | m683?2-* | m68k-* \ | m88110-* | m88k-* | maxq-* | mcore-* | metag-* \ | microblaze-* | microblazeel-* \ | mips-* | mipsbe-* | mipseb-* | mipsel-* | mipsle-* \ | mips16-* \ | mips64-* | mips64el-* \ | mips64octeon-* | mips64octeonel-* \ | mips64orion-* | mips64orionel-* \ | mips64r5900-* | mips64r5900el-* \ | mips64vr-* | mips64vrel-* \ | mips64vr4100-* | mips64vr4100el-* \ | mips64vr4300-* | mips64vr4300el-* \ | mips64vr5000-* | mips64vr5000el-* \ | mips64vr5900-* | mips64vr5900el-* \ | mipsisa32-* | mipsisa32el-* \ | mipsisa32r2-* | mipsisa32r2el-* \ | mipsisa64-* | mipsisa64el-* \ | mipsisa64r2-* | mipsisa64r2el-* \ | mipsisa64sb1-* | mipsisa64sb1el-* \ | mipsisa64sr71k-* | mipsisa64sr71kel-* \ | mipsr5900-* | mipsr5900el-* \ | mipstx39-* | mipstx39el-* \ | mmix-* \ | mt-* \ | msp430-* \ | nds32-* | nds32le-* | nds32be-* \ | nios-* | nios2-* | nios2eb-* | nios2el-* \ | none-* | np1-* | ns16k-* | ns32k-* \ | open8-* \ | orion-* \ | pdp10-* | pdp11-* | pj-* | pjl-* | pn-* | power-* \ | powerpc-* | powerpc64-* | powerpc64le-* | powerpcle-* \ | pyramid-* \ | rl78-* | romp-* | rs6000-* | rx-* \ | sh-* | sh[1234]-* | sh[24]a-* | sh[24]aeb-* | sh[23]e-* | sh[34]eb-* | sheb-* | shbe-* \ | shle-* | sh[1234]le-* | sh3ele-* | sh64-* | sh64le-* \ | sparc-* | sparc64-* | sparc64b-* | sparc64v-* | sparc86x-* | sparclet-* \ | sparclite-* \ | sparcv8-* | sparcv9-* | sparcv9b-* | sparcv9v-* | sv1-* | sx?-* \ | tahoe-* \ | tic30-* | tic4x-* | tic54x-* | tic55x-* | tic6x-* | tic80-* \ | tile*-* \ | tron-* \ | ubicom32-* \ | v850-* | v850e-* | v850e1-* | v850es-* | v850e2-* | v850e2v3-* \ | vax-* \ | we32k-* \ | x86-* | x86_64-* | xc16x-* | xps100-* \ | xstormy16-* | xtensa*-* \ | ymp-* \ | z8k-* | z80-*) ;; # Recognize the basic CPU types without company name, with glob match. xtensa*) basic_machine=$basic_machine-unknown ;; # Recognize the various machine names and aliases which stand # for a CPU type and a company and sometimes even an OS. 386bsd) basic_machine=i386-unknown os=-bsd ;; 3b1 | 7300 | 7300-att | att-7300 | pc7300 | safari | unixpc) basic_machine=m68000-att ;; 3b*) basic_machine=we32k-att ;; a29khif) basic_machine=a29k-amd os=-udi ;; abacus) basic_machine=abacus-unknown ;; adobe68k) basic_machine=m68010-adobe os=-scout ;; alliant | fx80) basic_machine=fx80-alliant ;; altos | altos3068) basic_machine=m68k-altos ;; am29k) basic_machine=a29k-none os=-bsd ;; amd64) basic_machine=x86_64-pc ;; amd64-*) basic_machine=x86_64-`echo $basic_machine | sed 's/^[^-]*-//'` ;; amdahl) basic_machine=580-amdahl os=-sysv ;; amiga | amiga-*) basic_machine=m68k-unknown ;; amigaos | amigados) basic_machine=m68k-unknown os=-amigaos ;; amigaunix | amix) basic_machine=m68k-unknown os=-sysv4 ;; apollo68) basic_machine=m68k-apollo os=-sysv ;; apollo68bsd) basic_machine=m68k-apollo os=-bsd ;; aros) basic_machine=i386-pc os=-aros ;; aux) basic_machine=m68k-apple os=-aux ;; balance) basic_machine=ns32k-sequent os=-dynix ;; blackfin) basic_machine=bfin-unknown os=-linux ;; blackfin-*) basic_machine=bfin-`echo $basic_machine | sed 's/^[^-]*-//'` os=-linux ;; bluegene*) basic_machine=powerpc-ibm os=-cnk ;; c54x-*) basic_machine=tic54x-`echo $basic_machine | sed 's/^[^-]*-//'` ;; c55x-*) basic_machine=tic55x-`echo $basic_machine | sed 's/^[^-]*-//'` ;; c6x-*) basic_machine=tic6x-`echo $basic_machine | sed 's/^[^-]*-//'` ;; c90) basic_machine=c90-cray os=-unicos ;; cegcc) basic_machine=arm-unknown os=-cegcc ;; convex-c1) basic_machine=c1-convex os=-bsd ;; convex-c2) basic_machine=c2-convex os=-bsd ;; convex-c32) basic_machine=c32-convex os=-bsd ;; convex-c34) basic_machine=c34-convex os=-bsd ;; convex-c38) basic_machine=c38-convex os=-bsd ;; cray | j90) basic_machine=j90-cray os=-unicos ;; craynv) basic_machine=craynv-cray os=-unicosmp ;; cr16 | cr16-*) basic_machine=cr16-unknown os=-elf ;; crds | unos) basic_machine=m68k-crds ;; crisv32 | crisv32-* | etraxfs*) basic_machine=crisv32-axis ;; cris | cris-* | etrax*) basic_machine=cris-axis ;; crx) basic_machine=crx-unknown os=-elf ;; da30 | da30-*) basic_machine=m68k-da30 ;; decstation | decstation-3100 | pmax | pmax-* | pmin | dec3100 | decstatn) basic_machine=mips-dec ;; decsystem10* | dec10*) basic_machine=pdp10-dec os=-tops10 ;; decsystem20* | dec20*) basic_machine=pdp10-dec os=-tops20 ;; delta | 3300 | motorola-3300 | motorola-delta \ | 3300-motorola | delta-motorola) basic_machine=m68k-motorola ;; delta88) basic_machine=m88k-motorola os=-sysv3 ;; dicos) basic_machine=i686-pc os=-dicos ;; djgpp) basic_machine=i586-pc os=-msdosdjgpp ;; dpx20 | dpx20-*) basic_machine=rs6000-bull os=-bosx ;; dpx2* | dpx2*-bull) basic_machine=m68k-bull os=-sysv3 ;; ebmon29k) basic_machine=a29k-amd os=-ebmon ;; elxsi) basic_machine=elxsi-elxsi os=-bsd ;; encore | umax | mmax) basic_machine=ns32k-encore ;; es1800 | OSE68k | ose68k | ose | OSE) basic_machine=m68k-ericsson os=-ose ;; fx2800) basic_machine=i860-alliant ;; genix) basic_machine=ns32k-ns ;; gmicro) basic_machine=tron-gmicro os=-sysv ;; go32) basic_machine=i386-pc os=-go32 ;; h3050r* | hiux*) basic_machine=hppa1.1-hitachi os=-hiuxwe2 ;; h8300hms) basic_machine=h8300-hitachi os=-hms ;; h8300xray) basic_machine=h8300-hitachi os=-xray ;; h8500hms) basic_machine=h8500-hitachi os=-hms ;; harris) basic_machine=m88k-harris os=-sysv3 ;; hp300-*) basic_machine=m68k-hp ;; hp300bsd) basic_machine=m68k-hp os=-bsd ;; hp300hpux) basic_machine=m68k-hp os=-hpux ;; hp3k9[0-9][0-9] | hp9[0-9][0-9]) basic_machine=hppa1.0-hp ;; hp9k2[0-9][0-9] | hp9k31[0-9]) basic_machine=m68000-hp ;; hp9k3[2-9][0-9]) basic_machine=m68k-hp ;; hp9k6[0-9][0-9] | hp6[0-9][0-9]) basic_machine=hppa1.0-hp ;; hp9k7[0-79][0-9] | hp7[0-79][0-9]) basic_machine=hppa1.1-hp ;; hp9k78[0-9] | hp78[0-9]) # FIXME: really hppa2.0-hp basic_machine=hppa1.1-hp ;; hp9k8[67]1 | hp8[67]1 | hp9k80[24] | hp80[24] | hp9k8[78]9 | hp8[78]9 | hp9k893 | hp893) # FIXME: really hppa2.0-hp basic_machine=hppa1.1-hp ;; hp9k8[0-9][13679] | hp8[0-9][13679]) basic_machine=hppa1.1-hp ;; hp9k8[0-9][0-9] | hp8[0-9][0-9]) basic_machine=hppa1.0-hp ;; hppa-next) os=-nextstep3 ;; hppaosf) basic_machine=hppa1.1-hp os=-osf ;; hppro) basic_machine=hppa1.1-hp os=-proelf ;; i370-ibm* | ibm*) basic_machine=i370-ibm ;; i*86v32) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-sysv32 ;; i*86v4*) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-sysv4 ;; i*86v) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-sysv ;; i*86sol2) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-solaris2 ;; i386mach) basic_machine=i386-mach os=-mach ;; i386-vsta | vsta) basic_machine=i386-unknown os=-vsta ;; iris | iris4d) basic_machine=mips-sgi case $os in -irix*) ;; *) os=-irix4 ;; esac ;; isi68 | isi) basic_machine=m68k-isi os=-sysv ;; m68knommu) basic_machine=m68k-unknown os=-linux ;; m68knommu-*) basic_machine=m68k-`echo $basic_machine | sed 's/^[^-]*-//'` os=-linux ;; m88k-omron*) basic_machine=m88k-omron ;; magnum | m3230) basic_machine=mips-mips os=-sysv ;; merlin) basic_machine=ns32k-utek os=-sysv ;; microblaze*) basic_machine=microblaze-xilinx ;; mingw64) basic_machine=x86_64-pc os=-mingw64 ;; mingw32) basic_machine=i386-pc os=-mingw32 ;; mingw32ce) basic_machine=arm-unknown os=-mingw32ce ;; miniframe) basic_machine=m68000-convergent ;; *mint | -mint[0-9]* | *MiNT | *MiNT[0-9]*) basic_machine=m68k-atari os=-mint ;; mips3*-*) basic_machine=`echo $basic_machine | sed -e 's/mips3/mips64/'` ;; mips3*) basic_machine=`echo $basic_machine | sed -e 's/mips3/mips64/'`-unknown ;; monitor) basic_machine=m68k-rom68k os=-coff ;; morphos) basic_machine=powerpc-unknown os=-morphos ;; msdos) basic_machine=i386-pc os=-msdos ;; ms1-*) basic_machine=`echo $basic_machine | sed -e 's/ms1-/mt-/'` ;; msys) basic_machine=i386-pc os=-msys ;; mvs) basic_machine=i370-ibm os=-mvs ;; nacl) basic_machine=le32-unknown os=-nacl ;; ncr3000) basic_machine=i486-ncr os=-sysv4 ;; netbsd386) basic_machine=i386-unknown os=-netbsd ;; netwinder) basic_machine=armv4l-rebel os=-linux ;; news | news700 | news800 | news900) basic_machine=m68k-sony os=-newsos ;; news1000) basic_machine=m68030-sony os=-newsos ;; news-3600 | risc-news) basic_machine=mips-sony os=-newsos ;; necv70) basic_machine=v70-nec os=-sysv ;; next | m*-next ) basic_machine=m68k-next case $os in -nextstep* ) ;; -ns2*) os=-nextstep2 ;; *) os=-nextstep3 ;; esac ;; nh3000) basic_machine=m68k-harris os=-cxux ;; nh[45]000) basic_machine=m88k-harris os=-cxux ;; nindy960) basic_machine=i960-intel os=-nindy ;; mon960) basic_machine=i960-intel os=-mon960 ;; nonstopux) basic_machine=mips-compaq os=-nonstopux ;; np1) basic_machine=np1-gould ;; neo-tandem) basic_machine=neo-tandem ;; nse-tandem) basic_machine=nse-tandem ;; nsr-tandem) basic_machine=nsr-tandem ;; op50n-* | op60c-*) basic_machine=hppa1.1-oki os=-proelf ;; openrisc | openrisc-*) basic_machine=or32-unknown ;; os400) basic_machine=powerpc-ibm os=-os400 ;; OSE68000 | ose68000) basic_machine=m68000-ericsson os=-ose ;; os68k) basic_machine=m68k-none os=-os68k ;; pa-hitachi) basic_machine=hppa1.1-hitachi os=-hiuxwe2 ;; paragon) basic_machine=i860-intel os=-osf ;; parisc) basic_machine=hppa-unknown os=-linux ;; parisc-*) basic_machine=hppa-`echo $basic_machine | sed 's/^[^-]*-//'` os=-linux ;; pbd) basic_machine=sparc-tti ;; pbb) basic_machine=m68k-tti ;; pc532 | pc532-*) basic_machine=ns32k-pc532 ;; pc98) basic_machine=i386-pc ;; pc98-*) basic_machine=i386-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pentium | p5 | k5 | k6 | nexgen | viac3) basic_machine=i586-pc ;; pentiumpro | p6 | 6x86 | athlon | athlon_*) basic_machine=i686-pc ;; pentiumii | pentium2 | pentiumiii | pentium3) basic_machine=i686-pc ;; pentium4) basic_machine=i786-pc ;; pentium-* | p5-* | k5-* | k6-* | nexgen-* | viac3-*) basic_machine=i586-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pentiumpro-* | p6-* | 6x86-* | athlon-*) basic_machine=i686-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pentiumii-* | pentium2-* | pentiumiii-* | pentium3-*) basic_machine=i686-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pentium4-*) basic_machine=i786-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pn) basic_machine=pn-gould ;; power) basic_machine=power-ibm ;; ppc | ppcbe) basic_machine=powerpc-unknown ;; ppc-* | ppcbe-*) basic_machine=powerpc-`echo $basic_machine | sed 's/^[^-]*-//'` ;; ppcle | powerpclittle | ppc-le | powerpc-little) basic_machine=powerpcle-unknown ;; ppcle-* | powerpclittle-*) basic_machine=powerpcle-`echo $basic_machine | sed 's/^[^-]*-//'` ;; ppc64) basic_machine=powerpc64-unknown ;; ppc64-*) basic_machine=powerpc64-`echo $basic_machine | sed 's/^[^-]*-//'` ;; ppc64le | powerpc64little | ppc64-le | powerpc64-little) basic_machine=powerpc64le-unknown ;; ppc64le-* | powerpc64little-*) basic_machine=powerpc64le-`echo $basic_machine | sed 's/^[^-]*-//'` ;; ps2) basic_machine=i386-ibm ;; pw32) basic_machine=i586-unknown os=-pw32 ;; rdos | rdos64) basic_machine=x86_64-pc os=-rdos ;; rdos32) basic_machine=i386-pc os=-rdos ;; rom68k) basic_machine=m68k-rom68k os=-coff ;; rm[46]00) basic_machine=mips-siemens ;; 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We pick the logical manufacturer. vendor=unknown case $basic_machine in *-unknown) case $os in -riscix*) vendor=acorn ;; -sunos*) vendor=sun ;; -cnk*|-aix*) vendor=ibm ;; -beos*) vendor=be ;; -hpux*) vendor=hp ;; -mpeix*) vendor=hp ;; -hiux*) vendor=hitachi ;; -unos*) vendor=crds ;; -dgux*) vendor=dg ;; -luna*) vendor=omron ;; -genix*) vendor=ns ;; -mvs* | -opened*) vendor=ibm ;; -os400*) vendor=ibm ;; -ptx*) vendor=sequent ;; -tpf*) vendor=ibm ;; -vxsim* | -vxworks* | -windiss*) vendor=wrs ;; -aux*) vendor=apple ;; -hms*) vendor=hitachi ;; -mpw* | -macos*) vendor=apple ;; -*mint | -mint[0-9]* | -*MiNT | -MiNT[0-9]*) vendor=atari ;; -vos*) vendor=stratus ;; esac basic_machine=`echo $basic_machine | sed "s/unknown/$vendor/"` ;; esac echo $basic_machine$os exit # Local variables: # eval: (add-hook 'write-file-hooks 'time-stamp) # time-stamp-start: "timestamp='" # time-stamp-format: "%:y-%02m-%02d" # time-stamp-end: "'" # End: nloptr/src/nlopt_src/api/0000755000176000001440000000000013263734242015171 5ustar ripleyusersnloptr/src/nlopt_src/api/nlopt.h0000644000176000001440000003320713355070635016504 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #ifndef NLOPT_H #define NLOPT_H #include /* for ptrdiff_t and size_t */ /* Change 0 to 1 to use stdcall convention under Win32 */ #if 0 && (defined(_WIN32) || defined(__WIN32__)) # if defined(__GNUC__) # define NLOPT_STDCALL __attribute__((stdcall)) # elif defined(_MSC_VER) || defined(_ICC) || defined(_STDCALL_SUPPORTED) # define NLOPT_STDCALL __stdcall # else # define NLOPT_STDCALL # endif #else # define NLOPT_STDCALL #endif /* for Windows compilers, you should add a line #define NLOPT_DLL when using NLopt from a DLL, in order to do the proper Windows importing nonsense. */ #if defined(NLOPT_DLL) && (defined(_WIN32) || defined(__WIN32__)) && !defined(__LCC__) /* annoying Windows syntax for calling functions in a DLL */ # if defined(NLOPT_DLL_EXPORT) # define NLOPT_EXTERN(T) extern __declspec(dllexport) T NLOPT_STDCALL # else # define NLOPT_EXTERN(T) extern __declspec(dllimport) T NLOPT_STDCALL # endif #else # define NLOPT_EXTERN(T) extern T NLOPT_STDCALL #endif #ifdef __cplusplus extern "C" { #endif /* __cplusplus */ typedef double (*nlopt_func)(unsigned n, const double *x, double *gradient, /* NULL if not needed */ void *func_data); typedef void (*nlopt_mfunc)(unsigned m, double *result, unsigned n, const double *x, double *gradient, /* NULL if not needed */ void *func_data); /* A preconditioner, which preconditions v at x to return vpre. (The meaning of "preconditioning" is algorithm-dependent.) */ typedef void (*nlopt_precond)(unsigned n, const double *x, const double *v, double *vpre, void *data); typedef enum { /* Naming conventions: NLOPT_{G/L}{D/N}_* = global/local derivative/no-derivative optimization, respectively *_RAND algorithms involve some randomization. *_NOSCAL algorithms are *not* scaled to a unit hypercube (i.e. they are sensitive to the units of x) */ NLOPT_GN_DIRECT = 0, NLOPT_GN_DIRECT_L, NLOPT_GN_DIRECT_L_RAND, NLOPT_GN_DIRECT_NOSCAL, NLOPT_GN_DIRECT_L_NOSCAL, NLOPT_GN_DIRECT_L_RAND_NOSCAL, NLOPT_GN_ORIG_DIRECT, NLOPT_GN_ORIG_DIRECT_L, NLOPT_GD_STOGO, NLOPT_GD_STOGO_RAND, NLOPT_LD_LBFGS_NOCEDAL, NLOPT_LD_LBFGS, NLOPT_LN_PRAXIS, NLOPT_LD_VAR1, NLOPT_LD_VAR2, NLOPT_LD_TNEWTON, NLOPT_LD_TNEWTON_RESTART, NLOPT_LD_TNEWTON_PRECOND, NLOPT_LD_TNEWTON_PRECOND_RESTART, NLOPT_GN_CRS2_LM, NLOPT_GN_MLSL, NLOPT_GD_MLSL, NLOPT_GN_MLSL_LDS, NLOPT_GD_MLSL_LDS, NLOPT_LD_MMA, NLOPT_LN_COBYLA, NLOPT_LN_NEWUOA, NLOPT_LN_NEWUOA_BOUND, NLOPT_LN_NELDERMEAD, NLOPT_LN_SBPLX, NLOPT_LN_AUGLAG, NLOPT_LD_AUGLAG, NLOPT_LN_AUGLAG_EQ, NLOPT_LD_AUGLAG_EQ, NLOPT_LN_BOBYQA, NLOPT_GN_ISRES, /* new variants that require local_optimizer to be set, not with older constants for backwards compatibility */ NLOPT_AUGLAG, NLOPT_AUGLAG_EQ, NLOPT_G_MLSL, NLOPT_G_MLSL_LDS, NLOPT_LD_SLSQP, NLOPT_LD_CCSAQ, NLOPT_GN_ESCH, NLOPT_NUM_ALGORITHMS /* not an algorithm, just the number of them */ } nlopt_algorithm; NLOPT_EXTERN(const char *) nlopt_algorithm_name(nlopt_algorithm a); typedef enum { NLOPT_FAILURE = -1, /* generic failure code */ NLOPT_INVALID_ARGS = -2, NLOPT_OUT_OF_MEMORY = -3, NLOPT_ROUNDOFF_LIMITED = -4, NLOPT_FORCED_STOP = -5, NLOPT_SUCCESS = 1, /* generic success code */ NLOPT_STOPVAL_REACHED = 2, NLOPT_FTOL_REACHED = 3, NLOPT_XTOL_REACHED = 4, NLOPT_MAXEVAL_REACHED = 5, NLOPT_MAXTIME_REACHED = 6 } nlopt_result; #define NLOPT_MINF_MAX_REACHED NLOPT_STOPVAL_REACHED NLOPT_EXTERN(void) nlopt_srand(unsigned long seed); NLOPT_EXTERN(void) nlopt_srand_time(void); NLOPT_EXTERN(void) nlopt_version(int *major, int *minor, int *bugfix); /*************************** OBJECT-ORIENTED API **************************/ /* The style here is that we create an nlopt_opt "object" (an opaque pointer), then set various optimization parameters, and then execute the algorithm. In this way, we can add more and more optimization parameters (including algorithm-specific ones) without breaking backwards compatibility, having functions with zillions of parameters, or relying non-reentrantly on global variables.*/ struct nlopt_opt_s; /* opaque structure, defined internally */ typedef struct nlopt_opt_s *nlopt_opt; /* the only immutable parameters of an optimization are the algorithm and the dimension n of the problem, since changing either of these could have side-effects on lots of other parameters */ NLOPT_EXTERN(nlopt_opt) nlopt_create(nlopt_algorithm algorithm, unsigned n); NLOPT_EXTERN(void) nlopt_destroy(nlopt_opt opt); NLOPT_EXTERN(nlopt_opt) nlopt_copy(const nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_optimize(nlopt_opt opt, double *x, double *opt_f); NLOPT_EXTERN(nlopt_result) nlopt_set_min_objective(nlopt_opt opt, nlopt_func f, void *f_data); NLOPT_EXTERN(nlopt_result) nlopt_set_max_objective(nlopt_opt opt, nlopt_func f, void *f_data); NLOPT_EXTERN(nlopt_result) nlopt_set_precond_min_objective(nlopt_opt opt, nlopt_func f, nlopt_precond pre, void *f_data); NLOPT_EXTERN(nlopt_result) nlopt_set_precond_max_objective(nlopt_opt opt, nlopt_func f, nlopt_precond pre, void *f_data); NLOPT_EXTERN(nlopt_algorithm) nlopt_get_algorithm(const nlopt_opt opt); NLOPT_EXTERN(unsigned) nlopt_get_dimension(const nlopt_opt opt); /* constraints: */ NLOPT_EXTERN(nlopt_result) nlopt_set_lower_bounds(nlopt_opt opt, const double *lb); NLOPT_EXTERN(nlopt_result) nlopt_set_lower_bounds1(nlopt_opt opt, double lb); NLOPT_EXTERN(nlopt_result) nlopt_get_lower_bounds(const nlopt_opt opt, double *lb); NLOPT_EXTERN(nlopt_result) nlopt_set_upper_bounds(nlopt_opt opt, const double *ub); NLOPT_EXTERN(nlopt_result) nlopt_set_upper_bounds1(nlopt_opt opt, double ub); NLOPT_EXTERN(nlopt_result) nlopt_get_upper_bounds(const nlopt_opt opt, double *ub); NLOPT_EXTERN(nlopt_result) nlopt_remove_inequality_constraints(nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_add_inequality_constraint(nlopt_opt opt, nlopt_func fc, void *fc_data, double tol); NLOPT_EXTERN(nlopt_result) nlopt_add_precond_inequality_constraint( nlopt_opt opt, nlopt_func fc, nlopt_precond pre, void *fc_data, double tol); NLOPT_EXTERN(nlopt_result) nlopt_add_inequality_mconstraint(nlopt_opt opt, unsigned m, nlopt_mfunc fc, void *fc_data, const double *tol); NLOPT_EXTERN(nlopt_result) nlopt_remove_equality_constraints(nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_add_equality_constraint(nlopt_opt opt, nlopt_func h, void *h_data, double tol); NLOPT_EXTERN(nlopt_result) nlopt_add_precond_equality_constraint( nlopt_opt opt, nlopt_func h, nlopt_precond pre, void *h_data, double tol); NLOPT_EXTERN(nlopt_result) nlopt_add_equality_mconstraint(nlopt_opt opt, unsigned m, nlopt_mfunc h, void *h_data, const double *tol); /* stopping criteria: */ NLOPT_EXTERN(nlopt_result) nlopt_set_stopval(nlopt_opt opt, double stopval); NLOPT_EXTERN(double) nlopt_get_stopval(const nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_set_ftol_rel(nlopt_opt opt, double tol); NLOPT_EXTERN(double) nlopt_get_ftol_rel(const nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_set_ftol_abs(nlopt_opt opt, double tol); NLOPT_EXTERN(double) nlopt_get_ftol_abs(const nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_set_xtol_rel(nlopt_opt opt, double tol); NLOPT_EXTERN(double) nlopt_get_xtol_rel(const nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_set_xtol_abs1(nlopt_opt opt, double tol); NLOPT_EXTERN(nlopt_result) nlopt_set_xtol_abs(nlopt_opt opt, const double *tol); NLOPT_EXTERN(nlopt_result) nlopt_get_xtol_abs(const nlopt_opt opt, double *tol); NLOPT_EXTERN(nlopt_result) nlopt_set_maxeval(nlopt_opt opt, int maxeval); NLOPT_EXTERN(int) nlopt_get_maxeval(const nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_set_maxtime(nlopt_opt opt, double maxtime); NLOPT_EXTERN(double) nlopt_get_maxtime(const nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_force_stop(nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_set_force_stop(nlopt_opt opt, int val); NLOPT_EXTERN(int) nlopt_get_force_stop(const nlopt_opt opt); /* more algorithm-specific parameters */ NLOPT_EXTERN(nlopt_result) nlopt_set_local_optimizer(nlopt_opt opt, const nlopt_opt local_opt); NLOPT_EXTERN(nlopt_result) nlopt_set_population(nlopt_opt opt, unsigned pop); NLOPT_EXTERN(unsigned) nlopt_get_population(const nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_set_vector_storage(nlopt_opt opt, unsigned dim); NLOPT_EXTERN(unsigned) nlopt_get_vector_storage(const nlopt_opt opt); NLOPT_EXTERN(nlopt_result) nlopt_set_default_initial_step(nlopt_opt opt, const double *x); NLOPT_EXTERN(nlopt_result) nlopt_set_initial_step(nlopt_opt opt, const double *dx); NLOPT_EXTERN(nlopt_result) nlopt_set_initial_step1(nlopt_opt opt, double dx); NLOPT_EXTERN(nlopt_result) nlopt_get_initial_step(const nlopt_opt opt, const double *x, double *dx); /* the following are functions mainly designed to be used internally by the Fortran and SWIG wrappers, allow us to tel nlopt_destroy and nlopt_copy to do something to the f_data pointers (e.g. free or duplicate them, respectively) */ typedef void* (*nlopt_munge)(void *p); NLOPT_EXTERN(void) nlopt_set_munge(nlopt_opt opt, nlopt_munge munge_on_destroy, nlopt_munge munge_on_copy); typedef void* (*nlopt_munge2)(void *p, void *data); NLOPT_EXTERN(void) nlopt_munge_data(nlopt_opt opt, nlopt_munge2 munge, void *data); /*************************** DEPRECATED API **************************/ /* The new "object-oriented" API is preferred, since it allows us to gracefully add new features and algorithm-specific options in a re-entrant way, and we can automatically assume reasonable defaults for unspecified parameters. */ /* Where possible (e.g. for gcc >= 3.1), enable a compiler warning for code that uses a deprecated function */ #if defined(__GNUC__) && (__GNUC__ > 3 || (__GNUC__==3 && __GNUC_MINOR__ > 0)) # define NLOPT_DEPRECATED __attribute__((deprecated)) #else # define NLOPT_DEPRECATED #endif typedef double (*nlopt_func_old)(int n, const double *x, double *gradient, /* NULL if not needed */ void *func_data); NLOPT_EXTERN(nlopt_result) nlopt_minimize( nlopt_algorithm algorithm, int n, nlopt_func_old f, void *f_data, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, /* out: minimum */ double minf_max, double ftol_rel, double ftol_abs, double xtol_rel, const double *xtol_abs, int maxeval, double maxtime) NLOPT_DEPRECATED; NLOPT_EXTERN(nlopt_result) nlopt_minimize_constrained( nlopt_algorithm algorithm, int n, nlopt_func_old f, void *f_data, int m, nlopt_func_old fc, void *fc_data, ptrdiff_t fc_datum_size, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, /* out: minimum */ double minf_max, double ftol_rel, double ftol_abs, double xtol_rel, const double *xtol_abs, int maxeval, double maxtime) NLOPT_DEPRECATED; NLOPT_EXTERN(nlopt_result) nlopt_minimize_econstrained( nlopt_algorithm algorithm, int n, nlopt_func_old f, void *f_data, int m, nlopt_func_old fc, void *fc_data, ptrdiff_t fc_datum_size, int p, nlopt_func_old h, void *h_data, ptrdiff_t h_datum_size, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, /* out: minimum */ double minf_max, double ftol_rel, double ftol_abs, double xtol_rel, const double *xtol_abs, double htol_rel, double htol_abs, int maxeval, double maxtime) NLOPT_DEPRECATED; NLOPT_EXTERN(void) nlopt_get_local_search_algorithm(nlopt_algorithm *deriv, nlopt_algorithm *nonderiv, int *maxeval) NLOPT_DEPRECATED; NLOPT_EXTERN(void) nlopt_set_local_search_algorithm(nlopt_algorithm deriv, nlopt_algorithm nonderiv, int maxeval) NLOPT_DEPRECATED; NLOPT_EXTERN(int) nlopt_get_stochastic_population(void) NLOPT_DEPRECATED; NLOPT_EXTERN(void) nlopt_set_stochastic_population(int pop) NLOPT_DEPRECATED; /*********************************************************************/ #ifdef __cplusplus } /* extern "C" */ #endif /* __cplusplus */ #endif nloptr/src/nlopt_src/api/deprecated.c0000644000176000001440000001430713355070635017443 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include "nlopt.h" /*************************************************************************/ nlopt_algorithm nlopt_local_search_alg_deriv = NLOPT_LD_MMA; nlopt_algorithm nlopt_local_search_alg_nonderiv = NLOPT_LN_COBYLA; int nlopt_local_search_maxeval = -1; /* no maximum by default */ void NLOPT_STDCALL nlopt_get_local_search_algorithm(nlopt_algorithm *deriv, nlopt_algorithm *nonderiv, int *maxeval) { *deriv = nlopt_local_search_alg_deriv; *nonderiv = nlopt_local_search_alg_nonderiv; *maxeval = nlopt_local_search_maxeval; } void NLOPT_STDCALL nlopt_set_local_search_algorithm(nlopt_algorithm deriv, nlopt_algorithm nonderiv, int maxeval) { nlopt_local_search_alg_deriv = deriv; nlopt_local_search_alg_nonderiv = nonderiv; nlopt_local_search_maxeval = maxeval; } /*************************************************************************/ int nlopt_stochastic_population = 0; int NLOPT_STDCALL nlopt_get_stochastic_population(void) { return nlopt_stochastic_population; } void NLOPT_STDCALL nlopt_set_stochastic_population(int pop) { nlopt_stochastic_population = pop <= 0 ? 0 : (unsigned) pop; } /*************************************************************************/ nlopt_result NLOPT_STDCALL nlopt_minimize_econstrained( nlopt_algorithm algorithm, int n, nlopt_func_old f, void *f_data, int m, nlopt_func_old fc, void *fc_data_, ptrdiff_t fc_datum_size, int p, nlopt_func_old h, void *h_data_, ptrdiff_t h_datum_size, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, /* out: minimum */ double minf_max, double ftol_rel, double ftol_abs, double xtol_rel, const double *xtol_abs, double htol_rel, double htol_abs, int maxeval, double maxtime) { char *fc_data = (char *) fc_data_; char *h_data = (char *) h_data_; nlopt_opt opt; nlopt_result ret; int i; if (n < 0 || m < 0 || p < 0) return NLOPT_INVALID_ARGS; opt = nlopt_create(algorithm, (unsigned) n); if (!opt) return NLOPT_INVALID_ARGS; ret = nlopt_set_min_objective(opt, (nlopt_func) f, f_data); if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; } for (i = 0; i < m; ++i) { ret = nlopt_add_inequality_constraint(opt, (nlopt_func) fc, fc_data + i*fc_datum_size, 0.0); if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; } } (void) htol_rel; /* unused */ for (i = 0; i < p; ++i) { ret = nlopt_add_equality_constraint(opt, (nlopt_func) h, h_data + i*h_datum_size, htol_abs); if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; } } ret = nlopt_set_lower_bounds(opt, lb); if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; } ret = nlopt_set_upper_bounds(opt, ub); if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; } ret = nlopt_set_stopval(opt, minf_max); if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; } ret = nlopt_set_ftol_rel(opt, ftol_rel); if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; } ret = nlopt_set_ftol_abs(opt, ftol_abs); if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; } ret = nlopt_set_xtol_rel(opt, xtol_rel); if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; } if (xtol_abs) ret = nlopt_set_xtol_abs(opt, xtol_abs); if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; } ret = nlopt_set_maxeval(opt, maxeval); if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; } ret = nlopt_set_maxtime(opt, maxtime); if (ret != NLOPT_SUCCESS) { nlopt_destroy(opt); return ret; } ret = nlopt_optimize(opt, x, minf); nlopt_destroy(opt); return ret; } nlopt_result NLOPT_STDCALL nlopt_minimize_constrained( nlopt_algorithm algorithm, int n, nlopt_func_old f, void *f_data, int m, nlopt_func_old fc, void *fc_data, ptrdiff_t fc_datum_size, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, /* out: minimum */ double minf_max, double ftol_rel, double ftol_abs, double xtol_rel, const double *xtol_abs, int maxeval, double maxtime) { return nlopt_minimize_econstrained( algorithm, n, f, f_data, m, fc, fc_data, fc_datum_size, 0, NULL, NULL, 0, lb, ub, x, minf, minf_max, ftol_rel, ftol_abs, xtol_rel, xtol_abs, ftol_rel, ftol_abs, maxeval, maxtime); } nlopt_result NLOPT_STDCALL nlopt_minimize( nlopt_algorithm algorithm, int n, nlopt_func_old f, void *f_data, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, /* out: minimum */ double minf_max, double ftol_rel, double ftol_abs, double xtol_rel, const double *xtol_abs, int maxeval, double maxtime) { return nlopt_minimize_constrained( algorithm, n, f, f_data, 0, NULL, NULL, 0, lb, ub, x, minf, minf_max, ftol_rel, ftol_abs, xtol_rel, xtol_abs, maxeval, maxtime); } nloptr/src/nlopt_src/api/nlopt.30000644000176000001440000006656413263734242016432 0ustar ripleyusers.\" .\" Copyright (c) 2007 Massachusetts Institute of Technology .\" .\" Copying and distribution of this file, with or without modification, .\" are permitted in any medium without royalty provided the copyright .\" notice and this notice are preserved. .\" .TH NLOPT 3 2007-08-23 "MIT" "NLopt programming manual" .SH NAME nlopt \- Nonlinear optimization library .SH SYNOPSIS .nf .B #include .sp .BI "nlopt_opt " "opt" " = nlopt_create(" "algorithm" , " n" ); .BI "nlopt_set_min_objective(" "opt" , " f" , " f_data" ); .BI "nlopt_set_ftol_rel(" "opt" , " tol"); .BI "..." .BI "nlopt_optimize(" "opt" , " x " , " &opt_f" ); .BI "nlopt_destroy(" "opt" ); .sp The "..." indicates any number of calls to NLopt functions, below, to set parameters of the optimization, constraints, and stopping criteria. Here, \fBnlopt_set_ftol_rel\fR is merely an example of a possible stopping criterion. You should link the resulting program with the linker flags \-lnlopt \-lm on Unix. .fi .SH DESCRIPTION NLopt is a library for nonlinear optimization. It attempts to minimize (or maximize) a given nonlinear objective function .I f of .I n design variables, using the specified .IR algorithm , possibly subject to linear or nonlinear constraints. The optimum function value found is returned in \fIopt_f\fR (type double) with the corresponding design variable values returned in the (double) array .I x of length .IR n . The input values in .I x should be a starting guess for the optimum. .sp The parameters of the optimization are controlled via the object .I opt of type .BR nlopt_opt , which is created by the function .B nlopt_create and disposed of by .BR nlopt_destroy . By calling various functions in the NLopt library, one can specify stopping criteria (e.g., a relative tolerance on the objective function value is specified by .BR nlopt_set_ftol_rel ), upper and/or lower bounds on the design parameters .IR x , and even arbitrary nonlinear inequality and equality constraints. .sp By changing the parameter .I algorithm among several predefined constants described below, one can switch easily between a variety of minimization algorithms. Some of these algorithms require the gradient (derivatives) of the function to be supplied via .IR f , and other algorithms do not require derivatives. Some of the algorithms attempt to find a global optimum within the given bounds, and others find only a local optimum. Most of the algorithms only handle the case where there are no nonlinear constraints. The NLopt library is a wrapper around several free/open-source minimization packages, as well as some new implementations of published optimization algorithms. You could, of course, compile and call these packages separately, and in some cases this will provide greater flexibility than is available via NLopt. However, depending upon the specific function being optimized, the different algorithms will vary in effectiveness. The intent of NLopt is to allow you to quickly switch between algorithms in order to experiment with them for your problem, by providing a simple unified interface to these subroutines. .SH OBJECTIVE FUNCTION The objective function is specified by calling one of: .sp .BI " nlopt_result nlopt_set_min_objective(nlopt_opt " "opt" , .br .BI " nlopt_func " "f" , .br .BI " void* " "f_data" ); .br .BI " nlopt_result nlopt_set_max_objective(nlopt_opt " "opt" , .br .BI " nlopt_func " "f" , .br .BI " void* " "f_data" ); .sp depending on whether one wishes to minimize or maximize the objective function .IR f , respectively. The function .I f should be of the form: .sp .BI " double f(unsigned " "n" , .br .BI " const double* " "x" , .br .BI " double* " "grad" , .br .BI " void* " "f_data" ); .sp The return value should be the value of the function at the point .IR x , where .I x points to an array of length .I n of the design variables. The dimension .I n is identical to the one passed to .BR nlopt_create . .sp In addition, if the argument .I grad is not NULL, then .I grad points to an array of length .I n which should (upon return) be set to the gradient of the function with respect to the design variables at .IR x . That is, .IR grad[i] should upon return contain the partial derivative df/dx[i], for 0 <= i < n, if .I grad is non-NULL. Not all of the optimization algorithms (below) use the gradient information: for algorithms listed as "derivative-free," the .I grad argument will always be NULL and need never be computed. (For algorithms that do use gradient information, however, .I grad may still be NULL for some calls.) .sp The .I f_data argument is the same as the one passed to .B nlopt_set_min_objective or .BR nlopt_set_max_objective , and may be used to pass any additional data through to the function. (That is, it may be a pointer to some caller-defined data structure/type containing information your function needs, which you convert from void* by a typecast.) .SH BOUND CONSTRAINTS Most of the algorithms in NLopt are designed for minimization of functions with simple bound constraints on the inputs. That is, the input vectors x[i] are constrainted to lie in a hyperrectangle lb[i] <= x[i] <= ub[i] for 0 <= i < n. These bounds are specified by passing arrays .I lb and .I ub of length .I n to one or both of the functions: .sp .BI " nlopt_result nlopt_set_lower_bounds(nlopt_opt " "opt" , .br .BI " const double* " "lb" ); .br .BI " nlopt_result nlopt_set_upper_bounds(nlopt_opt " "opt" , .br .BI " const double* " "ub" ); .sp If a lower/upper bound is not set, the default is no bound (unconstrained, i.e. a bound of infinity); it is possible to have lower bounds but not upper bounds or vice versa. Alternatively, the user can call one of the above functions and explicitly pass a lower bound of \-HUGE_VAL and/or an upper bound of +HUGE_VAL for some design variables to make them have no lower/upper bound, respectively. (HUGE_VAL is the standard C constant for a floating-point infinity, found in the math.h header file.) .sp Note, however, that some of the algorithms in NLopt, in particular most of the global-optimization algorithms, do not support unconstrained optimization and will return an error if you do not supply finite lower and upper bounds. .sp For convenience, the following two functions are supplied in order to set the lower/upper bounds for all design variables to a single constant (so that you don't have to fill an array with a constant value): .sp .BI " nlopt_result nlopt_set_lower_bounds1(nlopt_opt " "opt" , .br .BI " double " "lb" ); .br .BI " nlopt_result nlopt_set_upper_bounds1(nlopt_opt " "opt" , .br .BI " double " "ub" ); .sp .SH NONLINEAR CONSTRAINTS Several of the algorithms in NLopt (MMA and ORIG_DIRECT) also support arbitrary nonlinear inequality constraints, and some also allow nonlinear equality constraints (COBYLA, SLSQP, ISRES, and AUGLAG). For these algorithms, you can specify as many nonlinear constraints as you wish by calling the following functions multiple times. .sp In particular, a nonlinear inequality constraint of the form \fIfc\fR(\fIx\fR) <= 0, where the function .I fc is of the same form as the objective function described above, can be specified by calling: .sp .BI " nlopt_result nlopt_add_inequality_constraint(nlopt_opt " "opt" , .br .BI " nlopt_func " "fc" , .br .BI " void* " "fc_data" , .br .BI " double " "tol" ); .sp Just as for the objective function, .I fc_data is a pointer to arbitrary user data that will be passed through to the .I fc function whenever it is called. The parameter .I tol is a tolerance that is used for the purpose of stopping criteria only: a point .I x is considered feasible for judging whether to stop the optimization if \fIfc\fR(\fIx\fR) <= \fItol\fR. A tolerance of zero means that NLopt will try not to consider any \fIx\fR to be converged unless .I fc is strictly non-positive; generally, at least a small positive tolerance is advisable to reduce sensitivity to rounding errors. A nonlinear equality constraint of the form \fIh\fR(\fIx\fR) = 0, where the function .I h is of the same form as the objective function described above, can be specified by calling: .sp .BI " nlopt_result nlopt_add_equality_constraint(nlopt_opt " "opt" , .br .BI " nlopt_func " "h" , .br .BI " void* " "h_data" , .br .BI " double " "tol" ); .sp Just as for the objective function, .I h_data is a pointer to arbitrary user data that will be passed through to the .I h function whenever it is called. The parameter .I tol is a tolerance that is used for the purpose of stopping criteria only: a point .I x is considered feasible for judging whether to stop the optimization if |\fIh\fR(\fIx\fR)| <= \fItol\fR. For equality constraints, a small positive tolerance is strongly advised in order to allow NLopt to converge even if the equality constraint is slightly nonzero. .sp (For any algorithm listed as "derivative-free" below, the .I grad argument to \fIfc\fR or \fIh\fR will always be NULL and need never be computed.) .sp To remove all of the inequality and/or equality constraints from a given problem \fIopt\fR, you can call the following functions: .sp .BI " nlopt_result nlopt_remove_inequality_constraints(nlopt_opt " "opt" ); .br .BI " nlopt_result nlopt_remove_equality_constraints(nlopt_opt " "opt" ); .SH ALGORITHMS The .I algorithm parameter specifies the optimization algorithm (for more detail on these, see the README files in the source-code subdirectories), and can take on any of the following constant values. .sp Constants with .B _G{N,D}_ in their names refer to global optimization methods, whereas .B _L{N,D}_ refers to local optimization methods (that try to find a local optimum starting from the starting guess .IR x ). Constants with .B _{G,L}N_ refer to non-gradient (derivative-free) algorithms that do not require the objective function to supply a gradient, whereas .B _{G,L}D_ refers to derivative-based algorithms that require the objective function to supply a gradient. (Especially for local optimization, derivative-based algorithms are generally superior to derivative-free ones: the gradient is good to have .I if you can compute it cheaply, e.g. via an adjoint method.) .sp The algorithm specified for a given problem .I opt is returned by the function: .sp .BI " nlopt_algorithm nlopt_get_algorithm(nlopt_opt " "opt" ); .sp The available algorithms are: .TP .B NLOPT_GN_DIRECT_L Perform a global (G) derivative-free (N) optimization using the DIRECT-L search algorithm by Jones et al. as modified by Gablonsky et al. to be more weighted towards local search. Does not support unconstrainted optimization. There are also several other variants of the DIRECT algorithm that are supported: .BR NLOPT_GLOBAL_DIRECT , which is the original DIRECT algorithm; .BR NLOPT_GLOBAL_DIRECT_L_RAND , a slightly randomized version of DIRECT-L that may be better in high-dimensional search spaces; .BR NLOPT_GLOBAL_DIRECT_NOSCAL , .BR NLOPT_GLOBAL_DIRECT_L_NOSCAL , and .BR NLOPT_GLOBAL_DIRECT_L_RAND_NOSCAL , which are versions of DIRECT where the dimensions are not rescaled to a unit hypercube (which means that dimensions with larger bounds are given more weight). .TP .B NLOPT_GN_ORIG_DIRECT_L A global (G) derivative-free optimization using the DIRECT-L algorithm as above, along with .B NLOPT_GN_ORIG_DIRECT which is the original DIRECT algorithm. Unlike .B NLOPT_GN_DIRECT_L above, these two algorithms refer to code based on the original Fortran code of Gablonsky et al., which has some hard-coded limitations on the number of subdivisions etc. and does not support all of the NLopt stopping criteria, but on the other hand it supports arbitrary nonlinear inequality constraints. .TP .B NLOPT_GD_STOGO Global (G) optimization using the StoGO algorithm by Madsen et al. StoGO exploits gradient information (D) (which must be supplied by the objective) for its local searches, and performs the global search by a branch-and-bound technique. Only bound-constrained optimization is supported. There is also another variant of this algorithm, .BR NLOPT_GD_STOGO_RAND , which is a randomized version of the StoGO search scheme. The StoGO algorithms are only available if NLopt is compiled with C++ code enabled, and should be linked via \-lnlopt_cxx instead of \-lnlopt (via a C++ compiler, in order to link the C++ standard libraries). .TP .B NLOPT_LN_NELDERMEAD Perform a local (L) derivative-free (N) optimization, starting at .IR x , using the Nelder-Mead simplex algorithm, modified to support bound constraints. Nelder-Mead, while popular, is known to occasionally fail to converge for some objective functions, so it should be used with caution. Anecdotal evidence, on the other hand, suggests that it works fairly well for some cases that are hard to handle otherwise, e.g. noisy/discontinuous objectives. See also .B NLOPT_LN_SBPLX below. .TP .B NLOPT_LN_SBPLX Perform a local (L) derivative-free (N) optimization, starting at .IR x , using an algorithm based on the Subplex algorithm of Rowan et al., which is an improved variant of Nelder-Mead (above). Our implementation does not use Rowan's original code, and has some minor modifications such as explicit support for bound constraints. (Like Nelder-Mead, Subplex often works well in practice, even for noisy/discontinuous objectives, but there is no rigorous guarantee that it will converge.) .TP .B NLOPT_LN_PRAXIS Local (L) derivative-free (N) optimization using the principal-axis method, based on code by Richard Brent. Designed for unconstrained optimization, although bound constraints are supported too (via the inefficient method of returning +Inf when the constraints are violated). .TP .B NLOPT_LD_LBFGS Local (L) gradient-based (D) optimization using the limited-memory BFGS (L-BFGS) algorithm. (The objective function must supply the gradient.) Unconstrained optimization is supported in addition to simple bound constraints (see above). Based on an implementation by Luksan et al. .TP .B NLOPT_LD_VAR2 Local (L) gradient-based (D) optimization using a shifted limited-memory variable-metric method based on code by Luksan et al., supporting both unconstrained and bound-constrained optimization. .B NLOPT_LD_VAR2 uses a rank-2 method, while .B .B NLOPT_LD_VAR1 is another variant using a rank-1 method. .TP .B NLOPT_LD_TNEWTON_PRECOND_RESTART Local (L) gradient-based (D) optimization using an LBFGS-preconditioned truncated Newton method with steepest-descent restarting, based on code by Luksan et al., supporting both unconstrained and bound-constrained optimization. There are several other variants of this algorithm: .B NLOPT_LD_TNEWTON_PRECOND (same without restarting), .B NLOPT_LD_TNEWTON_RESTART (same without preconditioning), and .B NLOPT_LD_TNEWTON (same without restarting or preconditioning). .TP .B NLOPT_GN_CRS2_LM Global (G) derivative-free (N) optimization using the controlled random search (CRS2) algorithm of Price, with the "local mutation" (LM) modification suggested by Kaelo and Ali. .TP .B NLOPT_GN_ISRES Global (G) derivative-free (N) optimization using a genetic algorithm (mutation and differential evolution), using a stochastic ranking to handle nonlinear inequality and equality constraints as suggested by Runarsson and Yao. .TP \fBNLOPT_G_MLSL_LDS\fR, \fBNLOPT_G_MLSL\fR Global (G) optimization using the multi-level single-linkage (MLSL) algorithm with a low-discrepancy sequence (LDS) or pseudorandom numbers, respectively. This algorithm executes a low-discrepancy or pseudorandom sequence of local searches, with a clustering heuristic to avoid multiple local searches for the same local optimum. The local search algorithm must be specified, along with termination criteria/tolerances for the local searches, by \fInlopt_set_local_optimizer\fR. (This subsidiary algorithm can be with or without derivatives, and determines whether the objective function needs gradients.) .TP \fBNLOPT_LD_MMA\fR, \fBNLOPT_LD_CCSAQ\fR Local (L) gradient-based (D) optimization using the method of moving asymptotes (MMA), or rather a refined version of the algorithm as published by Svanberg (2002). (NLopt uses an independent free-software/open-source implementation of Svanberg's algorithm.) CCSAQ is a related algorithm from Svanberg's paper which uses a local quadratic approximation rather than the more-complicated MMA model; the two usually have similar convergence rates. The .B NLOPT_LD_MMA algorithm supports both bound-constrained and unconstrained optimization, and also supports an arbitrary number (\fIm\fR) of nonlinear inequality (not equality) constraints as described above. .TP .B NLOPT_LD_SLSQP Local (L) gradient-based (D) optimization using sequential quadratic programming and BFGS updates, supporting arbitrary nonlinear inequality and equality constraints, based on the code by Dieter Kraft (1988) adapted for use by the SciPy project. Note that this algorithm uses dense-matrix methods requiring O(\fIn\fR^2) storage and O(\fIn\fR^3) time, making it less practical for problems involving more than a few thousand parameters. .TP .B NLOPT_LN_COBYLA Local (L) derivative-free (N) optimization using the COBYLA algorithm of Powell (Constrained Optimization BY Linear Approximations). The .B NLOPT_LN_COBYLA algorithm supports both bound-constrained and unconstrained optimization, and also supports an arbitrary number (\fIm\fR) of nonlinear inequality/equality constraints as described above. .TP .B NLOPT_LN_NEWUOA Local (L) derivative-free (N) optimization using a variant of the NEWUOA algorithm of Powell, based on successive quadratic approximations of the objective function. We have modified the algorithm to support bound constraints. The original NEWUOA algorithm is also available, as .BR NLOPT_LN_NEWUOA , but this algorithm ignores the bound constraints .I lb and .IR ub , and so it should only be used for unconstrained problems. Mostly superseded by BOBYQA. .TP .B NLOPT_LN_BOBYQA Local (L) derivative-free (N) optimization using the BOBYQA algorithm of Powell, based on successive quadratic approximations of the objective function, supporting bound constraints. .TP .B NLOPT_AUGLAG Optimize an objective with nonlinear inequality/equality constraints via an unconstrained (or bound-constrained) optimization algorithm, using a gradually increasing "augmented Lagrangian" penalty for violated constraints. Requires you to specify another optimization algorithm for optimizing the objective+penalty function, using \fInlopt_set_local_optimizer\fR. (This subsidiary algorithm can be global or local and with or without derivatives, but you must specify its own termination criteria.) A variant, \fBNLOPT_AUGLAG_EQ\fR, only uses the penalty approach for equality constraints, while inequality constraints are handled directly by the subsidiary algorithm (restricting the choice of subsidiary algorithms to those that can handle inequality constraints). .SH STOPPING CRITERIA Multiple stopping criteria for the optimization are supported, as specified by the functions to modify a given optimization problem .BR opt . The optimization halts whenever any one of these criteria is satisfied. In some cases, the precise interpretation of the stopping criterion depends on the optimization algorithm above (although we have tried to make them as consistent as reasonably possible), and some algorithms do not support all of the stopping criteria. .sp Important: you do not need to use all of the stopping criteria! In most cases, you only need one or two, and can omit the remainder (all criteria are disabled by default). .TP .BI "nlopt_result nlopt_set_stopval(nlopt_opt " "opt" , .br .BI " double " stopval ); .sp Stop when an objective value of at least .I stopval is found: stop minimizing when a value <= \fIstopval\fR is found, or stop maximizing when a value >= \fIstopval\fR is found. (Setting \fIstopval\fR to \-HUGE_VAL for minimizing or +HUGE_VAL for maximizing disables this stopping criterion.) .TP .BI "nlopt_result nlopt_set_ftol_rel(nlopt_opt " "opt" , .br .BI " double " tol ); .sp Set relative tolerance on function value: stop when an optimization step (or an estimate of the optimum) changes the function value by less than .I tol multiplied by the absolute value of the function value. (If there is any chance that your optimum function value is close to zero, you might want to set an absolute tolerance with .B nlopt_set_ftol_abs as well.) Criterion is disabled if \fItol\fR is non-positive. .TP .BI "nlopt_result nlopt_set_ftol_abs(nlopt_opt " "opt" , .br .BI " double " tol ); .sp Set absolute tolerance on function value: stop when an optimization step (or an estimate of the optimum) changes the function value by less than .IR tol . Criterion is disabled if \fItol\fR is non-positive. .TP .BI "nlopt_result nlopt_set_xtol_rel(nlopt_opt " "opt" , .br .BI " double " tol ); .sp Set relative tolerance on design variables: stop when an optimization step (or an estimate of the optimum) changes every design variable by less than .I tol multiplied by the absolute value of the design variable. (If there is any chance that an optimal design variable is close to zero, you might want to set an absolute tolerance with .B nlopt_set_xtol_abs as well.) Criterion is disabled if \fItol\fR is non-positive. .TP .BI "nlopt_result nlopt_set_xtol_abs(nlopt_opt " "opt" , .br .BI " const double* " tol ); .sp Set absolute tolerances on design variables. \fItol\fR is a pointer to an array of length .I n giving the tolerances: stop when an optimization step (or an estimate of the optimum) changes every design variable .IR x [i] by less than .IR tol [i]. .sp For convenience, the following function may be used to set the absolute tolerances in all \fIn\fR design variables to the same value: .sp .BI " nlopt_result nlopt_set_xtol_abs1(nlopt_opt " "opt" , .br .BI " double " tol ); .sp Criterion is disabled if \fItol\fR is non-positive. .TP .BI "nlopt_result nlopt_set_maxeval(nlopt_opt " "opt" , .br .BI " int " maxeval ); .sp Stop when the number of function evaluations exceeds .IR maxeval . (This is not a strict maximum: the number of function evaluations may exceed .I maxeval slightly, depending upon the algorithm.) Criterion is disabled if \fImaxeval\fR is non-positive. .TP .BI "nlopt_result nlopt_set_maxtime(nlopt_opt " "opt" , .br .BI " double " maxtime ); .sp Stop when the optimization time (in seconds) exceeds .IR maxtime . (This is not a strict maximum: the time may exceed .I maxtime slightly, depending upon the algorithm and on how slow your function evaluation is.) Criterion is disabled if \fImaxtime\fR is non-positive. .SH RETURN VALUE Most of the NLopt functions return an enumerated constant of type .BR nlopt_result , which takes on one of the following values: .SS Successful termination (positive return values): .TP .B NLOPT_SUCCESS Generic success return value. .TP .B NLOPT_STOPVAL_REACHED Optimization stopped because .I stopval (above) was reached. .TP .B NLOPT_FTOL_REACHED Optimization stopped because .I ftol_rel or .I ftol_abs (above) was reached. .TP .B NLOPT_XTOL_REACHED Optimization stopped because .I xtol_rel or .I xtol_abs (above) was reached. .TP .B NLOPT_MAXEVAL_REACHED Optimization stopped because .I maxeval (above) was reached. .TP .B NLOPT_MAXTIME_REACHED Optimization stopped because .I maxtime (above) was reached. .SS Error codes (negative return values): .TP .B NLOPT_FAILURE Generic failure code. .TP .B NLOPT_INVALID_ARGS Invalid arguments (e.g. lower bounds are bigger than upper bounds, an unknown algorithm was specified, etcetera). .TP .B NLOPT_OUT_OF_MEMORY Ran out of memory. .TP .B NLOPT_ROUNDOFF_LIMITED Halted because roundoff errors limited progress. .TP .B NLOPT_FORCED_STOP Halted because the user called \fBnlopt_force_stop\fR(\fIopt\fR) on the optimization's \fBnlopt_opt\fR object \fIopt\fR from the user's objective function. .SH LOCAL OPTIMIZER Some of the algorithms, especially MLSL and AUGLAG, use a different optimization algorithm as a subroutine, typically for local optimization. You can change the local search algorithm and its tolerances by calling: .sp .BI " nlopt_result nlopt_set_local_optimizer(nlopt_opt " "opt" , .br .BI " const nlopt_opt " "local_opt" ); .sp Here, \fIlocal_opt\fR is another \fBnlopt_opt\fR object whose parameters are used to determine the local search algorithm and stopping criteria. (The objective function, bounds, and nonlinear-constraint parameters of \fIlocal_opt\fR are ignored.) The dimension \fIn\fR of \fIlocal_opt\fR must match that of \fIopt\fR. .sp This function makes a copy of the \fIlocal_opt\fR object, so you can freely destroy your original \fIlocal_opt\fR afterwards. .SH INITIAL STEP SIZE For derivative-free local-optimization algorithms, the optimizer must somehow decide on some initial step size to perturb \fIx\fR by when it begins the optimization. This step size should be big enough that the value of the objective changes significantly, but not too big if you want to find the local optimum nearest to \fIx\fR. By default, NLopt chooses this initial step size heuristically from the bounds, tolerances, and other information, but this may not always be the best choice. .sp You can modify the initial step size by calling: .sp .BI " nlopt_result nlopt_set_initial_step(nlopt_opt " "opt" , .br .BI " const double* " "dx" ); .sp Here, \fIdx\fR is an array of length \fIn\fR containing the (nonzero) initial step size for each component of the design parameters \fIx\fR. For convenience, if you want to set the step sizes in every direction to be the same value, you can instead call: .sp .BI " nlopt_result nlopt_set_initial_step1(nlopt_opt " "opt" , .br .BI " double " "dx" ); .SH STOCHASTIC POPULATION Several of the stochastic search algorithms (e.g., CRS, MLSL, and ISRES) start by generating some initial "population" of random points \fIx\fR. By default, this initial population size is chosen heuristically in some algorithm-specific way, but the initial population can by changed by calling: .sp .BI " nlopt_result nlopt_set_population(nlopt_opt " "opt" , .br .BI " unsigned " "pop" ); .sp (A \fIpop\fR of zero implies that the heuristic default will be used.) .SH PSEUDORANDOM NUMBERS For stochastic optimization algorithms, we use pseudorandom numbers generated by the Mersenne Twister algorithm, based on code from Makoto Matsumoto. By default, the seed for the random numbers is generated from the system time, so that they will be different each time you run the program. If you want to use deterministic random numbers, you can set the seed by calling: .sp .BI " void nlopt_srand(unsigned long " "seed" ); .sp Some of the algorithms also support using low-discrepancy sequences (LDS), sometimes known as quasi-random numbers. NLopt uses the Sobol LDS, which is implemented for up to 1111 dimensions. .SH AUTHORS Written by Steven G. Johnson. .PP Copyright (c) 2007-2014 Massachusetts Institute of Technology. .SH "SEE ALSO" nlopt_minimize(3) nloptr/src/nlopt_src/api/Makefile.am0000644000176000001440000000332013263734242017223 0ustar ripleyusersAM_CPPFLAGS = -I$(top_srcdir)/cdirect -I$(top_srcdir)/direct -I$(top_srcdir)/stogo -I$(top_srcdir)/praxis -I$(top_srcdir)/lbfgs -I$(top_srcdir)/luksan -I$(top_srcdir)/crs -I$(top_srcdir)/mlsl -I$(top_srcdir)/mma -I$(top_srcdir)/cobyla -I$(top_srcdir)/newuoa -I$(top_srcdir)/neldermead -I$(top_srcdir)/auglag -I$(top_srcdir)/bobyqa -I$(top_srcdir)/isres -I$(top_srcdir)/slsqp -I$(top_srcdir)/esch -I$(top_srcdir)/util include_HEADERS = nlopt.h nlopt.f nlopt.hpp noinst_LTLIBRARIES = libapi.la dist_man_MANS = nlopt.3 libapi_la_SOURCES = general.c options.c optimize.c deprecated.c \ nlopt-internal.h nlopt.h f77api.c f77funcs.h f77funcs_.h BUILT_SOURCES = nlopt.f nlopt.hpp EXTRA_DIST = nlopt-in.hpp if MAINTAINER_MODE # convert constants to F77 parameter statements & C++ nlopt.f: nlopt.h rm -f $@ (i=0; egrep 'NLOPT_[LG][DN]|NLOPT_AUGLAG|NLOPT_G_MLSL' $(srcdir)/nlopt.h | tr -d ' =0,' | while read n; do echo " integer $$n"; echo " parameter ($$n=$$i)"; i=`expr $$i + 1`; done; tail -n +`grep -n enum $(srcdir)/nlopt.h |cut -d: -f1 |tail -n 1` $(srcdir)/nlopt.h | grep NLOPT | egrep -v 'EXTERN|DEPRECATED|MINF_MAX' | cut -d, -f1 | tr -d ' ' | perl -pe 's/([A-Za-z0-9_]+)=([-+0-9]+)/ integer \1\n parameter (\1=\2)/') > $(srcdir)/$@ nlopt.hpp: nlopt.h nlopt-in.hpp rm -f $@ (n=`grep -n GEN_ENUMS_HERE $(srcdir)/nlopt-in.hpp | cut -d: -f1`; head -n $$n $(srcdir)/nlopt-in.hpp; echo " enum algorithm {"; egrep 'NLOPT_[LG][DN]|NLOPT_AUGLAG|NLOPT_G_MLSL|NLOPT_NUM_ALGORITHMS' $(srcdir)/nlopt.h | sed 's/NLOPT_//g'; echo " };"; echo " enum result {"; egrep 'NLOPT_[A-Z_]* =' $(srcdir)/nlopt.h | egrep -v 'NLOPT_[LG][DN]' | sed 's/NLOPT_//g'; echo " };"; tail -n +$$n $(srcdir)/nlopt-in.hpp) > $(srcdir)/$@ endif nloptr/src/nlopt_src/api/nlopt.hpp0000644000176000001440000005113413263734242017042 0ustar ripleyusers/* Copyright (c) 2007-2011 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ // C++ style wrapper around NLopt API // nlopt.hpp is AUTOMATICALLY GENERATED from nlopt-in.hpp - edit the latter! #ifndef NLOPT_HPP #define NLOPT_HPP #include #include #include #include #include #include #include // convenience overloading for below (not in nlopt:: since has nlopt_ prefix) inline nlopt_result nlopt_get_initial_step(const nlopt_opt opt, double *dx) { return nlopt_get_initial_step(opt, (const double *) NULL, dx); } namespace nlopt { ////////////////////////////////////////////////////////////////////// // nlopt::* namespace versions of the C enumerated types // AUTOMATICALLY GENERATED, DO NOT EDIT // GEN_ENUMS_HERE enum algorithm { GN_DIRECT = 0, GN_DIRECT_L, GN_DIRECT_L_RAND, GN_DIRECT_NOSCAL, GN_DIRECT_L_NOSCAL, GN_DIRECT_L_RAND_NOSCAL, GN_ORIG_DIRECT, GN_ORIG_DIRECT_L, GD_STOGO, GD_STOGO_RAND, LD_LBFGS_NOCEDAL, LD_LBFGS, LN_PRAXIS, LD_VAR1, LD_VAR2, LD_TNEWTON, LD_TNEWTON_RESTART, LD_TNEWTON_PRECOND, LD_TNEWTON_PRECOND_RESTART, GN_CRS2_LM, GN_MLSL, GD_MLSL, GN_MLSL_LDS, GD_MLSL_LDS, LD_MMA, LN_COBYLA, LN_NEWUOA, LN_NEWUOA_BOUND, LN_NELDERMEAD, LN_SBPLX, LN_AUGLAG, LD_AUGLAG, LN_AUGLAG_EQ, LD_AUGLAG_EQ, LN_BOBYQA, GN_ISRES, AUGLAG, AUGLAG_EQ, G_MLSL, G_MLSL_LDS, LD_SLSQP, LD_CCSAQ, GN_ESCH, NUM_ALGORITHMS /* not an algorithm, just the number of them */ }; enum result { FAILURE = -1, /* generic failure code */ INVALID_ARGS = -2, OUT_OF_MEMORY = -3, ROUNDOFF_LIMITED = -4, FORCED_STOP = -5, SUCCESS = 1, /* generic success code */ STOPVAL_REACHED = 2, FTOL_REACHED = 3, XTOL_REACHED = 4, MAXEVAL_REACHED = 5, MAXTIME_REACHED = 6 }; // GEN_ENUMS_HERE ////////////////////////////////////////////////////////////////////// typedef nlopt_func func; // nlopt::func synoynm typedef nlopt_mfunc mfunc; // nlopt::mfunc synoynm // alternative to nlopt_func that takes std::vector // ... unfortunately requires a data copy typedef double (*vfunc)(const std::vector &x, std::vector &grad, void *data); ////////////////////////////////////////////////////////////////////// // NLopt-specific exceptions (corresponding to error codes): class roundoff_limited : public std::runtime_error { public: roundoff_limited() : std::runtime_error("nlopt roundoff-limited") {} }; class forced_stop : public std::runtime_error { public: forced_stop() : std::runtime_error("nlopt forced stop") {} }; ////////////////////////////////////////////////////////////////////// class opt { private: nlopt_opt o; void mythrow(nlopt_result ret) const { switch (ret) { case NLOPT_FAILURE: throw std::runtime_error("nlopt failure"); case NLOPT_OUT_OF_MEMORY: throw std::bad_alloc(); case NLOPT_INVALID_ARGS: throw std::invalid_argument("nlopt invalid argument"); case NLOPT_ROUNDOFF_LIMITED: throw roundoff_limited(); case NLOPT_FORCED_STOP: throw forced_stop(); default: break; } } typedef struct { opt *o; mfunc mf; func f; void *f_data; vfunc vf; nlopt_munge munge_destroy, munge_copy; // non-NULL for SWIG wrappers } myfunc_data; // free/destroy f_data in nlopt_destroy and nlopt_copy, respectively static void *free_myfunc_data(void *p) { myfunc_data *d = (myfunc_data *) p; if (d) { if (d->f_data && d->munge_destroy) d->munge_destroy(d->f_data); delete d; } return NULL; } static void *dup_myfunc_data(void *p) { myfunc_data *d = (myfunc_data *) p; if (d) { void *f_data; if (d->f_data && d->munge_copy) { f_data = d->munge_copy(d->f_data); if (!f_data) return NULL; } else f_data = d->f_data; myfunc_data *dnew = new myfunc_data; if (dnew) { *dnew = *d; dnew->f_data = f_data; } return (void*) dnew; } else return NULL; } // nlopt_func wrapper that catches exceptions static double myfunc(unsigned n, const double *x, double *grad, void *d_) { myfunc_data *d = reinterpret_cast(d_); try { return d->f(n, x, grad, d->f_data); } catch (std::bad_alloc&) { d->o->forced_stop_reason = NLOPT_OUT_OF_MEMORY; } catch (std::invalid_argument&) { d->o->forced_stop_reason = NLOPT_INVALID_ARGS; } catch (roundoff_limited&) { d->o->forced_stop_reason = NLOPT_ROUNDOFF_LIMITED; } catch (forced_stop&) { d->o->forced_stop_reason = NLOPT_FORCED_STOP; } catch (...) { d->o->forced_stop_reason = NLOPT_FAILURE; } d->o->force_stop(); // stop gracefully, opt::optimize will re-throw return HUGE_VAL; } // nlopt_mfunc wrapper that catches exceptions static void mymfunc(unsigned m, double *result, unsigned n, const double *x, double *grad, void *d_) { myfunc_data *d = reinterpret_cast(d_); try { d->mf(m, result, n, x, grad, d->f_data); return; } catch (std::bad_alloc&) { d->o->forced_stop_reason = NLOPT_OUT_OF_MEMORY; } catch (std::invalid_argument&) { d->o->forced_stop_reason = NLOPT_INVALID_ARGS; } catch (roundoff_limited&) { d->o->forced_stop_reason = NLOPT_ROUNDOFF_LIMITED; } catch (forced_stop&) { d->o->forced_stop_reason = NLOPT_FORCED_STOP; } catch (...) { d->o->forced_stop_reason = NLOPT_FAILURE; } d->o->force_stop(); // stop gracefully, opt::optimize will re-throw for (unsigned i = 0; i < m; ++i) result[i] = HUGE_VAL; } std::vector xtmp, gradtmp, gradtmp0; // scratch for myvfunc // nlopt_func wrapper, using std::vector static double myvfunc(unsigned n, const double *x, double *grad, void *d_){ myfunc_data *d = reinterpret_cast(d_); try { std::vector &xv = d->o->xtmp; if (n) std::memcpy(&xv[0], x, n * sizeof(double)); double val=d->vf(xv, grad ? d->o->gradtmp : d->o->gradtmp0, d->f_data); if (grad && n) { std::vector &gradv = d->o->gradtmp; std::memcpy(grad, &gradv[0], n * sizeof(double)); } return val; } catch (std::bad_alloc&) { d->o->forced_stop_reason = NLOPT_OUT_OF_MEMORY; } catch (std::invalid_argument&) { d->o->forced_stop_reason = NLOPT_INVALID_ARGS; } catch (roundoff_limited&) { d->o->forced_stop_reason = NLOPT_ROUNDOFF_LIMITED; } catch (forced_stop&) { d->o->forced_stop_reason = NLOPT_FORCED_STOP; } catch (...) { d->o->forced_stop_reason = NLOPT_FAILURE; } d->o->force_stop(); // stop gracefully, opt::optimize will re-throw return HUGE_VAL; } void alloc_tmp() { if (xtmp.size() != nlopt_get_dimension(o)) { xtmp = std::vector(nlopt_get_dimension(o)); gradtmp = std::vector(nlopt_get_dimension(o)); } } result last_result; double last_optf; nlopt_result forced_stop_reason; public: // Constructors etc. opt() : o(NULL), xtmp(0), gradtmp(0), gradtmp0(0), last_result(nlopt::FAILURE), last_optf(HUGE_VAL), forced_stop_reason(NLOPT_FORCED_STOP) {} ~opt() { nlopt_destroy(o); } opt(algorithm a, unsigned n) : o(nlopt_create(nlopt_algorithm(a), n)), xtmp(0), gradtmp(0), gradtmp0(0), last_result(nlopt::FAILURE), last_optf(HUGE_VAL), forced_stop_reason(NLOPT_FORCED_STOP) { if (!o) throw std::bad_alloc(); nlopt_set_munge(o, free_myfunc_data, dup_myfunc_data); } opt(const opt& f) : o(nlopt_copy(f.o)), xtmp(f.xtmp), gradtmp(f.gradtmp), gradtmp0(0), last_result(f.last_result), last_optf(f.last_optf), forced_stop_reason(f.forced_stop_reason) { if (f.o && !o) throw std::bad_alloc(); } opt& operator=(opt const& f) { if (this == &f) return *this; // self-assignment nlopt_destroy(o); o = nlopt_copy(f.o); if (f.o && !o) throw std::bad_alloc(); xtmp = f.xtmp; gradtmp = f.gradtmp; last_result = f.last_result; last_optf = f.last_optf; forced_stop_reason = f.forced_stop_reason; return *this; } // Do the optimization: result optimize(std::vector &x, double &opt_f) { if (o && nlopt_get_dimension(o) != x.size()) throw std::invalid_argument("dimension mismatch"); forced_stop_reason = NLOPT_FORCED_STOP; nlopt_result ret = nlopt_optimize(o, x.empty() ? NULL : &x[0], &opt_f); last_result = result(ret); last_optf = opt_f; if (ret == NLOPT_FORCED_STOP) mythrow(forced_stop_reason); mythrow(ret); return last_result; } // variant mainly useful for SWIG wrappers: std::vector optimize(const std::vector &x0) { std::vector x(x0); last_result = optimize(x, last_optf); return x; } result last_optimize_result() const { return last_result; } double last_optimum_value() const { return last_optf; } // accessors: algorithm get_algorithm() const { if (!o) throw std::runtime_error("uninitialized nlopt::opt"); return algorithm(nlopt_get_algorithm(o)); } const char *get_algorithm_name() const { if (!o) throw std::runtime_error("uninitialized nlopt::opt"); return nlopt_algorithm_name(nlopt_get_algorithm(o)); } unsigned get_dimension() const { if (!o) throw std::runtime_error("uninitialized nlopt::opt"); return nlopt_get_dimension(o); } // Set the objective function void set_min_objective(func f, void *f_data) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_set_min_objective(o, myfunc, d)); // d freed via o } void set_min_objective(vfunc vf, void *f_data) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = NULL; d->f_data = f_data; d->mf = NULL; d->vf = vf; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_set_min_objective(o, myvfunc, d)); // d freed via o alloc_tmp(); } void set_max_objective(func f, void *f_data) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_set_max_objective(o, myfunc, d)); // d freed via o } void set_max_objective(vfunc vf, void *f_data) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = NULL; d->f_data = f_data; d->mf = NULL; d->vf = vf; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_set_max_objective(o, myvfunc, d)); // d freed via o alloc_tmp(); } // for internal use in SWIG wrappers -- variant that // takes ownership of f_data, with munging for destroy/copy void set_min_objective(func f, void *f_data, nlopt_munge md, nlopt_munge mc) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = md; d->munge_copy = mc; mythrow(nlopt_set_min_objective(o, myfunc, d)); // d freed via o } void set_max_objective(func f, void *f_data, nlopt_munge md, nlopt_munge mc) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = md; d->munge_copy = mc; mythrow(nlopt_set_max_objective(o, myfunc, d)); // d freed via o } // Nonlinear constraints: void remove_inequality_constraints() { nlopt_result ret = nlopt_remove_inequality_constraints(o); mythrow(ret); } void add_inequality_constraint(func f, void *f_data, double tol=0) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_add_inequality_constraint(o, myfunc, d, tol)); } void add_inequality_constraint(vfunc vf, void *f_data, double tol=0) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = NULL; d->f_data = f_data; d->mf = NULL; d->vf = vf; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_add_inequality_constraint(o, myvfunc, d, tol)); alloc_tmp(); } void add_inequality_mconstraint(mfunc mf, void *f_data, const std::vector &tol) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->mf = mf; d->f_data = f_data; d->f = NULL; d->vf = NULL; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_add_inequality_mconstraint(o, tol.size(), mymfunc, d, tol.empty() ? NULL : &tol[0])); } void remove_equality_constraints() { nlopt_result ret = nlopt_remove_equality_constraints(o); mythrow(ret); } void add_equality_constraint(func f, void *f_data, double tol=0) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_add_equality_constraint(o, myfunc, d, tol)); } void add_equality_constraint(vfunc vf, void *f_data, double tol=0) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = NULL; d->f_data = f_data; d->mf = NULL; d->vf = vf; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_add_equality_constraint(o, myvfunc, d, tol)); alloc_tmp(); } void add_equality_mconstraint(mfunc mf, void *f_data, const std::vector &tol) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->mf = mf; d->f_data = f_data; d->f = NULL; d->vf = NULL; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_add_equality_mconstraint(o, tol.size(), mymfunc, d, tol.empty() ? NULL : &tol[0])); } // For internal use in SWIG wrappers (see also above) void add_inequality_constraint(func f, void *f_data, nlopt_munge md, nlopt_munge mc, double tol=0) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = md; d->munge_copy = mc; mythrow(nlopt_add_inequality_constraint(o, myfunc, d, tol)); } void add_equality_constraint(func f, void *f_data, nlopt_munge md, nlopt_munge mc, double tol=0) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = md; d->munge_copy = mc; mythrow(nlopt_add_equality_constraint(o, myfunc, d, tol)); } void add_inequality_mconstraint(mfunc mf, void *f_data, nlopt_munge md, nlopt_munge mc, const std::vector &tol) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->mf = mf; d->f_data = f_data; d->f = NULL; d->vf = NULL; d->munge_destroy = md; d->munge_copy = mc; mythrow(nlopt_add_inequality_mconstraint(o, tol.size(), mymfunc, d, tol.empty() ? NULL : &tol[0])); } void add_equality_mconstraint(mfunc mf, void *f_data, nlopt_munge md, nlopt_munge mc, const std::vector &tol) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->mf = mf; d->f_data = f_data; d->f = NULL; d->vf = NULL; d->munge_destroy = md; d->munge_copy = mc; mythrow(nlopt_add_equality_mconstraint(o, tol.size(), mymfunc, d, tol.empty() ? NULL : &tol[0])); } #define NLOPT_GETSET_VEC(name) \ void set_##name(double val) { \ mythrow(nlopt_set_##name##1(o, val)); \ } \ void get_##name(std::vector &v) const { \ if (o && nlopt_get_dimension(o) != v.size()) \ throw std::invalid_argument("dimension mismatch"); \ mythrow(nlopt_get_##name(o, v.empty() ? NULL : &v[0])); \ } \ std::vector get_##name() const { \ if (!o) throw std::runtime_error("uninitialized nlopt::opt"); \ std::vector v(nlopt_get_dimension(o)); \ get_##name(v); \ return v; \ } \ void set_##name(const std::vector &v) { \ if (o && nlopt_get_dimension(o) != v.size()) \ throw std::invalid_argument("dimension mismatch"); \ mythrow(nlopt_set_##name(o, v.empty() ? NULL : &v[0])); \ } NLOPT_GETSET_VEC(lower_bounds) NLOPT_GETSET_VEC(upper_bounds) // stopping criteria: #define NLOPT_GETSET(T, name) \ T get_##name() const { \ if (!o) throw std::runtime_error("uninitialized nlopt::opt"); \ return nlopt_get_##name(o); \ } \ void set_##name(T name) { \ mythrow(nlopt_set_##name(o, name)); \ } NLOPT_GETSET(double, stopval) NLOPT_GETSET(double, ftol_rel) NLOPT_GETSET(double, ftol_abs) NLOPT_GETSET(double, xtol_rel) NLOPT_GETSET_VEC(xtol_abs) NLOPT_GETSET(int, maxeval) NLOPT_GETSET(double, maxtime) NLOPT_GETSET(int, force_stop) void force_stop() { set_force_stop(1); } // algorithm-specific parameters: void set_local_optimizer(const opt &lo) { nlopt_result ret = nlopt_set_local_optimizer(o, lo.o); mythrow(ret); } NLOPT_GETSET(unsigned, population) NLOPT_GETSET(unsigned, vector_storage) NLOPT_GETSET_VEC(initial_step) void set_default_initial_step(const std::vector &x) { nlopt_result ret = nlopt_set_default_initial_step(o, x.empty() ? NULL : &x[0]); mythrow(ret); } void get_initial_step(const std::vector &x, std::vector &dx) const { if (o && (nlopt_get_dimension(o) != x.size() || nlopt_get_dimension(o) != dx.size())) throw std::invalid_argument("dimension mismatch"); nlopt_result ret = nlopt_get_initial_step(o, x.empty() ? NULL : &x[0], dx.empty() ? NULL : &dx[0]); mythrow(ret); } std::vector get_initial_step_(const std::vector &x) const { if (!o) throw std::runtime_error("uninitialized nlopt::opt"); std::vector v(nlopt_get_dimension(o)); get_initial_step(x, v); return v; } }; #undef NLOPT_GETSET #undef NLOPT_GETSET_VEC ////////////////////////////////////////////////////////////////////// inline void srand(unsigned long seed) { nlopt_srand(seed); } inline void srand_time() { nlopt_srand_time(); } inline void version(int &major, int &minor, int &bugfix) { nlopt_version(&major, &minor, &bugfix); } inline int version_major() { int major, minor, bugfix; nlopt_version(&major, &minor, &bugfix); return major; } inline int version_minor() { int major, minor, bugfix; nlopt_version(&major, &minor, &bugfix); return minor; } inline int version_bugfix() { int major, minor, bugfix; nlopt_version(&major, &minor, &bugfix); return bugfix; } inline const char *algorithm_name(algorithm a) { return nlopt_algorithm_name(nlopt_algorithm(a)); } ////////////////////////////////////////////////////////////////////// } // namespace nlopt #endif /* NLOPT_HPP */ nloptr/src/nlopt_src/api/Makefile.in0000644000176000001440000006003613355070635017244 0ustar ripleyusers# Makefile.in generated by automake 1.14 from Makefile.am. # @configure_input@ # Copyright (C) 1994-2013 Free Software Foundation, Inc. # This Makefile.in is free software; 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egrep 'NLOPT_[LG][DN]|NLOPT_AUGLAG|NLOPT_G_MLSL' $(srcdir)/nlopt.h | tr -d ' =0,' | while read n; do echo " integer $$n"; echo " parameter ($$n=$$i)"; i=`expr $$i + 1`; done; tail -n +`grep -n enum $(srcdir)/nlopt.h |cut -d: -f1 |tail -n 1` $(srcdir)/nlopt.h | grep NLOPT | egrep -v 'EXTERN|DEPRECATED|MINF_MAX' | cut -d, -f1 | tr -d ' ' | perl -pe 's/([A-Za-z0-9_]+)=([-+0-9]+)/ integer \1\n parameter (\1=\2)/') > $(srcdir)/$@ @MAINTAINER_MODE_TRUE@nlopt.hpp: nlopt.h nlopt-in.hpp @MAINTAINER_MODE_TRUE@ rm -f $@ @MAINTAINER_MODE_TRUE@ (n=`grep -n GEN_ENUMS_HERE $(srcdir)/nlopt-in.hpp | cut -d: -f1`; head -n $$n $(srcdir)/nlopt-in.hpp; echo " enum algorithm {"; egrep 'NLOPT_[LG][DN]|NLOPT_AUGLAG|NLOPT_G_MLSL|NLOPT_NUM_ALGORITHMS' $(srcdir)/nlopt.h | sed 's/NLOPT_//g'; echo " };"; echo " enum result {"; egrep 'NLOPT_[A-Z_]* =' $(srcdir)/nlopt.h | egrep -v 'NLOPT_[LG][DN]' | sed 's/NLOPT_//g'; echo " };"; tail -n +$$n $(srcdir)/nlopt-in.hpp) > $(srcdir)/$@ # Tell versions [3.59,3.63) of GNU make to not export all variables. # Otherwise a system limit (for SysV at least) may be exceeded. .NOEXPORT: nloptr/src/nlopt_src/api/nlopt-in.hpp0000644000176000001440000004661213263734242017453 0ustar ripleyusers/* Copyright (c) 2007-2011 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ // C++ style wrapper around NLopt API // nlopt.hpp is AUTOMATICALLY GENERATED from nlopt-in.hpp - edit the latter! #ifndef NLOPT_HPP #define NLOPT_HPP #include #include #include #include #include #include #include // convenience overloading for below (not in nlopt:: since has nlopt_ prefix) inline nlopt_result nlopt_get_initial_step(const nlopt_opt opt, double *dx) { return nlopt_get_initial_step(opt, (const double *) NULL, dx); } namespace nlopt { ////////////////////////////////////////////////////////////////////// // nlopt::* namespace versions of the C enumerated types // AUTOMATICALLY GENERATED, DO NOT EDIT // GEN_ENUMS_HERE ////////////////////////////////////////////////////////////////////// typedef nlopt_func func; // nlopt::func synoynm typedef nlopt_mfunc mfunc; // nlopt::mfunc synoynm // alternative to nlopt_func that takes std::vector // ... unfortunately requires a data copy typedef double (*vfunc)(const std::vector &x, std::vector &grad, void *data); ////////////////////////////////////////////////////////////////////// // NLopt-specific exceptions (corresponding to error codes): class roundoff_limited : public std::runtime_error { public: roundoff_limited() : std::runtime_error("nlopt roundoff-limited") {} }; class forced_stop : public std::runtime_error { public: forced_stop() : std::runtime_error("nlopt forced stop") {} }; ////////////////////////////////////////////////////////////////////// class opt { private: nlopt_opt o; void mythrow(nlopt_result ret) const { switch (ret) { case NLOPT_FAILURE: throw std::runtime_error("nlopt failure"); case NLOPT_OUT_OF_MEMORY: throw std::bad_alloc(); case NLOPT_INVALID_ARGS: throw std::invalid_argument("nlopt invalid argument"); case NLOPT_ROUNDOFF_LIMITED: throw roundoff_limited(); case NLOPT_FORCED_STOP: throw forced_stop(); default: break; } } typedef struct { opt *o; mfunc mf; func f; void *f_data; vfunc vf; nlopt_munge munge_destroy, munge_copy; // non-NULL for SWIG wrappers } myfunc_data; // free/destroy f_data in nlopt_destroy and nlopt_copy, respectively static void *free_myfunc_data(void *p) { myfunc_data *d = (myfunc_data *) p; if (d) { if (d->f_data && d->munge_destroy) d->munge_destroy(d->f_data); delete d; } return NULL; } static void *dup_myfunc_data(void *p) { myfunc_data *d = (myfunc_data *) p; if (d) { void *f_data; if (d->f_data && d->munge_copy) { f_data = d->munge_copy(d->f_data); if (!f_data) return NULL; } else f_data = d->f_data; myfunc_data *dnew = new myfunc_data; if (dnew) { *dnew = *d; dnew->f_data = f_data; } return (void*) dnew; } else return NULL; } // nlopt_func wrapper that catches exceptions static double myfunc(unsigned n, const double *x, double *grad, void *d_) { myfunc_data *d = reinterpret_cast(d_); try { return d->f(n, x, grad, d->f_data); } catch (std::bad_alloc&) { d->o->forced_stop_reason = NLOPT_OUT_OF_MEMORY; } catch (std::invalid_argument&) { d->o->forced_stop_reason = NLOPT_INVALID_ARGS; } catch (roundoff_limited&) { d->o->forced_stop_reason = NLOPT_ROUNDOFF_LIMITED; } catch (forced_stop&) { d->o->forced_stop_reason = NLOPT_FORCED_STOP; } catch (...) { d->o->forced_stop_reason = NLOPT_FAILURE; } d->o->force_stop(); // stop gracefully, opt::optimize will re-throw return HUGE_VAL; } // nlopt_mfunc wrapper that catches exceptions static void mymfunc(unsigned m, double *result, unsigned n, const double *x, double *grad, void *d_) { myfunc_data *d = reinterpret_cast(d_); try { d->mf(m, result, n, x, grad, d->f_data); return; } catch (std::bad_alloc&) { d->o->forced_stop_reason = NLOPT_OUT_OF_MEMORY; } catch (std::invalid_argument&) { d->o->forced_stop_reason = NLOPT_INVALID_ARGS; } catch (roundoff_limited&) { d->o->forced_stop_reason = NLOPT_ROUNDOFF_LIMITED; } catch (forced_stop&) { d->o->forced_stop_reason = NLOPT_FORCED_STOP; } catch (...) { d->o->forced_stop_reason = NLOPT_FAILURE; } d->o->force_stop(); // stop gracefully, opt::optimize will re-throw for (unsigned i = 0; i < m; ++i) result[i] = HUGE_VAL; } std::vector xtmp, gradtmp, gradtmp0; // scratch for myvfunc // nlopt_func wrapper, using std::vector static double myvfunc(unsigned n, const double *x, double *grad, void *d_){ myfunc_data *d = reinterpret_cast(d_); try { std::vector &xv = d->o->xtmp; if (n) std::memcpy(&xv[0], x, n * sizeof(double)); double val=d->vf(xv, grad ? d->o->gradtmp : d->o->gradtmp0, d->f_data); if (grad && n) { std::vector &gradv = d->o->gradtmp; std::memcpy(grad, &gradv[0], n * sizeof(double)); } return val; } catch (std::bad_alloc&) { d->o->forced_stop_reason = NLOPT_OUT_OF_MEMORY; } catch (std::invalid_argument&) { d->o->forced_stop_reason = NLOPT_INVALID_ARGS; } catch (roundoff_limited&) { d->o->forced_stop_reason = NLOPT_ROUNDOFF_LIMITED; } catch (forced_stop&) { d->o->forced_stop_reason = NLOPT_FORCED_STOP; } catch (...) { d->o->forced_stop_reason = NLOPT_FAILURE; } d->o->force_stop(); // stop gracefully, opt::optimize will re-throw return HUGE_VAL; } void alloc_tmp() { if (xtmp.size() != nlopt_get_dimension(o)) { xtmp = std::vector(nlopt_get_dimension(o)); gradtmp = std::vector(nlopt_get_dimension(o)); } } result last_result; double last_optf; nlopt_result forced_stop_reason; public: // Constructors etc. opt() : o(NULL), xtmp(0), gradtmp(0), gradtmp0(0), last_result(nlopt::FAILURE), last_optf(HUGE_VAL), forced_stop_reason(NLOPT_FORCED_STOP) {} ~opt() { nlopt_destroy(o); } opt(algorithm a, unsigned n) : o(nlopt_create(nlopt_algorithm(a), n)), xtmp(0), gradtmp(0), gradtmp0(0), last_result(nlopt::FAILURE), last_optf(HUGE_VAL), forced_stop_reason(NLOPT_FORCED_STOP) { if (!o) throw std::bad_alloc(); nlopt_set_munge(o, free_myfunc_data, dup_myfunc_data); } opt(const opt& f) : o(nlopt_copy(f.o)), xtmp(f.xtmp), gradtmp(f.gradtmp), gradtmp0(0), last_result(f.last_result), last_optf(f.last_optf), forced_stop_reason(f.forced_stop_reason) { if (f.o && !o) throw std::bad_alloc(); } opt& operator=(opt const& f) { if (this == &f) return *this; // self-assignment nlopt_destroy(o); o = nlopt_copy(f.o); if (f.o && !o) throw std::bad_alloc(); xtmp = f.xtmp; gradtmp = f.gradtmp; last_result = f.last_result; last_optf = f.last_optf; forced_stop_reason = f.forced_stop_reason; return *this; } // Do the optimization: result optimize(std::vector &x, double &opt_f) { if (o && nlopt_get_dimension(o) != x.size()) throw std::invalid_argument("dimension mismatch"); forced_stop_reason = NLOPT_FORCED_STOP; nlopt_result ret = nlopt_optimize(o, x.empty() ? NULL : &x[0], &opt_f); last_result = result(ret); last_optf = opt_f; if (ret == NLOPT_FORCED_STOP) mythrow(forced_stop_reason); mythrow(ret); return last_result; } // variant mainly useful for SWIG wrappers: std::vector optimize(const std::vector &x0) { std::vector x(x0); last_result = optimize(x, last_optf); return x; } result last_optimize_result() const { return last_result; } double last_optimum_value() const { return last_optf; } // accessors: algorithm get_algorithm() const { if (!o) throw std::runtime_error("uninitialized nlopt::opt"); return algorithm(nlopt_get_algorithm(o)); } const char *get_algorithm_name() const { if (!o) throw std::runtime_error("uninitialized nlopt::opt"); return nlopt_algorithm_name(nlopt_get_algorithm(o)); } unsigned get_dimension() const { if (!o) throw std::runtime_error("uninitialized nlopt::opt"); return nlopt_get_dimension(o); } // Set the objective function void set_min_objective(func f, void *f_data) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_set_min_objective(o, myfunc, d)); // d freed via o } void set_min_objective(vfunc vf, void *f_data) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = NULL; d->f_data = f_data; d->mf = NULL; d->vf = vf; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_set_min_objective(o, myvfunc, d)); // d freed via o alloc_tmp(); } void set_max_objective(func f, void *f_data) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_set_max_objective(o, myfunc, d)); // d freed via o } void set_max_objective(vfunc vf, void *f_data) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = NULL; d->f_data = f_data; d->mf = NULL; d->vf = vf; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_set_max_objective(o, myvfunc, d)); // d freed via o alloc_tmp(); } // for internal use in SWIG wrappers -- variant that // takes ownership of f_data, with munging for destroy/copy void set_min_objective(func f, void *f_data, nlopt_munge md, nlopt_munge mc) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = md; d->munge_copy = mc; mythrow(nlopt_set_min_objective(o, myfunc, d)); // d freed via o } void set_max_objective(func f, void *f_data, nlopt_munge md, nlopt_munge mc) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = md; d->munge_copy = mc; mythrow(nlopt_set_max_objective(o, myfunc, d)); // d freed via o } // Nonlinear constraints: void remove_inequality_constraints() { nlopt_result ret = nlopt_remove_inequality_constraints(o); mythrow(ret); } void add_inequality_constraint(func f, void *f_data, double tol=0) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_add_inequality_constraint(o, myfunc, d, tol)); } void add_inequality_constraint(vfunc vf, void *f_data, double tol=0) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = NULL; d->f_data = f_data; d->mf = NULL; d->vf = vf; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_add_inequality_constraint(o, myvfunc, d, tol)); alloc_tmp(); } void add_inequality_mconstraint(mfunc mf, void *f_data, const std::vector &tol) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->mf = mf; d->f_data = f_data; d->f = NULL; d->vf = NULL; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_add_inequality_mconstraint(o, tol.size(), mymfunc, d, tol.empty() ? NULL : &tol[0])); } void remove_equality_constraints() { nlopt_result ret = nlopt_remove_equality_constraints(o); mythrow(ret); } void add_equality_constraint(func f, void *f_data, double tol=0) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_add_equality_constraint(o, myfunc, d, tol)); } void add_equality_constraint(vfunc vf, void *f_data, double tol=0) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = NULL; d->f_data = f_data; d->mf = NULL; d->vf = vf; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_add_equality_constraint(o, myvfunc, d, tol)); alloc_tmp(); } void add_equality_mconstraint(mfunc mf, void *f_data, const std::vector &tol) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->mf = mf; d->f_data = f_data; d->f = NULL; d->vf = NULL; d->munge_destroy = d->munge_copy = NULL; mythrow(nlopt_add_equality_mconstraint(o, tol.size(), mymfunc, d, tol.empty() ? NULL : &tol[0])); } // For internal use in SWIG wrappers (see also above) void add_inequality_constraint(func f, void *f_data, nlopt_munge md, nlopt_munge mc, double tol=0) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = md; d->munge_copy = mc; mythrow(nlopt_add_inequality_constraint(o, myfunc, d, tol)); } void add_equality_constraint(func f, void *f_data, nlopt_munge md, nlopt_munge mc, double tol=0) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->f = f; d->f_data = f_data; d->mf = NULL; d->vf = NULL; d->munge_destroy = md; d->munge_copy = mc; mythrow(nlopt_add_equality_constraint(o, myfunc, d, tol)); } void add_inequality_mconstraint(mfunc mf, void *f_data, nlopt_munge md, nlopt_munge mc, const std::vector &tol) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->mf = mf; d->f_data = f_data; d->f = NULL; d->vf = NULL; d->munge_destroy = md; d->munge_copy = mc; mythrow(nlopt_add_inequality_mconstraint(o, tol.size(), mymfunc, d, tol.empty() ? NULL : &tol[0])); } void add_equality_mconstraint(mfunc mf, void *f_data, nlopt_munge md, nlopt_munge mc, const std::vector &tol) { myfunc_data *d = new myfunc_data; if (!d) throw std::bad_alloc(); d->o = this; d->mf = mf; d->f_data = f_data; d->f = NULL; d->vf = NULL; d->munge_destroy = md; d->munge_copy = mc; mythrow(nlopt_add_equality_mconstraint(o, tol.size(), mymfunc, d, tol.empty() ? NULL : &tol[0])); } #define NLOPT_GETSET_VEC(name) \ void set_##name(double val) { \ mythrow(nlopt_set_##name##1(o, val)); \ } \ void get_##name(std::vector &v) const { \ if (o && nlopt_get_dimension(o) != v.size()) \ throw std::invalid_argument("dimension mismatch"); \ mythrow(nlopt_get_##name(o, v.empty() ? NULL : &v[0])); \ } \ std::vector get_##name() const { \ if (!o) throw std::runtime_error("uninitialized nlopt::opt"); \ std::vector v(nlopt_get_dimension(o)); \ get_##name(v); \ return v; \ } \ void set_##name(const std::vector &v) { \ if (o && nlopt_get_dimension(o) != v.size()) \ throw std::invalid_argument("dimension mismatch"); \ mythrow(nlopt_set_##name(o, v.empty() ? NULL : &v[0])); \ } NLOPT_GETSET_VEC(lower_bounds) NLOPT_GETSET_VEC(upper_bounds) // stopping criteria: #define NLOPT_GETSET(T, name) \ T get_##name() const { \ if (!o) throw std::runtime_error("uninitialized nlopt::opt"); \ return nlopt_get_##name(o); \ } \ void set_##name(T name) { \ mythrow(nlopt_set_##name(o, name)); \ } NLOPT_GETSET(double, stopval) NLOPT_GETSET(double, ftol_rel) NLOPT_GETSET(double, ftol_abs) NLOPT_GETSET(double, xtol_rel) NLOPT_GETSET_VEC(xtol_abs) NLOPT_GETSET(int, maxeval) NLOPT_GETSET(double, maxtime) NLOPT_GETSET(int, force_stop) void force_stop() { set_force_stop(1); } // algorithm-specific parameters: void set_local_optimizer(const opt &lo) { nlopt_result ret = nlopt_set_local_optimizer(o, lo.o); mythrow(ret); } NLOPT_GETSET(unsigned, population) NLOPT_GETSET(unsigned, vector_storage) NLOPT_GETSET_VEC(initial_step) void set_default_initial_step(const std::vector &x) { nlopt_result ret = nlopt_set_default_initial_step(o, x.empty() ? NULL : &x[0]); mythrow(ret); } void get_initial_step(const std::vector &x, std::vector &dx) const { if (o && (nlopt_get_dimension(o) != x.size() || nlopt_get_dimension(o) != dx.size())) throw std::invalid_argument("dimension mismatch"); nlopt_result ret = nlopt_get_initial_step(o, x.empty() ? NULL : &x[0], dx.empty() ? NULL : &dx[0]); mythrow(ret); } std::vector get_initial_step_(const std::vector &x) const { if (!o) throw std::runtime_error("uninitialized nlopt::opt"); std::vector v(nlopt_get_dimension(o)); get_initial_step(x, v); return v; } }; #undef NLOPT_GETSET #undef NLOPT_GETSET_VEC ////////////////////////////////////////////////////////////////////// inline void srand(unsigned long seed) { nlopt_srand(seed); } inline void srand_time() { nlopt_srand_time(); } inline void version(int &major, int &minor, int &bugfix) { nlopt_version(&major, &minor, &bugfix); } inline int version_major() { int major, minor, bugfix; nlopt_version(&major, &minor, &bugfix); return major; } inline int version_minor() { int major, minor, bugfix; nlopt_version(&major, &minor, &bugfix); return minor; } inline int version_bugfix() { int major, minor, bugfix; nlopt_version(&major, &minor, &bugfix); return bugfix; } inline const char *algorithm_name(algorithm a) { return nlopt_algorithm_name(nlopt_algorithm(a)); } ////////////////////////////////////////////////////////////////////// } // namespace nlopt #endif /* NLOPT_HPP */ nloptr/src/nlopt_src/api/nlopt-internal.h0000644000176000001440000000753013355070635020316 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #ifndef NLOPT_INTERNAL_H #define NLOPT_INTERNAL_H #include "nlopt.h" #include "nlopt-util.h" #ifdef __cplusplus extern "C" { #endif /* __cplusplus */ /*********************************************************************/ struct nlopt_opt_s { nlopt_algorithm algorithm; /* the optimization algorithm (immutable) */ unsigned n; /* the dimension of the problem (immutable) */ nlopt_func f; void *f_data; /* objective function to minimize */ nlopt_precond pre; /* optional preconditioner for f (NULL if none) */ int maximize; /* nonzero if we are maximizing, not minimizing */ double *lb, *ub; /* lower and upper bounds (length n) */ unsigned m; /* number of inequality constraints */ unsigned m_alloc; /* number of inequality constraints allocated */ nlopt_constraint *fc; /* inequality constraints, length m_alloc */ unsigned p; /* number of equality constraints */ unsigned p_alloc; /* number of inequality constraints allocated */ nlopt_constraint *h; /* equality constraints, length p_alloc */ nlopt_munge munge_on_destroy, munge_on_copy; /* hack for wrappers */ /* stopping criteria */ double stopval; /* stop when f reaches stopval or better */ double ftol_rel, ftol_abs; /* relative/absolute f tolerances */ double xtol_rel, *xtol_abs; /* rel/abs x tolerances */ int maxeval; /* max # evaluations */ double maxtime; /* max time (seconds) */ int force_stop; /* if nonzero, force a halt the next time we try to evaluate the objective during optimization */ /* when local optimization is used, we need a force_stop in the parent object to force a stop in child optimizations */ struct nlopt_opt_s *force_stop_child; /* algorithm-specific parameters */ nlopt_opt local_opt; /* local optimizer */ unsigned stochastic_population; /* population size for stochastic algs */ double *dx; /* initial step sizes (length n) for nonderivative algs */ unsigned vector_storage; /* max subspace dimension (0 for default) */ void *work; /* algorithm-specific workspace during optimization */ }; /*********************************************************************/ extern void nlopt_srand_time_default(void); /* init the rand. seed only if unset */ /*********************************************************************/ /* global defaults set by deprecated API: */ extern nlopt_algorithm nlopt_local_search_alg_deriv; extern nlopt_algorithm nlopt_local_search_alg_nonderiv; extern int nlopt_local_search_maxeval; extern unsigned nlopt_stochastic_population; /*********************************************************************/ #ifdef __cplusplus } /* extern "C" */ #endif /* __cplusplus */ #endif /* NLOPT_INTERNAL_H */ nloptr/src/nlopt_src/api/nlopt.f0000644000176000001440000000743013355070635016501 0ustar ripleyusers integer NLOPT_GN_DIRECT parameter (NLOPT_GN_DIRECT=0) integer NLOPT_GN_DIRECT_L parameter (NLOPT_GN_DIRECT_L=1) integer NLOPT_GN_DIRECT_L_RAND parameter (NLOPT_GN_DIRECT_L_RAND=2) integer NLOPT_GN_DIRECT_NOSCAL parameter (NLOPT_GN_DIRECT_NOSCAL=3) integer NLOPT_GN_DIRECT_L_NOSCAL parameter (NLOPT_GN_DIRECT_L_NOSCAL=4) integer NLOPT_GN_DIRECT_L_RAND_NOSCAL parameter (NLOPT_GN_DIRECT_L_RAND_NOSCAL=5) integer NLOPT_GN_ORIG_DIRECT parameter (NLOPT_GN_ORIG_DIRECT=6) integer NLOPT_GN_ORIG_DIRECT_L parameter (NLOPT_GN_ORIG_DIRECT_L=7) integer NLOPT_GD_STOGO parameter (NLOPT_GD_STOGO=8) integer NLOPT_GD_STOGO_RAND parameter (NLOPT_GD_STOGO_RAND=9) integer NLOPT_LD_LBFGS_NOCEDAL parameter (NLOPT_LD_LBFGS_NOCEDAL=10) integer NLOPT_LD_LBFGS parameter (NLOPT_LD_LBFGS=11) integer NLOPT_LN_PRAXIS parameter (NLOPT_LN_PRAXIS=12) integer NLOPT_LD_VAR1 parameter (NLOPT_LD_VAR1=13) integer NLOPT_LD_VAR2 parameter (NLOPT_LD_VAR2=14) integer NLOPT_LD_TNEWTON parameter (NLOPT_LD_TNEWTON=15) integer NLOPT_LD_TNEWTON_RESTART parameter (NLOPT_LD_TNEWTON_RESTART=16) integer NLOPT_LD_TNEWTON_PRECOND parameter (NLOPT_LD_TNEWTON_PRECOND=17) integer NLOPT_LD_TNEWTON_PRECOND_RESTART parameter (NLOPT_LD_TNEWTON_PRECOND_RESTART=18) integer NLOPT_GN_CRS2_LM parameter (NLOPT_GN_CRS2_LM=19) integer NLOPT_GN_MLSL parameter (NLOPT_GN_MLSL=20) integer NLOPT_GD_MLSL parameter (NLOPT_GD_MLSL=21) integer NLOPT_GN_MLSL_LDS parameter (NLOPT_GN_MLSL_LDS=22) integer NLOPT_GD_MLSL_LDS parameter (NLOPT_GD_MLSL_LDS=23) integer NLOPT_LD_MMA parameter (NLOPT_LD_MMA=24) integer NLOPT_LN_COBYLA parameter (NLOPT_LN_COBYLA=25) integer NLOPT_LN_NEWUOA parameter (NLOPT_LN_NEWUOA=26) integer NLOPT_LN_NEWUOA_BOUND parameter (NLOPT_LN_NEWUOA_BOUND=27) integer NLOPT_LN_NELDERMEAD parameter (NLOPT_LN_NELDERMEAD=28) integer NLOPT_LN_SBPLX parameter (NLOPT_LN_SBPLX=29) integer NLOPT_LN_AUGLAG parameter (NLOPT_LN_AUGLAG=30) integer NLOPT_LD_AUGLAG parameter (NLOPT_LD_AUGLAG=31) integer NLOPT_LN_AUGLAG_EQ parameter (NLOPT_LN_AUGLAG_EQ=32) integer NLOPT_LD_AUGLAG_EQ parameter (NLOPT_LD_AUGLAG_EQ=33) integer NLOPT_LN_BOBYQA parameter (NLOPT_LN_BOBYQA=34) integer NLOPT_GN_ISRES parameter (NLOPT_GN_ISRES=35) integer NLOPT_AUGLAG parameter (NLOPT_AUGLAG=36) integer NLOPT_AUGLAG_EQ parameter (NLOPT_AUGLAG_EQ=37) integer NLOPT_G_MLSL parameter (NLOPT_G_MLSL=38) integer NLOPT_G_MLSL_LDS parameter (NLOPT_G_MLSL_LDS=39) integer NLOPT_LD_SLSQP parameter (NLOPT_LD_SLSQP=40) integer NLOPT_LD_CCSAQ parameter (NLOPT_LD_CCSAQ=41) integer NLOPT_GN_ESCH parameter (NLOPT_GN_ESCH=42) integer NLOPT_FAILURE parameter (NLOPT_FAILURE=-1) integer NLOPT_INVALID_ARGS parameter (NLOPT_INVALID_ARGS=-2) integer NLOPT_OUT_OF_MEMORY parameter (NLOPT_OUT_OF_MEMORY=-3) integer NLOPT_ROUNDOFF_LIMITED parameter (NLOPT_ROUNDOFF_LIMITED=-4) integer NLOPT_FORCED_STOP parameter (NLOPT_FORCED_STOP=-5) integer NLOPT_SUCCESS parameter (NLOPT_SUCCESS=1) integer NLOPT_STOPVAL_REACHED parameter (NLOPT_STOPVAL_REACHED=2) integer NLOPT_FTOL_REACHED parameter (NLOPT_FTOL_REACHED=3) integer NLOPT_XTOL_REACHED parameter (NLOPT_XTOL_REACHED=4) integer NLOPT_MAXEVAL_REACHED parameter (NLOPT_MAXEVAL_REACHED=5) integer NLOPT_MAXTIME_REACHED parameter (NLOPT_MAXTIME_REACHED=6) nloptr/src/nlopt_src/api/f77api.c0000644000176000001440000001211513355070635016433 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include #include #include "nlopt.h" /*-----------------------------------------------------------------------*/ /* wrappers around f77 procedures */ typedef void (*nlopt_f77_func)(double *val, const int *n, const double *x, double *gradient, const int *need_gradient, void *func_data); typedef void (*nlopt_f77_mfunc)(const int *m, double *val, const int *n, const double *x, double *gradient, const int *need_gradient, void *func_data); typedef struct { nlopt_f77_func f; nlopt_f77_mfunc mf; void *f_data; } f77_func_data; static void *free_f77_func_data(void *p) { free(p); return NULL; } static void *dup_f77_func_data(void *p) { void *pnew = (void*) malloc(sizeof(f77_func_data)); if (pnew) memcpy(pnew, p, sizeof(f77_func_data)); return pnew; } static double f77_func_wrap_old(int n, const double *x, double *grad, void *data) { f77_func_data *d = (f77_func_data *) data; double val; int need_gradient = grad != 0; d->f(&val, &n, x, grad, &need_gradient, d->f_data); return val; } static double f77_func_wrap(unsigned n, const double *x, double *grad, void *data) { f77_func_data *d = (f77_func_data *) data; int ni = (int) n; double val; int need_gradient = grad != 0; d->f(&val, &ni, x, grad, &need_gradient, d->f_data); return val; } static void f77_mfunc_wrap(unsigned m, double *result, unsigned n, const double *x, double *grad, void *data) { f77_func_data *d = (f77_func_data *) data; int mi = (int) m; int ni = (int) n; int need_gradient = grad != 0; d->mf(&mi, result, &ni, x, grad, &need_gradient, d->f_data); } /*-----------------------------------------------------------------------*/ #define F77_GET(name,NAME,T) void F77_(nlo_get_##name,NLO_GET_##NAME)(T *val, nlopt_opt *opt) { *val = (T) nlopt_get_##name(*opt); } #define F77_SET(name,NAME,T) void F77_(nlo_set_##name,NLO_SET_##NAME)(int *ret, nlopt_opt *opt, T *val) { *ret = (int) nlopt_set_##name(*opt, *val); } #define F77_GETSET(name,NAME,T) F77_GET(name,NAME,T) F77_SET(name,NAME,T) #define F77_GETA(name,NAME,T) void F77_(nlo_get_##name,NLO_GET_##NAME)(int *ret, nlopt_opt *opt, T *val) { *ret = (int) nlopt_get_##name(*opt, val); } #define F77_SETA(name,NAME,T) void F77_(nlo_set_##name,NLO_SET_##NAME)(int *ret, nlopt_opt *opt, T *val) { *ret = (int) nlopt_set_##name(*opt, val); } #define F77_GETSETA(name,NAME,T) F77_GETA(name,NAME,T) F77_SETA(name,NAME,T) F77_SET(name##1,NAME##1,T) /*-----------------------------------------------------------------------*/ /* rather than trying to detect the Fortran name-mangling scheme with autoconf, we just include wrappers with all common name-mangling schemes ... this avoids problems and also allows us to work with multiple Fortran compilers on the same machine. Since the Fortran wrapper functions are so small, the library bloat of including them multiple times is negligible and seems well worth the benefit. */ # define F77CALL(a, A) F77(a, A) /* name + underscore is by far the most common (gfortran, g77, Intel, ...) */ # define F77(a, A) a ## _ # include "f77funcs.h" /* also include g77 convention of name + double underscore for identifiers containing underscores */ # define F77_(a, A) a ## __ # include "f77funcs_.h" # undef F77_ /* AIX and HPUX use just the lower-case name */ # undef F77 # define F77(a, A) a # include "f77funcs.h" /* Old Cray Unicos, as well as several Windows Fortran compilers (Digital/Compaq/HP Visual Fortran and Intel Fortran) use all-uppercase name */ /* Digital/Compaq/HP Visual Fortran, Intel Fortran. stdcall attribute is apparently required to adjust for calling conventions (callee pops stack in stdcall). See also: http://msdn.microsoft.com/library/en-us/vccore98/html/_core_mixed.2d.language_programming.3a_.overview.asp */ # undef F77 # undef F77CALL # define F77(a, A) NLOPT_STDCALL A # define F77CALL(a, A) A # include "f77funcs.h" nloptr/src/nlopt_src/api/general.c0000644000176000001440000001402513355070635016755 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include #include #include "nlopt-internal.h" /*************************************************************************/ int nlopt_isinf(double x) { return fabs(x) >= HUGE_VAL * 0.99 #ifdef HAVE_ISINF || isinf(x) #endif ; } /*************************************************************************/ void NLOPT_STDCALL nlopt_version(int *major, int *minor, int *bugfix) { *major = MAJOR_VERSION; *minor = MINOR_VERSION; *bugfix = BUGFIX_VERSION; } /*************************************************************************/ static const char nlopt_algorithm_names[NLOPT_NUM_ALGORITHMS][256] = { "DIRECT (global, no-derivative)", "DIRECT-L (global, no-derivative)", "Randomized DIRECT-L (global, no-derivative)", "Unscaled DIRECT (global, no-derivative)", "Unscaled DIRECT-L (global, no-derivative)", "Unscaled Randomized DIRECT-L (global, no-derivative)", "Original DIRECT version (global, no-derivative)", "Original DIRECT-L version (global, no-derivative)", #ifdef WITH_CXX "StoGO (global, derivative-based)", "StoGO with randomized search (global, derivative-based)", #else "StoGO (NOT COMPILED)", "StoGO randomized (NOT COMPILED)", #endif "original L-BFGS code by Nocedal et al. (NOT COMPILED)", "Limited-memory BFGS (L-BFGS) (local, derivative-based)", "Principal-axis, praxis (local, no-derivative)", "Limited-memory variable-metric, rank 1 (local, derivative-based)", "Limited-memory variable-metric, rank 2 (local, derivative-based)", "Truncated Newton (local, derivative-based)", "Truncated Newton with restarting (local, derivative-based)", "Preconditioned truncated Newton (local, derivative-based)", "Preconditioned truncated Newton with restarting (local, derivative-based)", "Controlled random search (CRS2) with local mutation (global, no-derivative)", "Multi-level single-linkage (MLSL), random (global, no-derivative)", "Multi-level single-linkage (MLSL), random (global, derivative)", "Multi-level single-linkage (MLSL), quasi-random (global, no-derivative)", "Multi-level single-linkage (MLSL), quasi-random (global, derivative)", "Method of Moving Asymptotes (MMA) (local, derivative)", "COBYLA (Constrained Optimization BY Linear Approximations) (local, no-derivative)", "NEWUOA unconstrained optimization via quadratic models (local, no-derivative)", "Bound-constrained optimization via NEWUOA-based quadratic models (local, no-derivative)", "Nelder-Mead simplex algorithm (local, no-derivative)", "Sbplx variant of Nelder-Mead (re-implementation of Rowan's Subplex) (local, no-derivative)", "Augmented Lagrangian method (local, no-derivative)", "Augmented Lagrangian method (local, derivative)", "Augmented Lagrangian method for equality constraints (local, no-derivative)", "Augmented Lagrangian method for equality constraints (local, derivative)", "BOBYQA bound-constrained optimization via quadratic models (local, no-derivative)", "ISRES evolutionary constrained optimization (global, no-derivative)", "Augmented Lagrangian method (needs sub-algorithm)", "Augmented Lagrangian method for equality constraints (needs sub-algorithm)", "Multi-level single-linkage (MLSL), random (global, needs sub-algorithm)", "Multi-level single-linkage (MLSL), quasi-random (global, needs sub-algorithm)", "Sequential Quadratic Programming (SQP) (local, derivative)", "CCSA (Conservative Convex Separable Approximations) with simple quadratic approximations (local, derivative)", "ESCH evolutionary strategy" }; const char * NLOPT_STDCALL nlopt_algorithm_name(nlopt_algorithm a) { if (((int) a) < 0 || a >= NLOPT_NUM_ALGORITHMS) return "UNKNOWN"; return nlopt_algorithm_names[a]; } /*************************************************************************/ /* get thread id, if possible, for use in nlopt_srand_time to ensure that different threads have a different default seed even if they are called simultaneously */ #if defined(_WIN32) || defined(__WIN32__) # include # define my_gettid GetCurrentThreadId #elif defined(HAVE_GETTID_SYSCALL) # include # include # define my_gettid() syscall(SYS_gettid) #elif defined(HAVE_GETPID) # include # include # define my_gettid getpid #else # define my_gettid() (0) #endif /*************************************************************************/ static THREADLOCAL int nlopt_srand_called = 0; void NLOPT_STDCALL nlopt_srand(unsigned long seed) { nlopt_srand_called = 1; nlopt_init_genrand(seed); } void NLOPT_STDCALL nlopt_srand_time(void) { nlopt_srand(nlopt_time_seed() + my_gettid() * 314159); } void nlopt_srand_time_default(void) { if (!nlopt_srand_called) nlopt_srand_time(); } /*************************************************************************/ nloptr/src/nlopt_src/api/f77funcs_.h0000644000176000001440000001430413355070635017146 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ /* Fortran API wrappers, using the F77 macro defined in f77api.c. This header file is #included one or more times from f77api.c in order to define verions of the Fortran API for various compilers. All of the functions in this file have underscores in their names, which means that they are treated differently for name-mangling (thank you, g77 and f2c) than names without underscores. The return value of a function is converted to the first argument of a subroutine. */ void F77_(nlo_create,NLO_CREATE)(nlopt_opt *opt, int *alg, int *n) { if (*n < 0) *opt = NULL; else { *opt = nlopt_create((nlopt_algorithm) *alg, (unsigned) *n); nlopt_set_munge(*opt, free_f77_func_data, dup_f77_func_data); } } void F77_(nlo_copy,NLO_COPY)(nlopt_opt *nopt, nlopt_opt *opt) { *nopt = nlopt_copy(*opt); } void F77_(nlo_destroy,NLO_DESTROY)(nlopt_opt *opt) { nlopt_destroy(*opt); } void F77_(nlo_optimize,NLO_OPTIMIZE)(int *ret, nlopt_opt *opt, double *x, double *optf) { *ret = (int) nlopt_optimize(*opt, x, optf); } void F77_(nlo_set_min_objective,NLO_SET_MIN_OBJECTIVE)( int *ret, nlopt_opt *opt, nlopt_f77_func f, void *f_data) { f77_func_data *d = (f77_func_data*) malloc(sizeof(f77_func_data)); if (!d) { *ret = (int) NLOPT_OUT_OF_MEMORY; return; } d->f = f; d->f_data = f_data; *ret = (int) nlopt_set_min_objective(*opt, f77_func_wrap, d); } void F77_(nlo_set_max_objective,NLO_SET_MAX_OBJECTIVE)( int *ret, nlopt_opt *opt, nlopt_f77_func f, void *f_data) { f77_func_data *d = (f77_func_data*) malloc(sizeof(f77_func_data)); if (!d) { *ret = (int) NLOPT_OUT_OF_MEMORY; return; } d->f = f; d->f_data = f_data; *ret = (int) nlopt_set_max_objective(*opt, f77_func_wrap, d); } F77_GET(algorithm, ALGORITHM, int) F77_GET(dimension, DIMENSION, int) F77_GETSETA(lower_bounds, LOWER_BOUNDS, double) F77_GETSETA(upper_bounds, UPPER_BOUNDS, double) void F77_(nlo_remove_inequality_constraints,NLO_REMOVE_INEQUALITY_CONSTRAINTS)( int *ret, nlopt_opt *opt) { *ret = (int) nlopt_remove_inequality_constraints(*opt); } void F77_(nlo_add_inequality_constraint,NLO_ADD_INEQUALITY_CONSTRAINT)( int *ret, nlopt_opt *opt, nlopt_f77_func fc, void *fc_data, double *tol) { f77_func_data *d = (f77_func_data*) malloc(sizeof(f77_func_data)); if (!d) { *ret = (int) NLOPT_OUT_OF_MEMORY; return; } d->f = fc; d->f_data = fc_data; *ret = (int) nlopt_add_inequality_constraint(*opt, f77_func_wrap,d, *tol); } void F77_(nlo_add_inequality_mconstraint,NLO_ADD_INEQUALITY_MCONSTRAINT)( int *ret, nlopt_opt *opt, int *m, nlopt_f77_mfunc mfc, void *mfc_data, double *tol) { f77_func_data *d; if (*m < 0) { *ret = (int) NLOPT_INVALID_ARGS; return; } if (*m == 0) { *ret = (int) NLOPT_SUCCESS; return; } d = (f77_func_data*) malloc(sizeof(f77_func_data)); if (!d) { *ret = (int) NLOPT_OUT_OF_MEMORY; return; } d->mf = mfc; d->f_data = mfc_data; *ret = (int) nlopt_add_inequality_mconstraint(*opt, (unsigned) *m, f77_mfunc_wrap, d, tol); } void F77_(nlo_remove_equality_constraints,NLO_REMOVE_EQUALITY_CONSTRAINTS)( int *ret, nlopt_opt *opt) { *ret = (int) nlopt_remove_equality_constraints(*opt); } void F77_(nlo_add_equality_constraint,NLO_ADD_EQUALITY_CONSTRAINT)( int *ret, nlopt_opt *opt, nlopt_f77_func fc, void *fc_data, double *tol) { f77_func_data *d = (f77_func_data*) malloc(sizeof(f77_func_data)); if (!d) { *ret = (int) NLOPT_OUT_OF_MEMORY; return; } d->f = fc; d->f_data = fc_data; *ret = (int) nlopt_add_equality_constraint(*opt, f77_func_wrap,d, *tol); } void F77_(nlo_add_equality_mconstraint,NLO_ADD_EQUALITY_MCONSTRAINT)( int *ret, nlopt_opt *opt, int *m, nlopt_f77_mfunc mfc, void *mfc_data, double *tol) { f77_func_data *d; if (*m < 0) { *ret = (int) NLOPT_INVALID_ARGS; return; } if (*m == 0) { *ret = (int) NLOPT_SUCCESS; return; } d = (f77_func_data*) malloc(sizeof(f77_func_data)); if (!d) { *ret = (int) NLOPT_OUT_OF_MEMORY; return; } d->mf = mfc; d->f_data = mfc_data; *ret = (int) nlopt_add_equality_mconstraint(*opt, (unsigned) *m, f77_mfunc_wrap, d, tol); } F77_GETSET(stopval, STOPVAL, double) F77_GETSET(ftol_rel, FTOL_REL, double) F77_GETSET(ftol_abs, FTOL_ABS, double) F77_GETSET(xtol_rel, XTOL_REL, double) F77_GETSETA(xtol_abs, XTOL_ABS, double) F77_GETSET(maxeval, MAXEVAL, int) F77_GETSET(maxtime, MAXTIME, double) F77_GETSET(force_stop, FORCE_STOP, int) void F77_(nlo_force_stop,NLO_FORCE_STOP)(int *ret, nlopt_opt *opt) { *ret = (int) nlopt_force_stop(*opt); } F77_SET(local_optimizer, LOCAL_OPTIMIZER, nlopt_opt) F77_GETSET(population, POPULATION, unsigned) F77_GETSET(vector_storage, vector_storage, unsigned) F77_SETA(default_initial_step, DEFAULT_INITIAL_STEP, double) F77_SETA(initial_step, INITIAL_STEP, double) F77_SET(initial_step1, INITIAL_STEP1, double) void F77_(nlo_get_initial_step, NLO_GET_INITIAL_STEP)( int *ret, nlopt_opt *opt, const double *x, double *dx) { *ret = (int) nlopt_get_initial_step(*opt, x, dx); } nloptr/src/nlopt_src/api/options.c0000644000176000001440000005336013355070635017040 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include #include #include #include #include #include "nlopt-internal.h" /*************************************************************************/ void NLOPT_STDCALL nlopt_destroy(nlopt_opt opt) { if (opt) { unsigned i; if (opt->munge_on_destroy) { nlopt_munge munge = opt->munge_on_destroy; munge(opt->f_data); for (i = 0; i < opt->m; ++i) munge(opt->fc[i].f_data); for (i = 0; i < opt->p; ++i) munge(opt->h[i].f_data); } for (i = 0; i < opt->m; ++i) free(opt->fc[i].tol); for (i = 0; i < opt->p; ++i) free(opt->h[i].tol); free(opt->lb); free(opt->ub); free(opt->xtol_abs); free(opt->fc); free(opt->h); nlopt_destroy(opt->local_opt); free(opt->dx); free(opt->work); free(opt); } } nlopt_opt NLOPT_STDCALL nlopt_create(nlopt_algorithm algorithm, unsigned n) { nlopt_opt opt; if (((int) algorithm) < 0 || algorithm >= NLOPT_NUM_ALGORITHMS) return NULL; opt = (nlopt_opt) malloc(sizeof(struct nlopt_opt_s)); if (opt) { opt->algorithm = algorithm; opt->n = n; opt->f = NULL; opt->f_data = NULL; opt->pre = NULL; opt->maximize = 0; opt->munge_on_destroy = opt->munge_on_copy = NULL; opt->lb = opt->ub = NULL; opt->m = opt->m_alloc = 0; opt->fc = NULL; opt->p = opt->p_alloc = 0; opt->h = NULL; opt->stopval = -HUGE_VAL; opt->ftol_rel = opt->ftol_abs = 0; opt->xtol_rel = 0; opt->xtol_abs = NULL; opt->maxeval = 0; opt->maxtime = 0; opt->force_stop = 0; opt->force_stop_child = NULL; opt->local_opt = NULL; opt->stochastic_population = 0; opt->vector_storage = 0; opt->dx = NULL; opt->work = NULL; if (n > 0) { opt->lb = (double *) malloc(sizeof(double) * (n)); if (!opt->lb) goto oom; opt->ub = (double *) malloc(sizeof(double) * (n)); if (!opt->ub) goto oom; opt->xtol_abs = (double *) malloc(sizeof(double) * (n)); if (!opt->xtol_abs) goto oom; nlopt_set_lower_bounds1(opt, -HUGE_VAL); nlopt_set_upper_bounds1(opt, +HUGE_VAL); nlopt_set_xtol_abs1(opt, 0.0); } } return opt; oom: nlopt_destroy(opt); return NULL; } nlopt_opt NLOPT_STDCALL nlopt_copy(const nlopt_opt opt) { nlopt_opt nopt = NULL; unsigned i; if (opt) { nlopt_munge munge; nopt = (nlopt_opt) malloc(sizeof(struct nlopt_opt_s)); *nopt = *opt; nopt->lb = nopt->ub = nopt->xtol_abs = NULL; nopt->fc = nopt->h = NULL; nopt->m_alloc = nopt->p_alloc = 0; nopt->local_opt = NULL; nopt->dx = NULL; nopt->work = NULL; opt->force_stop_child = NULL; munge = nopt->munge_on_copy; if (munge && nopt->f_data) if (!(nopt->f_data = munge(nopt->f_data))) goto oom; if (opt->n > 0) { nopt->lb = (double *) malloc(sizeof(double) * (opt->n)); if (!opt->lb) goto oom; nopt->ub = (double *) malloc(sizeof(double) * (opt->n)); if (!opt->ub) goto oom; nopt->xtol_abs = (double *) malloc(sizeof(double) * (opt->n)); if (!opt->xtol_abs) goto oom; memcpy(nopt->lb, opt->lb, sizeof(double) * (opt->n)); memcpy(nopt->ub, opt->ub, sizeof(double) * (opt->n)); memcpy(nopt->xtol_abs, opt->xtol_abs, sizeof(double) * (opt->n)); } if (opt->m) { nopt->m_alloc = opt->m; nopt->fc = (nlopt_constraint *) malloc(sizeof(nlopt_constraint) * (opt->m)); if (!nopt->fc) goto oom; memcpy(nopt->fc, opt->fc, sizeof(nlopt_constraint) * (opt->m)); for (i = 0; i < opt->m; ++i) nopt->fc[i].tol = NULL; if (munge) for (i = 0; i < opt->m; ++i) if (nopt->fc[i].f_data && !(nopt->fc[i].f_data = munge(nopt->fc[i].f_data))) goto oom; for (i = 0; i < opt->m; ++i) if (opt->fc[i].tol) { nopt->fc[i].tol = (double *) malloc(sizeof(double) * nopt->fc[i].m); if (!nopt->fc[i].tol) goto oom; memcpy(nopt->fc[i].tol, opt->fc[i].tol, sizeof(double) * nopt->fc[i].m); } } if (opt->p) { nopt->p_alloc = opt->p; nopt->h = (nlopt_constraint *) malloc(sizeof(nlopt_constraint) * (opt->p)); if (!nopt->h) goto oom; memcpy(nopt->h, opt->h, sizeof(nlopt_constraint) * (opt->p)); for (i = 0; i < opt->p; ++i) nopt->h[i].tol = NULL; if (munge) for (i = 0; i < opt->p; ++i) if (nopt->h[i].f_data && !(nopt->h[i].f_data = munge(nopt->h[i].f_data))) goto oom; for (i = 0; i < opt->p; ++i) if (opt->h[i].tol) { nopt->h[i].tol = (double *) malloc(sizeof(double) * nopt->h[i].m); if (!nopt->h[i].tol) goto oom; memcpy(nopt->h[i].tol, opt->h[i].tol, sizeof(double) * nopt->h[i].m); } } if (opt->local_opt) { nopt->local_opt = nlopt_copy(opt->local_opt); if (!nopt->local_opt) goto oom; } if (opt->dx) { nopt->dx = (double *) malloc(sizeof(double) * (opt->n)); if (!nopt->dx) goto oom; memcpy(nopt->dx, opt->dx, sizeof(double) * (opt->n)); } } return nopt; oom: nopt->munge_on_destroy = NULL; // better to leak mem than crash nlopt_destroy(nopt); return NULL; } /*************************************************************************/ nlopt_result NLOPT_STDCALL nlopt_set_precond_min_objective(nlopt_opt opt, nlopt_func f, nlopt_precond pre, void *f_data) { if (opt) { if (opt->munge_on_destroy) opt->munge_on_destroy(opt->f_data); opt->f = f; opt->f_data = f_data; opt->pre = pre; opt->maximize = 0; if (nlopt_isinf(opt->stopval) && opt->stopval > 0) opt->stopval = -HUGE_VAL; /* switch default from max to min */ return NLOPT_SUCCESS; } return NLOPT_INVALID_ARGS; } nlopt_result NLOPT_STDCALL nlopt_set_min_objective(nlopt_opt opt, nlopt_func f, void *f_data) { return nlopt_set_precond_min_objective(opt, f, NULL, f_data); } nlopt_result NLOPT_STDCALL nlopt_set_precond_max_objective(nlopt_opt opt, nlopt_func f, nlopt_precond pre, void *f_data) { if (opt) { if (opt->munge_on_destroy) opt->munge_on_destroy(opt->f_data); opt->f = f; opt->f_data = f_data; opt->pre = pre; opt->maximize = 1; if (nlopt_isinf(opt->stopval) && opt->stopval < 0) opt->stopval = +HUGE_VAL; /* switch default from min to max */ return NLOPT_SUCCESS; } return NLOPT_INVALID_ARGS; } nlopt_result NLOPT_STDCALL nlopt_set_max_objective(nlopt_opt opt, nlopt_func f, void *f_data) { return nlopt_set_precond_max_objective(opt, f, NULL, f_data); } /*************************************************************************/ nlopt_result NLOPT_STDCALL nlopt_set_lower_bounds(nlopt_opt opt, const double *lb) { if (opt && (opt->n == 0 || lb)) { memcpy(opt->lb, lb, sizeof(double) * (opt->n)); return NLOPT_SUCCESS; } return NLOPT_INVALID_ARGS; } nlopt_result NLOPT_STDCALL nlopt_set_lower_bounds1(nlopt_opt opt, double lb) { if (opt) { unsigned i; for (i = 0; i < opt->n; ++i) opt->lb[i] = lb; return NLOPT_SUCCESS; } return NLOPT_INVALID_ARGS; } nlopt_result NLOPT_STDCALL nlopt_get_lower_bounds(const nlopt_opt opt, double *lb) { if (opt && (opt->n == 0 || lb)) { memcpy(lb, opt->lb, sizeof(double) * (opt->n)); return NLOPT_SUCCESS; } return NLOPT_INVALID_ARGS; } nlopt_result NLOPT_STDCALL nlopt_set_upper_bounds(nlopt_opt opt, const double *ub) { if (opt && (opt->n == 0 || ub)) { memcpy(opt->ub, ub, sizeof(double) * (opt->n)); return NLOPT_SUCCESS; } return NLOPT_INVALID_ARGS; } nlopt_result NLOPT_STDCALL nlopt_set_upper_bounds1(nlopt_opt opt, double ub) { if (opt) { unsigned i; for (i = 0; i < opt->n; ++i) opt->ub[i] = ub; return NLOPT_SUCCESS; } return NLOPT_INVALID_ARGS; } nlopt_result NLOPT_STDCALL nlopt_get_upper_bounds(const nlopt_opt opt, double *ub) { if (opt && (opt->n == 0 || ub)) { memcpy(ub, opt->ub, sizeof(double) * (opt->n)); return NLOPT_SUCCESS; } return NLOPT_INVALID_ARGS; } /*************************************************************************/ #define AUGLAG_ALG(a) ((a) == NLOPT_AUGLAG || \ (a) == NLOPT_AUGLAG_EQ || \ (a) == NLOPT_LN_AUGLAG || \ (a) == NLOPT_LN_AUGLAG_EQ || \ (a) == NLOPT_LD_AUGLAG || \ (a) == NLOPT_LD_AUGLAG_EQ) nlopt_result NLOPT_STDCALL nlopt_remove_inequality_constraints(nlopt_opt opt) { unsigned i; if (!opt) return NLOPT_INVALID_ARGS; if (opt->munge_on_destroy) { nlopt_munge munge = opt->munge_on_destroy; for (i = 0; i < opt->m; ++i) munge(opt->fc[i].f_data); } for (i = 0; i < opt->m; ++i) free(opt->fc[i].tol); free(opt->fc); opt->fc = NULL; opt->m = opt->m_alloc = 0; return NLOPT_SUCCESS; } static nlopt_result add_constraint(unsigned *m, unsigned *m_alloc, nlopt_constraint **c, unsigned fm, nlopt_func fc, nlopt_mfunc mfc, nlopt_precond pre, void *fc_data, const double *tol) { double *tolcopy; unsigned i; if ((fc && mfc) || (fc && fm != 1) || (!fc && !mfc)) return NLOPT_INVALID_ARGS; if (tol) for (i = 0; i < fm; ++i) if (tol[i] < 0) return NLOPT_INVALID_ARGS; tolcopy = (double *) malloc(sizeof(double) * fm); if (fm && !tolcopy) return NLOPT_OUT_OF_MEMORY; if (tol) memcpy(tolcopy, tol, sizeof(double) * fm); else for (i = 0; i < fm; ++i) tolcopy[i] = 0; *m += 1; if (*m > *m_alloc) { /* allocate by repeated doubling so that we end up with O(log m) mallocs rather than O(m). */ *m_alloc = 2 * (*m); *c = (nlopt_constraint *) realloc(*c, sizeof(nlopt_constraint) * (*m_alloc)); if (!*c) { *m_alloc = *m = 0; free(tolcopy); return NLOPT_OUT_OF_MEMORY; } } (*c)[*m - 1].m = fm; (*c)[*m - 1].f = fc; (*c)[*m - 1].pre = pre; (*c)[*m - 1].mf = mfc; (*c)[*m - 1].f_data = fc_data; (*c)[*m - 1].tol = tolcopy; return NLOPT_SUCCESS; } static int inequality_ok(nlopt_algorithm algorithm) { /* nonlinear constraints are only supported with some algorithms */ return (algorithm == NLOPT_LD_MMA || algorithm == NLOPT_LD_CCSAQ || algorithm == NLOPT_LD_SLSQP || algorithm == NLOPT_LN_COBYLA || AUGLAG_ALG(algorithm) || algorithm == NLOPT_GN_ISRES || algorithm == NLOPT_GN_ORIG_DIRECT || algorithm == NLOPT_GN_ORIG_DIRECT_L); } nlopt_result NLOPT_STDCALL nlopt_add_inequality_mconstraint(nlopt_opt opt, unsigned m, nlopt_mfunc fc, void *fc_data, const double *tol) { nlopt_result ret; if (!m) { /* empty constraints are always ok */ if (opt && opt->munge_on_destroy) opt->munge_on_destroy(fc_data); return NLOPT_SUCCESS; } if (!opt || !inequality_ok(opt->algorithm)) ret = NLOPT_INVALID_ARGS; else ret = add_constraint(&opt->m, &opt->m_alloc, &opt->fc, m, NULL, fc, NULL, fc_data, tol); if (ret < 0 && opt && opt->munge_on_destroy) opt->munge_on_destroy(fc_data); return ret; } nlopt_result NLOPT_STDCALL nlopt_add_precond_inequality_constraint(nlopt_opt opt, nlopt_func fc, nlopt_precond pre, void *fc_data, double tol) { nlopt_result ret; if (!opt || !inequality_ok(opt->algorithm)) ret = NLOPT_INVALID_ARGS; else ret = add_constraint(&opt->m, &opt->m_alloc, &opt->fc, 1, fc, NULL, pre, fc_data, &tol); if (ret < 0 && opt && opt->munge_on_destroy) opt->munge_on_destroy(fc_data); return ret; } nlopt_result NLOPT_STDCALL nlopt_add_inequality_constraint(nlopt_opt opt, nlopt_func fc, void *fc_data, double tol) { return nlopt_add_precond_inequality_constraint(opt, fc, NULL, fc_data, tol); } nlopt_result NLOPT_STDCALL nlopt_remove_equality_constraints(nlopt_opt opt) { unsigned i; if (!opt) return NLOPT_INVALID_ARGS; if (opt->munge_on_destroy) { nlopt_munge munge = opt->munge_on_destroy; for (i = 0; i < opt->p; ++i) munge(opt->h[i].f_data); } for (i = 0; i < opt->p; ++i) free(opt->h[i].tol); free(opt->h); opt->h = NULL; opt->p = opt->p_alloc = 0; return NLOPT_SUCCESS; } static int equality_ok(nlopt_algorithm algorithm) { /* equality constraints (h(x) = 0) only via some algorithms */ return (AUGLAG_ALG(algorithm) || algorithm == NLOPT_LD_SLSQP || algorithm == NLOPT_GN_ISRES || algorithm == NLOPT_LN_COBYLA); } nlopt_result NLOPT_STDCALL nlopt_add_equality_mconstraint(nlopt_opt opt, unsigned m, nlopt_mfunc fc, void *fc_data, const double *tol) { nlopt_result ret; if (!m) { /* empty constraints are always ok */ if (opt && opt->munge_on_destroy) opt->munge_on_destroy(fc_data); return NLOPT_SUCCESS; } if (!opt || !equality_ok(opt->algorithm) || nlopt_count_constraints(opt->p, opt->h) + m > opt->n) ret = NLOPT_INVALID_ARGS; else ret = add_constraint(&opt->p, &opt->p_alloc, &opt->h, m, NULL, fc, NULL, fc_data, tol); if (ret < 0 && opt && opt->munge_on_destroy) opt->munge_on_destroy(fc_data); return ret; } nlopt_result NLOPT_STDCALL nlopt_add_precond_equality_constraint(nlopt_opt opt, nlopt_func fc, nlopt_precond pre, void *fc_data, double tol) { nlopt_result ret; if (!opt || !equality_ok(opt->algorithm) || nlopt_count_constraints(opt->p, opt->h) + 1 > opt->n) ret = NLOPT_INVALID_ARGS; else ret = add_constraint(&opt->p, &opt->p_alloc, &opt->h, 1, fc, NULL, pre, fc_data, &tol); if (ret < 0 && opt && opt->munge_on_destroy) opt->munge_on_destroy(fc_data); return ret; } nlopt_result NLOPT_STDCALL nlopt_add_equality_constraint(nlopt_opt opt, nlopt_func fc, void *fc_data, double tol) { return nlopt_add_precond_equality_constraint(opt, fc, NULL, fc_data, tol); } /*************************************************************************/ #define SET(param, T, arg) \ nlopt_result NLOPT_STDCALL nlopt_set_##param(nlopt_opt opt, T arg) \ { \ if (opt) { \ opt->arg = arg; \ return NLOPT_SUCCESS; \ } \ return NLOPT_INVALID_ARGS; \ } #define GET(param, T, arg) T NLOPT_STDCALL \ nlopt_get_##param(const nlopt_opt opt) { \ return opt->arg; \ } #define GETSET(param, T, arg) GET(param, T, arg) SET(param, T, arg) GETSET(stopval, double, stopval) GETSET(ftol_rel, double, ftol_rel) GETSET(ftol_abs, double, ftol_abs) GETSET(xtol_rel, double, xtol_rel) nlopt_result NLOPT_STDCALL nlopt_set_xtol_abs(nlopt_opt opt, const double *xtol_abs) { if (opt) { memcpy(opt->xtol_abs, xtol_abs, opt->n * sizeof(double)); return NLOPT_SUCCESS; } return NLOPT_INVALID_ARGS; } nlopt_result NLOPT_STDCALL nlopt_set_xtol_abs1(nlopt_opt opt, double xtol_abs) { if (opt) { unsigned i; for (i = 0; i < opt->n; ++i) opt->xtol_abs[i] = xtol_abs; return NLOPT_SUCCESS; } return NLOPT_INVALID_ARGS; } nlopt_result NLOPT_STDCALL nlopt_get_xtol_abs(const nlopt_opt opt, double *xtol_abs) { memcpy(xtol_abs, opt->xtol_abs, opt->n * sizeof(double)); return NLOPT_SUCCESS; } GETSET(maxeval, int, maxeval) GETSET(maxtime, double, maxtime) /*************************************************************************/ nlopt_result NLOPT_STDCALL nlopt_set_force_stop(nlopt_opt opt, int force_stop) { if (opt) { opt->force_stop = force_stop; if (opt->force_stop_child) return nlopt_set_force_stop(opt->force_stop_child, force_stop); return NLOPT_SUCCESS; } return NLOPT_INVALID_ARGS; } GET(force_stop, int, force_stop) nlopt_result NLOPT_STDCALL nlopt_force_stop(nlopt_opt opt) { return nlopt_set_force_stop(opt, 1); } /*************************************************************************/ GET(algorithm, nlopt_algorithm, algorithm) GET(dimension, unsigned, n) /*************************************************************************/ nlopt_result NLOPT_STDCALL nlopt_set_local_optimizer(nlopt_opt opt, const nlopt_opt local_opt) { if (opt) { if (local_opt && local_opt->n != opt->n) return NLOPT_INVALID_ARGS; nlopt_destroy(opt->local_opt); opt->local_opt = nlopt_copy(local_opt); if (local_opt) { if (!opt->local_opt) return NLOPT_OUT_OF_MEMORY; nlopt_set_lower_bounds(opt->local_opt, opt->lb); nlopt_set_upper_bounds(opt->local_opt, opt->ub); nlopt_remove_inequality_constraints(opt->local_opt); nlopt_remove_equality_constraints(opt->local_opt); nlopt_set_min_objective(opt->local_opt, NULL, NULL); nlopt_set_munge(opt->local_opt, NULL, NULL); opt->local_opt->force_stop = 0; } return NLOPT_SUCCESS; } return NLOPT_INVALID_ARGS; } /*************************************************************************/ GETSET(population, unsigned, stochastic_population) GETSET(vector_storage, unsigned, vector_storage) /*************************************************************************/ nlopt_result NLOPT_STDCALL nlopt_set_initial_step1(nlopt_opt opt, double dx) { unsigned i; if (!opt || dx == 0) return NLOPT_INVALID_ARGS; if (!opt->dx && opt->n > 0) { opt->dx = (double *) malloc(sizeof(double) * (opt->n)); if (!opt->dx) return NLOPT_OUT_OF_MEMORY; } for (i = 0; i < opt->n; ++i) opt->dx[i] = dx; return NLOPT_SUCCESS; } nlopt_result NLOPT_STDCALL nlopt_set_initial_step(nlopt_opt opt, const double *dx) { unsigned i; if (!opt) return NLOPT_INVALID_ARGS; if (!dx) { free(opt->dx); opt->dx = NULL; return NLOPT_SUCCESS; } for (i = 0; i < opt->n; ++i) if (dx[i] == 0) return NLOPT_INVALID_ARGS; if (!opt->dx && nlopt_set_initial_step1(opt, 1) == NLOPT_OUT_OF_MEMORY) return NLOPT_OUT_OF_MEMORY; memcpy(opt->dx, dx, sizeof(double) * (opt->n)); return NLOPT_SUCCESS; } nlopt_result NLOPT_STDCALL nlopt_get_initial_step(const nlopt_opt opt, const double *x, double *dx) { if (!opt) return NLOPT_INVALID_ARGS; if (!opt->n) return NLOPT_SUCCESS; if (!opt->dx) { nlopt_opt o = (nlopt_opt) opt; /* discard const temporarily */ nlopt_result ret = nlopt_set_default_initial_step(o, x); if (ret != NLOPT_SUCCESS) return ret; memcpy(dx, o->dx, sizeof(double) * (opt->n)); free(o->dx); o->dx = NULL; /* don't save, since x-dependent */ } else memcpy(dx, opt->dx, sizeof(double) * (opt->n)); return NLOPT_SUCCESS; } nlopt_result NLOPT_STDCALL nlopt_set_default_initial_step(nlopt_opt opt, const double *x) { const double *lb, *ub; unsigned i; if (!opt || !x) return NLOPT_INVALID_ARGS; lb = opt->lb; ub = opt->ub; if (!opt->dx && nlopt_set_initial_step1(opt, 1) == NLOPT_OUT_OF_MEMORY) return NLOPT_OUT_OF_MEMORY; /* crude heuristics for initial step size of nonderivative algorithms */ for (i = 0; i < opt->n; ++i) { double step = HUGE_VAL; if (!nlopt_isinf(ub[i]) && !nlopt_isinf(lb[i]) && (ub[i] - lb[i]) * 0.25 < step && ub[i] > lb[i]) step = (ub[i] - lb[i]) * 0.25; if (!nlopt_isinf(ub[i]) && ub[i] - x[i] < step && ub[i] > x[i]) step = (ub[i] - x[i]) * 0.75; if (!nlopt_isinf(lb[i]) && x[i] - lb[i] < step && x[i] > lb[i]) step = (x[i] - lb[i]) * 0.75; if (nlopt_isinf(step)) { if (!nlopt_isinf(ub[i]) && fabs(ub[i] - x[i]) < fabs(step)) step = (ub[i] - x[i]) * 1.1; if (!nlopt_isinf(lb[i]) && fabs(x[i] - lb[i]) < fabs(step)) step = (x[i] - lb[i]) * 1.1; } if (nlopt_isinf(step) || step == 0) { step = x[i]; } if (nlopt_isinf(step) || step == 0) step = 1; opt->dx[i] = step; } return NLOPT_SUCCESS; } /*************************************************************************/ void NLOPT_STDCALL nlopt_set_munge(nlopt_opt opt, nlopt_munge munge_on_destroy, nlopt_munge munge_on_copy) { if (opt) { opt->munge_on_destroy = munge_on_destroy; opt->munge_on_copy = munge_on_copy; } } void NLOPT_STDCALL nlopt_munge_data(nlopt_opt opt, nlopt_munge2 munge, void *data) { if (opt && munge) { unsigned i; opt->f_data = munge(opt->f_data, data); for (i = 0; i < opt->m; ++i) opt->fc[i].f_data = munge(opt->fc[i].f_data, data); for (i = 0; i < opt->p; ++i) opt->h[i].f_data = munge(opt->h[i].f_data, data); } } /*************************************************************************/ nloptr/src/nlopt_src/api/f77funcs.h0000644000176000001440000001031413355070635017004 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ /* Fortran API wrappers, using the F77 macro defined in f77api.c. This header file is #included one or more times from f77api.c in order to define verions of the Fortran API for various compilers. In homage to Fortran 77, we stick with 6-character subroutine names. The return value of the function is converted into the first argument of the subroutine. */ /* nlopt_minimize_constrained */ void F77(nloptc,NLOPTC)(int *info, const int *algorithm, const int *n, nlopt_f77_func f, void *f_data, const int *m, nlopt_f77_func fc, char *fc_data, char *fc_second_datum, const double *lb, const double *ub, double *x, double *minf, const double *minf_max, const double *ftol_rel, const double *ftol_abs, const double *xtol_rel, const double *xtol_abs, const int *have_xtol_abs, const int *maxeval, const double *maxtime) { f77_func_data d, *dc; int i; d.f = f; d.f_data = f_data; if (*m < 0) { *info = NLOPT_INVALID_ARGS; return; } dc = (f77_func_data *) malloc(sizeof(f77_func_data) * *m); if (*m > 0 && !dc) { *info = NLOPT_OUT_OF_MEMORY; return; } for (i = 0; i < *m; ++i) { dc[i].f = fc; dc[i].f_data = fc_data + i * (fc_second_datum - fc_data); } *info = nlopt_minimize_constrained((nlopt_algorithm) *algorithm, *n, f77_func_wrap_old, &d, *m, f77_func_wrap_old, dc, sizeof(f77_func_data), lb, ub, x, minf, *minf_max, *ftol_rel, *ftol_abs, *xtol_rel, *have_xtol_abs ? xtol_abs : 0, *maxeval, *maxtime); if (dc) free(dc); } /* nlopt_minimize */ void F77(nloptm,NLOPTM)(int *info, const int *algorithm, const int *n, nlopt_f77_func f, void *f_data, const double *lb, const double *ub, double *x, double *minf, const double *minf_max, const double *ftol_rel, const double *ftol_abs, const double *xtol_rel, const double *xtol_abs, const int *have_xtol_abs, const int *maxeval, const double *maxtime) { int m = 0; F77CALL(nloptc,NLOPTC)(info, algorithm, n, f, f_data, &m, 0, 0, 0, lb, ub, x, minf, minf_max, ftol_rel, ftol_abs, xtol_rel, xtol_abs, have_xtol_abs, maxeval, maxtime); } void F77(nlosr,NLOSR)(const int *seed) { nlopt_srand((unsigned long) *seed); } void F77(nlosrt,NLOSRT)(void) { nlopt_srand_time(); } void F77(nloptv,NLOPTV)(int *major, int *minor, int *bugfix) { nlopt_version(major, minor, bugfix); } void F77(nlogls,NLOGLS)(int *ideriv, int *inonderiv, int *maxeval) { nlopt_algorithm deriv, nonderiv; nlopt_get_local_search_algorithm(&deriv, &nonderiv, maxeval); *ideriv = deriv; *inonderiv = nonderiv; } void F77(nlosls,NLOSLS)(int *ideriv, int *inonderiv, int *maxeval) { nlopt_algorithm deriv = (nlopt_algorithm) *ideriv; nlopt_algorithm nonderiv = (nlopt_algorithm) *inonderiv; nlopt_set_local_search_algorithm(deriv, nonderiv, *maxeval); } void F77(nlogsp,NLOGSP)(int *pop) { *pop = nlopt_get_stochastic_population(); } void F77(nlossp,NLOSSP)(const int *pop) { nlopt_set_stochastic_population(*pop); } #define F77_(name,NAME) F77(name,NAME) # include "f77funcs_.h" #undef F77_ nloptr/src/nlopt_src/api/optimize.c0000644000176000001440000006427313355070635017212 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include #include #include #include "nlopt-internal.h" /*********************************************************************/ #ifndef HAVE_ISNAN static int my_isnan(double x) { return x != x; } # define isnan my_isnan #endif /*********************************************************************/ #include "praxis.h" #include "direct.h" #ifdef WITH_CXX # include "stogo.h" #endif #include "cdirect.h" #include "luksan.h" #include "crs.h" #include "mlsl.h" #include "mma.h" #include "cobyla.h" #include "newuoa.h" #include "neldermead.h" #include "auglag.h" #include "bobyqa.h" #include "isres.h" #include "esch.h" #include "slsqp.h" /*********************************************************************/ static double f_bound(int n, const double *x, void *data_) { int i; nlopt_opt data = (nlopt_opt) data_; double f; /* some methods do not support bound constraints, but support discontinuous objectives so we can just return Inf for invalid x */ for (i = 0; i < n; ++i) if (x[i] < data->lb[i] || x[i] > data->ub[i]) return HUGE_VAL; f = data->f((unsigned) n, x, NULL, data->f_data); return (isnan(f) || nlopt_isinf(f) ? HUGE_VAL : f); } static double f_noderiv(int n, const double *x, void *data_) { nlopt_opt data = (nlopt_opt) data_; return data->f((unsigned) n, x, NULL, data->f_data); } static double f_direct(int n, const double *x, int *undefined, void *data_) { nlopt_opt data = (nlopt_opt) data_; double *work = (double*) data->work; double f; unsigned i, j; f = data->f((unsigned) n, x, NULL, data->f_data); *undefined = isnan(f) || nlopt_isinf(f); if (nlopt_get_force_stop(data)) return f; for (i = 0; i < data->m && !*undefined; ++i) { nlopt_eval_constraint(work, NULL, data->fc+i, (unsigned) n, x); if (nlopt_get_force_stop(data)) return f; for (j = 0; j < data->fc[i].m; ++j) if (work[j] > 0) *undefined = 1; } return f; } /*********************************************************************/ /* get min(dx) for algorithms requiring a scalar initial step size */ static nlopt_result initial_step(nlopt_opt opt, const double *x, double *step) { unsigned freedx = 0, i; if (!opt->dx) { freedx = 1; if (nlopt_set_default_initial_step(opt, x) != NLOPT_SUCCESS) return NLOPT_OUT_OF_MEMORY; } *step = HUGE_VAL; for (i = 0; i < opt->n; ++i) if (*step > fabs(opt->dx[i])) *step = fabs(opt->dx[i]); if (freedx) { free(opt->dx); opt->dx = NULL; } return NLOPT_SUCCESS; } /*********************************************************************/ /* return true if [lb,ub] is finite in every dimension (n dimensions) */ static int finite_domain(unsigned n, const double *lb, const double *ub) { unsigned i; for (i = 0; i < n; ++i) if (nlopt_isinf(ub[i] - lb[i])) return 0; return 1; } /*********************************************************************/ /* wrapper functions, only for derivative-free methods, that eliminate dimensions with lb == ub. (The gradient-based methods should handle this case directly, since they operate on much larger vectors where I am loathe to make copies unnecessarily.) */ typedef struct { nlopt_func f; nlopt_mfunc mf; void *f_data; unsigned n; /* true dimension */ double *x; /* scratch vector of length n */ double *grad; /* optional scratch vector of length n */ const double *lb, *ub; /* bounds, of length n */ } elimdim_data; static void *elimdim_makedata(nlopt_func f, nlopt_mfunc mf, void *f_data, unsigned n, double *x, const double *lb, const double *ub, double *grad) { elimdim_data *d = (elimdim_data *) malloc(sizeof(elimdim_data)); if (!d) return NULL; d->f = f; d->mf = mf; d->f_data = f_data; d->n = n; d->x = x; d->lb = lb; d->ub = ub; d->grad = grad; return d; } static double elimdim_func(unsigned n0, const double *x0, double *grad, void *d_) { elimdim_data *d = (elimdim_data *) d_; double *x = d->x; const double *lb = d->lb, *ub = d->ub; double val; unsigned n = d->n, i, j; (void) n0; /* unused */ for (i = j = 0; i < n; ++i) { if (lb[i] == ub[i]) x[i] = lb[i]; else /* assert: j < n0 */ x[i] = x0[j++]; } val = d->f(n, x, grad ? d->grad : NULL, d->f_data); if (grad) { /* assert: d->grad != NULL */ for (i = j = 0; i < n; ++i) if (lb[i] != ub[i]) grad[j++] = d->grad[i]; } return val; } static void elimdim_mfunc(unsigned m, double *result, unsigned n0, const double *x0, double *grad, void *d_) { elimdim_data *d = (elimdim_data *) d_; double *x = d->x; const double *lb = d->lb, *ub = d->ub; unsigned n = d->n, i, j; (void) n0; /* unused */ (void) grad; /* assert: grad == NULL */ for (i = j = 0; i < n; ++i) { if (lb[i] == ub[i]) x[i] = lb[i]; else /* assert: j < n0 */ x[i] = x0[j++]; } d->mf(m, result, n, x, NULL, d->f_data); } /* compute the eliminated dimension: number of dims with lb[i] != ub[i] */ static unsigned elimdim_dimension(unsigned n, const double *lb, const double *ub) { unsigned n0 = 0, i; for (i = 0; i < n; ++i) n0 += lb[i] != ub[i] ? 