minqa/0000755000176000001440000000000012415506656011421 5ustar ripleyusersminqa/tests/0000755000176000001440000000000011730166327012557 5ustar ripleyusersminqa/tests/newuoa.R0000644000176000001440000000037511473252363014205 0ustar ripleyuserslibrary(minqa) maxfn <- function(x) 10 - (crossprod(x, seq_along(x)))^2 negmaxfn <- function(x) -maxfn(x) (ans.mx <- newuoa(rep(pi, 4), negmaxfn, control=list(iprint=1))) (ans.mxf<-newuoa(rep(pi, 4), negmaxfn, control=list(iprint=1,maxfun=25))) minqa/tests/cyq-minqa.R0000644000176000001440000000203111641412431014564 0ustar ripleyuserslibrary(minqa) # rm(list=ls()) cyq.f <- function (x) { rv<-cyq.res(x) f<-sum(rv*rv) } cyq.res <- function (x) { # Fletcher's chebyquad function m = n -- residuals n<-length(x) res<-rep(0,n) # initialize for (i in 1:n) { #loop over resids rr<-0.0 for (k in 1:n) { z7<-1.0 z2<-2.0*x[k]-1.0 z8<-z2 j<-1 while (j 0) test<-c(is.list(res), inherits(res, "minqa"), isTRUE(all(names(res) == c("par", "fval", "feval", "ierr", "msg"))), is.numeric(res$par), isTRUE(all.equal(res$par, 1:4, tol = 2e-4)), is.numeric(res$fval), isTRUE(all.equal(as.vector(res$fval), -10, check.attributes = FALSE, tol = 1e-4)), is.integer(res$feval), res$feval > 0) names(test)<-c("is.list", "inheritsOK", "namesOK", "is.numeric-par", "paramsOK","is.numeric-fn","fnOK","is.integer-fval", "feval>0") idx<-which(! test) msg<-paste("reschk failed",names(test)[idx],sep=', ') if ( ! all(test)) warning(msg) } # NOTE: we do not check ierr or msg here. JN 20100810 sessionInfo() reschk(ans.nd <- newuoa(x0, minfn, control = list(iprint = 2))) ans.nd reschk(ans.ud <- uobyqa(x0, minfn, control = list(iprint = 2))) ans.ud reschk(ans.bd <- bobyqa(x0, minfn, control = list(iprint = 2))) ans.bd minqa/src/0000755000176000001440000000000012415434561012203 5ustar ripleyusersminqa/src/minqa.cpp0000644000176000001440000001646112415434561014024 0ustar ripleyusers#include #include #include #include #include using namespace Rcpp; using namespace std; /// Wrapper for the objective function. It is initialized to R's "c" function static Function cf("c"); /** * Fortran callable objective function evaluation. * * @param n size of parameter vector * @param x parameter vector * @param ip print flag * * @return objective function evaluation */ extern "C" double F77_NAME(calfun)(int const *n, double const x[], int const *ip) { Environment rho(cf.environment()); IntegerVector cc(rho.get(".feval.")); int nn = *n; cc[0]++; // increment func eval count if (count_if(x, x + nn, R_finite) < nn) throw range_error("non-finite x values not allowed in calfun"); SEXP pp = PROTECT(::Rf_allocVector(REALSXP, nn)); copy(x, x + nn, REAL(pp)); double f = ::Rf_asReal(::Rf_eval(PROTECT(::Rf_lang2(as(cf), pp)), as(rho))); UNPROTECT(2); #if 0 double f; try { f = as(cf(pp)); // evaluate objective } catch( std::exception& __ex__ ) { forward_exception_to_r( __ex__ ); } catch(...) { ::Rf_error("c++ exception (unknown reason)"); } #endif if (!R_finite(f)) f = numeric_limits::max(); if (*ip == 3) { // print eval info when very verbose Rprintf("%3d:%#14.8g:", cc[0], f); for (int i = 0; i < *n; i++) Rprintf(" %#8g", x[i]); Rprintf("\n"); } if (*ip > 3 && cc[0] % *ip == 0) { // print eval info every *ip if *ip >3 Rprintf("%3d:%#14.8g:", cc[0], f); for (int i = 0; i < *n; i++) Rprintf(" %#8g", x[i]); Rprintf("\n"); } return f; } /** * Construct the named and classed list to return from the optimizer * * @param par parameter vector * @param cnm class name * * @return an Rcpp::List object */ static SEXP rval(NumericVector par, string cnm, int ierr = 0) { Environment rho(cf.environment()); SEXP feval = rho.get(".feval."); StringVector cl(2); cl[0] = cnm; cl[1] = "minqa"; double f = ::Rf_asReal(::Rf_eval(PROTECT(::Rf_lang2(as(cf), as(par))), as(rho))); UNPROTECT(1); List rr = List::create(_["par"] = par, _["fval"] = f, _["feval"] = feval, _["ierr"] = ierr); rr.attr("class") = cl; return rr; } /// Declaration of Powell's bobyqa extern "C" void F77_NAME(bobyqa)(const int *n, const int *npt, double X[], const double xl[], const double xu[], const double *rhobeg, const double *rhoend, const int *iprint, const int *maxfun, double w[], int *ierr); /// Interface for bobyqa extern "C" SEXP bobyqa_cpp(SEXP parp, SEXP xlp, SEXP xup, SEXP ccp, SEXP fnp) { try { NumericVector par(parp), xl(xlp), xu(xup); Environment cc(ccp); cf = Function(fnp); // install the objective function double rb = as(cc.get("rhobeg")), re = as(cc.get("rhoend")); int ierr = 0, ip = as(cc.get("iprint")), mxf = as(cc.get("maxfun")), n = par.size(), np = as(cc.get("npt")); vector w((np + 5) * (np + n) + (3 * n * (n + 5))/2); NumericVector pp = clone(par); // ensure that bobyqa doesn't modify the R object F77_NAME(bobyqa)(&n, &np, pp.begin(), xl.begin(), xu.begin(), &rb, &re, &ip, &mxf, &w[0], &ierr); return rval(pp, "bobyqa", ierr); } catch( std::exception& __ex__ ) { forward_exception_to_r( __ex__ ); } catch(...) { ::Rf_error("c++ exception (unknown reason)"); } return R_NilValue; // -Wall } extern "C" void F77_NAME(uobyqa)(const int *n, double X[], const double *rhobeg, const double *rhoend, const int *iprint, const int *maxfun, double w[], int *ierr); extern "C" SEXP uobyqa_cpp(SEXP parp, SEXP ccp, SEXP fnp) { try { NumericVector par(parp); Environment cc(ccp); cf = Function(fnp); double rb = as(cc.get("rhobeg")), re = as(cc.get("rhoend")); int ierr = 0, ip = as(cc.get("iprint")), mxf = as(cc.get("maxfun")), n = par.size(); Environment rho(cf.environment()); vector w((n*(42+n*(23+n*(8+n))) + max(2*n*n + 4, 18*n)) / 4); NumericVector pp = clone(par); // ensure that uobyqa doesn't modify the R object F77_NAME(uobyqa)(&n, pp.begin(), &rb, &re, &ip, &mxf, &w[0], &ierr); return rval(pp, "uobyqa", ierr); } catch( std::exception& __ex__ ) { forward_exception_to_r( __ex__ ); } catch(...) { ::Rf_error("c++ exception (unknown reason)"); } return R_NilValue; // -Wall } extern "C" void F77_NAME(newuoa)(const int *n, const int *npt, double X[], const double *rhobeg, const double *rhoend, const int *iprint, const int *maxfun, double w[], int *ierr); extern "C" SEXP newuoa_cpp(SEXP parp, SEXP ccp, SEXP fnp) { try { NumericVector par(parp); Environment cc(ccp); cf = Function(fnp); double rb = as(cc.get("rhobeg")), re = as(cc.get("rhoend")); int ierr = 0, ip = as(cc.get("iprint")), mxf = as(cc.get("maxfun")), n = par.size(), np = as(cc.get("npt")); vector w((np+13)*(np+n)+(3*n*(n+3))/2); NumericVector pp = clone(par); // ensure that newuoa doesn't modify the R object F77_NAME(newuoa)(&n, &np, pp.begin(), &rb, &re, &ip, &mxf, &w[0], &ierr); return rval(pp, "newuoa", ierr); } catch( std::exception& __ex__ ) { forward_exception_to_r( __ex__ ); } catch(...) { ::Rf_error("c++ exception (unknown reason)"); } return R_NilValue; // -Wall } /// Assorted error messages. extern "C" void F77_NAME(minqer)(const int *msgno) { BEGIN_RCPP const char *msg = (char*)NULL; switch(*msgno) { case 10: case 101: msg = "NPT is not in the required interval"; break; case 20: msg = "one of the differences XU(I)-XL(I) is less than 2*RHOBEG"; break; case 320: msg = "bobyqa detected too much cancellation in denominator"; break; case 390: msg = "maximum number of function evaluations exceeded"; break; case 430: case 3701: case 2101: msg = "a trust region step failed to reduce q"; break; default: throw range_error("minqer message number"); } throw runtime_error(msg); VOID_END_RCPP } /// Iteration output when rho changes and iprint >= 2 extern "C" void F77_NAME(minqit)(const int *iprint, const double *rho, const int *nf, const double *fopt, const int *n, const double xbase[], const double xopt[]) { if (*iprint >= 2) { Rprintf("rho: %#8.2g eval: %3d fn: %#12g par:", *rho, *nf, *fopt); for(int i = 0; i < *n; i++) Rprintf("%#8g ", xbase[i] + xopt[i]); Rprintf("\n"); } } /// Output at return (do we really need this - why not use the print method?) extern "C" void F77_NAME(minqir)(const int *iprint, const double *f, const int *nf, const int *n, const double x[]) { if (*iprint > 0) { Rprintf("At return\n"); Rprintf("eval: %3d fn: %#14.8g par:", *nf, *f); for (int i = 0; i < *n; i++) Rprintf(" %#8g", x[i]); Rprintf("\n"); } } #define CALLDEF(name, n) {#name, (DL_FUNC) &name, n} static R_CallMethodDef CallEntries[] = { CALLDEF(bobyqa_cpp, 5), CALLDEF(uobyqa_cpp, 3), CALLDEF(newuoa_cpp, 3), {NULL, NULL, 0} }; /// Initializer for the package. Registers the symbols for .Call. extern "C" void R_init_minqa(DllInfo *dll) { R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); R_useDynamicSymbols(dll, FALSE); } minqa/src/Makevars0000644000176000001440000000012511730166327013676 0ustar ripleyusers## -*- mode: makefile; -*- PKG_LIBS = `$(R_HOME)/bin/Rscript -e "Rcpp:::LdFlags()"` minqa/src/update.f0000644000176000001440000000654012415434561013641 0ustar ripleyusers SUBROUTINE UPDATE (N,NPT,BMAT,ZMAT,IDZ,NDIM,VLAG,BETA,KNEW,W) IMPLICIT DOUBLE PRECISION (A-H,O-Z) DIMENSION BMAT(NDIM,*),ZMAT(NPT,*),VLAG(*),W(*) C C The arrays BMAT and ZMAT with IDZ are updated, in order to shift the C interpolation point that has index KNEW. On entry, VLAG contains the C components of the vector Theta*Wcheck+e_b of the updating formula C (6.11), and BETA holds the value of the parameter that has this name. C The vector W is used for working space. C C Set some constants. C ONE=1.0D0 ZERO=0.0D0 NPTM=NPT-N-1 C C Apply the rotations that put zeros in the KNEW-th row of ZMAT. C JL=1 DO 20 J=2,NPTM IF (J .EQ. IDZ) THEN JL=IDZ ELSE IF (ZMAT(KNEW,J) .NE. ZERO) THEN TEMP=DSQRT(ZMAT(KNEW,JL)**2+ZMAT(KNEW,J)**2) TEMPA=ZMAT(KNEW,JL)/TEMP TEMPB=ZMAT(KNEW,J)/TEMP DO 10 I=1,NPT TEMP=TEMPA*ZMAT(I,JL)+TEMPB*ZMAT(I,J) ZMAT(I,J)=TEMPA*ZMAT(I,J)-TEMPB*ZMAT(I,JL) 10 ZMAT(I,JL)=TEMP ZMAT(KNEW,J)=ZERO END IF 20 CONTINUE C C Put the first NPT components of the KNEW-th column of HLAG into W, C and calculate the parameters of the updating formula. C TEMPA=ZMAT(KNEW,1) IF (IDZ .GE. 2) TEMPA=-TEMPA IF (JL .GT. 1) TEMPB=ZMAT(KNEW,JL) DO 30 I=1,NPT W(I)=TEMPA*ZMAT(I,1) IF (JL .GT. 1) W(I)=W(I)+TEMPB*ZMAT(I,JL) 30 CONTINUE ALPHA=W(KNEW) TAU=VLAG(KNEW) TAUSQ=TAU*TAU DENOM=ALPHA*BETA+TAUSQ VLAG(KNEW)=VLAG(KNEW)-ONE C C Complete the updating of ZMAT when there is only one nonzero element C in the KNEW-th row of the new matrix ZMAT, but, if IFLAG is set to one, C then the first column of ZMAT will be exchanged with another one later. C IFLAG=0 IF (JL .EQ. 1) THEN TEMP=DSQRT(DABS(DENOM)) TEMPB=TEMPA/TEMP TEMPA=TAU/TEMP DO 40 I=1,NPT 40 ZMAT(I,1)=TEMPA*ZMAT(I,1)-TEMPB*VLAG(I) IF (IDZ .EQ. 1 .AND. TEMP .LT. ZERO) IDZ=2 IF (IDZ .GE. 2 .AND. TEMP .GE. ZERO) IFLAG=1 ELSE C C Complete the updating of ZMAT in the alternative case. C JA=1 IF (BETA .GE. ZERO) JA=JL JB=JL+1-JA TEMP=ZMAT(KNEW,JB)/DENOM TEMPA=TEMP*BETA TEMPB=TEMP*TAU TEMP=ZMAT(KNEW,JA) SCALA=ONE/DSQRT(DABS(BETA)*TEMP*TEMP+TAUSQ) SCALB=SCALA*DSQRT(DABS(DENOM)) DO 50 I=1,NPT ZMAT(I,JA)=SCALA*(TAU*ZMAT(I,JA)-TEMP*VLAG(I)) 50 ZMAT(I,JB)=SCALB*(ZMAT(I,JB)-TEMPA*W(I)-TEMPB*VLAG(I)) IF (DENOM .LE. ZERO) THEN IF (BETA .LT. ZERO) IDZ=IDZ+1 IF (BETA .GE. ZERO) IFLAG=1 END IF END IF C C IDZ is reduced in the following case, and usually the first column C of ZMAT is exchanged with a later one. C IF (IFLAG .EQ. 1) THEN IDZ=IDZ-1 DO 60 I=1,NPT TEMP=ZMAT(I,1) ZMAT(I,1)=ZMAT(I,IDZ) 60 ZMAT(I,IDZ)=TEMP END IF C C Finally, update the matrix BMAT. C DO 70 J=1,N JP=NPT+J W(JP)=BMAT(KNEW,J) TEMPA=(ALPHA*VLAG(JP)-TAU*W(JP))/DENOM TEMPB=(-BETA*W(JP)-TAU*VLAG(JP))/DENOM DO 70 I=1,JP BMAT(I,J)=BMAT(I,J)+TEMPA*VLAG(I)+TEMPB*W(I) IF (I .GT. NPT) BMAT(JP,I-NPT)=BMAT(I,J) 70 CONTINUE RETURN END minqa/src/newuoa.f0000644000176000001440000000655212415434561013660 0ustar ripleyusers SUBROUTINE NEWUOA (N,NPT,X,RHOBEG,RHOEND,IPRINT,MAXFUN,W,IERR) IMPLICIT DOUBLE PRECISION (A-H,O-Z) DIMENSION X(*),W(*) CJN Declare error flag INTEGER IERR C C This subroutine seeks the least value of a function of many variables, C by a trust region method that forms quadratic models by interpolation. C There can be some freedom in the interpolation conditions, which is C taken up by minimizing the Frobenius norm of the change to the second C derivative of the quadratic model, beginning with a zero matrix. The C arguments of the subroutine are as follows. C C N must be set to the number of variables and must be at least two. C NPT is the number of interpolation conditions. Its value must be in the C interval [N+2,(N+1)(N+2)/2]. C Initial values of the variables must be set in X(1),X(2),...,X(N). They C will be changed to the values that give the least calculated F. C RHOBEG and RHOEND must be set to the initial and final values of a trust C region radius, so both must be positive with RHOEND<=RHOBEG. Typically C RHOBEG should be about one tenth of the greatest expected change to a C variable, and RHOEND should indicate the accuracy that is required in C the final values of the variables. C The value of IPRINT should be set to 0, 1, 2 or 3, which controls the C amount of printing. Specifically, there is no output if IPRINT=0 and C there is output only at the return if IPRINT=1. Otherwise, each new C value of RHO is printed, with the best vector of variables so far and C the corresponding value of the objective function. Further, each new C value of F with its variables are output if IPRINT=3. C MAXFUN must be set to an upper bound on the number of calls of CALFUN. C The array W will be used for working space. Its length must be at least C (NPT+13)*(NPT+N)+3*N*(N+3)/2. CJN Add IERR to tell what the error is to minqer. C IERR gives an error code that is interpreted by minqer interface routine. C and passed back to minqa.R C C SUBROUTINE CALFUN (N,X,F) must be provided by the user. It must set F to C the value of the objective function for the variables X(1),X(2),...,X(N). C C Partition the working space array, so that different parts of it can be C treated separately by the subroutine that performs the main calculation. C NP=N+1 NPTM=NPT-NP IF (NPT .LT. N+2 .OR. NPT .GT. ((N+2)*NP)/2) THEN CJN CALL minqer(101) c$$$ PRINT 10 c$$$ 10 FORMAT (/4X,'Return from NEWUOA because NPT is not in', c$$$ 1 ' the required interval') c$$$ GO TO 20 IERR = 10 GO TO 20 END IF 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 C C The above settings provide a partition of W for subroutine NEWUOB. C The partition requires the first NPT*(NPT+N)+5*N*(N+3)/2 elements of C W plus the space that is needed by the last array of NEWUOB. C CALL NEWUOB (N,NPT,X,RHOBEG,RHOEND,IPRINT,MAXFUN,W(IXB), 1 W(IXO),W(IXN),W(IXP),W(IFV),W(IGQ),W(IHQ),W(IPQ),W(IBMAT), 2 W(IZMAT),NDIM,W(ID),W(IVL),W(IW), IERR) 20 RETURN END minqa/src/bobyqb.f0000644000176000001440000005703312415434561013640 0ustar ripleyusers SUBROUTINE BOBYQB (N,NPT,X,XL,XU,RHOBEG,RHOEND,IPRINT, 1 MAXFUN,XBASE,XPT,FVAL,XOPT,GOPT,HQ,PQ,BMAT,ZMAT,NDIM, 2 SL,SU,XNEW,XALT,D,VLAG,W,IERR) IMPLICIT DOUBLE PRECISION (A-H,O-Z) DIMENSION X(*),XL(*),XU(*),XBASE(*),XPT(NPT,*),FVAL(*), 1 XOPT(*),GOPT(*),HQ(*),PQ(*),BMAT(NDIM,*),ZMAT(NPT,*), 2 SL(*),SU(*),XNEW(*),XALT(*),D(*),VLAG(*),W(*) C C The arguments N, NPT, X, XL, XU, RHOBEG, RHOEND, IPRINT and MAXFUN C are identical to the corresponding arguments in SUBROUTINE BOBYQA. C XBASE holds a shift of origin that should reduce the contributions C from rounding errors to values of the model and Lagrange functions. C XPT is a two-dimensional array that holds the coordinates of the C interpolation points relative to XBASE. C FVAL holds the values of F at the interpolation points. C XOPT is set to the displacement from XBASE of the trust region centre. C GOPT holds the gradient of the quadratic model at XBASE+XOPT. C HQ holds the explicit second derivatives of the quadratic model. C PQ contains the parameters of the implicit second derivatives of the C quadratic model. C BMAT holds the last N columns of H. C ZMAT holds the factorization of the leading NPT by NPT submatrix of H, C this factorization being ZMAT times ZMAT^T, which provides both the C correct rank and positive semi-definiteness. C NDIM is the first dimension of BMAT and has the value NPT+N. C SL and SU hold the differences XL-XBASE and XU-XBASE, respectively. C All the components of every XOPT are going to satisfy the bounds C SL(I) .LEQ. XOPT(I) .LEQ. SU(I), with appropriate equalities when C XOPT is on a constraint boundary. C XNEW is chosen by SUBROUTINE TRSBOX or ALTMOV. Usually XBASE+XNEW is the C vector of variables for the next call of CALFUN. XNEW also satisfies C the SL and SU constraints in the way that has just been mentioned. C XALT is an alternative to XNEW, chosen by ALTMOV, that may replace XNEW C in order to increase the denominator in the updating of UPDATE. C D is reserved for a trial step from XOPT, which is usually XNEW-XOPT. C VLAG contains the values of the Lagrange functions at a new point X. C They are part of a product that requires VLAG to be of length NDIM. C W is a one-dimensional array that is used for working space. Its length C must be at least 3*NDIM = 3*(NPT+N). CJN 100807 C IERR is an error code to tell calling program WHICH error occurred. CJN Note that it is defined in BOBYQA to 0 initially. C C Set some constants. C HALF=0.5D0 ONE=1.0D0 TEN=10.0D0 TENTH=0.1D0 TWO=2.0D0 ZERO=0.0D0 NP=N+1 NPTM=NPT-NP NH=(N*NP)/2 C C The call of PRELIM sets the elements of XBASE, XPT, FVAL, GOPT, HQ, PQ, C BMAT and ZMAT for the first iteration, with the corresponding values of C of NF and KOPT, which are the number of calls of CALFUN so far and the C index of the interpolation point at the trust region centre. Then the C initial XOPT is set too. The branch to label 720 occurs if MAXFUN is C less than NPT. GOPT will be updated if KOPT is different from KBASE. C CALL PRELIM (N,NPT,X,XL,XU,RHOBEG,IPRINT,MAXFUN,XBASE,XPT, 1 FVAL,GOPT,HQ,PQ,BMAT,ZMAT,NDIM,SL,SU,NF,KOPT) XOPTSQ=ZERO DO 10 I=1,N XOPT(I)=XPT(KOPT,I) 10 XOPTSQ=XOPTSQ+XOPT(I)**2 FSAVE=FVAL(1) IF (NF .LT. NPT) THEN CJN 100807 IERR=390 GOTO 720 CJN CALL minqer(390) c$$$ IF (IPRINT .GT. 0) PRINT 390 c$$$ GOTO 720 END IF KBASE=1 C C Complete the settings that are required for the iterative procedure. C RHO=RHOBEG DELTA=RHO NRESC=NF NTRITS=0 DIFFA=ZERO DIFFB=ZERO ITEST=0 NFSAV=NF C C Update GOPT if necessary before the first iteration and after each C call of RESCUE that makes a call of CALFUN. C 20 IF (KOPT .NE. KBASE) THEN IH=0 DO 30 J=1,N DO 30 I=1,J IH=IH+1 IF (I .LT. J) GOPT(J)=GOPT(J)+HQ(IH)*XOPT(I) 30 GOPT(I)=GOPT(I)+HQ(IH)*XOPT(J) IF (NF .GT. NPT) THEN DO 50 K=1,NPT TEMP=ZERO DO 40 J=1,N 40 TEMP=TEMP+XPT(K,J)*XOPT(J) TEMP=PQ(K)*TEMP DO 50 I=1,N 50 GOPT(I)=GOPT(I)+TEMP*XPT(K,I) END IF END IF C C Generate the next point in the trust region that provides a small value C of the quadratic model subject to the constraints on the variables. C The integer NTRITS is set to the number "trust region" iterations that C have occurred since the last "alternative" iteration. If the length C of XNEW-XOPT is less than HALF*RHO, however, then there is a branch to C label 650 or 680 with NTRITS=-1, instead of calculating F at XNEW. C 60 CALL TRSBOX (N,NPT,XPT,XOPT,GOPT,HQ,PQ,SL,SU,DELTA,XNEW,D, 1 W,W(NP),W(NP+N),W(NP+2*N),W(NP+3*N),DSQ,CRVMIN) DNORM=DMIN1(DELTA,DSQRT(DSQ)) IF (DNORM .LT. HALF*RHO) THEN NTRITS=-1 DISTSQ=(TEN*RHO)**2 IF (NF .LE. NFSAV+2) GOTO 650 C C The following choice between labels 650 and 680 depends on whether or C not our work with the current RHO seems to be complete. Either RHO is C decreased or termination occurs if the errors in the quadratic model at C the last three interpolation points compare favourably with predictions C of likely improvements to the model within distance HALF*RHO of XOPT. C ERRBIG=DMAX1(DIFFA,DIFFB,DIFFC) FRHOSQ=0.125D0*RHO*RHO IF (CRVMIN .GT. ZERO .AND. ERRBIG .GT. FRHOSQ*CRVMIN) 1 GOTO 650 BDTOL=ERRBIG/RHO DO 80 J=1,N BDTEST=BDTOL IF (XNEW(J) .EQ. SL(J)) BDTEST=W(J) IF (XNEW(J) .EQ. SU(J)) BDTEST=-W(J) IF (BDTEST .LT. BDTOL) THEN CURV=HQ((J+J*J)/2) DO 70 K=1,NPT 70 CURV=CURV+PQ(K)*XPT(K,J)**2 BDTEST=BDTEST+HALF*CURV*RHO IF (BDTEST .LT. BDTOL) GOTO 650 END IF 80 CONTINUE GOTO 680 END IF NTRITS=NTRITS+1 C C Severe cancellation is likely to occur if XOPT is too far from XBASE. C If the following test holds, then XBASE is shifted so that XOPT becomes C zero. The appropriate changes are made to BMAT and to the second C derivatives of the current model, beginning with the changes to BMAT C that do not depend on ZMAT. VLAG is used temporarily for working space. C 90 IF (DSQ .LE. 1.0D-3*XOPTSQ) THEN FRACSQ=0.25D0*XOPTSQ SUMPQ=ZERO DO 110 K=1,NPT SUMPQ=SUMPQ+PQ(K) SUM=-HALF*XOPTSQ DO 100 I=1,N 100 SUM=SUM+XPT(K,I)*XOPT(I) W(NPT+K)=SUM TEMP=FRACSQ-HALF*SUM DO 110 I=1,N W(I)=BMAT(K,I) VLAG(I)=SUM*XPT(K,I)+TEMP*XOPT(I) IP=NPT+I DO 110 J=1,I 110 BMAT(IP,J)=BMAT(IP,J)+W(I)*VLAG(J)+VLAG(I)*W(J) C C Then the revisions of BMAT that depend on ZMAT are calculated. C DO 150 JJ=1,NPTM SUMZ=ZERO SUMW=ZERO DO 120 K=1,NPT SUMZ=SUMZ+ZMAT(K,JJ) VLAG(K)=W(NPT+K)*ZMAT(K,JJ) 120 SUMW=SUMW+VLAG(K) DO 140 J=1,N SUM=(FRACSQ*SUMZ-HALF*SUMW)*XOPT(J) DO 130 K=1,NPT 130 SUM=SUM+VLAG(K)*XPT(K,J) W(J)=SUM DO 140 K=1,NPT 140 BMAT(K,J)=BMAT(K,J)+SUM*ZMAT(K,JJ) DO 150 I=1,N IP=I+NPT TEMP=W(I) DO 150 J=1,I 150 BMAT(IP,J)=BMAT(IP,J)+TEMP*W(J) C C The following instructions complete the shift, including the changes C to the second derivative parameters of the quadratic model. C IH=0 DO 170 J=1,N W(J)=-HALF*SUMPQ*XOPT(J) DO 160 K=1,NPT W(J)=W(J)+PQ(K)*XPT(K,J) 160 XPT(K,J)=XPT(K,J)-XOPT(J) DO 170 I=1,J IH=IH+1 HQ(IH)=HQ(IH)+W(I)*XOPT(J)+XOPT(I)*W(J) 170 BMAT(NPT+I,J)=BMAT(NPT+J,I) DO 180 I=1,N XBASE(I)=XBASE(I)+XOPT(I) XNEW(I)=XNEW(I)-XOPT(I) SL(I)=SL(I)-XOPT(I) SU(I)=SU(I)-XOPT(I) 180 XOPT(I)=ZERO XOPTSQ=ZERO END IF IF (NTRITS .EQ. 0) GOTO 210 GOTO 230 C C XBASE is also moved to XOPT by a call of RESCUE. This calculation is C more expensive than the previous shift, because new matrices BMAT and C ZMAT are generated from scratch, which may include the replacement of C interpolation points whose positions seem to be causing near linear C dependence in the interpolation conditions. Therefore RESCUE is called C only if rounding errors have reduced by at least a factor of two the C denominator of the formula for updating the H matrix. It provides a C useful safeguard, but is not invoked in most applications of BOBYQA. C 190 NFSAV=NF KBASE=KOPT CALL RESCUE (N,NPT,XL,XU,IPRINT,MAXFUN,XBASE,XPT,FVAL, 1 XOPT,GOPT,HQ,PQ,BMAT,ZMAT,NDIM,SL,SU,NF,DELTA,KOPT, 2 VLAG,W,W(N+NP),W(NDIM+NP)) C C XOPT is updated now in case the branch below to label 720 is taken. C Any updating of GOPT occurs after the branch below to label 20, which C leads to a trust region iteration as does the branch to label 60. C XOPTSQ=ZERO IF (KOPT .NE. KBASE) THEN DO 200 I=1,N XOPT(I)=XPT(KOPT,I) 200 XOPTSQ=XOPTSQ+XOPT(I)**2 END IF IF (NF .LT. 0) THEN NF=MAXFUN CJN CALL minqer(390) c$$$ IF (IPRINT .GT. 0) PRINT 390 c$$$ GOTO 720 CJN 100807 IERR=390 GOTO 720 END IF NRESC=NF IF (NFSAV .LT. NF) THEN NFSAV=NF GOTO 20 END IF IF (NTRITS .GT. 0) GOTO 60 C C Pick two alternative vectors of variables, relative to XBASE, that C are suitable as new positions of the KNEW-th interpolation point. C Firstly, XNEW is set to the point on a line through XOPT and another C interpolation point that minimizes the predicted value of the next C denominator, subject to ||XNEW - XOPT|| .LEQ. ADELT and to the SL C and SU bounds. Secondly, XALT is set to the best feasible point on C a constrained version of the Cauchy step of the KNEW-th Lagrange C function, the corresponding value of the square of this function C being returned in CAUCHY. The choice between these alternatives is C going to be made when the denominator is calculated. C 210 CALL ALTMOV (N,NPT,XPT,XOPT,BMAT,ZMAT,NDIM,SL,SU,KOPT, 1 KNEW,ADELT,XNEW,XALT,ALPHA,CAUCHY,W,W(NP),W(NDIM+1)) DO 220 I=1,N 220 D(I)=XNEW(I)-XOPT(I) C C Calculate VLAG and BETA for the current choice of D. The scalar C product of D with XPT(K,.) is going to be held in W(NPT+K) for C use when VQUAD is calculated. C 230 DO 250 K=1,NPT SUMA=ZERO SUMB=ZERO SUM=ZERO DO 240 J=1,N SUMA=SUMA+XPT(K,J)*D(J) SUMB=SUMB+XPT(K,J)*XOPT(J) 240 SUM=SUM+BMAT(K,J)*D(J) W(K)=SUMA*(HALF*SUMA+SUMB) VLAG(K)=SUM 250 W(NPT+K)=SUMA BETA=ZERO DO 270 JJ=1,NPTM SUM=ZERO DO 260 K=1,NPT 260 SUM=SUM+ZMAT(K,JJ)*W(K) BETA=BETA-SUM*SUM DO 270 K=1,NPT 270 VLAG(K)=VLAG(K)+SUM*ZMAT(K,JJ) DSQ=ZERO BSUM=ZERO DX=ZERO DO 300 J=1,N DSQ=DSQ+D(J)**2 SUM=ZERO DO 280 K=1,NPT 280 SUM=SUM+W(K)*BMAT(K,J) BSUM=BSUM+SUM*D(J) JP=NPT+J DO 290 I=1,N 290 SUM=SUM+BMAT(JP,I)*D(I) VLAG(JP)=SUM BSUM=BSUM+SUM*D(J) 300 DX=DX+D(J)*XOPT(J) BETA=DX*DX+DSQ*(XOPTSQ+DX+DX+HALF*DSQ)+BETA-BSUM VLAG(KOPT)=VLAG(KOPT)+ONE C C If NTRITS is zero, the denominator may be increased by replacing C the step D of ALTMOV by a Cauchy step. Then RESCUE may be called if C rounding errors have damaged the chosen denominator. C IF (NTRITS .EQ. 0) THEN DENOM=VLAG(KNEW)**2+ALPHA*BETA IF (DENOM .LT. CAUCHY .AND. CAUCHY .GT. ZERO) THEN DO 310 I=1,N XNEW(I)=XALT(I) 310 D(I)=XNEW(I)-XOPT(I) CAUCHY=ZERO GO TO 230 END IF IF (DENOM .LE. HALF*VLAG(KNEW)**2) THEN IF (NF .GT. NRESC) GOTO 190 CJN IF (IPRINT .GT. 0) CALL minqer(320) c$$$ PRINT 320 c$$$ 320 FORMAT (/5X,'Return from BOBYQA because of much', c$$$ 1 ' cancellation in a denominator.') c$$$ GOTO 720 CJN 100807 IERR=320 GOTO 720 END IF C C Alternatively, if NTRITS is positive, then set KNEW to the index of C the next interpolation point to be deleted to make room for a trust C region step. Again RESCUE may be called if rounding errors have damaged C the chosen denominator, which is the reason for attempting to select C KNEW before calculating the next value of the objective function. C ELSE DELSQ=DELTA*DELTA SCADEN=ZERO BIGLSQ=ZERO KNEW=0 DO 350 K=1,NPT IF (K .EQ. KOPT) GOTO 350 HDIAG=ZERO DO 330 JJ=1,NPTM 330 HDIAG=HDIAG+ZMAT(K,JJ)**2 DEN=BETA*HDIAG+VLAG(K)**2 DISTSQ=ZERO DO 340 J=1,N 340 DISTSQ=DISTSQ+(XPT(K,J)-XOPT(J))**2 TEMP=DMAX1(ONE,(DISTSQ/DELSQ)**2) IF (TEMP*DEN .GT. SCADEN) THEN SCADEN=TEMP*DEN KNEW=K DENOM=DEN END IF BIGLSQ=DMAX1(BIGLSQ,TEMP*VLAG(K)**2) 350 CONTINUE IF (SCADEN .LE. HALF*BIGLSQ) THEN IF (NF .GT. NRESC) GOTO 190 CJN IF (IPRINT .GT. 0) CALL minqer(320) c$$$ PRINT 320 c$$$ GOTO 720 CJN 100807 IERR=320 GOTO 720 END IF END IF C C Put the variables for the next calculation of the objective function C in XNEW, with any adjustments for the bounds. C C C Calculate the value of the objective function at XBASE+XNEW, unless C the limit on the number of calculations of F has been reached. C 360 DO 380 I=1,N X(I)=DMIN1(DMAX1(XL(I),XBASE(I)+XNEW(I)),XU(I)) IF (XNEW(I) .EQ. SL(I)) X(I)=XL(I) IF (XNEW(I) .EQ. SU(I)) X(I)=XU(I) 380 CONTINUE IF (NF .GE. MAXFUN) THEN CJN IF (IPRINT .GT. 0) CALL minqer(390) c$$$ PRINT 390 c$$$ 390 FORMAT (/4X,'Return from BOBYQA because CALFUN has been', c$$$ 1 ' called MAXFUN times.') c$$$ GOTO 720 CJN 100807 IERR=390 GOTO 720 END IF NF=NF+1 F = CALFUN (N,X,IPRINT) c$$$ CALL minqi3(IPRINT, F, NF, N, X) c$$$ IF (IPRINT .EQ. 3) THEN c$$$ PRINT 400, NF,F,(X(I),I=1,N) c$$$ 400 FORMAT (/4X,'Function number',I6,' F =',1PD18.10, c$$$ 1 ' The corresponding X is:'/(2X,5D15.6)) c$$$ END IF IF (NTRITS .EQ. -1) THEN FSAVE=F GOTO 720 END IF C C Use the quadratic model to predict the change in F due to the step D, C and set DIFF to the error of this prediction. C FOPT=FVAL(KOPT) VQUAD=ZERO IH=0 DO 410 J=1,N VQUAD=VQUAD+D(J)*GOPT(J) DO 410 I=1,J IH=IH+1 TEMP=D(I)*D(J) IF (I .EQ. J) TEMP=HALF*TEMP 410 VQUAD=VQUAD+HQ(IH)*TEMP DO 420 K=1,NPT 420 VQUAD=VQUAD+HALF*PQ(K)*W(NPT+K)**2 DIFF=F-FOPT-VQUAD DIFFC=DIFFB DIFFB=DIFFA DIFFA=DABS(DIFF) IF (DNORM .GT. RHO) NFSAV=NF C C Pick the next value of DELTA after a trust region step. C IF (NTRITS .GT. 0) THEN IF (VQUAD .GE. ZERO) THEN CJN IF (IPRINT .GT. 0) CALL minqer(430) c$$$ PRINT 430 c$$$ 430 FORMAT (/4X,'Return from BOBYQA because a trust', c$$$ 1 ' region step has failed to reduce Q.') c$$$ GOTO 720 CJN 100807 IERR=430 GOTO 720 END IF RATIO=(F-FOPT)/VQUAD IF (RATIO .LE. TENTH) THEN DELTA=DMIN1(HALF*DELTA,DNORM) ELSE IF (RATIO. LE. 0.7D0) THEN DELTA=DMAX1(HALF*DELTA,DNORM) ELSE DELTA=DMAX1(HALF*DELTA,DNORM+DNORM) END IF IF (DELTA .LE. 1.5D0*RHO) DELTA=RHO C C Recalculate KNEW and DENOM if the new F is less than FOPT. C IF (F .LT. FOPT) THEN KSAV=KNEW DENSAV=DENOM DELSQ=DELTA*DELTA SCADEN=ZERO BIGLSQ=ZERO KNEW=0 DO 460 K=1,NPT HDIAG=ZERO DO 440 JJ=1,NPTM 440 HDIAG=HDIAG+ZMAT(K,JJ)**2 DEN=BETA*HDIAG+VLAG(K)**2 DISTSQ=ZERO DO 450 J=1,N 450 DISTSQ=DISTSQ+(XPT(K,J)-XNEW(J))**2 TEMP=DMAX1(ONE,(DISTSQ/DELSQ)**2) IF (TEMP*DEN .GT. SCADEN) THEN SCADEN=TEMP*DEN KNEW=K DENOM=DEN END IF 460 BIGLSQ=DMAX1(BIGLSQ,TEMP*VLAG(K)**2) IF (SCADEN .LE. HALF*BIGLSQ) THEN KNEW=KSAV DENOM=DENSAV END IF END IF END IF C C Update BMAT and ZMAT, so that the KNEW-th interpolation point can be C moved. Also update the second derivative terms of the model. C CALL UPDATEBOBYQA (N,NPT,BMAT,ZMAT,NDIM,VLAG,BETA,DENOM,KNEW,W) IH=0 PQOLD=PQ(KNEW) PQ(KNEW)=ZERO DO 470 I=1,N TEMP=PQOLD*XPT(KNEW,I) DO 470 J=1,I IH=IH+1 470 HQ(IH)=HQ(IH)+TEMP*XPT(KNEW,J) DO 480 JJ=1,NPTM TEMP=DIFF*ZMAT(KNEW,JJ) DO 480 K=1,NPT 480 PQ(K)=PQ(K)+TEMP*ZMAT(K,JJ) C C Include the new interpolation point, and make the changes to GOPT at C the old XOPT that are caused by the updating of the quadratic model. C FVAL(KNEW)=F DO 490 I=1,N XPT(KNEW,I)=XNEW(I) 490 W(I)=BMAT(KNEW,I) DO 520 K=1,NPT SUMA=ZERO DO 500 JJ=1,NPTM 500 SUMA=SUMA+ZMAT(KNEW,JJ)*ZMAT(K,JJ) SUMB=ZERO DO 510 J=1,N 510 SUMB=SUMB+XPT(K,J)*XOPT(J) TEMP=SUMA*SUMB DO 520 I=1,N 520 W(I)=W(I)+TEMP*XPT(K,I) DO 530 I=1,N 530 GOPT(I)=GOPT(I)+DIFF*W(I) C C Update XOPT, GOPT and KOPT if the new calculated F is less than FOPT. C IF (F .LT. FOPT) THEN KOPT=KNEW