1U : 0; return n0; } /* modify v to "shrunk" version, with dimensions for lb[i] == ub[i] elim'ed */ static void elimdim_shrink(unsigned n, double *v, const double *lb, const double *ub) { unsigned i, j; if (v) for (i = j = 0; i < n; ++i) if (lb[i] != ub[i]) v[j++] = v[i]; } /* inverse of elimdim_shrink */ static void elimdim_expand(unsigned n, double *v, const double *lb, const double *ub) { unsigned i, j; if (v && n > 0) { j = elimdim_dimension(n, lb, ub) - 1; for (i = n - 1; i > 0; --i) { if (lb[i] != ub[i]) v[i] = v[j--]; else v[i] = lb[i]; } if (lb[0] == ub[0]) v[0] = lb[0]; } } /* given opt, create a new opt with equal-constraint dimensions eliminated */ static nlopt_opt elimdim_create(nlopt_opt opt) { nlopt_opt opt0 = nlopt_copy(opt); double *x, *grad = NULL; unsigned i; if (!opt0) return NULL; x = (double *) malloc(sizeof(double) * opt->n); if (opt->n && !x) { nlopt_destroy(opt0); return NULL; } if (opt->algorithm == NLOPT_GD_STOGO || opt->algorithm == NLOPT_GD_STOGO_RAND) { grad = (double *) malloc(sizeof(double) * opt->n); if (opt->n && !grad) goto bad; } opt0->n = elimdim_dimension(opt->n, opt->lb, opt->ub); elimdim_shrink(opt->n, opt0->lb, opt->lb, opt->ub); elimdim_shrink(opt->n, opt0->ub, opt->lb, opt->ub); elimdim_shrink(opt->n, opt0->xtol_abs, opt->lb, opt->ub); elimdim_shrink(opt->n, opt0->dx, opt->lb, opt->ub); opt0->munge_on_destroy = opt0->munge_on_copy = NULL; opt0->f = elimdim_func; opt0->f_data = elimdim_makedata(opt->f, NULL, opt->f_data, opt->n, x, opt->lb, opt->ub, grad); if (!opt0->f_data) goto bad; for (i = 0; i < opt->m; ++i) { opt0->fc[i].f = elimdim_func; opt0->fc[i].mf = elimdim_mfunc; opt0->fc[i].f_data = elimdim_makedata(opt->fc[i].f, opt->fc[i].mf, opt->fc[i].f_data, opt->n, x, opt->lb, opt->ub, NULL); if (!opt0->fc[i].f_data) goto bad; } for (i = 0; i < opt->p; ++i) { opt0->h[i].f = elimdim_func; opt0->h[i].mf = elimdim_mfunc; opt0->h[i].f_data = elimdim_makedata(opt->h[i].f, opt->h[i].mf, opt->h[i].f_data, opt->n, x, opt->lb, opt->ub, NULL); if (!opt0->h[i].f_data) goto bad; } return opt0; bad: free(grad); free(x); nlopt_destroy(opt0); return NULL; } /* like nlopt_destroy, but also frees elimdim_data */ static void elimdim_destroy(nlopt_opt opt) { unsigned i; if (!opt) return; free(((elimdim_data*) opt->f_data)->x); free(((elimdim_data*) opt->f_data)->grad); free(opt->f_data); opt->f_data = NULL; for (i = 0; i < opt->m; ++i) { free(opt->fc[i].f_data); opt->fc[i].f_data = NULL; } for (i = 0; i < opt->p; ++i) { free(opt->h[i].f_data); opt->h[i].f_data = NULL; } nlopt_destroy(opt); } /* return whether to use elimdim wrapping. */ static int elimdim_wrapcheck(nlopt_opt opt) { if (!opt) return 0; if (elimdim_dimension(opt->n, opt->lb, opt->ub) == opt->n) return 0; switch (opt->algorithm) { case NLOPT_GN_DIRECT: case NLOPT_GN_DIRECT_L: case NLOPT_GN_DIRECT_L_RAND: case NLOPT_GN_DIRECT_NOSCAL: case NLOPT_GN_DIRECT_L_NOSCAL: case NLOPT_GN_DIRECT_L_RAND_NOSCAL: case NLOPT_GN_ORIG_DIRECT: case NLOPT_GN_ORIG_DIRECT_L: case NLOPT_GN_CRS2_LM: case NLOPT_LN_PRAXIS: case NLOPT_LN_COBYLA: case NLOPT_LN_NEWUOA: case NLOPT_LN_NEWUOA_BOUND: case NLOPT_LN_BOBYQA: case NLOPT_LN_NELDERMEAD: case NLOPT_LN_SBPLX: case NLOPT_GN_ISRES: case NLOPT_GN_ESCH: case NLOPT_GD_STOGO: case NLOPT_GD_STOGO_RAND: return 1; default: return 0; } } /*********************************************************************/ #define POP(defaultpop) (opt->stochastic_population > 0 ? \ opt->stochastic_population : \ (nlopt_stochastic_population > 0 ? \ nlopt_stochastic_population : (defaultpop))) /* unlike nlopt_optimize() below, only handles minimization case */ static nlopt_result nlopt_optimize_(nlopt_opt opt, double *x, double *minf) { const double *lb, *ub; nlopt_algorithm algorithm; nlopt_func f; void *f_data; unsigned n, i; int ni; nlopt_stopping stop; if (!opt || !x || !minf || !opt->f || opt->maximize) return NLOPT_INVALID_ARGS; /* reset stopping flag */ nlopt_set_force_stop(opt, 0); opt->force_stop_child = NULL; /* copy a few params to local vars for convenience */ n = opt->n; ni = (int) n; /* most of the subroutines take "int" arg */ lb = opt->lb; ub = opt->ub; algorithm = opt->algorithm; f = opt->f; f_data = opt->f_data; if (n == 0) { /* trivial case: no degrees of freedom */ *minf = opt->f(n, x, NULL, opt->f_data); return NLOPT_SUCCESS; } *minf = HUGE_VAL; /* make sure rand generator is inited */ nlopt_srand_time_default(); /* default is non-deterministic */ /* check bound constraints */ for (i = 0; i < n; ++i) if (lb[i] > ub[i] || x[i] < lb[i] || x[i] > ub[i]) return NLOPT_INVALID_ARGS; stop.n = n; stop.minf_max = opt->stopval; stop.ftol_rel = opt->ftol_rel; stop.ftol_abs = opt->ftol_abs; stop.xtol_rel = opt->xtol_rel; stop.xtol_abs = opt->xtol_abs; stop.nevals = 0; stop.maxeval = opt->maxeval; stop.maxtime = opt->maxtime; stop.start = nlopt_seconds(); stop.force_stop = &(opt->force_stop); switch (algorithm) { case NLOPT_GN_DIRECT: case NLOPT_GN_DIRECT_L: case NLOPT_GN_DIRECT_L_RAND: if (!finite_domain(n, lb, ub)) return NLOPT_INVALID_ARGS; return cdirect(ni, f, f_data, lb, ub, x, minf, &stop, 0.0, (algorithm != NLOPT_GN_DIRECT) + 3 * (algorithm == NLOPT_GN_DIRECT_L_RAND ? 2 : (algorithm != NLOPT_GN_DIRECT)) + 9 * (algorithm == NLOPT_GN_DIRECT_L_RAND ? 1 : (algorithm != NLOPT_GN_DIRECT))); case NLOPT_GN_DIRECT_NOSCAL: case NLOPT_GN_DIRECT_L_NOSCAL: case NLOPT_GN_DIRECT_L_RAND_NOSCAL: if (!finite_domain(n, lb, ub)) return NLOPT_INVALID_ARGS; return cdirect_unscaled(ni, f, f_data, lb, ub, x, minf, &stop, 0.0, (algorithm != NLOPT_GN_DIRECT) + 3 * (algorithm == NLOPT_GN_DIRECT_L_RAND ? 2 : (algorithm != NLOPT_GN_DIRECT)) + 9 * (algorithm == NLOPT_GN_DIRECT_L_RAND ? 1 : (algorithm != NLOPT_GN_DIRECT))); case NLOPT_GN_ORIG_DIRECT: case NLOPT_GN_ORIG_DIRECT_L: { direct_return_code dret; if (!finite_domain(n, lb, ub)) return NLOPT_INVALID_ARGS; opt->work = malloc(sizeof(double) * nlopt_max_constraint_dim(opt->m, opt->fc)); if (!opt->work) return NLOPT_OUT_OF_MEMORY; dret = direct_optimize(f_direct, opt, ni, lb, ub, x, minf, stop.maxeval, -1, stop.start, stop.maxtime, 0.0, 0.0, pow(stop.xtol_rel, (double) n), -1.0, stop.force_stop, stop.minf_max, 0.0, NULL, algorithm == NLOPT_GN_ORIG_DIRECT ? DIRECT_ORIGINAL : DIRECT_GABLONSKY); free(opt->work); opt->work = NULL; switch (dret) { case DIRECT_INVALID_BOUNDS: case DIRECT_MAXFEVAL_TOOBIG: case DIRECT_INVALID_ARGS: return NLOPT_INVALID_ARGS; case DIRECT_INIT_FAILED: case DIRECT_SAMPLEPOINTS_FAILED: case DIRECT_SAMPLE_FAILED: return NLOPT_FAILURE; case DIRECT_MAXFEVAL_EXCEEDED: case DIRECT_MAXITER_EXCEEDED: return NLOPT_MAXEVAL_REACHED; case DIRECT_MAXTIME_EXCEEDED: return NLOPT_MAXTIME_REACHED; case DIRECT_GLOBAL_FOUND: return NLOPT_MINF_MAX_REACHED; case DIRECT_VOLTOL: case DIRECT_SIGMATOL: return NLOPT_XTOL_REACHED; case DIRECT_OUT_OF_MEMORY: return NLOPT_OUT_OF_MEMORY; case DIRECT_FORCED_STOP: return NLOPT_FORCED_STOP; } break; } case NLOPT_GD_STOGO: case NLOPT_GD_STOGO_RAND: #ifdef WITH_CXX if (!finite_domain(n, lb, ub)) return NLOPT_INVALID_ARGS; if (!stogo_minimize(ni, f, f_data, x, minf, lb, ub, &stop, algorithm == NLOPT_GD_STOGO ? 0 : (int) POP(2*n))) return NLOPT_FAILURE; break; #else return NLOPT_INVALID_ARGS; #endif #if 0 /* lacking a free/open-source license, we no longer use Rowan's code, and instead use by "sbplx" re-implementation */ case NLOPT_LN_SUBPLEX: { int iret, freedx = 0; if (!opt->dx) { freedx = 1; if (nlopt_set_default_initial_step(opt, x) != NLOPT_SUCCESS) return NLOPT_OUT_OF_MEMORY; } iret = nlopt_subplex(f_bound, minf, x, n, opt, &stop, opt->dx); if (freedx) { free(opt->dx); opt->dx = NULL; } switch (iret) { case -2: return NLOPT_INVALID_ARGS; case -20: return NLOPT_FORCED_STOP; case -10: return NLOPT_MAXTIME_REACHED; case -1: return NLOPT_MAXEVAL_REACHED; case 0: return NLOPT_XTOL_REACHED; case 1: return NLOPT_SUCCESS; case 2: return NLOPT_MINF_MAX_REACHED; case 20: return NLOPT_FTOL_REACHED; case -200: return NLOPT_OUT_OF_MEMORY; default: return NLOPT_FAILURE; /* unknown return code */ } break; } #endif case NLOPT_LN_PRAXIS: { double step; if (initial_step(opt, x, &step) != NLOPT_SUCCESS) return NLOPT_OUT_OF_MEMORY; return praxis_(0.0, DBL_EPSILON, step, ni, x, f_bound, opt, &stop, minf); } case NLOPT_LD_LBFGS: return luksan_plis(ni, f, f_data, lb, ub, x, minf, &stop, opt->vector_storage); case NLOPT_LD_VAR1: case NLOPT_LD_VAR2: return luksan_plip(ni, f, f_data, lb, ub, x, minf, &stop, opt->vector_storage, algorithm == NLOPT_LD_VAR1 ? 1 : 2); case NLOPT_LD_TNEWTON: case NLOPT_LD_TNEWTON_RESTART: case NLOPT_LD_TNEWTON_PRECOND: case NLOPT_LD_TNEWTON_PRECOND_RESTART: return luksan_pnet(ni, f, f_data, lb, ub, x, minf, &stop, opt->vector_storage, 1 + (algorithm - NLOPT_LD_TNEWTON) % 2, 1 + (algorithm - NLOPT_LD_TNEWTON) / 2); case NLOPT_GN_CRS2_LM: if (!finite_domain(n, lb, ub)) return NLOPT_INVALID_ARGS; return crs_minimize(ni, f, f_data, lb, ub, x, minf, &stop, (int) POP(0), 0); case NLOPT_G_MLSL: case NLOPT_G_MLSL_LDS: case NLOPT_GN_MLSL: case NLOPT_GD_MLSL: case NLOPT_GN_MLSL_LDS: case NLOPT_GD_MLSL_LDS: { nlopt_opt local_opt = opt->local_opt; nlopt_result ret; if (!finite_domain(n, lb, ub)) return NLOPT_INVALID_ARGS; if (!local_opt && (algorithm == NLOPT_G_MLSL || algorithm == NLOPT_G_MLSL_LDS)) return NLOPT_INVALID_ARGS; if (!local_opt) { /* default */ nlopt_algorithm local_alg = (algorithm == NLOPT_GN_MLSL || algorithm == NLOPT_GN_MLSL_LDS) ? nlopt_local_search_alg_nonderiv : nlopt_local_search_alg_deriv; /* don't call MLSL recursively! */ if (local_alg >= NLOPT_GN_MLSL && local_alg <= NLOPT_GD_MLSL_LDS) local_alg = (algorithm == NLOPT_GN_MLSL || algorithm == NLOPT_GN_MLSL_LDS) ? NLOPT_LN_COBYLA : NLOPT_LD_MMA; local_opt = nlopt_create(local_alg, n); if (!local_opt) return NLOPT_FAILURE; nlopt_set_ftol_rel(local_opt, opt->ftol_rel); nlopt_set_ftol_abs(local_opt, opt->ftol_abs); nlopt_set_xtol_rel(local_opt, opt->xtol_rel); nlopt_set_xtol_abs(local_opt, opt->xtol_abs); nlopt_set_maxeval(local_opt, nlopt_local_search_maxeval); } if (opt->dx) nlopt_set_initial_step(local_opt, opt->dx); for (i = 0; i < n && stop.xtol_abs[i] > 0; ++i) ; if (local_opt->ftol_rel <= 0 && local_opt->ftol_abs <= 0 && local_opt->xtol_rel <= 0 && i < n) { /* it is not sensible to call MLSL without *some* nonzero tolerance for the local search */ nlopt_set_ftol_rel(local_opt, 1e-15); nlopt_set_xtol_rel(local_opt, 1e-7); } opt->force_stop_child = local_opt; ret = mlsl_minimize(ni, f, f_data, lb, ub, x, minf, &stop, local_opt, (int) POP(0), algorithm >= NLOPT_GN_MLSL_LDS && algorithm != NLOPT_G_MLSL); opt->force_stop_child = NULL; if (!opt->local_opt) nlopt_destroy(local_opt); return ret; } case NLOPT_LD_MMA: case NLOPT_LD_CCSAQ: { nlopt_opt dual_opt; nlopt_result ret; #define LO(param, def) (opt->local_opt ? opt->local_opt->param : (def)) dual_opt = nlopt_create(LO(algorithm, nlopt_local_search_alg_deriv), nlopt_count_constraints(opt->m, opt->fc)); if (!dual_opt) return NLOPT_FAILURE; nlopt_set_ftol_rel(dual_opt, LO(ftol_rel, 1e-14)); nlopt_set_ftol_abs(dual_opt, LO(ftol_abs, 0.0)); nlopt_set_maxeval(dual_opt, LO(maxeval, 100000)); #undef LO if (algorithm == NLOPT_LD_MMA) ret = mma_minimize(n, f, f_data, opt->m, opt->fc, lb, ub, x, minf, &stop, dual_opt); else ret = ccsa_quadratic_minimize( n, f, f_data, opt->m, opt->fc, opt->pre, lb, ub, x, minf, &stop, dual_opt); nlopt_destroy(dual_opt); return ret; } case NLOPT_LN_COBYLA: { nlopt_result ret; int freedx = 0; if (!opt->dx) { freedx = 1; if (nlopt_set_default_initial_step(opt, x) != NLOPT_SUCCESS) return NLOPT_OUT_OF_MEMORY; } return cobyla_minimize(n, f, f_data, opt->m, opt->fc, opt->p, opt->h, lb, ub, x, minf, &stop, opt->dx); if (freedx) { free(opt->dx); opt->dx = NULL; } return ret; } case NLOPT_LN_NEWUOA: { double step; if (initial_step(opt, x, &step) != NLOPT_SUCCESS) return NLOPT_OUT_OF_MEMORY; return newuoa(ni, 2*n+1, x, 0, 0, step, &stop, minf, f_noderiv, opt); } case NLOPT_LN_NEWUOA_BOUND: { double step; if (initial_step(opt, x, &step) != NLOPT_SUCCESS) return NLOPT_OUT_OF_MEMORY; return newuoa(ni, 2*n+1, x, lb, ub, step, &stop, minf, f_noderiv, opt); } case NLOPT_LN_BOBYQA: { nlopt_result ret; int freedx = 0; if (!opt->dx) { freedx = 1; if (nlopt_set_default_initial_step(opt, x) != NLOPT_SUCCESS) return NLOPT_OUT_OF_MEMORY; } ret = bobyqa(ni, 2*n+1, x, lb, ub, opt->dx, &stop, minf, opt->f, opt->f_data); if (freedx) { free(opt->dx); opt->dx = NULL; } return ret; } case NLOPT_LN_NELDERMEAD: case NLOPT_LN_SBPLX: { nlopt_result ret; int freedx = 0; if (!opt->dx) { freedx = 1; if (nlopt_set_default_initial_step(opt, x) != NLOPT_SUCCESS) return NLOPT_OUT_OF_MEMORY; } if (algorithm == NLOPT_LN_NELDERMEAD) ret= nldrmd_minimize(ni,f,f_data,lb,ub,x,minf,opt->dx,&stop); else ret= sbplx_minimize(ni,f,f_data,lb,ub,x,minf,opt->dx,&stop); if (freedx) { free(opt->dx); opt->dx = NULL; } return ret; } case NLOPT_AUGLAG: case NLOPT_AUGLAG_EQ: case NLOPT_LN_AUGLAG: case NLOPT_LN_AUGLAG_EQ: case NLOPT_LD_AUGLAG: case NLOPT_LD_AUGLAG_EQ: { nlopt_opt local_opt = opt->local_opt; nlopt_result ret; if ((algorithm == NLOPT_AUGLAG || algorithm == NLOPT_AUGLAG_EQ) && !local_opt) return NLOPT_INVALID_ARGS; if (!local_opt) { /* default */ local_opt = nlopt_create( algorithm == NLOPT_LN_AUGLAG || algorithm == NLOPT_LN_AUGLAG_EQ ? nlopt_local_search_alg_nonderiv : nlopt_local_search_alg_deriv, n); if (!local_opt) return NLOPT_FAILURE; nlopt_set_ftol_rel(local_opt, opt->ftol_rel); nlopt_set_ftol_abs(local_opt, opt->ftol_abs); nlopt_set_xtol_rel(local_opt, opt->xtol_rel); nlopt_set_xtol_abs(local_opt, opt->xtol_abs); nlopt_set_maxeval(local_opt, nlopt_local_search_maxeval); } if (opt->dx) nlopt_set_initial_step(local_opt, opt->dx); opt->force_stop_child = local_opt; ret = auglag_minimize(ni, f, f_data, opt->m, opt->fc, opt->p, opt->h, lb, ub, x, minf, &stop, local_opt, algorithm == NLOPT_AUGLAG_EQ || algorithm == NLOPT_LN_AUGLAG_EQ || algorithm == NLOPT_LD_AUGLAG_EQ); opt->force_stop_child = NULL; if (!opt->local_opt) nlopt_destroy(local_opt); return ret; } case NLOPT_GN_ISRES: if (!finite_domain(n, lb, ub)) return NLOPT_INVALID_ARGS; return isres_minimize(ni, f, f_data, (int) (opt->m), opt->fc, (int) (opt->p), opt->h, lb, ub, x, minf, &stop, (int) POP(0)); case NLOPT_GN_ESCH: if (!finite_domain(n, lb, ub)) return NLOPT_INVALID_ARGS; return chevolutionarystrategy(n, f, f_data, lb, ub, x, minf, &stop, (unsigned) POP(0), (unsigned) (POP(0)*1.5)); case NLOPT_LD_SLSQP: return nlopt_slsqp(n, f, f_data, opt->m, opt->fc, opt->p, opt->h, lb, ub, x, minf, &stop); default: return NLOPT_INVALID_ARGS; } return NLOPT_SUCCESS; /* never reached */ } /*********************************************************************/ typedef struct { nlopt_func f; nlopt_precond pre; void *f_data; } f_max_data; /* wrapper for maximizing: just flip the sign of f and grad */ static double f_max(unsigned n, const double *x, double *grad, void *data) { f_max_data *d = (f_max_data *) data; double val = d->f(n, x, grad, d->f_data); if (grad) { unsigned i; for (i = 0; i < n; ++i) grad[i] = -grad[i]; } return -val; } static void pre_max(unsigned n, const double *x, const double *v, double *vpre, void *data) { f_max_data *d = (f_max_data *) data; unsigned i; d->pre(n, x, v, vpre, d->f_data); for (i = 0; i < n; ++i) vpre[i] = -vpre[i]; } nlopt_result NLOPT_STDCALL nlopt_optimize(nlopt_opt opt, double *x, double *opt_f) { nlopt_func f; void *f_data; nlopt_precond pre; f_max_data fmd; int maximize; nlopt_result ret; if (!opt || !opt_f || !opt->f) return NLOPT_INVALID_ARGS; f = opt->f; f_data = opt->f_data; pre = opt->pre; /* for maximizing, just minimize the f_max wrapper, which flips the sign of everything */ if ((maximize = opt->maximize)) { fmd.f = f; fmd.f_data = f_data; fmd.pre = pre; opt->f = f_max; opt->f_data = &fmd; if (opt->pre) opt->pre = pre_max; opt->stopval = -opt->stopval; opt->maximize = 0; } { /* possibly eliminate lb == ub dimensions for some algorithms */ nlopt_opt elim_opt = opt; if (elimdim_wrapcheck(opt)) { elim_opt = elimdim_create(opt); if (!elim_opt) { ret = NLOPT_OUT_OF_MEMORY; goto done; } elimdim_shrink(opt->n, x, opt->lb, opt->ub); } ret = nlopt_optimize_(elim_opt, x, opt_f); if (elim_opt != opt) { elimdim_destroy(elim_opt); elimdim_expand(opt->n, x, opt->lb, opt->ub); } } done: if (maximize) { /* restore original signs */ opt->maximize = maximize; opt->stopval = -opt->stopval; opt->f = f; opt->f_data = f_data; opt->pre = pre; *opt_f = -*opt_f; } return ret; } /*********************************************************************/ nlopt_result nlopt_optimize_limited(nlopt_opt opt, double *x, double *minf, int maxeval, double maxtime) { int save_maxeval; double save_maxtime; nlopt_result ret; if (!opt) return NLOPT_INVALID_ARGS; save_maxeval = nlopt_get_maxeval(opt); save_maxtime = nlopt_get_maxtime(opt); /* override opt limits if maxeval and/or maxtime are more stringent */ if (save_maxeval <= 0 || (maxeval > 0 && maxeval < save_maxeval)) nlopt_set_maxeval(opt, maxeval); if (save_maxtime <= 0 || (maxtime > 0 && maxtime < save_maxtime)) nlopt_set_maxtime(opt, maxtime); ret = nlopt_optimize(opt, x, minf); nlopt_set_maxeval(opt, save_maxeval); nlopt_set_maxtime(opt, save_maxtime); return ret; } /*********************************************************************/ nloptr/src/nlopt_src/autogen.sh0000644000176000001440000000170713263734242016423 0ustar ripleyusers#!/bin/sh configure_args="" configure="yes" while test $# -ge 1; do case $1 in --verbose) verbose=yes ;; --enable-*) configure_args="$configure_args $1" ;; --disable-*) configure_args="$configure_args $1" ;; --with-*) configure_args="$configure_args $1" ;; --without-*) configure_args="$configure_args $1" ;; --no-configure) configure="no" ;; *) echo "unknown argument $1"; exit 1 ;; esac shift done touch swig/nlopt.scm.in cp README.md README # paranoia: sometimes autoconf doesn't get things right the first time autoreconf --verbose --install --symlink --force autoreconf --verbose --install --symlink --force autoreconf --verbose --install --symlink --force if test "$configure" = "yes" then config=good # hackery so darcs_test still outputs config.log w/failed configure ./configure --enable-maintainer-mode $configure_args || config=bad if test x$verbose = xyes; 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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #ifndef CDIRECT_H #define CDIRECT_H #include "nlopt-util.h" #include "nlopt.h" #ifdef __cplusplus extern "C" { #endif /* __cplusplus */ extern nlopt_result cdirect_unscaled(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, double *x, double *minf, nlopt_stopping *stop, double magic_eps, int which_alg); extern nlopt_result cdirect(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, double *x, double *minf, nlopt_stopping *stop, double magic_eps, int which_alg); extern nlopt_result cdirect_hybrid(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, double *x, double *minf, nlopt_stopping *stop, nlopt_algorithm local_alg, int local_maxeval, int randomized_div); extern nlopt_result cdirect_hybrid_unscaled(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, double *x, double *minf, nlopt_stopping *stop, nlopt_algorithm local_alg, int local_maxeval, int randomized_div); /* internal routines and data structures: */ extern int cdirect_hyperrect_compare(double *a, double *b); typedef struct { nlopt_func f; void *f_data; double *x; const double *lb, *ub; } cdirect_uf_data; extern double cdirect_uf(unsigned n, const double *xu, double *grad, void *d_); #ifdef __cplusplus } /* extern "C" */ #endif /* __cplusplus */ #endif /* DIRECT_H */ nloptr/src/nlopt_src/cdirect/README0000644000176000001440000000225613263734242016722 0ustar ripleyusersFrom-scratch re-implementation of the DIRECT and DIRECT-L algorithms described in: D. R. Jones, C. D. Perttunen, and B. E. Stuckmann, "Lipschitzian optimization without the lipschitz constant," J. Optimization Theory and Applications, vol. 79, p. 157 (1993). J. M. Gablonsky and C. T. Kelley, "A locally-biased form of the DIRECT algorithm," J. Global Optimization 21 (1), p. 27-37 (2001). I re-implemented the algorithms for a couple of reasons. First, because I was interested in the algorithms and wanted to play with them by trying some variations (originally, because I wanted to experiment with a hybrid approach combining DIRECT with local search algorithms, see hybrid.c). Second, I wanted to remove some arbitrary restrictions in the original Fortran code, e.g. a fixed upper bound on the number of function evaluations. Third, because it was fun to code. As far as I can tell, my version converges in about the same number of iterations as Gablonsky's code (with occasional slight differences due to minor differences in how I break ties, etc.). The code in this directory is under the same MIT license as the rest of my code in NLopt (see ../COPYRIGHT). Steven G. Johnson nloptr/src/nlopt_src/cdirect/cdirect.c0000644000176000001440000004647613355070635017640 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include #include #include #include "nlopt-util.h" #include "nlopt.h" #include "cdirect.h" #include "redblack.h" #define MIN(a,b) ((a) < (b) ? (a) : (b)) #define MAX(a,b) ((a) > (b) ? (a) : (b)) /***************************************************************************/ /* basic data structure: * * a hyper-rectangle is stored as an array of length L = 2n+3, where [1] * is the value (f) of the function at the center, [0] is the "size" * measure (d) of the rectangle, [3..n+2] are the coordinates of the * center (c), [n+3..2n+2] are the widths of the sides (w), and [2] * is an "age" measure for tie-breaking purposes. * * we store the hyper-rectangles in a red-black tree, sorted by (d,f) * in lexographic order, to allow us to perform quick convex-hull * calculations (in the future, we might make this data structure * more sophisticated based on the dynamic convex-hull literature). * * n > 0 always, of course. */ /* parameters of the search algorithm and various information that needs to be passed around */ typedef struct { int n; /* dimension */ int L; /* size of each rectangle (2n+3) */ double magic_eps; /* Jones' epsilon parameter (1e-4 is recommended) */ int which_diam; /* which measure of hyper-rectangle diam to use: 0 = Jones, 1 = Gablonsky */ int which_div; /* which way to divide rects: 0: orig. Jones (divide all longest sides) 1: Gablonsky (cubes divide all, rects longest) 2: Jones Encyc. Opt.: pick random longest side */ int which_opt; /* which rects are considered "potentially optimal" 0: Jones (all pts on cvx hull, even equal pts) 1: Gablonsky DIRECT-L (pick one pt, if equal pts) 2: ~ 1, but pick points randomly if equal pts ... 2 seems to suck compared to just picking oldest pt */ const double *lb, *ub; nlopt_stopping *stop; /* stopping criteria */ nlopt_func f; void *f_data; double *work; /* workspace, of length >= 2*n */ int *iwork; /* workspace, length >= n */ double minf, *xmin; /* minimum so far */ /* red-black tree of hyperrects, sorted by (d,f,age) in lexographical order */ rb_tree rtree; int age; /* age for next new rect */ double **hull; /* array to store convex hull */ int hull_len; /* allocated length of hull array */ } params; /***************************************************************************/ /* Evaluate the "diameter" (d) of a rectangle of widths w[n] We round the result to single precision, which should be plenty for the use we put the diameter to (rect sorting), to allow our performance hack in convex_hull to work (in the Jones and Gablonsky DIRECT algorithms, all of the rects fall into a few diameter values, and we don't want rounding error to spoil this) */ static double rect_diameter(int n, const double *w, const params *p) { int i; if (p->which_diam == 0) { /* Jones measure */ double sum = 0; for (i = 0; i < n; ++i) sum += w[i] * w[i]; /* distance from center to a vertex */ return ((float) (sqrt(sum) * 0.5)); } else { /* Gablonsky measure */ double maxw = 0; for (i = 0; i < n; ++i) if (w[i] > maxw) maxw = w[i]; /* half-width of longest side */ return ((float) (maxw * 0.5)); } } #define ALLOC_RECT(rect, L) if (!(rect = (double*) malloc(sizeof(double)*(L)))) return NLOPT_OUT_OF_MEMORY static int sort_fv_compare(void *fv_, const void *a_, const void *b_) { const double *fv = (const double *) fv_; int a = *((const int *) a_), b = *((const int *) b_); double fa = MIN(fv[2*a], fv[2*a+1]); double fb = MIN(fv[2*b], fv[2*b+1]); if (fa < fb) return -1; else if (fa > fb) return +1; else return 0; } static void sort_fv(int n, double *fv, int *isort) { int i; for (i = 0; i < n; ++i) isort[i] = i; nlopt_qsort_r(isort, (unsigned) n, sizeof(int), fv, sort_fv_compare); } static double function_eval(const double *x, params *p) { double f = p->f(p->n, x, NULL, p->f_data); if (f < p->minf) { p->minf = f; memcpy(p->xmin, x, sizeof(double) * p->n); } p->stop->nevals++; return f; } #define FUNCTION_EVAL(fv,x,p,freeonerr) fv = function_eval(x, p); if (nlopt_stop_forced((p)->stop)) { free(freeonerr); return NLOPT_FORCED_STOP; } else if (p->minf < p->stop->minf_max) { free(freeonerr); return NLOPT_MINF_MAX_REACHED; } else if (nlopt_stop_evals((p)->stop)) { free(freeonerr); return NLOPT_MAXEVAL_REACHED; } else if (nlopt_stop_time((p)->stop)) { free(freeonerr); return NLOPT_MAXTIME_REACHED; } #define THIRD (0.3333333333333333333333) #define EQUAL_SIDE_TOL 5e-2 /* tolerance to equate side sizes */ /* divide rectangle idiv in the list p->rects */ static nlopt_result divide_rect(double *rdiv, params *p) { int i; const int n = p->n; const int L = p->L; double *c = rdiv + 3; /* center of rect to divide */ double *w = c + n; /* widths of rect to divide */ double wmax = w[0]; int imax = 0, nlongest = 0; rb_node *node; for (i = 1; i < n; ++i) if (w[i] > wmax) wmax = w[imax = i]; for (i = 0; i < n; ++i) if (wmax - w[i] <= wmax * EQUAL_SIDE_TOL) ++nlongest; if (p->which_div == 1 || (p->which_div == 0 && nlongest == n)) { /* trisect all longest sides, in increasing order of the average function value along that direction */ double *fv = p->work; int *isort = p->iwork; for (i = 0; i < n; ++i) { if (wmax - w[i] <= wmax * EQUAL_SIDE_TOL) { double csave = c[i]; c[i] = csave - w[i] * THIRD; FUNCTION_EVAL(fv[2*i], c, p, 0); c[i] = csave + w[i] * THIRD; FUNCTION_EVAL(fv[2*i+1], c, p, 0); c[i] = csave; } else { fv[2*i] = fv[2*i+1] = HUGE_VAL; } } sort_fv(n, fv, isort); if (!(node = rb_tree_find(&p->rtree, rdiv))) return NLOPT_FAILURE; for (i = 0; i < nlongest; ++i) { int k; w[isort[i]] *= THIRD; rdiv[0] = rect_diameter(n, w, p); rdiv[2] = p->age++; node = rb_tree_resort(&p->rtree, node); for (k = 0; k <= 1; ++k) { double *rnew; ALLOC_RECT(rnew, L); memcpy(rnew, rdiv, sizeof(double) * L); rnew[3 + isort[i]] += w[isort[i]] * (2*k-1); rnew[1] = fv[2*isort[i]+k]; rnew[2] = p->age++; if (!rb_tree_insert(&p->rtree, rnew)) { free(rnew); return NLOPT_OUT_OF_MEMORY; } } } } else { int k; if (nlongest > 1 && p->which_div == 2) { /* randomly choose longest side */ i = nlopt_iurand(nlongest); for (k = 0; k < n; ++k) if (wmax - w[k] <= wmax * EQUAL_SIDE_TOL) { if (!i) { i = k; break; } --i; } } else i = imax; /* trisect longest side */ if (!(node = rb_tree_find(&p->rtree, rdiv))) return NLOPT_FAILURE; w[i] *= THIRD; rdiv[0] = rect_diameter(n, w, p); rdiv[2] = p->age++; node = rb_tree_resort(&p->rtree, node); for (k = 0; k <= 1; ++k) { double *rnew; ALLOC_RECT(rnew, L); memcpy(rnew, rdiv, sizeof(double) * L); rnew[3 + i] += w[i] * (2*k-1); FUNCTION_EVAL(rnew[1], rnew + 3, p, rnew); rnew[2] = p->age++; if (!rb_tree_insert(&p->rtree, rnew)) { free(rnew); return NLOPT_OUT_OF_MEMORY; } } } return NLOPT_SUCCESS; } /***************************************************************************/ /* Convex hull algorithm, used later to find the potentially optimal points. What we really have in DIRECT is a "dynamic convex hull" problem, since we are dynamically adding/removing points and updating the hull, but I haven't implemented any of the fancy algorithms for this problem yet. */ /* Find the lower convex hull of a set of points (x,y) stored in a rb-tree of pointers to {x,y} arrays sorted in lexographic order by (x,y). Unlike standard convex hulls, we allow redundant points on the hull, and even allow duplicate points if allow_dups is nonzero. The return value is the number of points in the hull, with pointers stored in hull[i] (should be an array of length >= t->N). */ static int convex_hull(rb_tree *t, double **hull, int allow_dups) { int nhull = 0; double minslope; double xmin, xmax, yminmin, ymaxmin; rb_node *n, *nmax; /* Monotone chain algorithm [Andrew, 1979]. */ n = rb_tree_min(t); if (!n) return 0; nmax = rb_tree_max(t); xmin = n->k[0]; yminmin = n->k[1]; xmax = nmax->k[0]; if (allow_dups) do { /* include any duplicate points at (xmin,yminmin) */ hull[nhull++] = n->k; n = rb_tree_succ(n); } while (n && n->k[0] == xmin && n->k[1] == yminmin); else hull[nhull++] = n->k; if (xmin == xmax) return nhull; /* set nmax = min mode with x == xmax */ #if 0 while (nmax->k[0] == xmax) nmax = rb_tree_pred(nmax); /* non-NULL since xmin != xmax */ nmax = rb_tree_succ(nmax); #else /* performance hack (see also below) */ { double kshift[2]; kshift[0] = xmax * (1 - 1e-13); kshift[1] = -HUGE_VAL; nmax = rb_tree_find_gt(t, kshift); /* non-NULL since xmin != xmax */ } #endif ymaxmin = nmax->k[1]; minslope = (ymaxmin - yminmin) / (xmax - xmin); /* set n = first node with x != xmin */ #if 0 while (n->k[0] == xmin) n = rb_tree_succ(n); /* non-NULL since xmin != xmax */ #else /* performance hack (see also below) */ { double kshift[2]; kshift[0] = xmin * (1 + 1e-13); kshift[1] = -HUGE_VAL; n = rb_tree_find_gt(t, kshift); /* non-NULL since xmin != xmax */ } #endif for (; n != nmax; n = rb_tree_succ(n)) { double *k = n->k; if (k[1] > yminmin + (k[0] - xmin) * minslope) continue; /* performance hack: most of the points in DIRECT lie along vertical lines at a few x values, and we can exploit this */ if (nhull && k[0] == hull[nhull - 1][0]) { /* x == previous x */ if (k[1] > hull[nhull - 1][1]) { double kshift[2]; /* because of the round to float in rect_diameter, above, it shouldn't be possible for two diameters (x values) to have a fractional difference < 1e-13. Note that k[0] > 0 always in DIRECT */ kshift[0] = k[0] * (1 + 1e-13); kshift[1] = -HUGE_VAL; n = rb_tree_pred(rb_tree_find_gt(t, kshift)); continue; } else { /* equal y values, add to hull */ if (allow_dups) hull[nhull++] = k; continue; } } /* remove points until we are making a "left turn" to k */ while (nhull > 1) { double *t1 = hull[nhull - 1], *t2; /* because we allow equal points in our hull, we have to modify the standard convex-hull algorithm slightly: we need to look backwards in the hull list until we find a point t2 != t1 */ int it2 = nhull - 2; do { t2 = hull[it2--]; } while (it2 >= 0 && t2[0] == t1[0] && t2[1] == t1[1]); if (it2 < 0) break; /* cross product (t1-t2) x (k-t2) > 0 for a left turn: */ if ((t1[0]-t2[0]) * (k[1]-t2[1]) - (t1[1]-t2[1]) * (k[0]-t2[0]) >= 0) break; --nhull; } hull[nhull++] = k; } if (allow_dups) do { /* include any duplicate points at (xmax,ymaxmin) */ hull[nhull++] = nmax->k; nmax = rb_tree_succ(nmax); } while (nmax && nmax->k[0] == xmax && nmax->k[1] == ymaxmin); else hull[nhull++] = nmax->k; return nhull; } /***************************************************************************/ static int small(double *w, params *p) { int i; for (i = 0; i < p->n; ++i) if (w[i] > p->stop->xtol_abs[i] && w[i] > (p->ub[i] - p->lb[i]) * p->stop->xtol_rel) return 0; return 1; } static nlopt_result divide_good_rects(params *p) { const int n = p->n; double **hull; int nhull, i, xtol_reached = 1, divided_some = 0; double magic_eps = p->magic_eps; if (p->hull_len < p->rtree.N) { p->hull_len += p->rtree.N; p->hull = (double **) realloc(p->hull, sizeof(double*)*p->hull_len); if (!p->hull) return NLOPT_OUT_OF_MEMORY; } nhull = convex_hull(&p->rtree, hull = p->hull, p->which_opt != 1); divisions: for (i = 0; i < nhull; ++i) { double K1 = -HUGE_VAL, K2 = -HUGE_VAL, K; int im, ip; /* find unequal points before (im) and after (ip) to get slope */ for (im = i-1; im >= 0 && hull[im][0] == hull[i][0]; --im) ; for (ip = i+1; ip < nhull && hull[ip][0] == hull[i][0]; ++ip) ; if (im >= 0) K1 = (hull[i][1] - hull[im][1]) / (hull[i][0] - hull[im][0]); if (ip < nhull) K2 = (hull[i][1] - hull[ip][1]) / (hull[i][0] - hull[ip][0]); K = MAX(K1, K2); if (hull[i][1] - K * hull[i][0] <= p->minf - magic_eps * fabs(p->minf) || ip == nhull) { /* "potentially optimal" rectangle, so subdivide */ nlopt_result ret = divide_rect(hull[i], p); divided_some = 1; if (ret != NLOPT_SUCCESS) return ret; xtol_reached = xtol_reached && small(hull[i] + 3+n, p); } /* for the DIRECT-L variant, we only divide one rectangle out of all points with equal diameter and function values ... note that for p->which_opt == 1, i == ip-1 should be a no-op anyway, since we set allow_dups=0 in convex_hull above */ if (p->which_opt == 1) i = ip - 1; /* skip to next unequal point for next iteration */ else if (p->which_opt == 2) /* like DIRECT-L but randomized */ i += nlopt_iurand(ip - i); /* possibly do another equal pt */ } if (!divided_some) { if (magic_eps != 0) { magic_eps = 0; goto divisions; /* try again */ } else { /* WTF? divide largest rectangle with smallest f */ /* (note that this code actually gets called from time to time, and the heuristic here seems to work well, but I don't recall this situation being discussed in the references?) */ rb_node *max = rb_tree_max(&p->rtree); rb_node *pred = max; double wmax = max->k[0]; do { /* note: this loop is O(N) worst-case time */ max = pred; pred = rb_tree_pred(max); } while (pred && pred->k[0] == wmax); return divide_rect(max->k, p); } } return xtol_reached ? NLOPT_XTOL_REACHED : NLOPT_SUCCESS; } /***************************************************************************/ /* lexographic sort order (d,f,age) of hyper-rects, for red-black tree */ int cdirect_hyperrect_compare(double *a, double *b) { if (a[0] < b[0]) return -1; if (a[0] > b[0]) return +1; if (a[1] < b[1]) return -1; if (a[1] > b[1]) return +1; if (a[2] < b[2]) return -1; if (a[2] > b[2]) return +1; return (int) (a - b); /* tie-breaker, shouldn't be needed */ } /***************************************************************************/ nlopt_result cdirect_unscaled(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, double *x, double *minf, nlopt_stopping *stop, double magic_eps, int which_alg) { params p; int i; double *rnew; nlopt_result ret = NLOPT_OUT_OF_MEMORY; p.magic_eps = magic_eps; p.which_diam = which_alg % 3; p.which_div = (which_alg / 3) % 3; p.which_opt = (which_alg / (3*3)) % 3; p.lb = lb; p.ub = ub; p.stop = stop; p.n = n; p.L = 2*n+3; p.f = f; p.f_data = f_data; p.xmin = x; p.minf = HUGE_VAL; p.work = 0; p.iwork = 0; p.hull = 0; p.age = 0; rb_tree_init(&p.rtree, cdirect_hyperrect_compare); p.work = (double *) malloc(sizeof(double) * (2*n)); if (!p.work) goto done; p.iwork = (int *) malloc(sizeof(int) * n); if (!p.iwork) goto done; p.hull_len = 128; /* start with a reasonable number */ p.hull = (double **) malloc(sizeof(double *) * p.hull_len); if (!p.hull) goto done; if (!(rnew = (double *) malloc(sizeof(double) * p.L))) goto done; for (i = 0; i < n; ++i) { rnew[3+i] = 0.5 * (lb[i] + ub[i]); rnew[3+n+i] = ub[i] - lb[i]; } rnew[0] = rect_diameter(n, rnew+3+n, &p); rnew[1] = function_eval(rnew+3, &p); rnew[2] = p.age++; if (!rb_tree_insert(&p.rtree, rnew)) { free(rnew); goto done; } ret = divide_rect(rnew, &p); if (ret != NLOPT_SUCCESS) goto done; while (1) { double minf0 = p.minf; ret = divide_good_rects(&p); if (ret != NLOPT_SUCCESS) goto done; if (p.minf < minf0 && nlopt_stop_f(p.stop, p.minf, minf0)) { ret = NLOPT_FTOL_REACHED; goto done; } } done: rb_tree_destroy_with_keys(&p.rtree); free(p.hull); free(p.iwork); free(p.work); *minf = p.minf; return ret; } /* in the conventional DIRECT-type algorithm, we first rescale our coordinates to a unit hypercube ... we do this simply by wrapping cdirect() around cdirect_unscaled(). */ double cdirect_uf(unsigned n, const double *xu, double *grad, void *d_) { cdirect_uf_data *d = (cdirect_uf_data *) d_; double f; unsigned i; for (i = 0; i < n; ++i) d->x[i] = d->lb[i] + xu[i] * (d->ub[i] - d->lb[i]); f = d->f(n, d->x, grad, d->f_data); if (grad) for (i = 0; i < n; ++i) grad[i] *= d->ub[i] - d->lb[i]; return f; } nlopt_result cdirect(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, double *x, double *minf, nlopt_stopping *stop, double magic_eps, int which_alg) { cdirect_uf_data d; nlopt_result ret; const double *xtol_abs_save; int i; d.f = f; d.f_data = f_data; d.lb = lb; d.ub = ub; d.x = (double *) malloc(sizeof(double) * n*4); if (!d.x) return NLOPT_OUT_OF_MEMORY; for (i = 0; i < n; ++i) { x[i] = (x[i] - lb[i]) / (ub[i] - lb[i]); d.x[n+i] = 0; d.x[2*n+i] = 1; d.x[3*n+i] = stop->xtol_abs[i] / (ub[i] - lb[i]); } xtol_abs_save = stop->xtol_abs; stop->xtol_abs = d.x + 3*n; ret = cdirect_unscaled(n, cdirect_uf, &d, d.x+n, d.x+2*n, x, minf, stop, magic_eps, which_alg); stop->xtol_abs = xtol_abs_save; for (i = 0; i < n; ++i) x[i] = lb[i]+ x[i] * (ub[i] - lb[i]); free(d.x); return ret; } nloptr/src/nlopt_src/cdirect/hybrid.c0000644000176000001440000002354213355070635017471 0ustar ripleyusers/* Copyright (c) 2007-2014 Massachusetts Institute of Technology * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include #include #include #include "nlopt-util.h" #include "nlopt.h" #include "cdirect.h" #include "redblack.h" /* Hybrid algorithm, inspired by DIRECT, that uses another local * optimization algorithm within each rectangle, and then looks * in the largest remaining rectangle (breaking ties by minimum * function value and then by age. * * Each hyperrect is represented by an array of length 3*n+3 consisting * of (d, -f, -a, x, c, w), where d=diameter, f=f(x), a=age, x=local optimum * c=center, w=widths. */ typedef struct { int n; /* dimension */ int L; /* 3*n+3 */ const double *lb, *ub; nlopt_stopping *stop; /* stopping criteria */ nlopt_func f; void *f_data; double minf, *xmin; /* min so far */ rb_tree rtree; /* red-black tree of rects, sorted by (d,-f,-a) */ int age; /* age for next new rect */ double *work; /* workspace of length >= 2*n */ nlopt_algorithm local_alg; /* local search algorithm */ int local_maxeval; /* max # local iterations (0 if unlimited) */ int randomized_div; /* 1 to use randomized division algorithm */ } params; #define MIN(a,b) ((a) < (b) ? (a) : (b)) #define THIRD (0.3333333333333333333333) /* 1/3 */ /************************************************************************/ static double fcount(int n, const double *x, double *grad, void *p_) { params *p = (params *) p_; p->stop->nevals++; return p->f(n, x, grad, p->f_data); } static nlopt_result optimize_rect(double *r, params *p) { int i, n = p->n; double *lb = p->work, *ub = lb + n; double *x = r + 3, *c = x + n, *w = c + n; double t = nlopt_seconds(); double minf; nlopt_stopping *stop = p->stop; nlopt_result ret; if (stop->maxeval > 0 && stop->nevals >= stop->maxeval) return NLOPT_MAXEVAL_REACHED; if (stop->maxtime > 0 && t - stop->start >= stop->maxtime) return NLOPT_MAXTIME_REACHED; for (i = 0; i < n; ++i) { lb[i] = c[i] - 0.5 * w[i]; ub[i] = c[i] + 0.5 * w[i]; } ret = nlopt_minimize(p->local_alg, n, fcount, p, lb, ub, x, &minf, stop->minf_max, stop->ftol_rel, stop->ftol_abs, stop->xtol_rel, stop->xtol_abs, p->local_maxeval > 0 ? MIN(p->local_maxeval, stop->maxeval - stop->nevals) : stop->maxeval - stop->nevals, stop->maxtime - (t - stop->start)); r[1] = -minf; if (ret > 0) { if (minf < p->minf) { p->minf = minf; memcpy(p->xmin, x, sizeof(double) * n); if (ret == NLOPT_MINF_MAX_REACHED) return ret; } return NLOPT_SUCCESS; } return ret; } /* given a hyperrect r, randomize the starting guess within the middle third of the box (don't guess too close to edges) */ static void randomize_x(int n, double *r) { double *x = r + 3, *c = x + n, *w = c + n; int i; for (i = 0; i < n; ++i) x[i] = nlopt_urand(c[i] - w[i]*(0.5*THIRD), c[i] + w[i]*(0.5*THIRD)); } /************************************************************************/ static double longest(int n, const double *w) { double wmax = w[n-1]; for (n = n-2; n >= 0; n--) if (w[n] > wmax) wmax = w[n]; return wmax; } #define EQUAL_SIDE_TOL 5e-2 /* tolerance to equate side sizes */ static nlopt_result divide_largest(params *p) { int L = p->L; int n = p->n; rb_node *node = rb_tree_max(&p->rtree); /* just using it as a heap */ double minf_start = p->minf; double *r = node->k, *rnew = NULL; double *x = r + 3, *c = x + n, *w = c + n; const double *lb = p->lb, *ub = p->ub; int i, idiv; double wmax; nlopt_result ret; /* printf("rect:, %d, %g, %g, %g, %g\n", p->stop->nevals, c[0], c[1], w[0], w[1]); */ /* check xtol */ for (i = 0; i < n; ++i) if (w[i] > p->stop->xtol_rel * (ub[i] - lb[i]) && w[i] > p->stop->xtol_abs[i]) break; if (i == n) return NLOPT_XTOL_REACHED; if (p->randomized_div) { /* randomly pick among ~largest sides */ int nlongest = 0; wmax = longest(n, w); for (i = 0; i < n; ++i) if (wmax - w[i] < EQUAL_SIDE_TOL * wmax) ++nlongest; i = 1 + nlopt_iurand(nlongest); for (idiv = 0; idiv < n; ++idiv) { if (wmax - w[idiv] < EQUAL_SIDE_TOL * wmax) --i; if (!i) break; } } else { /* just pick first largest side */ wmax = w[idiv = 0]; for (i = 1; i < n; ++i) if (w[i] > wmax) wmax = w[idiv = i]; } if (fabs(x[idiv] - c[idiv]) > (0.5 * THIRD) * w[idiv]) { /* bisect */ double deltac = (x[idiv] > c[idiv] ? 0.25 : -0.25) * w[idiv]; w[idiv] *= 0.5; c[idiv] += deltac; r[0] = longest(n, w); /* new diameter */ /* r[1] unchanged since still contains local optimum x */ r[2] = p->age--; node = rb_tree_resort(&p->rtree, node); rnew = (double *) malloc(sizeof(double) * L); if (!rnew) return NLOPT_OUT_OF_MEMORY; memcpy(rnew, r, sizeof(double) * L); rnew[2] = p->age--; rnew[3+n+idiv] -= deltac*2; if (p->randomized_div) randomize_x(n, rnew); else memcpy(rnew+3, rnew+3+n, sizeof(double) * n); /* x = c */ ret = optimize_rect(rnew, p); if (ret != NLOPT_SUCCESS) { free(rnew); return ret; } if (!rb_tree_insert(&p->rtree, rnew)) { free(rnew); return NLOPT_OUT_OF_MEMORY; } } else { /* trisect */ w[idiv] *= THIRD; r[0] = longest(n, w); /* r[1] unchanged since still contains local optimum x */ r[2] = p->age--; node = rb_tree_resort(&p->rtree, node); for (i = -1; i <= +1; i += 2) { rnew = (double *) malloc(sizeof(double) * L); if (!rnew) return NLOPT_OUT_OF_MEMORY; memcpy(rnew, r, sizeof(double) * L); rnew[2] = p->age--; rnew[3+n+idiv] += w[i] * i; if (p->randomized_div) randomize_x(n, rnew); else memcpy(rnew+3, rnew+3+n, sizeof(double) * n); /* x = c */ ret = optimize_rect(rnew, p); if (ret != NLOPT_SUCCESS) { free(rnew); return ret; } if (!rb_tree_insert(&p->rtree, rnew)) { free(rnew); return NLOPT_OUT_OF_MEMORY; } } } if (p->minf < minf_start && nlopt_stop_f(p->stop, p->minf, minf_start)) return NLOPT_FTOL_REACHED; return NLOPT_SUCCESS; } /************************************************************************/ nlopt_result cdirect_hybrid_unscaled(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, double *x, double *minf, nlopt_stopping *stop, nlopt_algorithm local_alg, int local_maxeval, int randomized_div) { params p; int i; double *rnew; nlopt_result ret = NLOPT_OUT_OF_MEMORY; p.n = n; p.L = 3*n+3; p.lb = lb; p.ub = ub; p.stop = stop; p.f = f; p.f_data = f_data; p.minf = HUGE_VAL; p.xmin = x; p.age = 0; p.work = 0; p.local_alg = local_alg; p.local_maxeval = local_maxeval; p.randomized_div = randomized_div; rb_tree_init(&p.rtree, cdirect_hyperrect_compare); p.work = (double *) malloc(sizeof(double) * (2*n)); if (!p.work) goto done; if (!(rnew = (double *) malloc(sizeof(double) * p.L))) goto done; for (i = 0; i < n; ++i) { rnew[3+i] = rnew[3+n+i] = 0.5 * (lb[i] + ub[i]); rnew[3+2*n+i] = ub[i] - lb[i]; } rnew[0] = longest(n, rnew+2*n); rnew[2] = p.age--; ret = optimize_rect(rnew, &p); if (ret != NLOPT_SUCCESS) { free(rnew); goto done; } if (!rb_tree_insert(&p.rtree, rnew)) { free(rnew); goto done; } do { ret = divide_largest(&p); } while (ret == NLOPT_SUCCESS); done: rb_tree_destroy_with_keys(&p.rtree); free(p.work); *minf = p.minf; return ret; } /* rescaled to unit hypercube so that all x[i] are weighted equally */ nlopt_result cdirect_hybrid(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, double *x, double *minf, nlopt_stopping *stop, nlopt_algorithm local_alg, int local_maxeval, int randomized_div) { cdirect_uf_data d; nlopt_result ret; const double *xtol_abs_save; int i; d.f = f; d.f_data = f_data; d.lb = lb; d.ub = ub; d.x = (double *) malloc(sizeof(double) * n*4); if (!d.x) return NLOPT_OUT_OF_MEMORY; for (i = 0; i < n; ++i) { x[i] = (x[i] - lb[i]) / (ub[i] - lb[i]); d.x[n+i] = 0; d.x[2*n+i] = 1; d.x[3*n+i] = stop->xtol_abs[i] / (ub[i] - lb[i]); } xtol_abs_save = stop->xtol_abs; stop->xtol_abs = d.x + 3*n; ret = cdirect_hybrid_unscaled(n, cdirect_uf, &d, d.x+n, d.x+2*n, x, minf, stop, local_alg, local_maxeval, randomized_div); stop->xtol_abs = xtol_abs_save; for (i = 0; i < n; ++i) x[i] = lb[i]+ x[i] * (ub[i] - lb[i]); free(d.x); return ret; } nloptr/src/nlopt_src/luksan/0000755000176000001440000000000013263734242015715 5ustar ripleyusersnloptr/src/nlopt_src/luksan/plip.c0000644000176000001440000003650313355070635017035 0ustar ripleyusers#include #include #include #include #include "luksan.h" #define MAX2(a,b) ((a) > (b) ? (a) : (b)) #define MIN2(a,b) ((a) < (b) ? (a) : (b)) /* *********************************************************************** */ /* SUBROUTINE PLIP ALL SYSTEMS 01/09/22 */ /* PURPOSE : */ /* GENERAL SUBROUTINE FOR LARGE-SCALE BOX CONSTRAINED MINIMIZATION THAT */ /* USE THE SHIFTED LIMITED MEMORY VARIABLE METRIC METHOD BASED ON THE */ /* PRODUCT FORM UPDATES. */ /* PARAMETERS : */ /* II NF NUMBER OF VARIABLES. */ /* II NB CHOICE OF SIMPLE BOUNDS. NB=0-SIMPLE BOUNDS SUPPRESSED. */ /* NB>0-SIMPLE BOUNDS ACCEPTED. */ /* RI X(NF) VECTOR OF VARIABLES. */ /* II IX(NF) VECTOR CONTAINING TYPES OF BOUNDS. IX(I)=0-VARIABLE */ /* X(I) IS UNBOUNDED. IX(I)=1-LOVER BOUND XL(I).LE.X(I). */ /* IX(I)=2-UPPER BOUND X(I).LE.XU(I). IX(I)=3-TWO SIDE BOUND */ /* XL(I).LE.X(I).LE.XU(I). IX(I)=5-VARIABLE X(I) IS FIXED. */ /* RI XL(NF) VECTOR CONTAINING LOWER BOUNDS FOR VARIABLES. */ /* RI XU(NF) VECTOR CONTAINING UPPER BOUNDS FOR VARIABLES. */ /* RA GF(NF) GRADIENT OF THE OBJECTIVE FUNCTION. */ /* RO S(NF) DIRECTION VECTOR. */ /* RU XO(NF) VECTORS OF VARIABLES DIFFERENCE. */ /* RI GO(NF) GRADIENTS DIFFERENCE. */ /* RA SO(NF) AUXILIARY VECTOR. */ /* RA XM(NF*MF) AUXILIARY VECTOR. */ /* RA XR(MF) AUXILIARY VECTOR. */ /* RA GR(MF) AUXILIARY VECTOR. */ /* RI XMAX MAXIMUM STEPSIZE. */ /* RI TOLX TOLERANCE FOR CHANGE OF VARIABLES. */ /* RI TOLF TOLERANCE FOR CHANGE OF FUNCTION VALUES. */ /* RI TOLB TOLERANCE FOR THE FUNCTION VALUE. */ /* RI TOLG TOLERANCE FOR THE GRADIENT NORM. */ /* RI MINF_EST ESTIMATION OF THE MINIMUM FUNCTION VALUE. */ /* RO GMAX MAXIMUM PARTIAL DERIVATIVE. */ /* RO F VALUE OF THE OBJECTIVE FUNCTION. */ /* II MIT MAXIMUM NUMBER OF ITERATIONS. */ /* II MFV MAXIMUM NUMBER OF FUNCTION EVALUATIONS. */ /* II IEST ESTIMATION INDICATOR. IEST=0-MINIMUM IS NOT ESTIMATED. */ /* IEST=1-MINIMUM IS ESTIMATED BY THE VALUE MINF_EST. */ /* II MET METHOD USED. MET=1-RANK-ONE METHOD. MET=2-RANK-TWO */ /* METHOD. */ /* II MF NUMBER OF LIMITED MEMORY STEPS. */ /* IO ITERM VARIABLE THAT INDICATES THE CAUSE OF TERMINATION. */ /* ITERM=1-IF ABS(X-XO) WAS LESS THAN OR EQUAL TO TOLX IN */ /* MTESX (USUALLY TWO) SUBSEQUEBT ITERATIONS. */ /* ITERM=2-IF ABS(F-FO) WAS LESS THAN OR EQUAL TO TOLF IN */ /* MTESF (USUALLY TWO) SUBSEQUEBT ITERATIONS. */ /* ITERM=3-IF F IS LESS THAN OR EQUAL TO TOLB. */ /* ITERM=4-IF GMAX IS LESS THAN OR EQUAL TO TOLG. */ /* ITERM=6-IF THE TERMINATION CRITERION WAS NOT SATISFIED, */ /* BUT THE SOLUTION OBTAINED IS PROBABLY ACCEPTABLE. */ /* ITERM=11-IF NIT EXCEEDED MIT. ITERM=12-IF NFV EXCEEDED MFV. */ /* ITERM=13-IF NFG EXCEEDED MFG. ITERM<0-IF THE METHOD FAILED. */ /* VARIABLES IN COMMON /STAT/ (STATISTICS) : */ /* IO NRES NUMBER OF RESTARTS. */ /* IO NDEC NUMBER OF MATRIX DECOMPOSITION. */ /* IO NIN NUMBER OF INNER ITERATIONS. */ /* IO NIT NUMBER OF ITERATIONS. */ /* IO NFV NUMBER OF FUNCTION EVALUATIONS. */ /* IO NFG NUMBER OF GRADIENT EVALUATIONS. */ /* IO NFH NUMBER OF HESSIAN EVALUATIONS. */ /* SUBPROGRAMS USED : */ /* S PCBS04 ELIMINATION OF BOX CONSTRAINT VIOLATIONS. */ /* S PS1L01 STEPSIZE SELECTION USING LINE SEARCH. */ /* S PULSP3 SHIFTED VARIABLE METRIC UPDATE. */ /* S PULVP3 SHIFTED LIMITED-MEMORY VARIABLE METRIC UPDATE. */ /* S PYADC0 ADDITION OF A BOX CONSTRAINT. */ /* S PYFUT1 TEST ON TERMINATION. */ /* S PYRMC0 DELETION OF A BOX CONSTRAINT. */ /* S PYTRCD COMPUTATION OF PROJECTED DIFFERENCES FOR THE VARIABLE METRIC */ /* UPDATE. */ /* S PYTRCG COMPUTATION OF THE PROJECTED GRADIENT. */ /* S PYTRCS COMPUTATION OF THE PROJECTED DIRECTION VECTOR. */ /* S MXDRMM MULTIPLICATION OF A ROWWISE STORED DENSE RECTANGULAR */ /* MATRIX A BY A VECTOR X. */ /* S MXDCMD MULTIPLICATION OF A COLUMNWISE STORED DENSE RECTANGULAR */ /* MATRIX A BY A VECTOR X AND ADDITION OF THE SCALED VECTOR */ /* ALF*Y. */ /* S MXUCOP COPYING OF A VECTOR. */ /* S MXUDIR VECTOR AUGMENTED BY THE SCALED VECTOR. */ /* RF MXUDOT DOT PRODUCT OF TWO VECTORS. */ /* S MXUNEG COPYING OF A VECTOR WITH CHANGE OF THE SIGN. */ /* S MXUZER VECTOR ELEMENTS CORRESPONDING TO ACTIVE BOUNDS ARE SET */ /* TO ZERO. */ /* S MXVCOP COPYING OF A VECTOR. */ /* EXTERNAL SUBROUTINES : */ /* SE OBJ COMPUTATION OF THE VALUE OF THE OBJECTIVE FUNCTION. */ /* CALLING SEQUENCE: CALL OBJ(NF,X,FF) WHERE NF IS THE NUMBER */ /* OF VARIABLES, X(NF) IS THE VECTOR OF VARIABLES AND FF IS */ /* THE VALUE OF THE OBJECTIVE FUNCTION. */ /* SE DOBJ COMPUTATION OF THE GRADIENT OF THE OBJECTIVE FUNCTION. */ /* CALLING SEQUENCE: CALL DOBJ(NF,X,GF) WHERE NF IS THE NUMBER */ /* OF VARIABLES, X(NF) IS THE VECTOR OF VARIABLES AND GF(NF) */ /* IS THE GRADIENT OF THE OBJECTIVE FUNCTION. */ /* -- OBJ and DOBJ are replaced by a single function, objgrad, in NLopt */ /* METHOD : */ /* HYBRID METHOD WITH SPARSE MARWIL UPDATES FOR SPARSE LEAST SQUARES */ /* PROBLEMS. */ static void plip_(int *nf, int *nb, double *x, int * ix, double *xl, double *xu, double *gf, double *s, double *xo, double *go, double *so, double *xm, double *xr, double *gr, double *xmax, double *tolx, double *tolf, double *tolb, double *tolg, nlopt_stopping *stop, double * minf_est, double *gmax, double *f, int *mit, int *mfv, int *iest, int *met, int *mf, int *iterm, stat_common *stat_1, nlopt_func objgrad, void *objgrad_data) { /* System generated locals */ int i__1; double d__1, d__2; /* Builtin functions */ /* Local variables */ int i__, n; double p, r__; int kd, ld; double fo, fp; int nn; double po, pp, ro, rp; int kbf, mec, mfg; double par; int mes, kit; double alf1, alf2, eta0, eta9, par1, par2; int mes1, mes2, mes3, met3; double eps8, eps9; int meta, mred, nred, iold; double maxf, dmax__; int xstop = 0; int inew; double told; int ites; double rmin, rmax, umax, tolp, tols; int isys; int ires1, ires2; int iterd, iterh, mtesf, ntesf; double gnorm; int iters, irest, inits, kters, maxst; double snorm; int mtesx, ntesx; ps1l01_state state; /* INITIATION */ /* Parameter adjustments */ --gr; --xr; --xm; --so; --go; --xo; --s; --gf; --xu; --xl; --ix; --x; /* Function Body */ kbf = 0; if (*nb > 0) { kbf = 2; } stat_1->nres = 0; stat_1->ndec = 0; stat_1->nin = 0; stat_1->nit = 0; stat_1->nfg = 0; stat_1->nfh = 0; isys = 0; ites = 1; mtesx = 2; mtesf = 2; inits = 2; *iterm = 0; iterd = 0; iters = 2; iterh = 0; kters = 3; irest = 0; ires1 = 999; ires2 = 0; mred = 10; meta = 1; met3 = 4; mec = 4; mes = 4; mes1 = 2; mes2 = 2; mes3 = 2; eta0 = 1e-15; eta9 = 1e120; eps8 = 1.; eps9 = 1e-8; alf1 = 1e-10; alf2 = 1e10; rmax = eta9; dmax__ = eta9; maxf = 1e20; if (*iest <= 0) { *minf_est = -HUGE_VAL; /* changed from -1e60 by SGJ */ } if (*iest > 0) { *iest = 1; } if (*xmax <= 0.) { *xmax = 1e16; } if (*tolx <= 0.) { *tolx = 1e-16; } if (*tolf <= 0.) { *tolf = 1e-14; } if (*tolg <= 0.) { *tolg = 1e-8; /* SGJ: was 1e-6, but this sometimes stops too soon */ } #if 0 /* removed by SGJ: this check prevented us from using minf_max <= 0, which doesn't make sense. Instead, if you don't want to have a lower limit, you should set minf_max = -HUGE_VAL */ if (*tolb <= 0.) { *tolb = *minf_est + 1e-16; } #endif told = 1e-4; tols = 1e-4; tolp = .9; if (*met <= 0) { *met = 2; } /* changed by SGJ: default is no limit (INT_MAX) on # iterations/fevals */ if (*mit <= 0) { *mit = INT_MAX; } if (*mfv <= 0) { *mfv = INT_MAX; } mfg = *mfv; kd = 1; ld = -1; kit = -(ires1 * *nf + ires2); fo = *minf_est; /* INITIAL OPERATIONS WITH SIMPLE BOUNDS */ if (kbf > 0) { i__1 = *nf; for (i__ = 1; i__ <= i__1; ++i__) { if ((ix[i__] == 3 || ix[i__] == 4) && xu[i__] <= xl[i__]) { xu[i__] = xl[i__]; ix[i__] = 5; } else if (ix[i__] == 5 || ix[i__] == 6) { xl[i__] = x[i__]; xu[i__] = x[i__]; ix[i__] = 5; } /* L2: */ } luksan_pcbs04__(nf, &x[1], &ix[1], &xl[1], &xu[1], &eps9, &kbf); luksan_pyadc0__(nf, &n, &x[1], &ix[1], &xl[1], &xu[1], &inew); } if (*iterm != 0) { goto L11190; } *f = objgrad(*nf, &x[1], &gf[1], objgrad_data); ++stop->nevals; ++stat_1->nfg; if (nlopt_stop_time(stop)) { *iterm = 100; goto L11190; } L11120: luksan_pytrcg__(nf, nf, &ix[1], &gf[1], &umax, gmax, &kbf, &iold); luksan_pyfut1__(nf, f, &fo, &umax, gmax, xstop, stop, tolg, &kd, &stat_1->nit, &kit, mit, &stat_1->nfg, &mfg, &ntesx, &mtesx, &ntesf, &mtesf, &ites, &ires1, &ires2, &irest, & iters, iterm); if (*iterm != 0) { goto L11190; } if (nlopt_stop_time(stop)) { *iterm = 100; goto L11190; } if (kbf > 0 && rmax > 0.) { luksan_pyrmc0__(nf, &n, &ix[1], &gf[1], &eps8, &umax, gmax, &rmax, & iold, &irest); } L11130: if (irest > 0) { nn = 0; par = 1.; ld = MIN2(ld,1); if (kit < stat_1->nit) { ++stat_1->nres; kit = stat_1->nit; } else { *iterm = -10; if (iters < 0) { *iterm = iters - 5; } } } if (*iterm != 0) { goto L11190; } if (nlopt_stop_time(stop)) { *iterm = 100; goto L11190; } /* DIRECTION DETERMINATION */ gnorm = sqrt(luksan_mxudot__(nf, &gf[1], &gf[1], &ix[1], &kbf)); /* NEWTON LIKE STEP */ luksan_mxuneg__(nf, &gf[1], &s[1], &ix[1], &kbf); luksan_mxdrmm__(nf, &nn, &xm[1], &s[1], &gr[1]); luksan_mxdcmd__(nf, &nn, &xm[1], &gr[1], &par, &s[1], &s[1]); luksan_mxuzer__(nf, &s[1], &ix[1], &kbf); iterd = 1; snorm = sqrt(luksan_mxudot__(nf, &s[1], &s[1], &ix[1], &kbf)); /* TEST ON DESCENT DIRECTION AND PREPARATION OF LINE SEARCH */ if (kd > 0) { p = luksan_mxudot__(nf, &gf[1], &s[1], &ix[1], &kbf); } if (iterd < 0) { *iterm = iterd; } else { /* TEST ON DESCENT DIRECTION */ if (snorm <= 0.) { irest = MAX2(irest,1); } else if (p + told * gnorm * snorm <= 0.) { irest = 0; } else { /* UNIFORM DESCENT CRITERION */ irest = MAX2(irest,1); } if (irest == 0) { /* PREPARATION OF LINE SEARCH */ nred = 0; rmin = alf1 * gnorm / snorm; /* Computing MIN */ d__1 = alf2 * gnorm / snorm, d__2 = *xmax / snorm; rmax = MIN2(d__1,d__2); } } if (*iterm != 0) { goto L11190; } if (nlopt_stop_time(stop)) { *iterm = 100; goto L11190; } if (irest != 0) { goto L11130; } luksan_pytrcs__(nf, &x[1], &ix[1], &xo[1], &xl[1], &xu[1], &gf[1], &go[1], &s[1], &ro, &fp, &fo, f, &po, &p, &rmax, &eta9, &kbf); if (rmax == 0.) { goto L11175; } L11170: luksan_ps1l01__(&r__, &rp, f, &fo, &fp, &p, &po, &pp, minf_est, &maxf, &rmin, &rmax, &tols, &tolp, &par1, &par2, &kd, &ld, &stat_1->nit, &kit, & nred, &mred, &maxst, iest, &inits, &iters, &kters, &mes, &isys, &state); if (isys == 0) { goto L11174; } luksan_mxudir__(nf, &r__, &s[1], &xo[1], &x[1], &ix[1], &kbf); luksan_pcbs04__(nf, &x[1], &ix[1], &xl[1], &xu[1], &eps9, &kbf); *f = objgrad(*nf, &x[1], &gf[1], objgrad_data); ++stop->nevals; ++stat_1->nfg; p = luksan_mxudot__(nf, &gf[1], &s[1], &ix[1], &kbf); goto L11170; L11174: if (iters <= 0) { r__ = 0.; *f = fo; p = po; luksan_mxvcop__(nf, &xo[1], &x[1]); luksan_mxvcop__(nf, &go[1], &gf[1]); irest = MAX2(irest,1); ld = kd; goto L11130; } luksan_mxuneg__(nf, &go[1], &s[1], &ix[1], &kbf); luksan_pytrcd__(nf, &x[1], &ix[1], &xo[1], &gf[1], &go[1], &r__, f, &fo, & p, &po, &dmax__, &kbf, &kd, &ld, &iters); xstop = nlopt_stop_dx(stop, &x[1], &xo[1]); luksan_mxucop__(nf, &gf[1], &so[1], &ix[1], &kbf); if (nn < *mf) { luksan_pulsp3__(nf, &nn, mf, &xm[1], &gr[1], &xo[1], &go[1], &r__, & po, &par, &iterh, &met3); } else { luksan_pulvp3__(nf, &nn, &xm[1], &xr[1], &gr[1], &s[1], &so[1], &xo[1] , &go[1], &r__, &po, &par, &iterh, &mec, &met3, met); } L11175: if (iterh != 0) { irest = MAX2(irest,1); } if (kbf > 0) { luksan_pyadc0__(nf, &n, &x[1], &ix[1], &xl[1], &xu[1], &inew); } goto L11120; L11190: return; } /* plip_ */ /* NLopt wrapper around plip_, handling dynamic allocation etc. */ nlopt_result luksan_plip(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, nlopt_stopping *stop, int mf, /* subspace dimension (0 for default) */ int method) /* 1 or 2, see below */ { int i, *ix, nb = 1; double *work, *xl, *xu, *gf, *s, *xo, *go, *so, *xm, *xr, *gr; double gmax, minf_est; double xmax = 0; /* no maximum */ double tolg = 0; /* default gradient tolerance */ int iest = 0; /* we have no estimate of min function value */ int mit = 0; /* default no limit on #iterations */ int mfv = stop->maxeval; stat_common stat; int iterm; ix = (int*) malloc(sizeof(int) * n); if (!ix) return NLOPT_OUT_OF_MEMORY; if (mf <= 0) { mf = MAX2(MEMAVAIL/n, 10); if (stop->maxeval && stop->maxeval <= mf) mf = MAX2(stop->maxeval, 1); } retry_alloc: work = (double*) malloc(sizeof(double) * (n * 7 + MAX2(n,n*mf) + MAX2(n,mf)*2)); if (!work) { if (mf > 0) { mf = 0; /* allocate minimal memory */ goto retry_alloc; } free(ix); return NLOPT_OUT_OF_MEMORY; } xl = work; xu = xl + n; gf = xu + n; s = gf + n; xo = s + n; go = xo + n; so = go + n; xm = so + n; xr = xm + MAX2(n*mf,n); gr = xr + MAX2(n,mf); for (i = 0; i < n; ++i) { int lbu = lb[i] <= -0.99 * HUGE_VAL; /* lb unbounded */ int ubu = ub[i] >= 0.99 * HUGE_VAL; /* ub unbounded */ ix[i] = lbu ? (ubu ? 0 : 2) : (ubu ? 1 : (lb[i] == ub[i] ? 5 : 3)); xl[i] = lb[i]; xu[i] = ub[i]; } /* ? xo does not seem to be initialized in the original Fortran code, but it is used upon input to plip if mf > 0 ... perhaps ALLOCATE initializes arrays to zero by default? */ memset(xo, 0, sizeof(double) * MAX2(n,n*mf)); plip_(&n, &nb, x, ix, xl, xu, gf, s, xo, go, so, xm, xr, gr, &xmax, /* fixme: pass tol_rel and tol_abs and use NLopt check */ &stop->xtol_rel, &stop->ftol_rel, &stop->minf_max, &tolg, stop, &minf_est, &gmax, minf, &mit, &mfv, &iest, &method, /* 1 == rank-one method VAR1, 2 == rank-two method VAR2 */ &mf, &iterm, &stat, f, f_data); free(work); free(ix); switch (iterm) { case 1: return NLOPT_XTOL_REACHED; case 2: return NLOPT_FTOL_REACHED; case 3: return NLOPT_MINF_MAX_REACHED; case 4: return NLOPT_SUCCESS; /* gradient tolerance reached */ case 6: return NLOPT_SUCCESS; case 12: case 13: return NLOPT_MAXEVAL_REACHED; case 100: return NLOPT_MAXTIME_REACHED; case -999: return NLOPT_FORCED_STOP; default: return NLOPT_FAILURE; } } nloptr/src/nlopt_src/luksan/COPYRIGHT0000644000176000001440000000376613263734242017224 0ustar ripleyusers--------------------------------------------------------------------------- Copyright: Subroutines PBUN, PNEW, PVAR, PSEN, Copyright ACM, 2001. The original versions were published in Transactions on Mathematical Software, Vol.27, 2001, pp.193-213. Here are the author's modifications. They are posted here by permission of ACM for your personal use. Not for redistribution. Subroutines PLIP, PSEN, Copyright Jan Vlcek, 2007. The remaining subroutines, Copyright Ladislav Luksan, 2007. Many of sparse matrix modules were prepared by Miroslav Tuma. License: This library (with exception of PBUN, PNEW, PVAR, PSEN) is a free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version (see http://www.gnu.org/copyleft/gpl.html). This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. Permission is hereby granted to use or copy this program under the terms of the GNU LGPL, provided that the Copyright, this License, and the Availability of the original version is retained on all copies. User documentation of any code that uses this code or any modified version of this code must cite the Copyright, this License, the Availability note, and "Used by permission." Permission to modify the code and to distribute modified code is granted, provided the Copyright, this License, and the Availability note are retained, and a notice that the code was modified is included. Availability: http://www.cs.cas.cz/~luksan/subroutines.html Acknowledgements: This work was supported by the Grant Agency of the Czech Academy of Sciences, under grant IAA1030405. nloptr/src/nlopt_src/luksan/pnet.c0000644000176000001440000004634613355070635017045 0ustar ripleyusers#include #include #include #include #include "luksan.h" #define MAX2(a,b) ((a) > (b) ? (a) : (b)) #define MIN2(a,b) ((a) < (b) ? (a) : (b)) /* Table of constant values */ static double c_b7 = 0.; /* *********************************************************************** */ /* SUBROUTINE PNET ALL SYSTEMS 01/09/22 */ /* PURPOSE : */ /* GENERAL SUBROUTINE FOR LARGE-SCALE BOX CONSTRAINED MINIMIZATION THAT */ /* USE THE LIMITED MEMORY VARIABLE METRIC METHOD BASED ON THE STRANG */ /* RECURRENCES. */ /* PARAMETERS : */ /* II NF NUMBER OF VARIABLES. */ /* II NB CHOICE OF SIMPLE BOUNDS. NB=0-SIMPLE BOUNDS SUPPRESSED. */ /* NB>0-SIMPLE BOUNDS ACCEPTED. */ /* RI X(NF) VECTOR OF VARIABLES. */ /* II IX(NF) VECTOR CONTAINING TYPES OF BOUNDS. IX(I)=0-VARIABLE */ /* X(I) IS UNBOUNDED. IX(I)=1-LOVER BOUND XL(I).LE.X(I). */ /* IX(I)=2-UPPER BOUND X(I).LE.XU(I). IX(I)=3-TWO SIDE BOUND */ /* XL(I).LE.X(I).LE.XU(I). IX(I)=5-VARIABLE X(I) IS FIXED. */ /* RI XL(NF) VECTOR CONTAINING LOWER BOUNDS FOR VARIABLES. */ /* RI XU(NF) VECTOR CONTAINING UPPER BOUNDS FOR VARIABLES. */ /* RO GF(NF) GRADIENT OF THE OBJECTIVE FUNCTION. */ /* RA GN(NF) OLD GRADIENT OF THE OBJECTIVE FUNCTION. */ /* RO S(NF) DIRECTION VECTOR. */ /* RA XO(NF) ARRAY CONTAINING INCREMENTS OF VARIABLES. */ /* RA GO(NF) ARRAY CONTAINING INCREMENTS OF GRADIENTS. */ /* RA XS(NF) AUXILIARY VECTOR. */ /* RA GS(NF) AUXILIARY VECTOR. */ /* RA XM(NF*MF) ARRAY CONTAINING INCREMENTS OF VARIABLES. */ /* RA GM(NF*MF) ARRAY CONTAINING INCREMENTS OF GRADIENTS. */ /* RA U1(MF) AUXILIARY VECTOR. */ /* RA U2(MF) AUXILIARY VECTOR. */ /* RI XMAX MAXIMUM STEPSIZE. */ /* RI TOLX TOLERANCE FOR CHANGE OF VARIABLES. */ /* RI TOLF TOLERANCE FOR CHANGE OF FUNCTION VALUES. */ /* RI TOLB TOLERANCE FOR THE FUNCTION VALUE. */ /* RI TOLG TOLERANCE FOR THE GRADIENT NORM. */ /* RI MINF_EST ESTIMATION OF THE MINIMUM FUNCTION VALUE. */ /* RO GMAX MAXIMUM PARTIAL DERIVATIVE. */ /* RO F VALUE OF THE OBJECTIVE FUNCTION. */ /* II MIT MAXIMUM NUMBER OF ITERATIONS. */ /* II MFV MAXIMUM NUMBER OF FUNCTION EVALUATIONS. */ /* II MFG MAXIMUM NUMBER OF GRADIENT EVALUATIONS. */ /* II IEST ESTIMATION INDICATOR. IEST=0-MINIMUM IS NOT ESTIMATED. */ /* IEST=1-MINIMUM IS ESTIMATED BY THE VALUE MINF_EST. */ /* II MOS1 CHOICE OF RESTARTS AFTER A CONSTRAINT CHANGE. */ /* MOS1=1-RESTARTS ARE SUPPRESSED. MOS1=2-RESTARTS WITH */ /* STEEPEST DESCENT DIRECTIONS ARE USED. */ /* II MOS1 CHOICE OF DIRECTION VECTORS AFTER RESTARTS. MOS1=1-THE */ /* NEWTON DIRECTIONS ARE USED. MOS1=2-THE STEEPEST DESCENT */ /* DIRECTIONS ARE USED. */ /* II MOS2 CHOICE OF PRECONDITIONING STRATEGY. MOS2=1-PRECONDITIONING */ /* IS NOT USED. MOS2=2-PRECONDITIONING BY THE LIMITED MEMORY */ /* BFGS METHOD IS USED. */ /* II MF THE NUMBER OF LIMITED-MEMORY VARIABLE METRIC UPDATES */ /* IN EACH ITERATION (THEY USE 2*MF STORED VECTORS). */ /* IO ITERM VARIABLE THAT INDICATES THE CAUSE OF TERMINATION. */ /* ITERM=1-IF ABS(X-XO) WAS LESS THAN OR EQUAL TO TOLX IN */ /* MTESX (USUALLY TWO) SUBSEQUEBT ITERATIONS. */ /* ITERM=2-IF ABS(F-FO) WAS LESS THAN OR EQUAL TO TOLF IN */ /* MTESF (USUALLY TWO) SUBSEQUEBT ITERATIONS. */ /* ITERM=3-IF F IS LESS THAN OR EQUAL TO TOLB. */ /* ITERM=4-IF GMAX IS LESS THAN OR EQUAL TO TOLG. */ /* ITERM=6-IF THE TERMINATION CRITERION WAS NOT SATISFIED, */ /* BUT THE SOLUTION OBTAINED IS PROBABLY ACCEPTABLE. */ /* ITERM=11-IF NIT EXCEEDED MIT. ITERM=12-IF NFV EXCEEDED MFV. */ /* ITERM=13-IF NFG EXCEEDED MFG. ITERM<0-IF THE METHOD FAILED. */ /* VARIABLES IN COMMON /STAT/ (STATISTICS) : */ /* IO NRES NUMBER OF RESTARTS. */ /* IO NDEC NUMBER OF MATRIX DECOMPOSITION. */ /* IO NIN NUMBER OF INNER ITERATIONS. */ /* IO NIT NUMBER OF ITERATIONS. */ /* IO NFV NUMBER OF FUNCTION EVALUATIONS. */ /* IO NFG NUMBER OF GRADIENT EVALUATIONS. */ /* IO NFH NUMBER OF HESSIAN EVALUATIONS. */ /* SUBPROGRAMS USED : */ /* S PCBS04 ELIMINATION OF BOX CONSTRAINT VIOLATIONS. */ /* S PS1L01 STEPSIZE SELECTION USING LINE SEARCH. */ /* S PYADC0 ADDITION OF A BOX CONSTRAINT. */ /* S PYFUT1 TEST ON TERMINATION. */ /* S PYRMC0 DELETION OF A BOX CONSTRAINT. */ /* S PYTRCD COMPUTATION OF PROJECTED DIFFERENCES FOR THE VARIABLE METRIC */ /* UPDATE. */ /* S PYTRCG COMPUTATION OF THE PROJECTED GRADIENT. */ /* S PYTRCS COMPUTATION OF THE PROJECTED DIRECTION VECTOR. */ /* S MXDRCB BACKWARD PART OF THE STRANG FORMULA FOR PREMULTIPLICATION */ /* OF THE VECTOR X BY AN IMPLICIT BFGS UPDATE. */ /* S MXDRCF FORWARD PART OF THE STRANG FORMULA FOR PREMULTIPLICATION */ /* OF THE VECTOR X BY AN IMPLICIT BFGS UPDATE. */ /* S MXDRSU SHIFT OF COLUMNS OF THE RECTANGULAR MATRICES A AND B. */ /* SHIFT OF ELEMENTS OF THE VECTOR U. THESE SHIFTS ARE USED IN */ /* THE LIMITED MEMORY BFGS METHOD. */ /* S MXUDIR VECTOR AUGMENTED BY THE SCALED VECTOR. */ /* RF MXUDOT DOT PRODUCT OF TWO VECTORS. */ /* S MXVNEG COPYING OF A VECTOR WITH CHANGE OF THE SIGN. */ /* S MXVCOP COPYING OF A VECTOR. */ /* S MXVSCL SCALING OF A VECTOR. */ /* S MXVSET INITIATINON OF A VECTOR. */ /* S MXVDIF DIFFERENCE OF TWO VECTORS. */ /* EXTERNAL SUBROUTINES : */ /* SE OBJ COMPUTATION OF THE VALUE OF THE OBJECTIVE FUNCTION. */ /* CALLING SEQUENCE: CALL OBJ(NF,X,FF) WHERE NF IS THE NUMBER */ /* OF VARIABLES, X(NF) IS THE VECTOR OF VARIABLES AND FF IS */ /* THE VALUE OF THE OBJECTIVE FUNCTION. */ /* SE DOBJ COMPUTATION OF THE GRADIENT OF THE OBJECTIVE FUNCTION. */ /* CALLING SEQUENCE: CALL DOBJ(NF,X,GF) WHERE NF IS THE NUMBER */ /* OF VARIABLES, X(NF) IS THE VECTOR OF VARIABLES AND GF(NF) */ /* IS THE GRADIENT OF THE OBJECTIVE FUNCTION. */ /* -- OBJ and DOBJ are replaced by a single function, objgrad, in NLopt */ /* METHOD : */ /* LIMITED MEMORY VARIABLE METRIC METHOD BASED ON THE STRANG */ /* RECURRENCES. */ static void pnet_(int *nf, int *nb, double *x, int * ix, double *xl, double *xu, double *gf, double *gn, double *s, double *xo, double *go, double *xs, double *gs, double *xm, double *gm, double *u1, double *u2, double *xmax, double *tolx, double *tolf, double *tolb, double *tolg, nlopt_stopping *stop, double *minf_est, double * gmax, double *f, int *mit, int *mfv, int *mfg, int *iest, int *mos1, int *mos2, int *mf, int *iterm, stat_common *stat_1, nlopt_func objgrad, void *objgrad_data) { /* System generated locals */ int i__1; double d__1, d__2; /* Builtin functions */ /* Local variables */ double a, b; int i__, n; double p, r__; int kd, ld; double fo, fp, po, pp, ro, rp; int mx, kbf; double alf; double par; int mes, kit; double rho, eps; int mmx; double alf1, alf2, eta0, eta9, par1, par2; int mes1, mes2, mes3; double rho1, rho2, eps8, eps9; int mred, iold, nred; double maxf, dmax__; int xstop = 0; int inew; double told; int ites; double rmin, rmax, umax, tolp, tols; int isys; int ires1, ires2; int iterd, mtesf, ntesf; double gnorm; int iters, irest, inits, kters, maxst; double snorm; int mtesx, ntesx; ps1l01_state state; /* INITIATION */ /* Parameter adjustments */ --u2; --u1; --gm; --xm; --gs; --xs; --go; --xo; --s; --gn; --gf; --xu; --xl; --ix; --x; /* Function Body */ kbf = 0; if (*nb > 0) { kbf = 2; } stat_1->nres = 0; stat_1->ndec = 0; stat_1->nin = 0; stat_1->nit = 0; stat_1->nfg = 0; stat_1->nfh = 0; isys = 0; ites = 1; mtesx = 2; mtesf = 2; inits = 2; *iterm = 0; iterd = 0; iters = 2; kters = 3; irest = 0; ires1 = 999; ires2 = 0; mred = 10; mes = 4; mes1 = 2; mes2 = 2; mes3 = 2; eps = .8; eta0 = 1e-15; eta9 = 1e120; eps8 = 1.; eps9 = 1e-8; alf1 = 1e-10; alf2 = 1e10; rmax = eta9; dmax__ = eta9; maxf = 1e20; if (*iest <= 0) { *minf_est = -HUGE_VAL; /* changed from -1e60 by SGJ */ } if (*iest > 0) { *iest = 1; } if (*xmax <= 0.) { *xmax = 1e16; } if (*tolx <= 0.) { *tolx = 1e-16; } if (*tolf <= 0.) { *tolf = 1e-14; } if (*tolg <= 0.) { *tolg = 1e-8; /* SGJ: was 1e-6, but this sometimes stops too soon */ } #if 0 /* removed by SGJ: this check prevented us from using minf_max <= 0, which doesn't make sense. Instead, if you don't want to have a lower limit, you should set minf_max = -HUGE_VAL */ if (*tolb <= 0.) { *tolb = *minf_est + 1e-16; } #endif told = 1e-4; tols = 1e-4; tolp = .9; /* changed by SGJ: default is no limit (INT_MAX) on # iterations/fevals */ if (*mit <= 0) { *mit = INT_MAX; } if (*mfv <= 0) { *mfv = INT_MAX; } if (*mfg <= 0) { *mfg = INT_MAX; } if (*mos1 <= 0) { *mos1 = 1; } if (*mos2 <= 0) { *mos2 = 1; } kd = 1; ld = -1; kit = -(ires1 * *nf + ires2); fo = *minf_est; /* INITIAL OPERATIONS WITH SIMPLE BOUNDS */ if (kbf > 0) { i__1 = *nf; for (i__ = 1; i__ <= i__1; ++i__) { if ((ix[i__] == 3 || ix[i__] == 4) && xu[i__] <= xl[i__]) { xu[i__] = xl[i__]; ix[i__] = 5; } else if (ix[i__] == 5 || ix[i__] == 6) { xl[i__] = x[i__]; xu[i__] = x[i__]; ix[i__] = 5; } /* L2: */ } luksan_pcbs04__(nf, &x[1], &ix[1], &xl[1], &xu[1], &eps9, &kbf); luksan_pyadc0__(nf, &n, &x[1], &ix[1], &xl[1], &xu[1], &inew); } *f = objgrad(*nf, &x[1], &gf[1], objgrad_data); ++stop->nevals; ++stat_1->nfg; if (nlopt_stop_time(stop)) { *iterm = 100; goto L11080; } ld = kd; L11020: luksan_pytrcg__(nf, nf, &ix[1], &gf[1], &umax, gmax, &kbf, &iold); luksan_mxvcop__(nf, &gf[1], &gn[1]); luksan_pyfut1__(nf, f, &fo, &umax, gmax, xstop, stop, tolg, &kd, &stat_1->nit, &kit, mit, &stat_1->nfg, mfg, & ntesx, &mtesx, &ntesf, &mtesf, &ites, &ires1, &ires2, &irest, & iters, iterm); if (*iterm != 0) { goto L11080; } if (nlopt_stop_time(stop)) { *iterm = 100; goto L11080; } if (kbf > 0) { luksan_pyrmc0__(nf, &n, &ix[1], &gn[1], &eps8, &umax, gmax, &rmax, & iold, &irest); if (umax > eps8 * *gmax) { irest = MAX2(irest,1); } } luksan_mxvcop__(nf, &x[1], &xo[1]); L11040: /* DIRECTION DETERMINATION */ if (irest != 0) { if (kit < stat_1->nit) { mx = 0; ++stat_1->nres; kit = stat_1->nit; } else { *iterm = -10; if (iters < 0) { *iterm = iters - 5; } goto L11080; } if (*mos1 > 1) { luksan_mxvneg__(nf, &gn[1], &s[1]); gnorm = sqrt(luksan_mxudot__(nf, &gn[1], &gn[1], &ix[1], &kbf)); snorm = gnorm; goto L12560; } } rho1 = luksan_mxudot__(nf, &gn[1], &gn[1], &ix[1], &kbf); gnorm = sqrt(rho1); /* Computing MIN */ d__1 = eps, d__2 = sqrt(gnorm); par = MIN2(d__1,d__2); if (par > .01) { /* Computing MIN */ d__1 = par, d__2 = 1. / (double) stat_1->nit; par = MIN2(d__1,d__2); } par *= par; /* CG INITIATION */ rho = rho1; snorm = 0.; luksan_mxvset__(nf, &c_b7, &s[1]); luksan_mxvneg__(nf, &gn[1], &gs[1]); luksan_mxvcop__(nf, &gs[1], &xs[1]); if (*mos2 > 1) { if (mx == 0) { b = 0.; } else { b = luksan_mxudot__(nf, &xm[1], &gm[1], &ix[1], &kbf); } if (b > 0.) { u1[1] = 1. / b; luksan_mxdrcb__(nf, &mx, &xm[1], &gm[1], &u1[1], &u2[1], &xs[1], & ix[1], &kbf); a = luksan_mxudot__(nf, &gm[1], &gm[1], &ix[1], &kbf); if (a > 0.) { d__1 = b / a; luksan_mxvscl__(nf, &d__1, &xs[1], &xs[1]); } luksan_mxdrcf__(nf, &mx, &xm[1], &gm[1], &u1[1], &u2[1], &xs[1], & ix[1], &kbf); } } rho = luksan_mxudot__(nf, &gs[1], &xs[1], &ix[1], &kbf); /* SIG=RHO */ mmx = *nf + 3; nred = 0; L12520: ++nred; if (nred > mmx) { goto L12550; } fo = *f; pp = sqrt(eta0 / luksan_mxudot__(nf, &xs[1], &xs[1], &ix[1], &kbf)); ld = 0; luksan_mxudir__(nf, &pp, &xs[1], &xo[1], &x[1], &ix[1], &kbf); objgrad(*nf, &x[1], &gf[1], objgrad_data); ++stop->nevals; ++stat_1->nfg; ld = kd; luksan_mxvdif__(nf, &gf[1], &gn[1], &go[1]); *f = fo; d__1 = 1. / pp; luksan_mxvscl__(nf, &d__1, &go[1], &go[1]); alf = luksan_mxudot__(nf, &xs[1], &go[1], &ix[1], &kbf); if (alf <= 1. / eta9) { /* IF (ALF.LE.1.0D-8*SIG) THEN */ /* CG FAILS (THE MATRIX IS NOT POSITIVE DEFINITE) */ if (nred == 1) { luksan_mxvneg__(nf, &gn[1], &s[1]); snorm = gnorm; } iterd = 0; goto L12560; } else { iterd = 2; } /* CG STEP */ alf = rho / alf; luksan_mxudir__(nf, &alf, &xs[1], &s[1], &s[1], &ix[1], &kbf); d__1 = -alf; luksan_mxudir__(nf, &d__1, &go[1], &gs[1], &gs[1], &ix[1], &kbf); rho2 = luksan_mxudot__(nf, &gs[1], &gs[1], &ix[1], &kbf); snorm = sqrt(luksan_mxudot__(nf, &s[1], &s[1], &ix[1], &kbf)); if (rho2 <= par * rho1) { goto L12560; } if (nred >= mmx) { goto L12550; } if (*mos2 > 1) { if (b > 0.) { luksan_mxvcop__(nf, &gs[1], &go[1]); luksan_mxdrcb__(nf, &mx, &xm[1], &gm[1], &u1[1], &u2[1], &go[1], & ix[1], &kbf); if (a > 0.) { d__1 = b / a; luksan_mxvscl__(nf, &d__1, &go[1], &go[1]); } luksan_mxdrcf__(nf, &mx, &xm[1], &gm[1], &u1[1], &u2[1], &go[1], & ix[1], &kbf); rho2 = luksan_mxudot__(nf, &gs[1], &go[1], &ix[1], &kbf); alf = rho2 / rho; luksan_mxudir__(nf, &alf, &xs[1], &go[1], &xs[1], &ix[1], &kbf); } else { alf = rho2 / rho; luksan_mxudir__(nf, &alf, &xs[1], &gs[1], &xs[1], &ix[1], &kbf); } } else { alf = rho2 / rho; luksan_mxudir__(nf, &alf, &xs[1], &gs[1], &xs[1], &ix[1], &kbf); } rho = rho2; /* SIG=RHO2+ALF*ALF*SIG */ goto L12520; L12550: /* AN INEXACT SOLUTION IS OBTAINED */ L12560: /* ------------------------------ */ /* END OF DIRECTION DETERMINATION */ /* ------------------------------ */ luksan_mxvcop__(nf, &xo[1], &x[1]); luksan_mxvcop__(nf, &gn[1], &gf[1]); if (kd > 0) { p = luksan_mxudot__(nf, &gn[1], &s[1], &ix[1], &kbf); } if (iterd < 0) { *iterm = iterd; } else { /* TEST ON DESCENT DIRECTION */ if (snorm <= 0.) { irest = MAX2(irest,1); } else if (p + told * gnorm * snorm <= 0.) { irest = 0; } else { /* UNIFORM DESCENT CRITERION */ irest = MAX2(irest,1); } if (irest == 0) { /* PREPARATION OF LINE SEARCH */ nred = 0; rmin = alf1 * gnorm / snorm; /* Computing MIN */ d__1 = alf2 * gnorm / snorm, d__2 = *xmax / snorm; rmax = MIN2(d__1,d__2); } } ld = kd; if (*iterm != 0) { goto L11080; } if (nlopt_stop_time(stop)) { *iterm = 100; goto L11080; } if (irest != 0) { goto L11040; } luksan_pytrcs__(nf, &x[1], &ix[1], &xo[1], &xl[1], &xu[1], &gf[1], &go[1], &s[1], &ro, &fp, &fo, f, &po, &p, &rmax, &eta9, &kbf); if (rmax == 0.) { goto L11075; } L11060: luksan_ps1l01__(&r__, &rp, f, &fo, &fp, &p, &po, &pp, minf_est, &maxf, &rmin, &rmax, &tols, &tolp, &par1, &par2, &kd, &ld, &stat_1->nit, &kit, & nred, &mred, &maxst, iest, &inits, &iters, &kters, &mes, &isys, &state); if (isys == 0) { goto L11064; } luksan_mxudir__(nf, &r__, &s[1], &xo[1], &x[1], &ix[1], &kbf); luksan_pcbs04__(nf, &x[1], &ix[1], &xl[1], &xu[1], &eps9, &kbf); *f = objgrad(*nf, &x[1], &gf[1], objgrad_data); ++stop->nevals; ++stat_1->nfg; ld = kd; p = luksan_mxudot__(nf, &gf[1], &s[1], &ix[1], &kbf); goto L11060; L11064: if (iters <= 0) { r__ = 0.; *f = fo; p = po; luksan_mxvcop__(nf, &xo[1], &x[1]); luksan_mxvcop__(nf, &go[1], &gf[1]); irest = MAX2(irest,1); ld = kd; goto L11040; } luksan_pytrcd__(nf, &x[1], &ix[1], &xo[1], &gf[1], &go[1], &r__, f, &fo, & p, &po, &dmax__, &kbf, &kd, &ld, &iters); xstop = nlopt_stop_dx(stop, &x[1], &xo[1]); if (*mos2 > 1) { /* Computing MIN */ i__1 = mx + 1; mx = MIN2(i__1,*mf); luksan_mxdrsu__(nf, &mx, &xm[1], &gm[1], &u1[1]); luksan_mxvcop__(nf, &xo[1], &xm[1]); luksan_mxvcop__(nf, &go[1], &gm[1]); } L11075: if (kbf > 0) { luksan_pyadc0__(nf, &n, &x[1], &ix[1], &xl[1], &xu[1], &inew); if (inew > 0) { irest = MAX2(irest,1); } } goto L11020; L11080: return; } /* pnet_ */ /* NLopt wrapper around pnet_, handling dynamic allocation etc. */ nlopt_result luksan_pnet(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, nlopt_stopping *stop, int mf, /* subspace dimension (0 for default) */ int mos1, int mos2) /* 1 or 2 */ { int i, *ix, nb = 1; double *work; double *xl, *xu, *gf, *gn, *s, *xo, *go, *xs, *gs, *xm, *gm, *u1, *u2; double gmax, minf_est; double xmax = 0; /* no maximum */ double tolg = 0; /* default gradient tolerance */ int iest = 0; /* we have no estimate of min function value */ int mit = 0, mfg = 0; /* default no limit on #iterations */ int mfv = stop->maxeval; stat_common stat; int iterm; ix = (int*) malloc(sizeof(int) * n); if (!ix) return NLOPT_OUT_OF_MEMORY; if (mf <= 0) { mf = MAX2(MEMAVAIL/n, 10); if (stop->maxeval && stop->maxeval <= mf) mf = MAX2(stop->maxeval, 1); } retry_alloc: work = (double*) malloc(sizeof(double) * (n * 9 + MAX2(n,n*mf)*2 + MAX2(n,mf)*2)); if (!work) { if (mf > 0) { mf = 0; /* allocate minimal memory */ goto retry_alloc; } free(ix); return NLOPT_OUT_OF_MEMORY; } xl = work; xu = xl + n; gf = xu + n; gn = gf + n; s = gn + n; xo = s + n; go = xo + n; xs = go + n; gs = xs + n; xm = gs + n; gm = xm + MAX2(n*mf,n); u1 = gm + MAX2(n*mf,n); u2 = u1 + MAX2(n,mf); for (i = 0; i < n; ++i) { int lbu = lb[i] <= -0.99 * HUGE_VAL; /* lb unbounded */ int ubu = ub[i] >= 0.99 * HUGE_VAL; /* ub unbounded */ ix[i] = lbu ? (ubu ? 