XOPTSQ=ZERO IH=0 DO 540 J=1,N XOPT(J)=XNEW(J) XOPTSQ=XOPTSQ+XOPT(J)**2 DO 540 I=1,J IH=IH+1 IF (I .LT. J) GOPT(J)=GOPT(J)+HQ(IH)*D(I) 540 GOPT(I)=GOPT(I)+HQ(IH)*D(J) DO 560 K=1,NPT TEMP=ZERO DO 550 J=1,N 550 TEMP=TEMP+XPT(K,J)*D(J) TEMP=PQ(K)*TEMP DO 560 I=1,N 560 GOPT(I)=GOPT(I)+TEMP*XPT(K,I) END IF C C Calculate the parameters of the least Frobenius norm interpolant to C the current data, the gradient of this interpolant at XOPT being put C into VLAG(NPT+I), I=1,2,...,N. C IF (NTRITS .GT. 0) THEN DO 570 K=1,NPT VLAG(K)=FVAL(K)-FVAL(KOPT) 570 W(K)=ZERO DO 590 J=1,NPTM SUM=ZERO DO 580 K=1,NPT 580 SUM=SUM+ZMAT(K,J)*VLAG(K) DO 590 K=1,NPT 590 W(K)=W(K)+SUM*ZMAT(K,J) DO 610 K=1,NPT SUM=ZERO DO 600 J=1,N 600 SUM=SUM+XPT(K,J)*XOPT(J) W(K+NPT)=W(K) 610 W(K)=SUM*W(K) GQSQ=ZERO GISQ=ZERO DO 630 I=1,N SUM=ZERO DO 620 K=1,NPT 620 SUM=SUM+BMAT(K,I)*VLAG(K)+XPT(K,I)*W(K) IF (XOPT(I) .EQ. SL(I)) THEN GQSQ=GQSQ+DMIN1(ZERO,GOPT(I))**2 GISQ=GISQ+DMIN1(ZERO,SUM)**2 ELSE IF (XOPT(I) .EQ. SU(I)) THEN GQSQ=GQSQ+DMAX1(ZERO,GOPT(I))**2 GISQ=GISQ+DMAX1(ZERO,SUM)**2 ELSE GQSQ=GQSQ+GOPT(I)**2 GISQ=GISQ+SUM*SUM END IF 630 VLAG(NPT+I)=SUM C C Test whether to replace the new quadratic model by the least Frobenius C norm interpolant, making the replacement if the test is satisfied. C ITEST=ITEST+1 IF (GQSQ .LT. TEN*GISQ) ITEST=0 IF (ITEST .GE. 3) THEN DO 640 I=1,MAX0(NPT,NH) IF (I .LE. N) GOPT(I)=VLAG(NPT+I) IF (I .LE. NPT) PQ(I)=W(NPT+I) IF (I .LE. NH) HQ(I)=ZERO ITEST=0 640 CONTINUE END IF END IF C C If a trust region step has provided a sufficient decrease in F, then C branch for another trust region calculation. The case NTRITS=0 occurs C when the new interpolation point was reached by an alternative step. C IF (NTRITS .EQ. 0) GOTO 60 IF (F .LE. FOPT+TENTH*VQUAD) GOTO 60 C C Alternatively, find out if the interpolation points are close enough C to the best point so far. C DISTSQ=DMAX1((TWO*DELTA)**2,(TEN*RHO)**2) 650 KNEW=0 DO 670 K=1,NPT SUM=ZERO DO 660 J=1,N 660 SUM=SUM+(XPT(K,J)-XOPT(J))**2 IF (SUM .GT. DISTSQ) THEN KNEW=K DISTSQ=SUM END IF 670 CONTINUE C C If KNEW is positive, then ALTMOV finds alternative new positions for C the KNEW-th interpolation point within distance ADELT of XOPT. It is C reached via label 90. Otherwise, there is a branch to label 60 for C another trust region iteration, unless the calculations with the C current RHO are complete. C IF (KNEW .GT. 0) THEN DIST=DSQRT(DISTSQ) IF (NTRITS .EQ. -1) THEN DELTA=DMIN1(TENTH*DELTA,HALF*DIST) IF (DELTA .LE. 1.5D0*RHO) DELTA=RHO END IF NTRITS=0 ADELT=DMAX1(DMIN1(TENTH*DIST,DELTA),RHO) DSQ=ADELT*ADELT GOTO 90 END IF IF (NTRITS .EQ. -1) GOTO 680 IF (RATIO .GT. ZERO) GOTO 60 IF (DMAX1(DELTA,DNORM) .GT. RHO) GOTO 60 C C The calculations with the current value of RHO are complete. Pick the C next values of RHO and DELTA. C 680 IF (RHO .GT. RHOEND) THEN DELTA=HALF*RHO RATIO=RHO/RHOEND IF (RATIO .LE. 16.0D0) THEN RHO=RHOEND ELSE IF (RATIO .LE. 250.0D0) THEN RHO=DSQRT(RATIO)*RHOEND ELSE RHO=TENTH*RHO END IF DELTA=DMAX1(DELTA,RHO) CALL minqit(IPRINT, RHO, NF, FVAL(KOPT), N, XBASE, XOPT) c$$$ IF (IPRINT .GE. 2) THEN c$$$ IF (IPRINT .GE. 3) PRINT 690 c$$$ 690 FORMAT (5X) c$$$ PRINT 700, RHO,NF c$$$ 700 FORMAT (/4X,'New RHO =',1PD11.4,5X,'Number of', c$$$ 1 ' function values =',I6) c$$$ PRINT 710, FVAL(KOPT),(XBASE(I)+XOPT(I),I=1,N) c$$$ 710 FORMAT (4X,'Least value of F =',1PD23.15,9X, c$$$ 1 'The corresponding X is:'/(2X,5D15.6)) c$$$ END IF NTRITS=0 NFSAV=NF GOTO 60 END IF C C Return from the calculation, after another Newton-Raphson step, if C it is too short to have been tried before. C IF (NTRITS .EQ. -1) GOTO 360 720 IF (FVAL(KOPT) .LE. FSAVE) THEN DO 730 I=1,N X(I)=DMIN1(DMAX1(XL(I),XBASE(I)+XOPT(I)),XU(I)) IF (XOPT(I) .EQ. SL(I)) X(I)=XL(I) IF (XOPT(I) .EQ. SU(I)) X(I)=XU(I) 730 CONTINUE F=FVAL(KOPT) END IF CJN 100807 Do we want to add IERR to minqir as a diagnostic. If zero, not print, CJN if not, then use minqer output or similar. CJN ?? IF (IERR.NE.0) CALL minqer(IERR) CALL minqir(IPRINT, F, NF, N, X) c$$$ IF (IPRINT .GE. 1) THEN c$$$ PRINT 740, NF c$$$ 740 FORMAT (/4X,'At the return from BOBYQA',5X, c$$$ 1 'Number of function values =',I6) c$$$ PRINT 710, F,(X(I),I=1,N) c$$$ END IF RETURN END minqa/src/altmov.f0000644000176000001440000002216412415434561013661 0ustar ripleyusers SUBROUTINE ALTMOV (N,NPT,XPT,XOPT,BMAT,ZMAT,NDIM,SL,SU,KOPT, 1 KNEW,ADELT,XNEW,XALT,ALPHA,CAUCHY,GLAG,HCOL,W) IMPLICIT DOUBLE PRECISION (A-H,O-Z) DIMENSION XPT(NPT,*),XOPT(*),BMAT(NDIM,*),ZMAT(NPT,*),SL(*), 1 SU(*),XNEW(*),XALT(*),GLAG(*),HCOL(*),W(*) C C The arguments N, NPT, XPT, XOPT, BMAT, ZMAT, NDIM, SL and SU all have C the same meanings as the corresponding arguments of BOBYQB. C KOPT is the index of the optimal interpolation point. C KNEW is the index of the interpolation point that is going to be moved. C ADELT is the current trust region bound. C XNEW will be set to a suitable new position for the interpolation point C XPT(KNEW,.). Specifically, it satisfies the SL, SU and trust region C bounds and it should provide a large denominator in the next call of C UPDATE. The step XNEW-XOPT from XOPT is restricted to moves along the C straight lines through XOPT and another interpolation point. C XALT also provides a large value of the modulus of the KNEW-th Lagrange C function subject to the constraints that have been mentioned, its main C difference from XNEW being that XALT-XOPT is a constrained version of C the Cauchy step within the trust region. An exception is that XALT is C not calculated if all components of GLAG (see below) are zero. C ALPHA will be set to the KNEW-th diagonal element of the H matrix. C CAUCHY will be set to the square of the KNEW-th Lagrange function at C the step XALT-XOPT from XOPT for the vector XALT that is returned, C except that CAUCHY is set to zero if XALT is not calculated. C GLAG is a working space vector of length N for the gradient of the C KNEW-th Lagrange function at XOPT. C HCOL is a working space vector of length NPT for the second derivative C coefficients of the KNEW-th Lagrange function. C W is a working space vector of length 2N that is going to hold the C constrained Cauchy step from XOPT of the Lagrange function, followed C by the downhill version of XALT when the uphill step is calculated. C C Set the first NPT components of W to the leading elements of the C KNEW-th column of the H matrix. C HALF=0.5D0 ONE=1.0D0 ZERO=0.0D0 CONST=ONE+DSQRT(2.0D0) DO 10 K=1,NPT 10 HCOL(K)=ZERO DO 20 J=1,NPT-N-1 TEMP=ZMAT(KNEW,J) DO 20 K=1,NPT 20 HCOL(K)=HCOL(K)+TEMP*ZMAT(K,J) ALPHA=HCOL(KNEW) HA=HALF*ALPHA C C Calculate the gradient of the KNEW-th Lagrange function at XOPT. C DO 30 I=1,N 30 GLAG(I)=BMAT(KNEW,I) DO 50 K=1,NPT TEMP=ZERO DO 40 J=1,N 40 TEMP=TEMP+XPT(K,J)*XOPT(J) TEMP=HCOL(K)*TEMP DO 50 I=1,N 50 GLAG(I)=GLAG(I)+TEMP*XPT(K,I) C C Search for a large denominator along the straight lines through XOPT C and another interpolation point. SLBD and SUBD will be lower and upper C bounds on the step along each of these lines in turn. PREDSQ will be C set to the square of the predicted denominator for each line. PRESAV C will be set to the largest admissible value of PREDSQ that occurs. C PRESAV=ZERO DO 80 K=1,NPT IF (K .EQ. KOPT) GOTO 80 DDERIV=ZERO DISTSQ=ZERO DO 60 I=1,N TEMP=XPT(K,I)-XOPT(I) DDERIV=DDERIV+GLAG(I)*TEMP 60 DISTSQ=DISTSQ+TEMP*TEMP SUBD=ADELT/DSQRT(DISTSQ) SLBD=-SUBD ILBD=0 IUBD=0 SUMIN=DMIN1(ONE,SUBD) C C Revise SLBD and SUBD if necessary because of the bounds in SL and SU. C DO 70 I=1,N TEMP=XPT(K,I)-XOPT(I) IF (TEMP .GT. ZERO) THEN IF (SLBD*TEMP .LT. SL(I)-XOPT(I)) THEN SLBD=(SL(I)-XOPT(I))/TEMP ILBD=-I END IF IF (SUBD*TEMP .GT. SU(I)-XOPT(I)) THEN SUBD=DMAX1(SUMIN,(SU(I)-XOPT(I))/TEMP) IUBD=I END IF ELSE IF (TEMP .LT. ZERO) THEN IF (SLBD*TEMP .GT. SU(I)-XOPT(I)) THEN SLBD=(SU(I)-XOPT(I))/TEMP ILBD=I END IF IF (SUBD*TEMP .LT. SL(I)-XOPT(I)) THEN SUBD=DMAX1(SUMIN,(SL(I)-XOPT(I))/TEMP) IUBD=-I END IF END IF 70 CONTINUE C C Seek a large modulus of the KNEW-th Lagrange function when the index C of the other interpolation point on the line through XOPT is KNEW. C IF (K .EQ. KNEW) THEN DIFF=DDERIV-ONE STEP=SLBD VLAG=SLBD*(DDERIV-SLBD*DIFF) ISBD=ILBD TEMP=SUBD*(DDERIV-SUBD*DIFF) IF (DABS(TEMP) .GT. DABS(VLAG)) THEN STEP=SUBD VLAG=TEMP ISBD=IUBD END IF TEMPD=HALF*DDERIV TEMPA=TEMPD-DIFF*SLBD TEMPB=TEMPD-DIFF*SUBD IF (TEMPA*TEMPB .LT. ZERO) THEN TEMP=TEMPD*TEMPD/DIFF IF (DABS(TEMP) .GT. DABS(VLAG)) THEN STEP=TEMPD/DIFF VLAG=TEMP ISBD=0 END IF END IF C C Search along each of the other lines through XOPT and another point. C ELSE STEP=SLBD VLAG=SLBD*(ONE-SLBD) ISBD=ILBD TEMP=SUBD*(ONE-SUBD) IF (DABS(TEMP) .GT. DABS(VLAG)) THEN STEP=SUBD VLAG=TEMP ISBD=IUBD END IF IF (SUBD .GT. HALF) THEN IF (DABS(VLAG) .LT. 0.25D0) THEN STEP=HALF VLAG=0.25D0 ISBD=0 END IF END IF VLAG=VLAG*DDERIV END IF C C Calculate PREDSQ for the current line search and maintain PRESAV. C TEMP=STEP*(ONE-STEP)*DISTSQ PREDSQ=VLAG*VLAG*(VLAG*VLAG+HA*TEMP*TEMP) IF (PREDSQ .GT. PRESAV) THEN PRESAV=PREDSQ KSAV=K STPSAV=STEP IBDSAV=ISBD END IF 80 CONTINUE C C Construct XNEW in a way that satisfies the bound constraints exactly. C DO 90 I=1,N TEMP=XOPT(I)+STPSAV*(XPT(KSAV,I)-XOPT(I)) 90 XNEW(I)=DMAX1(SL(I),DMIN1(SU(I),TEMP)) IF (IBDSAV .LT. 0) XNEW(-IBDSAV)=SL(-IBDSAV) IF (IBDSAV .GT. 0) XNEW(IBDSAV)=SU(IBDSAV) C C Prepare for the iterative method that assembles the constrained Cauchy C step in W. The sum of squares of the fixed components of W is formed in C WFIXSQ, and the free components of W are set to BIGSTP. C BIGSTP=ADELT+ADELT IFLAG=0 100 WFIXSQ=ZERO GGFREE=ZERO DO 110 I=1,N W(I)=ZERO TEMPA=DMIN1(XOPT(I)-SL(I),GLAG(I)) TEMPB=DMAX1(XOPT(I)-SU(I),GLAG(I)) IF (TEMPA .GT. ZERO .OR. TEMPB .LT. ZERO) THEN W(I)=BIGSTP GGFREE=GGFREE+GLAG(I)**2 END IF 110 CONTINUE IF (GGFREE .EQ. ZERO) THEN CAUCHY=ZERO GOTO 200 END IF C C Investigate whether more components of W can be fixed. C 120 TEMP=ADELT*ADELT-WFIXSQ IF (TEMP .GT. ZERO) THEN WSQSAV=WFIXSQ STEP=DSQRT(TEMP/GGFREE) GGFREE=ZERO DO 130 I=1,N IF (W(I) .EQ. BIGSTP) THEN TEMP=XOPT(I)-STEP*GLAG(I) IF (TEMP .LE. SL(I)) THEN W(I)=SL(I)-XOPT(I) WFIXSQ=WFIXSQ+W(I)**2 ELSE IF (TEMP .GE. SU(I)) THEN W(I)=SU(I)-XOPT(I) WFIXSQ=WFIXSQ+W(I)**2 ELSE GGFREE=GGFREE+GLAG(I)**2 END IF END IF 130 CONTINUE IF (WFIXSQ .GT. WSQSAV .AND. GGFREE .GT. ZERO) GOTO 120 END IF C C Set the remaining free components of W and all components of XALT, C except that W may be scaled later. C GW=ZERO DO 140 I=1,N IF (W(I) .EQ. BIGSTP) THEN W(I)=-STEP*GLAG(I) XALT(I)=DMAX1(SL(I),DMIN1(SU(I),XOPT(I)+W(I))) ELSE IF (W(I) .EQ. ZERO) THEN XALT(I)=XOPT(I) ELSE IF (GLAG(I) .GT. ZERO) THEN XALT(I)=SL(I) ELSE XALT(I)=SU(I) END IF 140 GW=GW+GLAG(I)*W(I) C C Set CURV to the curvature of the KNEW-th Lagrange function along W. C Scale W by a factor less than one if that can reduce the modulus of C the Lagrange function at XOPT+W. Set CAUCHY to the final value of C the square of this function. C CURV=ZERO DO 160 K=1,NPT TEMP=ZERO DO 150 J=1,N 150 TEMP=TEMP+XPT(K,J)*W(J) 160 CURV=CURV+HCOL(K)*TEMP*TEMP IF (IFLAG .EQ. 1) CURV=-CURV IF (CURV .GT. -GW .AND. CURV .LT. -CONST*GW) THEN SCALE=-GW/CURV DO 170 I=1,N TEMP=XOPT(I)+SCALE*W(I) 170 XALT(I)=DMAX1(SL(I),DMIN1(SU(I),TEMP)) CAUCHY=(HALF*GW*SCALE)**2 ELSE CAUCHY=(GW+HALF*CURV)**2 END IF C C If IFLAG is zero, then XALT is calculated as before after reversing C the sign of GLAG. Thus two XALT vectors become available. The one that C is chosen is the one that gives the larger value of CAUCHY. C IF (IFLAG .EQ. 0) THEN DO 180 I=1,N GLAG(I)=-GLAG(I) 180 W(N+I)=XALT(I) CSAVE=CAUCHY IFLAG=1 GOTO 100 END IF IF (CSAVE .GT. CAUCHY) THEN DO 190 I=1,N 190 XALT(I)=W(N+I) CAUCHY=CSAVE END IF 200 RETURN END minqa/src/newuob.f0000644000176000001440000004242512415434561013660 0ustar ripleyusers SUBROUTINE NEWUOB (N,NPT,X,RHOBEG,RHOEND,IPRINT,MAXFUN,XBASE, 1 XOPT,XNEW,XPT,FVAL,GQ,HQ,PQ,BMAT,ZMAT,NDIM,D,VLAG,W,IERR) CJN Remember IERR here. IMPLICIT DOUBLE PRECISION (A-H,O-Z) DIMENSION X(*),XBASE(*),XOPT(*),XNEW(*),XPT(NPT,*),FVAL(*), 1 GQ(*),HQ(*),PQ(*),BMAT(NDIM,*),ZMAT(NPT,*),D(*),VLAG(*),W(*) C C The arguments N, NPT, X, RHOBEG, RHOEND, IPRINT and MAXFUN are identical C to the corresponding arguments in SUBROUTINE NEWUOA. C XBASE will hold a shift of origin that should reduce the contributions C from rounding errors to values of the model and Lagrange functions. C XOPT will be set to the displacement from XBASE of the vector of C variables that provides the least calculated F so far. C XNEW will be set to the displacement from XBASE of the vector of C variables for the current calculation of F. C XPT will contain the interpolation point coordinates relative to XBASE. C FVAL will hold the values of F at the interpolation points. C GQ will hold the gradient of the quadratic model at XBASE. C HQ will hold the explicit second derivatives of the quadratic model. C PQ will contain the parameters of the implicit second derivatives of C the quadratic model. C BMAT will hold the last N columns of H. C ZMAT will hold the factorization of the leading NPT by NPT submatrix of C H, this factorization being ZMAT times Diag(DZ) times ZMAT^T, where C the elements of DZ are plus or minus one, as specified by IDZ. C NDIM is the first dimension of BMAT and has the value NPT+N. C D is reserved for trial steps from XOPT. C VLAG will contain the values of the Lagrange functions at a new point X. C They are part of a product that requires VLAG to be of length NDIM. C The array W will be used for working space. Its length must be at least C 10*NDIM = 10*(NPT+N). C C Set some constants. C HALF=0.5D0 ONE=1.0D0 TENTH=0.1D0 ZERO=0.0D0 NP=N+1 NH=(N*NP)/2 NPTM=NPT-NP NFTEST=MAX0(MAXFUN,1) C C Set the initial elements of XPT, BMAT, HQ, PQ and ZMAT to zero. C DO 20 J=1,N XBASE(J)=X(J) DO 10 K=1,NPT 10 XPT(K,J)=ZERO DO 20 I=1,NDIM 20 BMAT(I,J)=ZERO DO 30 IH=1,NH 30 HQ(IH)=ZERO DO 40 K=1,NPT PQ(K)=ZERO DO 40 J=1,NPTM 40 ZMAT(K,J)=ZERO C C Begin the initialization procedure. NF becomes one more than the number C of function values so far. The coordinates of the displacement of the C next initial interpolation point from XBASE are set in XPT(NF,.). C RHOSQ=RHOBEG*RHOBEG RECIP=ONE/RHOSQ RECIQ=DSQRT(HALF)/RHOSQ NF=0 50 NFM=NF NFMM=NF-N NF=NF+1 IF (NFM .LE. 2*N) THEN IF (NFM .GE. 1 .AND. NFM .LE. N) THEN XPT(NF,NFM)=RHOBEG ELSE IF (NFM .GT. N) THEN XPT(NF,NFMM)=-RHOBEG END IF ELSE ITEMP=(NFMM-1)/N JPT=NFM-ITEMP*N-N IPT=JPT+ITEMP IF (IPT .GT. N) THEN ITEMP=JPT JPT=IPT-N IPT=ITEMP END IF XIPT=RHOBEG IF (FVAL(IPT+NP) .LT. FVAL(IPT+1)) XIPT=-XIPT XJPT=RHOBEG IF (FVAL(JPT+NP) .LT. FVAL(JPT+1)) XJPT=-XJPT XPT(NF,IPT)=XIPT XPT(NF,JPT)=XJPT END IF C C Calculate the next value of F, label 70 being reached immediately C after this calculation. The least function value so far and its index C are required. C DO 60 J=1,N 60 X(J)=XPT(NF,J)+XBASE(J) GOTO 310 70 FVAL(NF)=F IF (NF .EQ. 1) THEN FBEG=F FOPT=F KOPT=1 ELSE IF (F .LT. FOPT) THEN FOPT=F KOPT=NF END IF C C Set the nonzero initial elements of BMAT and the quadratic model in C the cases when NF is at most 2*N+1. C IF (NFM .LE. 2*N) THEN IF (NFM .GE. 1 .AND. NFM .LE. N) THEN GQ(NFM)=(F-FBEG)/RHOBEG IF (NPT .LT. NF+N) THEN BMAT(1,NFM)=-ONE/RHOBEG BMAT(NF,NFM)=ONE/RHOBEG BMAT(NPT+NFM,NFM)=-HALF*RHOSQ END IF ELSE IF (NFM .GT. N) THEN BMAT(NF-N,NFMM)=HALF/RHOBEG BMAT(NF,NFMM)=-HALF/RHOBEG ZMAT(1,NFMM)=-RECIQ-RECIQ ZMAT(NF-N,NFMM)=RECIQ ZMAT(NF,NFMM)=RECIQ IH=(NFMM*(NFMM+1))/2 TEMP=(FBEG-F)/RHOBEG HQ(IH)=(GQ(NFMM)-TEMP)/RHOBEG GQ(NFMM)=HALF*(GQ(NFMM)+TEMP) END IF C C Set the off-diagonal second derivatives of the Lagrange functions and C the initial quadratic model. C ELSE IH=(IPT*(IPT-1))/2+JPT IF (XIPT .LT. ZERO) IPT=IPT+N IF (XJPT .LT. ZERO) JPT=JPT+N ZMAT(1,NFMM)=RECIP ZMAT(NF,NFMM)=RECIP ZMAT(IPT+1,NFMM)=-RECIP ZMAT(JPT+1,NFMM)=-RECIP HQ(IH)=(FBEG-FVAL(IPT+1)-FVAL(JPT+1)+F)/(XIPT*XJPT) END IF IF (NF .LT. NPT) GOTO 50 C C Begin the iterative procedure, because the initial model is complete. C RHO=RHOBEG DELTA=RHO IDZ=1 DIFFA=ZERO DIFFB=ZERO ITEST=0 XOPTSQ=ZERO DO 80 I=1,N XOPT(I)=XPT(KOPT,I) 80 XOPTSQ=XOPTSQ+XOPT(I)**2 90 NFSAV=NF C C Generate the next trust region step and test its length. Set KNEW C to -1 if the purpose of the next F will be to improve the model. C 100 KNEW=0 CALL TRSAPP (N,NPT,XOPT,XPT,GQ,HQ,PQ,DELTA,D,W,W(NP), 1 W(NP+N),W(NP+2*N),CRVMIN) DSQ=ZERO DO 110 I=1,N 110 DSQ=DSQ+D(I)**2 DNORM=DMIN1(DELTA,DSQRT(DSQ)) IF (DNORM .LT. HALF*RHO) THEN KNEW=-1 DELTA=TENTH*DELTA RATIO=-1.0D0 IF (DELTA .LE. 1.5D0*RHO) DELTA=RHO IF (NF .LE. NFSAV+2) GOTO 460 TEMP=0.125D0*CRVMIN*RHO*RHO IF (TEMP .LE. DMAX1(DIFFA,DIFFB,DIFFC)) GOTO 460 GOTO 490 END IF C C Shift XBASE if XOPT may be too far from XBASE. First make the changes C to BMAT that do not depend on ZMAT. C 120 IF (DSQ .LE. 1.0D-3*XOPTSQ) THEN TEMPQ=0.25D0*XOPTSQ DO 140 K=1,NPT SUM=ZERO DO 130 I=1,N 130 SUM=SUM+XPT(K,I)*XOPT(I) TEMP=PQ(K)*SUM SUM=SUM-HALF*XOPTSQ W(NPT+K)=SUM DO 140 I=1,N GQ(I)=GQ(I)+TEMP*XPT(K,I) XPT(K,I)=XPT(K,I)-HALF*XOPT(I) VLAG(I)=BMAT(K,I) W(I)=SUM*XPT(K,I)+TEMPQ*XOPT(I) IP=NPT+I DO 140 J=1,I 140 BMAT(IP,J)=BMAT(IP,J)+VLAG(I)*W(J)+W(I)*VLAG(J) C C Then the revisions of BMAT that depend on ZMAT are calculated. C DO 180 K=1,NPTM SUMZ=ZERO DO 150 I=1,NPT SUMZ=SUMZ+ZMAT(I,K) 150 W(I)=W(NPT+I)*ZMAT(I,K) DO 170 J=1,N SUM=TEMPQ*SUMZ*XOPT(J) DO 160 I=1,NPT 160 SUM=SUM+W(I)*XPT(I,J) VLAG(J)=SUM IF (K .LT. IDZ) SUM=-SUM DO 170 I=1,NPT 170 BMAT(I,J)=BMAT(I,J)+SUM*ZMAT(I,K) DO 180 I=1,N IP=I+NPT TEMP=VLAG(I) IF (K .LT. IDZ) TEMP=-TEMP DO 180 J=1,I 180 BMAT(IP,J)=BMAT(IP,J)+TEMP*VLAG(J) C C The following instructions complete the shift of XBASE, including C the changes to the parameters of the quadratic model. C IH=0 DO 200 J=1,N W(J)=ZERO DO 190 K=1,NPT W(J)=W(J)+PQ(K)*XPT(K,J) 190 XPT(K,J)=XPT(K,J)-HALF*XOPT(J) DO 200 I=1,J IH=IH+1 IF (I .LT. J) GQ(J)=GQ(J)+HQ(IH)*XOPT(I) GQ(I)=GQ(I)+HQ(IH)*XOPT(J) HQ(IH)=HQ(IH)+W(I)*XOPT(J)+XOPT(I)*W(J) 200 BMAT(NPT+I,J)=BMAT(NPT+J,I) DO 210 J=1,N XBASE(J)=XBASE(J)+XOPT(J) 210 XOPT(J)=ZERO XOPTSQ=ZERO END IF C C Pick the model step if KNEW is positive. A different choice of D C may be made later, if the choice of D by BIGLAG causes substantial C cancellation in DENOM. C IF (KNEW .GT. 0) THEN CALL BIGLAG (N,NPT,XOPT,XPT,BMAT,ZMAT,IDZ,NDIM,KNEW,DSTEP, 1 D,ALPHA,VLAG,VLAG(NPT+1),W,W(NP),W(NP+N)) END IF C C Calculate VLAG and BETA for the current choice of D. The first NPT C components of W_check will be held in W. C DO 230 K=1,NPT SUMA=ZERO SUMB=ZERO SUM=ZERO DO 220 J=1,N SUMA=SUMA+XPT(K,J)*D(J) SUMB=SUMB+XPT(K,J)*XOPT(J) 220 SUM=SUM+BMAT(K,J)*D(J) W(K)=SUMA*(HALF*SUMA+SUMB) 230 VLAG(K)=SUM BETA=ZERO DO 250 K=1,NPTM SUM=ZERO DO 240 I=1,NPT 240 SUM=SUM+ZMAT(I,K)*W(I) IF (K .LT. IDZ) THEN BETA=BETA+SUM*SUM SUM=-SUM ELSE BETA=BETA-SUM*SUM END IF DO 250 I=1,NPT 250 VLAG(I)=VLAG(I)+SUM*ZMAT(I,K) BSUM=ZERO DX=ZERO DO 280 J=1,N SUM=ZERO DO 260 I=1,NPT 260 SUM=SUM+W(I)*BMAT(I,J) BSUM=BSUM+SUM*D(J) JP=NPT+J DO 270 K=1,N 270 SUM=SUM+BMAT(JP,K)*D(K) VLAG(JP)=SUM BSUM=BSUM+SUM*D(J) 280 DX=DX+D(J)*XOPT(J) BETA=DX*DX+DSQ*(XOPTSQ+DX+DX+HALF*DSQ)+BETA-BSUM VLAG(KOPT)=VLAG(KOPT)+ONE C C If KNEW is positive and if the cancellation in DENOM is unacceptable, C then BIGDEN calculates an alternative model step, XNEW being used for C working space. C IF (KNEW .GT. 0) THEN TEMP=ONE+ALPHA*BETA/VLAG(KNEW)**2 IF (DABS(TEMP) .LE. 0.8D0) THEN CALL BIGDEN (N,NPT,XOPT,XPT,BMAT,ZMAT,IDZ,NDIM,KOPT, 1 KNEW,D,W,VLAG,BETA,XNEW,W(NDIM+1),W(6*NDIM+1)) END IF END IF C C Calculate the next value of the objective function. C 290 DO 300 I=1,N XNEW(I)=XOPT(I)+D(I) 300 X(I)=XBASE(I)+XNEW(I) NF=NF+1 310 IF (NF .GT. NFTEST) THEN NF=NF-1 CJN CALL MINQER (390) C$$$ IF (IPRINT .GT. 0) PRINT 320 C$$$ 320 FORMAT (/4X,'Return from NEWUOA because CALFUN has