0 : 2) : (ubu ? 1 : (lb[i] == ub[i] ? 5 : 3)); xl[i] = lb[i]; xu[i] = ub[i]; } /* ? xo does not seem to be initialized in the original Fortran code, but it is used upon input to pnet if mf > 0 ... perhaps ALLOCATE initializes arrays to zero by default? */ memset(xo, 0, sizeof(double) * MAX2(n,n*mf)); pnet_(&n, &nb, x, ix, xl, xu, gf, gn, s, xo, go, xs, gs, xm, gm, u1, u2, &xmax, /* fixme: pass tol_rel and tol_abs and use NLopt check */ &stop->xtol_rel, &stop->ftol_rel, &stop->minf_max, &tolg, stop, &minf_est, &gmax, minf, &mit, &mfv, &mfg, &iest, &mos1, &mos2, &mf, &iterm, &stat, f, f_data); free(work); free(ix); switch (iterm) { case 1: return NLOPT_XTOL_REACHED; case 2: return NLOPT_FTOL_REACHED; case 3: return NLOPT_MINF_MAX_REACHED; case 4: return NLOPT_SUCCESS; /* gradient tolerance reached */ case 6: return NLOPT_SUCCESS; case 12: case 13: return NLOPT_MAXEVAL_REACHED; case 100: return NLOPT_MAXTIME_REACHED; case -999: return NLOPT_FORCED_STOP; default: return NLOPT_FAILURE; } } nloptr/src/nlopt_src/luksan/Makefile.am0000644000176000001440000000032513263734242017751 0ustar ripleyusersAM_CPPFLAGS = -I$(top_srcdir)/util -I$(top_srcdir)/api noinst_LTLIBRARIES = libluksan.la libluksan_la_SOURCES = plis.c plip.c pnet.c mssubs.c pssubs.c luksan.h EXTRA_DIST = README COPYRIGHT plis.txt v999-07.pdf nloptr/src/nlopt_src/luksan/v999-07.pdf0000644000176000001440000145530713263734242017373 0ustar ripleyusers%PDF-1.4 %쏢 5 0 obj <> stream x}I&quXMk2"cIE3Hh:tl؈isL ef?oi>9iYﯭ6O8ޟ{OOs?5u9Q/Ox=˳G{~|ў>_$z&4'A试82bΓщ+ѩ!O$)('3]_;Fyԋ$';K}Mi-׌'zW/tzJS׃kNBs94LҌ @_'~R's " 3"OxЉ]̫ItdjhIkɗ_W&OYBy򖡝:<5Q' V!ʙѧ: 4'U6wl,=Z2,CfPi'><щru֦hPJ$q#]'WtG2CB%VNH`|lNpueD5ƞf;ΦҞq D ~zur=dç D>Sm*sP ֏e'ͅ6k;l$9 2S$ =).4q);jzct jpVuI0,>'〦8lY}te./u:~` :fqi8Ne,L-O]8#N;zYh`s;'e(`5чq Gݸŗ>F-C'؟1`ƑxdNVUU rLXS'QǔqL K~p2 ,,DC5I%4pgV>,q+'N)#;0Cvؤ5*褽H>W;)Wuc^7=&ҙdu +|0^e_ 33?"2 &:(9^~'Nζ)1A 3?%zTs uE-OX ޔxTxRl"%1)@{kP xF"ewia6)=S̅]eO?[##^\S *rH(DYǃ_͓TDy" &b)׬^z,;]uq\Nk=d1UL7Ojj )[ykhϭ c-fꇜsʞt2ܗGф^t"߳ ]Dz\_*X5*V~y(e" CE Z:qFϤӦo' D|u=TX+(Ҥdm{\澨<|xekZލ(ia}kC驒= 90T1siv֍+0hIpugɘvmHq1K n҅2]P˚]kBg*iWAu~NTĬ *6^zErNi22FB롪>[˲\+7lԖ\3@ #ԉHvTN[:̡ f&b:Z!2>bN8&&tqoV[/0&{C D&t0>84iخ7{Q6͠XN!഻ ~/":9re >9rB@V!o+;rGN󜘗"W³Zڝ1/׷znJ 9/NZtܲ+zg[}]\ x:s: pq|>7D/'ZX3?QbKӚtf{ I-u'ڊBif1d mu'UB ' _v97ɞ ֝.H*>%9&r[r,w@*q2X!$hu~ک eEq׬|>Q T[I؀P&*CtkK{aEk.-*oК[Žqn3ƍ^v_ vbyd dRԲUsXVjMYU/-"[pnz-bT%RTG1,-!]G7#QNk1B.jۮ^u|7`N0*[Yc5 gPԭ26 Ws( V rZb4zadE,B;b+<{앮AΈ @.Hg0*O?IgIU3@PD'i Xs"'+1r~Sv6iYEϢ'x6Ī' oUhʉ8JsNeo|%<.eRU9͊E<V:AII*HU%2 K+REh o<ۛT开q!Z%~x:4JԂE)-Oh  7*q u#O`f[.\Ҋ0XYh8KYբTcf*Z!?Qo-q~x.uPKkYiT~5ٺb *Ml-+y&Ib$:pEbH֠ ',:iqnɪ8w87p487xI^8\I1zݠ|L1`(4Y j`6;Dt^[']iPhf͂E809\qoySl=UفQ+OʚX3U/)DVVoѪ@9]%:NfSe[iߴ5A)bleoCPt([;-KV η?!X}{qTus5>1.m58Ih&Y@c弤4\g ⤵:ۙR`Ju6ԒJZ  ´2a:l+cY.;3dqh!,բ^@s*Xee/(G(|VSD#RF|ZɒBDtBEi~"\@ie8ox ztBCoȸ3'4ID'juR0t@u-ּXVO.'_\Ԩ-7Pư&/b6ؒx֢{HD m>vAwg$bgtt1gEہ8\O^‡]R0%ޜU"͒FpWK5ajP$E%,2Ή3dYVjpnE8uwՃ]OdSlg, ԙ=%/ 4i3[^6 7˖HmIpҼWSWdz4\6IlbkH=)z7 `0 5y1(V=#ĤWOOZ%ؔ UHۨ,˾ Q(1ih4|)LϪiIhIU UPVԺ֐F/`Gt^VX rZ.{4oJ/̇WR 2M\=p!QɝnNhH7 KA2C#6o$Z%,m(m• ]/͸7}iphaQf9mWaE"nNV.-9W.iQJUaaՏ]M(wί;x pBI_ 8/Om2wM[/iPO mqGt=L9NڭAwoKS9ϗ8$j%AkC݋TM*#yvgWžN`j,'}V"o8ᦅ#K[i ; _ZapYduvxf4jvjmaή(Ӷc֒*#v [eĘ + [Or;=G^B<>u  !@HYuCd -D^8Ϭ S) q\7=3@ \*Y:\aMgR]IxV' %ZU%S1jbkzmCozmDM[( #Pm8!s|knWVt34]ݢ`Co.o-!߄'2/yvE|펿]"^ @Ų6CH+0m>av\~zV{%~Fٰh)P&\2,;ob>=8ra"L6,6Dv(v6mmx\+yEbX%PflEiT-®E`mvpv\s;<{uo.b/E _0N`|3x"PvxxL~ID\!#hcua#0;h>)7ʀo_n@3l g} zPUL#R78wV#  ꠯vxvroD)D7j3 -2VmjŦwըOOZ墪+wm1*ͨ]Tިo:sTҽ]A0v͓~ M0#fc}ylhm;&hdfh0T!ԍp0wsz3AL)ѭ5=g#x>cdwDJtΙྉ".N膊nNJ]e:Ӣmタ.v_p Fbt;tetlF\ݯA7xs"G7spCo~ݑݛ+<7Wl|{ BaHE h<yc"!bETb%db&tbgm!RڃNT Z=kWmqW a=0Bhom>d P>a g >@jnqGq0a-C!c0;ĺ`x`n-`cK 1QjB] [CLؒ'􊐀4%lY"1$l(!SeKdS]B.LLٓiBMLySz?{JЖ4NR:iI1m(iOU{ULЊ \{WH)bif1 QZd n{*ܖ,rdn }oRI$kaLL y[bcL}R#w+c%A3p )!I4n9[jLRXCkLSec2mLqcn~cJpSc='yYޒc%N=:f={K{B~~`fX‚vg€L`x |zz'~-}4_П[য়%) E7v/_NG3K87X&T x 8N4ne%yZT'sN=3\ f:ppifBb 3ApqCK-8¬ Z!dр߸<RxB0 x,?1us Yd5:sV0k("9210U!=qqHhYFq۩8}\֖"xCrV8p TeyŇAotv"fϙѩ0I mZLYmy®17SYc1|%Uj(bƼYG7dx+ZV? 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nlopt_result luksan_plip(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, nlopt_stopping *stop, int mf, int method); nlopt_result luksan_pnet(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, nlopt_stopping *stop, int mf, int mos1, int mos2); typedef struct { double fl, fu, pl, rl, pu, ru; int mes1, mes2, mes3, mode, mtyp; } ps1l01_state; /***************************** internal routines *************************/ /* mssubs.c: */ void luksan_mxdcmd__(int *n, int *m, double *a, double *x, double *alf, double *y, double *z__); void luksan_mxdrcb__(int *n, int *m, double *a, double *b, double *u, double *v, double *x, int * ix, int *job); void luksan_mxdrcf__(int *n, int *m, double *a, double *b, double *u, double *v, double *x, int * ix, int *job); void luksan_mxdrmm__(int *n, int *m, double *a, double *x, double *y); void luksan_mxdrsu__(int *n, int *m, double *a, double *b, double *u); void luksan_mxucop__(int *n, double *x, double *y, int *ix, int *job); void luksan_mxudir__(int *n, double *a, double *x, double *y, double *z__, int *ix, int *job); void luksan_mxuneg__(int *n, double *x, double *y, int *ix, int *job); void luksan_mxuzer__(int *n, double *x, int *ix, int *job); void luksan_mxvcop__(int *n, double *x, double *y); void luksan_mxvdif__(int *n, double *x, double *y, double *z__); void luksan_mxvneg__(int *n, double *x, double *y); void luksan_mxvscl__(int *n, double *a, double *x, double *y); void luksan_mxvset__(int *n, double *a, double *x); double luksan_mxudot__(int *n, double *x, double *y, int *ix, int *job); double luksan_mxvdot__(int *n, double *x, double *y); void luksan_mxvdir__(int *n, double *a, double *x, double *y, double *z__); void luksan_mxdcmu__(int *n, int *m, double *a, double *alf, double *x, double *y); void luksan_mxvlin__(int *n, double *a, double *x, double *b, double *y, double *z__); void luksan_mxdcmv__(int *n, int *m, double *a, double *alf, double *x, double *u, double *bet, double *y, double *v); void luksan_mxvsav__(int *n, double *x, double *y); void luksan_mxvine__(int *n, int *ix); double luksan_mxvmax__(int *n, double *x); /* pssubs.c: */ void luksan_pcbs04__(int *nf, double *x, int *ix, double *xl, double *xu, double *eps9, int *kbf); void luksan_ps1l01__(double *r__, double *rp, double *f, double *fo, double *fp, double *p, double *po, double *pp, double *minf, double *fmax, double *rmin, double *rmax, double *tols, double * tolp, double *par1, double *par2, int *kd, int *ld, int *nit, int *kit, int *nred, int *mred, int * maxst, int *iest, int *inits, int *iters, int *kters, int *mes, int *isys, ps1l01_state *state); void luksan_pulsp3__(int *n, int *m, int *mf, double *xm, double *gr, double *xo, double *go, double *r__, double *po, double *sig, int *iterh, int *met3); void luksan_pulvp3__(int *n, int *m, double *xm, double *xr, double *gr, double *s, double *so, double *xo, double *go, double *r__, double *po, double *sig, int *iterh, int *met2, int *met3, int *met5); void luksan_pyadc0__(int *nf, int *n, double *x, int *ix, double *xl, double *xu, int *inew); void luksan_pyfut1__(int *n, double *f, double * fo, double *umax, double *gmax, int xstop, const nlopt_stopping *stop, double *tolg, int *kd, int *nit, int *kit, int *mit, int *nfg, int *mfg, int *ntesx, int *mtesx, int *ntesf, int *mtesf, int *ites, int *ires1, int *ires2, int *irest, int *iters, int *iterm); void luksan_pyrmc0__(int *nf, int *n, int *ix, double *g, double *eps8, double *umax, double *gmax, double *rmax, int *iold, int *irest); void luksan_pytrcd__(int *nf, double *x, int *ix, double *xo, double *g, double *go, double *r__, double *f, double *fo, double *p, double *po, double *dmax__, int *kbf, int *kd, int *ld, int * iters); void luksan_pytrcg__(int *nf, int *n, int *ix, double *g, double *umax, double *gmax, int *kbf, int *iold); void luksan_pytrcs__(int *nf, double *x, int *ix, double *xo, double *xl, double *xu, double *g, double *go, double *s, double *ro, double *fp, double *fo, double *f, double *po, double *p, double *rmax, double *eta9, int *kbf); void luksan_pnint1__(double *rl, double *ru, double *fl, double *fu, double *pl, double *pu, double *r__, int *mode, int *mtyp, int *merr); /* Common Block Declarations */ typedef struct { int nres, ndec, nin, nit; /* int nfv; -- now stored in stop->nevals */ int nfg, nfh; } stat_common; /* number of double variables that can be stored in scratch memory ... it's >= 2007, and this is in the context of scientific computation, so assume that at least 10M are available, and that sizeof(double)==8 */ #define MEMAVAIL 1310720 #ifdef __cplusplus } /* extern "C" */ #endif /* __cplusplus */ #endif /* LUKSAN_H */ nloptr/src/nlopt_src/luksan/plis.c0000644000176000001440000003571013355070635017037 0ustar ripleyusers#include #include #include #include #include "luksan.h" #define MAX2(a,b) ((a) > (b) ? (a) : (b)) #define MIN2(a,b) ((a) < (b) ? (a) : (b)) /* *********************************************************************** */ /* SUBROUTINE PLIS ALL SYSTEMS 01/09/22 */ /* PURPOSE : */ /* GENERAL SUBROUTINE FOR LARGE-SCALE BOX CONSTRAINED MINIMIZATION THAT */ /* USE THE LIMITED MEMORY VARIABLE METRIC METHOD BASED ON THE STRANG */ /* RECURRENCES. */ /* PARAMETERS : */ /* II NF NUMBER OF VARIABLES. */ /* II NB CHOICE OF SIMPLE BOUNDS. NB=0-SIMPLE BOUNDS SUPPRESSED. */ /* NB>0-SIMPLE BOUNDS ACCEPTED. */ /* RI X(NF) VECTOR OF VARIABLES. */ /* II IX(NF) VECTOR CONTAINING TYPES OF BOUNDS. IX(I)=0-VARIABLE */ /* X(I) IS UNBOUNDED. IX(I)=1-LOVER BOUND XL(I).LE.X(I). */ /* IX(I)=2-UPPER BOUND X(I).LE.XU(I). IX(I)=3-TWO SIDE BOUND */ /* XL(I).LE.X(I).LE.XU(I). IX(I)=5-VARIABLE X(I) IS FIXED. */ /* RI XL(NF) VECTOR CONTAINING LOWER BOUNDS FOR VARIABLES. */ /* RI XU(NF) VECTOR CONTAINING UPPER BOUNDS FOR VARIABLES. */ /* RO GF(NF) GRADIENT OF THE OBJECTIVE FUNCTION. */ /* RO S(NF) DIRECTION VECTOR. */ /* RU XO(NF) VECTORS OF VARIABLES DIFFERENCE. */ /* RI GO(NF) GRADIENTS DIFFERENCE. */ /* RA UO(NF) AUXILIARY VECTOR. */ /* RA VO(NF) AUXILIARY VECTOR. */ /* RI XMAX MAXIMUM STEPSIZE. */ /* RI TOLX TOLERANCE FOR CHANGE OF VARIABLES. */ /* RI TOLF TOLERANCE FOR CHANGE OF FUNCTION VALUES. */ /* RI TOLB TOLERANCE FOR THE FUNCTION VALUE. */ /* RI TOLG TOLERANCE FOR THE GRADIENT NORM. */ /* RI MINF_EST ESTIMATION OF THE MINIMUM FUNCTION VALUE. */ /* RO GMAX MAXIMUM PARTIAL DERIVATIVE. */ /* RO F VALUE OF THE OBJECTIVE FUNCTION. */ /* II MIT MAXIMUM NUMBER OF ITERATIONS. */ /* II MFV MAXIMUM NUMBER OF FUNCTION EVALUATIONS. */ /* II IEST ESTIMATION INDICATOR. IEST=0-MINIMUM IS NOT ESTIMATED. */ /* IEST=1-MINIMUM IS ESTIMATED BY THE VALUE MINF_EST. */ /* II MF NUMBER OF LIMITED MEMORY STEPS. */ /* IO ITERM VARIABLE THAT INDICATES THE CAUSE OF TERMINATION. */ /* ITERM=1-IF ABS(X-XO) WAS LESS THAN OR EQUAL TO TOLX IN */ /* MTESX (USUALLY TWO) SUBSEQUEBT ITERATIONS. */ /* ITERM=2-IF ABS(F-FO) WAS LESS THAN OR EQUAL TO TOLF IN */ /* MTESF (USUALLY TWO) SUBSEQUEBT ITERATIONS. */ /* ITERM=3-IF F IS LESS THAN OR EQUAL TO TOLB. */ /* ITERM=4-IF GMAX IS LESS THAN OR EQUAL TO TOLG. */ /* ITERM=6-IF THE TERMINATION CRITERION WAS NOT SATISFIED, */ /* BUT THE SOLUTION OBTAINED IS PROBABLY ACCEPTABLE. */ /* ITERM=11-IF NIT EXCEEDED MIT. ITERM=12-IF NFV EXCEEDED MFV. */ /* ITERM=13-IF NFG EXCEEDED MFG. ITERM<0-IF THE METHOD FAILED. */ /* VARIABLES IN COMMON /STAT/ (STATISTICS) : */ /* IO NRES NUMBER OF RESTARTS. */ /* IO NDEC NUMBER OF MATRIX DECOMPOSITION. */ /* IO NIN NUMBER OF INNER ITERATIONS. */ /* IO NIT NUMBER OF ITERATIONS. */ /* IO NFV NUMBER OF FUNCTION EVALUATIONS. */ /* IO NFG NUMBER OF GRADIENT EVALUATIONS. */ /* IO NFH NUMBER OF HESSIAN EVALUATIONS. */ /* SUBPROGRAMS USED : */ /* S PCBS04 ELIMINATION OF BOX CONSTRAINT VIOLATIONS. */ /* S PS1L01 STEPSIZE SELECTION USING LINE SEARCH. */ /* S PYADC0 ADDITION OF A BOX CONSTRAINT. */ /* S PYFUT1 TEST ON TERMINATION. */ /* S PYRMC0 DELETION OF A BOX CONSTRAINT. */ /* S PYTRCD COMPUTATION OF PROJECTED DIFFERENCES FOR THE VARIABLE METRIC */ /* UPDATE. */ /* S PYTRCG COMPUTATION OF THE PROJECTED GRADIENT. */ /* S PYTRCS COMPUTATION OF THE PROJECTED DIRECTION VECTOR. */ /* S MXDRCB BACKWARD PART OF THE STRANG FORMULA FOR PREMULTIPLICATION */ /* OF THE VECTOR X BY AN IMPLICIT BFGS UPDATE. */ /* S MXDRCF FORWARD PART OF THE STRANG FORMULA FOR PREMULTIPLICATION */ /* OF THE VECTOR X BY AN IMPLICIT BFGS UPDATE. */ /* S MXDRSU SHIFT OF COLUMNS OF THE RECTANGULAR MATRICES A AND B. */ /* SHIFT OF ELEMENTS OF THE VECTOR U. THESE SHIFTS ARE USED IN */ /* THE LIMITED MEMORY BFGS METHOD. */ /* S MXUDIR VECTOR AUGMENTED BY THE SCALED VECTOR. */ /* RF MXUDOT DOT PRODUCT OF TWO VECTORS. */ /* S MXUNEG COPYING OF A VECTOR WITH CHANGE OF THE SIGN. */ /* S MXVCOP COPYING OF A VECTOR. */ /* S MXVSCL SCALING OF A VECTOR. */ /* EXTERNAL SUBROUTINES : */ /* SE OBJ COMPUTATION OF THE VALUE OF THE OBJECTIVE FUNCTION. */ /* CALLING SEQUENCE: CALL OBJ(NF,X,FF) WHERE NF IS THE NUMBER */ /* OF VARIABLES, X(NF) IS THE VECTOR OF VARIABLES AND FF IS */ /* THE VALUE OF THE OBJECTIVE FUNCTION. */ /* SE DOBJ COMPUTATION OF THE GRADIENT OF THE OBJECTIVE FUNCTION. */ /* CALLING SEQUENCE: CALL DOBJ(NF,X,GF) WHERE NF IS THE NUMBER */ /* OF VARIABLES, X(NF) IS THE VECTOR OF VARIABLES AND GF(NF) */ /* IS THE GRADIENT OF THE OBJECTIVE FUNCTION. */ /* -- OBJ and DOBJ are replaced by a single function, objgrad, in NLopt */ /* METHOD : */ /* LIMITED MEMORY VARIABLE METRIC METHOD BASED ON THE STRANG */ /* RECURRENCES. */ static void plis_(int *nf, int *nb, double *x, int * ix, double *xl, double *xu, double *gf, double *s, double *xo, double *go, double *uo, double *vo, double *xmax, double *tolx, double *tolf, double * tolb, double *tolg, nlopt_stopping *stop, double *minf_est, double *gmax, double *f, int *mit, int *mfv, int *iest, int *mf, int *iterm, stat_common *stat_1, nlopt_func objgrad, void *objgrad_data) { /* System generated locals */ int i__1; double d__1, d__2; /* Builtin functions */ /* Local variables */ double a, b; int i__, k, n; double p, r__; int kd, ld; double fo, fp, po, pp, ro, rp; int kbf, mfg; int mes, kit; double alf1, alf2, eta0, eta9, par1, par2; int mes1, mes2, mes3; double eps8, eps9; int mred, iold, nred; double maxf, dmax__; int xstop = 0; int inew; double told; int ites; double rmin, rmax, umax, tolp, tols; int isys; int ires1, ires2; int iterd, mtesf, ntesf; double gnorm; int iters, irest, inits, kters, maxst; double snorm; int mtesx, ntesx; ps1l01_state state; /* INITIATION */ /* Parameter adjustments */ --vo; --uo; --go; --xo; --s; --gf; --xu; --xl; --ix; --x; /* Function Body */ kbf = 0; if (*nb > 0) { kbf = 2; } stat_1->nres = 0; stat_1->ndec = 0; stat_1->nin = 0; stat_1->nit = 0; stat_1->nfg = 0; stat_1->nfh = 0; isys = 0; ites = 1; mtesx = 2; mtesf = 2; inits = 2; *iterm = 0; iterd = 0; iters = 2; kters = 3; irest = 0; ires1 = 999; ires2 = 0; mred = 10; mes = 4; mes1 = 2; mes2 = 2; mes3 = 2; eta0 = 1e-15; eta9 = 1e120; eps8 = 1.; eps9 = 1e-8; alf1 = 1e-10; alf2 = 1e10; rmax = eta9; dmax__ = eta9; maxf = 1e20; if (*iest <= 0) { *minf_est = -HUGE_VAL; /* changed from -1e60 by SGJ */ } if (*iest > 0) { *iest = 1; } if (*xmax <= 0.) { *xmax = 1e16; } if (*tolx <= 0.) { *tolx = 1e-16; } if (*tolf <= 0.) { *tolf = 1e-14; } if (*tolg <= 0.) { *tolg = 1e-8; /* SGJ: was 1e-6, but this sometimes stops too soon */ } #if 0 /* removed by SGJ: this check prevented us from using minf_max <= 0, which doesn't make sense. Instead, if you don't want to have a lower limit, you should set minf_max = -HUGE_VAL */ if (*tolb <= 0.) { *tolb = *minf_est + 1e-16; } #endif told = 1e-4; tols = 1e-4; tolp = .8; /* changed by SGJ: default is no limit (INT_MAX) on # iterations/fevals */ if (*mit <= 0) { *mit = INT_MAX; } if (*mfv <= 0) { *mfv = INT_MAX; } mfg = *mfv; kd = 1; ld = -1; kit = -(ires1 * *nf + ires2); fo = *minf_est; /* INITIAL OPERATIONS WITH SIMPLE BOUNDS */ if (kbf > 0) { i__1 = *nf; for (i__ = 1; i__ <= i__1; ++i__) { if ((ix[i__] == 3 || ix[i__] == 4) && xu[i__] <= xl[i__]) { xu[i__] = xl[i__]; ix[i__] = 5; } else if (ix[i__] == 5 || ix[i__] == 6) { xl[i__] = x[i__]; xu[i__] = x[i__]; ix[i__] = 5; } /* L2: */ } luksan_pcbs04__(nf, &x[1], &ix[1], &xl[1], &xu[1], &eps9, &kbf); luksan_pyadc0__(nf, &n, &x[1], &ix[1], &xl[1], &xu[1], &inew); } if (*iterm != 0) { goto L11190; } *f = objgrad(*nf, &x[1], &gf[1], objgrad_data); ++stop->nevals; ++stat_1->nfg; if (nlopt_stop_time(stop)) { *iterm = 100; goto L11190; } L11120: luksan_pytrcg__(nf, nf, &ix[1], &gf[1], &umax, gmax, &kbf, &iold); luksan_pyfut1__(nf, f, &fo, &umax, gmax, xstop, stop, tolg, &kd, &stat_1->nit, &kit, mit, &stat_1->nfg, &mfg, &ntesx, &mtesx, &ntesf, &mtesf, &ites, &ires1, &ires2, &irest, & iters, iterm); if (*iterm != 0) { goto L11190; } if (nlopt_stop_time(stop)) { *iterm = 100; goto L11190; } if (kbf > 0 && rmax > 0.) { luksan_pyrmc0__(nf, &n, &ix[1], &gf[1], &eps8, &umax, gmax, &rmax, & iold, &irest); } L11130: /* DIRECTION DETERMINATION */ gnorm = sqrt(luksan_mxudot__(nf, &gf[1], &gf[1], &ix[1], &kbf)); if (irest != 0) { goto L12620; } /* Computing MIN */ i__1 = stat_1->nit - kit; k = MIN2(i__1,*mf); if (k <= 0) { irest = MAX2(irest,1); goto L12620; } /* DETERMINATION OF THE PARAMETER B */ b = luksan_mxudot__(nf, &xo[1], &go[1], &ix[1], &kbf); if (b <= 0.) { irest = MAX2(irest,1); goto L12620; } uo[1] = 1. / b; luksan_mxuneg__(nf, &gf[1], &s[1], &ix[1], &kbf); luksan_mxdrcb__(nf, &k, &xo[1], &go[1], &uo[1], &vo[1], &s[1], &ix[1], & kbf); a = luksan_mxudot__(nf, &go[1], &go[1], &ix[1], &kbf); if (a > 0.) { d__1 = b / a; luksan_mxvscl__(nf, &d__1, &s[1], &s[1]); } luksan_mxdrcf__(nf, &k, &xo[1], &go[1], &uo[1], &vo[1], &s[1], &ix[1], & kbf); snorm = sqrt(luksan_mxudot__(nf, &s[1], &s[1], &ix[1], &kbf)); /* Computing MIN */ i__1 = k + 1; k = MIN2(i__1,*mf); luksan_mxdrsu__(nf, &k, &xo[1], &go[1], &uo[1]); L12620: iterd = 0; if (irest != 0) { /* STEEPEST DESCENT DIRECTION */ luksan_mxuneg__(nf, &gf[1], &s[1], &ix[1], &kbf); snorm = gnorm; if (kit < stat_1->nit) { ++stat_1->nres; kit = stat_1->nit; } else { *iterm = -10; if (iters < 0) { *iterm = iters - 5; } } } /* TEST ON DESCENT DIRECTION AND PREPARATION OF LINE SEARCH */ if (kd > 0) { p = luksan_mxudot__(nf, &gf[1], &s[1], &ix[1], &kbf); } if (iterd < 0) { *iterm = iterd; } else { /* TEST ON DESCENT DIRECTION */ if (snorm <= 0.) { irest = MAX2(irest,1); } else if (p + told * gnorm * snorm <= 0.) { irest = 0; } else { /* UNIFORM DESCENT CRITERION */ irest = MAX2(irest,1); } if (irest == 0) { /* PREPARATION OF LINE SEARCH */ nred = 0; rmin = alf1 * gnorm / snorm; /* Computing MIN */ d__1 = alf2 * gnorm / snorm, d__2 = *xmax / snorm; rmax = MIN2(d__1,d__2); } } if (*iterm != 0) { goto L11190; } if (nlopt_stop_time(stop)) { *iterm = 100; goto L11190; } if (irest != 0) { goto L11130; } luksan_pytrcs__(nf, &x[1], &ix[1], &xo[1], &xl[1], &xu[1], &gf[1], &go[1], &s[1], &ro, &fp, &fo, f, &po, &p, &rmax, &eta9, &kbf); if (rmax == 0.) { goto L11175; } L11170: luksan_ps1l01__(&r__, &rp, f, &fo, &fp, &p, &po, &pp, minf_est, &maxf, &rmin, &rmax, &tols, &tolp, &par1, &par2, &kd, &ld, &stat_1->nit, &kit, & nred, &mred, &maxst, iest, &inits, &iters, &kters, &mes, &isys, &state); if (isys == 0) { goto L11174; } luksan_mxudir__(nf, &r__, &s[1], &xo[1], &x[1], &ix[1], &kbf); luksan_pcbs04__(nf, &x[1], &ix[1], &xl[1], &xu[1], &eps9, &kbf); *f = objgrad(*nf, &x[1], &gf[1], objgrad_data); ++stop->nevals; ++stat_1->nfg; p = luksan_mxudot__(nf, &gf[1], &s[1], &ix[1], &kbf); goto L11170; L11174: if (iters <= 0) { r__ = 0.; *f = fo; p = po; luksan_mxvcop__(nf, &xo[1], &x[1]); luksan_mxvcop__(nf, &go[1], &gf[1]); irest = MAX2(irest,1); ld = kd; goto L11130; } luksan_pytrcd__(nf, &x[1], &ix[1], &xo[1], &gf[1], &go[1], &r__, f, &fo, & p, &po, &dmax__, &kbf, &kd, &ld, &iters); xstop = nlopt_stop_dx(stop, &x[1], &xo[1]); L11175: if (kbf > 0) { luksan_mxvine__(nf, &ix[1]); luksan_pyadc0__(nf, &n, &x[1], &ix[1], &xl[1], &xu[1], &inew); } goto L11120; L11190: return; } /* plis_ */ /* NLopt wrapper around plis_, handling dynamic allocation etc. */ nlopt_result luksan_plis(int n, nlopt_func f, void *f_data, const double *lb, const double *ub, /* bounds */ double *x, /* in: initial guess, out: minimizer */ double *minf, nlopt_stopping *stop, int mf) /* subspace dimension, 0 for default */ { int i, *ix, nb = 1; double *work, *xl, *xu, *xo, *gf, *s, *go, *uo, *vo; double gmax, minf_est; double xmax = 0; /* no maximum */ double tolg = 0; /* default gradient tolerance */ int iest = 0; /* we have no estimate of min function value */ int mit = 0; /* default no limit on #iterations */ int mfv = stop->maxeval; stat_common stat; int iterm; ix = (int*) malloc(sizeof(int) * n); if (!ix) return NLOPT_OUT_OF_MEMORY; if (mf <= 0) { mf = MAX2(MEMAVAIL/n, 10); if (stop->maxeval && stop->maxeval <= mf) mf = MAX2(stop->maxeval, 1); } retry_alloc: work = (double*) malloc(sizeof(double) * (n * 4 + MAX2(n,n*mf)*2 + MAX2(n,mf)*2)); if (!work) { if (mf > 0) { mf = 0; /* allocate minimal memory */ goto retry_alloc; } free(ix); return NLOPT_OUT_OF_MEMORY; } xl = work; xu = xl + n; gf = xu + n; s = gf + n; xo = s + n; go = xo + MAX2(n,n*mf); uo = go + MAX2(n,n*mf); vo = uo + MAX2(n,mf); for (i = 0; i < n; ++i) { int lbu = lb[i] <= -0.99 * HUGE_VAL; /* lb unbounded */ int ubu = ub[i] >= 0.99 * HUGE_VAL; /* ub unbounded */ ix[i] = lbu ? (ubu ? 0 : 2) : (ubu ? 1 : (lb[i] == ub[i] ? 5 : 3)); xl[i] = lb[i]; xu[i] = ub[i]; } /* ? xo does not seem to be initialized in the original Fortran code, but it is used upon input to plis if mf > 0 ... perhaps ALLOCATE initializes arrays to zero by default? */ memset(xo, 0, sizeof(double) * MAX2(n,n*mf)); plis_(&n, &nb, x, ix, xl, xu, gf, s, xo, go, uo, vo, &xmax, /* fixme: pass tol_rel and tol_abs and use NLopt check */ &stop->xtol_rel, &stop->ftol_rel, &stop->minf_max, &tolg, stop, &minf_est, &gmax, minf, &mit, &mfv, &iest, &mf, &iterm, &stat, f, f_data); free(work); free(ix); switch (iterm) { case 1: return NLOPT_XTOL_REACHED; case 2: return NLOPT_FTOL_REACHED; case 3: return NLOPT_MINF_MAX_REACHED; case 4: return NLOPT_SUCCESS; /* gradient tolerance reached */ case 6: return NLOPT_SUCCESS; case 12: case 13: return NLOPT_MAXEVAL_REACHED; case 100: return NLOPT_MAXTIME_REACHED; case -999: return NLOPT_FORCED_STOP; default: return NLOPT_FAILURE; } } nloptr/src/nlopt_src/luksan/README0000644000176000001440000000212013263734242016570 0ustar ripleyusersThese routines for nonlinear optimization are based on the Fortran 77 code placed online by Professor Ladislav Luksan at his web site: http://www.uivt.cas.cz/~luksan/subroutines.html and graciously licensed under the GNU Lesser General Public License (LGPL). See also the COPYRIGHT file for details. The C conversions were done via f2c by S. G. Johnson, with the f2c output manually cleaned up somewhat, and then converted to use the NLopt termination conditions, C dynamic allocation, etc. In particular, we converted the subroutines PLIS, PLIP, and PNET (as well as various auxiliary routines required by this code), all of which solve general nonlinear unconstrained or box-constrained optimization problems. Prof. Luksan also provides a large number of more specialized routines for cases where one has additional knowledge of the objective function, but these cases seem outside the scope of NLopt. [We also did not convert the PMIN, PBUN, PNEW, or PVAR subroutines since these were published in ACM Trans. on Math. Software (TOMS) and are subject to the non-free ACM licensing conditions.] nloptr/src/nlopt_src/luksan/pssubs.c0000644000176000001440000010477213355070635017414 0ustar ripleyusers#include #include "luksan.h" #define FALSE_ 0 #define MAX2(a,b) ((a) > (b) ? (a) : (b)) #define MIN2(a,b) ((a) < (b) ? (a) : (b)) #define iabs(a) ((a) < 0 ? -(a) : (a)) /* subroutines extracted from pssubs.for */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE PCBS04 ALL SYSTEMS 98/12/01 * PURPOSE : * INITIATION OF THE VECTOR CONTAINING TYPES OF CONSTRAINTS. * * PARAMETERS : * II NF NUMBER OF VARIABLES. * RI X(NF) VECTOR OF VARIABLES. * II IX(NF) VECTOR CONTAINING TYPES OF BOUNDS. * RI XL(NF) VECTOR CONTAINING LOWER BOUNDS FOR VARIABLES. * RI XU(NF) VECTOR CONTAINING UPPER BOUNDS FOR VARIABLES. * RI EPS9 TOLERANCE FOR ACTIVE CONSTRAINTS. * II KBF SPECIFICATION OF SIMPLE BOUNDS. KBF=0-NO SIMPLE BOUNDS. * KBF=1-ONE SIDED SIMPLE BOUNDS. KBF=2=TWO SIDED SIMPLE BOUNDS. */ void luksan_pcbs04__(int *nf, double *x, int *ix, double *xl, double *xu, double *eps9, int *kbf) { /* System generated locals */ int i__1, i__2; double d__1, d__2; /* Local variables */ int i__, ixi; double temp; /* Parameter adjustments */ --xu; --xl; --ix; --x; /* Function Body */ if (*kbf > 0) { i__1 = *nf; for (i__ = 1; i__ <= i__1; ++i__) { temp = 1.; ixi = (i__2 = ix[i__], iabs(i__2)); /* Computing MAX */ d__2 = (d__1 = xl[i__], fabs(d__1)); if ((ixi == 1 || ixi == 3 || ixi == 4) && x[i__] <= xl[i__] + * eps9 * MAX2(d__2,temp)) { x[i__] = xl[i__]; } /* Computing MAX */ d__2 = (d__1 = xu[i__], fabs(d__1)); if ((ixi == 2 || ixi == 3 || ixi == 4) && x[i__] >= xu[i__] - * eps9 * MAX2(d__2,temp)) { x[i__] = xu[i__]; } /* L1: */ } } return; } /* luksan_pcbs04__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE PNINT1 ALL SYSTEMS 91/12/01 */ /* PURPOSE : */ /* EXTRAPOLATION OR INTERPOLATION FOR LINE SEARCH WITH DIRECTIONAL */ /* DERIVATIVES. */ /* PARAMETERS : */ /* RI RL LOWER VALUE OF THE STEPSIZE PARAMETER. */ /* RI RU UPPER VALUE OF THE STEPSIZE PARAMETER. */ /* RI FL VALUE OF THE OBJECTIVE FUNCTION FOR R=RL. */ /* RI FU VALUE OF THE OBJECTIVE FUNCTION FOR R=RU. */ /* RI PL DIRECTIONAL DERIVATIVE FOR R=RL. */ /* RI PU DIRECTIONAL DERIVATIVE FOR R=RU. */ /* RO R VALUE OF THE STEPSIZE PARAMETER OBTAINED. */ /* II MODE MODE OF LINE SEARCH. */ /* II MTYP METHOD SELECTION. MTYP=1-BISECTION. MTYP=2-QUADRATIC */ /* INTERPOLATION (WITH ONE DIRECTIONAL DERIVATIVE). */ /* MTYP=3-QUADRATIC INTERPOLATION (WITH TWO DIRECTIONAL */ /* DERIVATIVES). MTYP=4-CUBIC INTERPOLATION. MTYP=5-CONIC */ /* INTERPOLATION. */ /* IO MERR ERROR INDICATOR. MERR=0 FOR NORMAL RETURN. */ /* METHOD : */ /* EXTRAPOLATION OR INTERPOLATION WITH STANDARD MODEL FUNCTIONS. */ void luksan_pnint1__(double *rl, double *ru, double *fl, double *fu, double *pl, double *pu, double *r__, int *mode, int *mtyp, int *merr) { /* System generated locals */ double d__1, d__2; /* Local variables */ double a, b, c__, d__, den, dis; int ntyp; *merr = 0; if (*mode <= 0) { return; } if (*pl >= 0.) { *merr = 2; return; } else if (*ru <= *rl) { *merr = 3; return; } for (ntyp = *mtyp; ntyp >= 1; --ntyp) { if (ntyp == 1) { /* BISECTION */ if (*mode == 1) { *r__ = *ru * 4.; return; } else { *r__ = (*rl + *ru) * .5; return; } } else if (ntyp == *mtyp) { a = (*fu - *fl) / (*pl * (*ru - *rl)); b = *pu / *pl; } if (ntyp == 2) { /* QUADRATIC EXTRAPOLATION OR INTERPOLATION WITH ONE DIRECTIONAL */ /* DERIVATIVE */ den = (1. - a) * 2.; } else if (ntyp == 3) { /* QUADRATIC EXTRAPOLATION OR INTERPOLATION WITH TWO DIRECTIONAL */ /* DERIVATIVES */ den = 1. - b; } else if (ntyp == 4) { /* CUBIC EXTRAPOLATION OR INTERPOLATION */ c__ = b - a * 2. + 1.; d__ = b - a * 3. + 2.; dis = d__ * d__ - c__ * 3.; if (dis < 0.) { goto L1; } den = d__ + sqrt(dis); } else if (ntyp == 5) { /* CONIC EXTRAPOLATION OR INTERPOLATION */ dis = a * a - b; if (dis < 0.) { goto L1; } den = a + sqrt(dis); if (den <= 0.) { goto L1; } /* Computing 3rd power */ d__1 = 1. / den; den = 1. - b * (d__1 * (d__1 * d__1)); } if (*mode == 1 && den > 0. && den < 1.) { /* EXTRAPOLATION ACCEPTED */ *r__ = *rl + (*ru - *rl) / den; /* Computing MAX */ d__1 = *r__, d__2 = *ru * 1.1; *r__ = MAX2(d__1,d__2); /* Computing MIN */ d__1 = *r__, d__2 = *ru * 1e3; *r__ = MIN2(d__1,d__2); return; } else if (*mode == 2 && den > 1.) { /* INTERPOLATION ACCEPTED */ *r__ = *rl + (*ru - *rl) / den; if (*rl == 0.) { /* Computing MAX */ d__1 = *r__, d__2 = *rl + (*ru - *rl) * .01; *r__ = MAX2(d__1,d__2); } else { /* Computing MAX */ d__1 = *r__, d__2 = *rl + (*ru - *rl) * .1; *r__ = MAX2(d__1,d__2); } /* Computing MIN */ d__1 = *r__, d__2 = *rl + (*ru - *rl) * .9; *r__ = MIN2(d__1,d__2); return; } L1: ; } return; } /* luksan_pnint1__ */ /* save and restore state, replacing old non-reeentrant implementation that used static local variables */ #define SS(var) state->var = var #define SAVE_STATE SS(fl); SS(fu); SS(pl); SS(rl); SS(pu); SS(ru); \ SS(mes1); SS(mes2); SS(mes3); SS(mode); SS(mtyp) #define RS(var) var = state->var #define RESTORE_STATE RS(fl); RS(fu); RS(pl); RS(rl); RS(pu); RS(ru); \ RS(mes1); RS(mes2); RS(mes3); RS(mode); RS(mtyp) /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE PS1L01 ALL SYSTEMS 97/12/01 * PURPOSE : * STANDARD LINE SEARCH WITH DIRECTIONAL DERIVATIVES. * * PARAMETERS : * RO R VALUE OF THE STEPSIZE PARAMETER. * RO RP PREVIOUS VALUE OF THE STEPSIZE PARAMETER. * RO F VALUE OF THE OBJECTIVE FUNCTION. * RI FO INITIAL VALUE OF THE OBJECTIVE FUNCTION. * RO FP PREVIOUS VALUE OF THE OBJECTIVE FUNCTION. * RO P VALUE OF THE DIRECTIONAL DERIVATIVE. * RI PO INITIAL VALUE OF THE DIRECTIONAL DERIVATIVE. * RO PP PREVIOUS VALUE OF THE DIRECTIONAL DERIVATIVE. * RI FMIN LOWER BOUND FOR VALUE OF THE OBJECTIVE FUNCTION. * RI MAXF UPPER BOUND FOR VALUE OF THE OBJECTIVE FUNCTION. * RI RMIN MINIMUM VALUE OF THE STEPSIZE PARAMETER * RI RMAX MAXIMUM VALUE OF THE STEPSIZE PARAMETER * RI TOLS TERMINATION TOLERANCE FOR LINE SEARCH (IN TEST ON THE * CHANGE OF THE FUNCTION VALUE). * RI TOLP TERMINATION TOLERANCE FOR LINE SEARCH (IN TEST ON THE * CHANGE OF THE DIRECTIONAL DERIVATIVE). * RO PAR1 PARAMETER FOR CONTROLLED SCALING OF VARIABLE METRIC * UPDATES. * RO PAR2 PARAMETER FOR CONTROLLED SCALING OF VARIABLE METRIC * UPDATES. * II KD DEGREE OF REQUIRED DERIVATIVES. * IO LD DEGREE OF PREVIOUSLY COMPUTED DERIVATIVES OF OBJECTIVE * II NIT ACTUAL NUMBER OF ITERATIONS. * II KIT NUMBER OF THE ITERATION AFTER LAST RESTART. * IO NRED ACTUAL NUMBER OF EXTRAPOLATIONS OR INTERPOLATIONS. * II MRED MAXIMUM NUMBER OF EXTRAPOLATIONS OR INTERPOLATIONS. * IO MAXST MAXIMUM STEPSIZE INDICATOR. MAXST=0 OR MAXST=1 IF MAXIMUM * STEPSIZE WAS NOT OR WAS REACHED. * II IEST LOWER BOUND SPECIFICATION. IEST=0 OR IEST=1 IF LOWER BOUND * IS NOT OR IS GIVEN. * II INITS CHOICE OF THE INITIAL STEPSIZE. INITS=0-INITIAL STEPSIZE * IS SPECIFIED IN THE CALLING PROGRAM. INITS=1-UNIT INITIAL * STEPSIZE. INITS=2-COMBINED UNIT AND QUADRATICALLY ESTIMATED * INITIAL STEPSIZE. INITS=3-QUADRATICALLY ESTIMATED INITIAL * STEPSIZE. * IO ITERS TERMINATION INDICATOR. ITERS=0-ZERO STEP. ITERS=1-PERFECT * LINE SEARCH. ITERS=2 GOLDSTEIN STEPSIZE. ITERS=3-CURRY * STEPSIZE. ITERS=4-EXTENDED CURRY STEPSIZE. * ITERS=5-ARMIJO STEPSIZE. ITERS=6-FIRST STEPSIZE. * ITERS=7-MAXIMUM STEPSIZE. ITERS=8-UNBOUNDED FUNCTION. * ITERS=-1-MRED REACHED. ITERS=-2-POSITIVE DIRECTIONAL * DERIVATIVE. ITERS=-3-ERROR IN INTERPOLATION. * II KTERS TERMINATION SELECTION. KTERS=1-PERFECT LINE SEARCH. * KTERS=2-GOLDSTEIN STEPSIZE. KTERS=3-CURRY STEPSIZE. * KTERS=4-EXTENDED CURRY STEPSIZE. KTERS=5-ARMIJO STEPSIZE. * KTERS=6-FIRST STEPSIZE. * II MES METHOD SELECTION. MES=1-BISECTION. MES=2-QUADRATIC * INTERPOLATION (WITH ONE DIRECTIONAL DERIVATIVE). * MES=3-QUADRATIC INTERPOLATION (WITH TWO DIRECTIONAL * DERIVATIVES). MES=4-CUBIC INTERPOLATION. MES=5-CONIC * INTERPOLATION. * IU ISYS CONTROL PARAMETER. * * SUBPROGRAM USED : * S PNINT1 EXTRAPOLATION OR INTERPOLATION WITH DIRECTIONAL * DERIVATIVES. * * METHOD : * SAFEGUARDED EXTRAPOLATION AND INTERPOLATION WITH STANDARD TERMINATION * CRITERIA. */ void luksan_ps1l01__(double *r__, double *rp, double *f, double *fo, double *fp, double *p, double *po, double *pp, double *minf, double *maxf, double *rmin, double *rmax, double *tols, double * tolp, double *par1, double *par2, int *kd, int *ld, int *nit, int *kit, int *nred, int *mred, int * maxst, int *iest, int *inits, int *iters, int *kters, int *mes, int *isys, ps1l01_state *state) { /* System generated locals */ double d__1, d__2; /* Local variables */ unsigned l1, l2, l3, m1, l5, m2, l7, m3; double fl, fu, pl, rl, pu, ru; int mes1, mes2, mes3, mode; int merr; int mtyp; int init1; double rtemp; RESTORE_STATE; if (*isys == 1) { goto L3; } mes1 = 2; mes2 = 2; mes3 = 2; *iters = 0; if (*po >= 0.) { *r__ = 0.; *iters = -2; goto L4; } if (*rmax <= 0.) { *iters = 0; goto L4; } /* INITIAL STEPSIZE SELECTION */ if (*inits > 0) { rtemp = *minf - *f; } else if (*iest == 0) { rtemp = *f - *fp; } else { /* Computing MAX */ d__1 = *f - *fp, d__2 = *minf - *f; rtemp = MAX2(d__1,d__2); } init1 = iabs(*inits); *rp = 0.; *fp = *fo; *pp = *po; if (init1 == 0) { } else if (init1 == 1 || (*inits >= 1 && *iest == 0)) { *r__ = 1.; } else if (init1 == 2) { /* Computing MIN */ d__1 = 1., d__2 = rtemp * 4. / *po; *r__ = MIN2(d__1,d__2); } else if (init1 == 3) { /* Computing MIN */ d__1 = 1., d__2 = rtemp * 2. / *po; *r__ = MIN2(d__1,d__2); } else if (init1 == 4) { *r__ = rtemp * 2. / *po; } *r__ = MAX2(*r__,*rmin); *r__ = MIN2(*r__,*rmax); mode = 0; ru = 0.; fu = *fo; pu = *po; /* NEW STEPSIZE SELECTION (EXTRAPOLATION OR INTERPOLATION) */ L2: luksan_pnint1__(&rl, &ru, &fl, &fu, &pl, &pu, r__, &mode, &mtyp, &merr); if (merr > 0) { *iters = -merr; goto L4; } else if (mode == 1) { --(*nred); *r__ = MIN2(*r__,*rmax); } else if (mode == 2) { ++(*nred); } /* COMPUTATION OF THE NEW FUNCTION VALUE AND THE NEW DIRECTIONAL */ /* DERIVATIVE */ *kd = 1; *ld = -1; *isys = 1; SAVE_STATE; return; L3: if (mode == 0) { *par1 = *p / *po; *par2 = *f - *fo; } if (*iters != 0) { goto L4; } if (*f <= *minf) { *iters = 7; goto L4; } else { l1 = *r__ <= *rmin && *nit != *kit; l2 = *r__ >= *rmax; l3 = *f - *fo <= *tols * *r__ * *po; l5 = *p >= *tolp * *po || (mes2 == 2 && mode == 2); l7 = mes2 <= 2 || mode != 0; m1 = FALSE_; m2 = FALSE_; m3 = l3; if (mes3 >= 1) { m1 = fabs(*p) <= fabs(*po) * .01 && *fo - *f >= fabs(*fo) * 9.9999999999999994e-12; l3 = l3 || m1; } if (mes3 >= 2) { m2 = fabs(*p) <= fabs(*po) * .5 && (d__1 = *fo - *f, fabs(d__1)) <= fabs(*fo) * 2.0000000000000001e-13; l3 = l3 || m2; } *maxst = 0; if (l2) { *maxst = 1; } } /* TEST ON TERMINATION */ if (l1 && ! l3) { *iters = 0; goto L4; } else if (l2 && l3 && ! l5) { *iters = 7; goto L4; } else if (m3 && mes1 == 3) { *iters = 5; goto L4; } else if (l3 && l5 && l7) { *iters = 4; goto L4; } else if (*kters < 0 || (*kters == 6 && l7)) { *iters = 6; goto L4; } else if (iabs(*nred) >= *mred) { *iters = -1; goto L4; } else { *rp = *r__; *fp = *f; *pp = *p; mode = MAX2(mode,1); mtyp = iabs(*mes); if (*f >= *maxf) { mtyp = 1; } } if (mode == 1) { /* INTERVAL CHANGE AFTER EXTRAPOLATION */ rl = ru; fl = fu; pl = pu; ru = *r__; fu = *f; pu = *p; if (! l3) { *nred = 0; mode = 2; } else if (mes1 == 1) { mtyp = 1; } } else { /* INTERVAL CHANGE AFTER INTERPOLATION */ if (! l3) { ru = *r__; fu = *f; pu = *p; } else { rl = *r__; fl = *f; pl = *p; } } goto L2; L4: *isys = 0; SAVE_STATE; return; } /* luksan_ps1l01__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE PULSP3 ALL SYSTEMS 02/12/01 * PURPOSE : * LIMITED STORAGE VARIABLE METRIC UPDATE. * * PARAMETERS : * II N NUMBER OF VARIABLES (NUMBER OF ROWS OF XM). * II M NUMBER OF COLUMNS OF XM. * II MF MAXIMUM NUMBER OF COLUMNS OF XM. * RI XM(N*M) RECTANGULAR MATRIX IN THE PRODUCT FORM SHIFTED BROYDEN * METHOD (STORED COLUMNWISE): H-SIGMA*I=XM*TRANS(XM) * RO GR(M) MATRIX TRANS(XM)*GO. * RU XO(N) VECTORS OF VARIABLES DIFFERENCE XO AND VECTOR XO-TILDE. * RU GO(N) GRADIENT DIFFERENCE GO AND VECTOR XM*TRANS(XM)*GO. * RI R STEPSIZE PARAMETER. * RI PO OLD DIRECTIONAL DERIVATIVE (MULTIPLIED BY R) * RU SIG SCALING PARAMETER (ZETA AND SIGMA). * IO ITERH TERMINATION INDICATOR. ITERH<0-BAD DECOMPOSITION. * ITERH=0-SUCCESSFUL UPDATE. ITERH>0-NONPOSITIVE PARAMETERS. * II MET3 CHOICE OF SIGMA (1-CONSTANT, 2-QUADRATIC EQUATION). * * SUBPROGRAMS USED : * S MXDRMM MULTIPLICATION OF A ROWWISE STORED DENSE RECTANGULAR * MATRIX BY A VECTOR. * S MXDCMU UPDATE OF A COLUMNWISE STORED DENSE RECTANGULAR MATRIX. * WITH CONTROLLING OF POSITIVE DEFINITENESS. * S MXVDIR VECTOR AUGMENTED BY A SCALED VECTOR. * RF MXVDOT DOT PRODUCT OF VECTORS. * S MXVSCL SCALING OF A VECTOR. * * METHOD : * SHIFTED BFGS METHOD IN THE PRODUCT FORM. */ void luksan_pulsp3__(int *n, int *m, int *mf, double *xm, double *gr, double *xo, double *go, double *r__, double *po, double *sig, int *iterh, int *met3) { /* System generated locals */ double d__1, d__2, d__3, d__4; /* Builtin functions */ /* Local variables */ double a, b, c__, aa, bb, ah, den, par, pom; /* Parameter adjustments */ --go; --xo; --gr; --xm; /* Function Body */ if (*m >= *mf) { return; } b = luksan_mxvdot__(n, &xo[1], &go[1]); if (b <= 0.) { *iterh = 2; goto L22; } luksan_mxdrmm__(n, m, &xm[1], &go[1], &gr[1]); ah = luksan_mxvdot__(n, &go[1], &go[1]); aa = luksan_mxvdot__(m, &gr[1], &gr[1]); a = aa + ah * *sig; c__ = -(*r__) * *po; /* DETERMINATION OF THE PARAMETER SIG (SHIFT) */ par = 1.; pom = b / ah; if (a > 0.) { den = luksan_mxvdot__(n, &xo[1], &xo[1]); if (*met3 <= 4) { /* Computing MAX */ d__1 = 0., d__2 = 1. - aa / a; /* Computing MAX */ d__3 = 0., d__4 = 1. - b * b / (den * ah); *sig = sqrt((MAX2(d__1,d__2))) / (sqrt((MAX2(d__3,d__4))) + 1.) * pom; } else { /* Computing MAX */ d__1 = 0., d__2 = *sig * ah / a; /* Computing MAX */ d__3 = 0., d__4 = 1. - b * b / (den * ah); *sig = sqrt((MAX2(d__1,d__2))) / (sqrt((MAX2(d__3,d__4))) + 1.) * pom; } /* Computing MAX */ d__1 = *sig, d__2 = pom * .2; *sig = MAX2(d__1,d__2); /* Computing MIN */ d__1 = *sig, d__2 = pom * .8; *sig = MIN2(d__1,d__2); } else { *sig = pom * .25; } /* COMPUTATION OF SHIFTED XO AND SHIFTED B */ bb = b - ah * *sig; d__1 = -(*sig); luksan_mxvdir__(n, &d__1, &go[1], &xo[1], &xo[1]); /* BFGS-BASED SHIFTED BFGS UPDATE */ pom = 1.; d__1 = -1. / bb; luksan_mxdcmu__(n, m, &xm[1], &d__1, &xo[1], &gr[1]); d__1 = sqrt(par / bb); luksan_mxvscl__(n, &d__1, &xo[1], &xm[*n * *m + 1]); ++(*m); L22: *iterh = 0; return; } /* luksan_pulsp3__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE PULVP3 ALL SYSTEMS 03/12/01 * PURPOSE : * RANK-TWO LIMITED-STORAGE VARIABLE-METRIC METHODS IN THE PRODUCT FORM. * * PARAMETERS : * II N NUMBER OF VARIABLES (NUMBER OF ROWS OF XM). * II M NUMBER OF COLUMNS OF XM. * RI XM(N*M) RECTANGULAR MATRIX IN THE PRODUCT FORM SHIFTED BROYDEN * METHOD (STORED COLUMNWISE): H-SIGMA*I=XM*TRANS(XM) * RO XR(M) VECTOR TRANS(XM)*H**(-1)*XO. * RO GR(M) MATRIX TRANS(XM)*GO. * RA S(N) AUXILIARY VECTORS (H**(-1)*XO AND U). * RA SO(N) AUXILIARY VECTORS ((H-SIGMA*I)*H**(-1)*XO AND V). * RU XO(N) VECTORS OF VARIABLES DIFFERENCE XO AND VECTOR XO-TILDE. * RU GO(N) GRADIENT DIFFERENCE GO AND VECTOR XM*TRANS(XM)*GO. * RI R STEPSIZE PARAMETER. * RI PO OLD DIRECTIONAL DERIVATIVE (MULTIPLIED BY R) * RU SIG SCALING PARAMETER (ZETA AND SIGMA). * IO ITERH TERMINATION INDICATOR. ITERH<0-BAD DECOMPOSITION. * ITERH=0-SUCCESSFUL UPDATE. ITERH>0-NONPOSITIVE PARAMETERS. * II MET2 CHOICE OF THE CORRECTION PARAMETER (1-THE UNIT VALUE, * 2-THE BALANCING VALUE, 3-THE SQUARE ROOT, 4-THE GEOMETRIC * MEAN). * II MET3 CHOICE OF THE SHIFT PARAMETER (4-THE FIRST FORMULA, * 5-THE SECOND FORMULA). * II MET5 CHOICE OF THE METHOD (1-RANK-ONE METHOD, 2-RANK-TWO * METHOD). * * SUBPROGRAMS USED : * S MXDRMM MULTIPLICATION OF A ROWWISE STORED DENSE RECTANGULAR * MATRIX BY A VECTOR. * S MXDCMU UPDATE OF A COLUMNWISE STORED DENSE RECTANGULAR MATRIX. * WITH CONTROLLING OF POSITIVE DEFINITENESS. RANK-ONE FORMULA. * S MXDCMV UPDATE OF A COLUMNWISE STORED DENSE RECTANGULAR MATRIX. * WITH CONTROLLING OF POSITIVE DEFINITENESS. RANK-TWO FORMULA. * S MXVDIR VECTOR AUGMENTED BY A SCALED VECTOR. * RF MXVDOT DOT PRODUCT OF VECTORS. * S MXVLIN LINEAR COMBINATION OF TWO VECTORS. * S MXVSCL SCALING OF A VECTOR. * * METHOD : * RANK-ONE LIMITED-STORAGE VARIABLE-METRIC METHOD IN THE PRODUCT FORM. */ void luksan_pulvp3__(int *n, int *m, double *xm, double *xr, double *gr, double *s, double *so, double *xo, double *go, double *r__, double *po, double *sig, int *iterh, int *met2, int *met3, int *met5) { /* System generated locals */ double d__1, d__2, d__3, d__4; /* Builtin functions */ /* Local variables */ double a, b, c__, aa, bb, cc, ah, den, par, pom, zet; /* Parameter adjustments */ --go; --xo; --so; --s; --gr; --xr; --xm; /* Function Body */ zet = *sig; /* COMPUTATION OF B */ b = luksan_mxvdot__(n, &xo[1], &go[1]); if (b <= 0.) { *iterh = 2; goto L22; } /* COMPUTATION OF GR=TRANS(XM)*GO, XR=TRANS(XM)*H**(-1)*XO */ /* AND S=H**(-1)*XO, SO=(H-SIGMA*I)*H**(-1)*XO. COMPUTATION */ /* OF AA=GR*GR, BB=GR*XR, CC=XR*XR. COMPUTATION OF A AND C. */ luksan_mxdrmm__(n, m, &xm[1], &go[1], &gr[1]); luksan_mxvscl__(n, r__, &s[1], &s[1]); luksan_mxdrmm__(n, m, &xm[1], &s[1], &xr[1]); d__1 = -(*sig); luksan_mxvdir__(n, &d__1, &s[1], &xo[1], &so[1]); ah = luksan_mxvdot__(n, &go[1], &go[1]); aa = luksan_mxvdot__(m, &gr[1], &gr[1]); bb = luksan_mxvdot__(m, &gr[1], &xr[1]); cc = luksan_mxvdot__(m, &xr[1], &xr[1]); a = aa + ah * *sig; c__ = -(*r__) * *po; /* DETERMINATION OF THE PARAMETER SIG (SHIFT) */ pom = b / ah; if (a > 0.) { den = luksan_mxvdot__(n, &xo[1], &xo[1]); if (*met3 <= 4) { /* Computing MAX */ d__1 = 0., d__2 = 1. - aa / a; /* Computing MAX */ d__3 = 0., d__4 = 1. - b * b / (den * ah); *sig = sqrt((MAX2(d__1,d__2))) / (sqrt((MAX2(d__3,d__4))) + 1.) * pom; } else { /* Computing MAX */ d__1 = 0., d__2 = *sig * ah / a; /* Computing MAX */ d__3 = 0., d__4 = 1. - b * b / (den * ah); *sig = sqrt((MAX2(d__1,d__2))) / (sqrt((MAX2(d__3,d__4))) + 1.) * pom; } /* Computing MAX */ d__1 = *sig, d__2 = pom * .2; *sig = MAX2(d__1,d__2); /* Computing MIN */ d__1 = *sig, d__2 = pom * .8; *sig = MIN2(d__1,d__2); } else { *sig = pom * .25; } /* COMPUTATION OF SHIFTED XO AND SHIFTED B */ b -= ah * *sig; d__1 = -(*sig); luksan_mxvdir__(n, &d__1, &go[1], &xo[1], &xo[1]); /* COMPUTATION OF THE PARAMETER RHO (CORRECTION) */ if (*met2 <= 1) { par = 1.; } else if (*met2 == 2) { par = *sig * ah / b; } else if (*met2 == 3) { par = sqrt(1. - aa / a); } else if (*met2 == 4) { par = sqrt(sqrt(1. - aa / a) * (*sig * ah / b)); } else { par = zet / (zet + *sig); } /* COMPUTATION OF THE PARAMETER THETA (BFGS) */ d__1 = sqrt(par * b / cc); pom = copysign(d__1, bb); /* COMPUTATION OF Q AND P */ if (*met5 == 1) { /* RANK ONE UPDATE OF XM */ luksan_mxvdir__(m, &pom, &xr[1], &gr[1], &xr[1]); luksan_mxvlin__(n, &par, &xo[1], &pom, &so[1], &s[1]); d__1 = -1. / (par * b + pom * bb); luksan_mxdcmu__(n, m, &xm[1], &d__1, &s[1], &xr[1]); } else { /* RANK TWO UPDATE OF XM */ d__1 = par / pom - bb / b; luksan_mxvdir__(n, &d__1, &xo[1], &so[1], &s[1]); d__1 = -1. / b; d__2 = -1. / cc; luksan_mxdcmv__(n, m, &xm[1], &d__1, &xo[1], &gr[1], &d__2, &s[1], &xr[1]); } L22: *iterh = 0; return; } /* luksan_pulvp3__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE PYADC0 ALL SYSTEMS 98/12/01 * PURPOSE : * NEW SIMPLE BOUNDS ARE ADDED TO THE ACTIVE SET. * * PARAMETERS : * II NF DECLARED NUMBER OF VARIABLES. * II N REDUCED NUMBER OF VARIABLES. * RI X(NF) VECTOR OF VARIABLES. * II IX(NF) VECTOR CONTAINING TYPES OF BOUNDS. * RI XL(NF) VECTOR CONTAINING LOWER BOUNDS FOR VARIABLES. * RI XU(NF) VECTOR CONTAINING UPPER BOUNDS FOR VARIABLES. * IO INEW NUMBER OF ACTIVE CONSTRAINTS. */ void luksan_pyadc0__(int *nf, int *n, double *x, int *ix, double *xl, double *xu, int *inew) { /* System generated locals */ int i__1; /* Local variables */ int i__, ii, ixi; /* Parameter adjustments */ --ix; --x; --xl; --xu; /* Function Body */ *n = *nf; *inew = 0; i__1 = *nf; for (i__ = 1; i__ <= i__1; ++i__) { ii = ix[i__]; ixi = iabs(ii); if (ixi >= 5) { ix[i__] = -ixi; } else if ((ixi == 1 || ixi == 3 || ixi == 4) && x[i__] <= xl[i__]) { x[i__] = xl[i__]; if (ixi == 4) { ix[i__] = -3; } else { ix[i__] = -ixi; } --(*n); if (ii > 0) { ++(*inew); } } else if ((ixi == 2 || ixi == 3 || ixi == 4) && x[i__] >= xu[i__]) { x[i__] = xu[i__]; if (ixi == 3) { ix[i__] = -4; } else { ix[i__] = -ixi; } --(*n); if (ii > 0) { ++(*inew); } } /* L1: */ } return; } /* luksan_pyadc0__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE PYFUT1 ALL SYSTEMS 98/12/01 * PURPOSE : * TERMINATION CRITERIA AND TEST ON RESTART. * * PARAMETERS : * II N ACTUAL NUMBER OF VARIABLES. * RI F NEW VALUE OF THE OBJECTIVE FUNCTION. * RI FO OLD VALUE OF THE OBJECTIVE FUNCTION. * RI UMAX MAXIMUM ABSOLUTE VALUE OF THE NEGATIVE LAGRANGE MULTIPLIER. * RO GMAX NORM OF THE TRANSFORMED GRADIENT. * RI DMAX MAXIMUM RELATIVE DIFFERENCE OF VARIABLES. * RI TOLX LOWER BOUND FOR STEPLENGTH. * RI TOLF LOWER BOUND FOR FUNCTION DECREASE. * RI TOLB LOWER BOUND FOR FUNCTION VALUE. * RI TOLG LOWER BOUND FOR GRADIENT. * II KD DEGREE OF REQUIRED DERIVATIVES. * IU NIT ACTUAL NUMBER OF ITERATIONS. * II KIT NUMBER OF THE ITERATION AFTER RESTART. * II MIT MAXIMUM NUMBER OF ITERATIONS. * IU NFV ACTUAL NUMBER OF COMPUTED FUNCTION VALUES. * II MFV MAXIMUM NUMBER OF COMPUTED FUNCTION VALUES. * IU NFG ACTUAL NUMBER OF COMPUTED GRADIENT VALUES. * II MFG MAXIMUM NUMBER OF COMPUTED GRADIENT VALUES. * IU NTESX ACTUAL NUMBER OF TESTS ON STEPLENGTH. * II MTESX MAXIMUM NUMBER OF TESTS ON STEPLENGTH. * IU NTESF ACTUAL NUMBER OF TESTS ON FUNCTION DECREASE. * II MTESF MAXIMUM NUMBER OF TESTS ON FUNCTION DECREASE. * II IRES1 RESTART SPECIFICATION. RESTART IS PERFORMED AFTER * IRES1*N+IRES2 ITERATIONS. * II IRES2 RESTART SPECIFICATION. RESTART IS PERFORMED AFTER * IRES1*N+IRES2 ITERATIONS. * IU IREST RESTART INDICATOR. RESTART IS PERFORMED IF IREST>0. * II ITERS TERMINATION INDICATOR FOR STEPLENGTH DETERMINATION. * ITERS=0 FOR ZERO STEP. * IO ITERM TERMINATION INDICATOR. ITERM=1-TERMINATION AFTER MTESX * UNSUFFICIENT STEPLENGTHS. ITERM=2-TERMINATION AFTER MTESF * UNSUFFICIENT FUNCTION DECREASES. ITERM=3-TERMINATION ON LOWER * BOUND FOR FUNCTION VALUE. ITERM=4-TERMINATION ON LOWER BOUND * FOR GRADIENT. ITERM=11-TERMINATION AFTER MAXIMUM NUMBER OF * ITERATIONS. ITERM=12-TERMINATION AFTER MAXIMUM NUMBER OF * COMPUTED FUNCTION VALUES. */ void luksan_pyfut1__(int *n, double *f, double *fo, double *umax, double *gmax, int xstop, /* double *dmax__, */ const nlopt_stopping *stop, double *tolg, int *kd, int *nit, int *kit, int *mit, int *nfg, int *mfg, int *ntesx, int *mtesx, int *ntesf, int *mtesf, int *ites, int *ires1, int *ires2, int *irest, int *iters, int *iterm) { /* System generated locals */ double d__1, d__2; /* Builtin functions */ if (*iterm < 0) { return; } if (*ites <= 0) { goto L1; } if (*iters == 0) { goto L1; } if (*nit <= 0) { /* Computing MIN */ d__1 = sqrt((fabs(*f))), d__2 = fabs(*f) / 10.; *fo = *f + MIN2(d__1,d__2); } if (nlopt_stop_forced(stop)) { *iterm = -999; return; } if (*f <= stop->minf_max /* *tolb */) { *iterm = 3; return; } if (*kd > 0) { if (*gmax <= *tolg && *umax <= *tolg) { *iterm = 4; return; } } if (*nit <= 0) { *ntesx = 0; *ntesf = 0; } if (xstop) /* (*dmax__ <= *tolx) */ { *iterm = 1; ++(*ntesx); if (*ntesx >= *mtesx) { return; } } else { *ntesx = 0; } if (nlopt_stop_ftol(stop, *f, *fo)) { *iterm = 2; ++(*ntesf); if (*ntesf >= *mtesf) { return; } } else { *ntesf = 0; } L1: if (*nit >= *mit) { *iterm = 11; return; } if (nlopt_stop_evals(stop)) /* (*nfv >= *mfv) */ { *iterm = 12; return; } if (*nfg >= *mfg) { *iterm = 13; return; } *iterm = 0; if (*n > 0 && *nit - *kit >= *ires1 * *n + *ires2) { *irest = MAX2(*irest,1); } ++(*nit); return; } /* luksan_pyfut1__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE PYRMC0 ALL SYSTEMS 98/12/01 * PURPOSE : * OLD SIMPLE BOUND IS REMOVED FROM THE ACTIVE SET. TRANSFORMED * GRADIENT OF THE OBJECTIVE FUNCTION IS UPDATED. * * PARAMETERS : * II NF DECLARED NUMBER OF VARIABLES. * II N REDUCED NUMBER OF VARIABLES. * II IX(NF) VECTOR CONTAINING TYPES OF BOUNDS. * RI G(NF) GRADIENT OF THE OBJECTIVE FUNCTION. * RI EPS8 TOLERANCE FOR CONSTRAINT TO BE REMOVED. * RI UMAX MAXIMUM ABSOLUTE VALUE OF THE NEGATIVE LAGRANGE MULTIPLIER. * RI GMAX NORM OF THE TRANSFORMED GRADIENT. * RO RMAX MAXIMUM VALUE OF THE STEPSIZE PARAMETER. * II IOLD NUMBER OF REMOVED CONSTRAINTS. * IU IREST RESTART INDICATOR. */ void luksan_pyrmc0__(int *nf, int *n, int *ix, double *g, double *eps8, double *umax, double *gmax, double *rmax, int *iold, int *irest) { /* System generated locals */ int i__1, i__2, i__3; /* Local variables */ int i__, ixi; /* Parameter adjustments */ --g; --ix; /* Function Body */ if (*n == 0 || *rmax > 0.) { if (*umax > *eps8 * *gmax) { *iold = 0; i__1 = *nf; for (i__ = 1; i__ <= i__1; ++i__) { ixi = ix[i__]; if (ixi >= 0) { } else if (ixi <= -5) { } else if ((ixi == -1 || ixi == -3) && -g[i__] <= 0.) { } else if ((ixi == -2 || ixi == -4) && g[i__] <= 0.) { } else { ++(*iold); /* Computing MIN */ i__3 = (i__2 = ix[i__], iabs(i__2)); ix[i__] = MIN2(i__3,3); if (*rmax == 0.) { goto L2; } } /* L1: */ } L2: if (*iold > 1) { *irest = MAX2(*irest,1); } } } return; } /* luksan_pyrmc0__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE PYTRCD ALL SYSTEMS 98/12/01 * PURPOSE : * VECTORS OF VARIABLES DIFFERENCE AND GRADIENTS DIFFERENCE ARE COMPUTED * AND SCALED AND REDUCED. TEST VALUE DMAX IS DETERMINED. * * PARAMETERS : * II NF DECLARED NUMBER OF VARIABLES. * RI X(NF) VECTOR OF VARIABLES. * II IX(NF) VECTOR CONTAINING TYPES OF BOUNDS. * RU XO(NF) VECTORS OF VARIABLES DIFFERENCE. * RI G(NF) GRADIENT OF THE OBJECTIVE FUNCTION. * RU GO(NF) GRADIENTS DIFFERENCE. * RO R VALUE OF THE STEPSIZE PARAMETER. * RO F NEW VALUE OF THE OBJECTIVE FUNCTION. * RI FO OLD VALUE OF THE OBJECTIVE FUNCTION. * RO P NEW VALUE OF THE DIRECTIONAL DERIVATIVE. * RI PO OLD VALUE OF THE DIRECTIONAL DERIVATIVE. * RO DMAX MAXIMUM RELATIVE DIFFERENCE OF VARIABLES. * II KBF SPECIFICATION OF SIMPLE BOUNDS. KBF=0-NO SIMPLE BOUNDS. * KBF=1-ONE SIDED SIMPLE BOUNDS. KBF=2=TWO SIDED SIMPLE BOUNDS. * IO KD DEGREE OF REQUIRED DERIVATIVES. * IO LD DEGREE OF COMPUTED DERIVATIVES. * II ITERS TERMINATION INDICATOR FOR STEPLENGTH DETERMINATION. * ITERS=0 FOR ZERO STEP. * * SUBPROGRAMS USED : * S MXVDIF DIFFERENCE OF TWO VECTORS. * S MXVSAV DIFFERENCE OF TWO VECTORS WITH COPYING AND SAVING THE * SUBSTRACTED ONE. */ void luksan_pytrcd__(int *nf, double *x, int *ix, double *xo, double *g, double *go, double *r__, double *f, double *fo, double *p, double *po, double *dmax__, int *kbf, int *kd, int *ld, int * iters) { /* System generated locals */ int i__1; double d__1, d__2, d__3, d__4, d__5; /* Local variables */ int i__; /* Parameter adjustments */ --go; --g; --xo; --ix; --x; /* Function Body */ if (*iters > 0) { luksan_mxvdif__(nf, &x[1], &xo[1], &xo[1]); luksan_mxvdif__(nf, &g[1], &go[1], &go[1]); *po = *r__ * *po; *p = *r__ * *p; } else { *f = *fo; *p = *po; luksan_mxvsav__(nf, &x[1], &xo[1]); luksan_mxvsav__(nf, &g[1], &go[1]); *ld = *kd; } *dmax__ = 0.; i__1 = *nf; for (i__ = 1; i__ <= i__1; ++i__) { if (*kbf > 0) { if (ix[i__] < 0) { xo[i__] = 0.; go[i__] = 0.; goto L1; } } /* Computing MAX */ /* Computing MAX */ d__5 = (d__2 = x[i__], fabs(d__2)); d__3 = *dmax__, d__4 = (d__1 = xo[i__], fabs(d__1)) / MAX2(d__5,1.); *dmax__ = MAX2(d__3,d__4); L1: ; } return; } /* luksan_pytrcd__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE PYTRCG ALL SYSTEMS 99/12/01 * PURPOSE : * GRADIENT OF THE OBJECTIVE FUNCTION IS SCALED AND REDUCED. TEST VALUES * GMAX AND UMAX ARE COMPUTED. * * PARAMETERS : * II NF DECLARED NUMBER OF VARIABLES. * II N ACTUAL NUMBER OF VARIABLES. * II IX(NF) VECTOR CONTAINING TYPES OF BOUNDS. * RI G(NF) GRADIENT OF THE OBJECTIVE FUNCTION. * RI UMAX MAXIMUM ABSOLUTE VALUE OF THE NEGATIVE LAGRANGE MULTIPLIER. * RI GMAX NORM OF THE TRANSFORMED GRADIENT. * II KBF SPECIFICATION OF SIMPLE BOUNDS. KBF=0-NO SIMPLE BOUNDS. * KBF=1-ONE SIDED SIMPLE BOUNDS. KBF=2=TWO SIDED SIMPLE BOUNDS. * II IOLD INDEX OF THE REMOVED CONSTRAINT. * * SUBPROGRAMS USED : * RF MXVMAX L-INFINITY NORM OF A VECTOR. */ void luksan_pytrcg__(int *nf, int *n, int *ix, double *g, double *umax, double *gmax, int *kbf, int *iold) { /* System generated locals */ int i__1; double d__1, d__2; /* Local variables */ int i__; double temp; /* Parameter adjustments */ --g; --ix; /* Function Body */ if (*kbf > 0) { *gmax = 0.; *umax = 0.; *iold = 0; i__1 = *nf; for (i__ = 1; i__ <= i__1; ++i__) { temp = g[i__]; if (ix[i__] >= 0) { /* Computing MAX */ d__1 = *gmax, d__2 = fabs(temp); *gmax = MAX2(d__1,d__2); } else if (ix[i__] <= -5) { } else if ((ix[i__] == -1 || ix[i__] == -3) && *umax + temp >= 0.) { } else if ((ix[i__] == -2 || ix[i__] == -4) && *umax - temp >= 0.) { } else { *iold = i__; *umax = fabs(temp); } /* L1: */ } } else { *umax = 0.; *gmax = luksan_mxvmax__(nf, &g[1]); } *n = *nf; return; } /* luksan_pytrcg__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE PYTRCS ALL SYSTEMS 98/12/01 * PURPOSE : * SCALED AND REDUCED DIRECTION VECTOR IS BACK TRANSFORMED. VECTORS * X,G AND VALUES F,P ARE SAVED. * * PARAMETERS : * II NF DECLARED NUMBER OF VARIABLES. * RI X(NF) VECTOR OF VARIABLES. * II IX(NF) VECTOR CONTAINING TYPES OF BOUNDS. * RO XO(NF) SAVED VECTOR OF VARIABLES. * RI XL(NF) VECTOR CONTAINING LOWER BOUNDS FOR VARIABLES. * RI XU(NF) VECTOR CONTAINING UPPER BOUNDS FOR VARIABLES. * RI G(NF) GRADIENT OF THE OBJECTIVE FUNCTION. * RO GO(NF) SAVED GRADIENT OF THE OBJECTIVE FUNCTION. * RO S(NF) DIRECTION VECTOR. * RO RO SAVED VALUE OF THE STEPSIZE PARAMETER. * RO FP PREVIOUS VALUE OF THE OBJECTIVE FUNCTION. * RU FO SAVED VALUE OF THE OBJECTIVE FUNCTION. * RI F VALUE OF THE OBJECTIVE FUNCTION. * RO PO SAVED VALUE OF THE DIRECTIONAL DERIVATIVE. * RI P VALUE OF THE DIRECTIONAL DERIVATIVE. * RO RMAX MAXIMUM VALUE OF THE STEPSIZE PARAMETER. * RI ETA9 MAXIMUM FOR REAL NUMBERS. * II KBF SPECIFICATION OF SIMPLE BOUNDS. KBF=0-NO SIMPLE BOUNDS. * KBF=1-ONE SIDED SIMPLE BOUNDS. KBF=2=TWO SIDED SIMPLE BOUNDS. * * SUBPROGRAMS USED : * S MXVCOP COPYING OF A VECTOR. */ void luksan_pytrcs__(int *nf, double *x, int *ix, double *xo, double *xl, double *xu, double *g, double *go, double *s, double *ro, double *fp, double *fo, double *f, double *po, double *p, double *rmax, double *eta9, int *kbf) { /* System generated locals */ int i__1; double d__1, d__2; /* Local variables */ int i__; /* Parameter adjustments */ --s; --go; --g; --xu; --xl; --xo; --ix; --x; /* Function Body */ *fp = *fo; *ro = 0.; *fo = *f; *po = *p; luksan_mxvcop__(nf, &x[1], &xo[1]); luksan_mxvcop__(nf, &g[1], &go[1]); if (*kbf > 0) { i__1 = *nf; for (i__ = 1; i__ <= i__1; ++i__) { if (ix[i__] < 0) { s[i__] = 0.; } else { if (ix[i__] == 1 || ix[i__] >= 3) { if (s[i__] < -1. / *eta9) { /* Computing MIN */ d__1 = *rmax, d__2 = (xl[i__] - x[i__]) / s[i__]; *rmax = MIN2(d__1,d__2); } } if (ix[i__] == 2 || ix[i__] >= 3) { if (s[i__] > 1. / *eta9) { /* Computing MIN */ d__1 = *rmax, d__2 = (xu[i__] - x[i__]) / s[i__]; *rmax = MIN2(d__1,d__2); } } } /* L1: */ } } return; } /* luksan_pytrcs__ */ nloptr/src/nlopt_src/luksan/mssubs.c0000644000176000001440000005632313355070635017407 0ustar ripleyusers#include #include "luksan.h" #define MAX2(a,b) ((a) > (b) ? (a) : (b)) #define iabs(a) ((a) < 0 ? -(a) : (a)) /* subroutines extracted from mssubs.for */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* FUNCTION MXVMAX ALL SYSTEMS 91/12/01 */ /* PURPOSE : */ /* L-INFINITY NORM OF A VECTOR. */ /* PARAMETERS : */ /* II N VECTOR DIMENSION. */ /* RI X(N) INPUT VECTOR. */ /* RR MXVMAX L-INFINITY NORM OF THE VECTOR X. */ double luksan_mxvmax__(int *n, double *x) { /* System generated locals */ int i__1; double d__1, d__2, d__3; /* Local variables */ int i__; double mxvmax; /* Parameter adjustments */ --x; /* Function Body */ mxvmax = 0.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { /* Computing MAX */ d__2 = mxvmax, d__3 = (d__1 = x[i__], fabs(d__1)); mxvmax = MAX2(d__2,d__3); /* L1: */ } return mxvmax; } /* luksan_mxvmax__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXVINE ALL SYSTEMS 94/12/01 */ /* PURPOSE : */ /* ELEMENTS OF THE INTEGER VECTOR ARE REPLACED BY THEIR ABSOLUTE VALUES. */ /* PARAMETERS : */ /* II N DIMENSION OF THE INTEGER VECTOR. */ /* IU IX(N) INTEGER VECTOR WHICH IS UPDATED SO THAT IX(I):=ABS(IX(I)) */ /* FOR ALL I. */ void luksan_mxvine__(int *n, int *ix) { /* System generated locals */ int i__1, i__2; /* Local variables */ int i__; /* Parameter adjustments */ --ix; /* Function Body */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { ix[i__] = (i__2 = ix[i__], iabs(i__2)); /* L1: */ } return; } /* luksan_mxvine__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXDCMV ALL SYSTEMS 91/12/01 */ /* PURPOSE : */ /* RANK-TWO UPDATE OF A COLUMNWISE STORED DENSE RECTANGULAR MATRIX A. */ /* THIS MATRIX IS UPDATED BY THE RULE A:=A+ALF*X*TRANS(U)+BET*Y*TRANS(V). */ /* PARAMETERS : */ /* II N NUMBER OF ROWS OF THE MATRIX A. */ /* II M NUMBER OF COLUMNS OF THE MATRIX A. */ /* RU A(N*M) RECTANGULAR MATRIX STORED COLUMNWISE IN THE */ /* ONE-DIMENSIONAL ARRAY. */ /* RI ALF SCALAR PARAMETER. */ /* RI X(N) INPUT VECTOR. */ /* RI U(M) INPUT VECTOR. */ /* RI BET SCALAR PARAMETER. */ /* RI Y(N) INPUT VECTOR. */ /* RI V(M) INPUT VECTOR. */ void luksan_mxdcmv__(int *n, int *m, double *a, double *alf, double *x, double *u, double *bet, double *y, double *v) { /* System generated locals */ int i__1, i__2; /* Local variables */ int i__, j, k; double tempa, tempb; /* Parameter adjustments */ --v; --y; --u; --x; --a; /* Function Body */ k = 0; i__1 = *m; for (j = 1; j <= i__1; ++j) { tempa = *alf * u[j]; tempb = *bet * v[j]; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { a[k + i__] = a[k + i__] + tempa * x[i__] + tempb * y[i__]; /* L1: */ } k += *n; /* L2: */ } return; } /* luksan_mxdcmv__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXVSAV ALL SYSTEMS 91/12/01 */ /* PORTABILITY : ALL SYSTEMS */ /* 91/12/01 LU : ORIGINAL VERSION */ /* PURPOSE : */ /* DIFFERENCE OF TWO VECTORS RETURNED IN THE SUBSTRACTED ONE. */ /* PARAMETERS : */ /* II N VECTOR DIMENSION. */ /* RI X(N) INPUT VECTOR. */ /* RU Y(N) UPDATE VECTOR WHERE Y:= X - Y. */ void luksan_mxvsav__(int *n, double *x, double *y) { /* System generated locals */ int i__1; /* Local variables */ int i__; double temp; /* Parameter adjustments */ --y; --x; /* Function Body */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp = y[i__]; y[i__] = x[i__] - y[i__]; x[i__] = temp; /* L10: */ } return; } /* luksan_mxvsav__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXVLIN ALL SYSTEMS 92/12/01 */ /* PURPOSE : */ /* LINEAR COMBINATION OF TWO VECTORS. */ /* PARAMETERS : */ /* II N VECTOR DIMENSION. */ /* RI A SCALING FACTOR. */ /* RI X(N) INPUT VECTOR. */ /* RI B SCALING FACTOR. */ /* RI Y(N) INPUT VECTOR. */ /* RO Z(N) OUTPUT VECTOR WHERE Z:= A*X + B*Y. */ void luksan_mxvlin__(int *n, double *a, double *x, double *b, double *y, double *z__) { /* System generated locals */ int i__1; /* Local variables */ int i__; /* Parameter adjustments */ --z__; --y; --x; /* Function Body */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { z__[i__] = *a * x[i__] + *b * y[i__]; /* L1: */ } return; } /* luksan_mxvlin__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXDCMU ALL SYSTEMS 91/12/01 */ /* PURPOSE : */ /* UPDATE OF A COLUMNWISE STORED DENSE RECTANGULAR MATRIX A. THIS MATRIX */ /* IS UPDATED BY THE RULE A:=A+ALF*X*TRANS(Y). */ /* PARAMETERS : */ /* II N NUMBER OF ROWS OF THE MATRIX A. */ /* II M NUMBER OF COLUMNS OF THE MATRIX A. */ /* RU A(N*M) RECTANGULAR MATRIX STORED COLUMNWISE IN THE */ /* ONE-DIMENSIONAL ARRAY. */ /* RI ALF SCALAR PARAMETER. */ /* RI X(N) INPUT VECTOR. */ /* RI Y(M) INPUT VECTOR. */ void luksan_mxdcmu__(int *n, int *m, double *a, double *alf, double *x, double *y) { /* System generated locals */ int i__1, i__2; /* Local variables */ int i__, j, k; double temp; /* Parameter adjustments */ --y; --x; --a; /* Function Body */ k = 0; i__1 = *m; for (j = 1; j <= i__1; ++j) { temp = *alf * y[j]; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { a[k + i__] += temp * x[i__]; /* L1: */ } k += *n; /* L2: */ } return; } /* luksan_mxdcmu__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXVDIR ALL SYSTEMS 91/12/01 */ /* PURPOSE : */ /* VECTOR AUGMENTED BY THE SCALED VECTOR. */ /* PARAMETERS : */ /* II N VECTOR DIMENSION. */ /* RI A SCALING FACTOR. */ /* RI X(N) INPUT VECTOR. */ /* RI Y(N) INPUT VECTOR. */ /* RO Z(N) OUTPUT VECTOR WHERE Z:= Y + A*X. */ void luksan_mxvdir__(int *n, double *a, double *x, double *y, double *z__) { /* System generated locals */ int i__1; /* Local variables */ int i__; /* Parameter adjustments */ --z__; --y; --x; /* Function Body */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { z__[i__] = y[i__] + *a * x[i__]; /* L10: */ } } /* luksan_mxvdir__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXDCMD ALL SYSTEMS 91/12/01 * PURPOSE : * MULTIPLICATION OF A COLUMNWISE STORED DENSE RECTANGULAR MATRIX A * BY A VECTOR X AND ADDITION OF THE SCALED VECTOR ALF*Y. * * PARAMETERS : * II N NUMBER OF ROWS OF THE MATRIX A. * II M NUMBER OF COLUMNS OF THE MATRIX A. * RI A(N*M) RECTANGULAR MATRIX STORED COLUMNWISE IN THE * ONE-DIMENSIONAL ARRAY. * RI X(M) INPUT VECTOR. * RI ALF SCALING FACTOR. * RI Y(N) INPUT VECTOR. * RO Z(N) OUTPUT VECTOR EQUAL TO A*X+ALF*Y. * * SUBPROGRAMS USED : * S MXVDIR VECTOR AUGMENTED BY THE SCALED VECTOR. * S MXVSCL SCALING OF A VECTOR. */ void luksan_mxdcmd__(int *n, int *m, double *a, double *x, double *alf, double *y, double *z__) { /* System generated locals */ int i__1; /* Local variables */ int j, k; /* Parameter adjustments */ --z__; --y; --x; --a; /* Function Body */ luksan_mxvscl__(n, alf, &y[1], &z__[1]); k = 0; i__1 = *m; for (j = 1; j <= i__1; ++j) { luksan_mxvdir__(n, &x[j], &a[k + 1], &z__[1], &z__[1]); k += *n; /* L1: */ } return; } /* luksan_mxdcmd__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXDRCB ALL SYSTEMS 91/12/01 * PURPOSE : * BACKWARD PART OF THE STRANG FORMULA FOR PREMULTIPLICATION OF * THE VECTOR X BY AN IMPLICIT BFGS UPDATE. * * PARAMETERS : * II N NUMBER OF ROWS OF THE MATRICES A AND B. * II M NUMBER OF COLUMNS OF THE MATRICES A AND B. * RI A(N*M) RECTANGULAR MATRIX STORED AS A ONE-DIMENSIONAL ARRAY. * RI B(N*M) RECTANGULAR MATRIX STORED AS A ONE-DIMENSIONAL ARRAY. * RI U(M) VECTOR OF SCALAR COEFFICIENTS. * RO V(M) VECTOR OF SCALAR COEFFICIENTS. * RU X(N) PREMULTIPLIED VECTOR. * II IX(N) VECTOR CONTAINING TYPES OF BOUNDS. * II JOB OPTION. IF JOB.GT.0 THEN INDEX I IS NOT USED WHENEVER * IX(I).LE.-1. IF JOB.LT.0 THEN INDEX I IS NOT USED WHENEVER * IX(I).EQ.-5. * * SUBPROGRAM USED : * S MXUDIR VECTOR AUGMENTED BY THE SCALED VECTOR. * RF MXUDOT DOT PRODUCT OF VECTORS. * * METHOD : * H.MATTHIES, G.STRANG: THE SOLUTION OF NONLINEAR FINITE ELEMENT * EQUATIONS. INT.J.NUMER. METHODS ENGN. 14 (1979) 1613-1626. */ void luksan_mxdrcb__(int *n, int *m, double *a, double *b, double *u, double *v, double *x, int * ix, int *job) { /* System generated locals */ int i__1; double d__1; /* Local variables */ int i__, k; /* Parameter adjustments */ --ix; --x; --v; --u; --b; --a; /* Function Body */ k = 1; i__1 = *m; for (i__ = 1; i__ <= i__1; ++i__) { v[i__] = u[i__] * luksan_mxudot__(n, &x[1], &a[k], &ix[1], job); d__1 = -v[i__]; luksan_mxudir__(n, &d__1, &b[k], &x[1], &x[1], &ix[1], job); k += *n; /* L1: */ } return; } /* luksan_mxdrcb__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXDRCF ALL SYSTEMS 91/12/01 * PURPOSE : * FORWARD PART OF THE STRANG FORMULA FOR PREMULTIPLICATION OF * THE VECTOR X BY AN IMPLICIT BFGS UPDATE. * * PARAMETERS : * II N NUMBER OF ROWS OF THE MATRICES A AND B. * II M NUMBER OF COLUMNS OF THE MATRICES A AND B. * RI A(N*M) RECTANGULAR MATRIX STORED AS A ONE-DIMENSIONAL ARRAY. * RI B(N*M) RECTANGULAR MATRIX STORED AS A ONE-DIMENSIONAL ARRAY. * RI U(M) VECTOR OF SCALAR COEFFICIENTS. * RI V(M) VECTOR OF SCALAR COEFFICIENTS. * RU X(N) PREMULTIPLIED VECTOR. * II IX(N) VECTOR CONTAINING TYPES OF BOUNDS. * II JOB OPTION. IF JOB.GT.0 THEN INDEX I IS NOT USED WHENEVER * IX(I).LE.