been', C$$$ 1 ' called MAXFUN times.') C$$$ GOTO 530 IERR = 390 GO TO 530 END IF F = CALFUN (N,X,IPRINT) c$$$ IF (IPRINT .EQ. 3) THEN c$$$ PRINT 70, NF,F,(X(I),I=1,N) c$$$ 70 FORMAT (/4X,'Function number',I6,' F =',1PD18.10, c$$$ 1 ' The corresponding X is:'/(2X,5D15.6)) c$$$ END IF c$$$ CALL minqi3 (IPRINT, F, NF, N, X) IF (NF .LE. NPT) GOTO 70 IF (KNEW .EQ. -1) GOTO 530 C C Use the quadratic model to predict the change in F due to the step D, C and set DIFF to the error of this prediction. C VQUAD=ZERO IH=0 DO 340 J=1,N VQUAD=VQUAD+D(J)*GQ(J) DO 340 I=1,J IH=IH+1 TEMP=D(I)*XNEW(J)+D(J)*XOPT(I) IF (I .EQ. J) TEMP=HALF*TEMP 340 VQUAD=VQUAD+TEMP*HQ(IH) DO 350 K=1,NPT 350 VQUAD=VQUAD+PQ(K)*W(K) DIFF=F-FOPT-VQUAD DIFFC=DIFFB DIFFB=DIFFA DIFFA=DABS(DIFF) IF (DNORM .GT. RHO) NFSAV=NF C C Update FOPT and XOPT if the new F is the least value of the objective C function so far. The branch when KNEW is positive occurs if D is not C a trust region step. C FSAVE=FOPT IF (F .LT. FOPT) THEN FOPT=F XOPTSQ=ZERO DO 360 I=1,N XOPT(I)=XNEW(I) 360 XOPTSQ=XOPTSQ+XOPT(I)**2 END IF KSAVE=KNEW IF (KNEW .GT. 0) GOTO 410 C C Pick the next value of DELTA after a trust region step. C IF (VQUAD .GE. ZERO) THEN CJN IF (IPRINT .GT. 0) CALL minqer(3701) C$$$ IF (IPRINT .GT. 0) PRINT 370 C$$$ 370 FORMAT (/4X,'Return from NEWUOA because a trust', C$$$ 1 ' region step has failed to reduce Q.') IERR = 3701 GOTO 530 END IF RATIO=(F-FSAVE)/VQUAD IF (RATIO .LE. TENTH) THEN DELTA=HALF*DNORM ELSE IF (RATIO. LE. 0.7D0) THEN DELTA=DMAX1(HALF*DELTA,DNORM) ELSE DELTA=DMAX1(HALF*DELTA,DNORM+DNORM) END IF IF (DELTA .LE. 1.5D0*RHO) DELTA=RHO C C Set KNEW to the index of the next interpolation point to be deleted. C RHOSQ=DMAX1(TENTH*DELTA,RHO)**2 KTEMP=0 DETRAT=ZERO IF (F .GE. FSAVE) THEN KTEMP=KOPT DETRAT=ONE END IF DO 400 K=1,NPT HDIAG=ZERO DO 380 J=1,NPTM TEMP=ONE IF (J .LT. IDZ) TEMP=-ONE 380 HDIAG=HDIAG+TEMP*ZMAT(K,J)**2 TEMP=DABS(BETA*HDIAG+VLAG(K)**2) DISTSQ=ZERO DO 390 J=1,N 390 DISTSQ=DISTSQ+(XPT(K,J)-XOPT(J))**2 IF (DISTSQ .GT. RHOSQ) TEMP=TEMP*(DISTSQ/RHOSQ)**3 IF (TEMP .GT. DETRAT .AND. K .NE. KTEMP) THEN DETRAT=TEMP KNEW=K END IF 400 CONTINUE IF (KNEW .EQ. 0) GOTO 460 C C Update BMAT, ZMAT and IDZ, so that the KNEW-th interpolation point C can be moved. Begin the updating of the quadratic model, starting C with the explicit second derivative term. C 410 CALL UPDATE (N,NPT,BMAT,ZMAT,IDZ,NDIM,VLAG,BETA,KNEW,W) FVAL(KNEW)=F IH=0 DO 420 I=1,N TEMP=PQ(KNEW)*XPT(KNEW,I) DO 420 J=1,I IH=IH+1 420 HQ(IH)=HQ(IH)+TEMP*XPT(KNEW,J) PQ(KNEW)=ZERO C C Update the other second derivative parameters, and then the gradient C vector of the model. Also include the new interpolation point. C DO 440 J=1,NPTM TEMP=DIFF*ZMAT(KNEW,J) IF (J .LT. IDZ) TEMP=-TEMP DO 440 K=1,NPT 440 PQ(K)=PQ(K)+TEMP*ZMAT(K,J) GQSQ=ZERO DO 450 I=1,N GQ(I)=GQ(I)+DIFF*BMAT(KNEW,I) GQSQ=GQSQ+GQ(I)**2 450 XPT(KNEW,I)=XNEW(I) C C If a trust region step makes a small change to the objective function, C then calculate the gradient of the least Frobenius norm interpolant at C XBASE, and store it in W, using VLAG for a vector of right hand sides. C IF (KSAVE .EQ. 0 .AND. DELTA .EQ. RHO) THEN IF (DABS(RATIO) .GT. 1.0D-2) THEN ITEST=0 ELSE DO 700 K=1,NPT 700 VLAG(K)=FVAL(K)-FVAL(KOPT) GISQ=ZERO DO 720 I=1,N SUM=ZERO DO 710 K=1,NPT 710 SUM=SUM+BMAT(K,I)*VLAG(K) GISQ=GISQ+SUM*SUM 720 W(I)=SUM C C Test whether to replace the new quadratic model by the least Frobenius C norm interpolant, making the replacement if the test is satisfied. C ITEST=ITEST+1 IF (GQSQ .LT. 1.0D2*GISQ) ITEST=0 IF (ITEST .GE. 3) THEN DO 730 I=1,N 730 GQ(I)=W(I) DO 740 IH=1,NH 740 HQ(IH)=ZERO DO 760 J=1,NPTM W(J)=ZERO DO 750 K=1,NPT 750 W(J)=W(J)+VLAG(K)*ZMAT(K,J) 760 IF (J .LT. IDZ) W(J)=-W(J) DO 770 K=1,NPT PQ(K)=ZERO DO 770 J=1,NPTM 770 PQ(K)=PQ(K)+ZMAT(K,J)*W(J) ITEST=0 END IF END IF END IF IF (F .LT. FSAVE) KOPT=KNEW C C If a trust region step has provided a sufficient decrease in F, then C branch for another trust region calculation. The case KSAVE>0 occurs C when the new function value was calculated by a model step. C IF (F .LE. FSAVE+TENTH*VQUAD) GOTO 100 IF (KSAVE .GT. 0) GOTO 100 C C Alternatively, find out if the interpolation points are close enough C to the best point so far. C KNEW=0 460 DISTSQ=4.0D0*DELTA*DELTA DO 480 K=1,NPT SUM=ZERO DO 470 J=1,N 470 SUM=SUM+(XPT(K,J)-XOPT(J))**2 IF (SUM .GT. DISTSQ) THEN KNEW=K DISTSQ=SUM END IF 480 CONTINUE C C If KNEW is positive, then set DSTEP, and branch back for the next C iteration, which will generate a "model step". C IF (KNEW .GT. 0) THEN DSTEP=DMAX1(DMIN1(TENTH*DSQRT(DISTSQ),HALF*DELTA),RHO) DSQ=DSTEP*DSTEP GOTO 120 END IF IF (RATIO .GT. ZERO) GOTO 100 IF (DMAX1(DELTA,DNORM) .GT. RHO) GOTO 100 C C The calculations with the current value of RHO are complete. Pick the C next values of RHO and DELTA. C 490 IF (RHO .GT. RHOEND) THEN DELTA=HALF*RHO RATIO=RHO/RHOEND IF (RATIO .LE. 16.0D0) THEN RHO=RHOEND ELSE IF (RATIO .LE. 250.0D0) THEN RHO=DSQRT(RATIO)*RHOEND ELSE RHO=TENTH*RHO END IF DELTA=DMAX1(DELTA,RHO) IF (IPRINT .GE. 2) THEN CALL minqit(IPRINT, RHO, NF, FOPT, N, XBASE, XOPT) C$$$ IF (IPRINT .GE. 3) PRINT 500 C$$$ 500 FORMAT (5X) C$$$ PRINT 510, RHO,NF C$$$ 510 FORMAT (/4X,'New RHO =',1PD11.4,5X,'Number of', C$$$ 1 ' function values =',I6) C$$$ PRINT 520, FOPT,(XBASE(I)+XOPT(I),I=1,N) C$$$ 520 FORMAT (4X,'Least value of F =',1PD23.15,9X, C$$$ 1 'The corresponding X is:'/(2X,5D15.6)) END IF GOTO 90 END IF C C Return from the calculation, after another Newton-Raphson step, if C it is too short to have been tried before. C IF (KNEW .EQ. -1) GOTO 290 530 IF (FOPT .LE. F) THEN DO 540 I=1,N 540 X(I)=XBASE(I)+XOPT(I) F=FOPT END IF IF (IPRINT .GE. 1) THEN CALL minqir(IPRINT, F, NF, N, X) C$$$ PRINT 550, NF C$$$ 550 FORMAT (/4X,'At the return from NEWUOA',5X, C$$$ 1 'Number of function values =',I6) C$$$ PRINT 520, F,(X(I),I=1,N) END IF RETURN END minqa/src/rescue.f0000644000176000001440000003201412415434561013640 0ustar ripleyusers SUBROUTINE RESCUE (N,NPT,XL,XU,IPRINT,MAXFUN,XBASE,XPT, 1 FVAL,XOPT,GOPT,HQ,PQ,BMAT,ZMAT,NDIM,SL,SU,NF,DELTA, 2 KOPT,VLAG,PTSAUX,PTSID,W) IMPLICIT DOUBLE PRECISION (A-H,O-Z) DIMENSION XL(*),XU(*),XBASE(*),XPT(NPT,*),FVAL(*),XOPT(*), 1 GOPT(*),HQ(*),PQ(*),BMAT(NDIM,*),ZMAT(NPT,*),SL(*),SU(*), 2 VLAG(*),PTSAUX(2,*),PTSID(*),W(*) C C The arguments N, NPT, XL, XU, IPRINT, MAXFUN, XBASE, XPT, FVAL, XOPT, C GOPT, HQ, PQ, BMAT, ZMAT, NDIM, SL and SU have the same meanings as C the corresponding arguments of BOBYQB on the entry to RESCUE. C NF is maintained as the number of calls of CALFUN so far, except that C NF is set to -1 if the value of MAXFUN prevents further progress. C KOPT is maintained so that FVAL(KOPT) is the least calculated function C value. Its correct value must be given on entry. It is updated if a C new least function value is found, but the corresponding changes to C XOPT and GOPT have to be made later by the calling program. C DELTA is the current trust region radius. C VLAG is a working space vector that will be used for the values of the C provisional Lagrange functions at each of the interpolation points. C They are part of a product that requires VLAG to be of length NDIM. C PTSAUX is also a working space array. For J=1,2,...,N, PTSAUX(1,J) and C PTSAUX(2,J) specify the two positions of provisional interpolation C points when a nonzero step is taken along e_J (the J-th coordinate C direction) through XBASE+XOPT, as specified below. Usually these C steps have length DELTA, but other lengths are chosen if necessary C in order to satisfy the given bounds on the variables. C PTSID is also a working space array. It has NPT components that denote C provisional new positions of the original interpolation points, in C case changes are needed to restore the linear independence of the C interpolation conditions. The K-th point is a candidate for change C if and only if PTSID(K) is nonzero. In this case let p and q be the C integer parts of PTSID(K) and (PTSID(K)-p) multiplied by N+1. If p C and q are both positive, the step from XBASE+XOPT to the new K-th C interpolation point is PTSAUX(1,p)*e_p + PTSAUX(1,q)*e_q. Otherwise C the step is PTSAUX(1,p)*e_p or PTSAUX(2,q)*e_q in the cases q=0 or C p=0, respectively. C The first NDIM+NPT elements of the array W are used for working space. C The final elements of BMAT and ZMAT are set in a well-conditioned way C to the values that are appropriate for the new interpolation points. C The elements of GOPT, HQ and PQ are also revised to the values that are C appropriate to the final quadratic model. C C Set some constants. C HALF=0.5D0 ONE=1.0D0 ZERO=0.0D0 NP=N+1 SFRAC=HALF/DFLOAT(NP) NPTM=NPT-NP C C Shift the interpolation points so that XOPT becomes the origin, and set C the elements of ZMAT to zero. The value of SUMPQ is required in the C updating of HQ below. The squares of the distances from XOPT to the C other interpolation points are set at the end of W. Increments of WINC C may be added later to these squares to balance the consideration of C the choice of point that is going to become current. C SUMPQ=ZERO WINC=ZERO DO 20 K=1,NPT DISTSQ=ZERO DO 10 J=1,N XPT(K,J)=XPT(K,J)-XOPT(J) 10 DISTSQ=DISTSQ+XPT(K,J)**2 SUMPQ=SUMPQ+PQ(K) W(NDIM+K)=DISTSQ WINC=DMAX1(WINC,DISTSQ) DO 20 J=1,NPTM 20 ZMAT(K,J)=ZERO C C Update HQ so that HQ and PQ define the second derivatives of the model C after XBASE has been shifted to the trust region centre. C IH=0 DO 40 J=1,N W(J)=HALF*SUMPQ*XOPT(J) DO 30 K=1,NPT 30 W(J)=W(J)+PQ(K)*XPT(K,J) DO 40 I=1,J IH=IH+1 40 HQ(IH)=HQ(IH)+W(I)*XOPT(J)+W(J)*XOPT(I) C C Shift XBASE, SL, SU and XOPT. Set the elements of BMAT to zero, and C also set the elements of PTSAUX. C DO 50 J=1,N XBASE(J)=XBASE(J)+XOPT(J) SL(J)=SL(J)-XOPT(J) SU(J)=SU(J)-XOPT(J) XOPT(J)=ZERO PTSAUX(1,J)=DMIN1(DELTA,SU(J)) PTSAUX(2,J)=DMAX1(-DELTA,SL(J)) IF (PTSAUX(1,J)+PTSAUX(2,J) .LT. ZERO) THEN