-1. IF JOB.LT.0 THEN INDEX I IS NOT USED WHENEVER * IX(I).EQ.-5. * * SUBPROGRAM USED : * S MXUDIR VECTOR AUGMENTED BY THE SCALED VECTOR. * RF MXUDOT DOT PRODUCT OF VECTORS. * * METHOD : * H.MATTHIES, G.STRANG: THE SOLUTION OF NONLINEAR FINITE ELEMENT * EQUATIONS. INT.J.NUMER. METHODS ENGN. 14 (1979) 1613-1626. */ void luksan_mxdrcf__(int *n, int *m, double *a, double *b, double *u, double *v, double *x, int * ix, int *job) { /* System generated locals */ double d__1; /* Local variables */ int i__, k; double temp; /* Parameter adjustments */ --ix; --x; --v; --u; --b; --a; /* Function Body */ k = (*m - 1) * *n + 1; for (i__ = *m; i__ >= 1; --i__) { temp = u[i__] * luksan_mxudot__(n, &x[1], &b[k], &ix[1], job); d__1 = v[i__] - temp; luksan_mxudir__(n, &d__1, &a[k], &x[1], &x[1], &ix[1], job); k -= *n; /* L1: */ } return; } /* luksan_mxdrcf__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXDRMM ALL SYSTEMS 91/12/01 * PURPOSE : * MULTIPLICATION OF A ROWWISE STORED DENSE RECTANGULAR MATRIX A BY * A VECTOR X. * * PARAMETERS : * II N NUMBER OF COLUMNS OF THE MATRIX A. * II M NUMBER OF ROWS OF THE MATRIX A. * RI A(M*N) RECTANGULAR MATRIX STORED ROWWISE IN THE * ONE-DIMENSIONAL ARRAY. * RI X(N) INPUT VECTOR. * RO Y(M) OUTPUT VECTOR EQUAL TO A*X. */ void luksan_mxdrmm__(int *n, int *m, double *a, double *x, double *y) { /* System generated locals */ int i__1, i__2; /* Local variables */ int i__, j, k; double temp; /* Parameter adjustments */ --y; --x; --a; /* Function Body */ k = 0; i__1 = *m; for (j = 1; j <= i__1; ++j) { temp = 0.; i__2 = *n; for (i__ = 1; i__ <= i__2; ++i__) { temp += a[k + i__] * x[i__]; /* L1: */ } y[j] = temp; k += *n; /* L2: */ } return; } /* luksan_mxdrmm__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXDRSU ALL SYSTEMS 91/12/01 * PURPOSE : * SHIFT OF COLUMNS OF THE RECTANGULAR MATRICES A AND B. SHIFT OF * ELEMENTS OF THE VECTOR U. THESE SHIFTS ARE USED IN THE LIMITED * MEMORY BFGS METHOD. * * PARAMETERS : * II N NUMBER OF ROWS OF THE MATRIX A AND B. * II M NUMBER OF COLUMNS OF THE MATRIX A AND B. * RU A(N*M) RECTANGULAR MATRIX STORED AS A ONE-DIMENSIONAL ARRAY. * RU B(N*M) RECTANGULAR MATRIX STORED AS A ONE-DIMENSIONAL ARRAY. * RU U(M) VECTOR. */ void luksan_mxdrsu__(int *n, int *m, double *a, double *b, double *u) { int i__, k, l; /* Parameter adjustments */ --u; --b; --a; /* Function Body */ k = (*m - 1) * *n + 1; for (i__ = *m - 1; i__ >= 1; --i__) { l = k - *n; luksan_mxvcop__(n, &a[l], &a[k]); luksan_mxvcop__(n, &b[l], &b[k]); u[i__ + 1] = u[i__]; k = l; /* L1: */ } return; } /* luksan_mxdrsu__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXUCOP ALL SYSTEMS 99/12/01 * PURPOSE : * COPY OF THE VECTOR WITH INITIATION OF THE ACTIVE PART. * * PARAMETERS : * II N VECTOR DIMENSION. * RI X(N) INPUT VECTOR. * RO Y(N) OUTPUT VECTOR WHERE Y:= X. * II IX(N) VECTOR CONTAINING TYPES OF BOUNDS. * II JOB OPTION. IF JOB.GT.0 THEN INDEX I IS NOT USED WHENEVER * IX(I).LE.-1. IF JOB.LT.0 THEN INDEX I IS NOT USED WHENEVER * IX(I).EQ.-5. */ void luksan_mxucop__(int *n, double *x, double *y, int *ix, int *job) { /* System generated locals */ int i__1; /* Local variables */ int i__; /* Parameter adjustments */ --ix; --y; --x; /* Function Body */ if (*job == 0) { i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { y[i__] = x[i__]; /* L1: */ } } else if (*job > 0) { i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (ix[i__] >= 0) { y[i__] = x[i__]; } else { y[i__] = 0.; } /* L2: */ } } else { i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (ix[i__] != -5) { y[i__] = x[i__]; } else { y[i__] = 0.; } /* L3: */ } } return; } /* luksan_mxucop__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXUDIR ALL SYSTEMS 99/12/01 * PURPOSE : * VECTOR AUGMENTED BY THE SCALED VECTOR IN A BOUND CONSTRAINED CASE. * * PARAMETERS : * II N VECTOR DIMENSION. * RI A SCALING FACTOR. * RI X(N) INPUT VECTOR. * RI Y(N) INPUT VECTOR. * RO Z(N) OUTPUT VECTOR WHERE Z:= Y + A*X. * II IX(N) VECTOR CONTAINING TYPES OF BOUNDS. * II JOB OPTION. IF JOB.GT.0 THEN INDEX I IS NOT USED WHENEVER * IX(I).LE.-1. IF JOB.LT.0 THEN INDEX I IS NOT USED WHENEVER * IX(I).EQ.-5. */ void luksan_mxudir__(int *n, double *a, double *x, double *y, double *z__, int *ix, int *job) { /* System generated locals */ int i__1; /* Local variables */ int i__; /* Parameter adjustments */ --ix; --z__; --y; --x; /* Function Body */ if (*job == 0) { i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { z__[i__] = y[i__] + *a * x[i__]; /* L1: */ } } else if (*job > 0) { i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (ix[i__] >= 0) { z__[i__] = y[i__] + *a * x[i__]; } /* L2: */ } } else { i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (ix[i__] != -5) { z__[i__] = y[i__] + *a * x[i__]; } /* L3: */ } } return; } /* luksan_mxudir__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* FUNCTION MXVDOT ALL SYSTEMS 91/12/01 */ /* PURPOSE : */ /* DOT PRODUCT OF TWO VECTORS. */ /* PARAMETERS : */ /* II N VECTOR DIMENSION. */ /* RI X(N) INPUT VECTOR. */ /* RI Y(N) INPUT VECTOR. */ /* RR MXVDOT VALUE OF DOT PRODUCT MXVDOT=TRANS(X)*Y. */ double luksan_mxvdot__(int *n, double *x, double *y) { /* System generated locals */ int i__1; /* Local variables */ int i__; double temp; /* Parameter adjustments */ --y; --x; /* Function Body */ temp = 0.; i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp += x[i__] * y[i__]; /* L10: */ } return temp; } /* luksan_mxvdot__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* FUNCTION MXUDOT ALL SYSTEMS 99/12/01 * PURPOSE : * DOT PRODUCT OF VECTORS IN A BOUND CONSTRAINED CASE. * * PARAMETERS : * II N VECTOR DIMENSION. * RI X(N) INPUT VECTOR. * RI Y(N) INPUT VECTOR. * II IX(N) VECTOR CONTAINING TYPES OF BOUNDS. * II JOB OPTION. IF JOB.GT.0 THEN INDEX I IS NOT USED WHENEVER * IX(I).LE.-1. IF JOB.LT.0 THEN INDEX I IS NOT USED WHENEVER * IX(I).EQ.-5. * RR MXUDOT VALUE OF DOT PRODUCT MXUDOT=TRANS(X)*Y. */ double luksan_mxudot__(int *n, double *x, double *y, int *ix, int *job) { /* System generated locals */ int i__1; /* Local variables */ int i__; double temp; /* Parameter adjustments */ --ix; --y; --x; /* Function Body */ temp = 0.; if (*job == 0) { i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { temp += x[i__] * y[i__]; /* L1: */ } } else if (*job > 0) { i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (ix[i__] >= 0) { temp += x[i__] * y[i__]; } /* L2: */ } } else { i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (ix[i__] != -5) { temp += x[i__] * y[i__]; } /* L3: */ } } return temp; } /* luksan_mxudot__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXUNEG ALL SYSTEMS 00/12/01 * PURPOSE : * COPY OF THE VECTOR WITH INITIATION OF THE ACTIVE PART. * * PARAMETERS : * II N VECTOR DIMENSION. * RI X(N) INPUT VECTOR. * RO Y(N) OUTPUT VECTOR WHERE Y:= X. * II IX(N) VECTOR CONTAINING TYPES OF BOUNDS. * II JOB OPTION. IF JOB.GT.0 THEN INDEX I IS NOT USED WHENEVER * IX(I).LE.-1. IF JOB.LT.0 THEN INDEX I IS NOT USED WHENEVER * IX(I).EQ.-5. */ void luksan_mxuneg__(int *n, double *x, double *y, int *ix, int *job) { /* System generated locals */ int i__1; /* Local variables */ int i__; /* Parameter adjustments */ --ix; --y; --x; /* Function Body */ if (*job == 0) { i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { y[i__] = -x[i__]; /* L1: */ } } else if (*job > 0) { i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (ix[i__] >= 0) { y[i__] = -x[i__]; } else { y[i__] = 0.; } /* L2: */ } } else { i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (ix[i__] != -5) { y[i__] = -x[i__]; } else { y[i__] = 0.; } /* L3: */ } } return; } /* luksan_mxuneg__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXUZER ALL SYSTEMS 99/12/01 * PURPOSE : * VECTOR ELEMENTS CORRESPONDING TO ACTIVE BOUNDS ARE SET TO ZERO. * * PARAMETERS : * II N VECTOR DIMENSION. * RO X(N) OUTPUT VECTOR SUCH THAT X(I)=A FOR ALL I. * II IX(N) VECTOR CONTAINING TYPES OF BOUNDS. * II JOB OPTION. IF JOB.GT.0 THEN INDEX I IS NOT USED WHENEVER * IX(I).LE.-1. IF JOB.LT.0 THEN INDEX I IS NOT USED WHENEVER * IX(I).EQ.-5. */ void luksan_mxuzer__(int *n, double *x, int *ix, int *job) { /* System generated locals */ int i__1; /* Local variables */ int i__; /* Parameter adjustments */ --ix; --x; /* Function Body */ if (*job == 0) { return; } i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (ix[i__] < 0) { x[i__] = 0.; } /* L1: */ } return; } /* luksan_mxuzer__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXVCOP ALL SYSTEMS 88/12/01 * PURPOSE : * COPYING OF A VECTOR. * * PARAMETERS : * II N VECTOR DIMENSION. * RI X(N) INPUT VECTOR. * RO Y(N) OUTPUT VECTOR WHERE Y:= X. */ void luksan_mxvcop__(int *n, double *x, double *y) { /* System generated locals */ int i__1; /* Local variables */ int i__; /* Parameter adjustments */ --y; --x; /* Function Body */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { y[i__] = x[i__]; /* L10: */ } return; } /* luksan_mxvcop__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXVDIF ALL SYSTEMS 88/12/01 * PURPOSE : * VECTOR DIFFERENCE. * * PARAMETERS : * RI X(N) INPUT VECTOR. * RI Y(N) INPUT VECTOR. * RO Z(N) OUTPUT VECTOR WHERE Z:= X - Y. */ void luksan_mxvdif__(int *n, double *x, double *y, double *z__) { /* System generated locals */ int i__1; /* Local variables */ int i__; /* Parameter adjustments */ --z__; --y; --x; /* Function Body */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { z__[i__] = x[i__] - y[i__]; /* L10: */ } return; } /* luksan_mxvdif__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXVNEG ALL SYSTEMS 88/12/01 * PURPOSE : * CHANGE THE SIGNS OF VECTOR ELEMENTS. * * PARAMETERS : * II N VECTOR DIMENSION. * RI X(N) INPUT VECTOR. * RO Y(N) OUTPUT VECTOR WHERE Y:= - X. */ void luksan_mxvneg__(int *n, double *x, double *y) { /* System generated locals */ int i__1; /* Local variables */ int i__; /* Parameter adjustments */ --y; --x; /* Function Body */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { y[i__] = -x[i__]; /* L10: */ } return; } /* luksan_mxvneg__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXVSCL ALL SYSTEMS 88/12/01 * PURPOSE : * SCALING OF A VECTOR. * * PARAMETERS : * II N VECTOR DIMENSION. * RI X(N) INPUT VECTOR. * RI A SCALING FACTOR. * RO Y(N) OUTPUT VECTOR WHERE Y:= A*X. */ void luksan_mxvscl__(int *n, double *a, double *x, double *y) { /* System generated locals */ int i__1; /* Local variables */ int i__; /* Parameter adjustments */ --y; --x; /* Function Body */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { y[i__] = *a * x[i__]; /* L1: */ } return; } /* luksan_mxvscl__ */ /* cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc */ /* SUBROUTINE MXVSET ALL SYSTEMS 88/12/01 * PURPOSE : * A SCALAR IS SET TO ALL THE ELEMENTS OF A VECTOR. * * PARAMETERS : * II N VECTOR DIMENSION. * RI A INITIAL VALUE. * RO X(N) OUTPUT VECTOR SUCH THAT X(I)=A FOR ALL I. */ void luksan_mxvset__(int *n, double *a, double *x) { /* System generated locals */ int i__1; /* Local variables */ int i__; /* Parameter adjustments */ --x; /* Function Body */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { x[i__] = *a; /* L10: */ } return; } /* luksan_mxvset__ */ nloptr/src/nlopt_src/luksan/plis.txt0000644000176000001440000003730613263734242017436 0ustar ripleyusers*********************************************************************** * * * PLIS - A LIMITED MEMORY VARIABLE METRIC ALGORITHM FOR * * LARGE-SCALE OPTIMIZATION. * * * *********************************************************************** 1. Introduction: ---------------- The double-precision FORTRAN 77 basic subroutine PLIS is designed to find a close approximation to a local minimum of a nonlinear function F(X) with simple bounds on variables. Here X is a vector of NF variables and F(X) is a smooth function. We suppose that NF is large but the sparsity pattern of the Hessian matrix is not known (or the Hessian matrix is dense). Simple bounds are assumed in the form X(I) unbounded if IX(I) = 0, XL(I) <= X(I) if IX(I) = 1, X(I) <= XU(I) if IX(I) = 2, XL(I) <= X(I) <= XU(I) if IX(I) = 3, XL(I) = X(I) = XU(I) if IX(I) = 5, where 1 <= I <= NF. To simplify user's work, two additional easy to use subroutines are added. They call the basic general subroutine PLIS: PLISU - unconstrained large-scale optimization, PLISS - large-scale optimization with simple bounds. All subroutines contain a description of formal parameters and extensive comments. Furthermore, two test programs TLISU and TLISS are included, which contain several test problems (see e.g. [2]). These test programs serve as examples for using the subroutines, verify their correctness and demonstrate their efficiency. In this short guide, we describe all subroutines which can be called from the user's program. A detailed description of the method is given in [1]. In the description of formal parameters, we introduce a type of the argument that specifies whether the argument must have a value defined on entry to the subroutine (I), whether it is a value which will be returned (O), or both (U), or whether it is an auxiliary value (A). Note that the arguments of the type I can be changed on output under some circumstances, especially if improper input values were given. Besides formal parameters, we can use a COMMON /STAT/ block containing statistical information. This block, used in each subroutine has the following form: COMMON /STAT/ NRES,NDEC,NIN,NIT,NFV,NFG,NFH The arguments have the following meaning: Argument Type Significance ---------------------------------------------------------------------- NRES O Positive INTEGER variable that indicates the number of restarts. NDEC O Positive INTEGER variable that indicates the number of matrix decompositions. NIN O Positive INTEGER variable that indicates the number of inner iterations (for solving linear systems). NIT O Positive INTEGER variable that indicates the number of iterations. NFV O Positive INTEGER variable that indicates the number of function evaluations. NFG O Positive INTEGER variable that specifies the number of gradient evaluations. NFH O Positive INTEGER variable that specifies the number of Hessian evaluations. 2. Subroutines PLISU, PLISS: ---------------------------- The calling sequences are CALL PLISU(NF,X,IPAR,RPAR,F,GMAX,IPRNT,ITERM) CALL PLISS(NF,X,IX,XL,XU,IPAR,RPAR,F,GMAX,IPRNT,ITERM) The arguments have the following meaning. Argument Type Significance ---------------------------------------------------------------------- NF I Positive INTEGER variable that specifies the number of variables of the objective function. X(NF) U On input, DOUBLE PRECISION vector with the initial estimate to the solution. On output, the approximation to the minimum. IX(NF) I On input (significant only if NB>0) INTEGER vector containing the simple bounds types: IX(I)=0 - the variable X(I) is unbounded, IX(I)=1 - the lower bound X(I) >= XL(I), IX(I)=2 - the upper bound X(I) <= XU(I), IX(I)=3 - the two side bound XL(I) <= X(I) <= XU(I), IX(I)=5 - the variable X(I) is fixed (given by its initial estimate). XL(NF) I DOUBLE PRECISION vector with lower bounds for variables (significant only if NB>0). XU(NF) I DOUBLE PRECISION vector with upper bounds for variables (significant only if NB>0). IPAR(7) I INTEGER parameters: IPAR(1)=MIT, IPAR(2)=MFV, IPAR(3)-NONE, IPAR(4)=IEST, IPAR(5)-NONE, IPAR(6)-NONE, IPAR(7)=MF. Parameters MIT, MFV, IEST, MF are described in Section 3 together with other parameters of the subroutine PLIS. RPAR(9) I DOUBLE PRECISION parameters: RPAR(1)=XMAX, RPAR(2)=TOLX, RPAR(3)=TOLF, RPAR(4)=TOLB, RPAR(5)=TOLG, RPAR(6)=FMIN, RPAR(7)-NONE, RPAR(6)-NONE, RPAR(9)-NONE. Parameters XMAX, TOLX, TOLF, TOLB, TOLG, FMIN are described in Section 3 together with other parameters of the subroutine PLIS. F O DOUBLE PRECISION value of the objective function at the solution X. GMAX O DOUBLE PRECISION maximum absolute value of a partial derivative of the Lagrangian function. IPRNT I INTEGER variable that specifies PRINT: IPRNT= 0 - print is suppressed, IPRNT= 1 - basic print of final results, IPRNT=-1 - extended print of final results, IPRNT= 2 - basic print of intermediate and final results, IPRNT=-2 - extended print of intermediate and final results. ITERM O INTEGER variable that indicates the cause of termination: ITERM= 1 - if |X - XO| was less than or equal to TOLX in two subsequent iterations, ITERM= 2 - if |F - FO| was less than or equal to TOLF in two subsequent iterations, ITERM= 3 - if F is less than or equal to TOLB, ITERM= 4 - if GMAX is less than or equal to TOLG, ITERM= 6 - if termination criterion was not satisfied, but the solution is probably acceptable, ITERM=11 - if NIT exceeded MIT, ITERM=12 - if NFV exceeded MFV, ITERM< 0 - if the method failed. The subroutines PLISU, PLISS require the user supplied subroutines OBJ and DOBJ that define the objective function and its gradient and have the form SUBROUTINE OBJ(NF,X,F) SUBROUTINE DOBJ(NF,X,G) The arguments of the user supplied subroutines have the following meaning. Argument Type Significance ---------------------------------------------------------------------- NF I Positive INTEGER variable that specifies the number of variables of the objective function. X(NF) I DOUBLE PRECISION an estimate to the solution. F O DOUBLE PRECISION value of the objective function at the point X. G(NF) O DOUBLE PRECISION gradient of the objective function at the point X. 3. Subroutine PLIS: ------------------- This general subroutine is called from all subroutines described in Section 2. The calling sequence is CALL PLIS(NF,NB,X,IX,XL,XU,GF,S,XO,GO,UO,VO,XMAX,TOLX,TOLF,TOLB, & TOLG,FMIN,GMAX,F,MIT,MFV,IEST,MF,IPRNT,ITERM) The arguments NF, NB, X, IX, XL, XU, GMAX, F, IPRNT, ITERM, have the same meaning as in Section 2. Other arguments have the following meaning: Argument Type Significance ---------------------------------------------------------------------- GF(NF) A DOUBLE PRECISION gradient of the objective function. S(NF) A DOUBLE PRECISION direction vector. XO(NF*MF) A DOUBLE PRECISION array which contains increments of variables. GO(NF*MF) A DOUBLE PRECISION array which contains increments of gradients. UO(MF) A DOUBLE PRECISION Auxiliary array. VO(MF) A DOUBLE PRECISION Auxiliary array. XMAX I DOUBLE PRECISION maximum stepsize; the choice XMAX=0 causes that the default value 1.0D+16 will be taken. TOLX I DOUBLE PRECISION tolerance for the change of the coordinate vector X; the choice TOLX=0 causes that the default value TOLX=1.0D-16 will be taken. TOLF I DOUBLE PRECISION tolerance for the change of function values; the choice TOLF=0 causes that the default value TOLF=1.0D-14 will be taken. TOLB I DOUBLE PRECISION minimum acceptable function value; the choice TOLB=0 causes that the default value TOLB=FMIN+1.0D-16 will be taken. TOLG I DOUBLE PRECISION tolerance for the Lagrangian function gradient; the choice TOLG=0 causes that the default value TOLG=1.0D-6 will be taken. FMIN I DOUBLE PRECISION lower bound for the minimum function value. MIT I INTEGER variable that specifies the maximum number of iterations; the choice MIT=0 causes that the default value 9000 will be taken. MFV I INTEGER variable that specifies the maximum number of function evaluations; the choice MFV=0 causes that the default value 9000 will be taken. IEST I INTEGER estimation of the minimum functiom value for the line search: IEST=0 - estimation is not used, IEST=1 - lower bound FMIN is used as an estimation for the minimum function value. MF I The number of limited-memory variable metric updates in each iteration (they use 2*MF stored vectors). The choice of parameter XMAX can be sensitive in many cases. First, the objective function can be evaluated only in a relatively small region (if it contains exponentials) so that the maximum stepsize is necessary. Secondly, the problem can be very ill-conditioned far from the solution point so that large steps can be unsuitable. Finally, if the problem has more local solutions, a suitably chosen maximum stepsize can lead to obtaining a better local solution. The subroutine PLIS requires the user supplied subroutines OBJ and DOBJ which are described in Section 2. 4. Verification of the subroutines: ----------------------------------- Subroutine PLISU can be verified and tested using the program TLISU. This program calls the subroutines TIUD14 (initiation), TFFU14 (function evaluation) and TFGU14 (gradient evaluation) containing 22 unconstrained test problems with at most 1000 variables [2]. The results obtained by the program TLISU on a PC computer with Microsoft Power Station Fortran compiler have the following form. NIT= 4988 NFV= 5554 NFG= 5554 F= 0.963780013E-14 G= 0.891E-06 ITERM= 4 NIT= 425 NFV= 454 NFG= 454 F= 14.9944763 G= 0.773E-05 ITERM= 2 NIT= 74 NFV= 78 NFG= 78 F= 0.655101686E-09 G= 0.539E-06 ITERM= 4 NIT= 103 NFV= 112 NFG= 112 F= 269.499543 G= 0.899E-06 ITERM= 4 NIT= 24 NFV= 26 NFG= 26 F= 0.130639280E-11 G= 0.671E-06 ITERM= 4 NIT= 30 NFV= 31 NFG= 31 F= 0.216102227E-10 G= 0.946E-06 ITERM= 4 NIT= 38 NFV= 43 NFG= 43 F= 335.137433 G= 0.730E-06 ITERM= 4 NIT= 29 NFV= 33 NFG= 33 F= 761774.954 G= 0.432E-03 ITERM= 2 NIT= 13 NFV= 16 NFG= 16 F= 316.436141 G= 0.369E-06 ITERM= 4 NIT= 1540 NFV= 1582 NFG= 1582 F= -124.630000 G= 0.124E-04 ITERM= 2 NIT= 114 NFV= 138 NFG= 138 F= 10.7765879 G= 0.380E-06 ITERM= 4 NIT= 248 NFV= 267 NFG= 267 F= 982.273617 G= 0.123E-04 ITERM= 2 NIT= 7 NFV= 8 NFG= 8 F= 0.165734137E-12 G= 0.453E-06 ITERM= 4 NIT= 10 NFV= 12 NFG= 12 F= 0.128729169E-08 G= 0.916E-06 ITERM= 4 NIT= 2830 NFV= 2929 NFG= 2929 F= 1.92401599 G= 0.936E-06 ITERM= 4 NIT= 196 NFV= 210 NFG= 210 F= -427.404476 G= 0.991E-05 ITERM= 2 NIT= 1007 NFV= 1032 NFG= 1032 F=-0.379921091E-01 G= 0.876E-06 ITERM= 4 NIT= 1449 NFV= 1474 NFG= 1474 F=-0.245741193E-01 G= 0.862E-06 ITERM= 4 NIT= 1393 NFV= 1431 NFG= 1431 F= 59.5986241 G= 0.259E-05 ITERM= 2 NIT= 2129 NFV= 2191 NFG= 2191 F= -1.00013520 G= 0.908E-06 ITERM= 4 NIT= 2120 NFV= 2169 NFG= 2169 F= 2.13866377 G= 0.927E-06 ITERM= 4 NIT= 1305 NFV= 1346 NFG= 1346 F= 1.00000000 G= 0.982E-06 ITERM= 4 NITER =20072 NFVAL =21136 NSUCC = 22 TIME= 0:00:10.78 The rows corresponding to individual test problems contain the number of iterations NIT, the number of function evaluations NFV, the number of gradient evaluations NFG, the final value of the objective function F, the norm of gradient G and the cause of termination ITERM. Subroutine PLISS can be verified and tested using the program TLISS. This program calls the subroutines TIUD14 (initiation), TFFU14 (function evaluation), TFGU14 (gradient evaluation) containing 22 box constrained test problems with at most 1000 variables [2]. The results obtained by the program TLISS on a PC computer with Microsoft Power Station Fortran compiler have the following form. NIT= 5063 NFV= 5738 NFG= 5738 F= 0.00000000 G= 0.000E+00 ITERM= 3 NIT= 3167 NFV= 4664 NFG= 4664 F= 3926.45961 G= 0.626E-04 ITERM= 2 NIT= 113 NFV= 124 NFG= 124 F= 0.459503394E-12 G= 0.600E-06 ITERM= 4 NIT= 59 NFV= 64 NFG= 64 F= 269.522686 G= 0.838E-06 ITERM= 4 NIT= 24 NFV= 26 NFG= 26 F= 0.130639280E-11 G= 0.671E-06 ITERM= 4 NIT= 30 NFV= 31 NFG= 31 F= 0.216102227E-10 G= 0.946E-06 ITERM= 4 NIT= 33 NFV= 40 NFG= 40 F= 337.722479 G= 0.592E-06 ITERM= 4 NIT= 50 NFV= 55 NFG= 55 F= 761925.725 G= 0.240E-03 ITERM= 2 NIT= 505 NFV= 508 NFG= 508 F= 428.056916 G= 0.334E-07 ITERM= 4 NIT= 1167 NFV= 1227 NFG= 1227 F= -81.0913589 G= 0.100E-04 ITERM= 2 NIT= 20 NFV= 26 NFG= 26 F= 96517.2947 G= 0.745E-05 ITERM= 2 NIT= 91 NFV= 109 NFG= 109 F= 4994.21410 G= 0.104E-04 ITERM= 2 NIT= 7 NFV= 8 NFG= 8 F= 0.165734137E-12 G= 0.453E-06 ITERM= 4 NIT= 10 NFV= 12 NFG= 12 F= 0.128729169E-08 G= 0.916E-06 ITERM= 4 NIT= 2830 NFV= 2929 NFG= 2929 F= 1.92401599 G= 0.936E-06 ITERM= 4 NIT= 178 NFV= 184 NFG= 184 F= -427.391653 G= 0.107E-04 ITERM= 2 NIT= 1007 NFV= 1032 NFG= 1032 F=-0.379921091E-01 G= 0.876E-06 ITERM= 4 NIT= 1449 NFV= 1474 NFG= 1474 F=-0.245741193E-01 G= 0.862E-06 ITERM= 4 NIT= 1561 NFV= 1595 NFG= 1595 F= 1654.94525 G= 0.112E-04 ITERM= 2 NIT= 2075 NFV= 2121 NFG= 2121 F= -1.00013520 G= 0.916E-06 ITERM= 4 NIT= 1361 NFV= 1389 NFG= 1389 F= 2.41354873 G= 0.709E-06 ITERM= 4 NIT= 1562 NFV= 1598 NFG= 1598 F= 1.00000000 G= 0.786E-06 ITERM= 4 NITER =22362 NFVAL =24954 NSUCC = 22 TIME= 0:00:12.39 References: ----------- [1] Luksan L., Matonoha C., Vlcek J.: LSA: Algorithms for large-scale unconstrained and box constrained optimization Technical Report V-896. Prague, ICS AS CR, 2004. [2] Luksan L., Vlcek J.: Sparse and partially separable test problems for unconstrained and equality constrained optimization. Research Report V-767, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic, 1998. nloptr/src/init_nloptr.c0000644000176000001440000001620713355070635015131 0ustar ripleyusers/* * Copyright (C) 2017 Jelmer Ypma. All Rights Reserved. * This code is published under the L-GPL. * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU Lesser General Public License as published * by the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU Lesser General Public License for more details. * * You should have received a copy of the GNU Lesser General Public License * along with this program. If not, see . * * File: init_nloptr.c * Author: Jelmer Ypma * Date: 3 October 2017 * * This file registers C functions to be used from R. * * 03/10/2017: Included registering of C functions to be used by external R packages. * 01/10/2017: Initial version. */ #include #include #include // for NULL #include #include "nloptr.h" #include "nlopt.h" static const R_CallMethodDef CallEntries[] = { {"NLoptR_Optimize", (DL_FUNC) &NLoptR_Optimize, 1}, {NULL, NULL, 0} }; void R_init_nloptr(DllInfo *info) { // Register C functions that can be used by external packages // linking to internal NLopt code from C. R_RegisterCCallable("nloptr", "nlopt_algorithm_name", (DL_FUNC) &nlopt_algorithm_name); R_RegisterCCallable("nloptr", "nlopt_srand", (DL_FUNC) &nlopt_srand); R_RegisterCCallable("nloptr", "nlopt_srand_time", (DL_FUNC) &nlopt_srand_time); R_RegisterCCallable("nloptr", "nlopt_version", (DL_FUNC) &nlopt_version); R_RegisterCCallable("nloptr", "nlopt_create", (DL_FUNC) &nlopt_create); R_RegisterCCallable("nloptr", "nlopt_destroy", (DL_FUNC) &nlopt_destroy); R_RegisterCCallable("nloptr", "nlopt_copy", (DL_FUNC) &nlopt_copy); R_RegisterCCallable("nloptr", "nlopt_optimize", (DL_FUNC) &nlopt_optimize); R_RegisterCCallable("nloptr", "nlopt_set_min_objective", (DL_FUNC) &nlopt_set_min_objective); R_RegisterCCallable("nloptr", "nlopt_set_max_objective", (DL_FUNC) &nlopt_set_max_objective); R_RegisterCCallable("nloptr", "nlopt_set_precond_min_objective", (DL_FUNC) &nlopt_set_precond_min_objective); R_RegisterCCallable("nloptr", "nlopt_set_precond_max_objective", (DL_FUNC) &nlopt_set_precond_max_objective); R_RegisterCCallable("nloptr", "nlopt_get_algorithm", (DL_FUNC) &nlopt_get_algorithm); R_RegisterCCallable("nloptr", "nlopt_get_dimension", (DL_FUNC) &nlopt_get_dimension); R_RegisterCCallable("nloptr", "nlopt_set_lower_bounds", (DL_FUNC) &nlopt_set_lower_bounds); R_RegisterCCallable("nloptr", "nlopt_set_lower_bounds1", (DL_FUNC) &nlopt_set_lower_bounds1); R_RegisterCCallable("nloptr", "nlopt_get_lower_bounds", (DL_FUNC) &nlopt_get_lower_bounds); R_RegisterCCallable("nloptr", "nlopt_set_upper_bounds", (DL_FUNC) &nlopt_set_upper_bounds); R_RegisterCCallable("nloptr", "nlopt_set_upper_bounds1", (DL_FUNC) &nlopt_set_upper_bounds1); R_RegisterCCallable("nloptr", "nlopt_get_upper_bounds", (DL_FUNC) &nlopt_get_upper_bounds); R_RegisterCCallable("nloptr", "nlopt_remove_inequality_constraints", (DL_FUNC) &nlopt_remove_inequality_constraints); R_RegisterCCallable("nloptr", "nlopt_add_inequality_constraint", (DL_FUNC) &nlopt_add_inequality_constraint); R_RegisterCCallable("nloptr", "nlopt_add_precond_inequality_constraint", (DL_FUNC) &nlopt_add_precond_inequality_constraint); R_RegisterCCallable("nloptr", "nlopt_add_inequality_mconstraint", (DL_FUNC) &nlopt_add_inequality_mconstraint); R_RegisterCCallable("nloptr", "nlopt_remove_equality_constraints", (DL_FUNC) &nlopt_remove_equality_constraints); R_RegisterCCallable("nloptr", "nlopt_add_equality_constraint", (DL_FUNC) &nlopt_add_equality_constraint); R_RegisterCCallable("nloptr", "nlopt_add_precond_equality_constraint", (DL_FUNC) &nlopt_add_precond_equality_constraint); R_RegisterCCallable("nloptr", "nlopt_add_equality_mconstraint", (DL_FUNC) &nlopt_add_equality_mconstraint); R_RegisterCCallable("nloptr", "nlopt_set_stopval", (DL_FUNC) &nlopt_set_stopval); R_RegisterCCallable("nloptr", "nlopt_get_stopval", (DL_FUNC) &nlopt_get_stopval); R_RegisterCCallable("nloptr", "nlopt_set_ftol_rel", (DL_FUNC) &nlopt_set_ftol_rel); R_RegisterCCallable("nloptr", "nlopt_get_ftol_rel", (DL_FUNC) &nlopt_get_ftol_rel); R_RegisterCCallable("nloptr", "nlopt_set_ftol_abs", (DL_FUNC) &nlopt_set_ftol_abs); R_RegisterCCallable("nloptr", "nlopt_get_ftol_abs", (DL_FUNC) &nlopt_get_ftol_abs); R_RegisterCCallable("nloptr", "nlopt_set_xtol_rel", (DL_FUNC) &nlopt_set_xtol_rel); R_RegisterCCallable("nloptr", "nlopt_get_xtol_rel", (DL_FUNC) &nlopt_get_xtol_rel); R_RegisterCCallable("nloptr", "nlopt_set_xtol_abs1", (DL_FUNC) &nlopt_set_xtol_abs1); R_RegisterCCallable("nloptr", "nlopt_set_xtol_abs", (DL_FUNC) &nlopt_set_xtol_abs); R_RegisterCCallable("nloptr", "nlopt_get_xtol_abs", (DL_FUNC) &nlopt_get_xtol_abs); R_RegisterCCallable("nloptr", "nlopt_set_maxeval", (DL_FUNC) &nlopt_set_maxeval); R_RegisterCCallable("nloptr", "nlopt_get_maxeval", (DL_FUNC) &nlopt_get_maxeval); R_RegisterCCallable("nloptr", "nlopt_set_maxtime", (DL_FUNC) &nlopt_set_maxtime); R_RegisterCCallable("nloptr", "nlopt_get_maxtime", (DL_FUNC) &nlopt_get_maxtime); R_RegisterCCallable("nloptr", "nlopt_force_stop", (DL_FUNC) &nlopt_force_stop); R_RegisterCCallable("nloptr", "nlopt_set_force_stop", (DL_FUNC) &nlopt_set_force_stop); R_RegisterCCallable("nloptr", "nlopt_get_force_stop", (DL_FUNC) &nlopt_get_force_stop); R_RegisterCCallable("nloptr", "nlopt_set_local_optimizer", (DL_FUNC) &nlopt_set_local_optimizer); R_RegisterCCallable("nloptr", "nlopt_set_population", (DL_FUNC) &nlopt_set_population); R_RegisterCCallable("nloptr", "nlopt_get_population", (DL_FUNC) &nlopt_get_population); R_RegisterCCallable("nloptr", "nlopt_set_vector_storage", (DL_FUNC) &nlopt_set_vector_storage); R_RegisterCCallable("nloptr", "nlopt_get_vector_storage", (DL_FUNC) &nlopt_get_vector_storage); R_RegisterCCallable("nloptr", "nlopt_set_default_initial_step", (DL_FUNC) &nlopt_set_default_initial_step); R_RegisterCCallable("nloptr", "nlopt_set_initial_step", (DL_FUNC) &nlopt_set_initial_step); R_RegisterCCallable("nloptr", "nlopt_set_initial_step1", (DL_FUNC) &nlopt_set_initial_step1); R_RegisterCCallable("nloptr", "nlopt_get_initial_step", (DL_FUNC) &nlopt_get_initial_step); // Register routines to improve lookup from R using .Call interface. R_registerRoutines(info, NULL, CallEntries, NULL, NULL); R_useDynamicSymbols(info, FALSE); } nloptr/src/nloptr.h0000644000176000001440000000021513355070635014103 0ustar ripleyusers#include #include #ifndef __NLOPTR_H__ #define __NLOPTR_H__ SEXP NLoptR_Optimize( SEXP args ); #endif /*__NLOPTR_H__*/ nloptr/src/Makevars.in0000644000176000001440000001220413355070635014516 0ustar ripleyusers ## We now use configure to find where NLopt is installed. ## ## If it is not found, it is downloaded and installed from source -- but such ## a build scheme is not permitted within Debian, or on Launchpad, or ... so ## the preference is use a supplied version of NLopt PKG_CFLAGS=@MAKEVARS_PKG_CFLAGS@ PKG_LIBS=@MAKEVARS_PKG_LIBS@ ## Old Makevars code below # Copyright (C) 2010 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: Makevars.in # Author: Jelmer Ypma # Date: 10 June 2010 # # 16-01-2011: Changed --disable-shared to --enable-shared to compile with -fPIC # 16-06-2011: Updated version number # 09-07-2011: Download and untar NLopt code using Rscript # 01-09-2011: Added check on empty SRCDIR # Added CC, CFLAGS, etc. to NLopt ./configure # 09-09-2011: Changed CURDIR to PWD which is not supported on Solaris Make # 21-10-2011: Changed ${PWD} to $(shell pwd) which is not supported on Solaris Make # 24-10-2011: Changed $(shell pwd) to `pwd` which is supported on Solaris Make # 24-10-2011: Removed SRCDIR macro, using `pwd` when needed instead # 28-10-2011: Changed shell to `` in downloading and extracting code # 18-11-2011: Changed CRLF and CR line endings to LF # 21-02-2013: Changed NLOPT_VERSION to 2.3 # Included --with-cxx option in configure to allow for StoGo algorithm. # Linking to libnlopt_cxx.a instead of libnlopt.a # 08-07-2013: Changed CFLAGS to NLOPT_CFLAGS and similarly for CC, CPP, CXX, # CXXFLAGS, and CXXCPP to prevent overriding user customizations. # For clarity some of the other macros are now prefixed with NLOPTR # instead of NLOPT. # 08-07-2013: Added --vanilla flag to Rscript. This solves problems if someone sets # a different home directory with setwd in Rprofile. # 08-07-2013: Added `pwd` to NLOPTR_LIBS and NLOPTR_INCL. # 05-11-2013: Changed NLOPT_VERSION to 2.4 # 07-11-2013: Removed -lstdc++ from linker statement. A file dummy.cpp (with C++ extension) # is added to the source directory to ensure linking with C++. Thanks to # Brian Ripley for bringing this up. # 12-11-2013: Replace 'sqrt(' by 'sqrt((double) ' in isres/isres/c in NLopt. This cast # is not implemented in NLopt, but causes compilation errors on some systems. # 19-11-2013: Wrap code in util/qsort_r.c in NLopt in extern "C" as requested by Brian # Ripley to fix compilation on Solaris. # 07-07-2014: Changed NLOPT_VERSION to 2.4.2 # # define NLopt version # NLOPT_VERSION = 2.4.2 # # C Compiler options # NLOPTR_CFLAGS = # # additional C Compiler options for linking # NLOPTR_CLINKFLAGS = # # Libraries necessary to link with NLopt # NLOPTR_LIBS = -lm `pwd`/nlopt-${NLOPT_VERSION}/lib/libnlopt_cxx.a # # Necessary Include dirs # NLOPTR_INCL = -I`pwd`/nlopt-${NLOPT_VERSION}/include # # Get R compilers and flags # NLOPT_CC=`"${R_HOME}/bin/R" CMD config CC` # NLOPT_CFLAGS=`"${R_HOME}/bin/R" CMD config CFLAGS` # NLOPT_CPP=`"${R_HOME}/bin/R" CMD config CPP` # NLOPT_CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS` # NLOPT_CXX=`"${R_HOME}/bin/R" CMD config CXX` # NLOPT_CXXFLAGS=`"${R_HOME}/bin/R" CMD config CXXFLAGS` # NLOPT_CXXCPP=`"${R_HOME}/bin/R" CMD config CXXCPP` # # Define objects for R to build # OBJECTS = nloptr.o # # Convert to R macros # PKG_LIBS=${NLOPTR_CLINKFLAGS} ${NLOPTR_LIBS} # PKG_CFLAGS=${NLOPTR_CFLAGS} ${NLOPTR_INCL} # .PHONY: all # all: $(SHLIB) # $(SHLIB): nlopt-${NLOPT_VERSION}/lib/libnlopt_cxx.a ${OBJECTS} # nloptr.o: nlopt-${NLOPT_VERSION}/include/nlopt.h # # Compile NLopt source code and clean up # # --prefix="`pwd`", which is the directory we want to # # install in, after we changed our current directory # nlopt-${NLOPT_VERSION}/include/nlopt.h nlopt-${NLOPT_VERSION}/lib/libnlopt_cxx.a: nlopt-${NLOPT_VERSION}/configure # echo "Installing library to: `pwd`/nlopt-${NLOPT_VERSION}" # cd nlopt-${NLOPT_VERSION}; \ # ed -s isres/isres.c <<< $$'H\n,s/sqrt(/sqrt((double) /g\nw'; \ # ed -s util/qsort_r.c <<< $$'H\n1i\nextern "C" {\n.\nw'; \ # ed -s util/qsort_r.c <<< $$'H\n$$a\n}\n.\nw'; \ # ./configure --prefix="`pwd`" --enable-shared --enable-static --without-octave --without-matlab --without-guile --without-python --with-cxx CC="${NLOPT_CC}" CFLAGS="${NLOPT_CFLAGS}" CPP="${NLOPT_CPP}" CPPFLAGS="${NLOPT_CPPFLAGS}" CXX="${NLOPT_CXX}" CXXFLAGS="${NLOPT_CXXFLAGS}" CXXCPP="${NLOPT_CXXCPP}"; \ # make; \ # make install; \ # ls | grep -v ^include$$ | grep -v ^lib$$ | xargs rm -rf; \ # rm -rf .libs; # # Extract NLopt source code and remove .tar.gz # # tar -xzvf nlopt-${NLOPT_VERSION}.tar.gz # nlopt-${NLOPT_VERSION}/configure: nlopt-${NLOPT_VERSION}.tar.gz # `"${R_HOME}/bin/Rscript" --vanilla -e "untar(tarfile='nlopt-${NLOPT_VERSION}.tar.gz')"` # rm -rf nlopt-${NLOPT_VERSION}.tar.gz # # Download NLopt source code # # curl -O http://ab-initio.mit.edu/nlopt/nlopt-${NLOPT_VERSION}.tar.gz # nlopt-${NLOPT_VERSION}.tar.gz: # `"${R_HOME}/bin/Rscript" --vanilla -e "download.file(url='http://ab-initio.mit.edu/nlopt/nlopt-${NLOPT_VERSION}.tar.gz', destfile='nlopt-${NLOPT_VERSION}.tar.gz')"` # clean: # rm -rf *.so # rm -rf *.o # rm -rf nlopt-${NLOPT_VERSION} nloptr/src/dummy.cpp0000644000176000001440000000000013355070635014243 0ustar ripleyusersnloptr/src/Makevars.win0000644000176000001440000000225613355070635014713 0ustar ripleyusers# Copyright (C) 2010 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: Makevars.win # Author: Jelmer Ypma # Date: 18 August 2010 # # 09 June 2011: Windows support added thanks to Stefan Theussl and Uwe Ligges. # NLOPT_HOME is the directory where a working installation of # NLopt is located (within subdirectories NLOPT_VERSION/R_ARCH) # 18 November 2011: Removed NLOPT_VERSION macro and adopted some other changes # proposed by Brian Ripley to make nloptr work with his new toolchain. # NLopt should now be located in NLOPT_HOME/R_ARCH (i.e. without # version number) # 19 February 2013: NLopt is compiled with --with-cxx option, in order to include # the StoGo algorithm. This means that we now need to link to # with -lnlopt_cxx and also link to the c++ library, -lstdc++. # 7 November 2013: Changed PKG_CPPFLAGS to PKG_CFLAGS. # 2 May 2014: Added quotes around include paths to allow for spaces in NLOPT_HOME. # C Compiler options PKG_CFLAGS = -I"$(NLOPT_HOME)$(R_ARCH)/include" PKG_LIBS = -L"$(NLOPT_HOME)$(R_ARCH)/lib" -lnlopt_cxx nloptr/src/nloptr.c0000644000176000001440000010530113355070635014100 0ustar ripleyusers/* * Copyright (C) 2010 Jelmer Ypma. All Rights Reserved. * This code is published under the L-GPL. * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU Lesser General Public License as published * by the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU Lesser General Public License for more details. * * You should have received a copy of the GNU Lesser General Public License * along with this program. If not, see . * * File: nloptr.cpp * Author: Jelmer Ypma * Date: 9 June 2010 * * This file defines the main function NLoptR_Minimize, which * provides an interface to NLopt from R. * * The function takes an R object containing objective function, * constraints, options, etc. as argument, solve the problem * using NLopt and return the results. * * Financial support of the UK Economic and Social Research Council * through a grant (RES-589-28-0001) to the ESRC Centre for Microdata * Methods and Practice (CeMMAP) is gratefully acknowledged. * * 13/01/2011: added print_level option * 24/07/2011: added checks on return value when setting equality constraints etc. * 05/11/2013: Moved declaration of ineq_constr_data and eq_constr_data outside if-statement to solve segfault on Ubuntu. */ // TODO: add minimize/maximize option (objective = "maximize") // nlopt_result nlopt_set_min_objective(nlopt_opt opt, nlopt_func f, void* f_data); // nlopt_result nlopt_set_max_objective(nlopt_opt opt, nlopt_func f, void* f_data); #include "nlopt.h" #include #include // Rdefines.h is somewhat more higher level then Rinternal.h, and is preferred if the code might be shared with S at any stage. // #include /* * Extracts element with name 'str' from R object 'list' * and returns that element. */ SEXP getListElement (SEXP list, char *str) { SEXP elmt = R_NilValue, names = getAttrib(list, R_NamesSymbol); int i; for (i = 0; i < length(list); i++) { if(strcmp(CHAR(STRING_ELT(names, i)), str) == 0) { elmt = VECTOR_ELT(list, i); break; } } return elmt; } // convert string to nlopt_alogirthm nlopt_algorithm getAlgorithmCode( const char *algorithm_str ) { nlopt_algorithm algorithm; if ( strcmp( algorithm_str, "NLOPT_GN_DIRECT" ) == 0 ) { algorithm = NLOPT_GN_DIRECT; } else if ( strcmp( algorithm_str, "NLOPT_GN_DIRECT_L" ) == 0 ) { algorithm = NLOPT_GN_DIRECT_L; } else if ( strcmp( algorithm_str, "NLOPT_GN_DIRECT_L_RAND" ) == 0 ) { algorithm = NLOPT_GN_DIRECT_L_RAND; } else if ( strcmp( algorithm_str, "NLOPT_GN_DIRECT_NOSCAL" ) == 0 ) { algorithm = NLOPT_GN_DIRECT_NOSCAL; } else if ( strcmp( algorithm_str, "NLOPT_GN_DIRECT_L_NOSCAL" ) == 0 ) { algorithm = NLOPT_GN_DIRECT_L_NOSCAL; } else if ( strcmp( algorithm_str, "NLOPT_GN_DIRECT_L_RAND_NOSCAL" ) == 0 ) { algorithm = NLOPT_GN_DIRECT_L_RAND_NOSCAL; } else if ( strcmp( algorithm_str, "NLOPT_GN_ORIG_DIRECT" ) == 0 ) { algorithm = NLOPT_GN_ORIG_DIRECT; } else if ( strcmp( algorithm_str, "NLOPT_GN_ORIG_DIRECT_L" ) == 0 ) { algorithm = NLOPT_GN_ORIG_DIRECT_L; } else if ( strcmp( algorithm_str, "NLOPT_GD_STOGO" ) == 0 ) { algorithm = NLOPT_GD_STOGO; } else if ( strcmp( algorithm_str, "NLOPT_GD_STOGO_RAND" ) == 0 ) { algorithm = NLOPT_GD_STOGO_RAND; } else if ( strcmp( algorithm_str, "NLOPT_LD_SLSQP" ) == 0 ) { algorithm = NLOPT_LD_SLSQP; } else if ( strcmp( algorithm_str, "NLOPT_LD_LBFGS_NOCEDAL" ) == 0 ) { algorithm = NLOPT_LD_LBFGS_NOCEDAL; } else if ( strcmp( algorithm_str, "NLOPT_LD_LBFGS" ) == 0 ) { algorithm = NLOPT_LD_LBFGS; } else if ( strcmp( algorithm_str, "NLOPT_LN_PRAXIS" ) == 0 ) { algorithm = NLOPT_LN_PRAXIS; } else if ( strcmp( algorithm_str, "NLOPT_LD_VAR1" ) == 0 ) { algorithm = NLOPT_LD_VAR1; } else if ( strcmp( algorithm_str, "NLOPT_LD_VAR2" ) == 0 ) { algorithm = NLOPT_LD_VAR2; } else if ( strcmp( algorithm_str, "NLOPT_LD_TNEWTON" ) == 0 ) { algorithm = NLOPT_LD_TNEWTON; } else if ( strcmp( algorithm_str, "NLOPT_LD_TNEWTON_RESTART" ) == 0 ) { algorithm = NLOPT_LD_TNEWTON_RESTART; } else if ( strcmp( algorithm_str, "NLOPT_LD_TNEWTON_PRECOND" ) == 0 ) { algorithm = NLOPT_LD_TNEWTON_PRECOND; } else if ( strcmp( algorithm_str, "NLOPT_LD_TNEWTON_PRECOND_RESTART" ) == 0 ) { algorithm = NLOPT_LD_TNEWTON_PRECOND_RESTART; } else if ( strcmp( algorithm_str, "NLOPT_GN_CRS2_LM" ) == 0 ) { algorithm = NLOPT_GN_CRS2_LM; } else if ( strcmp( algorithm_str, "NLOPT_GN_MLSL" ) == 0 ) { algorithm = NLOPT_GN_MLSL; } else if ( strcmp( algorithm_str, "NLOPT_GD_MLSL" ) == 0 ) { algorithm = NLOPT_GD_MLSL; } else if ( strcmp( algorithm_str, "NLOPT_GN_MLSL_LDS" ) == 0 ) { algorithm = NLOPT_GN_MLSL_LDS; } else if ( strcmp( algorithm_str, "NLOPT_GD_MLSL_LDS" ) == 0 ) { algorithm = NLOPT_GD_MLSL_LDS; } else if ( strcmp( algorithm_str, "NLOPT_LD_MMA" ) == 0 ) { algorithm = NLOPT_LD_MMA; } else if ( strcmp( algorithm_str, "NLOPT_LN_COBYLA" ) == 0 ) { algorithm = NLOPT_LN_COBYLA; } else if ( strcmp( algorithm_str, "NLOPT_LN_NEWUOA" ) == 0 ) { algorithm = NLOPT_LN_NEWUOA; } else if ( strcmp( algorithm_str, "NLOPT_LN_NEWUOA_BOUND" ) == 0 ) { algorithm = NLOPT_LN_NEWUOA_BOUND; } else if ( strcmp( algorithm_str, "NLOPT_LN_NELDERMEAD" ) == 0 ) { algorithm = NLOPT_LN_NELDERMEAD; } else if ( strcmp( algorithm_str, "NLOPT_LN_SBPLX" ) == 0 ) { algorithm = NLOPT_LN_SBPLX; } else if ( strcmp( algorithm_str, "NLOPT_LN_AUGLAG" ) == 0 ) { algorithm = NLOPT_LN_AUGLAG; } else if ( strcmp( algorithm_str, "NLOPT_LD_AUGLAG" ) == 0 ) { algorithm = NLOPT_LD_AUGLAG; } else if ( strcmp( algorithm_str, "NLOPT_LN_AUGLAG_EQ" ) == 0 ) { algorithm = NLOPT_LN_AUGLAG_EQ; } else if ( strcmp( algorithm_str, "NLOPT_LD_AUGLAG_EQ" ) == 0 ) { algorithm = NLOPT_LD_AUGLAG_EQ; } else if ( strcmp( algorithm_str, "NLOPT_LN_BOBYQA" ) == 0 ) { algorithm = NLOPT_LN_BOBYQA; } else if ( strcmp( algorithm_str, "NLOPT_GN_ISRES" ) == 0 ) { algorithm = NLOPT_GN_ISRES; } else { // unknown algorithm code Rprintf("Error: unknown algorithm %s.\n", algorithm_str); algorithm = NLOPT_NUM_ALGORITHMS; // Not an algorithm, so this should result in a runtime error. } return algorithm; } /* * Define structure that contains data to pass to the objective function */ typedef struct { SEXP R_eval_f; SEXP R_environment; int num_iterations; int print_level; } func_objective_data; /* * Define function that calls user-defined objective function in R */ double func_objective(unsigned n, const double *x, double *grad, void *data) { // return the value (and the gradient) of the objective function // Check for user interruption from R R_CheckUserInterrupt(); // declare counter unsigned i; func_objective_data *d = (func_objective_data *) data; // increase number of function evaluations d->num_iterations++; // print status if ( d->print_level >= 1 ) { Rprintf( "iteration: %d\n", d->num_iterations ); } // print values of x if ( d->print_level >= 3 ) { if ( n == 1 ) { Rprintf( "\tx = %f\n", x[ 0 ] ); } else { Rprintf( "\tx = ( %f", x[ 0 ] ); for (i=1;iR_eval_f,rargs)); PROTECT(result = eval(Rcall,d->R_environment)); // recode the return value from SEXP to double double obj_value; if ( isNumeric( result ) ) { // objective value is only element of result obj_value = REAL(result)[0]; } else { // objective value should be parsed from the list of return values SEXP R_obj_value; PROTECT( R_obj_value = getListElement( result, "objective" ) ); // recode the return value from SEXP to double obj_value = REAL( R_obj_value )[0]; UNPROTECT( 1 ); } // print objective value if ( d->print_level >= 1 ) { Rprintf( "\tf(x) = %f\n", obj_value ); } // gradient if (grad) { // result needs to be a list in this case SEXP R_gradient; PROTECT( R_gradient = getListElement( result, "gradient" ) ); // recode the return value from SEXP to double for (i=0;iR_eval_g, rargs_x ) ); PROTECT( result = eval( Rcall, d->R_environment ) ); // get the value of the constraint from the result if ( isNumeric( result ) ) { // constraint values are the only element of result // recode the return value from SEXP to double*, by looping over constraints for (i=0;iprint_level >= 2 ) { if ( m == 1 ) { Rprintf( "\tg(x) = %f\n", constraints[ 0 ] ); } else { Rprintf( "\tg(x) = ( %f", constraints[ 0 ] ); for (i=1;iR_eval_g, rargs_x ) ); PROTECT( result = eval( Rcall, d->R_environment ) ); // get the value of the constraint from the result if ( isNumeric( result ) ) { // constraint values are the only element of result // recode the return value from SEXP to double*, by looping over constraints for (i=0;iprint_level >= 2 ) { if ( m == 1 ) { Rprintf( "\th(x) = %f\n", constraints[ 0 ] ); } else { Rprintf( "\th(x) = ( %f", constraints[ 0 ] ); for (i=1;i= stopval for maximizing PROTECT( R_opts_stopval = getListElement( R_options, "stopval" ) ); double stopval = REAL( R_opts_stopval )[0]; res = nlopt_set_stopval(opts, stopval); if ( res == NLOPT_INVALID_ARGS ) { *flag_encountered_error = 1; Rprintf("Error: nlopt_set_stopval returned NLOPT_INVALID_ARGS.\n"); } SEXP R_opts_ftol_rel; PROTECT( R_opts_ftol_rel = getListElement( R_options, "ftol_rel" ) ); double ftol_rel = REAL( R_opts_ftol_rel )[0]; res = nlopt_set_ftol_rel(opts, ftol_rel); if ( res == NLOPT_INVALID_ARGS ) { *flag_encountered_error = 1; Rprintf("Error: nlopt_set_ftol_rel returned NLOPT_INVALID_ARGS.\n"); } SEXP R_opts_ftol_abs; PROTECT( R_opts_ftol_abs = getListElement( R_options, "ftol_abs" ) ); double ftol_abs = REAL( R_opts_ftol_abs )[0]; res = nlopt_set_ftol_abs(opts, ftol_abs); if ( res == NLOPT_INVALID_ARGS ) { *flag_encountered_error = 1; Rprintf("Error: nlopt_set_ftol_abs returned NLOPT_INVALID_ARGS.\n"); } SEXP R_opts_xtol_rel; PROTECT( R_opts_xtol_rel = getListElement( R_options, "xtol_rel" ) ); double xtol_rel = REAL( R_opts_xtol_rel )[0]; res = nlopt_set_xtol_rel(opts, xtol_rel); if ( res == NLOPT_INVALID_ARGS ) { *flag_encountered_error = 1; Rprintf("Error: nlopt_set_xtol_rel returned NLOPT_INVALID_ARGS.\n"); } SEXP R_opts_xtol_abs; PROTECT( R_opts_xtol_abs = getListElement( R_options, "xtol_abs" ) ); double xtol_abs[ num_controls ]; int i; for (i=0;i 0. // by default a random seed is generated from system time. if ( ranseed > 0 ) { nlopt_srand(ranseed); } UNPROTECT( 11 ); return opts; } SEXP convertStatusToMessage( nlopt_result status ) { // convert message to an R object SEXP R_status_message; PROTECT(R_status_message = allocVector(STRSXP, 1)); switch ( status ) { // Successful termination (positive return values): // (= +1) case NLOPT_SUCCESS: SET_STRING_ELT(R_status_message, 0, mkChar("NLOPT_SUCCESS: Generic success return value.")); break; // (= +2) case NLOPT_STOPVAL_REACHED: SET_STRING_ELT(R_status_message, 0, mkChar("NLOPT_STOPVAL_REACHED: Optimization stopped because stopval (above) was reached.")); break; // (= +3) case NLOPT_FTOL_REACHED: SET_STRING_ELT(R_status_message, 0, mkChar("NLOPT_FTOL_REACHED: Optimization stopped because ftol_rel or ftol_abs (above) was reached.")); break; // (= +4) case NLOPT_XTOL_REACHED: SET_STRING_ELT(R_status_message, 0, mkChar("NLOPT_XTOL_REACHED: Optimization stopped because xtol_rel or xtol_abs (above) was reached.")); break; // (= +5) case NLOPT_MAXEVAL_REACHED: SET_STRING_ELT(R_status_message, 0, mkChar("NLOPT_MAXEVAL_REACHED: Optimization stopped because maxeval (above) was reached.")); break; // (= +6) case NLOPT_MAXTIME_REACHED: SET_STRING_ELT(R_status_message, 0, mkChar("NLOPT_MAXTIME_REACHED: Optimization stopped because maxtime (above) was reached.")); break; // Error codes (negative return values): // (= -1) case NLOPT_FAILURE: SET_STRING_ELT(R_status_message, 0, mkChar("NLOPT_FAILURE: Generic failure code.")); break; // (= -2) case NLOPT_INVALID_ARGS: SET_STRING_ELT(R_status_message, 0, mkChar("NLOPT_INVALID_ARGS: Invalid arguments (e.g. lower bounds are bigger than upper bounds, an unknown algorithm was specified, etcetera).")); break; // (= -3) case NLOPT_OUT_OF_MEMORY: SET_STRING_ELT(R_status_message, 0, mkChar("NLOPT_OUT_OF_MEMORY: Ran out of memory.")); break; // (= -4) case NLOPT_ROUNDOFF_LIMITED: SET_STRING_ELT(R_status_message, 0, mkChar("NLOPT_ROUNDOFF_LIMITED: Roundoff errors led to a breakdown of the optimization algorithm. In this case, the returned minimum may still be useful. (e.g. this error occurs in NEWUOA if one tries to achieve a tolerance too close to machine precision.)")); break; case NLOPT_FORCED_STOP: SET_STRING_ELT(R_status_message, 0, mkChar("Halted because of a forced termination: the user called nlopt_force_stop(opt) on the optimization's nlopt_opt object opt from the user's objective function.")); default: SET_STRING_ELT(R_status_message, 0, mkChar("Return status not recognized.")); } UNPROTECT( 1 ); return R_status_message; } // // Constrained minimization // SEXP NLoptR_Optimize( SEXP args ) { // declare counter unsigned i; // declare nlopt_result, to capture error codes from setting options nlopt_result res; int flag_encountered_error = 0; // get initial values SEXP R_init_values; PROTECT( R_init_values = getListElement( args, "x0" ) ); // number of control variables unsigned num_controls = length( R_init_values ); // set initial values of the controls double x0[ num_controls ]; for (i=0;i 0 ) { // get tolerances from R_options double tol_constraints_ineq[ num_constraints_ineq ]; SEXP R_tol_constraints_ineq; PROTECT( R_tol_constraints_ineq = getListElement( R_options, "tol_constraints_ineq" ) ); for (i=0;i 0 ) { // get tolerances from R_options double tol_constraints_eq[ num_constraints_eq ]; SEXP R_tol_constraints_eq; PROTECT( R_tol_constraints_eq = getListElement( R_options, "tol_constraints_eq" ) ); for (i=0;i= to <= in a future nloptr version. 22 February 2015, version 1.0.8: * Changed description in DESCRIPTION such that it does not start with the package name (as requested by CRAN). 14 February 2015, version 1.0.7: * Changed title field in DESCRIPTION to title case (as requested by CRAN). * Added donttest around example in mma documentation. 8 February 2015, version 1.0.6: * Updated description to better reflect installation procedure on Linux when NLopt is pre-installed (as requested by CRAN). 28 January 2015, version 1.0.5: * Added non-exported functions CFlags and LdFlags to be used in packages that want to link to the NLopt C library. * For consistency with the rest of the package the inequality sign in the functions auglag, cobyla, isres, mma, and slsqp will be switched from >= to <= in the next nloptr version. The current version of nloptr shows a warning when using these functions with inequality constraints. This warning can be turned off with options('nloptr.show.inequality.warning' = FALSE). 02 August 2014, version 1.0.4: * Increased version number to re-submit package to CRAN with CRLF line endings removed for configure and configure.ac. 25 July 2014, version 1.0.3: * Changed NLOPT_VERSION to 2.4.2 for Linux. * Changed nloptr.default.options from a data.frame to a function returning the data.frame. 25 July 2014, version 1.0.2: * Added configure script which tests for a system NLopt library via pkg-config and uses it if it is sufficiently recent (ie 2.4.*), and otherwise configure downloads, patches and builds the NLopt sources just how src/Makevars used to (thanks to Dirk Eddelbuettel). 05 May 2014, version 1.0.1: * All unit tests are now enabled and use the package testthat. Install the package with argument INSTALL_opt = "--install-tests" supplied to install.packages to install the tests. The tests can be run after installation with test_package('nloptr'). The testthat package needs to be installed and loaded to be able to run the tests. * Changed default value for maxtime option from 0.0 to -1.0. In some cases nloptr returned NLOPT_MAXTIME_REACHED without running any iterations with the default setting. This change solves this. * Replaced cat by message or warning. Messages can be suppressed by suppressMessages. 27 January 2014, version 1.0.0: * Merged wrappers from the 'nloptwrap' package. 19 November 2013, version 0.9.6: * Added a line in Makevars to replace some code in NLopt to fix compilation on Solaris as requested by Brian Ripley. 12 November 2013, version 0.9.5: * Updated references from NLopt version 2.3 to NLopt version 2.4 in installation instructions in INSTALL.windows. * Added a line in Makevars that replaces some code related to type-casting in NLopt-2.4/isres/isres.c. * Changed encoding of src/nloptr.c from CP1252 to UTF-8. 09 November 2013, version 0.9.4: * Updated NLopt to version 2.4. * Changed tests to use unit testing package testthat (these are currently disabled). * Fixed a segfault that started to occur on the latest version of Ubuntu. * Slightly changed the build process (Removed -lstdc++ from linker statement. A file dummy.cpp (with C++ extension) is added to the source directory to ensure linking with C++. Thanks to Brian Ripley for bringing this up.) 31 July 2013, version 0.9.3: * Split lines longer than 100 characters in check.derivatives examples in two lines to comply with new package rules. * Moved vignettes from inst/doc to vignettes to comply with new package rules. * Removed dependency on apacite in vignette as an update of apacite on CRAN resulted in errors. 11 July 2013, version 0.9.2: * Made changes in bibtex file of documentation. * Removed CFLAGS, CXXFLAGS from Makevars. version 0.9.0: * Introduced new print_level = 3. Shows values of controls (16 April 2012 on R-Forge). * Changed Makevars and Makevars.win to link to version 2.3 of NLopt compiled with --with-cxx option. This makes the StoGo algorithm available. (31 April 2013 on R-Forge). 18 November 2011, version 0.8.9: * Changed CRLF and CR line endings in src/Makevars to LF line endings to remove a warning from R CMD check. * Adopted some changes proposed by Brian Ripley to src/Makevars.win in order for nloptr to work with his new toolchain. 28 September 2011, version 0.8.8: * Updated src/Makevars to compile on Solaris. 24 September 2011, version 0.8.7: * Updated src/Makevars to compile on Solaris. 19 September 2011, version 0.8.6: * Updated src/Makevars to compile on Solaris. 03 September 2011, version 0.8.5: * Updated src/Makevars to compile a working binary for MacOS. 12 August 2011, version 0.8.4: * added new options: - data/nloptr.default.options.R: new file with a description of all options, mostly taken from the NLopt website (for internal use). - R/nloptr.print.options.R: function to show the description for a specific (set of) option(s). E.g. nloptr.print.options( option="maxeval" ) nloptr.print.options() shows a description of all options if called without arguments. - added option to print a description of all options and their values ('print_options_doc' = TRUE/FALSE). - added option population to set the population of stochastic/global solvers ('population' = 1000). - added option ranseed which sets the random seed for stochastic solvers ('ranseed' = 3141). A value of 0 uses a random seed generated by system time. - option check_derivatives is no longer listed as a termination condition. - documented the option to set the tolerance of (in)equality constraints (tol_constraints_eq, tol_constraints_ineq). * tests/banana_global.R: new test file that uses the algorithms (CRS, ISRES, MLSL) and options ranseed and population. * src/nloptr.c: capture error codes from setting options. * R/nloptr.print.R: output gives 'optimal value of controls' when status = -4 (some error code), this is changed to 'current value of controls'. nloptr/R/0000755000176000001440000000000013266702727012035 5ustar ripleyusersnloptr/R/slsqp.R0000644000176000001440000001274413267037255013330 0ustar ripleyusers# Copyright (C) 2014 Hans W. Borchers. All Rights Reserved. # This code is published under the L-GPL. # # File: slsqp.R # Author: Hans W. Borchers # Date: 27 January 2014 # # Wrapper to solve optimization problem using Sequential Quadratic Programming. #' Sequential Quadratic Programming (SQP) #' #' Sequential (least-squares) quadratic programming (SQP) algorithm for #' nonlinearly constrained, gradient-based optimization, supporting both #' equality and inequality constraints. #' #' The algorithm optimizes successive second-order (quadratic/least-squares) #' approximations of the objective function (via BFGS updates), with #' first-order (affine) approximations of the constraints. #' #' @param x0 starting point for searching the optimum. #' @param fn objective function that is to be minimized. #' @param gr gradient of function \code{fn}; will be calculated numerically if #' not specified. #' @param lower,upper lower and upper bound constraints. #' @param hin function defining the inequality constraints, that is #' \code{hin>=0} for all components. #' @param hinjac Jacobian of function \code{hin}; will be calculated #' numerically if not specified. #' @param heq function defining the equality constraints, that is \code{heq==0} #' for all components. #' @param heqjac Jacobian of function \code{heq}; will be calculated #' numerically if not specified. #' @param nl.info logical; shall the original NLopt info been shown. #' @param control list of options, see \code{nl.opts} for help. #' @param ... additional arguments passed to the function. #' #' @return List with components: #' \item{par}{the optimal solution found so far.} #' \item{value}{the function value corresponding to \code{par}.} #' \item{iter}{number of (outer) iterations, see \code{maxeval}.} #' \item{convergence}{integer code indicating successful completion (> 1) #' or a possible error number (< 0).} #' \item{message}{character string produced by NLopt and giving additional #' information.} #' #' @export slsqp #' #' @author Hans W. Borchers #' #' @note See more infos at #' \url{http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms}. #' #' @seealso \code{alabama::auglag}, \code{Rsolnp::solnp}, #' \code{Rdonlp2::donlp2} #' #' @references Dieter Kraft, ``A software package for sequential quadratic #' programming'', Technical Report DFVLR-FB 88-28, Institut fuer Dynamik der #' Flugsysteme, Oberpfaffenhofen, July 1988. #' #' @examples #' #' ## Solve the Hock-Schittkowski problem no. 100 #' x0.hs100 <- c(1, 2, 0, 4, 0, 1, 1) #' fn.hs100 <- function(x) { #' (x[1]-10)^2 + 5*(x[2]-12)^2 + x[3]^4 + 3*(x[4]-11)^2 + 10*x[5]^6 + #' 7*x[6]^2 + x[7]^4 - 4*x[6]*x[7] - 10*x[6] - 8*x[7] #' } #' hin.hs100 <- function(x) { #' h <- numeric(4) #' h[1] <- 127 - 2*x[1]^2 - 3*x[2]^4 - x[3] - 4*x[4]^2 - 5*x[5] #' h[2] <- 282 - 7*x[1] - 3*x[2] - 10*x[3]^2 - x[4] + x[5] #' h[3] <- 196 - 23*x[1] - x[2]^2 - 6*x[6]^2 + 8*x[7] #' h[4] <- -4*x[1]^2 - x[2]^2 + 3*x[1]*x[2] -2*x[3]^2 - 5*x[6] +11*x[7] #' return(h) #' } #' #' S <- slsqp(x0.hs100, fn = fn.hs100, # no gradients and jacobians provided #' hin = hin.hs100, #' control = list(xtol_rel = 1e-8, check_derivatives = TRUE)) #' S #' ## Optimal value of objective function: 690.622270249131 *** WRONG *** #' #' # Even the numerical derivatives seem to be too tight. #' # Let's try with a less accurate jacobian. #' #' hinjac.hs100 <- function(x) nl.jacobian(x, hin.hs100, heps = 1e-2) #' S <- slsqp(x0.hs100, fn = fn.hs100, #' hin = hin.hs100, hinjac = hinjac.hs100, #' control = list(xtol_rel = 1e-8)) #' S #' ## Optimal value of objective function: 680.630057392593 *** CORRECT *** #' slsqp <- function(x0, fn, gr = NULL, lower = NULL, upper = NULL, hin = NULL, hinjac = NULL, heq = NULL, heqjac = NULL, nl.info = FALSE, control = list(), ...) { opts <- nl.opts(control) opts["algorithm"] <- "NLOPT_LD_SLSQP" fun <- match.fun(fn) fn <- function(x) fun(x, ...) if (is.null(gr)) { gr <- function(x) nl.grad(x, fn) } else { .gr <- match.fun(gr) gr <- function(x) .gr(x, ...) } if (!is.null(hin)) { if ( getOption('nloptr.show.inequality.warning') ) { message('For consistency with the rest of the package the inequality sign may be switched from >= to <= in a future nloptr version.') } .hin <- match.fun(hin) hin <- function(x) (-1) * .hin(x) # change hin >= 0 to hin <= 0 ! if (is.null(hinjac)) { hinjac <- function(x) nl.jacobian(x, hin) } else { .hinjac <- match.fun(hinjac) hinjac <- function(x) (-1) * .hinjac(x) } } if (!is.null(heq)) { .heq <- match.fun(heq) heq <- function(x) .heq(x) if (is.null(heqjac)) { heqjac <- function(x) nl.jacobian(x, heq) } else { .heqjac <- match.fun(heqjac) heqjac <- function(x) .heqjac(x) } } S0 <- nloptr(x0, eval_f = fn, eval_grad_f = gr, lb = lower, ub = upper, eval_g_ineq = hin, eval_jac_g_ineq = hinjac, eval_g_eq = heq, eval_jac_g_eq = heqjac, opts = opts) if (nl.info) print(S0) S1 <- list(par = S0$solution, value = S0$objective, iter = S0$iterations, convergence = S0$status, message = S0$message) return(S1) } nloptr/R/check.derivatives.R0000644000176000001440000001501313266704353015556 0ustar ripleyusers# Copyright (C) 2011 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: check.derivatives.R # Author: Jelmer Ypma # Date: 24 July 2011 # # Compare analytic derivatives wih finite difference approximations. # # Input: # .x : compare at this point # func : calculate finite difference approximation for the gradient of this function # func_grad : function to calculate analytic gradients # check_derivatives_tol : show deviations larger than this value (optional) # check_derivatives_print : print the values of the function (optional) # func_grad_name : name of function to show in output (optional) # ... : arguments that are passed to the user-defined function (func and func_grad) # # Output: list with analytic gradients, finite difference approximations, relative errors # and a comparison of the relative errors to the tolerance. # # CHANGELOG: # 27/10/2013: Added relative_error and flag_derivative_warning to output list. # 05/05/2014: Replaced cat by message, so messages can now be suppressed by suppressMessages. #' Check analytic gradients of a function using finite difference #' approximations #' #' This function compares the analytic gradients of a function with a finite #' difference approximation and prints the results of these checks. #' #' @param .x point at which the comparison is done. #' @param func function to be evaluated. #' @param func_grad function calculating the analytic gradients. #' @param check_derivatives_tol option determining when differences between the #' analytic gradient and its finite difference approximation are flagged as an #' error. #' @param check_derivatives_print option related to the amount of output. 'all' #' means that all comparisons are shown, 'errors' only shows comparisons that #' are flagged as an error, and 'none' shows the number of errors only. #' @param func_grad_name option to change the name of the gradient function #' that shows up in the output. #' @param ... further arguments passed to the functions func and func_grad. #' #' @return The return value contains a list with the analytic gradient, its #' finite difference approximation, the relative errors, and vector comparing #' the relative errors to the tolerance. #' #' @export #' #' @author Jelmer Ypma #' #' @seealso \code{\link[nloptr:nloptr]{nloptr}} #' #' @keywords optimize interface #' #' @examples #' #' library('nloptr') #' #' # example with correct gradient #' f <- function( x, a ) { #' return( sum( ( x - a )^2 ) ) #' } #' #' f_grad <- function( x, a ) { #' return( 2*( x - a ) ) #' } #' #' check.derivatives( .x=1:10, func=f, func_grad=f_grad, #' check_derivatives_print='none', a=runif(10) ) #' #' # example with incorrect gradient #' f_grad <- function( x, a ) { #' return( 2*( x - a ) + c(0,.1,rep(0,8)) ) #' } #' #' check.derivatives( .x=1:10, func=f, func_grad=f_grad, #' check_derivatives_print='errors', a=runif(10) ) #' #' # example with incorrect gradient of vector-valued function #' g <- function( x, a ) { #' return( c( sum(x-a), sum( (x-a)^2 ) ) ) #' } #' #' g_grad <- function( x, a ) { #' return( rbind( rep(1,length(x)) + c(0,.01,rep(0,8)), 2*(x-a) + c(0,.1,rep(0,8)) ) ) #' } #' #' check.derivatives( .x=1:10, func=g, func_grad=g_grad, #' check_derivatives_print='all', a=runif(10) ) #' check.derivatives <- function( .x, func, func_grad, check_derivatives_tol = 1e-04, check_derivatives_print = 'all', func_grad_name = 'grad_f', ... ) { analytic_grad <- func_grad( .x, ... ) finite_diff_grad <- finite.diff( func, .x, ... ) relative_error <- ifelse( finite_diff_grad == 0, analytic_grad, abs( ( analytic_grad - finite_diff_grad ) / finite_diff_grad ) ) flag_derivative_warning <- relative_error > check_derivatives_tol if ( ! ( check_derivatives_print %in% c('all','errors','none') ) ) { warning( paste( "Value '", check_derivatives_print, "' for check_derivatives_print is unknown; use 'all' (default), 'errors', or 'none'.", sep='' ) ) check_derivatives_print <- 'none' } # determine indices of vector / matrix for printing # format indices with width, such that they are aligned vertically if ( is.matrix( analytic_grad ) ) { indices <- paste( format( rep( 1:nrow(analytic_grad), times=ncol(analytic_grad) ), width=1 + sum( nrow(analytic_grad) > 10^(1:10) ) ), format( rep( 1:ncol(analytic_grad), each=nrow(analytic_grad) ), width=1 + sum( ncol(analytic_grad) > 10^(1:10) ) ), sep=', ' ) } else { indices <- format( 1:length(analytic_grad), width=1 + sum( length(analytic_grad) > 10^(1:10) ) ) } # Print results. message( "Derivative checker results: ", sum( flag_derivative_warning ), " error(s) detected." ) if ( check_derivatives_print == 'all' ) { message( "\n", paste( ifelse( flag_derivative_warning, "*"," "), " ", func_grad_name, "[ ", indices, " ] = ", format(analytic_grad, scientific=TRUE), " ~ ", format(finite_diff_grad, scientific=TRUE), " [", format( relative_error, scientific=TRUE), "]", sep='', collapse="\n" ), "\n\n" ) } else if ( check_derivatives_print == 'errors' ) { if ( sum( flag_derivative_warning ) > 0 ) { message( "\n", paste( ifelse( flag_derivative_warning[ flag_derivative_warning ], "*"," "), " ", func_grad_name, "[ ", indices[ flag_derivative_warning ], " ] = ", format(analytic_grad[ flag_derivative_warning ], scientific=TRUE), " ~ ", format(finite_diff_grad[ flag_derivative_warning ], scientific=TRUE), " [", format( relative_error[ flag_derivative_warning ], scientific=TRUE), "]", sep='', collapse="\n" ), "\n\n" ) } } else if ( check_derivatives_print == 'none' ) { } return( list( "analytic" = analytic_grad, "finite_difference" = finite_diff_grad, "relative_error" = relative_error, "flag_derivative_warning" = flag_derivative_warning ) ) } nloptr/R/cobyla.R0000644000176000001440000002064313267037364013435 0ustar ripleyusers# Copyright (C) 2014 Hans W. Borchers. All Rights Reserved. # This code is published under the L-GPL. # # File: cobyla.R # Author: Hans W. Borchers # Date: 27 January 2014 # # Wrapper to solve optimization problem using COBYLA, BOBYQA, and NEWUOA. #' Constrained Optimization by Linear Approximations #' #' COBYLA is an algorithm for derivative-free optimization with nonlinear #' inequality and equality constraints (but see below). #' #' It constructs successive linear approximations of the objective function and #' constraints via a simplex of n+1 points (in n dimensions), and optimizes #' these approximations in a trust region at each step. #' #' COBYLA supports equality constraints by transforming them into two #' inequality constraints. As this does not give full satisfaction with the #' implementation in NLOPT, it has not been made available here. #' #' @param x0 starting point for searching the optimum. #' @param fn objective function that is to be minimized. #' @param lower,upper lower and upper bound constraints. #' @param hin function defining the inequality constraints, that is #' \code{hin>=0} for all components. #' @param nl.info logical; shall the original NLopt info been shown. #' @param control list of options, see \code{nl.opts} for help. #' @param ... additional arguments passed to the function. #' #' @return List with components: #' \item{par}{the optimal solution found so far.} #' \item{value}{the function value corresponding to \code{par}.} #' \item{iter}{number of (outer) iterations, see \code{maxeval}.} #' \item{convergence}{integer code indicating successful completion (> 0) #' or a possible error number (< 0).} #' \item{message}{character string produced by NLopt and giving additional #' information.} #' #' @author Hans W. Borchers #' #' @note The original code, written in Fortran by Powell, was converted in C #' for the Scipy project. #' #' @seealso \code{\link{bobyqa}}, \code{\link{newuoa}} #' #' @references M. J. D. Powell, ``A direct search optimization method that #' models the objective and constraint functions by linear interpolation,'' in #' Advances in Optimization and Numerical Analysis, eds. S. Gomez and J.-P. #' Hennart (Kluwer Academic: Dordrecht, 1994), p. 51-67. #' #' @examples #' #' ### Solve Hock-Schittkowski no. 100 #' x0.hs100 <- c(1, 2, 0, 4, 0, 1, 1) #' fn.hs100 <- function(x) { #' (x[1]-10)^2 + 5*(x[2]-12)^2 + x[3]^4 + 3*(x[4]-11)^2 + 10*x[5]^6 + #' 7*x[6]^2 + x[7]^4 - 4*x[6]*x[7] - 10*x[6] - 8*x[7] #' } #' hin.hs100 <- function(x) { #' h <- numeric(4) #' h[1] <- 127 - 2*x[1]^2 - 3*x[2]^4 - x[3] - 4*x[4]^2 - 5*x[5] #' h[2] <- 282 - 7*x[1] - 3*x[2] - 10*x[3]^2 - x[4] + x[5] #' h[3] <- 196 - 23*x[1] - x[2]^2 - 6*x[6]^2 + 8*x[7] #' h[4] <- -4*x[1]^2 - x[2]^2 + 3*x[1]*x[2] -2*x[3]^2 - 5*x[6] +11*x[7] #' return(h) #' } #' #' S <- cobyla(x0.hs100, fn.hs100, hin = hin.hs100, #' nl.info = TRUE, control = list(xtol_rel = 1e-8, maxeval = 2000)) #' ## Optimal value of objective function: 680.630057374431 #' #' @export cobyla cobyla <- function(x0, fn, lower = NULL, upper = NULL, hin = NULL, nl.info = FALSE, control = list(), ...) { opts <- nl.opts(control) opts["algorithm"] <- "NLOPT_LN_COBYLA" f1 <- match.fun(fn) fn <- function(x) f1(x, ...) if (!is.null(hin)) { if ( getOption('nloptr.show.inequality.warning') ) { message('For consistency with the rest of the package the inequality sign may be switched from >= to <= in a future nloptr version.') } f2 <- match.fun(hin) hin <- function(x) (-1)*f2(x, ...) # NLOPT expects hin <= 0 } S0 <- nloptr(x0, eval_f = fn, lb = lower, ub = upper, eval_g_ineq = hin, opts = opts) if (nl.info) print(S0) S1 <- list(par = S0$solution, value = S0$objective, iter = S0$iterations, convergence = S0$status, message = S0$message) return(S1) } #' Bound Optimization by Quadratic Approximation #' #' BOBYQA performs derivative-free bound-constrained optimization using an #' iteratively constructed quadratic approximation for the objective function. #' #' This is an algorithm derived from the BOBYQA Fortran subroutine of Powell, #' converted to C and modified for the NLOPT stopping criteria. #' #' @param x0 starting point for searching the optimum. #' @param fn objective function that is to be minimized. #' @param lower,upper lower and upper bound constraints. #' @param nl.info logical; shall the original NLopt info been shown. #' @param control list of options, see \code{nl.opts} for help. #' @param ... additional arguments passed to the function. #' #' @return List with components: #' \item{par}{the optimal solution found so far.} #' \item{value}{the function value corresponding to \code{par}.} #' \item{iter}{number of (outer) iterations, see \code{maxeval}.} #' \item{convergence}{integer code indicating successful completion (> 0) #' or a possible error number (< 0).} #' \item{message}{character string produced by NLopt and giving additional #' information.} #' #' @export bobyqa #' #' @note Because BOBYQA constructs a quadratic approximation of the objective, #' it may perform poorly for objective functions that are not #' twice-differentiable. #' #' @seealso \code{\link{cobyla}}, \code{\link{newuoa}} #' #' @references M. J. D. Powell. ``The BOBYQA algorithm for bound constrained #' optimization without derivatives,'' Department of Applied Mathematics and #' Theoretical Physics, Cambridge England, technical reportNA2009/06 (2009). #' #' @examples #' #' fr <- function(x) { ## Rosenbrock Banana function #' 100 * (x[2] - x[1]^2)^2 + (1 - x[1])^2 #' } #' (S <- bobyqa(c(0, 0, 0), fr, lower = c(0, 0, 0), upper = c(0.5, 0.5, 0.5))) #' bobyqa <- function(x0, fn, lower = NULL, upper = NULL, nl.info = FALSE, control = list(), ...) { opts <- nl.opts(control) opts["algorithm"] <- "NLOPT_LN_BOBYQA" fun <- match.fun(fn) fn <- function(x) fun(x, ...) S0 <- nloptr(x0, fn, lb = lower, ub = upper, opts = opts) if (nl.info) print(S0) S1 <- list(par = S0$solution, value = S0$objective, iter = S0$iterations, convergence = S0$status, message = S0$message) return(S1) } #' New Unconstrained Optimization with quadratic Approximation #' #' NEWUOA solves quadratic subproblems in a spherical trust regionvia a #' truncated conjugate-gradient algorithm. For bound-constrained problems, #' BOBYQA shold be used instead, as Powell developed it as an enhancement #' thereof for bound constraints. #' #' This is an algorithm derived from the NEWUOA Fortran subroutine of Powell, #' converted to C and modified for the NLOPT stopping criteria. #' #' @param x0 starting point for searching the optimum. #' @param fn objective function that is to be minimized. #' @param nl.info logical; shall the original NLopt info been shown. #' @param control list of options, see \code{nl.opts} for help. #' @param ... additional arguments passed to the function. #' #' @return List with components: #' \item{par}{the optimal solution found so far.} #' \item{value}{the function value corresponding to \code{par}.} #' \item{iter}{number of (outer) iterations, see \code{maxeval}.} #' \item{convergence}{integer code indicating successful completion (> 0) #' or a possible error number (< 0).} #' \item{message}{character string produced by NLopt and giving additional #' information.} #' #' @export newuoa #' #' @author Hans W. Borchers #' #' @note NEWUOA may be largely superseded by BOBYQA. #' #' @seealso \code{\link{bobyqa}}, \code{\link{cobyla}} #' #' @references M. J. D. Powell. ``The BOBYQA algorithm for bound constrained #' optimization without derivatives,'' Department of Applied Mathematics and #' Theoretical Physics, Cambridge England, technical reportNA2009/06 (2009). #' #' @examples #' #' fr <- function(x) { ## Rosenbrock Banana function #' 100 * (x[2] - x[1]^2)^2 + (1 - x[1])^2 #' } #' (S <- newuoa(c(1, 2), fr)) #' newuoa <- function(x0, fn, nl.info = FALSE, control = list(), ...) { opts <- nl.opts(control) opts["algorithm"] <- "NLOPT_LN_NEWUOA" fun <- match.fun(fn) fn <- function(x) fun(x, ...) S0 <- nloptr(x0, fn, opts = opts) if (nl.info) print(S0) S1 <- list(par = S0$solution, value = S0$objective, iter = S0$iterations, convergence = S0$status, message = S0$message) return(S1) } nloptr/R/tnewton.R0000644000176000001440000000677713266716134013674 0ustar ripleyusers# Copyright (C) 2014 Hans W. Borchers. All Rights Reserved. # This code is published under the L-GPL. # # File: tnewton.R # Author: Hans W. Borchers # Date: 27 January 2014 # # Wrapper to solve optimization problem using Preconditioned Truncated Newton. #' Preconditioned Truncated Newton #' #' Truncated Newton methods, also calledNewton-iterative methods, solve an #' approximating Newton system using a conjugate-gradient approach and are #' related to limited-memory BFGS. #' #' Truncated Newton methods are based on approximating the objective with a #' quadratic function and applying an iterative scheme such as the linear #' conjugate-gradient algorithm. #' #' @param x0 starting point for searching the optimum. #' @param fn objective function that is to be minimized. #' @param gr gradient of function \code{fn}; will be calculated numerically if #' not specified. #' @param lower,upper lower and upper bound constraints. #' @param precond logical; preset L-BFGS with steepest descent. #' @param restart logical; restarting L-BFGS with steepest descent. #' @param nl.info logical; shall the original NLopt info been shown. #' @param control list of options, see \code{nl.opts} for help. #' @param ... additional arguments passed to the function. #' #' @return List with components: #' \item{par}{the optimal solution found so far.} #' \item{value}{the function value corresponding to \code{par}.} #' \item{iter}{number of (outer) iterations, see \code{maxeval}.} #' \item{convergence}{integer code indicating successful completion (> 1) #' or a possible error number (< 0).} #' \item{message}{character string produced by NLopt and giving additional #' information.} #' #' @export tnewton #' #' @author Hans W. Borchers #' #' @note Less reliable than Newton's method, but can handle very large #' problems. #' #' @seealso \code{\link{lbfgs}} #' #' @references R. S. Dembo and T. Steihaug, ``Truncated Newton algorithms for #' large-scale optimization,'' Math. Programming 26, p. 190-212 (1982). #' #' @examples #' #' flb <- function(x) { #' p <- length(x) #' sum(c(1, rep(4, p-1)) * (x - c(1, x[-p])^2)^2) #' } #' # 25-dimensional box constrained: par[24] is *not* at boundary #' S <- tnewton(rep(3, 25), flb, lower=rep(2, 25), upper=rep(4, 25), #' nl.info = TRUE, control = list(xtol_rel=1e-8)) #' ## Optimal value of objective function: 368.105912874334 #' ## Optimal value of controls: 2 ... 2 2.109093 4 #' tnewton <- function(x0, fn, gr = NULL, lower = NULL, upper = NULL, precond = TRUE, restart = TRUE, nl.info = FALSE, control = list(), ...) { opts <- nl.opts(control) if (precond) { if (restart) opts["algorithm"] <- "NLOPT_LD_TNEWTON_PRECOND_RESTART" else opts["algorithm"] <- "NLOPT_LD_TNEWTON_PRECOND" } else { if (restart) opts["algorithm"] <- "NLOPT_LD_TNEWTON_RESTART" else opts["algorithm"] <- "NLOPT_LD_TNEWTON" } fun <- match.fun(fn) fn <- function(x) fun(x, ...) if (is.null(gr)) { gr <- function(x) nl.grad(x, fn) } else { .gr <- match.fun(gr) gr <- function(x) .gr(x, ...) } S0 <- nloptr(x0, eval_f = fn, eval_grad_f = gr, lb = lower, ub = upper, opts = opts) if (nl.info) print(S0) S1 <- list(par = S0$solution, value = S0$objective, iter = S0$iterations, convergence = S0$status, message = S0$message) return(S1) } nloptr/R/varmetric.R0000644000176000001440000000616513266716153014162 0ustar ripleyusers# Copyright (C) 2014 Hans W. Borchers. All Rights Reserved. # This code is published under the L-GPL. # # File: varmetric.R # Author: Hans W. Borchers # Date: 27 January 2014 # # Wrapper to solve optimization problem using Varmetric. #' Shifted Limited-memory Variable-metric #' #' Shifted limited-memory variable-metric algorithm. #' #' Variable-metric methods are a variant of the quasi-Newton methods, #' especially adapted to large-scale unconstrained (or bound constrained) #' minimization. #' #' @param x0 initial point for searching the optimum. #' @param fn objective function to be minimized. #' @param gr gradient of function \code{fn}; will be calculated numerically if #' not specified. #' @param rank2 logical; if true uses a rank-2 update method, else rank-1. #' @param lower,upper lower and upper bound constraints. #' @param nl.info logical; shall the original NLopt info been shown. #' @param control list of control parameters, see \code{nl.opts} for help. #' @param ... further arguments to be passed to the function. #' #' @return List with components: #' \item{par}{the optimal solution found so far.} #' \item{value}{the function value corresponding to \code{par}.} #' \item{iter}{number of (outer) iterations, see \code{maxeval}.} #' \item{convergence}{integer code indicating successful completion (> 0) #' or a possible error number (< 0).} #' \item{message}{character string produced by NLopt and giving additional #' information.} #' #' @export varmetric #' #' @author Hans W. Borchers #' #' @note Based on L. Luksan's Fortran implementation of a shifted #' limited-memory variable-metric algorithm. #' #' @seealso \code{\link{lbfgs}} #' #' @references J. Vlcek and L. Luksan, ``Shifted limited-memory variable metric #' methods for large-scale unconstrained minimization,'' J. Computational Appl. #' Math. 186, p. 365-390 (2006). #' #' @examples #' #' flb <- function(x) { #' p <- length(x) #' sum(c(1, rep(4, p-1)) * (x - c(1, x[-p])^2)^2) #' } #' # 25-dimensional box constrained: par[24] is *not* at the boundary #' S <- varmetric(rep(3, 25), flb, lower=rep(2, 25), upper=rep(4, 25), #' nl.info = TRUE, control = list(xtol_rel=1e-8)) #' ## Optimal value of objective function: 368.105912874334 #' ## Optimal value of controls: 2 ... 2 2.109093 4 #' varmetric <- function(x0, fn, gr = NULL, rank2 = TRUE, lower = NULL, upper = NULL, nl.info = FALSE, control = list(), ...) { opts <- nl.opts(control) if (rank2) opts["algorithm"] <- "NLOPT_LD_VAR2" else opts["algorithm"] <- "NLOPT_LD_VAR1" fun <- match.fun(fn) fn <- function(x) fun(x, ...) if (is.null(gr)) { gr <- function(x) nl.grad(x, fn) } else { .gr <- match.fun(gr) gr <- function(x) .gr(x, ...) } S0 <- nloptr(x0, eval_f = fn, eval_grad_f = gr, lb = lower, ub = upper, opts = opts) if (nl.info) print(S0) S1 <- list(par = S0$solution, value = S0$objective, iter = S0$iterations, convergence = S0$status, message = S0$message) return(S1) } nloptr/R/onLoad.R0000644000176000001440000000035712553776710013402 0ustar ripleyusers.onLoad <- function(libname, pkgname) { # Set the option to TRUE if it has not been set before. if( is.null( getOption('nloptr.show.inequality.warning') ) ) { options('nloptr.show.inequality.warning' = TRUE ) } } nloptr/R/direct.R0000644000176000001440000001365113266717641013441 0ustar ripleyusers# Copyright (C) 2014 Hans W. Borchers. All Rights Reserved. # This code is published under the L-GPL. # # File: direct.R # Author: Hans W. Borchers # Date: 27 January 2014 # # Wrapper to solve optimization problem using Direct. #' DIviding RECTangles Algorithm for Global Optimization #' #' DIRECT is a deterministic search algorithm based on systematic division of #' the search domain into smaller and smaller hyperrectangles. The DIRECT_L #' makes the algorithm more biased towards local search (more efficient for #' functions without too many minima). #' #' The DIRECT and DIRECT-L algorithms start by rescaling the bound constraints #' to a hypercube, which gives all dimensions equal weight in the search #' procedure. If your dimensions do not have equal weight, e.g. if you have a #' ``long and skinny'' search space and your function varies at about the same #' speed in all directions, it may be better to use unscaled variant of the #' DIRECT algorithm. #' #' The algorithms only handle finite bound constraints which must be provided. #' The original versions may include some support for arbitrary nonlinear #' inequality, but this has not been tested. #' #' The original versions do not have randomized or unscaled variants, so these #' options will be disregarded for these versions. #' #' @aliases direct directL #' #' @param fn objective function that is to be minimized. #' @param lower,upper lower and upper bound constraints. #' @param scaled logical; shall the hypercube be scaled before starting. #' @param randomized logical; shall some randomization be used to decide which #' dimension to halve next in the case of near-ties. #' @param original logical; whether to use the original implementation by #' Gablonsky -- the performance is mostly similar. #' @param nl.info logical; shall the original NLopt info been shown. #' @param control list of options, see \code{nl.opts} for help. #' @param ... additional arguments passed to the function. #' #' @return List with components: #' \item{par}{the optimal solution found so far.} #' \item{value}{the function value corresponding to \code{par}.} #' \item{iter}{number of (outer) iterations, see \code{maxeval}.} #' \item{convergence}{integer code indicating successful completion (> 0) #' or a possible error number (< 0).} #' \item{message}{character string produced by NLopt and giving additional #' information.} #' #' @export direct #' #' @author Hans W. Borchers #' #' @note The DIRECT_L algorithm should be tried first. #' #' @seealso The \code{dfoptim} package will provide a pure R version of this #' algorithm. #' #' @references D. R. Jones, C. D. Perttunen, and B. E. Stuckmann, #' ``Lipschitzian optimization without the lipschitz constant,'' J. #' Optimization Theory and Applications, vol. 79, p. 157 (1993). #' #' J. M. Gablonsky and C. T. Kelley, ``A locally-biased form of the DIRECT #' algorithm," J. Global Optimization, vol. 21 (1), p. 27-37 (2001). #' #' @examples #' #' ### Minimize the Hartmann6 function #' hartmann6 <- function(x) { #' n <- length(x) #' a <- c(1.0, 1.2, 3.0, 3.2) #' A <- matrix(c(10.0, 0.05, 3.0, 17.0, #' 3.0, 10.0, 3.5, 8.0, #' 17.0, 17.0, 1.7, 0.05, #' 3.5, 0.1, 10.0, 10.0, #' 1.7, 8.0, 17.0, 0.1, #' 8.0, 14.0, 8.0, 14.0), nrow=4, ncol=6) #' B <- matrix(c(.1312,.2329,.2348,.4047, #' .1696,.4135,.1451,.8828, #' .5569,.8307,.3522,.8732, #' .0124,.3736,.2883,.5743, #' .8283,.1004,.3047,.1091, #' .5886,.9991,.6650,.0381), nrow=4, ncol=6) #' fun <- 0.0 #' for (i in 1:4) { #' fun <- fun - a[i] * exp(-sum(A[i,]*(x-B[i,])^2)) #' } #' return(fun) #' } #' S <- directL(hartmann6, rep(0,6), rep(1,6), #' nl.info = TRUE, control=list(xtol_rel=1e-8, maxeval=1000)) #' ## Number of Iterations....: 500 #' ## Termination conditions: stopval: -Inf #' ## xtol_rel: 1e-08, maxeval: 500, ftol_rel: 0, ftol_abs: 0 #' ## Number of inequality constraints: 0 #' ## Number of equality constraints: 0 #' ## Current value of objective function: -3.32236800687327 #' ## Current value of controls: #' ## 0.2016884 0.1500025 0.4768667 0.2753391 0.311648 0.6572931 #' direct <- function(fn, lower, upper, scaled = TRUE, original = FALSE, nl.info = FALSE, control = list(), ...) { opts <- nl.opts(control) if (scaled) { opts["algorithm"] <- "NLOPT_GN_DIRECT" } else { opts["algorithm"] <- "NLOPT_GN_DIRECT_NOSCAL" } if (original) opts["algorithm"] <- "NLOPT_GN_ORIG_DIRECT" fun <- match.fun(fn) fn <- function(x) fun(x, ...) x0 <- (lower + upper) / 2 S0 <- nloptr(x0, eval_f = fn, lb = lower, ub = upper, opts = opts) if (nl.info) print(S0) S1 <- list(par = S0$solution, value = S0$objective, iter = S0$iterations, convergence = S0$status, message = S0$message) return(S1) } #' @export directL #' @rdname direct directL <- function(fn, lower, upper, randomized = FALSE, original = FALSE, nl.info = FALSE, control = list(), ...) { opts <- nl.opts(control) if (randomized) { opts["algorithm"] <- "NLOPT_GN_DIRECT_L_RAND" } else { opts["algorithm"] <- "NLOPT_GN_DIRECT_L" } if (original) opts["algorithm"] <- "NLOPT_GN_ORIG_DIRECT_L" fun <- match.fun(fn) fn <- function(x) fun(x, ...) x0 <- (lower + upper) / 2 S0 <- nloptr(x0, eval_f = fn, lb = lower, ub = upper, opts = opts) if (nl.info) print(S0) S1 <- list(par = S0$solution, value = S0$objective, iter = S0$iterations, convergence = S0$status, message = S0$message) return(S1) } nloptr/R/mlsl.R0000644000176000001440000001272213266716050013125 0ustar ripleyusers# Copyright (C) 2014 Hans W. Borchers. All Rights Reserved. # This code is published under the L-GPL. # # File: mlsl.R # Author: Hans W. Borchers # Date: 27 January 2014 # # Wrapper to solve optimization problem using Multi-Level Single-Linkage. # # CHANGELOG: # 05/05/2014: Replaced cat by warning. #' Multi-level Single-linkage #' #' The ``Multi-Level Single-Linkage'' (MLSL) algorithm for global optimization #' searches by a sequence of local optimizations from random starting points. #' A modification of MLSL is included using a low-discrepancy sequence (LDS) #' instead of pseudorandom numbers. #' #' MLSL is a `multistart' algorithm: it works by doing a sequence of local #' optimizations (using some other local optimization algorithm) from random or #' low-discrepancy starting points. MLSL is distinguished, however by a #' `clustering' heuristic that helps it to avoid repeated searches of the same #' local optima, and has some theoretical guarantees of finding all local #' optima in a finite number of local minimizations. #' #' The local-search portion of MLSL can use any of the other algorithms in #' NLopt, and in particular can use either gradient-based or derivative-free #' algorithms. For this wrapper only gradient-based \code{L-BFGS} is available #' as local method. #' #' @param x0 initial point for searching the optimum. #' @param fn objective function that is to be minimized. #' @param gr gradient of function \code{fn}; will be calculated numerically if #' not specified. #' @param lower,upper lower and upper bound constraints. #' @param local.method only \code{BFGS} for the moment. #' @param low.discrepancy logical; shall a low discrepancy variation be used. #' @param nl.info logical; shall the original NLopt info been shown. #' @param control list of options, see \code{nl.opts} for help. #' @param ... additional arguments passed to the function. #' #' @return List with components: #' \item{par}{the optimal solution found so far.} #' \item{value}{the function value corresponding to \code{par}.} #' \item{iter}{number of (outer) iterations, see \code{maxeval}.} #' \item{convergence}{integer code indicating successful completion (> 0) #' or a possible error number (< 0).} #' \item{message}{character string produced by NLopt and giving additional #' information.} #' #' @export mlsl #' #' @author Hans W. Borchers #' #' @note If you don't set a stopping tolerance for your local-optimization #' algorithm, MLSL defaults to \code{ftol_rel=1e-15} and \code{xtol_rel=1e-7} #' for the local searches. #' #' @seealso \code{\link{direct}} #' #' @references A. H. G. Rinnooy Kan and G. T. Timmer, ``Stochastic global #' optimization methods'' Mathematical Programming, vol. 39, p. 27-78 (1987). #' #' Sergei Kucherenko and Yury Sytsko, ``Application of deterministic #' low-discrepancy sequences in global optimization,'' Computational #' Optimization and Applications, vol. 30, p. 297-318 (2005). #' #' @examples #' #' ### Minimize the Hartmann6 function #' hartmann6 <- function(x) { #' n <- length(x) #' a <- c(1.0, 1.2, 3.0, 3.2) #' A <- matrix(c(10.0, 0.05, 3.0, 17.0, #' 3.0, 10.0, 3.5, 8.0, #' 17.0, 17.0, 1.7, 0.05, #' 3.5, 0.1, 10.0, 10.0, #' 1.7, 8.0, 17.0, 0.1, #' 8.0, 14.0, 8.0, 14.0), nrow=4, ncol=6) #' B <- matrix(c(.1312,.2329,.2348,.4047, #' .1696,.4135,.1451,.8828, #' .5569,.8307,.3522,.8732, #' .0124,.3736,.2883,.5743, #' .8283,.1004,.3047,.1091, #' .5886,.9991,.6650,.0381), nrow=4, ncol=6) #' fun <- 0.0 #' for (i in 1:4) { #' fun <- fun - a[i] * exp(-sum(A[i,]*(x-B[i,])^2)) #' } #' return(fun) #' } #' S <- mlsl(x0 = rep(0, 6), hartmann6, lower = rep(0,6), upper = rep(1,6), #' nl.info = TRUE, control=list(xtol_rel=1e-8, maxeval=1000)) #' ## Number of Iterations....: 1000 #' ## Termination conditions: #' ## stopval: -Inf, xtol_rel: 1e-08, maxeval: 1000, ftol_rel: 0, ftol_abs: 0 #' ## Number of inequality constraints: 0 #' ## Number of equality constraints: 0 #' ## Current value of objective function: -3.32236801141552 #' ## Current value of controls: #' ## 0.2016895 0.1500107 0.476874 0.2753324 0.3116516 0.6573005 #' mlsl <- function(x0, fn, gr = NULL, lower, upper, local.method = "LBFGS", low.discrepancy = TRUE, nl.info = FALSE, control = list(), ...) { local_opts <- list(algorithm = "NLOPT_LD_LBFGS", xtol_rel = 1e-4) opts <- nl.opts(control) if (low.discrepancy) { opts["algorithm"] <- "NLOPT_GD_MLSL_LDS" } else { opts["algorithm"] <- "NLOPT_GD_MLSL" } opts[["local_opts"]] <- local_opts fun <- match.fun(fn) fn <- function(x) fun(x, ...) if (local.method == "LBFGS") { if (is.null(gr)) { gr <- function(x) nl.grad(x, fn) } else { .gr <- match.fun(gr) gr <- function(x) .gr(x, ...) } } else { warning("Only gradient-based LBFGS available as local method.") gr <- NULL } S0 <- nloptr(x0 = x0, eval_f = fn, eval_grad_f = gr, lb = lower, ub = upper, opts = opts) if (nl.info) print(S0) S1 <- list(par = S0$solution, value = S0$objective, iter = S0$iterations, convergence = S0$status, message = S0$message) return(S1) } nloptr/R/PkgFlags.R0000644000176000001440000000204013262730360013640 0ustar ripleyusers# Note: This file will be created from PkgFlags.R.in when nloptr is # installed with configure (i.e. on *nix systems). Since this file # is not created on Windows, the file PkgFlags.R should not be # removed from the R build. ## Provide compiler flags. CFlags <- function( print = TRUE ) { if (.Platform$OS.type=="windows") { flags <- paste0('-I"', system.file("include", package = "nloptr"), '"') } else { flags <- '@PKG_CFLAGS@' # Replace flags by include directory on R source path if flag is empty. if (nchar(trimws(flags)) == 0) { flags <- paste0('-I"', system.file("include", package = "nloptr"), '"') } } if ( print ) { cat( flags ) } else { return( flags ) } } ## Provide linker flags. LdFlags <- function( print = TRUE ) { if (.Platform$OS.type=="windows") { flags <- '' } else { flags <- '@PKG_LIBS@' } if ( print ) { cat( flags ) } else { return( flags ) } } nloptr/R/nloptr.R0000644000176000001440000005505113266715235013502 0ustar ripleyusers# Copyright (C) 2010 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: nloptr.R # Author: Jelmer Ypma # Date: 9 June 2010 # # Input: # x0 : vector with initial values # eval_f : function to evaluate objective function (and optionally its gradient) # eval_grad_f : function evaluate the gradient of the objective function (optional) # lb : lower bounds of the control (optional) # ub : upper bounds of the control (optional) # eval_g_ineq : function to evaluate (non-)linear inequality constraints that should # hold in the solution and its jacobian (optional) # eval_jac_g_ineq : function to evaluate jacobian of the (non-)linear inequality constraints (optional) # eval_g_eq : function to evaluate (non-)linear equality constraints that should hold # in the solution and its jacobian (optional) # eval_jac_g_eq : function to evaluate jacobian of the (non-)linear equality constraints (optional) # opts : list with options that are passed to Ipopt # ... : arguments that are passed to user-defined functions # # Output: structure with inputs and # call : the call that was made to solve # status : integer value with the status of the optimization (0 is success) # message : more informative message with the status of the optimization # iterations : number of iterations that were executed # objective : value if the objective function in the solution # solution : optimal value of the controls # # CHANGELOG: # 13/01/2011: added print_level option # 24/07/2011: added finite difference gradient checker # 07/08/2011: moved addition of default options to separate function # show documentation of options if print_options_doc == TRUE # 05/05/2014: Replaced cat by message, so messages can now be suppressed by suppressMessages. # 22/03/2015: Added while-loop around solve statement. This should solve the problem that NLopt sometimes exits with NLOPT_MAXTIME_REACHED when no maxtime was set in the options. #' R interface to NLopt #' #' nloptr is an R interface to NLopt, a free/open-source library for nonlinear #' optimization started by Steven G. Johnson, providing a common interface for #' a number of different free optimization routines available online as well as #' original implementations of various other algorithms. The NLopt library is #' available under the GNU Lesser General Public License (LGPL), and the #' copyrights are owned by a variety of authors. Most of the information here #' has been taken from \href{http://ab-initio.mit.edu/nlopt}{the NLopt website}, #' where more details are available. #' #' NLopt addresses general nonlinear optimization problems of the form: #' #' min f(x) x in R^n #' #' s.t. g(x) <= 0 h(x) = 0 lb <= x <= ub #' #' where f is the objective function to be minimized and x represents the n #' optimization parameters. This problem may optionally be subject to the bound #' constraints (also called box constraints), lb and ub. For partially or #' totally unconstrained problems the bounds can take -Inf or Inf. One may also #' optionally have m nonlinear inequality constraints (sometimes called a #' nonlinear programming problem), which can be specified in g(x), and equality #' constraints that can be specified in h(x). Note that not all of the #' algorithms in NLopt can handle constraints. #' #' @param x0 vector with starting values for the optimization. #' @param eval_f function that returns the value of the objective function. It #' can also return gradient information at the same time in a list with #' elements "objective" and "gradient" (see below for an example). #' @param eval_grad_f function that returns the value of the gradient of the #' objective function. Not all of the algorithms require a gradient. #' @param lb vector with lower bounds of the controls (use -Inf for controls #' without lower bound), by default there are no lower bounds for any of the #' controls. #' @param ub vector with upper bounds of the controls (use Inf for controls #' without upper bound), by default there are no upper bounds for any of the #' controls. #' @param eval_g_ineq function to evaluate (non-)linear inequality constraints #' that should hold in the solution. It can also return gradient information #' at the same time in a list with elements "objective" and "jacobian" (see #' below for an example). #' @param eval_jac_g_ineq function to evaluate the jacobian of the (non-)linear #' inequality constraints that should hold in the solution. #' @param eval_g_eq function to evaluate (non-)linear equality constraints that #' should hold in the solution. It can also return gradient information at the #' same time in a list with elements "objective" and "jacobian" (see below for #' an example). #' @param eval_jac_g_eq function to evaluate the jacobian of the (non-)linear #' equality constraints that should hold in the solution. #' @param opts list with options. The option "algorithm" is required. Check the #' \href{http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms}{NLopt website} #' for a full list of available algorithms. Other options control the #' termination conditions (minf_max, ftol_rel, ftol_abs, xtol_rel, xtol_abs, #' maxeval, maxtime). Default is xtol_rel = 1e-4. More information #' \href{http://ab-initio.mit.edu/wiki/index.php/NLopt_Introduction\#Termination_conditions}{here}. #' A full description of all options is shown by the function #' \code{nloptr.print.options()}. #' #' Some algorithms with equality constraints require the option local_opts, #' which contains a list with an algorithm and a termination condition for the #' local algorithm. See ?`nloptr-package` for an example. #' #' The option print_level controls how much output is shown during the #' optimization process. Possible values: \tabular{ll}{ 0 (default) \tab no #' output \cr 1 \tab show iteration number and value of objective function \cr #' 2 \tab 1 + show value of (in)equalities \cr 3 \tab 2 + show value of #' controls } #' #' The option check_derivatives (default = FALSE) can be used to run to compare #' the analytic gradients with finite difference approximations. The option #' check_derivatives_print ('all' (default), 'errors', 'none') controls the #' output of the derivative checker, if it is run, showing all comparisons, #' only those that resulted in an error, or none. The option #' check_derivatives_tol (default = 1e-04), determines when a difference #' between an analytic gradient and its finite difference approximation is #' flagged as an error. #' #' @param ... arguments that will be passed to the user-defined objective and #' constraints functions. #' #' @return The return value contains a list with the inputs, and additional #' elements #' \item{call}{the call that was made to solve} #' \item{status}{integer value with the status of the optimization (0 is #' success)} #' \item{message}{more informative message with the status of the optimization} #' \item{iterations}{number of iterations that were executed} #' \item{objective}{value if the objective function in the solution} #' \item{solution}{optimal value of the controls} #' \item{version}{version of NLopt that was used} #' #' @export nloptr #' #' @author Steven G. Johnson and others (C code) \cr Jelmer Ypma (R interface) #' #' @note See ?`nloptr-package` for an extended example. #' #' @seealso #' \code{\link[nloptr:nloptr.print.options]{nloptr.print.options}} #' \code{\link[nloptr:check.derivatives]{check.derivatives}} #' \code{\link{optim}} #' \code{\link{nlm}} #' \code{\link{nlminb}} #' \code{Rsolnp::Rsolnp} #' \code{Rsolnp::solnp} #' #' @references Steven G. Johnson, The NLopt nonlinear-optimization package, #' \url{http://ab-initio.mit.edu/nlopt} #' #' @keywords optimize interface #' #' @examples #' #' library('nloptr') #' #' ## Rosenbrock Banana function and gradient in separate functions #' eval_f <- function(x) { #' return( 100 * (x[2] - x[1] * x[1])^2 + (1 - x[1])^2 ) #' } #' #' eval_grad_f <- function(x) { #' return( c( -400 * x[1] * (x[2] - x[1] * x[1]) - 2 * (1 - x[1]), #' 200 * (x[2] - x[1] * x[1])) ) #' } #' #' #' # initial values #' x0 <- c( -1.2, 1 ) #' #' opts <- list("algorithm"="NLOPT_LD_LBFGS", #' "xtol_rel"=1.0e-8) #' #' # solve Rosenbrock Banana function #' res <- nloptr( x0=x0, #' eval_f=eval_f, #' eval_grad_f=eval_grad_f, #' opts=opts) #' print( res ) #' #' #' ## Rosenbrock Banana function and gradient in one function #' # this can be used to economize on calculations #' eval_f_list <- function(x) { #' return( list( "objective" = 100 * (x[2] - x[1] * x[1])^2 + (1 - x[1])^2, #' "gradient" = c( -400 * x[1] * (x[2] - x[1] * x[1]) - 2 * (1 - x[1]), #' 200 * (x[2] - x[1] * x[1])) ) ) #' } #' #' # solve Rosenbrock Banana function using an objective function that #' # returns a list with the objective value and its gradient #' res <- nloptr( x0=x0, #' eval_f=eval_f_list, #' opts=opts) #' print( res ) #' #' #' #' # Example showing how to solve the problem from the NLopt tutorial. #' # #' # min sqrt( x2 ) #' # s.t. x2 >= 0 #' # x2 >= ( a1*x1 + b1 )^3 #' # x2 >= ( a2*x1 + b2 )^3 #' # where #' # a1 = 2, b1 = 0, a2 = -1, b2 = 1 #' # #' # re-formulate constraints to be of form g(x) <= 0 #' # ( a1*x1 + b1 )^3 - x2 <= 0 #' # ( a2*x1 + b2 )^3 - x2 <= 0 #' #' library('nloptr') #' #' #' # objective function #' eval_f0 <- function( x, a, b ){ #' return( sqrt(x[2]) ) #' } #' #' # constraint function #' eval_g0 <- function( x, a, b ) { #' return( (a*x[1] + b)^3 - x[2] ) #' } #' #' # gradient of objective function #' eval_grad_f0 <- function( x, a, b ){ #' return( c( 0, .5/sqrt(x[2]) ) ) #' } #' #' # jacobian of constraint #' eval_jac_g0 <- function( x, a, b ) { #' return( rbind( c( 3*a[1]*(a[1]*x[1] + b[1])^2, -1.0 ), #' c( 3*a[2]*(a[2]*x[1] + b[2])^2, -1.0 ) ) ) #' } #' #' #' # functions with gradients in objective and constraint function #' # this can be useful if the same calculations are needed for #' # the function value and the gradient #' eval_f1 <- function( x, a, b ){ #' return( list("objective"=sqrt(x[2]), #' "gradient"=c(0,.5/sqrt(x[2])) ) ) #' } #' #' eval_g1 <- function( x, a, b ) { #' return( list( "constraints"=(a*x[1] + b)^3 - x[2], #' "jacobian"=rbind( c( 3*a[1]*(a[1]*x[1] + b[1])^2, -1.0 ), #' c( 3*a[2]*(a[2]*x[1] + b[2])^2, -1.0 ) ) ) ) #' } #' #' #' # define parameters #' a <- c(2,-1) #' b <- c(0, 1) #' #' # Solve using NLOPT_LD_MMA with gradient information supplied in separate function #' res0 <- nloptr( x0=c(1.234,5.678), #' eval_f=eval_f0, #' eval_grad_f=eval_grad_f0, #' lb = c(-Inf,0), #' ub = c(Inf,Inf), #' eval_g_ineq = eval_g0, #' eval_jac_g_ineq = eval_jac_g0, #' opts = list("algorithm"="NLOPT_LD_MMA"), #' a = a, #' b = b ) #' print( res0 ) #' #' # Solve using NLOPT_LN_COBYLA without gradient information #' res1 <- nloptr( x0=c(1.234,5.678), #' eval_f=eval_f0, #' lb = c(-Inf,0), #' ub = c(Inf,Inf), #' eval_g_ineq = eval_g0, #' opts = list("algorithm"="NLOPT_LN_COBYLA"), #' a = a, #' b = b ) #' print( res1 ) #' #' #' # Solve using NLOPT_LD_MMA with gradient information in objective function #' res2 <- nloptr( x0=c(1.234,5.678), #' eval_f=eval_f1, #' lb = c(-Inf,0), #' ub = c(Inf,Inf), #' eval_g_ineq = eval_g1, #' opts = list("algorithm"="NLOPT_LD_MMA", "check_derivatives"=TRUE), #' a = a, #' b = b ) #' print( res2 ) #' nloptr <- function( x0, eval_f, eval_grad_f = NULL, lb = NULL, ub = NULL, eval_g_ineq = NULL, eval_jac_g_ineq = NULL, eval_g_eq = NULL, eval_jac_g_eq = NULL, opts = list(), ... ) { # internal function to check the arguments of the functions .checkfunargs = function( fun, arglist, funname ) { if( !is.function(fun) ) stop(paste(funname, " must be a function\n", sep = "")) flist = formals(fun) if ( length(flist) > 1 ) { fnms = names(flist)[2:length(flist)] # remove first argument, which is x rnms = names(arglist) m1 = match(fnms, rnms) if( any(is.na(m1)) ){ mx1 = which( is.na(m1) ) for( i in 1:length(mx1) ){ stop(paste(funname, " requires argument '", fnms[mx1[i]], "' but this has not been passed to the 'nloptr' function.\n", sep = "")) } } m2 = match(rnms, fnms) if( any(is.na(m2)) ){ mx2 = which( is.na(m2) ) for( i in 1:length(mx2) ){ stop(paste(rnms[mx2[i]], "' passed to (...) in 'nloptr' but this is not required in the ", funname, " function.\n", sep = "")) } } } return( 0 ) } # extract list of additional arguments and check user-defined functions arglist = list(...) .checkfunargs( eval_f, arglist, 'eval_f' ) if( !is.null( eval_grad_f ) ) { .checkfunargs( eval_grad_f, arglist, 'eval_grad_f' ) } if( !is.null( eval_g_ineq ) ) { .checkfunargs( eval_g_ineq, arglist, 'eval_g_ineq' ) } if( !is.null( eval_jac_g_ineq ) ) { .checkfunargs( eval_jac_g_ineq, arglist, 'eval_jac_g_ineq' ) } if( !is.null( eval_g_eq ) ) { .checkfunargs( eval_g_eq, arglist, 'eval_g_eq' ) } if( !is.null( eval_jac_g_eq ) ) { .checkfunargs( eval_jac_g_eq, arglist, 'eval_jac_g_eq' ) } # define 'infinite' lower and upper bounds of the control if they haven't been set if ( is.null( lb ) ) { lb <- rep( -Inf, length(x0) ) } if ( is.null( ub ) ) { ub <- rep( Inf, length(x0) ) } # if eval_f does not return a list, write a wrapper function combining eval_f and eval_grad_f if ( is.list( eval_f( x0, ... ) ) | is.null( eval_grad_f ) ) { eval_f_wrapper <- function(x) { eval_f(x, ...) } } else { eval_f_wrapper <- function( x ) { return( list( "objective" = eval_f( x, ... ), "gradient" = eval_grad_f( x, ... ) ) ) } } # change the environment of the inequality constraint functions that we're calling num_constraints_ineq <- 0 if ( !is.null( eval_g_ineq ) ) { # if eval_g_ineq does not return a list, write a wrapper function combining eval_g_ineq and eval_jac_g_ineq if ( is.list( eval_g_ineq( x0, ... ) ) | is.null( eval_jac_g_ineq ) ) { eval_g_ineq_wrapper <- function(x) { eval_g_ineq(x, ...) } } else { eval_g_ineq_wrapper <- function( x ) { return( list( "constraints" = eval_g_ineq( x, ... ), "jacobian" = eval_jac_g_ineq( x, ... ) ) ) } } # determine number of constraints tmp_constraints <- eval_g_ineq_wrapper( x0 ) if ( is.list( tmp_constraints ) ) { num_constraints_ineq <- length( tmp_constraints$constraints ) } else { num_constraints_ineq <- length( tmp_constraints ) } } else { # define dummy function eval_g_ineq_wrapper <- NULL } # change the environment of the equality constraint functions that we're calling num_constraints_eq <- 0 if ( !is.null( eval_g_eq ) ) { # if eval_g_eq does not return a list, write a wrapper function combining eval_g_eq and eval_jac_g_eq if ( is.list( eval_g_eq( x0, ... ) ) | is.null( eval_jac_g_eq ) ) { eval_g_eq_wrapper <- function(x) { eval_g_eq(x, ...) } } else { eval_g_eq_wrapper <- function( x ) { return( list( "constraints" = eval_g_eq( x, ... ), "jacobian" = eval_jac_g_eq( x, ... ) ) ) } } # determine number of constraints tmp_constraints <- eval_g_eq_wrapper( x0 ) if ( is.list( tmp_constraints ) ) { num_constraints_eq <- length( tmp_constraints$constraints ) } else { num_constraints_eq <- length( tmp_constraints ) } } else { # define dummy function eval_g_eq_wrapper <- NULL } # extract local options from list of options if they exist if ( "local_opts" %in% names(opts) ) { res.opts.add <- nloptr.add.default.options( opts.user = opts$local_opts, x0 = x0, num_constraints_ineq = num_constraints_ineq, num_constraints_eq = num_constraints_eq ) local_opts <- res.opts.add$opts.user opts$local_opts <- NULL } else { local_opts <- NULL } # add defaults to list of options res.opts.add <- nloptr.add.default.options( opts.user = opts, x0 = x0, num_constraints_ineq = num_constraints_ineq, num_constraints_eq = num_constraints_eq ) opts <- res.opts.add$opts.user # add the termination criteria to the list termination_conditions <- res.opts.add$termination_conditions # print description of options if requested if (opts$print_options_doc) { nloptr.print.options( opts.user = opts ) } # define list with all algorithms # nloptr.options.description is a data.frame with options # that is loaded when nloptr is loaded. nloptr.default.options <- nloptr.get.default.options() list_algorithms <- unlist( strsplit( nloptr.default.options[ nloptr.default.options$name=="algorithm", "possible_values" ], ", " ) ) # run derivative checker if ( opts$check_derivatives ) { if ( opts$algorithm %in% list_algorithms[ grep( "NLOPT_[G,L]N", list_algorithms ) ] ) { warning( paste("Skipping derivative checker because algorithm '", opts$algorithm, "' does not use gradients.", sep='') ) } else { # check derivatives of objective function message( "Checking gradients of objective function." ) check.derivatives( .x = x0, func = function( x ) { eval_f_wrapper( x )$objective }, func_grad = function( x ) { eval_f_wrapper( x )$gradient }, check_derivatives_tol = opts$check_derivatives_tol, check_derivatives_print = opts$check_derivatives_print, func_grad_name = 'eval_grad_f' ) if ( num_constraints_ineq > 0 ) { # check derivatives of inequality constraints message( "Checking gradients of inequality constraints.\n" ) check.derivatives( .x = x0, func = function( x ) { eval_g_ineq_wrapper( x )$constraints }, func_grad = function( x ) { eval_g_ineq_wrapper( x )$jacobian }, check_derivatives_tol = opts$check_derivatives_tol, check_derivatives_print = opts$check_derivatives_print, func_grad_name = 'eval_jac_g_ineq' ) } if ( num_constraints_eq > 0 ) { # check derivatives of equality constraints message( "Checking gradients of equality constraints.\n" ) check.derivatives( .x = x0, func = function( x ) { eval_g_eq_wrapper( x )$constraints }, func_grad = function( x ) { eval_g_eq_wrapper( x )$jacobian }, check_derivatives_tol = opts$check_derivatives_tol, check_derivatives_print = opts$check_derivatives_print, func_grad_name = 'eval_jac_g_eq' ) } } } ret <- list( "x0" = x0, "eval_f" = eval_f_wrapper, "lower_bounds" = lb, "upper_bounds" = ub, "num_constraints_ineq" = num_constraints_ineq, "eval_g_ineq" = eval_g_ineq_wrapper, "num_constraints_eq" = num_constraints_eq, "eval_g_eq" = eval_g_eq_wrapper, "options" = opts, "local_options" = local_opts, "nloptr_environment" = new.env() ) attr(ret, "class") <- "nloptr" # add the current call to the list ret$call <- match.call() # add the termination criteria to the list ret$termination_conditions <- termination_conditions # check whether we have a correctly formed ipoptr object is.nloptr( ret ) # Count the number of times that we try to solve the problem. num.evals <- 0 solve.continue <- TRUE while ( num.evals <= 10 & solve.continue ) { # Update the number of evaluations. num.evals <- num.evals + 1 # choose correct minimzation function based on wether constrained were supplied solution <- .Call( "NLoptR_Optimize", ret, PACKAGE = "nloptr" ) # remove the environment from the return object ret$environment <- NULL # add solution variables to object ret$status <- solution$status ret$message <- solution$message ret$iterations <- solution$iterations ret$objective <- solution$objective ret$solution <- solution$solution ret$version <- paste( c(solution$version_major, solution$version_minor, solution$version_bugfix), collapse='.' ) ret$num.evals <- num.evals # If maxtime is set to a positive number in the options # or if the return status of the solver is not equal to # 6, we can stop trying to solve the problem. # # Solution status 6: NLOPT_MAXTIME_REACHED: Optimization # stopped because maxtime (above) was reached. # # This loop is need, because sometimes the solver exits # with this code, even if maxtime is set to 0 or a negative # number. if ( opts$maxtime > 0 | solution$status != 6 ) { solve.continue <- FALSE } } return( ret ) } nloptr/R/auglag.R0000644000176000001440000002053313267037411013413 0ustar ripleyusers# Copyright (C) 2014 Hans W. Borchers. All Rights Reserved. # This code is published under the L-GPL. # # File: auglag.R # Author: Hans W. Borchers # Date: 27 January 2014 # # Wrapper to solve optimization problem using Augmented Lagrangian. # # Changelog: # 2017-09-26: Fixed bug, BOBYQA is allowed as local solver (thanks to Leo Belzile). #' Augmented Lagrangian Algorithm #' #' The Augmented Lagrangian method adds additional terms to the unconstrained #' objective function, designed to emulate a Lagrangian multiplier. #' #' This method combines the objective function and the nonlinear #' inequality/equality constraints (if any) in to a single function: #' essentially, the objective plus a `penalty' for any violated constraints. #' #' This modified objective function is then passed to another optimization #' algorithm with no nonlinear constraints. If the constraints are violated by #' the solution of this sub-problem, then the size of the penalties is #' increased and the process is repeated; eventually, the process must converge #' to the desired solution (if it exists). #' #' Since all of the actual optimization is performed in this subsidiary #' optimizer, the subsidiary algorithm that you specify determines whether the #' optimization is gradient-based or derivative-free. #' #' The local solvers available at the moment are ``COBYLA'' (for the #' derivative-free approach) and ``LBFGS'', ``MMA'', or ``SLSQP'' (for smooth #' functions). The tolerance for the local solver has to be provided. #' #' There is a variant that only uses penalty functions for equality constraints #' while inequality constraints are passed through to the subsidiary algorithm #' to be handled directly; in this case, the subsidiary algorithm must handle #' inequality constraints. (At the moment, this variant has been turned off #' because of problems with the NLOPT library.) #' #' @param x0 starting point for searching the optimum. #' @param fn objective function that is to be minimized. #' @param gr gradient of the objective function; will be provided provided is #' \code{NULL} and the solver requires derivatives. #' @param lower,upper lower and upper bound constraints. #' @param hin,hinjac defines the inequalty constraints, \code{hin(x) >= 0} #' @param heq,heqjac defines the equality constraints, \code{heq(x) = 0}. #' @param localsolver available local solvers: COBYLA, LBFGS, MMA, or SLSQP. #' @param localtol tolerance applied in the selected local solver. #' @param ineq2local logical; shall the inequality constraints be treated by #' the local solver?; not possible at the moment. #' @param nl.info logical; shall the original NLopt info been shown. #' @param control list of options, see \code{nl.opts} for help. #' @param ... additional arguments passed to the function. #' #' @return List with components: #' \item{par}{the optimal solution found so far.} #' \item{value}{the function value corresponding to \code{par}.} #' \item{iter}{number of (outer) iterations, see \code{maxeval}.} #' \item{global_solver}{the global NLOPT solver used.} #' \item{local_solver}{the local NLOPT solver used, LBFGS or COBYLA.} #' \item{convergence}{integer code indicating successful completion #' (> 0) or a possible error number (< 0).} #' \item{message}{character string produced by NLopt and giving additional #' information.} #' #' @export #' #' @author Hans W. Borchers #' #' @note Birgin and Martinez provide their own free implementation of the #' method as part of the TANGO project; other implementations can be found in #' semi-free packages like LANCELOT. #' #' @seealso \code{alabama::auglag}, \code{Rsolnp::solnp} #' #' @references Andrew R. Conn, Nicholas I. M. Gould, and Philippe L. Toint, ``A #' globally convergent augmented Lagrangian algorithm for optimization with #' general constraints and simple bounds,'' SIAM J. Numer. Anal. vol. 28, no. #' 2, p. 545-572 (1991). #' #' E. G. Birgin and J. M. Martinez, ``Improving ultimate convergence of an #' augmented Lagrangian method," Optimization Methods and Software vol. 23, no. #' 2, p. 177-195 (2008). #' #' @examples #' #' x0 <- c(1, 1) #' fn <- function(x) (x[1]-2)^2 + (x[2]-1)^2 #' hin <- function(x) -0.25*x[1]^2 - x[2]^2 + 1 # hin >= 0 #' heq <- function(x) x[1] - 2*x[2] + 1 # heq == 0 #' gr <- function(x) nl.grad(x, fn) #' hinjac <- function(x) nl.jacobian(x, hin) #' heqjac <- function(x) nl.jacobian(x, heq) #' #' auglag(x0, fn, gr = NULL, hin = hin, heq = heq) # with COBYLA #' # $par: 0.8228761 0.9114382 #' # $value: 1.393464 #' # $iter: 1001 #' #' auglag(x0, fn, gr = NULL, hin = hin, heq = heq, localsolver = "SLSQP") #' # $par: 0.8228757 0.9114378 #' # $value: 1.393465 #' # $iter 173 #' #' ## Example from the alabama::auglag help page #' fn <- function(x) (x[1] + 3*x[2] + x[3])^2 + 4 * (x[1] - x[2])^2 #' heq <- function(x) x[1] + x[2] + x[3] - 1 #' hin <- function(x) c(6*x[2] + 4*x[3] - x[1]^3 - 3, x[1], x[2], x[3]) #' #' auglag(runif(3), fn, hin = hin, heq = heq, localsolver="lbfgs") #' # $par: 2.380000e-09 1.086082e-14 1.000000e+00 #' # $value: 1 #' # $iter: 289 #' #' ## Powell problem from the Rsolnp::solnp help page #' x0 <- c(-2, 2, 2, -1, -1) #' fn1 <- function(x) exp(x[1]*x[2]*x[3]*x[4]*x[5]) #' eqn1 <-function(x) #' c(x[1]*x[1]+x[2]*x[2]+x[3]*x[3]+x[4]*x[4]+x[5]*x[5], #' x[2]*x[3]-5*x[4]*x[5], #' x[1]*x[1]*x[1]+x[2]*x[2]*x[2]) #' #' auglag(x0, fn1, heq = eqn1, localsolver = "mma") #' # $par: -3.988458e-10 -1.654201e-08 -3.752028e-10 8.904445e-10 8.926336e-10 #' # $value: 1 #' # $iter: 1001 #' auglag <- function(x0, fn, gr = NULL, lower = NULL, upper = NULL, hin = NULL, hinjac = NULL, heq = NULL, heqjac = NULL, localsolver = c("COBYLA"), localtol = 1e-6, ineq2local = FALSE, nl.info = FALSE, control = list(), ...) { if (ineq2local) { # gsolver <- "NLOPT_LN_AUGLAG_EQ" stop("Inequalities to local solver: feature not yet implemented.") } localsolver <- toupper(localsolver) if (localsolver %in% c("COBYLA", "BOBYQA")) { # derivative-free dfree <- TRUE gsolver <- "NLOPT_LN_AUGLAG" lsolver <- paste("NLOPT_LN_", localsolver, sep = "") } else if (localsolver %in% c("LBFGS", "MMA", "SLSQP")) { # with derivatives dfree <- FALSE gsolver <- "NLOPT_LD_AUGLAG" lsolver <- paste("NLOPT_LD_", localsolver, sep = "") } else { stop("Only local solvers allowed: BOBYQA, COBYLA, LBFGS, MMA, SLSQP.") } # Function and gradient, if needed .fn <- match.fun(fn) fn <- function(x) .fn(x, ...) if (!dfree && is.null(gr)) { gr <- function(x) nl.grad(x, fn) } # Global and local options opts <- nl.opts(control) opts$algorithm <- gsolver local_opts <- list(algorithm = lsolver, xtol_rel = localtol, eval_grad_f = if (!dfree) gr else NULL) opts$local_opts <- local_opts # Inequality constraints if (!is.null(hin)) { if ( getOption('nloptr.show.inequality.warning') ) { message('For consistency with the rest of the package the inequality sign may be switched from >= to <= in a future nloptr version.') } .hin <- match.fun(hin) hin <- function(x) (-1) * .hin(x) # change hin >= 0 to hin <= 0 ! } if (!dfree) { if (is.null(hinjac)) { hinjac <- function(x) nl.jacobian(x, hin) } else { .hinjac <- match.fun(hinjac) hinjac <- function(x) (-1) * .hinjac(x) } } # Equality constraints if (!is.null(heq)) { .heq <- match.fun(heq) heq <- function(x) .heq(x) } if (!dfree) { if (is.null(heqjac)) { heqjac <- function(x) nl.jacobian(x, heq) } else { .heqjac <- match.fun(heqjac) heqjac <- function(x) .heqjac(x) } } S0 <- nloptr(x0, eval_f = fn, eval_grad_f = gr, lb = lower, ub = upper, eval_g_ineq = hin, eval_jac_g_ineq = hinjac, eval_g_eq = heq, eval_jac_g_eq = heqjac, opts = opts) if (nl.info) print(S0) S1 <- list( par = S0$solution, value = S0$objective, iter = S0$iterations, global_solver = gsolver, local_solver = lsolver, convergence = S0$status, message = S0$message) return(S1) } nloptr/R/nloptions.R0000644000176000001440000000455113266715711014207 0ustar ripleyusers# Copyright (C) 2014 Hans W. Borchers. All Rights Reserved. # This code is published under the L-GPL. # # File: nloptions.R # Author: Hans W. Borchers # Date: 27 January 2014 # # Function to define nloptr options list. #' Setting NL Options #' #' Sets and changes the NLOPT options. #' #' The following options can be set (here with default values): #' #' \code{stopval = -Inf, # stop minimization at this value}\cr \code{xtol_rel = #' 1e-6, # stop on small optimization step}\cr \code{maxeval = 1000, # stop on #' this many function evaluations}\cr \code{ftol_rel = 0.0, # stop on change #' times function value}\cr \code{ftol_abs = 0.0, # stop on small change of #' function value}\cr \code{check_derivatives = FALSE} #' #' @param optlist list of options, see below. #' #' @return returns a list with default and changed options. #' #' @export #' #' @author Hans W. Borchers #' #' @note There are more options that can be set for solvers in NLOPT. These #' cannot be set through their wrapper functions. To see the full list of #' options and algorithms, type \code{nloptr.print.options()}. #' #' @examples #' #' nl.opts(list(xtol_rel = 1e-8, maxeval = 2000)) #' nl.opts <- function(optlist = NULL) { opts <- list(stopval = -Inf, # stop minimization at this value xtol_rel = 1e-6, # stop on small optimization step maxeval = 1000, # stop on this many function evaluations ftol_rel = 0.0, # stop on change times function value ftol_abs = 0.0, # stop on small change of function value check_derivatives = FALSE, algorithm = NULL # will be filled by each single function ) if (is.null(optlist)) return(opts) if (!is.list(optlist) || "" %in% names(optlist)) stop("Argument 'optlist' must be a list of named (character) objects.") namc <- match.arg(names(optlist), choices=names(opts), several.ok=TRUE) if (!all(names(optlist) %in% names(opts))) warning("Unknown names in control: ", names(optlist)[!(names(optlist) %in% names(opts))]) if (!is.null(namc)) opts[namc] <- optlist[namc] if ("algorithm" %in% namc) { warning("Option 'algorithm can not be set here; will be overwritten.") opts["algorithm"] <- NULL } return(opts) } nloptr/R/finite.diff.R0000644000176000001440000000267712553776710014362 0ustar ripleyusers# Copyright (C) 2011 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: finite.diff.R # Author: Jelmer Ypma # Date: 24 July 2011 # # Approximate the gradient of a function using finite differences. # # Input: # func : calculate finite difference approximation for the gradient of this function # .x : evaluate at this point # indices : calculate finite difference approcximation only for .x-indices in this vector (optional) # stepSize : relative size of the step between x and x+h (optional) # ... : arguments that are passed to the user-defined function (func) # # Output: matrix with finite difference approximations # http://en.wikipedia.org/wiki/Numerical_differentiation finite.diff <- function( func, .x, indices=1:length(.x), stepSize=sqrt( .Machine$double.eps ), ... ) { # if we evaluate at values close to 0, we need a different step size stepSizeVec <- pmax( abs(.x), 1 ) * stepSize fx <- func( .x, ... ) approx.gradf.index <- function(i, .x, func, fx, stepSizeVec, ...) { x_prime <- .x x_prime[i] <- .x[i] + stepSizeVec[i] stepSizeVec[i] <- x_prime[i] - .x[i] fx_prime <- func( x_prime, ... ) return( ( fx_prime - fx )/stepSizeVec[i] ) } grad_fx <- sapply (indices, approx.gradf.index, .x=.x, func=func, fx=fx, stepSizeVec=stepSizeVec, ... ) return( grad_fx ) } nloptr/R/PkgFlags.R.in0000644000176000001440000000146713262730360014261 0ustar ripleyusers## Provide compiler flags. CFlags <- function( print = TRUE ) { if (.Platform$OS.type=="windows") { flags <- paste0('-I"', system.file("include", package = "nloptr"), '"') } else { flags <- '@PKG_CFLAGS@' # Replace flags by include directory on R source path if flag is empty. if (nchar(trimws(flags)) == 0) { flags <- paste0('-I"', system.file("include", package = "nloptr"), '"') } } if ( print ) { cat( flags ) } else { return( flags ) } } ## Provide linker flags. LdFlags <- function( print = TRUE ) { if (.Platform$OS.type=="windows") { flags <- '' } else { flags <- '@PKG_LIBS@' } if ( print ) { cat( flags ) } else { return( flags ) } } nloptr/R/nm.R0000644000176000001440000001320113266717014012562 0ustar ripleyusers# Copyright (C) 2014 Hans W. Borchers. All Rights Reserved. # This code is published under the L-GPL. # # File: nm.R # Author: Hans W. Borchers # Date: 27 January 2014 # # Wrapper to solve optimization problem using Nelder-Mead and Subplex. #' Nelder-Mead Simplex #' #' An implementation of almost the original Nelder-Mead simplex algorithm. #' #' Provides xplicit support for bound constraints, using essentially the method #' proposed in [Box]. Whenever a new point would lie outside the bound #' constraints the point is moved back exactly onto the constraint. #' #' @param x0 starting point for searching the optimum. #' @param fn objective function that is to be minimized. #' @param lower,upper lower and upper bound constraints. #' @param nl.info logical; shall the original NLopt info been shown. #' @param control list of options, see \code{nl.opts} for help. #' @param ... additional arguments passed to the function. #' #' @return List with components: #' \item{par}{the optimal solution found so far.} #' \item{value}{the function value corresponding to \code{par}.} #' \item{iter}{number of (outer) iterations, see \code{maxeval}.} #' \item{convergence}{integer code indicating successful completion (> 0) #' or a possible error number (< 0).} #' \item{message}{character string produced by NLopt and giving additional #' information.} #' #' @export neldermead #' #' @author Hans W. Borchers #' #' @note The author of NLopt would tend to recommend the Subplex method #' instead. #' #' @seealso \code{dfoptim::nmk} #' #' @references J. A. Nelder and R. Mead, ``A simplex method for function #' minimization,'' The Computer Journal 7, p. 308-313 (1965). #' #' M. J. Box, ``A new method of constrained optimization and a comparison with #' other methods,'' Computer J. 8 (1), 42-52 (1965). #' #' @examples #' #' # Fletcher and Powell's helic valley #' fphv <- function(x) #' 100*(x[3] - 10*atan2(x[2], x[1])/(2*pi))^2 + #' (sqrt(x[1]^2 + x[2]^2) - 1)^2 +x[3]^2 #' x0 <- c(-1, 0, 0) #' neldermead(x0, fphv) # 1 0 0 #' #' # Powell's Singular Function (PSF) #' psf <- function(x) (x[1] + 10*x[2])^2 + 5*(x[3] - x[4])^2 + #' (x[2] - 2*x[3])^4 + 10*(x[1] - x[4])^4 #' x0 <- c(3, -1, 0, 1) #' neldermead(x0, psf) # 0 0 0 0, needs maximum number of function calls #' #' \dontrun{ #' # Bounded version of Nelder-Mead #' lower <- c(-Inf, 0, 0) #' upper <- c( Inf, 0.5, 1) #' x0 <- c(0, 0.1, 0.1) #' S <- neldermead(c(0, 0.1, 0.1), rosenbrock, lower, upper, nl.info = TRUE) #' # $xmin = c(0.7085595, 0.5000000, 0.2500000) #' # $fmin = 0.3353605} #' neldermead <- function(x0, fn, lower = NULL, upper = NULL, nl.info = FALSE, control = list(), ...) { opts <- nl.opts(control) opts["algorithm"] <- "NLOPT_LN_NELDERMEAD" fun <- match.fun(fn) fn <- function(x) fun(x, ...) S0 <- nloptr(x0, fn, lb = lower, ub = upper, opts = opts) if (nl.info) print(S0) S1 <- list(par = S0$solution, value = S0$objective, iter = S0$iterations, convergence = S0$status, message = S0$message) return(S1) } #' Subplex Algorithm #' #' Subplex is a variant of Nelder-Mead that uses Nelder-Mead on a sequence of #' subspaces. #' #' SUBPLEX is claimed to be much more efficient and robust than the original #' Nelder-Mead, while retaining the latter's facility with discontinuous #' objectives. #' #' This implementation has explicit support for bound constraints (via the #' method in the Box paper as described on the \code{neldermead} help page). #' #' @param x0 starting point for searching the optimum. #' @param fn objective function that is to be minimized. #' @param lower,upper lower and upper bound constraints. #' @param nl.info logical; shall the original NLopt info been shown. #' @param control list of options, see \code{nl.opts} for help. #' @param ... additional arguments passed to the function. #' #' @return List with components: #' \item{par}{the optimal solution found so far.} #' \item{value}{the function value corresponding to \code{par}.} #' \item{iter}{number of (outer) iterations, see \code{maxeval}.} #' \item{convergence}{integer code indicating successful completion (> 0) #' or a possible error number (< 0).} #' \item{message}{character string produced by NLopt and giving additional #' information.} #' #' @export sbplx #' #' @note It is the request of Tom Rowan that reimplementations of his algorithm #' shall not use the name `subplex'. #' #' @seealso \code{subplex::subplex} #' #' @references T. Rowan, ``Functional Stability Analysis of Numerical #' Algorithms'', Ph.D. thesis, Department of Computer Sciences, University of #' Texas at Austin, 1990. #' #' @examples #' #' # Fletcher and Powell's helic valley #' fphv <- function(x) #' 100*(x[3] - 10*atan2(x[2], x[1])/(2*pi))^2 + #' (sqrt(x[1]^2 + x[2]^2) - 1)^2 +x[3]^2 #' x0 <- c(-1, 0, 0) #' sbplx(x0, fphv) # 1 0 0 #' #' # Powell's Singular Function (PSF) #' psf <- function(x) (x[1] + 10*x[2])^2 + 5*(x[3] - x[4])^2 + #' (x[2] - 2*x[3])^4 + 10*(x[1] - x[4])^4 #' x0 <- c(3, -1, 0, 1) #' sbplx(x0, psf, control = list(maxeval = Inf, ftol_rel = 1e-6)) # 0 0 0 0 (?) #' sbplx <- function(x0, fn, lower = NULL, upper = NULL, nl.info = FALSE, control = list(), ...) { opts <- nl.opts(control) opts["algorithm"] <- "NLOPT_LN_SBPLX" fun <- match.fun(fn) fn <- function(x) fun(x, ...) S0 <- nloptr(x0, fn, lb = lower, ub = upper, opts = opts) if (nl.info) print(S0) S1 <- list(par = S0$solution, value = S0$objective, iter = S0$iterations, convergence = S0$status, message = S0$message) return(S1) } nloptr/R/lbfgs.R0000644000176000001440000000650413266716541013261 0ustar ripleyusers# Copyright (C) 2014 Hans W. Borchers. All Rights Reserved. # This code is published under the L-GPL. # # File: lbfgs.R # Author: Hans W. Borchers # Date: 27 January 2014 # # Wrapper to solve optimization problem using Low-storage BFGS. #' Low-storage BFGS #' #' Low-storage version of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. #' #' The low-storage (or limited-memory) algorithm is a member of the class of #' quasi-Newton optimization methods. It is well suited for optimization #' problems with a large number of variables. #' #' One parameter of this algorithm is the number \code{m} of gradients to #' remember from previous optimization steps. NLopt sets \code{m} to a #' heuristic value by default. It can be changed by the NLopt function #' \code{set_vector_storage}. #' #' @param x0 initial point for searching the optimum. #' @param fn objective function to be minimized. #' @param gr gradient of function \code{fn}; will be calculated numerically if #' not specified. #' @param lower,upper lower and upper bound constraints. #' @param nl.info logical; shall the original NLopt info been shown. #' @param control list of control parameters, see \code{nl.opts} for help. #' @param ... further arguments to be passed to the function. #' #' @return List with components: #' \item{par}{the optimal solution found so far.} #' \item{value}{the function value corresponding to \code{par}.} #' \item{iter}{number of (outer) iterations, see \code{maxeval}.} #' \item{convergence}{integer code indicating successful completion (> 0) #' or a possible error number (< 0).} #' \item{message}{character string produced by NLopt and giving additional #' information.} #' #' @export lbfgs #' #' @author Hans W. Borchers #' #' @note Based on a Fortran implementation of the low-storage BFGS algorithm #' written by L. Luksan, and posted under the GNU LGPL license. #' #' @seealso \code{\link{optim}} #' #' @references J. Nocedal, ``Updating quasi-Newton matrices with limited #' storage,'' Math. Comput. 35, 773-782 (1980). #' #' D. C. Liu and J. Nocedal, ``On the limited memory BFGS method for large #' scale optimization,'' Math. Programming 45, p. 503-528 (1989). #' #' @examples #' #' flb <- function(x) { #' p <- length(x) #' sum(c(1, rep(4, p-1)) * (x - c(1, x[-p])^2)^2) #' } #' # 25-dimensional box constrained: par[24] is *not* at the boundary #' S <- lbfgs(rep(3, 25), flb, lower=rep(2, 25), upper=rep(4, 25), #' nl.info = TRUE, control = list(xtol_rel=1e-8)) #' ## Optimal value of objective function: 368.105912874334 #' ## Optimal value of controls: 2 ... 2 2.109093 4 #' lbfgs <- function(x0, fn, gr = NULL, lower = NULL, upper = NULL, nl.info = FALSE, control = list(), ...) { opts <- nl.opts(control) opts["algorithm"] <- "NLOPT_LD_LBFGS" fun <- match.fun(fn) fn <- function(x) fun(x, ...) if (is.null(gr)) { gr <- function(x) nl.grad(x, fn) } else { .gr <- match.fun(gr) gr <- function(x) .gr(x, ...) } S0 <- nloptr(x0, eval_f = fn, eval_grad_f = gr, lb = lower, ub = upper, opts = opts) if (nl.info) print(S0) S1 <- list(par = S0$solution, value = S0$objective, iter = S0$iterations, convergence = S0$status, message = S0$message) return(S1) } nloptr/R/print.nloptr.R0000644000176000001440000000662213266713641014634 0ustar ripleyusers# Copyright (C) 2010 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: print.nloptr.R # Author: Jelmer Ypma # Date: 10 June 2010 # # This function prints some basic output of a nloptr # ojbect. The information is only available after it # has been solved. # # show.controls is TRUE, FALSE or a vector of indices. # Use this option to show none, or only a subset of # the controls. # # 13/01/2011: added show.controls option # 07/08/2011: show 'optimal value' instead of 'current value' if status == 1, 2, 3, or 4 #' Print results after running nloptr #' #' This function prints the nloptr object that holds the results from a #' minimization using \code{nloptr}. #' #' @param x object containing result from minimization. #' @param show.controls Logical or vector with indices. Should we show the #' value of the control variables in the solution? If \code{show.controls} is a #' vector with indices, it is used to select which control variables should be #' shown. This can be useful if the model contains a set of parameters of #' interest and a set of nuisance parameters that are not of immediate #' interest. #' @param ... further arguments passed to or from other methods. #' #' @method print nloptr #' @export #' #' @author Jelmer Ypma #' #' @seealso \code{\link[nloptr:nloptr]{nloptr}} #' #' @keywords optimize interface #' print.nloptr <- function(x, show.controls=TRUE, ...) { cat( "\nCall:\n", deparse(x$call), "\n\n", sep = "", fill=TRUE ) cat( paste( "Minimization using NLopt version", x$version, "\n" ), fill=TRUE ) cat( unlist(strsplit(paste( "NLopt solver status:", x$status, "(", x$message, ")\n" ),' ')), fill=TRUE ) cat( paste( "Number of Iterations....:", x$iterations, "\n" ) ) cat( paste( "Termination conditions: ", x$termination_conditions, "\n" ) ) cat( paste( "Number of inequality constraints: ", x$num_constraints_ineq, "\n" ) ) cat( paste( "Number of equality constraints: ", x$num_constraints_eq, "\n" ) ) # if show.controls is TRUE or FALSE, show all or none of the controls if ( is.logical( show.controls ) ) { # show all control variables if ( show.controls ) { controls.indices = 1:length(x$solution) } } # if show.controls is a vector with indices, rename this vector # and define show.controls as TRUE if ( is.numeric( show.controls ) ) { controls.indices = show.controls show.controls = TRUE } # if solved successfully if ( x$status >= 1 & x$status <=4 ) { cat( paste( "Optimal value of objective function: ", x$objective, "\n" ) ) if ( show.controls ) { if ( length( controls.indices ) < length(x$solution) ) { cat( "Optimal value of user-defined subset of controls: " ) } else { cat( "Optimal value of controls: " ) } cat( x$solution[ controls.indices ], fill=TRUE) cat("\n") } } else { cat( paste( "Current value of objective function: ", x$objective, "\n" ) ) if ( show.controls ) { if ( length( controls.indices ) < length(x$solution) ) { cat( "Current value of user-defined subset of controls: " ) } else { cat( "Current value of controls: " ) } cat( x$solution[ controls.indices ], fill=TRUE ) cat("\n") } } cat("\n") } nloptr/R/nloptr.get.default.options.R0000644000176000001440000002220213266707003017357 0ustar ripleyusers# Copyright (C) 2011 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: nloptr.default.options.R # Author: Jelmer Ypma # Date: 7 August 2011 # # Defines a data.frame with fields: # name: name of the option. # type: type (numeric, logical, integer, character). # possible_values: string explaining the values the option can take. # default: default value of the option (as a string). # is_termination_condition: is this option part of the termination conditions? # description: description of the option (taken from NLopt website # if it's an option that is passed on to NLopt). # # CHANGELOG: # 12/07/2014: Changed from creating a data.frame to a function returning a data.frame. #' Return a data.frame with all the options that can be supplied to nloptr. #' #' This function returns a data.frame with all the options that can be supplied #' to \code{\link[nloptr:nloptr]{nloptr}}. The data.frame contains the default #' values of the options and an explanation. A user-friendly way to show these #' options is by using the function #' \code{\link[nloptr:nloptr.print.options]{nloptr.print.options}}. #' #' @return The return value contains a \code{data.frame} with the following #' elements #' \item{name}{name of the option} #' \item{type}{type (numeric, logical, integer, character)} #' \item{possible_values}{string explaining the values the option can take} #' \item{default}{default value of the option (as a string)} #' \item{is_termination_condition}{is this option part of the termination #' conditions?} #' \item{description}{description of the option (taken from NLopt website #' if it's an option that is passed on to NLopt).} #' #' @export nloptr.get.default.options #' #' @author Jelmer Ypma #' #' @seealso #' \code{\link[nloptr:nloptr]{nloptr}} #' \code{\link[nloptr:nloptr.print.options]{nloptr.print.options}} #' #' @keywords optimize interface #' nloptr.get.default.options <- function() { dat.opts <- data.frame( rbind( c("algorithm", "character", "NLOPT_GN_DIRECT, NLOPT_GN_DIRECT_L, NLOPT_GN_DIRECT_L_RAND, NLOPT_GN_DIRECT_NOSCAL, NLOPT_GN_DIRECT_L_NOSCAL, NLOPT_GN_DIRECT_L_RAND_NOSCAL, NLOPT_GN_ORIG_DIRECT, NLOPT_GN_ORIG_DIRECT_L, NLOPT_GD_STOGO, NLOPT_GD_STOGO_RAND, NLOPT_LD_SLSQP, NLOPT_LD_LBFGS_NOCEDAL, NLOPT_LD_LBFGS, NLOPT_LN_PRAXIS, NLOPT_LD_VAR1, NLOPT_LD_VAR2, NLOPT_LD_TNEWTON, NLOPT_LD_TNEWTON_RESTART, NLOPT_LD_TNEWTON_PRECOND, NLOPT_LD_TNEWTON_PRECOND_RESTART, NLOPT_GN_CRS2_LM, NLOPT_GN_MLSL, NLOPT_GD_MLSL, NLOPT_GN_MLSL_LDS, NLOPT_GD_MLSL_LDS, NLOPT_LD_MMA, NLOPT_LN_COBYLA, NLOPT_LN_NEWUOA, NLOPT_LN_NEWUOA_BOUND, NLOPT_LN_NELDERMEAD, NLOPT_LN_SBPLX, NLOPT_LN_AUGLAG, NLOPT_LD_AUGLAG, NLOPT_LN_AUGLAG_EQ, NLOPT_LD_AUGLAG_EQ, NLOPT_LN_BOBYQA, NLOPT_GN_ISRES", "none", FALSE, "This option is required. Check the NLopt website for a description of the algorithms."), c("stopval", "numeric", "-Inf <= stopval <= Inf", "-Inf", TRUE, "Stop minimization when an objective value <= stopval is found. Setting stopval to -Inf disables this stopping criterion (default)."), c("ftol_rel", "numeric", "ftol_rel > 0", "0.0", TRUE, "Stop when an optimization step (or an estimate of the optimum) changes the objective function value by less than ftol_rel multiplied by the absolute value of the function value. If there is any chance that your optimum function value is close to zero, you might want to set an absolute tolerance with ftol_abs as well. Criterion is disabled if ftol_rel is non-positive (default)."), c("ftol_abs", "numeric", "ftol_abs > 0", "0.0", TRUE, "Stop when an optimization step (or an estimate of the optimum) changes the function value by less than ftol_abs. Criterion is disabled if ftol_abs is non-positive (default)."), c("xtol_rel", "numeric", "xtol_rel > 0", "1.0e-04", TRUE, "Stop when an optimization step (or an estimate of the optimum) changes every parameter by less than xtol_rel multiplied by the absolute value of the parameter. If there is any chance that an optimal parameter is close to zero, you might want to set an absolute tolerance with xtol_abs as well. Criterion is disabled if xtol_rel is non-positive."), c("xtol_abs", "numeric", "xtol_abs > 0", "rep( 0.0, length(x0) )", TRUE, "xtol_abs is a vector of length n (the number of elements in x) giving the tolerances: stop when an optimization step (or an estimate of the optimum) changes every parameter x[i] by less than xtol_abs[i]. Criterion is disabled if all elements of xtol_abs are non-positive (default)."), c("maxeval", "integer", "maxeval is a positive integer", "100", TRUE, "Stop when the number of function evaluations exceeds maxeval. This is not a strict maximum: the number of function evaluations may exceed maxeval slightly, depending upon the algorithm. Criterion is disabled if maxeval is non-positive."), c("maxtime", "numeric", "maxtime > 0", "-1.0", TRUE, "Stop when the optimization time (in seconds) exceeds maxtime. This is not a strict maximum: the time may exceed maxtime slightly, depending upon the algorithm and on how slow your function evaluation is. Criterion is disabled if maxtime is non-positive (default)."), c("tol_constraints_ineq", "numeric", "tol_constraints_ineq > 0.0", "rep( 1e-8, num_constraints_ineq )", FALSE, "The parameter tol_constraints_ineq is a vector of tolerances. Each tolerance corresponds to one of the inequality constraints. The tolerance is used for the purpose of stopping criteria only: a point x is considered feasible for judging whether to stop the optimization if eval_g_ineq(x) <= tol. A tolerance of zero means that NLopt will try not to consider any x to be converged unless eval_g_ineq(x) is strictly non-positive; generally, at least a small positive tolerance is advisable to reduce sensitivity to rounding errors. By default the tolerances for all inequality constraints are set to 1e-8."), c("tol_constraints_eq", "numeric", "tol_constraints_eq > 0.0", "rep( 1e-8, num_constraints_eq )", FALSE, "The parameter tol_constraints_eq is a vector of tolerances. Each tolerance corresponds to one of the equality constraints. The tolerance is used for the purpose of stopping criteria only: a point x is considered feasible for judging whether to stop the optimization if abs( eval_g_ineq(x) ) <= tol. For equality constraints, a small positive tolerance is strongly advised in order to allow NLopt to converge even if the equality constraint is slightly nonzero. By default the tolerances for all equality constraints are set to 1e-8."), c("print_level", "interger", "0, 1, 2, or 3", "0", FALSE, "The option print_level controls how much output is shown during the optimization process. Possible values: 0 (default): no output; 1: show iteration number and value of objective function; 2: 1 + show value of (in)equalities; 3: 2 + show value of controls."), c("check_derivatives", "logical", "TRUE or FALSE", "FALSE", FALSE, "The option check_derivatives can be activated to compare the user-supplied analytic gradients with finite difference approximations."), c("check_derivatives_tol", "numeric", "check_derivatives_tol > 0.0", "1e-04", FALSE, "The option check_derivatives_tol determines when a difference between an analytic gradient and its finite difference approximation is flagged as an error."), c("check_derivatives_print", "character", "'none', 'all', 'errors',", "all", FALSE, "The option check_derivatives_print controls the output of the derivative checker (if check_derivatives==TRUE). All comparisons are shown ('all'), only those comparisions that resulted in an error ('error'), or only the number of errors is shown ('none')."), c("print_options_doc", "logical", "TRUE or FALSE", "FALSE", FALSE, "If TRUE, a description of all options and their current and default values is printed to the screen."), c("population", "integer", "population is a positive integer", "0", FALSE, "Several of the stochastic search algorithms (e.g., CRS, MLSL, and ISRES) start by generating some initial population of random points x. By default, this initial population size is chosen heuristically in some algorithm-specific way, but the initial population can by changed by setting a positive integer value for population. A population of zero implies that the heuristic default will be used."), c("ranseed", "integer", "ranseed is a positive integer", "0", FALSE, "For stochastic optimization algorithms, pseudorandom numbers are generated. Set the random seed using ranseed if you want to use a 'deterministic' sequence of pseudorandom numbers, i.e. the same sequence from run to run. If ranseed is 0 (default), the seed for the random numbers is generated from the system time, so that you will get a different sequence of pseudorandom numbers each time you run your program.") ), stringsAsFactors = FALSE ) names( dat.opts ) <- c( "name", "type", "possible_values", "default", "is_termination_condition", "description" ) return( dat.opts ) } nloptr/R/gradients.R0000644000176000001440000000451313266715715014144 0ustar ripleyusers# Copyright (C) 2014 Hans W. Borchers. All Rights Reserved. # This code is published under the L-GPL. # # File: gradients.R # Author: Hans W. Borchers # Date: 27 January 2014 # # Functions to calculate numerical Gradient and Jacobian. #' Numerical Gradients and Jacobians #' #' Provides numerical gradients and jacobians. #' #' Both functions apply the ``central difference formula'' with step size as #' recommended in the literature. #' #' @aliases nl.grad nl.jacobian #' #' @param x0 point as a vector where the gradient is to be calculated. #' @param fn scalar function of one or several variables. #' @param heps step size to be used. #' @param \dots additional arguments passed to the function. #' #' @return \code{grad} returns the gradient as a vector; \code{jacobian} #' returns the Jacobian as a matrix of usual dimensions. #' #' @export #' #' @author Hans W. Borchers #' #' @examples #' #' fn1 <- function(x) sum(x^2) #' nl.grad(seq(0, 1, by = 0.2), fn1) #' ## [1] 0.0 0.4 0.8 1.2 1.6 2.0 #' nl.grad(rep(1, 5), fn1) #' ## [1] 2 2 2 2 2 #' #' fn2 <- function(x) c(sin(x), cos(x)) #' x <- (0:1)*2*pi #' nl.jacobian(x, fn2) #' ## [,1] [,2] #' ## [1,] 1 0 #' ## [2,] 0 1 #' ## [3,] 0 0 #' ## [4,] 0 0 #' nl.grad <- function (x0, fn, heps = .Machine$double.eps^(1/3), ...) { if (!is.numeric(x0)) stop("Argument 'x0' must be a numeric value.") fun <- match.fun(fn) fn <- function(x) fun(x, ...) if (length(fn(x0)) != 1) stop("Function 'f' must be a univariate function of 2 variables.") n <- length(x0) hh <- rep(0, n) gr <- numeric(n) for (i in 1:n) { hh[i] <- heps gr[i] <- (fn(x0 + hh) - fn(x0 - hh)) / (2*heps) hh[i] <- 0 } return(gr) } #' @export nl.jacobian <- function(x0, fn, heps = .Machine$double.eps^(1/3), ...) { if (!is.numeric(x0) || length(x0) == 0) stop("Argument 'x' must be a non-empty numeric vector.") fun <- match.fun(fn) fn <- function(x) fun(x, ...) n <- length(x0) m <- length(fn(x0)) jacob <- matrix(NA, m, n) hh <- numeric(n) for (i in 1:n) { hh[i] <- heps jacob[, i] <- (fn(x0 + hh) - fn(x0 - hh)) / (2*heps) hh[i] <- 0 } return(jacob) } nloptr/R/nloptr.add.default.options.R0000644000176000001440000000564212756545216017352 0ustar ripleyusers# Copyright (C) 2011 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: nloptr.add.default.options.R # Author: Jelmer Ypma # Date: 17 August 2016 # # Add default options to a user supplied list of options. # # Input: # opts.user: list with user defined options # x0: initial value for control variables # num_constraints_ineq: number of inequality constraints # num_constraints_eq: number of equality constraints # # Output: # opts.user with default options added for those options # that were not part of the original opts.user. # # Changelog: # 17-08-2016: Fixed bug that sometimes caused segmentation faults # due to uninitialized tolerances for the (in)equality # constraints (thanks to Florian Schwendiger). nloptr.add.default.options <- function( opts.user, x0=0, num_constraints_ineq=0, num_constraints_eq=0 ) { nloptr.default.options <- nloptr.get.default.options() rownames(nloptr.default.options) <- nloptr.default.options[,"name"] # get names of options that define a termination condition termination.opts <- nloptr.default.options[ nloptr.default.options$is_termination_condition==TRUE, "name" ] if ( sum(termination.opts %in% names( opts.user )) == 0 ) { # get default xtol_rel xtol_rel_default <- as.numeric( nloptr.default.options[ nloptr.default.options$name=="xtol_rel", "default" ] ) warning( paste("No termination criterium specified, using default (relative x-tolerance = ", xtol_rel_default, ")", sep='') ) termination_conditions <- paste("relative x-tolerance = ", xtol_rel_default, " (DEFAULT)", sep='') } else { conv_options <- unlist(opts.user[names(opts.user) %in% termination.opts]) termination_conditions <- paste( paste( names(conv_options) ), ": ", paste( conv_options ), sep='', collapse='\t' ) } # determine list with names of options that contain character values. # we need to add quotes around these options below. nloptr.list.character.options <- nloptr.default.options[ nloptr.default.options$type=="character", "name" ] opts <- vector(mode="list", nrow(nloptr.default.options)) names(opts) <- nloptr.default.options[,"name"] for ( name in names(opts) ) { if ( !is.null(opts.user[[name]]) ) { opts[[name]] <- opts.user[[name]] } else if ( name %in% nloptr.list.character.options ) { opts[[name]] <- nloptr.default.options[name, "default"] } else { opts[[name]] <- eval(parse(text=nloptr.default.options[name, "default"])) } } return( list( "opts.user" = opts, "termination_conditions" = termination_conditions ) ) } nloptr/R/nloptr-package.R0000644000176000001440000001151313266714625015070 0ustar ripleyusers#' R interface to NLopt #' #' nloptr is an R interface to NLopt, a free/open-source library for nonlinear #' optimization started by Steven G. Johnson, providing a common interface for #' a number of different free optimization routines available online as well as #' original implementations of various other algorithms. The NLopt library is #' available under the GNU Lesser General Public License (LGPL), and the #' copyrights are owned by a variety of authors. Most of the information here #' has been taken from \href{http://ab-initio.mit.edu/nlopt}{the NLopt website}, #' where more details are available. #' #' NLopt addresses general nonlinear optimization problems of the form: #' #' min f(x) x in R^n #' #' s.t. g(x) <= 0 h(x) = 0 lb <= x <= ub #' #' where f is the objective function to be minimized and x represents the n #' optimization parameters. This problem may optionally be subject to the bound #' constraints (also called box constraints), lb and ub. For partially or #' totally unconstrained problems the bounds can take -Inf or Inf. One may also #' optionally have m nonlinear inequality constraints (sometimes called a #' nonlinear programming problem), which can be specified in g(x), and equality #' constraints that can be specified in h(x). Note that not all of the #' algorithms in NLopt can handle constraints. #' #' An optimization problem can be solved with the general nloptr interface, or #' using one of the wrapper functions for the separate algorithms; auglag, #' bobyqa, cobyla, crs2lm, direct, lbfgs, mlsl, mma, neldermead, newuoa, sbplx, #' slsqp, stogo, tnewton, varmetric. #' #' \tabular{ll}{ Package: \tab nloptr\cr Type: \tab Package\cr Version: \tab #' 0.9.9\cr Date: \tab 2013-11-22\cr License: \tab L-GPL\cr LazyLoad: \tab #' yes\cr } #' #' @docType package #' #' @name nloptr-package #' #' @useDynLib nloptr, .registration = TRUE #' #' @author Steven G. Johnson and others (C code) \cr Jelmer Ypma (R interface) #' \cr Hans W. Borchers (wrappers) #' #' @note See ?nloptr for more examples. #' #' @seealso \code{\link{optim}} \code{\link{nlm}} \code{\link{nlminb}} #' \code{Rsolnp::Rsolnp} \code{Rsolnp::solnp} \code{\link{nloptr}} #' \code{\link{auglag}} \code{\link{bobyqa}} \code{\link{cobyla}} #' \code{\link{crs2lm}} \code{\link{direct}} \code{\link{isres}} #' \code{\link{lbfgs}} \code{\link{mlsl}} \code{\link{mma}} #' \code{\link{neldermead}} \code{\link{newuoa}} \code{\link{sbplx}} #' \code{\link{slsqp}} \code{\link{stogo}} \code{\link{tnewton}} #' \code{\link{varmetric}} #' #' @references Steven G. Johnson, The NLopt nonlinear-optimization package, #' \url{http://ab-initio.mit.edu/nlopt} #' #' @keywords optimize interface #' #' @examples #' #' # Example problem, number 71 from the Hock-Schittkowsky test suite. #' # #' # \min_{x} x1*x4*(x1 + x2 + x3) + x3 #' # s.t. #' # x1*x2*x3*x4 >= 25 #' # x1^2 + x2^2 + x3^2 + x4^2 = 40 #' # 1 <= x1,x2,x3,x4 <= 5 #' # #' # we re-write the inequality as #' # 25 - x1*x2*x3*x4 <= 0 #' # #' # and the equality as #' # x1^2 + x2^2 + x3^2 + x4^2 - 40 = 0 #' # #' # x0 = (1,5,5,1) #' # #' # optimal solution = (1.00000000, 4.74299963, 3.82114998, 1.37940829) #' #' #' library('nloptr') #' #' # #' # f(x) = x1*x4*(x1 + x2 + x3) + x3 #' # #' eval_f <- function( x ) { #' return( list( "objective" = x[1]*x[4]*(x[1] + x[2] + x[3]) + x[3], #' "gradient" = c( x[1] * x[4] + x[4] * (x[1] + x[2] + x[3]), #' x[1] * x[4], #' x[1] * x[4] + 1.0, #' x[1] * (x[1] + x[2] + x[3]) ) ) ) #' } #' #' # constraint functions #' # inequalities #' eval_g_ineq <- function( x ) { #' constr <- c( 25 - x[1] * x[2] * x[3] * x[4] ) #' #' grad <- c( -x[2]*x[3]*x[4], #' -x[1]*x[3]*x[4], #' -x[1]*x[2]*x[4], #' -x[1]*x[2]*x[3] ) #' return( list( "constraints"=constr, "jacobian"=grad ) ) #' } #' #' # equalities #' eval_g_eq <- function( x ) { #' constr <- c( x[1]^2 + x[2]^2 + x[3]^2 + x[4]^2 - 40 ) #' #' grad <- c( 2.0*x[1], #' 2.0*x[2], #' 2.0*x[3], #' 2.0*x[4] ) #' return( list( "constraints"=constr, "jacobian"=grad ) ) #' } #' #' # initial values #' x0 <- c( 1, 5, 5, 1 ) #' #' # lower and upper bounds of control #' lb <- c( 1, 1, 1, 1 ) #' ub <- c( 5, 5, 5, 5 ) #' #' #' local_opts <- list( "algorithm" = "NLOPT_LD_MMA", #' "xtol_rel" = 1.0e-7 ) #' opts <- list( "algorithm" = "NLOPT_LD_AUGLAG", #' "xtol_rel" = 1.0e-7, #' "maxeval" = 1000, #' "local_opts" = local_opts ) #' #' res <- nloptr( x0=x0, #' eval_f=eval_f, #' lb=lb, #' ub=ub, #' eval_g_ineq=eval_g_ineq, #' eval_g_eq=eval_g_eq, #' opts=opts) #' print( res ) #' NULL nloptr/R/nloptr.print.options.R0000644000176000001440000000560513266707062016326 0ustar ripleyusers# Copyright (C) 2011 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: nloptr.print.options.R # Author: Jelmer Ypma # Date: 8 August 2011 # # Print list of all options with description. # # Input: # opts.show: the description of the options in this list are shown (optional, default is to show all options) # opts.user: list with user defined options (optional) # Output: options, default values, user values if supplied) # and description printed to screen. No return value. # # 08/08/2011: Added opts.show argument to only show a subset of all options. #' Print description of nloptr options #' #' This function prints a list of all the options that can be set when solving #' a minimization problem using \code{nloptr}. #' #' @param opts.show list or vector with names of options. A description will be #' shown for the options in this list. By default, a description of all options #' is shown. #' @param opts.user object containing user supplied options. This argument is #' optional. It is used when \code{nloptr.print.options} is called from #' \code{nloptr}. In that case options are listed if \code{print_options_doc} #' is set to \code{TRUE} when passing a minimization problem to \code{nloptr}. #' #' @export nloptr.print.options #' #' @author Jelmer Ypma #' #' @seealso \code{\link[nloptr:nloptr]{nloptr}} #' #' @keywords optimize interface #' #' @examples #' #' library('nloptr') #' nloptr.print.options() #' #' nloptr.print.options( opts.show = c("algorithm", "check_derivatives") ) #' #' opts <- list("algorithm"="NLOPT_LD_LBFGS", #' "xtol_rel"=1.0e-8) #' nloptr.print.options( opts.user = opts ) #' nloptr.print.options <- function( opts.show=NULL, opts.user=NULL ) { # show all options if no list of options is supplied if ( is.null( opts.show ) ) { nloptr.show.options <- nloptr.get.default.options() } else { nloptr.show.options <- nloptr.get.default.options() nloptr.show.options <- nloptr.show.options[ nloptr.show.options$name %in% opts.show, ] } # loop over all options and print values for( row.cnt in 1:nrow( nloptr.show.options ) ) { opt <- nloptr.show.options[row.cnt,] value.current <- ifelse( is.null(opts.user[[opt$name]]), "(default)", opts.user[[opt$name]] ) cat( opt$name, "\n", sep='' ) cat( "\tpossible values: ", paste(strwrap(opt$possible_values, width=50), collapse="\n\t "), "\n", sep='' ) cat( "\tdefault value: ", opt$default, "\n", sep='' ) if ( !is.null( opts.user ) ) { cat( "\tcurrent value: ", value.current, "\n", sep='' ) } cat( "\n\t", paste(strwrap(opt$description, width=70), collapse="\n\t"), "\n", sep='' ) cat( "\n") } } nloptr/R/is.nloptr.R0000644000176000001440000002375213266705727014124 0ustar ripleyusers# Copyright (C) 2010 Jelmer Ypma. All Rights Reserved. # This code is published under the L-GPL. # # File: is.nloptr.R # Author: Jelmer Ypma # Date: 10 June 2010 # # Input: object # Output: bool telling whether the object is an nloptr or not # # CHANGELOG # 16/06/2011: separated local optimzer check and equality constraints check # 05/05/2014: Replaced cat by warning. #' R interface to NLopt #' #' is.nloptr preforms checks to see if a fully specified problem is supplied to #' nloptr. Mostly for internal use. #' #' @param x object to be tested. #' #' @return Logical. Return TRUE if all tests were passed, otherwise return #' FALSE or exit with Error. #' #' @export is.nloptr #' #' @author Jelmer Ypma #' #' @seealso \code{\link[nloptr:nloptr]{nloptr}} #' #' @keywords optimize interface is.nloptr <- function( x ) { # Check whether the object exists and is a list if( is.null(x) ) { return( FALSE ) } if( !is.list(x) ) { return( FALSE ) } # Check whether the needed wrapper functions are supplied if ( !is.function(x$eval_f) ) { stop('eval_f is not a function') } if ( !is.null(x$eval_g_ineq) ) { if ( !is.function(x$eval_g_ineq) ) { stop('eval_g_ineq is not a function') } } if ( !is.null(x$eval_g_eq) ) { if ( !is.function(x$eval_g_eq) ) { stop('eval_g_eq is not a function') } } # Check whether bounds are defined for all controls if ( any( is.na( x$x0 ) ) ) { stop('x0 contains NA') } if ( length( x$x0 ) != length( x$lower_bounds ) ) { stop('length(lb) != length(x0)') } if ( length( x$x0 ) != length( x$upper_bounds ) ) { stop('length(ub) != length(x0)') } # Check whether the initial value is within the bounds if ( any( x$x0 < x$lower_bounds ) ) { stop('at least one element in x0 < lb') } if ( any( x$x0 > x$upper_bounds ) ) { stop('at least one element in x0 > ub') } # define list with all algorithms list_algorithms <- c( "NLOPT_GN_DIRECT", "NLOPT_GN_DIRECT_L", "NLOPT_GN_DIRECT_L_RAND", "NLOPT_GN_DIRECT_NOSCAL", "NLOPT_GN_DIRECT_L_NOSCAL", "NLOPT_GN_DIRECT_L_RAND_NOSCAL", "NLOPT_GN_ORIG_DIRECT", "NLOPT_GN_ORIG_DIRECT_L", "NLOPT_GD_STOGO", "NLOPT_GD_STOGO_RAND", "NLOPT_LD_SLSQP", "NLOPT_LD_LBFGS_NOCEDAL", "NLOPT_LD_LBFGS", "NLOPT_LN_PRAXIS", "NLOPT_LD_VAR1", "NLOPT_LD_VAR2", "NLOPT_LD_TNEWTON", "NLOPT_LD_TNEWTON_RESTART", "NLOPT_LD_TNEWTON_PRECOND", "NLOPT_LD_TNEWTON_PRECOND_RESTART", "NLOPT_GN_CRS2_LM", "NLOPT_GN_MLSL", "NLOPT_GD_MLSL", "NLOPT_GN_MLSL_LDS", "NLOPT_GD_MLSL_LDS", "NLOPT_LD_MMA", "NLOPT_LN_COBYLA", "NLOPT_LN_NEWUOA", "NLOPT_LN_NEWUOA_BOUND", "NLOPT_LN_NELDERMEAD", "NLOPT_LN_SBPLX", "NLOPT_LN_AUGLAG", "NLOPT_LD_AUGLAG", "NLOPT_LN_AUGLAG_EQ", "NLOPT_LD_AUGLAG_EQ", "NLOPT_LN_BOBYQA", "NLOPT_GN_ISRES" ) # check if an existing algorithm was supplied if ( !( x$options$algorithm %in% list_algorithms ) ) { stop( paste('Incorrect algorithm supplied. Use one of the following:\n', paste( list_algorithms, collapse='\n' ) ) ) } # determine subset of algorithms that need a derivative list_algorithmsD <- list_algorithms[ grep( "NLOPT_[G,L]D", list_algorithms ) ] list_algorithmsN <- list_algorithms[ grep( "NLOPT_[G,L]N", list_algorithms ) ] # Check the whether we don't have NA's if we evaluate the objective function in x0 f0 <- x$eval_f( x$x0 ) if ( is.list( f0 ) ) { if ( is.na( f0$objective ) ) { stop('objective in x0 returns NA') } if ( any( is.na( f0$gradient ) ) ) { stop('gradient of objective in x0 returns NA') } if ( length( f0$gradient ) != length( x$x0 ) ) { stop('wrong number of elements in gradient of objective') } # check whether algorihtm needs a derivative if ( x$options$algorithm %in% list_algorithmsN ) { warning( 'a gradient was supplied for the objective function, but algorithm ', x$options$algorithm, ' does not use gradients.' ) } } else { if ( is.na( f0 ) ) { stop('objective in x0 returns NA') } # check whether algorihtm needs a derivative if ( x$options$algorithm %in% list_algorithmsD ) { stop( paste( 'A gradient for the objective function is needed by algorithm', x$options$algorithm, 'but was not supplied.\n' ) ) } } # Check the whether we don't have NA's if we evaluate the inequality constraints in x0 if ( !is.null( x$eval_g_ineq ) ) { g0_ineq <- x$eval_g_ineq( x$x0 ) if ( is.list( g0_ineq ) ) { if ( any( is.na( g0_ineq$constraints ) ) ) { stop('inequality constraints in x0 returns NA') } if ( any( is.na( g0_ineq$jacobian ) ) ) { stop('jacobian of inequality constraints in x0 returns NA') } if ( length( g0_ineq$jacobian ) != length( g0_ineq$constraints )*length( x$x0 ) ) { stop(paste('wrong number of elements in jacobian of inequality constraints (is ', length( g0_ineq$jacobian ), ', but should be ', length( g0_ineq$constraints ), ' x ', length( x$x0 ), ' = ', length( g0_ineq$constraints )*length( x$x0 ), ')', sep='')) } # check whether algorihtm needs a derivative if ( x$options$algorithm %in% list_algorithmsN ) { warning( 'a gradient was supplied for the inequality constraints, but algorithm ', x$options$algorithm, ' does not use gradients.' ) } } else { if ( any( is.na( g0_ineq ) ) ) { stop('inequality constraints in x0 returns NA') } # check whether algorihtm needs a derivative if ( x$options$algorithm %in% list_algorithmsD ) { stop( paste( 'A gradient for the inequality constraints is needed by algorithm', x$options$algorithm, 'but was not supplied.\n' ) ) } } } # Check the whether we don't have NA's if we evaluate the equality constraints in x0 if ( !is.null( x$eval_g_eq ) ) { g0_eq <- x$eval_g_eq( x$x0 ) if ( is.list( g0_eq ) ) { if ( any( is.na( g0_eq$constraints ) ) ) { stop('equality constraints in x0 returns NA') } if ( any( is.na( g0_eq$jacobian ) ) ) { stop('jacobian of equality constraints in x0 returns NA') } if ( length( g0_eq$jacobian ) != length( g0_eq$constraints )*length( x$x0 ) ) { stop(paste('wrong number of elements in jacobian of equality constraints (is ', length( g0_eq$jacobian ), ', but should be ', length( g0_eq$constraints ), ' x ', length( x$x0 ), ' = ', length( g0_eq$constraints )*length( x$x0 ), ')', sep='')) } # check whether algorihtm needs a derivative if ( x$options$algorithm %in% list_algorithmsN ) { warning( 'a gradient was supplied for the equality constraints, but algorithm ', x$options$algorithm, ' does not use gradients.' ) } } else { if ( any( is.na( g0_eq ) ) ) { stop('equality constraints in x0 returns NA') } # check whether algorihtm needs a derivative if ( x$options$algorithm %in% list_algorithmsD ) { stop( paste( 'A gradient for the equality constraints is needed by algorithm', x$options$algorithm, 'but was not supplied.\n' ) ) } } } # check if we have a correct algorithm for the equality constraints if ( x$num_constraints_eq > 0 ) { eq_algorithms <- c("NLOPT_LD_AUGLAG", "NLOPT_LN_AUGLAG", "NLOPT_LD_AUGLAG_EQ", "NLOPT_LN_AUGLAG_EQ", "NLOPT_GN_ISRES", "NLOPT_LD_SLSQP") if( !( x$options$algorithm %in% eq_algorithms ) ) { stop(paste('If you want to use equality constraints, then you should use one of these algorithms', paste(eq_algorithms, collapse=', '))) } } # check if a local optimizer was supplied, which is needed by some algorithms if ( x$options$algorithm %in% c("NLOPT_LD_AUGLAG", "NLOPT_LN_AUGLAG", "NLOPT_LD_AUGLAG_EQ", "NLOPT_LN_AUGLAG_EQ", "NLOPT_GN_MLSL", "NLOPT_GD_MLSL", "NLOPT_GN_MLSL_LDS", "NLOPT_GD_MLSL_LDS") ) { if ( is.null( x$local_options ) ) { stop(paste('The algorithm', x$options$algorithm, 'needs a local optimizer; specify an algorithm and termination condition in local_opts')) } } # Check if the vector with tolerances for the inequality # constraints is of the same size as the number of constraints. if ( x$num_constraints_ineq != length( x$options$tol_constraints_ineq ) ) { stop(paste('The vector tol_constraints_ineq in the options list has size', length(x$options$tol_constraints_ineq), 'which is unequal to the number of inequality constraints,', x$num_constraints_ineq, '.')) } # Check if the vector with tolerances for the equality # constraints is of the same size as the number of constraints. if ( x$num_constraints_eq != length( x$options$tol_constraints_eq ) ) { stop(paste('The vector tol_constraints_eq in the options list has size', length(x$options$tol_constraints_eq), 'which is unequal to the number of equality constraints,', x$num_constraints_eq, '.')) } return( TRUE ) } nloptr/R/global.R0000644000176000001440000002677513267037333013434 0ustar ripleyusers# Copyright (C) 2014 Hans W. Borchers. All Rights Reserved. # This code is published under the L-GPL. # # File: global.R # Author: Hans W. Borchers # Date: 27 January 2014 # # Wrapper to solve optimization problem using StoGo. #-- Does not work: maybe C++ support missing? ---------------------- StoGo --- #' Stochastic Global Optimization #' #' StoGO is a global optimization algorithm that works by systematically #' dividing the search space into smaller hyper-rectangles. #' #' StoGO is a global optimization algorithm that works by systematically #' dividing the search space (which must be bound-constrained) into smaller #' hyper-rectangles via a branch-and-bound technique, and searching them by a #' gradient-based local-search algorithm (a BFGS variant), optionally including #' some randomness. #' #' @param x0 initial point for searching the optimum. #' @param fn objective function that is to be minimized. #' @param gr optional gradient of the objective function. #' @param lower,upper lower and upper bound constraints. #' @param maxeval maximum number of function evaluations. #' @param xtol_rel stopping criterion for relative change reached. #' @param randomized logical; shall a randomizing variant be used? #' @param nl.info logical; shall the original NLopt info been shown. #' @param ... additional arguments passed to the function. #' #' @return List with components: #' \item{par}{the optimal solution found so far.} #' \item{value}{the function value corresponding to \code{par}.} #' \item{iter}{number of (outer) iterations, see \code{maxeval}.} #' \item{convergence}{integer code indicating successful completion (> 0) #' or a possible error number (< 0).} #' \item{message}{character string produced by NLopt and giving additional #' information.} #' #' @export stogo #' #' @author Hans W. Borchers #' #' @note Only bound-constrained problems are supported by this algorithm. #' #' @references S. Zertchaninov and K. Madsen, ``A C++ Programme for Global #' Optimization,'' IMM-REP-1998-04, Department of Mathematical Modelling, #' Technical University of Denmark. #' #' @examples #' #' ### Rosenbrock Banana objective function #' fn <- function(x) #' return( 100 * (x[2] - x[1] * x[1])^2 + (1 - x[1])^2 ) #' #' x0 <- c( -1.2, 1 ) #' lb <- c( -3, -3 ) #' ub <- c( 3, 3 ) #' #' stogo(x0 = x0, fn = fn, lower = lb, upper = ub) #' stogo <- function(x0, fn, gr = NULL, lower = NULL, upper = NULL, maxeval = 10000, xtol_rel = 1e-6, randomized = FALSE, nl.info = FALSE, ...) # control = list() { # opts <- nl.opts(control) opts <- list() opts$maxeval <- maxeval opts$xtol_rel <- xtol_rel if (randomized) opts["algorithm"] <- "NLOPT_GD_STOGO_RAND" else opts["algorithm"] <- "NLOPT_GD_STOGO" fun <- match.fun(fn) fn <- function(x) fun(x, ...) if (is.null(gr)) { gr <- function(x) nl.grad(x, fn) } S0 <- nloptr(x0, eval_f = fn, eval_grad_f = gr, lb = lower, ub = upper, opts = opts) if (nl.info) print(S0) S1 <- list(par = S0$solution, value = S0$objective, iter = S0$iterations, convergence = S0$status, message = S0$message) return(S1) } #-- Supports nonlinear constraints: quite inaccurate! -------------- ISRES --- #' Improved Stochastic Ranking Evolution Strategy #' #' The Improved Stochastic Ranking Evolution Strategy (ISRES) algorithm for #' nonlinearly constrained global optimization (or at least semi-global: #' although it has heuristics to escape local optima. #' #' The evolution strategy is based on a combination of a mutation rule (with a #' log-normal step-size update and exponential smoothing) and differential #' variation (a Nelder-Mead-like update rule). The fitness ranking is simply #' via the objective function for problems without nonlinear constraints, but #' when nonlinear constraints are included the stochastic ranking proposed by #' Runarsson and Yao is employed. #' #' This method supports arbitrary nonlinear inequality and equality constraints #' in addition to the bound constraints. #' #' @param x0 initial point for searching the optimum. #' @param fn objective function that is to be minimized. #' @param lower,upper lower and upper bound constraints. #' @param hin function defining the inequality constraints, that is #' \code{hin>=0} for all components. #' @param heq function defining the equality constraints, that is \code{heq==0} #' for all components. #' @param maxeval maximum number of function evaluations. #' @param pop.size population size. #' @param xtol_rel stopping criterion for relative change reached. #' @param nl.info logical; shall the original NLopt info been shown. #' @param ... additional arguments passed to the function. #' #' @return List with components: #' \item{par}{the optimal solution found so far.} #' \item{value}{the function value corresponding to \code{par}.} #' \item{iter}{number of (outer) iterations, see \code{maxeval}.} #' \item{convergence}{integer code indicating successful completion (> 0) #' or a possible error number (< 0).} #' \item{message}{character string produced by NLopt and giving additional #' information.} #' #' @export isres #' #' @author Hans W. Borchers #' #' @note The initial population size for CRS defaults to \code{20x(n+1)} in #' \code{n} dimensions, but this can be changed; the initial population must be #' at least \code{n+1}. #' #' @references Thomas Philip Runarsson and Xin Yao, ``Search biases in #' constrained evolutionary optimization,'' IEEE Trans. on Systems, Man, and #' Cybernetics Part C: Applications and Reviews, vol. 35 (no. 2), pp. 233-243 #' (2005). #' #' @examples #' #' ### Rosenbrock Banana objective function #' fn <- function(x) #' return( 100 * (x[2] - x[1] * x[1])^2 + (1 - x[1])^2 ) #' #' x0 <- c( -1.2, 1 ) #' lb <- c( -3, -3 ) #' ub <- c( 3, 3 ) #' #' isres(x0 = x0, fn = fn, lower = lb, upper = ub) #' isres <- function(x0, fn, lower, upper, hin = NULL, heq = NULL, maxeval = 10000, pop.size = 20*(length(x0)+1), xtol_rel = 1e-6, nl.info = FALSE, ...) { #opts <- nl.opts(control) opts <- list() opts$maxeval <- maxeval opts$xtol_rel <- xtol_rel opts$population <- pop.size opts$algorithm <- "NLOPT_GN_ISRES" fun <- match.fun(fn) fn <- function(x) fun(x, ...) if (!is.null(hin)) { if ( getOption('nloptr.show.inequality.warning') ) { message('For consistency with the rest of the package the inequality sign may be switched from >= to <= in a future nloptr version.') } .hin <- match(hin) hin <- function(x) (-1) * .hin(x) # change hin >= 0 to hin <= 0 ! } if (!is.null(heq)) { .heq <- match.fun(heq) heq <- function(x) .heq(x) } S0 <- nloptr(x0 = x0, eval_f = fn, lb = lower, ub = upper, eval_g_ineq = hin, eval_g_eq = heq, opts = opts) if (nl.info) print(S0) S1 <- list(par = S0$solution, value = S0$objective, iter = S0$iterations, convergence = S0$status, message = S0$message) return(S1) } #-- ------------------------------------------------------------------ CRS --- #' Controlled Random Search #' #' The Controlled Random Search (CRS) algorithm (and in particular, the CRS2 #' variant) with the `local mutation' modification. #' #' The CRS algorithms are sometimes compared to genetic algorithms, in that #' they start with a random population of points, and randomly evolve these #' points by heuristic rules. In this case, the evolution somewhat resembles a #' randomized Nelder-Mead algorithm. #' #' The published results for CRS seem to be largely empirical. #' #' @param x0 initial point for searching the optimum. #' @param fn objective function that is to be minimized. #' @param lower,upper lower and upper bound constraints. #' @param maxeval maximum number of function evaluations. #' @param pop.size population size. #' @param ranseed prescribe seed for random number generator. #' @param xtol_rel stopping criterion for relative change reached. #' @param nl.info logical; shall the original NLopt info been shown. #' @param ... additional arguments passed to the function. #' #' @return List with components: #' \item{par}{the optimal solution found so far.} #' \item{value}{the function value corresponding to \code{par}.} #' \item{iter}{number of (outer) iterations, see \code{maxeval}.} #' \item{convergence}{integer code indicating successful completion (> 0) #' or a possible error number (< 0).} #' \item{message}{character string produced by NLopt and giving additional #' information.} #' #' @export crs2lm #' #' @note The initial population size for CRS defaults to \code{10x(n+1)} in #' \code{n} dimensions, but this can be changed; the initial population must be #' at least \code{n+1}. #' #' @references W. L. Price, ``Global optimization by controlled random #' search,'' J. Optim. Theory Appl. 40 (3), p. 333-348 (1983). #' #' P. Kaelo and M. M. Ali, ``Some variants of the controlled random search #' algorithm for global optimization,'' J. Optim. Theory Appl. 130 (2), 253-264 #' (2006). #' #' @examples #' #' ### Minimize the Hartmann6 function #' hartmann6 <- function(x) { #' n <- length(x) #' a <- c(1.0, 1.2, 3.0, 3.2) #' A <- matrix(c(10.0, 0.05, 3.0, 17.0, #' 3.0, 10.0, 3.5, 8.0, #' 17.0, 17.0, 1.7, 0.05, #' 3.5, 0.1, 10.0, 10.0, #' 1.7, 8.0, 17.0, 0.1, #' 8.0, 14.0, 8.0, 14.0), nrow=4, ncol=6) #' B <- matrix(c(.1312,.2329,.2348,.4047, #' .1696,.4135,.1451,.8828, #' .5569,.8307,.3522,.8732, #' .0124,.3736,.2883,.5743, #' .8283,.1004,.3047,.1091, #' .5886,.9991,.6650,.0381), nrow=4, ncol=6) #' fun <- 0.0 #' for (i in 1:4) { #' fun <- fun - a[i] * exp(-sum(A[i,]*(x-B[i,])^2)) #' } #' return(fun) #' } #' #' S <- mlsl(x0 = rep(0, 6), hartmann6, lower = rep(0,6), upper = rep(1,6), #' nl.info = TRUE, control=list(xtol_rel=1e-8, maxeval=1000)) #' ## Number of Iterations....: 4050 #' ## Termination conditions: maxeval: 10000 xtol_rel: 1e-06 #' ## Number of inequality constraints: 0 #' ## Number of equality constraints: 0 #' ## Optimal value of objective function: -3.32236801141328 #' ## Optimal value of controls: #' ## 0.2016893 0.1500105 0.4768738 0.2753326 0.3116516 0.6573004 #' crs2lm <- function(x0, fn, lower, upper, maxeval = 10000, pop.size = 10*(length(x0)+1), ranseed = NULL, xtol_rel = 1e-6, nl.info = FALSE, ...) { #opts <- nl.opts(control) opts <- list() opts$maxeval <- maxeval opts$xtol_rel <- xtol_rel opts$population <- pop.size if (!is.null(ranseed)) opts$ranseed <- as.integer(ranseed) opts$algorithm <- "NLOPT_GN_CRS2_LM" fun <- match.fun(fn) fn <- function(x) fun(x, ...) S0 <- nloptr(x0, eval_f = fn, lb = lower, ub = upper, opts = opts) if (nl.info) print(S0) S1 <- list(par = S0$solution, value = S0$objective, iter = S0$iterations, convergence = S0$status, message = S0$message) return(S1) } nloptr/R/mma.R0000644000176000001440000001276613267037303012736 0ustar ripleyusers# Copyright (C) 2014 Hans W. Borchers. All Rights Reserved. # This code is published under the L-GPL. # # File: mma.R # Author: Hans W. Borchers # Date: 27 January 2014 # # Wrapper to solve optimization problem using MMA. #' Method of Moving Asymptotes #' #' Globally-convergent method-of-moving-asymptotes (MMA) algorithm for #' gradient-based local optimization, including nonlinear inequality #' constraints (but not equality constraints). #' #' This is an improved CCSA ("conservative convex separable approximation") #' variant of the original MMA algorithm published by Svanberg in 1987, which #' has become popular for topology optimization. Note: #' #' @param x0 starting point for searching the optimum. #' @param fn objective function that is to be minimized. #' @param gr gradient of function \code{fn}; will be calculated numerically if #' not specified. #' @param lower,upper lower and upper bound constraints. #' @param hin function defining the inequality constraints, that is #' \code{hin>=0} for all components. #' @param hinjac Jacobian of function \code{hin}; will be calculated #' numerically if not specified. #' @param nl.info logical; shall the original NLopt info been shown. #' @param control list of options, see \code{nl.opts} for help. #' @param ... additional arguments passed to the function. #' #' @return List with components: #' \item{par}{the optimal solution found so far.} #' \item{value}{the function value corresponding to \code{par}.} #' \item{iter}{number of (outer) iterations, see \code{maxeval}.} #' \item{convergence}{integer code indicating successful completion (> 1) #' or a possible error number (< 0).} #' \item{message}{character string produced by NLopt and giving additional #' information.} #' #' @export mma #' #' @author Hans W. Borchers #' #' @note ``Globally convergent'' does not mean that this algorithm converges to #' the global optimum; it means that it is guaranteed to converge to some local #' minimum from any feasible starting point. #' #' @seealso \code{\link{slsqp}} #' #' @references Krister Svanberg, ``A class of globally convergent optimization #' methods based on conservative convex separable approximations,'' SIAM J. #' Optim. 12 (2), p. 555-573 (2002). #' #' @examples #' #' ## Solve the Hock-Schittkowski problem no. 100 with analytic gradients #' x0.hs100 <- c(1, 2, 0, 4, 0, 1, 1) #' fn.hs100 <- function(x) { #' (x[1]-10)^2 + 5*(x[2]-12)^2 + x[3]^4 + 3*(x[4]-11)^2 + 10*x[5]^6 + #' 7*x[6]^2 + x[7]^4 - 4*x[6]*x[7] - 10*x[6] - 8*x[7] #' } #' hin.hs100 <- function(x) { #' h <- numeric(4) #' h[1] <- 127 - 2*x[1]^2 - 3*x[2]^4 - x[3] - 4*x[4]^2 - 5*x[5] #' h[2] <- 282 - 7*x[1] - 3*x[2] - 10*x[3]^2 - x[4] + x[5] #' h[3] <- 196 - 23*x[1] - x[2]^2 - 6*x[6]^2 + 8*x[7] #' h[4] <- -4*x[1]^2 - x[2]^2 + 3*x[1]*x[2] -2*x[3]^2 - 5*x[6] +11*x[7] #' return(h) #' } #' gr.hs100 <- function(x) { #' c( 2 * x[1] - 20, #' 10 * x[2] - 120, #' 4 * x[3]^3, #' 6 * x[4] - 66, #' 60 * x[5]^5, #' 14 * x[6] - 4 * x[7] - 10, #' 4 * x[7]^3 - 4 * x[6] - 8 )} #' hinjac.hs100 <- function(x) { #' matrix(c(4*x[1], 12*x[2]^3, 1, 8*x[4], 5, 0, 0, #' 7, 3, 20*x[3], 1, -1, 0, 0, #' 23, 2*x[2], 0, 0, 0, 12*x[6], -8, #' 8*x[1]-3*x[2], 2*x[2]-3*x[1], 4*x[3], 0, 0, 5, -11), 4, 7, byrow=TRUE) #' } #' #' # incorrect result with exact jacobian #' S <- mma(x0.hs100, fn.hs100, gr = gr.hs100, #' hin = hin.hs100, hinjac = hinjac.hs100, #' nl.info = TRUE, control = list(xtol_rel = 1e-8)) #' #' \donttest{ #' # This example is put in donttest because it runs for more than #' # 40 seconds under 32-bit Windows. The difference in time needed #' # to execute the code between 32-bit Windows and 64-bit Windows #' # can probably be explained by differences in rounding/truncation #' # on the different systems. On Windows 32-bit more iterations #' # are needed resulting in a longer runtime. #' # correct result with inexact jacobian #' S <- mma(x0.hs100, fn.hs100, hin = hin.hs100, #' nl.info = TRUE, control = list(xtol_rel = 1e-8)) #' } #' mma <- function(x0, fn, gr = NULL, lower = NULL, upper = NULL, hin = NULL, hinjac = NULL, nl.info = FALSE, control = list(), ...) { opts <- nl.opts(control) opts["algorithm"] <- "NLOPT_LD_MMA" fun <- match.fun(fn) fn <- function(x) fun(x, ...) if (is.null(gr)) { gr <- function(x) nl.grad(x, fn) } else { .gr <- match.fun(gr) gr <- function(x) .gr(x, ...) } if (!is.null(hin)) { if ( getOption('nloptr.show.inequality.warning') ) { message('For consistency with the rest of the package the inequality sign may be switched from >= to <= in a future nloptr version.') } .hin <- match.fun(hin) hin <- function(x) (-1) * .hin(x) # change hin >= 0 to hin <= 0 ! if (is.null(hinjac)) { hinjac <- function(x) nl.jacobian(x, hin) } else { .hinjac <- match.fun(hinjac) hinjac <- function(x) (-1) * .hinjac(x) } } S0 <- nloptr(x0, eval_f = fn, eval_grad_f = gr, lb = lower, ub = upper, eval_g_ineq = hin, eval_jac_g_ineq = hinjac, opts = opts) if (nl.info) print(S0) S1 <- list(par = S0$solution, value = S0$objective, iter = S0$iterations, convergence = S0$status, message = S0$message) return(S1) } nloptr/R/onUnLoad.R0000644000176000001440000000012313262730360013661 0ustar ripleyusers.onUnload <- function (libpath) { library.dynam.unload("nloptr", libpath) } nloptr/vignettes/0000755000176000001440000000000013332372163013633 5ustar ripleyusersnloptr/vignettes/nloptr.Rmd0000644000176000001440000003513013150612565015620 0ustar ripleyusers--- title: "Introduction to `nloptr`: an R interface to NLopt^[This package should be considered in beta and comments about any aspect of the package are welcome. This document is an R vignette prepared with the aid of `knitr` [@Xie:2014; @Xie:2015; @Xie:2016]. Financial support of the UK Economic and Social Research Council through a grant (RES-589-28-0001) to the ESRC Centre for Microdata Methods and Practice (CeMMAP) is gratefully acknowledged.]" author: "Jelmer Ypma" date: "`r Sys.Date()`" output: rmarkdown::pdf_document bibliography: reflist.bib nocite: | @NLopt:website vignette: > %\VignetteIndexEntry{Introduction to nloptr: an R interface to NLopt} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setSweaveOptions,echo=FALSE} # have an (invisible) initialization noweb chunk # to remove the default continuation prompt '>' options(continue = " ") options(width = 60) # eliminate margin space above plots options(SweaveHooks=list(fig=function() par(mar=c(5.1, 4.1, 1.1, 2.1)))) ``` This document describes how to use `nloptr`, which is an R interface to NLopt. NLopt is a free/open-source library for nonlinear optimization started by Steven G. Johnson, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. The NLopt library is available under the GNU Lesser General Public License (LGPL), and the copyrights are owned by a variety of authors. # Introduction NLopt addresses general nonlinear optimization problems of the form: $$ \begin{aligned} &\min_{x \in R^n} f(x) \\ s.t.& g(x) \leq 0 \\ & h(x) = 0 \\ & x_L \leq x \leq x_U \end{aligned} $$ where $f(\cdot)$ is the objective function and $x$ represents the $n$ optimization parameters. This problem may optionally be subject to the bound constraints (also called box constraints), $x_L$ and $x_U$. For partially or totally unconstrained problems the bounds can take values $-\infty$ or $\infty$. One may also optionally have $m$ nonlinear inequality constraints (sometimes called a nonlinear programming problem), which can be specified in $g(\cdot)$, and equality constraints that can be specified in $h(\cdot)$. Note that not all of the algorithms in NLopt can handle constraints. This vignette describes how to formulate minimization problems to be solved with the R interface to NLopt. If you want to use the C interface directly or are interested in the Matlab interface, there are other sources of documentation avialable. Some of the information here has been taken from the [NLopt website](\texttt{http://ab-initio.mit.edu/nlopt), where more details are available. All credit for implementing the C code for the different algorithms available in NLopt should go to the respective authors. See the [website](http://ab-initio.mit.edu/wiki/index.php/Citing\_NLopt) for information on how to cite NLopt and the algorithms you use. # Installation This package is on CRAN and can be installed from within R using ```{r installNLopt, eval=FALSE} install.packages("nloptr") ``` You should now be able to load the R interface to NLopt and read the help. ```{r testNLoptInstallation, eval=FALSE} library('nloptr') ?nloptr ``` The most recent experimental version of `nloptr` can be installed from R-Forge using ```{r installNLoptRForge, eval=FALSE} install.packages("nloptr",repos="http://R-Forge.R-project.org") ``` or from source using ```{r installNLoptRForgeSource, eval=FALSE} install.packages("nloptr",type="source",repos="http://R-Forge.R-project.org") ``` # Minimizing the Rosenbrock Banana function As a first example we will solve an unconstrained minimization problem. The function we look at is the Rosenbrock Banana function $$ f( x ) = 100 \left( x_2 - x_1^2 \right)^2 + \left(1 - x_1 \right)^2, $$ which is also used as an example in the documentation for the standard R optimizer `optim`. The gradient of the objective function is given by $$ \nabla f( x ) = \left( \begin{array}[1]{c} -400 \cdot x_1 \cdot (x_2 - x_1^2) - 2 \cdot (1 - x_1) \\ 200 \cdot (x_2 - x_1^2) \end{array} \right). $$ Not all of the algorithms in NLopt need gradients to be supplied by the user. We will show examples with and without supplying the gradient. After loading the library ```{r loadLibrary} library(nloptr) ``` we start by specifying the objective function and its gradient ```{r defineRosenbrockBanana} ## Rosenbrock Banana function eval_f <- function(x) { return( 100 * (x[2] - x[1] * x[1])^2 + (1 - x[1])^2 ) } ## Gradient of Rosenbrock Banana function eval_grad_f <- function(x) { return( c( -400 * x[1] * (x[2] - x[1] * x[1]) - 2 * (1 - x[1]), 200 * (x[2] - x[1] * x[1]) ) ) } ``` We define initial values ```{r setRosenbrockBananaInitialValues} # initial values x0 <- c( -1.2, 1 ) ``` and then minimize the function using the `nloptr` command. This command runs some checks on the supplied inputs and returns an object with the exit code of the solver, the optimal value of the objective function and the solution. Before we can minimize the function we need to specify which algorithm we want to use ```{r setRosenbrockBananaOptions} opts <- list("algorithm"="NLOPT_LD_LBFGS", "xtol_rel"=1.0e-8) ``` Here we use the L-BFGS algorithm [@Nocedal:1980; @LiuNocedal:1989]. The characters `LD` in the algorithm show that this algorithm looks for local minima (`L`) using a derivative-based (`D`) algorithm. Other algorithms look for global (`G`) minima, or they don't need derivatives (`N`). We also specified the termination criterium in terms of the relative x-tolerance. Other termination criteria are available (see Appendix `\ref{sec:descoptions}` for a full list of options). We then solve the minimization problem using ```{r solveRosenbrockBanana} # solve Rosenbrock Banana function res <- nloptr( x0=x0, eval_f=eval_f, eval_grad_f=eval_grad_f, opts=opts) ``` We can see the results by printing the resulting object. ```{r printRosenbrockBanana} print( res ) ``` Sometimes the objective function and its gradient contain common terms. To economize on calculations, we can return the objective and its gradient in a list. For the Rosenbrock Banana function we have for instance ```{r defineRosenbrockBananaList} ## Rosenbrock Banana function and gradient in one function eval_f_list <- function(x) { common_term <- x[2] - x[1] * x[1] return( list( "objective" = 100 * common_term^2 + (1 - x[1])^2, "gradient" = c( -400 * x[1] * common_term - 2 * (1 - x[1]), 200 * common_term) ) ) } ``` which we minimize using ```{r solveRosenbrockBananaList} res <- nloptr( x0=x0, eval_f=eval_f_list, opts=opts) print( res ) ``` This gives the same results as before. # Minimization with inequality constraints This section shows how to minimize a function subject to inequality constraints. This example is the same as the one used in the tutorial on the NLopt website. The problem we want to solve is $$ \begin{aligned} &\min_{x \in R^n} \sqrt{x_2} \\ s.t.& x_2 \geq 0 \\ & x_2 \geq ( a_1 x_1 + b_1 )^3 \\ & x_2 \geq ( a_2 x_1 + b_2 )^3, \end{aligned} $$ where $a_1 = 2$, $b_1 = 0$, $a_2 = -1$, and $b_2 = 1$. In order to solve this problem, we first have to re-formulate the constraints to be of the form $g(x) \leq 0$. Note that the first constraint is a bound on $x_2$, which we will add later. The other two constraints can be re-written as $$ \begin{aligned} ( a_1 x_1 + b_1 )^3 - x_2 &\leq 0 \\ ( a_2 x_1 + b_2 )^3 - x_2 &\leq 0 \end{aligned} $$ First we define R functions to calculate the objective function and its gradient ```{r defineTutorialObjective} # objective function eval_f0 <- function( x, a, b ){ return( sqrt(x[2]) ) } # gradient of objective function eval_grad_f0 <- function( x, a, b ){ return( c( 0, .5/sqrt(x[2]) ) ) } ``` If needed, these can of course be calculated in the same function as before. Then we define the two constraints and the jacobian of the constraints ```{r defineTutorialConstraints} # constraint function eval_g0 <- function( x, a, b ) { return( (a*x[1] + b)^3 - x[2] ) } # jacobian of constraint eval_jac_g0 <- function( x, a, b ) { return( rbind( c( 3*a[1]*(a[1]*x[1] + b[1])^2, -1.0 ), c( 3*a[2]*(a[2]*x[1] + b[2])^2, -1.0 ) ) ) } ``` Note that all of the functions above depend on additional parameters, `a` and `b`. We have to supply specific values for these when we invoke the optimization command. The constraint function `eval_g0` returns a vector with in this case the same length as the vectors `a` and `b`. The function calculating the jacobian of the constraint should return a matrix where the number of rows equal the number of constraints (in this case two). The number of columns should equal the number of control variables (two in this case as well). After defining values for the parameters ```{r defineTutorialParameters} # define parameters a <- c(2,-1) b <- c(0, 1) ``` we can minimize the function subject to the constraints with the following command ```{r solveTutorialWithGradient} # Solve using NLOPT_LD_MMA with gradient information supplied in separate function res0 <- nloptr( x0=c(1.234,5.678), eval_f=eval_f0, eval_grad_f=eval_grad_f0, lb = c(-Inf,0), ub = c(Inf,Inf), eval_g_ineq = eval_g0, eval_jac_g_ineq = eval_jac_g0, opts = list("algorithm" = "NLOPT_LD_MMA", "xtol_rel"=1.0e-8, "print_level" = 2, "check_derivatives" = TRUE, "check_derivatives_print" = "all"), a = a, b = b ) print( res0 ) ``` Here we supplied lower bounds for $x_2$ in `lb`. There are no upper bounds for both control variables, so we supply `Inf` values. If we don't supply lower or upper bounds, plus or minus infinity is chosen by default. The inequality constraints and its jacobian are defined using `eval_g_ineq` and `eval_jac_g_ineq`. Not all algorithms can handle inequality constraints, so we have to specifiy one that does, `NLOPT_LD_MMA` [@Svanberg:2002]. We also specify the option `print_level` to obtain output during the optimization process. For the available `print_level` values, see `?nloptr`. Setting the `check_derivatives` option to `TRUE`, compares the gradients supplied by the user with a finite difference approximation in the initial point (`x0`). When this check is run, the option `check_derivatives_print` can be used to print all values of the derivative checker (`all` (default)), only those values that result in an error (`errors`) or no output (`none`), in which case only the number of errors is shown. The tolerance that determines if a difference between the analytic gradient and the finite difference approximation results in an error can be set using the option `check_derivatives_tol` (default = 1e-04). The first column shows the value of the analytic gradient, the second column shows the value of the finite difference approximation, and the third column shows the relative error. Stars are added at the front of a line if the relative error is larger than the specified tolerance. Finally, we add all the parameters that have to be passed on to the objective and constraint functions, `a` and `b`. We can also use a different algorithm to solve the same minimization problem. The only thing we have to change is the algorithm that we want to use, in this case `NLOPT_LN_COBYLA`, which is an algorithm that doesn't need gradient information [@Powell:1994; @Powell:1998]. ```{r solveTutorialWithoutGradient} # Solve using NLOPT_LN_COBYLA without gradient information res1 <- nloptr( x0=c(1.234,5.678), eval_f=eval_f0, lb = c(-Inf,0), ub = c(Inf,Inf), eval_g_ineq = eval_g0, opts = list("algorithm"="NLOPT_LN_COBYLA", "xtol_rel"=1.0e-8), a = a, b = b ) print( res1 ) ``` # Derivative checker The derivative checker can be called when supplying a minimization problem to `nloptr`, using the options `check_derivatives`, `check_derivatives_tol` and `check_derivatives_print`, but it can also be used separately. For example, define the function `g`, with vector outcome, and its gradient `g_grad` ```{r derivativeCheckerDefineFunctions} g <- function( x, a ) { return( c( x[1] - a[1], x[2] - a[2], (x[1] - a[1])^2, (x[2] - a[2])^2, (x[1] - a[1])^3, (x[2] - a[2])^3 ) ) } g_grad <- function( x, a ) { return( rbind( c( 1, 0 ), c( 0, 1 ), c( 2*(x[1] - a[1]), 0 ), c( 2*(x[1] - a[1]), 2*(x[2] - a[2]) ), c( 3*(x[1] - a[2])^2, 0 ), c( 0, 3*(x[2] - a[2])^2 ) ) ) } ``` `a` is some vector containing data. The gradient contains some errors in this case. By calling the function `check.derivatives` we can check the user-supplied analytic gradients with a finite difference approximation at a point `.x`. ```{r derivativeCheckerPrint} res <- check.derivatives( .x=c(1,2), func=g, func_grad=g_grad, check_derivatives_print='all', a=c(.3, .8) ) ``` The errors are shown on screen, where the option `check_derivatives_print` determines the amount of output you see. The value of the analytic gradient and the value of the finite difference approximation at the supplied point is returned in a list. ```{r derivativeCheckerResult} res ``` Note that not all errors will be picked up by the derivative checker. For instance, if we run the check with `a = c(.5, .5)`, one of the errors is not flagged as an error. # Notes The `.R` scripts in the `tests` directory contain more examples. For instance, `hs071.R` and `systemofeq.R` show how to solve problems with equality constraints. See the [NLopt website](http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms#Augmented_Lagrangian_algorithm) for more details. Please let me know if you're missing any of the features that are implemented in NLopt. Sometimes the optimization procedure terminates with a message `maxtime was reached` without evaluating the objective function. Submitting the same problem again usually solves this problem. # Description of options ```{r printAllOptions} nloptr::nloptr.print.options() ``` # References nloptr/vignettes/reflist.bib0000644000176000001440000000460012756645506015776 0ustar ripleyusers@MISC{NLopt:website, author = {Steven G. Johnson}, title = {The {N}{L}opt nonlinear-optimization package}, url = {http://ab-initio.mit.edu/nlopt} } @inproceedings{Powell:1994, author = {M. J. D. Powell}, title = {A direct search optimization method that models the objective and constraint functions by linear interpolation}, booktitle = {Advances in Optimization and Numerical Analysis}, editor = {S. Gomez and J.-P. Hennart}, publisher = {Kluwer Academic, Dordrecht}, pages = {51--67}, year = {1994} } @article{Powell:1998, author = {M. J. D. Powell}, title = {Direct search algorithms for optimization calculations}, journal = {Acta Numerica}, volume = {7}, pages = {287--336}, year = {1998} } @article{Svanberg:2002, author = {Krister Svanberg}, title = {A class of globally convergent optimization methods based on conservative convex separable approximations}, journal = {SIAM J. Optim.}, volume = {12}, number = {2}, pages = {555--573}, year = {2002} } @article{LiuNocedal:1989, author = {D. C. Liu and J. Nocedal}, title = {On the limited memory {B}{F}{G}{S} method for large scale optimization}, journal = {Math. Programming}, volume = {45}, pages = {503--528}, year = {1989} } @article{Nocedal:1980, author = {J. Nocedal}, title = {Updating quasi-{N}ewton matrices with limited storage}, journal = {Math. Comput.}, volume = {35}, pages = {773--782}, year = {1980} } @Manual{Xie:2016, title = {knitr: A General-Purpose Package for Dynamic Report Generation in R}, author = {Yihui Xie}, year = {2016}, note = {R package version 1.14}, url = {http://yihui.name/knitr/}, } @Book{Xie:2015, title = {Dynamic Documents with {R} and knitr}, author = {Yihui Xie}, publisher = {Chapman and Hall/CRC}, address = {Boca Raton, Florida}, year = {2015}, edition = {2nd}, note = {ISBN 978-1498716963}, url = {http://yihui.name/knitr/}, } @InCollection{Xie:2014, booktitle = {Implementing Reproducible Computational Research}, editor = {Victoria Stodden and Friedrich Leisch and Roger D. Peng}, title = {knitr: A Comprehensive Tool for Reproducible Research in {R}}, author = {Yihui Xie}, publisher = {Chapman and Hall/CRC}, year = {2014}, note = {ISBN 978-1466561595}, url = {http://www.crcpress.com/product/isbn/9781466561595}, } nloptr/README.md0000644000176000001440000001075613266723316013121 0ustar ripleyusers# nloptr [![Build Status](https://travis-ci.org/jyypma/nloptr.svg?branch=master)](https://travis-ci.org/jyypma/nloptr) `nloptr` is an R interface to [NLopt](http://ab-initio.mit.edu/wiki/index.php/NLopt). NLopt is a free/open-source library for nonlinear optimization started by Steven G. Johnson, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. It can be used to solve general nonlinear programming problems with nonlinear constraints and lower and upper bounds for the controls, such as ``` min f(x) x in R^n s.t. g(x) <= 0 h(x) = 0 lb <= x <= ub ``` The NLopt library is available under the GNU Lesser General Public License (LGPL), and the copyrights are owned by a variety of authors. See the [website](http://ab-initio.mit.edu/wiki/index.php/Citing_NLopt) for information on how to cite NLopt and the algorithms you use. The R interface to NLopt, also under LGPL, can be downloaded from [CRAN](https://CRAN.R-project.org/package=nloptr/index.html) or [GitHub](https://github.com/jyypma/nloptr) (development version). ## Installation For most versions of R `nloptr` can be installed from R with ``` install.packages('nloptr') ``` ### Development version The most recent (experimental) version can be installed from source from GitHub ``` library('devtools') install_github("jyypma/nloptr") ``` For this to work on Windows, you need to have Rtools and NLopt installed, and set an environment variable NLOPT_HOME with the location of the NLopt library. ## Disclaimer This package is distributed in the hope that it may be useful to some. The usual disclaimers apply (downloading and installing this software is at your own risk, and no support or guarantee is provided, I don't take liability and so on), but please let me know if you have any problems, suggestions, comments, etc. ## Files * [NLopt-2.4-win-build.zip](http://www.ucl.ac.uk/~uctpjyy/downloads/NLopt-2.4-win-build.zip) - static libraries of NLopt 2.4 compiled for Windows 32-bit and 64-bit. * [nloptr.pdf](https://CRAN.R-project.org/package=nloptr/vignettes/nloptr.pdf) - an R vignette describing how to use the R interface to NLopt. * [INSTALL.windows](https://github.com/jyypma/nloptr/blob/master/INSTALL.windows) - description of how to install `nloptr` from source for Windows. ## Changelog A full version of the changelog can be found on [CRAN](https://CRAN.R-project.org/package=nloptr/ChangeLog) | Date | Description | | :---------- | :----------- | | 27/01/2014 | Version 1.0.0 merged wrappers from the 'nloptwrap' package. | | 19/11/2013 | Version 0.9.6 Added a line in Makevars to replace some code in NLopt to fix compilation on Solaris as requested by Brian Ripley. | | 12/11/2013 | Version 0.9.5 Updated references from NLopt version 2.3 to NLopt version 2.4 in installation instructions. | in INSTALL.windows. Added a line in Makevars that replaces some code related to type-casting in NLopt-2.4/isres/isres.c. Changed encoding of src/nloptr.c from CP1252 to UTF-8. | | 09/11/2013 | Version 0.9.4 updated NLopt to version 2.4. | | 31/07/2013 | Version 0.9.3 was a maintainance release. | | 11/07/2013 | Version 0.9.2 was a maintainance release. | | 31/04/2013 | Version 0.9.0 has a new `print_level = 3`, and is compiled with NLopt version 2.3 with `--with-cxx option`. This makes the StoGo algorithm available. | | 18/11/2011 | Version 0.8.9 removed some warnings from R CMD check and included some changes to the build process. | | 28/09/2011 | Version 0.8.8 updated to compile on Solaris. | | 03/09/2011 | Version 0.8.5 includes a working binary for MacOS. | | 12/08/2011 | Version 0.8.4 includes a new function `nloptr.print.options`, and has new options (`print_options_doc`, and `population` and `ranseed` for stochastic global solvers). | | 24/07/2011 | Version 0.8.3 has a finite difference derivative checker and includes checks to prevent adding constraints to a problem when the chosen algortihm does not allow for constraints. | | 09/07/2011 | Version 0.8.2 is on CRAN with an updated build process and a newer version of NLopt. | | 13/01/2011 | Version 0.8.1 contains an option print_level to control intermediate output. | ## Reference Steven G. 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*vignettes/reflist.bib nloptr/build/0000755000176000001440000000000013332372163012722 5ustar ripleyusersnloptr/build/vignette.rds0000644000176000001440000000034013332372163015256 0ustar ripleyusersuQ 0!& tz9;}]BBArQg(JYkg5Nh%5\ %[L0G@ia ˩%L̵?x k "a5~[onTfGM}\/)IliUwh^\4DP~x^|nloptr/DESCRIPTION0000644000176000001440000000235113355073540013334 0ustar ripleyusersPackage: nloptr Type: Package Title: R Interface to NLopt Version: 1.2.1 Date: 2018-10-02 Author: Jelmer Ypma , with contributions by Hans W. Borchers and Dirk Eddelbuettel Maintainer: Jelmer Ypma Description: Solve optimization problems using an R interface to NLopt. NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. See for more information on the available algorithms. During installation of nloptr on Unix-based systems, the installer checks whether the NLopt library is installed on the system. If the NLopt library cannot be found, the code is compiled using the NLopt source included in the nloptr package. 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In this case, the evolution somewhat resembles a randomized Nelder-Mead algorithm. The published results for CRS seem to be largely empirical. } \note{ The initial population size for CRS defaults to \code{10x(n+1)} in \code{n} dimensions, but this can be changed; the initial population must be at least \code{n+1}. } \examples{ ### Minimize the Hartmann6 function hartmann6 <- function(x) { n <- length(x) a <- c(1.0, 1.2, 3.0, 3.2) A <- matrix(c(10.0, 0.05, 3.0, 17.0, 3.0, 10.0, 3.5, 8.0, 17.0, 17.0, 1.7, 0.05, 3.5, 0.1, 10.0, 10.0, 1.7, 8.0, 17.0, 0.1, 8.0, 14.0, 8.0, 14.0), nrow=4, ncol=6) B <- matrix(c(.1312,.2329,.2348,.4047, .1696,.4135,.1451,.8828, .5569,.8307,.3522,.8732, .0124,.3736,.2883,.5743, .8283,.1004,.3047,.1091, .5886,.9991,.6650,.0381), nrow=4, ncol=6) fun <- 0.0 for (i in 1:4) { fun <- fun - a[i] * exp(-sum(A[i,]*(x-B[i,])^2)) } return(fun) } S <- mlsl(x0 = rep(0, 6), hartmann6, lower = rep(0,6), upper = rep(1,6), nl.info = TRUE, control=list(xtol_rel=1e-8, maxeval=1000)) ## Number of Iterations....: 4050 ## Termination conditions: maxeval: 10000 xtol_rel: 1e-06 ## Number of inequality constraints: 0 ## Number of equality constraints: 0 ## Optimal value of objective function: -3.32236801141328 ## Optimal value of controls: ## 0.2016893 0.1500105 0.4768738 0.2753326 0.3116516 0.6573004 } \references{ W. L. Price, ``Global optimization by controlled random search,'' J. Optim. Theory Appl. 40 (3), p. 333-348 (1983). P. Kaelo and M. M. Ali, ``Some variants of the controlled random search algorithm for global optimization,'' J. Optim. Theory Appl. 130 (2), 253-264 (2006). } nloptr/man/varmetric.Rd0000644000176000001440000000442013266716212014664 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/varmetric.R \name{varmetric} \alias{varmetric} \title{Shifted Limited-memory Variable-metric} \usage{ varmetric(x0, fn, gr = NULL, rank2 = TRUE, lower = NULL, upper = NULL, nl.info = FALSE, control = list(), ...) } \arguments{ \item{x0}{initial point for searching the optimum.} \item{fn}{objective function to be minimized.} \item{gr}{gradient of function \code{fn}; will be calculated numerically if not specified.} \item{rank2}{logical; if true uses a rank-2 update method, else rank-1.} \item{lower, upper}{lower and upper bound constraints.} \item{nl.info}{logical; shall the original NLopt info been shown.} \item{control}{list of control parameters, see \code{nl.opts} for help.} \item{...}{further arguments to be passed to the function.} } \value{ List with components: \item{par}{the optimal solution found so far.} \item{value}{the function value corresponding to \code{par}.} \item{iter}{number of (outer) iterations, see \code{maxeval}.} \item{convergence}{integer code indicating successful completion (> 0) or a possible error number (< 0).} \item{message}{character string produced by NLopt and giving additional information.} } \description{ Shifted limited-memory variable-metric algorithm. } \details{ Variable-metric methods are a variant of the quasi-Newton methods, especially adapted to large-scale unconstrained (or bound constrained) minimization. } \note{ Based on L. Luksan's Fortran implementation of a shifted limited-memory variable-metric algorithm. } \examples{ flb <- function(x) { p <- length(x) sum(c(1, rep(4, p-1)) * (x - c(1, x[-p])^2)^2) } # 25-dimensional box constrained: par[24] is *not* at the boundary S <- varmetric(rep(3, 25), flb, lower=rep(2, 25), upper=rep(4, 25), nl.info = TRUE, control = list(xtol_rel=1e-8)) ## Optimal value of objective function: 368.105912874334 ## Optimal value of controls: 2 ... 2 2.109093 4 } \references{ J. Vlcek and L. Luksan, ``Shifted limited-memory variable metric methods for large-scale unconstrained minimization,'' J. Computational Appl. Math. 186, p. 365-390 (2006). } \seealso{ \code{\link{lbfgs}} } \author{ Hans W. Borchers } nloptr/man/check.derivatives.Rd0000644000176000001440000000470113266711050016266 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/check.derivatives.R \name{check.derivatives} \alias{check.derivatives} \title{Check analytic gradients of a function using finite difference approximations} \usage{ check.derivatives(.x, func, func_grad, check_derivatives_tol = 1e-04, check_derivatives_print = "all", func_grad_name = "grad_f", ...) } \arguments{ \item{.x}{point at which the comparison is done.} \item{func}{function to be evaluated.} \item{func_grad}{function calculating the analytic gradients.} \item{check_derivatives_tol}{option determining when differences between the analytic gradient and its finite difference approximation are flagged as an error.} \item{check_derivatives_print}{option related to the amount of output. 'all' means that all comparisons are shown, 'errors' only shows comparisons that are flagged as an error, and 'none' shows the number of errors only.} \item{func_grad_name}{option to change the name of the gradient function that shows up in the output.} \item{...}{further arguments passed to the functions func and func_grad.} } \value{ The return value contains a list with the analytic gradient, its finite difference approximation, the relative errors, and vector comparing the relative errors to the tolerance. } \description{ This function compares the analytic gradients of a function with a finite difference approximation and prints the results of these checks. } \examples{ library('nloptr') # example with correct gradient f <- function( x, a ) { return( sum( ( x - a )^2 ) ) } f_grad <- function( x, a ) { return( 2*( x - a ) ) } check.derivatives( .x=1:10, func=f, func_grad=f_grad, check_derivatives_print='none', a=runif(10) ) # example with incorrect gradient f_grad <- function( x, a ) { return( 2*( x - a ) + c(0,.1,rep(0,8)) ) } check.derivatives( .x=1:10, func=f, func_grad=f_grad, check_derivatives_print='errors', a=runif(10) ) # example with incorrect gradient of vector-valued function g <- function( x, a ) { return( c( sum(x-a), sum( (x-a)^2 ) ) ) } g_grad <- function( x, a ) { return( rbind( rep(1,length(x)) + c(0,.01,rep(0,8)), 2*(x-a) + c(0,.1,rep(0,8)) ) ) } check.derivatives( .x=1:10, func=g, func_grad=g_grad, check_derivatives_print='all', a=runif(10) ) } \seealso{ \code{\link[nloptr:nloptr]{nloptr}} } \author{ Jelmer Ypma } \keyword{interface} \keyword{optimize} nloptr/man/cobyla.Rd0000644000176000001440000000572113266716212014146 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/cobyla.R \name{cobyla} \alias{cobyla} \title{Constrained Optimization by Linear Approximations} \usage{ cobyla(x0, fn, lower = NULL, upper = NULL, hin = NULL, nl.info = FALSE, control = list(), ...) } \arguments{ \item{x0}{starting point for searching the optimum.} \item{fn}{objective function that is to be minimized.} \item{lower, upper}{lower and upper bound constraints.} \item{hin}{function defining the inequality constraints, that is \code{hin>=0} for all components.} \item{nl.info}{logical; shall the original NLopt info been shown.} \item{control}{list of options, see \code{nl.opts} for help.} \item{...}{additional arguments passed to the function.} } \value{ List with components: \item{par}{the optimal solution found so far.} \item{value}{the function value corresponding to \code{par}.} \item{iter}{number of (outer) iterations, see \code{maxeval}.} \item{convergence}{integer code indicating successful completion (> 0) or a possible error number (< 0).} \item{message}{character string produced by NLopt and giving additional information.} } \description{ COBYLA is an algorithm for derivative-free optimization with nonlinear inequality and equality constraints (but see below). } \details{ It constructs successive linear approximations of the objective function and constraints via a simplex of n+1 points (in n dimensions), and optimizes these approximations in a trust region at each step. COBYLA supports equality constraints by transforming them into two inequality constraints. As this does not give full satisfaction with the implementation in NLOPT, it has not been made available here. } \note{ The original code, written in Fortran by Powell, was converted in C for the Scipy project. } \examples{ ### Solve Hock-Schittkowski no. 100 x0.hs100 <- c(1, 2, 0, 4, 0, 1, 1) fn.hs100 <- function(x) { (x[1]-10)^2 + 5*(x[2]-12)^2 + x[3]^4 + 3*(x[4]-11)^2 + 10*x[5]^6 + 7*x[6]^2 + x[7]^4 - 4*x[6]*x[7] - 10*x[6] - 8*x[7] } hin.hs100 <- function(x) { h <- numeric(4) h[1] <- 127 - 2*x[1]^2 - 3*x[2]^4 - x[3] - 4*x[4]^2 - 5*x[5] h[2] <- 282 - 7*x[1] - 3*x[2] - 10*x[3]^2 - x[4] + x[5] h[3] <- 196 - 23*x[1] - x[2]^2 - 6*x[6]^2 + 8*x[7] h[4] <- -4*x[1]^2 - x[2]^2 + 3*x[1]*x[2] -2*x[3]^2 - 5*x[6] +11*x[7] return(h) } S <- cobyla(x0.hs100, fn.hs100, hin = hin.hs100, nl.info = TRUE, control = list(xtol_rel = 1e-8, maxeval = 2000)) ## Optimal value of objective function: 680.630057374431 } \references{ M. J. D. Powell, ``A direct search optimization method that models the objective and constraint functions by linear interpolation,'' in Advances in Optimization and Numerical Analysis, eds. S. Gomez and J.-P. Hennart (Kluwer Academic: Dordrecht, 1994), p. 51-67. } \seealso{ \code{\link{bobyqa}}, \code{\link{newuoa}} } \author{ Hans W. Borchers } nloptr/man/sbplx.Rd0000644000176000001440000000436213266717646014041 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nm.R \name{sbplx} \alias{sbplx} \title{Subplex Algorithm} \usage{ sbplx(x0, fn, lower = NULL, upper = NULL, nl.info = FALSE, control = list(), ...) } \arguments{ \item{x0}{starting point for searching the optimum.} \item{fn}{objective function that is to be minimized.} \item{lower, upper}{lower and upper bound constraints.} \item{nl.info}{logical; shall the original NLopt info been shown.} \item{control}{list of options, see \code{nl.opts} for help.} \item{...}{additional arguments passed to the function.} } \value{ List with components: \item{par}{the optimal solution found so far.} \item{value}{the function value corresponding to \code{par}.} \item{iter}{number of (outer) iterations, see \code{maxeval}.} \item{convergence}{integer code indicating successful completion (> 0) or a possible error number (< 0).} \item{message}{character string produced by NLopt and giving additional information.} } \description{ Subplex is a variant of Nelder-Mead that uses Nelder-Mead on a sequence of subspaces. } \details{ SUBPLEX is claimed to be much more efficient and robust than the original Nelder-Mead, while retaining the latter's facility with discontinuous objectives. This implementation has explicit support for bound constraints (via the method in the Box paper as described on the \code{neldermead} help page). } \note{ It is the request of Tom Rowan that reimplementations of his algorithm shall not use the name `subplex'. } \examples{ # Fletcher and Powell's helic valley fphv <- function(x) 100*(x[3] - 10*atan2(x[2], x[1])/(2*pi))^2 + (sqrt(x[1]^2 + x[2]^2) - 1)^2 +x[3]^2 x0 <- c(-1, 0, 0) sbplx(x0, fphv) # 1 0 0 # Powell's Singular Function (PSF) psf <- function(x) (x[1] + 10*x[2])^2 + 5*(x[3] - x[4])^2 + (x[2] - 2*x[3])^4 + 10*(x[1] - x[4])^4 x0 <- c(3, -1, 0, 1) sbplx(x0, psf, control = list(maxeval = Inf, ftol_rel = 1e-6)) # 0 0 0 0 (?) } \references{ T. Rowan, ``Functional Stability Analysis of Numerical Algorithms'', Ph.D. thesis, Department of Computer Sciences, University of Texas at Austin, 1990. } \seealso{ \code{subplex::subplex} } nloptr/man/nloptr.print.options.Rd0000644000176000001440000000237713266711050017037 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nloptr.print.options.R \name{nloptr.print.options} \alias{nloptr.print.options} \title{Print description of nloptr options} \usage{ nloptr.print.options(opts.show = NULL, opts.user = NULL) } \arguments{ \item{opts.show}{list or vector with names of options. A description will be shown for the options in this list. By default, a description of all options is shown.} \item{opts.user}{object containing user supplied options. This argument is optional. It is used when \code{nloptr.print.options} is called from \code{nloptr}. In that case options are listed if \code{print_options_doc} is set to \code{TRUE} when passing a minimization problem to \code{nloptr}.} } \description{ This function prints a list of all the options that can be set when solving a minimization problem using \code{nloptr}. } \examples{ library('nloptr') nloptr.print.options() nloptr.print.options( opts.show = c("algorithm", "check_derivatives") ) opts <- list("algorithm"="NLOPT_LD_LBFGS", "xtol_rel"=1.0e-8) nloptr.print.options( opts.user = opts ) } \seealso{ \code{\link[nloptr:nloptr]{nloptr}} } \author{ Jelmer Ypma } \keyword{interface} \keyword{optimize} nloptr/man/isres.Rd0000644000176000001440000000552113266716212014020 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/global.R \name{isres} \alias{isres} \title{Improved Stochastic Ranking Evolution Strategy} \usage{ isres(x0, fn, lower, upper, hin = NULL, heq = NULL, maxeval = 10000, pop.size = 20 * (length(x0) + 1), xtol_rel = 1e-06, nl.info = FALSE, ...) } \arguments{ \item{x0}{initial point for searching the optimum.} \item{fn}{objective function that is to be minimized.} \item{lower, upper}{lower and upper bound constraints.} \item{hin}{function defining the inequality constraints, that is \code{hin>=0} for all components.} \item{heq}{function defining the equality constraints, that is \code{heq==0} for all components.} \item{maxeval}{maximum number of function evaluations.} \item{pop.size}{population size.} \item{xtol_rel}{stopping criterion for relative change reached.} \item{nl.info}{logical; shall the original NLopt info been shown.} \item{...}{additional arguments passed to the function.} } \value{ List with components: \item{par}{the optimal solution found so far.} \item{value}{the function value corresponding to \code{par}.} \item{iter}{number of (outer) iterations, see \code{maxeval}.} \item{convergence}{integer code indicating successful completion (> 0) or a possible error number (< 0).} \item{message}{character string produced by NLopt and giving additional information.} } \description{ The Improved Stochastic Ranking Evolution Strategy (ISRES) algorithm for nonlinearly constrained global optimization (or at least semi-global: although it has heuristics to escape local optima. } \details{ The evolution strategy is based on a combination of a mutation rule (with a log-normal step-size update and exponential smoothing) and differential variation (a Nelder-Mead-like update rule). The fitness ranking is simply via the objective function for problems without nonlinear constraints, but when nonlinear constraints are included the stochastic ranking proposed by Runarsson and Yao is employed. This method supports arbitrary nonlinear inequality and equality constraints in addition to the bound constraints. } \note{ The initial population size for CRS defaults to \code{20x(n+1)} in \code{n} dimensions, but this can be changed; the initial population must be at least \code{n+1}. } \examples{ ### Rosenbrock Banana objective function fn <- function(x) return( 100 * (x[2] - x[1] * x[1])^2 + (1 - x[1])^2 ) x0 <- c( -1.2, 1 ) lb <- c( -3, -3 ) ub <- c( 3, 3 ) isres(x0 = x0, fn = fn, lower = lb, upper = ub) } \references{ Thomas Philip Runarsson and Xin Yao, ``Search biases in constrained evolutionary optimization,'' IEEE Trans. on Systems, Man, and Cybernetics Part C: Applications and Reviews, vol. 35 (no. 2), pp. 233-243 (2005). } \author{ Hans W. Borchers } nloptr/man/auglag.Rd0000644000176000001440000001256613266716212014142 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/auglag.R \name{auglag} \alias{auglag} \title{Augmented Lagrangian Algorithm} \usage{ auglag(x0, fn, gr = NULL, lower = NULL, upper = NULL, hin = NULL, hinjac = NULL, heq = NULL, heqjac = NULL, localsolver = c("COBYLA"), localtol = 1e-06, ineq2local = FALSE, nl.info = FALSE, control = list(), ...) } \arguments{ \item{x0}{starting point for searching the optimum.} \item{fn}{objective function that is to be minimized.} \item{gr}{gradient of the objective function; will be provided provided is \code{NULL} and the solver requires derivatives.} \item{lower, upper}{lower and upper bound constraints.} \item{hin, hinjac}{defines the inequalty constraints, \code{hin(x) >= 0}} \item{heq, heqjac}{defines the equality constraints, \code{heq(x) = 0}.} \item{localsolver}{available local solvers: COBYLA, LBFGS, MMA, or SLSQP.} \item{localtol}{tolerance applied in the selected local solver.} \item{ineq2local}{logical; shall the inequality constraints be treated by the local solver?; not possible at the moment.} \item{nl.info}{logical; shall the original NLopt info been shown.} \item{control}{list of options, see \code{nl.opts} for help.} \item{...}{additional arguments passed to the function.} } \value{ List with components: \item{par}{the optimal solution found so far.} \item{value}{the function value corresponding to \code{par}.} \item{iter}{number of (outer) iterations, see \code{maxeval}.} \item{global_solver}{the global NLOPT solver used.} \item{local_solver}{the local NLOPT solver used, LBFGS or COBYLA.} \item{convergence}{integer code indicating successful completion (> 0) or a possible error number (< 0).} \item{message}{character string produced by NLopt and giving additional information.} } \description{ The Augmented Lagrangian method adds additional terms to the unconstrained objective function, designed to emulate a Lagrangian multiplier. } \details{ This method combines the objective function and the nonlinear inequality/equality constraints (if any) in to a single function: essentially, the objective plus a `penalty' for any violated constraints. This modified objective function is then passed to another optimization algorithm with no nonlinear constraints. If the constraints are violated by the solution of this sub-problem, then the size of the penalties is increased and the process is repeated; eventually, the process must converge to the desired solution (if it exists). Since all of the actual optimization is performed in this subsidiary optimizer, the subsidiary algorithm that you specify determines whether the optimization is gradient-based or derivative-free. The local solvers available at the moment are ``COBYLA'' (for the derivative-free approach) and ``LBFGS'', ``MMA'', or ``SLSQP'' (for smooth functions). The tolerance for the local solver has to be provided. There is a variant that only uses penalty functions for equality constraints while inequality constraints are passed through to the subsidiary algorithm to be handled directly; in this case, the subsidiary algorithm must handle inequality constraints. (At the moment, this variant has been turned off because of problems with the NLOPT library.) } \note{ Birgin and Martinez provide their own free implementation of the method as part of the TANGO project; other implementations can be found in semi-free packages like LANCELOT. } \examples{ x0 <- c(1, 1) fn <- function(x) (x[1]-2)^2 + (x[2]-1)^2 hin <- function(x) -0.25*x[1]^2 - x[2]^2 + 1 # hin >= 0 heq <- function(x) x[1] - 2*x[2] + 1 # heq == 0 gr <- function(x) nl.grad(x, fn) hinjac <- function(x) nl.jacobian(x, hin) heqjac <- function(x) nl.jacobian(x, heq) auglag(x0, fn, gr = NULL, hin = hin, heq = heq) # with COBYLA # $par: 0.8228761 0.9114382 # $value: 1.393464 # $iter: 1001 auglag(x0, fn, gr = NULL, hin = hin, heq = heq, localsolver = "SLSQP") # $par: 0.8228757 0.9114378 # $value: 1.393465 # $iter 173 ## Example from the alabama::auglag help page fn <- function(x) (x[1] + 3*x[2] + x[3])^2 + 4 * (x[1] - x[2])^2 heq <- function(x) x[1] + x[2] + x[3] - 1 hin <- function(x) c(6*x[2] + 4*x[3] - x[1]^3 - 3, x[1], x[2], x[3]) auglag(runif(3), fn, hin = hin, heq = heq, localsolver="lbfgs") # $par: 2.380000e-09 1.086082e-14 1.000000e+00 # $value: 1 # $iter: 289 ## Powell problem from the Rsolnp::solnp help page x0 <- c(-2, 2, 2, -1, -1) fn1 <- function(x) exp(x[1]*x[2]*x[3]*x[4]*x[5]) eqn1 <-function(x) c(x[1]*x[1]+x[2]*x[2]+x[3]*x[3]+x[4]*x[4]+x[5]*x[5], x[2]*x[3]-5*x[4]*x[5], x[1]*x[1]*x[1]+x[2]*x[2]*x[2]) auglag(x0, fn1, heq = eqn1, localsolver = "mma") # $par: -3.988458e-10 -1.654201e-08 -3.752028e-10 8.904445e-10 8.926336e-10 # $value: 1 # $iter: 1001 } \references{ Andrew R. Conn, Nicholas I. M. Gould, and Philippe L. Toint, ``A globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds,'' SIAM J. Numer. Anal. vol. 28, no. 2, p. 545-572 (1991). E. G. Birgin and J. M. Martinez, ``Improving ultimate convergence of an augmented Lagrangian method," Optimization Methods and Software vol. 23, no. 2, p. 177-195 (2008). } \seealso{ \code{alabama::auglag}, \code{Rsolnp::solnp} } \author{ Hans W. Borchers } nloptr/man/nloptr.get.default.options.Rd0000644000176000001440000000254213266711050020077 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nloptr.get.default.options.R \name{nloptr.get.default.options} \alias{nloptr.get.default.options} \title{Return a data.frame with all the options that can be supplied to nloptr.} \usage{ nloptr.get.default.options() } \value{ The return value contains a \code{data.frame} with the following elements \item{name}{name of the option} \item{type}{type (numeric, logical, integer, character)} \item{possible_values}{string explaining the values the option can take} \item{default}{default value of the option (as a string)} \item{is_termination_condition}{is this option part of the termination conditions?} \item{description}{description of the option (taken from NLopt website if it's an option that is passed on to NLopt).} } \description{ This function returns a data.frame with all the options that can be supplied to \code{\link[nloptr:nloptr]{nloptr}}. The data.frame contains the default values of the options and an explanation. A user-friendly way to show these options is by using the function \code{\link[nloptr:nloptr.print.options]{nloptr.print.options}}. } \seealso{ \code{\link[nloptr:nloptr]{nloptr}} \code{\link[nloptr:nloptr.print.options]{nloptr.print.options}} } \author{ Jelmer Ypma } \keyword{interface} \keyword{optimize} nloptr/man/nl.grad.Rd0000644000176000001440000000227513266711050014216 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gradients.R \name{nl.grad} \alias{nl.grad} \alias{nl.jacobian} \title{Numerical Gradients and Jacobians} \usage{ nl.grad(x0, fn, heps = .Machine$double.eps^(1/3), ...) } \arguments{ \item{x0}{point as a vector where the gradient is to be calculated.} \item{fn}{scalar function of one or several variables.} \item{heps}{step size to be used.} \item{\dots}{additional arguments passed to the function.} } \value{ \code{grad} returns the gradient as a vector; \code{jacobian} returns the Jacobian as a matrix of usual dimensions. } \description{ Provides numerical gradients and jacobians. } \details{ Both functions apply the ``central difference formula'' with step size as recommended in the literature. } \examples{ fn1 <- function(x) sum(x^2) nl.grad(seq(0, 1, by = 0.2), fn1) ## [1] 0.0 0.4 0.8 1.2 1.6 2.0 nl.grad(rep(1, 5), fn1) ## [1] 2 2 2 2 2 fn2 <- function(x) c(sin(x), cos(x)) x <- (0:1)*2*pi nl.jacobian(x, fn2) ## [,1] [,2] ## [1,] 1 0 ## [2,] 0 1 ## [3,] 0 0 ## [4,] 0 0 } \author{ Hans W. Borchers } nloptr/man/bobyqa.Rd0000644000176000001440000000400113266716212014140 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/cobyla.R \name{bobyqa} \alias{bobyqa} \title{Bound Optimization by Quadratic Approximation} \usage{ bobyqa(x0, fn, lower = NULL, upper = NULL, nl.info = FALSE, control = list(), ...) } \arguments{ \item{x0}{starting point for searching the optimum.} \item{fn}{objective function that is to be minimized.} \item{lower, upper}{lower and upper bound constraints.} \item{nl.info}{logical; shall the original NLopt info been shown.} \item{control}{list of options, see \code{nl.opts} for help.} \item{...}{additional arguments passed to the function.} } \value{ List with components: \item{par}{the optimal solution found so far.} \item{value}{the function value corresponding to \code{par}.} \item{iter}{number of (outer) iterations, see \code{maxeval}.} \item{convergence}{integer code indicating successful completion (> 0) or a possible error number (< 0).} \item{message}{character string produced by NLopt and giving additional information.} } \description{ BOBYQA performs derivative-free bound-constrained optimization using an iteratively constructed quadratic approximation for the objective function. } \details{ This is an algorithm derived from the BOBYQA Fortran subroutine of Powell, converted to C and modified for the NLOPT stopping criteria. } \note{ Because BOBYQA constructs a quadratic approximation of the objective, it may perform poorly for objective functions that are not twice-differentiable. } \examples{ fr <- function(x) { ## Rosenbrock Banana function 100 * (x[2] - x[1]^2)^2 + (1 - x[1])^2 } (S <- bobyqa(c(0, 0, 0), fr, lower = c(0, 0, 0), upper = c(0.5, 0.5, 0.5))) } \references{ M. J. D. Powell. ``The BOBYQA algorithm for bound constrained optimization without derivatives,'' Department of Applied Mathematics and Theoretical Physics, Cambridge England, technical reportNA2009/06 (2009). } \seealso{ \code{\link{cobyla}}, \code{\link{newuoa}} } nloptr/man/newuoa.Rd0000644000176000001440000000362713266717646014212 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/cobyla.R \name{newuoa} \alias{newuoa} \title{New Unconstrained Optimization with quadratic Approximation} \usage{ newuoa(x0, fn, nl.info = FALSE, control = list(), ...) } \arguments{ \item{x0}{starting point for searching the optimum.} \item{fn}{objective function that is to be minimized.} \item{nl.info}{logical; shall the original NLopt info been shown.} \item{control}{list of options, see \code{nl.opts} for help.} \item{...}{additional arguments passed to the function.} } \value{ List with components: \item{par}{the optimal solution found so far.} \item{value}{the function value corresponding to \code{par}.} \item{iter}{number of (outer) iterations, see \code{maxeval}.} \item{convergence}{integer code indicating successful completion (> 0) or a possible error number (< 0).} \item{message}{character string produced by NLopt and giving additional information.} } \description{ NEWUOA solves quadratic subproblems in a spherical trust regionvia a truncated conjugate-gradient algorithm. For bound-constrained problems, BOBYQA shold be used instead, as Powell developed it as an enhancement thereof for bound constraints. } \details{ This is an algorithm derived from the NEWUOA Fortran subroutine of Powell, converted to C and modified for the NLOPT stopping criteria. } \note{ NEWUOA may be largely superseded by BOBYQA. } \examples{ fr <- function(x) { ## Rosenbrock Banana function 100 * (x[2] - x[1]^2)^2 + (1 - x[1])^2 } (S <- newuoa(c(1, 2), fr)) } \references{ M. J. D. Powell. ``The BOBYQA algorithm for bound constrained optimization without derivatives,'' Department of Applied Mathematics and Theoretical Physics, Cambridge England, technical reportNA2009/06 (2009). } \seealso{ \code{\link{bobyqa}}, \code{\link{cobyla}} } \author{ Hans W. Borchers } nloptr/man/mlsl.Rd0000644000176000001440000001021113266716212013632 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mlsl.R \name{mlsl} \alias{mlsl} \title{Multi-level Single-linkage} \usage{ mlsl(x0, fn, gr = NULL, lower, upper, local.method = "LBFGS", low.discrepancy = TRUE, nl.info = FALSE, control = list(), ...) } \arguments{ \item{x0}{initial point for searching the optimum.} \item{fn}{objective function that is to be minimized.} \item{gr}{gradient of function \code{fn}; will be calculated numerically if not specified.} \item{lower, upper}{lower and upper bound constraints.} \item{local.method}{only \code{BFGS} for the moment.} \item{low.discrepancy}{logical; shall a low discrepancy variation be used.} \item{nl.info}{logical; shall the original NLopt info been shown.} \item{control}{list of options, see \code{nl.opts} for help.} \item{...}{additional arguments passed to the function.} } \value{ List with components: \item{par}{the optimal solution found so far.} \item{value}{the function value corresponding to \code{par}.} \item{iter}{number of (outer) iterations, see \code{maxeval}.} \item{convergence}{integer code indicating successful completion (> 0) or a possible error number (< 0).} \item{message}{character string produced by NLopt and giving additional information.} } \description{ The ``Multi-Level Single-Linkage'' (MLSL) algorithm for global optimization searches by a sequence of local optimizations from random starting points. A modification of MLSL is included using a low-discrepancy sequence (LDS) instead of pseudorandom numbers. } \details{ MLSL is a `multistart' algorithm: it works by doing a sequence of local optimizations (using some other local optimization algorithm) from random or low-discrepancy starting points. MLSL is distinguished, however by a `clustering' heuristic that helps it to avoid repeated searches of the same local optima, and has some theoretical guarantees of finding all local optima in a finite number of local minimizations. The local-search portion of MLSL can use any of the other algorithms in NLopt, and in particular can use either gradient-based or derivative-free algorithms. For this wrapper only gradient-based \code{L-BFGS} is available as local method. } \note{ If you don't set a stopping tolerance for your local-optimization algorithm, MLSL defaults to \code{ftol_rel=1e-15} and \code{xtol_rel=1e-7} for the local searches. } \examples{ ### Minimize the Hartmann6 function hartmann6 <- function(x) { n <- length(x) a <- c(1.0, 1.2, 3.0, 3.2) A <- matrix(c(10.0, 0.05, 3.0, 17.0, 3.0, 10.0, 3.5, 8.0, 17.0, 17.0, 1.7, 0.05, 3.5, 0.1, 10.0, 10.0, 1.7, 8.0, 17.0, 0.1, 8.0, 14.0, 8.0, 14.0), nrow=4, ncol=6) B <- matrix(c(.1312,.2329,.2348,.4047, .1696,.4135,.1451,.8828, .5569,.8307,.3522,.8732, .0124,.3736,.2883,.5743, .8283,.1004,.3047,.1091, .5886,.9991,.6650,.0381), nrow=4, ncol=6) fun <- 0.0 for (i in 1:4) { fun <- fun - a[i] * exp(-sum(A[i,]*(x-B[i,])^2)) } return(fun) } S <- mlsl(x0 = rep(0, 6), hartmann6, lower = rep(0,6), upper = rep(1,6), nl.info = TRUE, control=list(xtol_rel=1e-8, maxeval=1000)) ## Number of Iterations....: 1000 ## Termination conditions: ## stopval: -Inf, xtol_rel: 1e-08, maxeval: 1000, ftol_rel: 0, ftol_abs: 0 ## Number of inequality constraints: 0 ## Number of equality constraints: 0 ## Current value of objective function: -3.32236801141552 ## Current value of controls: ## 0.2016895 0.1500107 0.476874 0.2753324 0.3116516 0.6573005 } \references{ A. H. G. Rinnooy Kan and G. T. Timmer, ``Stochastic global optimization methods'' Mathematical Programming, vol. 39, p. 27-78 (1987). Sergei Kucherenko and Yury Sytsko, ``Application of deterministic low-discrepancy sequences in global optimization,'' Computational Optimization and Applications, vol. 30, p. 297-318 (2005). } \seealso{ \code{\link{direct}} } \author{ Hans W. Borchers } nloptr/man/nloptr-package.Rd0000644000176000001440000001131713266716212015602 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nloptr-package.R \docType{package} \name{nloptr-package} \alias{nloptr-package} \title{R interface to NLopt} \description{ nloptr is an R interface to NLopt, a free/open-source library for nonlinear optimization started by Steven G. Johnson, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. The NLopt library is available under the GNU Lesser General Public License (LGPL), and the copyrights are owned by a variety of authors. Most of the information here has been taken from \href{http://ab-initio.mit.edu/nlopt}{the NLopt website}, where more details are available. } \details{ NLopt addresses general nonlinear optimization problems of the form: min f(x) x in R^n s.t. g(x) <= 0 h(x) = 0 lb <= x <= ub where f is the objective function to be minimized and x represents the n optimization parameters. This problem may optionally be subject to the bound constraints (also called box constraints), lb and ub. For partially or totally unconstrained problems the bounds can take -Inf or Inf. One may also optionally have m nonlinear inequality constraints (sometimes called a nonlinear programming problem), which can be specified in g(x), and equality constraints that can be specified in h(x). Note that not all of the algorithms in NLopt can handle constraints. An optimization problem can be solved with the general nloptr interface, or using one of the wrapper functions for the separate algorithms; auglag, bobyqa, cobyla, crs2lm, direct, lbfgs, mlsl, mma, neldermead, newuoa, sbplx, slsqp, stogo, tnewton, varmetric. \tabular{ll}{ Package: \tab nloptr\cr Type: \tab Package\cr Version: \tab 0.9.9\cr Date: \tab 2013-11-22\cr License: \tab L-GPL\cr LazyLoad: \tab yes\cr } } \note{ See ?nloptr for more examples. } \examples{ # Example problem, number 71 from the Hock-Schittkowsky test suite. # # \\min_{x} x1*x4*(x1 + x2 + x3) + x3 # s.t. # x1*x2*x3*x4 >= 25 # x1^2 + x2^2 + x3^2 + x4^2 = 40 # 1 <= x1,x2,x3,x4 <= 5 # # we re-write the inequality as # 25 - x1*x2*x3*x4 <= 0 # # and the equality as # x1^2 + x2^2 + x3^2 + x4^2 - 40 = 0 # # x0 = (1,5,5,1) # # optimal solution = (1.00000000, 4.74299963, 3.82114998, 1.37940829) library('nloptr') # # f(x) = x1*x4*(x1 + x2 + x3) + x3 # eval_f <- function( x ) { return( list( "objective" = x[1]*x[4]*(x[1] + x[2] + x[3]) + x[3], "gradient" = c( x[1] * x[4] + x[4] * (x[1] + x[2] + x[3]), x[1] * x[4], x[1] * x[4] + 1.0, x[1] * (x[1] + x[2] + x[3]) ) ) ) } # constraint functions # inequalities eval_g_ineq <- function( x ) { constr <- c( 25 - x[1] * x[2] * x[3] * x[4] ) grad <- c( -x[2]*x[3]*x[4], -x[1]*x[3]*x[4], -x[1]*x[2]*x[4], -x[1]*x[2]*x[3] ) return( list( "constraints"=constr, "jacobian"=grad ) ) } # equalities eval_g_eq <- function( x ) { constr <- c( x[1]^2 + x[2]^2 + x[3]^2 + x[4]^2 - 40 ) grad <- c( 2.0*x[1], 2.0*x[2], 2.0*x[3], 2.0*x[4] ) return( list( "constraints"=constr, "jacobian"=grad ) ) } # initial values x0 <- c( 1, 5, 5, 1 ) # lower and upper bounds of control lb <- c( 1, 1, 1, 1 ) ub <- c( 5, 5, 5, 5 ) local_opts <- list( "algorithm" = "NLOPT_LD_MMA", "xtol_rel" = 1.0e-7 ) opts <- list( "algorithm" = "NLOPT_LD_AUGLAG", "xtol_rel" = 1.0e-7, "maxeval" = 1000, "local_opts" = local_opts ) res <- nloptr( x0=x0, eval_f=eval_f, lb=lb, ub=ub, eval_g_ineq=eval_g_ineq, eval_g_eq=eval_g_eq, opts=opts) print( res ) } \references{ Steven G. Johnson, The NLopt nonlinear-optimization package, \url{http://ab-initio.mit.edu/nlopt} } \seealso{ \code{\link{optim}} \code{\link{nlm}} \code{\link{nlminb}} \code{Rsolnp::Rsolnp} \code{Rsolnp::solnp} \code{\link{nloptr}} \code{\link{auglag}} \code{\link{bobyqa}} \code{\link{cobyla}} \code{\link{crs2lm}} \code{\link{direct}} \code{\link{isres}} \code{\link{lbfgs}} \code{\link{mlsl}} \code{\link{mma}} \code{\link{neldermead}} \code{\link{newuoa}} \code{\link{sbplx}} \code{\link{slsqp}} \code{\link{stogo}} \code{\link{tnewton}} \code{\link{varmetric}} } \author{ Steven G. Johnson and others (C code) \cr Jelmer Ypma (R interface) \cr Hans W. Borchers (wrappers) } \keyword{interface} \keyword{optimize} nloptr/man/tnewton.Rd0000644000176000001440000000463113266716212014372 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tnewton.R \name{tnewton} \alias{tnewton} \title{Preconditioned Truncated Newton} \usage{ tnewton(x0, fn, gr = NULL, lower = NULL, upper = NULL, precond = TRUE, restart = TRUE, nl.info = FALSE, control = list(), ...) } \arguments{ \item{x0}{starting point for searching the optimum.} \item{fn}{objective function that is to be minimized.} \item{gr}{gradient of function \code{fn}; will be calculated numerically if not specified.} \item{lower, upper}{lower and upper bound constraints.} \item{precond}{logical; preset L-BFGS with steepest descent.} \item{restart}{logical; restarting L-BFGS with steepest descent.} \item{nl.info}{logical; shall the original NLopt info been shown.} \item{control}{list of options, see \code{nl.opts} for help.} \item{...}{additional arguments passed to the function.} } \value{ List with components: \item{par}{the optimal solution found so far.} \item{value}{the function value corresponding to \code{par}.} \item{iter}{number of (outer) iterations, see \code{maxeval}.} \item{convergence}{integer code indicating successful completion (> 1) or a possible error number (< 0).} \item{message}{character string produced by NLopt and giving additional information.} } \description{ Truncated Newton methods, also calledNewton-iterative methods, solve an approximating Newton system using a conjugate-gradient approach and are related to limited-memory BFGS. } \details{ Truncated Newton methods are based on approximating the objective with a quadratic function and applying an iterative scheme such as the linear conjugate-gradient algorithm. } \note{ Less reliable than Newton's method, but can handle very large problems. } \examples{ flb <- function(x) { p <- length(x) sum(c(1, rep(4, p-1)) * (x - c(1, x[-p])^2)^2) } # 25-dimensional box constrained: par[24] is *not* at boundary S <- tnewton(rep(3, 25), flb, lower=rep(2, 25), upper=rep(4, 25), nl.info = TRUE, control = list(xtol_rel=1e-8)) ## Optimal value of objective function: 368.105912874334 ## Optimal value of controls: 2 ... 2 2.109093 4 } \references{ R. S. Dembo and T. Steihaug, ``Truncated Newton algorithms for large-scale optimization,'' Math. Programming 26, p. 190-212 (1982). } \seealso{ \code{\link{lbfgs}} } \author{ Hans W. Borchers } nloptr/man/neldermead.Rd0000644000176000001440000000476413266716212015003 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nm.R \name{neldermead} \alias{neldermead} \title{Nelder-Mead Simplex} \usage{ neldermead(x0, fn, lower = NULL, upper = NULL, nl.info = FALSE, control = list(), ...) } \arguments{ \item{x0}{starting point for searching the optimum.} \item{fn}{objective function that is to be minimized.} \item{lower, upper}{lower and upper bound constraints.} \item{nl.info}{logical; shall the original NLopt info been shown.} \item{control}{list of options, see \code{nl.opts} for help.} \item{...}{additional arguments passed to the function.} } \value{ List with components: \item{par}{the optimal solution found so far.} \item{value}{the function value corresponding to \code{par}.} \item{iter}{number of (outer) iterations, see \code{maxeval}.} \item{convergence}{integer code indicating successful completion (> 0) or a possible error number (< 0).} \item{message}{character string produced by NLopt and giving additional information.} } \description{ An implementation of almost the original Nelder-Mead simplex algorithm. } \details{ Provides xplicit support for bound constraints, using essentially the method proposed in [Box]. Whenever a new point would lie outside the bound constraints the point is moved back exactly onto the constraint. } \note{ The author of NLopt would tend to recommend the Subplex method instead. } \examples{ # Fletcher and Powell's helic valley fphv <- function(x) 100*(x[3] - 10*atan2(x[2], x[1])/(2*pi))^2 + (sqrt(x[1]^2 + x[2]^2) - 1)^2 +x[3]^2 x0 <- c(-1, 0, 0) neldermead(x0, fphv) # 1 0 0 # Powell's Singular Function (PSF) psf <- function(x) (x[1] + 10*x[2])^2 + 5*(x[3] - x[4])^2 + (x[2] - 2*x[3])^4 + 10*(x[1] - x[4])^4 x0 <- c(3, -1, 0, 1) neldermead(x0, psf) # 0 0 0 0, needs maximum number of function calls \dontrun{ # Bounded version of Nelder-Mead lower <- c(-Inf, 0, 0) upper <- c( Inf, 0.5, 1) x0 <- c(0, 0.1, 0.1) S <- neldermead(c(0, 0.1, 0.1), rosenbrock, lower, upper, nl.info = TRUE) # $xmin = c(0.7085595, 0.5000000, 0.2500000) # $fmin = 0.3353605} } \references{ J. A. Nelder and R. Mead, ``A simplex method for function minimization,'' The Computer Journal 7, p. 308-313 (1965). M. J. Box, ``A new method of constrained optimization and a comparison with other methods,'' Computer J. 8 (1), 42-52 (1965). } \seealso{ \code{dfoptim::nmk} } \author{ Hans W. Borchers } nloptr/man/stogo.Rd0000644000176000001440000000441613266716212014030 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/global.R \name{stogo} \alias{stogo} \title{Stochastic Global Optimization} \usage{ stogo(x0, fn, gr = NULL, lower = NULL, upper = NULL, maxeval = 10000, xtol_rel = 1e-06, randomized = FALSE, nl.info = FALSE, ...) } \arguments{ \item{x0}{initial point for searching the optimum.} \item{fn}{objective function that is to be minimized.} \item{gr}{optional gradient of the objective function.} \item{lower, upper}{lower and upper bound constraints.} \item{maxeval}{maximum number of function evaluations.} \item{xtol_rel}{stopping criterion for relative change reached.} \item{randomized}{logical; shall a randomizing variant be used?} \item{nl.info}{logical; shall the original NLopt info been shown.} \item{...}{additional arguments passed to the function.} } \value{ List with components: \item{par}{the optimal solution found so far.} \item{value}{the function value corresponding to \code{par}.} \item{iter}{number of (outer) iterations, see \code{maxeval}.} \item{convergence}{integer code indicating successful completion (> 0) or a possible error number (< 0).} \item{message}{character string produced by NLopt and giving additional information.} } \description{ StoGO is a global optimization algorithm that works by systematically dividing the search space into smaller hyper-rectangles. } \details{ StoGO is a global optimization algorithm that works by systematically dividing the search space (which must be bound-constrained) into smaller hyper-rectangles via a branch-and-bound technique, and searching them by a gradient-based local-search algorithm (a BFGS variant), optionally including some randomness. } \note{ Only bound-constrained problems are supported by this algorithm. } \examples{ ### Rosenbrock Banana objective function fn <- function(x) return( 100 * (x[2] - x[1] * x[1])^2 + (1 - x[1])^2 ) x0 <- c( -1.2, 1 ) lb <- c( -3, -3 ) ub <- c( 3, 3 ) stogo(x0 = x0, fn = fn, lower = lb, upper = ub) } \references{ S. Zertchaninov and K. Madsen, ``A C++ Programme for Global Optimization,'' IMM-REP-1998-04, Department of Mathematical Modelling, Technical University of Denmark. } \author{ Hans W. Borchers } nloptr/man/is.nloptr.Rd0000644000176000001440000000111113266711050014605 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/is.nloptr.R \name{is.nloptr} \alias{is.nloptr} \title{R interface to NLopt} \usage{ is.nloptr(x) } \arguments{ \item{x}{object to be tested.} } \value{ Logical. Return TRUE if all tests were passed, otherwise return FALSE or exit with Error. } \description{ is.nloptr preforms checks to see if a fully specified problem is supplied to nloptr. Mostly for internal use. } \seealso{ \code{\link[nloptr:nloptr]{nloptr}} } \author{ Jelmer Ypma } \keyword{interface} \keyword{optimize} nloptr/man/print.nloptr.Rd0000644000176000001440000000176113266711050015341 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/print.nloptr.R \name{print.nloptr} \alias{print.nloptr} \title{Print results after running nloptr} \usage{ \method{print}{nloptr}(x, show.controls = TRUE, ...) } \arguments{ \item{x}{object containing result from minimization.} \item{show.controls}{Logical or vector with indices. Should we show the value of the control variables in the solution? If \code{show.controls} is a vector with indices, it is used to select which control variables should be shown. This can be useful if the model contains a set of parameters of interest and a set of nuisance parameters that are not of immediate interest.} \item{...}{further arguments passed to or from other methods.} } \description{ This function prints the nloptr object that holds the results from a minimization using \code{nloptr}. } \seealso{ \code{\link[nloptr:nloptr]{nloptr}} } \author{ Jelmer Ypma } \keyword{interface} \keyword{optimize} nloptr/man/slsqp.Rd0000644000176000001440000000716713266716212014045 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/slsqp.R \name{slsqp} \alias{slsqp} \title{Sequential Quadratic Programming (SQP)} \usage{ slsqp(x0, fn, gr = NULL, lower = NULL, upper = NULL, hin = NULL, hinjac = NULL, heq = NULL, heqjac = NULL, nl.info = FALSE, control = list(), ...) } \arguments{ \item{x0}{starting point for searching the optimum.} \item{fn}{objective function that is to be minimized.} \item{gr}{gradient of function \code{fn}; will be calculated numerically if not specified.} \item{lower, upper}{lower and upper bound constraints.} \item{hin}{function defining the inequality constraints, that is \code{hin>=0} for all components.} \item{hinjac}{Jacobian of function \code{hin}; will be calculated numerically if not specified.} \item{heq}{function defining the equality constraints, that is \code{heq==0} for all components.} \item{heqjac}{Jacobian of function \code{heq}; will be calculated numerically if not specified.} \item{nl.info}{logical; shall the original NLopt info been shown.} \item{control}{list of options, see \code{nl.opts} for help.} \item{...}{additional arguments passed to the function.} } \value{ List with components: \item{par}{the optimal solution found so far.} \item{value}{the function value corresponding to \code{par}.} \item{iter}{number of (outer) iterations, see \code{maxeval}.} \item{convergence}{integer code indicating successful completion (> 1) or a possible error number (< 0).} \item{message}{character string produced by NLopt and giving additional information.} } \description{ Sequential (least-squares) quadratic programming (SQP) algorithm for nonlinearly constrained, gradient-based optimization, supporting both equality and inequality constraints. } \details{ The algorithm optimizes successive second-order (quadratic/least-squares) approximations of the objective function (via BFGS updates), with first-order (affine) approximations of the constraints. } \note{ See more infos at \url{http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms}. } \examples{ ## Solve the Hock-Schittkowski problem no. 100 x0.hs100 <- c(1, 2, 0, 4, 0, 1, 1) fn.hs100 <- function(x) { (x[1]-10)^2 + 5*(x[2]-12)^2 + x[3]^4 + 3*(x[4]-11)^2 + 10*x[5]^6 + 7*x[6]^2 + x[7]^4 - 4*x[6]*x[7] - 10*x[6] - 8*x[7] } hin.hs100 <- function(x) { h <- numeric(4) h[1] <- 127 - 2*x[1]^2 - 3*x[2]^4 - x[3] - 4*x[4]^2 - 5*x[5] h[2] <- 282 - 7*x[1] - 3*x[2] - 10*x[3]^2 - x[4] + x[5] h[3] <- 196 - 23*x[1] - x[2]^2 - 6*x[6]^2 + 8*x[7] h[4] <- -4*x[1]^2 - x[2]^2 + 3*x[1]*x[2] -2*x[3]^2 - 5*x[6] +11*x[7] return(h) } S <- slsqp(x0.hs100, fn = fn.hs100, # no gradients and jacobians provided hin = hin.hs100, control = list(xtol_rel = 1e-8, check_derivatives = TRUE)) S ## Optimal value of objective function: 690.622270249131 *** WRONG *** # Even the numerical derivatives seem to be too tight. # Let's try with a less accurate jacobian. hinjac.hs100 <- function(x) nl.jacobian(x, hin.hs100, heps = 1e-2) S <- slsqp(x0.hs100, fn = fn.hs100, hin = hin.hs100, hinjac = hinjac.hs100, control = list(xtol_rel = 1e-8)) S ## Optimal value of objective function: 680.630057392593 *** CORRECT *** } \references{ Dieter Kraft, ``A software package for sequential quadratic programming'', Technical Report DFVLR-FB 88-28, Institut fuer Dynamik der Flugsysteme, Oberpfaffenhofen, July 1988. } \seealso{ \code{alabama::auglag}, \code{Rsolnp::solnp}, \code{Rdonlp2::donlp2} } \author{ Hans W. Borchers } nloptr/man/direct.Rd0000644000176000001440000001042613266717646014161 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/direct.R \name{direct} \alias{direct} \alias{directL} \alias{directL} \title{DIviding RECTangles Algorithm for Global Optimization} \usage{ direct(fn, lower, upper, scaled = TRUE, original = FALSE, nl.info = FALSE, control = list(), ...) directL(fn, lower, upper, randomized = FALSE, original = FALSE, nl.info = FALSE, control = list(), ...) } \arguments{ \item{fn}{objective function that is to be minimized.} \item{lower, upper}{lower and upper bound constraints.} \item{scaled}{logical; shall the hypercube be scaled before starting.} \item{original}{logical; whether to use the original implementation by Gablonsky -- the performance is mostly similar.} \item{nl.info}{logical; shall the original NLopt info been shown.} \item{control}{list of options, see \code{nl.opts} for help.} \item{...}{additional arguments passed to the function.} \item{randomized}{logical; shall some randomization be used to decide which dimension to halve next in the case of near-ties.} } \value{ List with components: \item{par}{the optimal solution found so far.} \item{value}{the function value corresponding to \code{par}.} \item{iter}{number of (outer) iterations, see \code{maxeval}.} \item{convergence}{integer code indicating successful completion (> 0) or a possible error number (< 0).} \item{message}{character string produced by NLopt and giving additional information.} } \description{ DIRECT is a deterministic search algorithm based on systematic division of the search domain into smaller and smaller hyperrectangles. The DIRECT_L makes the algorithm more biased towards local search (more efficient for functions without too many minima). } \details{ The DIRECT and DIRECT-L algorithms start by rescaling the bound constraints to a hypercube, which gives all dimensions equal weight in the search procedure. If your dimensions do not have equal weight, e.g. if you have a ``long and skinny'' search space and your function varies at about the same speed in all directions, it may be better to use unscaled variant of the DIRECT algorithm. The algorithms only handle finite bound constraints which must be provided. The original versions may include some support for arbitrary nonlinear inequality, but this has not been tested. The original versions do not have randomized or unscaled variants, so these options will be disregarded for these versions. } \note{ The DIRECT_L algorithm should be tried first. } \examples{ ### Minimize the Hartmann6 function hartmann6 <- function(x) { n <- length(x) a <- c(1.0, 1.2, 3.0, 3.2) A <- matrix(c(10.0, 0.05, 3.0, 17.0, 3.0, 10.0, 3.5, 8.0, 17.0, 17.0, 1.7, 0.05, 3.5, 0.1, 10.0, 10.0, 1.7, 8.0, 17.0, 0.1, 8.0, 14.0, 8.0, 14.0), nrow=4, ncol=6) B <- matrix(c(.1312,.2329,.2348,.4047, .1696,.4135,.1451,.8828, .5569,.8307,.3522,.8732, .0124,.3736,.2883,.5743, .8283,.1004,.3047,.1091, .5886,.9991,.6650,.0381), nrow=4, ncol=6) fun <- 0.0 for (i in 1:4) { fun <- fun - a[i] * exp(-sum(A[i,]*(x-B[i,])^2)) } return(fun) } S <- directL(hartmann6, rep(0,6), rep(1,6), nl.info = TRUE, control=list(xtol_rel=1e-8, maxeval=1000)) ## Number of Iterations....: 500 ## Termination conditions: stopval: -Inf ## xtol_rel: 1e-08, maxeval: 500, ftol_rel: 0, ftol_abs: 0 ## Number of inequality constraints: 0 ## Number of equality constraints: 0 ## Current value of objective function: -3.32236800687327 ## Current value of controls: ## 0.2016884 0.1500025 0.4768667 0.2753391 0.311648 0.6572931 } \references{ D. R. Jones, C. D. Perttunen, and B. E. Stuckmann, ``Lipschitzian optimization without the lipschitz constant,'' J. Optimization Theory and Applications, vol. 79, p. 157 (1993). J. M. Gablonsky and C. T. Kelley, ``A locally-biased form of the DIRECT algorithm," J. Global Optimization, vol. 21 (1), p. 27-37 (2001). } \seealso{ The \code{dfoptim} package will provide a pure R version of this algorithm. } \author{ Hans W. Borchers } nloptr/man/lbfgs.Rd0000644000176000001440000000503313266717646014002 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/lbfgs.R \name{lbfgs} \alias{lbfgs} \title{Low-storage BFGS} \usage{ lbfgs(x0, fn, gr = NULL, lower = NULL, upper = NULL, nl.info = FALSE, control = list(), ...) } \arguments{ \item{x0}{initial point for searching the optimum.} \item{fn}{objective function to be minimized.} \item{gr}{gradient of function \code{fn}; will be calculated numerically if not specified.} \item{lower, upper}{lower and upper bound constraints.} \item{nl.info}{logical; shall the original NLopt info been shown.} \item{control}{list of control parameters, see \code{nl.opts} for help.} \item{...}{further arguments to be passed to the function.} } \value{ List with components: \item{par}{the optimal solution found so far.} \item{value}{the function value corresponding to \code{par}.} \item{iter}{number of (outer) iterations, see \code{maxeval}.} \item{convergence}{integer code indicating successful completion (> 0) or a possible error number (< 0).} \item{message}{character string produced by NLopt and giving additional information.} } \description{ Low-storage version of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. } \details{ The low-storage (or limited-memory) algorithm is a member of the class of quasi-Newton optimization methods. It is well suited for optimization problems with a large number of variables. One parameter of this algorithm is the number \code{m} of gradients to remember from previous optimization steps. NLopt sets \code{m} to a heuristic value by default. It can be changed by the NLopt function \code{set_vector_storage}. } \note{ Based on a Fortran implementation of the low-storage BFGS algorithm written by L. Luksan, and posted under the GNU LGPL license. } \examples{ flb <- function(x) { p <- length(x) sum(c(1, rep(4, p-1)) * (x - c(1, x[-p])^2)^2) } # 25-dimensional box constrained: par[24] is *not* at the boundary S <- lbfgs(rep(3, 25), flb, lower=rep(2, 25), upper=rep(4, 25), nl.info = TRUE, control = list(xtol_rel=1e-8)) ## Optimal value of objective function: 368.105912874334 ## Optimal value of controls: 2 ... 2 2.109093 4 } \references{ J. Nocedal, ``Updating quasi-Newton matrices with limited storage,'' Math. Comput. 35, 773-782 (1980). D. C. Liu and J. Nocedal, ``On the limited memory BFGS method for large scale optimization,'' Math. Programming 45, p. 503-528 (1989). } \seealso{ \code{\link{optim}} } \author{ Hans W. Borchers } nloptr/man/nl.opts.Rd0000644000176000001440000000213113266711050014255 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nloptions.R \name{nl.opts} \alias{nl.opts} \title{Setting NL Options} \usage{ nl.opts(optlist = NULL) } \arguments{ \item{optlist}{list of options, see below.} } \value{ returns a list with default and changed options. } \description{ Sets and changes the NLOPT options. } \details{ The following options can be set (here with default values): \code{stopval = -Inf, # stop minimization at this value}\cr \code{xtol_rel = 1e-6, # stop on small optimization step}\cr \code{maxeval = 1000, # stop on this many function evaluations}\cr \code{ftol_rel = 0.0, # stop on change times function value}\cr \code{ftol_abs = 0.0, # stop on small change of function value}\cr \code{check_derivatives = FALSE} } \note{ There are more options that can be set for solvers in NLOPT. These cannot be set through their wrapper functions. To see the full list of options and algorithms, type \code{nloptr.print.options()}. } \examples{ nl.opts(list(xtol_rel = 1e-8, maxeval = 2000)) } \author{ Hans W. Borchers } nloptr/man/mma.Rd0000644000176000001440000000777013266716212013455 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mma.R \name{mma} \alias{mma} \title{Method of Moving Asymptotes} \usage{ mma(x0, fn, gr = NULL, lower = NULL, upper = NULL, hin = NULL, hinjac = NULL, nl.info = FALSE, control = list(), ...) } \arguments{ \item{x0}{starting point for searching the optimum.} \item{fn}{objective function that is to be minimized.} \item{gr}{gradient of function \code{fn}; will be calculated numerically if not specified.} \item{lower, upper}{lower and upper bound constraints.} \item{hin}{function defining the inequality constraints, that is \code{hin>=0} for all components.} \item{hinjac}{Jacobian of function \code{hin}; will be calculated numerically if not specified.} \item{nl.info}{logical; shall the original NLopt info been shown.} \item{control}{list of options, see \code{nl.opts} for help.} \item{...}{additional arguments passed to the function.} } \value{ List with components: \item{par}{the optimal solution found so far.} \item{value}{the function value corresponding to \code{par}.} \item{iter}{number of (outer) iterations, see \code{maxeval}.} \item{convergence}{integer code indicating successful completion (> 1) or a possible error number (< 0).} \item{message}{character string produced by NLopt and giving additional information.} } \description{ Globally-convergent method-of-moving-asymptotes (MMA) algorithm for gradient-based local optimization, including nonlinear inequality constraints (but not equality constraints). } \details{ This is an improved CCSA ("conservative convex separable approximation") variant of the original MMA algorithm published by Svanberg in 1987, which has become popular for topology optimization. Note: } \note{ ``Globally convergent'' does not mean that this algorithm converges to the global optimum; it means that it is guaranteed to converge to some local minimum from any feasible starting point. } \examples{ ## Solve the Hock-Schittkowski problem no. 100 with analytic gradients x0.hs100 <- c(1, 2, 0, 4, 0, 1, 1) fn.hs100 <- function(x) { (x[1]-10)^2 + 5*(x[2]-12)^2 + x[3]^4 + 3*(x[4]-11)^2 + 10*x[5]^6 + 7*x[6]^2 + x[7]^4 - 4*x[6]*x[7] - 10*x[6] - 8*x[7] } hin.hs100 <- function(x) { h <- numeric(4) h[1] <- 127 - 2*x[1]^2 - 3*x[2]^4 - x[3] - 4*x[4]^2 - 5*x[5] h[2] <- 282 - 7*x[1] - 3*x[2] - 10*x[3]^2 - x[4] + x[5] h[3] <- 196 - 23*x[1] - x[2]^2 - 6*x[6]^2 + 8*x[7] h[4] <- -4*x[1]^2 - x[2]^2 + 3*x[1]*x[2] -2*x[3]^2 - 5*x[6] +11*x[7] return(h) } gr.hs100 <- function(x) { c( 2 * x[1] - 20, 10 * x[2] - 120, 4 * x[3]^3, 6 * x[4] - 66, 60 * x[5]^5, 14 * x[6] - 4 * x[7] - 10, 4 * x[7]^3 - 4 * x[6] - 8 )} hinjac.hs100 <- function(x) { matrix(c(4*x[1], 12*x[2]^3, 1, 8*x[4], 5, 0, 0, 7, 3, 20*x[3], 1, -1, 0, 0, 23, 2*x[2], 0, 0, 0, 12*x[6], -8, 8*x[1]-3*x[2], 2*x[2]-3*x[1], 4*x[3], 0, 0, 5, -11), 4, 7, byrow=TRUE) } # incorrect result with exact jacobian S <- mma(x0.hs100, fn.hs100, gr = gr.hs100, hin = hin.hs100, hinjac = hinjac.hs100, nl.info = TRUE, control = list(xtol_rel = 1e-8)) \donttest{ # This example is put in donttest because it runs for more than # 40 seconds under 32-bit Windows. The difference in time needed # to execute the code between 32-bit Windows and 64-bit Windows # can probably be explained by differences in rounding/truncation # on the different systems. On Windows 32-bit more iterations # are needed resulting in a longer runtime. # correct result with inexact jacobian S <- mma(x0.hs100, fn.hs100, hin = hin.hs100, nl.info = TRUE, control = list(xtol_rel = 1e-8)) } } \references{ Krister Svanberg, ``A class of globally convergent optimization methods based on conservative convex separable approximations,'' SIAM J. Optim. 12 (2), p. 555-573 (2002). } \seealso{ \code{\link{slsqp}} } \author{ Hans W. Borchers } nloptr/man/nloptr.Rd0000644000176000001440000002330213266716212014206 0ustar ripleyusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nloptr.R \name{nloptr} \alias{nloptr} \title{R interface to NLopt} \usage{ nloptr(x0, eval_f, eval_grad_f = NULL, lb = NULL, ub = NULL, eval_g_ineq = NULL, eval_jac_g_ineq = NULL, eval_g_eq = NULL, eval_jac_g_eq = NULL, opts = list(), ...) } \arguments{ \item{x0}{vector with starting values for the optimization.} \item{eval_f}{function that returns the value of the objective function. It can also return gradient information at the same time in a list with elements "objective" and "gradient" (see below for an example).} \item{eval_grad_f}{function that returns the value of the gradient of the objective function. Not all of the algorithms require a gradient.} \item{lb}{vector with lower bounds of the controls (use -Inf for controls without lower bound), by default there are no lower bounds for any of the controls.} \item{ub}{vector with upper bounds of the controls (use Inf for controls without upper bound), by default there are no upper bounds for any of the controls.} \item{eval_g_ineq}{function to evaluate (non-)linear inequality constraints that should hold in the solution. It can also return gradient information at the same time in a list with elements "objective" and "jacobian" (see below for an example).} \item{eval_jac_g_ineq}{function to evaluate the jacobian of the (non-)linear inequality constraints that should hold in the solution.} \item{eval_g_eq}{function to evaluate (non-)linear equality constraints that should hold in the solution. It can also return gradient information at the same time in a list with elements "objective" and "jacobian" (see below for an example).} \item{eval_jac_g_eq}{function to evaluate the jacobian of the (non-)linear equality constraints that should hold in the solution.} \item{opts}{list with options. The option "algorithm" is required. Check the \href{http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms}{NLopt website} for a full list of available algorithms. Other options control the termination conditions (minf_max, ftol_rel, ftol_abs, xtol_rel, xtol_abs, maxeval, maxtime). Default is xtol_rel = 1e-4. More information \href{http://ab-initio.mit.edu/wiki/index.php/NLopt_Introduction\#Termination_conditions}{here}. A full description of all options is shown by the function \code{nloptr.print.options()}. Some algorithms with equality constraints require the option local_opts, which contains a list with an algorithm and a termination condition for the local algorithm. See ?`nloptr-package` for an example. The option print_level controls how much output is shown during the optimization process. Possible values: \tabular{ll}{ 0 (default) \tab no output \cr 1 \tab show iteration number and value of objective function \cr 2 \tab 1 + show value of (in)equalities \cr 3 \tab 2 + show value of controls } The option check_derivatives (default = FALSE) can be used to run to compare the analytic gradients with finite difference approximations. The option check_derivatives_print ('all' (default), 'errors', 'none') controls the output of the derivative checker, if it is run, showing all comparisons, only those that resulted in an error, or none. The option check_derivatives_tol (default = 1e-04), determines when a difference between an analytic gradient and its finite difference approximation is flagged as an error.} \item{...}{arguments that will be passed to the user-defined objective and constraints functions.} } \value{ The return value contains a list with the inputs, and additional elements \item{call}{the call that was made to solve} \item{status}{integer value with the status of the optimization (0 is success)} \item{message}{more informative message with the status of the optimization} \item{iterations}{number of iterations that were executed} \item{objective}{value if the objective function in the solution} \item{solution}{optimal value of the controls} \item{version}{version of NLopt that was used} } \description{ nloptr is an R interface to NLopt, a free/open-source library for nonlinear optimization started by Steven G. Johnson, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. The NLopt library is available under the GNU Lesser General Public License (LGPL), and the copyrights are owned by a variety of authors. Most of the information here has been taken from \href{http://ab-initio.mit.edu/nlopt}{the NLopt website}, where more details are available. } \details{ NLopt addresses general nonlinear optimization problems of the form: min f(x) x in R^n s.t. g(x) <= 0 h(x) = 0 lb <= x <= ub where f is the objective function to be minimized and x represents the n optimization parameters. This problem may optionally be subject to the bound constraints (also called box constraints), lb and ub. For partially or totally unconstrained problems the bounds can take -Inf or Inf. One may also optionally have m nonlinear inequality constraints (sometimes called a nonlinear programming problem), which can be specified in g(x), and equality constraints that can be specified in h(x). Note that not all of the algorithms in NLopt can handle constraints. } \note{ See ?`nloptr-package` for an extended example. } \examples{ library('nloptr') ## Rosenbrock Banana function and gradient in separate functions eval_f <- function(x) { return( 100 * (x[2] - x[1] * x[1])^2 + (1 - x[1])^2 ) } eval_grad_f <- function(x) { return( c( -400 * x[1] * (x[2] - x[1] * x[1]) - 2 * (1 - x[1]), 200 * (x[2] - x[1] * x[1])) ) } # initial values x0 <- c( -1.2, 1 ) opts <- list("algorithm"="NLOPT_LD_LBFGS", "xtol_rel"=1.0e-8) # solve Rosenbrock Banana function res <- nloptr( x0=x0, eval_f=eval_f, eval_grad_f=eval_grad_f, opts=opts) print( res ) ## Rosenbrock Banana function and gradient in one function # this can be used to economize on calculations eval_f_list <- function(x) { return( list( "objective" = 100 * (x[2] - x[1] * x[1])^2 + (1 - x[1])^2, "gradient" = c( -400 * x[1] * (x[2] - x[1] * x[1]) - 2 * (1 - x[1]), 200 * (x[2] - x[1] * x[1])) ) ) } # solve Rosenbrock Banana function using an objective function that # returns a list with the objective value and its gradient res <- nloptr( x0=x0, eval_f=eval_f_list, opts=opts) print( res ) # Example showing how to solve the problem from the NLopt tutorial. # # min sqrt( x2 ) # s.t. x2 >= 0 # x2 >= ( a1*x1 + b1 )^3 # x2 >= ( a2*x1 + b2 )^3 # where # a1 = 2, b1 = 0, a2 = -1, b2 = 1 # # re-formulate constraints to be of form g(x) <= 0 # ( a1*x1 + b1 )^3 - x2 <= 0 # ( a2*x1 + b2 )^3 - x2 <= 0 library('nloptr') # objective function eval_f0 <- function( x, a, b ){ return( sqrt(x[2]) ) } # constraint function eval_g0 <- function( x, a, b ) { return( (a*x[1] + b)^3 - x[2] ) } # gradient of objective function eval_grad_f0 <- function( x, a, b ){ return( c( 0, .5/sqrt(x[2]) ) ) } # jacobian of constraint eval_jac_g0 <- function( x, a, b ) { return( rbind( c( 3*a[1]*(a[1]*x[1] + b[1])^2, -1.0 ), c( 3*a[2]*(a[2]*x[1] + b[2])^2, -1.0 ) ) ) } # functions with gradients in objective and constraint function # this can be useful if the same calculations are needed for # the function value and the gradient eval_f1 <- function( x, a, b ){ return( list("objective"=sqrt(x[2]), "gradient"=c(0,.5/sqrt(x[2])) ) ) } eval_g1 <- function( x, a, b ) { return( list( "constraints"=(a*x[1] + b)^3 - x[2], "jacobian"=rbind( c( 3*a[1]*(a[1]*x[1] + b[1])^2, -1.0 ), c( 3*a[2]*(a[2]*x[1] + b[2])^2, -1.0 ) ) ) ) } # define parameters a <- c(2,-1) b <- c(0, 1) # Solve using NLOPT_LD_MMA with gradient information supplied in separate function res0 <- nloptr( x0=c(1.234,5.678), eval_f=eval_f0, eval_grad_f=eval_grad_f0, lb = c(-Inf,0), ub = c(Inf,Inf), eval_g_ineq = eval_g0, eval_jac_g_ineq = eval_jac_g0, opts = list("algorithm"="NLOPT_LD_MMA"), a = a, b = b ) print( res0 ) # Solve using NLOPT_LN_COBYLA without gradient information res1 <- nloptr( x0=c(1.234,5.678), eval_f=eval_f0, lb = c(-Inf,0), ub = c(Inf,Inf), eval_g_ineq = eval_g0, opts = list("algorithm"="NLOPT_LN_COBYLA"), a = a, b = b ) print( res1 ) # Solve using NLOPT_LD_MMA with gradient information in objective function res2 <- nloptr( x0=c(1.234,5.678), eval_f=eval_f1, lb = c(-Inf,0), ub = c(Inf,Inf), eval_g_ineq = eval_g1, opts = list("algorithm"="NLOPT_LD_MMA", "check_derivatives"=TRUE), a = a, b = b ) print( res2 ) } \references{ Steven G. Johnson, The NLopt nonlinear-optimization package, \url{http://ab-initio.mit.edu/nlopt} } \seealso{ \code{\link[nloptr:nloptr.print.options]{nloptr.print.options}} \code{\link[nloptr:check.derivatives]{check.derivatives}} \code{\link{optim}} \code{\link{nlm}} \code{\link{nlminb}} \code{Rsolnp::Rsolnp} \code{Rsolnp::solnp} } \author{ Steven G. Johnson and others (C code) \cr Jelmer Ypma (R interface) } \keyword{interface} \keyword{optimize} nloptr/INSTALL.windows0000644000176000001440000000673612754654377014402 0ustar ripleyusersThis file describes how to install nloptr from source for Windows. Compiling nloptr for Windows consists of two steps: 1. configure and compile NLopt from source or download and unpack a pre-compiled binary (NLopt-2.4-win-build.zip from http://www.ucl.ac.uk/~uctpjyy/nloptr.html) 2. compile the R interface *** To install NLopt from source (optional) Requirements: Cygwin or MSYS 1. Download the latest source of NLopt from http://ab-initio.mit.edu/wiki/index.php/NLopt#Download_and_installation, e.g. nlopt-2.4.tar.gz and unpack this archive somewhere (e.g. c:/tmp/nlopt-2.4). 2. Create a bash script somewhere, e.g. c:/tmp/NLoptInstall.sh with the following contents #!/bin/bash R_HOME="C:/tools/R/R-3.0.2" R_ARCH="i386" NLOPT_SRC_DIR="C:/tmp/nlopt-2.4" NLOPT_LIB_DIR="C:/tmp/nlopt" # Create directory for output mkdir ${NLOPT_LIB_DIR} mkdir ${NLOPT_LIB_DIR}/${R_ARCH} # Start in source directory cd ${NLOPT_SRC_DIR} # Get R compilers and flags CC=`"${R_HOME}/bin/${R_ARCH}/R.exe" CMD config CC` CFLAGS=`"${R_HOME}/bin/${R_ARCH}/R.exe" CMD config CFLAGS` CPP=`"${R_HOME}/bin/${R_ARCH}/R.exe" CMD config CPP` CPPFLAGS=`"${R_HOME}/bin/${R_ARCH}/R.exe" CMD config CPPFLAGS` CXX=`"${R_HOME}/bin/${R_ARCH}/R.exe" CMD config CXX` CXXFLAGS=`"${R_HOME}/bin/${R_ARCH}/R.exe" CMD config CXXFLAGS` CXXCPP=`"${R_HOME}/bin/${R_ARCH}/R.exe" CMD config CXXCPP` F77=`"${R_HOME}/bin/${R_ARCH}/R.exe" CMD config F77` FFLAGS=`"${R_HOME}/bin/${R_ARCH}/R.exe" CMD config FFLAGS` echo "./configure --prefix=\"${NLOPT_LIB_DIR}/${R_ARCH}\" --disable-shared --enable-static --without-octave --without-matlab --without-guile --without-python --with-cxx CC=\"${CC}\" ADD_CFLAGS=\"${CFLAGS}\" CPP=\"${CPP}\" ADD_CPPFLAGS=\"${CPPFLAGS}\" CXX=\"${CXX}\" ADD_CXXFLAGS=\"${CXXFLAGS}\" CXXCPP=\"${CXXCPP}\" F77=\"${F77}\" ADD_FFLAGS=\"${FFLAGS}\"" # Configure ./configure --prefix="${NLOPT_LIB_DIR}/${R_ARCH}" --disable-shared --enable-static --without-octave --without-matlab --without-guile --without-python --with-cxx CC="${CC}" ADD_CFLAGS="${CFLAGS}" CPP="${CPP}" ADD_CPPFLAGS="${CPPFLAGS}" CXX="${CXX}" ADD_CXXFLAGS="${CXXFLAGS}" CXXCPP="${CXXCPP}" F77="${F77}" ADD_FFLAGS="${FFLAGS}" # Compile make make install 3. In c:/tmp/NLoptInstall.sh adjust the values of the following macros R_HOME (The directory where R is located) NLOPT_SRC_DIR (The directory where the NLopt source is located, i.e. the directory created in 1.) NLOPT_LIB_DIR (The directory where the NLopt binary will be after compilation. This directory is created by the script NLoptInstall.sh) 4. Start MSYS, go to the directory where the script is located and execute the bash script > cd c:/tmp > ./NLoptInstall.sh The bash script sets macros for the correct compilers, as used by R, and configures and compiles the NLopt code. You do not need to use the script to compile NLopt, but it simplifies the process. 5. Optionally compile NLopt for 64-bit. Change the line R_ARCH="i386" to R_ARCH="x64" in c:/tmp/NLoptInstall.sh and execture the script again from MSYS. *** To install nloptr Requirements: Rtools 1. Download the source of nloptr (from GitHub https://github.com/jyypma/nloptr or CRAN http://cran.r-project.org/web/packages/nloptr/index.html) and unpack this somewhere. 2. Before building from the MS-DOS prompt, define an enviroment variable with the location of the NLopt library, e.g. set NLOPT_HOME=c:/tmp/nlopt 3. Compile the R interface R CMD build nloptr nloptr/cleanup0000755000176000001440000000030113355070635013176 0ustar ripleyusers#!/bin/sh rm -rf autom4te.cache/ config.log config.status \ src/Makevars src/*.o src/*.so src/*.dylib \ src/*.dll *~ rm -rf src/nlopt_src/lib cd src/nlopt_src make clean cd ../..

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