TEMP=PTSAUX(1,J) PTSAUX(1,J)=PTSAUX(2,J) PTSAUX(2,J)=TEMP END IF IF (DABS(PTSAUX(2,J)) .LT. HALF*DABS(PTSAUX(1,J))) THEN PTSAUX(2,J)=HALF*PTSAUX(1,J) END IF DO 50 I=1,NDIM 50 BMAT(I,J)=ZERO FBASE=FVAL(KOPT) C C Set the identifiers of the artificial interpolation points that are C along a coordinate direction from XOPT, and set the corresponding C nonzero elements of BMAT and ZMAT. C PTSID(1)=SFRAC DO 60 J=1,N JP=J+1 JPN=JP+N PTSID(JP)=DFLOAT(J)+SFRAC IF (JPN .LE. NPT) THEN PTSID(JPN)=DFLOAT(J)/DFLOAT(NP)+SFRAC TEMP=ONE/(PTSAUX(1,J)-PTSAUX(2,J)) BMAT(JP,J)=-TEMP+ONE/PTSAUX(1,J) BMAT(JPN,J)=TEMP+ONE/PTSAUX(2,J) BMAT(1,J)=-BMAT(JP,J)-BMAT(JPN,J) ZMAT(1,J)=DSQRT(2.0D0)/DABS(PTSAUX(1,J)*PTSAUX(2,J)) ZMAT(JP,J)=ZMAT(1,J)*PTSAUX(2,J)*TEMP ZMAT(JPN,J)=-ZMAT(1,J)*PTSAUX(1,J)*TEMP ELSE BMAT(1,J)=-ONE/PTSAUX(1,J) BMAT(JP,J)=ONE/PTSAUX(1,J) BMAT(J+NPT,J)=-HALF*PTSAUX(1,J)**2 END IF 60 CONTINUE C C Set any remaining identifiers with their nonzero elements of ZMAT. C IF (NPT .GE. N+NP) THEN DO 70 K=2*NP,NPT IW=(DFLOAT(K-NP)-HALF)/DFLOAT(N) IP=K-NP-IW*N IQ=IP+IW IF (IQ .GT. N) IQ=IQ-N PTSID(K)=DFLOAT(IP)+DFLOAT(IQ)/DFLOAT(NP)+SFRAC TEMP=ONE/(PTSAUX(1,IP)*PTSAUX(1,IQ)) ZMAT(1,K-NP)=TEMP ZMAT(IP+1,K-NP)=-TEMP ZMAT(IQ+1,K-NP)=-TEMP 70 ZMAT(K,K-NP)=TEMP END IF NREM=NPT KOLD=1 KNEW=KOPT C C Reorder the provisional points in the way that exchanges PTSID(KOLD) C with PTSID(KNEW). C 80 DO 90 J=1,N TEMP=BMAT(KOLD,J) BMAT(KOLD,J)=BMAT(KNEW,J) 90 BMAT(KNEW,J)=TEMP DO 100 J=1,NPTM TEMP=ZMAT(KOLD,J) ZMAT(KOLD,J)=ZMAT(KNEW,J) 100 ZMAT(KNEW,J)=TEMP PTSID(KOLD)=PTSID(KNEW) PTSID(KNEW)=ZERO W(NDIM+KNEW)=ZERO NREM=NREM-1 IF (KNEW .NE. KOPT) THEN TEMP=VLAG(KOLD) VLAG(KOLD)=VLAG(KNEW) VLAG(KNEW)=TEMP C C Update the BMAT and ZMAT matrices so that the status of the KNEW-th C interpolation point can be changed from provisional to original. The C branch to label 350 occurs if all the original points are reinstated. C The nonnegative values of W(NDIM+K) are required in the search below. C CALL UPDATEBOBYQA (N,NPT,BMAT,ZMAT,NDIM,VLAG,BETA,DENOM,KNEW, + W) IF (NREM .EQ. 0) GOTO 350 DO 110 K=1,NPT 110 W(NDIM+K)=DABS(W(NDIM+K)) END IF C C Pick the index KNEW of an original interpolation point that has not C yet replaced one of the provisional interpolation points, giving C attention to the closeness to XOPT and to previous tries with KNEW. C 120 DSQMIN=ZERO DO 130 K=1,NPT IF (W(NDIM+K) .GT. ZERO) THEN IF (DSQMIN .EQ. ZERO .OR. W(NDIM+K) .LT. DSQMIN) THEN KNEW=K DSQMIN=W(NDIM+K) END IF END IF 130 CONTINUE IF (DSQMIN .EQ. ZERO) GOTO 260 C C Form the W-vector of the chosen original interpolation point. C DO 140 J=1,N 140 W(NPT+J)=XPT(KNEW,J) DO 160 K=1,NPT SUM=ZERO IF (K .EQ. KOPT) THEN CONTINUE ELSE IF (PTSID(K) .EQ. ZERO) THEN DO 150 J=1,N 150 SUM=SUM+W(NPT+J)*XPT(K,J) ELSE IP=PTSID(K) IF (IP .GT. 0) SUM=W(NPT+IP)*PTSAUX(1,IP) IQ=DFLOAT(NP)*PTSID(K)-DFLOAT(IP*NP) IF (IQ .GT. 0) THEN IW=1 IF (IP .EQ. 0) IW=2 SUM=SUM+W(NPT+IQ)*PTSAUX(IW,IQ) END IF END IF 160 W(K)=HALF*SUM*SUM C C Calculate VLAG and BETA for the required updating of the H matrix if C XPT(KNEW,.) is reinstated in the set of interpolation points. C DO 180 K=1,NPT SUM=ZERO DO 170 J=1,N 170 SUM=SUM+BMAT(K,J)*W(NPT+J) 180 VLAG(K)=SUM BETA=ZERO DO 200 J=1,NPTM SUM=ZERO DO 190 K=1,NPT 190 SUM=SUM+ZMAT(K,J)*W(K) BETA=BETA-SUM*SUM DO 200 K=1,NPT 200 VLAG(K)=VLAG(K)+SUM*ZMAT(K,J) BSUM=ZERO DISTSQ=ZERO DO 230 J=1,N SUM=ZERO DO 210 K=1,NPT 210 SUM=SUM+BMAT(K,J)*W(K) JP=J+NPT BSUM=BSUM+SUM*W(JP) DO 220 IP=NPT+1,NDIM 220 SUM=SUM+BMAT(IP,J)*W(IP) BSUM=BSUM+SUM*W(JP) VLAG(JP)=SUM 230 DISTSQ=DISTSQ+XPT(KNEW,J)**2 BETA=HALF*DISTSQ*DISTSQ+BETA-BSUM VLAG(KOPT)=VLAG(KOPT)+ONE C C KOLD is set to the index of the provisional interpolation point that is C going to be deleted to make way for the KNEW-th original interpolation C point. The choice of KOLD is governed by the avoidance of a small value C of the denominator in the updating calculation of UPDATE. C DENOM=ZERO VLMXSQ=ZERO DO 250 K=1,NPT IF (PTSID(K) .NE. ZERO) THEN HDIAG=ZERO DO 240 J=1,NPTM 240 HDIAG=HDIAG+ZMAT(K,J)**2 DEN=BETA*HDIAG+VLAG(K)**2 IF (DEN .GT. DENOM) THEN KOLD=K DENOM=DEN END IF END IF 250 VLMXSQ=DMAX1(VLMXSQ,VLAG(K)**2) IF (DENOM .LE. 1.0D-2*VLMXSQ) THEN W(NDIM+KNEW)=-W(NDIM+KNEW)-WINC GOTO 120 END IF GOTO 80 C C When label 260 is reached, all the final positions of the interpolation C points have been chosen although any changes have not been included yet C in XPT. Also the final BMAT and ZMAT matrices are complete, but, apart C from the shift of XBASE, the updating of the quadratic model remains to C be done. The following cycle through the new interpolation points begins C by putting the new point in XPT(KPT,.) and by setting PQ(KPT) to zero, C except that a RETURN occurs if MAXFUN prohibits another value of F. C 260 DO 340 KPT=1,NPT IF (PTSID(KPT) .EQ. ZERO) GOTO 340 IF (NF .GE. MAXFUN) THEN NF=-1 GOTO 350 END IF IH=0 DO 270 J=1,N W(J)=XPT(KPT,J) XPT(KPT,J)=ZERO TEMP=PQ(KPT)*W(J) DO 270 I=1,J IH=IH+1 270 HQ(IH)=HQ(IH)+TEMP*W(I) PQ(KPT)=ZERO IP=PTSID(KPT) IQ=DFLOAT(NP)*PTSID(KPT)-DFLOAT(IP*NP) IF (IP .GT. 0) THEN XP=PTSAUX(1,IP) XPT(KPT,IP)=XP END IF IF (IQ .GT. 0) THEN XQ=PTSAUX(1,IQ) IF (IP .EQ. 0) XQ=PTSAUX(2,IQ) XPT(KPT,IQ)=XQ END IF C C Set VQUAD to the value of the current model at the new point. C VQUAD=FBASE IF (IP .GT. 0) THEN IHP=(IP+IP*IP)/2 VQUAD=VQUAD+XP*(GOPT(IP)+HALF*XP*HQ(IHP)) END IF IF (IQ .GT. 0) THEN IHQ=(IQ+IQ*IQ)/2 VQUAD=VQUAD+XQ*(GOPT(IQ)+HALF*XQ*HQ(IHQ)) IF (IP .GT. 0) THEN IW=MAX0(IHP,IHQ)-IABS(IP-IQ) VQUAD=VQUAD+XP*XQ*HQ(IW) END IF END IF DO 280 K=1,NPT TEMP=ZERO IF (IP .GT. 0) TEMP=TEMP+XP*XPT(K,IP) IF (IQ .GT. 0) TEMP=TEMP+XQ*XPT(K,IQ) 280 VQUAD=VQUAD+HALF*PQ(K)*TEMP*TEMP C C Calculate F at the new interpolation point, and set DIFF to the factor C that is going to multiply the KPT-th Lagrange function when the model C is updated to provide interpolation to the new function value. C DO 290 I=1,N W(I)=DMIN1(DMAX1(XL(I),XBASE(I)+XPT(KPT,I)),XU(I)) IF (XPT(KPT,I) .EQ. SL(I)) W(I)=XL(I) IF (XPT(KPT,I) .EQ. SU(I)) W(I)=XU(I) 290 CONTINUE NF=NF+1 F = CALFUN (N,X,IPRINT) c$$$ IF (IPRINT .EQ. 3) THEN c$$$ PRINT 70, NF,F,(X(I),I=1,N) c$$$ 70 FORMAT (/4X,'Function number',I6,' F =',1PD18.10, c$$$ 1 ' The corresponding X is:'/(2X,5D15.6)) c$$$ END IF c$$$ CALL minqi3 (IPRINT, F, NF, N, X) FVAL(KPT)=F IF (F .LT. FVAL(KOPT)) KOPT=KPT DIFF=F-VQUAD C C Update the quadratic model. The RETURN from the subroutine occurs when C all the new interpolation points are included in the model. C DO 310 I=1,N 310 GOPT(I)=GOPT(I)+DIFF*BMAT(KPT,I) DO 330 K=1,NPT SUM=ZERO DO 320 J=1,NPTM 320 SUM=SUM+ZMAT(K,J)*ZMAT(KPT,J) TEMP=DIFF*SUM IF (PTSID(K) .EQ. ZERO) THEN PQ(K)=PQ(K)+TEMP ELSE IP=PTSID(K) IQ=DFLOAT(NP)*PTSID(K)-DFLOAT(IP*NP) IHQ=(IQ*IQ+IQ)/2 IF (IP .EQ. 0) THEN HQ(IHQ)=HQ(IHQ)+TEMP*PTSAUX(2,IQ)**2 ELSE IHP=(IP*IP+IP)/2 HQ(IHP)=HQ(IHP)+TEMP*PTSAUX(1,IP)**2 IF (IQ .GT. 0) THEN HQ(IHQ)=HQ(IHQ)+TEMP*PTSAUX(1,IQ)**2 IW=MAX0(IHP,IHQ)-IABS(IQ-IP) HQ(IW)=HQ(IW)+TEMP*PTSAUX(1,IP)*PTSAUX(1,IQ) END IF END IF END IF 330 CONTINUE PTSID(KPT)=ZERO 340 CONTINUE 350 RETURN END minqa/src/trsapp.f0000644000176000001440000001274612415434561013675 0ustar ripleyusers SUBROUTINE TRSAPP (N,NPT,XOPT,XPT,GQ,HQ,PQ,DELTA,STEP, 1 D,G,HD,HS,CRVMIN) IMPLICIT DOUBLE PRECISION (A-H,O-Z) DIMENSION XOPT(*),XPT(NPT,*),GQ(*),HQ(*),PQ(*),STEP(*), 1 D(*),G(*),HD(*),HS(*) C C N is the number of variables of a quadratic objective function, Q say. C The arguments NPT, XOPT, XPT, GQ, HQ and PQ have their usual meanings, C in order to define the current quadratic model Q. C DELTA is the trust region radius, and has to be positive. C STEP will be set to the calculated trial step. C The arrays D, G, HD and HS will be used for working space. C CRVMIN will be set to the least curvature of H along the conjugate C directions that occur, except that it is set to zero if STEP goes C all the way to the trust region boundary. C C The calculation of STEP begins with the truncated conjugate gradient C method. If the boundary of the trust region is reached, then further C changes to STEP may be made, each one being in the 2D space spanned C by the current STEP and the corresponding gradient of Q. Thus STEP C should provide a substantial reduction to Q within the trust region. C C Initialization, which includes setting HD to H times XOPT. C HALF=0.5D0 ZERO=0.0D0 TWOPI=8.0D0*DATAN(1.0D0) DELSQ=DELTA*DELTA ITERC=0 ITERMAX=N ITERSW=ITERMAX DO 10 I=1,N 10 D(I)=XOPT(I) GOTO 170 C C Prepare for the first line search. C 20 QRED=ZERO DD=ZERO DO 30 I=1,N STEP(I)=ZERO HS(I)=ZERO G(I)=GQ(I)+HD(I) D(I)=-G(I) 30 DD=DD+D(I)**2 CRVMIN=ZERO IF (DD .EQ. ZERO) GOTO 160 DS=ZERO SS=ZERO GG=DD GGBEG=GG C C Calculate the step to the trust region boundary and the product HD. C 40 ITERC=ITERC+1 TEMP=DELSQ-SS BSTEP=TEMP/(DS+DSQRT(DS*DS+DD*TEMP)) GOTO 170 50 DHD=ZERO DO 60 J=1,N 60 DHD=DHD+D(J)*HD(J) C C Update CRVMIN and set the step-length ALPHA. C ALPHA=BSTEP IF (DHD .GT. ZERO) THEN TEMP=DHD/DD IF (ITERC .EQ. 1) CRVMIN=TEMP CRVMIN=DMIN1(CRVMIN,TEMP) ALPHA=DMIN1(ALPHA,GG/DHD) END IF QADD=ALPHA*(GG-HALF*ALPHA*DHD) QRED=QRED+QADD C C Update STEP and HS. C GGSAV=GG GG=ZERO DO 70 I=1,N STEP(I)=STEP(I)+ALPHA*D(I) HS(I)=HS(I)+ALPHA*HD(I) 70 GG=GG+(G(I)+HS(I))**2 C C Begin another conjugate direction iteration if required. C IF (ALPHA .LT. BSTEP) THEN IF (QADD .LE. 0.01D0*QRED) GOTO 160 IF (GG .LE. 1.0D-4*GGBEG) GOTO 160 IF (ITERC .EQ. ITERMAX) GOTO 160 TEMP=GG/GGSAV DD=ZERO DS=ZERO SS=ZERO DO 80 I=1,N D(I)=TEMP*D(I)-G(I)-HS(I) DD=DD+D(I)**2 DS=DS+D(I)*STEP(I) 80 SS=SS+STEP(I)**2 IF (DS .LE. ZERO) GOTO 160 IF (SS .LT. DELSQ) GOTO 40 END IF CRVMIN=ZERO ITERSW=ITERC C C Test whether an alternative iteration is required. C 90 IF (GG .LE. 1.0D-4*GGBEG) GOTO 160 SG=ZERO SHS=ZERO DO 100 I=1,N SG=SG+STEP(I)*G(I) 100 SHS=SHS+STEP(I)*HS(I) SGK=SG+SHS ANGTEST=SGK/DSQRT(GG*DELSQ) IF (ANGTEST .LE. -0.99D0) GOTO 160 C C Begin the alternative iteration by calculating D and HD and some C scalar products. C ITERC=ITERC+1 TEMP=DSQRT(DELSQ*GG-SGK*SGK) TEMPA=DELSQ/TEMP TEMPB=SGK/TEMP DO 110 I=1,N 110 D(I)=TEMPA*(G(I)+HS(I))-TEMPB*STEP(I) GOTO 170 120 DG=ZERO DHD=ZERO DHS=ZERO DO 130 I=1,N DG=DG+D(I)*G(I) DHD=DHD+HD(I)*D(I) 130 DHS=DHS+HD(I)*STEP(I) C C Seek the value of the angle that minimizes Q. C CF=HALF*(SHS-DHD) QBEG=SG+CF QSAV=QBEG QMIN=QBEG ISAVE=0 IU=49 TEMP=TWOPI/DFLOAT(IU+1) DO 140 I=1,IU ANGLE=DFLOAT(I)*TEMP CTH=DCOS(ANGLE) STH=DSIN(ANGLE) QNEW=(SG+CF*CTH)*CTH+(DG+DHS*CTH)*STH IF (QNEW .LT. QMIN) THEN QMIN=QNEW ISAVE=I TEMPA=QSAV ELSE IF (I .EQ. ISAVE+1) THEN TEMPB=QNEW END IF 140 QSAV=QNEW IF (ISAVE .EQ. ZERO) TEMPA=QNEW IF (ISAVE .EQ. IU) TEMPB=QBEG ANGLE=ZERO IF (TEMPA .NE. TEMPB) THEN TEMPA=TEMPA-QMIN TEMPB=TEMPB-QMIN ANGLE=HALF*(TEMPA-TEMPB)/(TEMPA+TEMPB) END IF ANGLE=TEMP*(DFLOAT(ISAVE)+ANGLE) C C Calculate the new STEP and HS. Then test for convergence. C CTH=DCOS(ANGLE) STH=DSIN(ANGLE) REDUC=QBEG-(SG+CF*CTH)*CTH-(DG+DHS*CTH)*STH GG=ZERO DO 150 I=1,N STEP(I)=CTH*STEP(I)+STH*D(I) HS(I)=CTH*HS(I)+STH*HD(I) 150 GG=GG+(G(I)+HS(I))**2 QRED=QRED+REDUC RATIO=REDUC/QRED IF (ITERC .LT. ITERMAX .AND. RATIO .GT. 0.01D0) GOTO 90 160 RETURN C C The following instructions act as a subroutine for setting the vector C HD to the vector D multiplied by the second derivative matrix of Q. C They are called from three different places, which are distinguished C by the value of ITERC. C 170 DO 180 I=1,N 180 HD(I)=ZERO DO 200 K=1,NPT TEMP=ZERO DO 190 J=1,N 190 TEMP=TEMP+XPT(K,J)*D(J) TEMP=TEMP*PQ(K) DO 200 I=1,N 200 HD(I)=HD(I)+TEMP*XPT(K,I) IH=0 DO 210 J=1,N DO 210 I=1,J IH=IH+1 IF (I .LT. J) HD(J)=HD(J)+HQ(IH)*D(I) 210 HD(I)=HD(I)+HQ(IH)*D(J) IF (ITERC .EQ. 0) GOTO 20 IF (ITERC .LE. ITERSW) GOTO 50 GOTO 120 END minqa/src/updatebobyqa.f0000644000176000001440000000434312415434561015036 0ustar ripleyusers SUBROUTINE UPDATEBOBYQA (N,NPT,BMAT,ZMAT,NDIM,VLAG,BETA,DENOM, 1 KNEW,W) IMPLICIT DOUBLE PRECISION (A-H,O-Z) DIMENSION BMAT(NDIM,*),ZMAT(NPT,*),VLAG(*),W(*) C C The arrays BMAT and ZMAT are updated, as required by the new position C of the interpolation point that has the index KNEW. The vector VLAG has C N+NPT components, set on entry to the first NPT and last N components C of the product Hw in equation (4.11) of the Powell (2006) paper on C NEWUOA. Further, BETA is set on entry to the value of the parameter C with that name, and DENOM is set to the denominator of the updating C formula. Elements of ZMAT may be treated as zero if their moduli are C at most ZTEST. The first NDIM elements of W are used for working space. C C Set some constants. C ONE=1.0D0 ZERO=0.0D0 NPTM=NPT-N-1 ZTEST=ZERO DO 10 K=1,NPT DO 10 J=1,NPTM 10 ZTEST=DMAX1(ZTEST,DABS(ZMAT(K,J))) ZTEST=1.0D-20*ZTEST C C Apply the rotations that put zeros in the KNEW-th row of ZMAT. C JL=1 DO 30 J=2,NPTM IF (DABS(ZMAT(KNEW,J)) .GT. ZTEST) THEN TEMP=DSQRT(ZMAT(KNEW,1)**2+ZMAT(KNEW,J)**2) TEMPA=ZMAT(KNEW,1)/TEMP TEMPB=ZMAT(KNEW,J)/TEMP DO 20 I=1,NPT TEMP=TEMPA*ZMAT(I,1)+TEMPB*ZMAT(I,J) ZMAT(I,J)=TEMPA*ZMAT(I,J)-TEMPB*ZMAT(I,1) 20 ZMAT(I,1)=TEMP END IF ZMAT(KNEW,J)=ZERO 30 CONTINUE C C Put the first NPT components of the KNEW-th column of HLAG into W, C and calculate the parameters of the updating formula. C DO 40 I=1,NPT W(I)=ZMAT(KNEW,1)*ZMAT(I,1) 40 CONTINUE ALPHA=W(KNEW) TAU=VLAG(KNEW) VLAG(KNEW)=VLAG(KNEW)-ONE C C Complete the updating of ZMAT. C TEMP=DSQRT(DENOM) TEMPB=ZMAT(KNEW,1)/TEMP TEMPA=TAU/TEMP DO 50 I=1,NPT 50 ZMAT(I,1)=TEMPA*ZMAT(I,1)-TEMPB*VLAG(I) C C Finally, update the matrix BMAT. C DO 60 J=1,N JP=NPT+J W(JP)=BMAT(KNEW,J) TEMPA=(ALPHA*VLAG(JP)-TAU*W(JP))/DENOM TEMPB=(-BETA*W(JP)-TAU*VLAG(JP))/DENOM DO 60 I=1,JP BMAT(I,J)=BMAT(I,J)+TEMPA*VLAG(I)+TEMPB*W(I) IF (I .GT. NPT) BMAT(JP,I-NPT)=BMAT(I,J) 60 CONTINUE RETURN END minqa/src/prelim.f0000644000176000001440000001302312415434561013641 0ustar ripleyusers SUBROUTINE PRELIM (N,NPT,X,XL,XU,RHOBEG,IPRINT,MAXFUN,XBASE, 1 XPT,FVAL,GOPT,HQ,PQ,BMAT,ZMAT,NDIM,SL,SU,NF,KOPT) IMPLICIT DOUBLE PRECISION (A-H,O-Z) DIMENSION X(*),XL(*),XU(*),XBASE(*),XPT(NPT,*),FVAL(*),GOPT(*), 1 HQ(*),PQ(*),BMAT(NDIM,*),ZMAT(NPT,*),SL(*),SU(*) C C The arguments N, NPT, X, XL, XU, RHOBEG, IPRINT and MAXFUN are the C same as the corresponding arguments in SUBROUTINE BOBYQA. C The arguments XBASE, XPT, FVAL, HQ, PQ, BMAT, ZMAT, NDIM, SL and SU C are the same as the corresponding arguments in BOBYQB, the elements C of SL and SU being set in BOBYQA. C GOPT is usually the gradient of the quadratic model at XOPT+XBASE, but C it is set by PRELIM to the gradient of the quadratic model at XBASE. C If XOPT is nonzero, BOBYQB will change it to its usual value later. C NF is maintaned as the number of calls of CALFUN so far. C KOPT will be such that the least calculated value of F so far is at C the point XPT(KOPT,.)+XBASE in the space of the variables. C C SUBROUTINE PRELIM sets the elements of XBASE, XPT, FVAL, GOPT, HQ, PQ, C BMAT and ZMAT for the first iteration, and it maintains the values of C NF and KOPT. The vector X is also changed by PRELIM. C C Set some constants. C HALF=0.5D0 ONE=1.0D0 TWO=2.0D0 ZERO=0.0D0 RHOSQ=RHOBEG*RHOBEG RECIP=ONE/RHOSQ NP=N+1 C C Set XBASE to the initial vector of variables, and set the initial C elements of XPT, BMAT, HQ, PQ and ZMAT to zero. C DO 20 J=1,N XBASE(J)=X(J) DO 10 K=1,NPT 10 XPT(K,J)=ZERO DO 20 I=1,NDIM 20 BMAT(I,J)=ZERO DO 30 IH=1,(N*NP)/2 30 HQ(IH)=ZERO DO 40 K=1,NPT PQ(K)=ZERO DO 40 J=1,NPT-NP 40 ZMAT(K,J)=ZERO C C Begin the initialization procedure. NF becomes one more than the number C of function values so far. The coordinates of the displacement of the C next initial interpolation point from XBASE are set in XPT(NF+1,.). C NF=0 50 NFM=NF NFX=NF-N NF=NF+1 IF (NFM .LE. 2*N) THEN IF (NFM .GE. 1 .AND. NFM .LE. N) THEN STEPA=RHOBEG IF (SU(NFM) .EQ. ZERO) STEPA=-STEPA XPT(NF,NFM)=STEPA ELSE IF (NFM .GT. N) THEN STEPA=XPT(NF-N,NFX) STEPB=-RHOBEG IF (SL(NFX) .EQ. ZERO) STEPB=DMIN1(TWO*RHOBEG,SU(NFX)) IF (SU(NFX) .EQ. ZERO) STEPB=DMAX1(-TWO*RHOBEG,SL(NFX)) XPT(NF,NFX)=STEPB END IF ELSE ITEMP=(NFM-NP)/N JPT=NFM-ITEMP*N-N IPT=JPT+ITEMP IF (IPT .GT. N) THEN ITEMP=JPT JPT=IPT-N IPT=ITEMP END IF XPT(NF,IPT)=XPT(IPT+1,IPT) XPT(NF,JPT)=XPT(JPT+1,JPT) END IF C C Calculate the next value of F. The least function value so far and C its index are required. C DO 60 J=1,N X(J)=DMIN1(DMAX1(XL(J),XBASE(J)+XPT(NF,J)),XU(J)) IF (XPT(NF,J) .EQ. SL(J)) X(J)=XL(J) IF (XPT(NF,J) .EQ. SU(J)) X(J)=XU(J) 60 CONTINUE F = CALFUN (N,X,IPRINT) c$$$ IF (IPRINT .EQ. 3) THEN c$$$ PRINT 70, NF,F,(X(I),I=1,N) c$$$ 70 FORMAT (/4X,'Function number',I6,' F =',1PD18.10, c$$$ 1 ' The corresponding X is:'/(2X,5D15.6)) c$$$ END IF c$$$ CALL minqi3 (IPRINT, F, NF, N, X) FVAL(NF)=F IF (NF .EQ. 1) THEN FBEG=F KOPT=1 ELSE IF (F .LT. FVAL(KOPT)) THEN KOPT=NF END IF C C Set the nonzero initial elements of BMAT and the quadratic model in the C cases when NF is at most 2*N+1. If NF exceeds N+1, then the positions C of the NF-th and (NF-N)-th interpolation points may be switched, in C order that the function value at the first of them contributes to the C off-diagonal second derivative terms of the initial quadratic model. C IF (NF .LE. 2*N+1) THEN IF (NF .GE. 2 .AND. NF .LE. N+1) THEN GOPT(NFM)=(F-FBEG)/STEPA IF (NPT .LT. NF+N) THEN BMAT(1,NFM)=-ONE/STEPA BMAT(NF,NFM)=ONE/STEPA BMAT(NPT+NFM,NFM)=-HALF*RHOSQ END IF ELSE IF (NF .GE. N+2) THEN 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 .LT. ZERO) THEN IF (F .LT. FVAL(NF-N)) THEN FVAL(NF)=FVAL(NF-N) FVAL(NF-N)=F IF (KOPT .EQ. NF) KOPT=NF-N XPT(NF-N,NFX)=STEPB XPT(NF,NFX)=STEPA END IF END IF BMAT(1,NFX)=-(STEPA+STEPB)/(STEPA*STEPB) BMAT(NF,NFX)=-HALF/XPT(NF-N,NFX) BMAT(NF-N,NFX)=-BMAT(1,NFX)-BMAT(NF,NFX) ZMAT(1,NFX)=DSQRT(TWO)/(STEPA*STEPB) ZMAT(NF,NFX)=DSQRT(HALF)/RHOSQ ZMAT(NF-N,NFX)=-ZMAT(1,NFX)-ZMAT(NF,NFX) END IF C C Set the off-diagonal second derivatives of the Lagrange functions and C the initial quadratic model. C ELSE IH=(IPT*(IPT-1))/2+JPT ZMAT(1,NFX)=RECIP ZMAT(NF,NFX)=RECIP ZMAT(IPT+1,NFX)=-RECIP ZMAT(JPT+1,NFX)=-RECIP TEMP=XPT(NF,IPT)*XPT(NF,JPT) HQ(IH)=(FBEG-FVAL(IPT+1)-FVAL(JPT+1)+F)/TEMP END IF IF (NF .LT. NPT .AND. NF .LT. MAXFUN) GOTO 50 RETURN END minqa/src/trstep.f0000644000176000001440000002466212415434561013705 0ustar ripleyusersC%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% trstep.f %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% SUBROUTINE TRSTEP (N,G,H,DELTA,TOL,D,GG,TD,TN,W,PIV,Z,EVALUE) IMPLICIT DOUBLE PRECISION (A-H,O-Z) DIMENSION G(*),H(N,*),D(*),GG(*),TD(*),TN(*),W(*),PIV(*),Z(*) C C N is the number of variables of a quadratic objective function, Q say. C G is the gradient of Q at the origin. C H is the Hessian matrix of Q. Only the upper triangular and diagonal C parts need be set. The lower triangular part is used to store the C elements of a Householder similarity transformation. C DELTA is the trust region radius, and has to be positive. C TOL is the value of a tolerance from the open interval (0,1). C D will be set to the calculated vector of variables. C The arrays GG, TD, TN, W, PIV and Z will be used for working space. C EVALUE will be set to the least eigenvalue of H if and only if D is a C Newton-Raphson step. Then EVALUE will be positive, but otherwise it C will be set to zero. C C Let MAXRED be the maximum of Q(0)-Q(D) subject to ||D|| .LEQ. DELTA, C and let ACTRED be the value of Q(0)-Q(D) that is actually calculated. C We take the view that any D is acceptable if it has the properties C C ||D|| .LEQ. DELTA and ACTRED .LEQ. (1-TOL)*MAXRED. C C The calculation of D is done by the method of Section 2 of the paper C by MJDP in the 1997 Dundee Numerical Analysis Conference Proceedings, C after transforming H to tridiagonal form. C C Initialization. C ONE=1.0D0 TWO=2.0D0 ZERO=0.0D0 DELSQ=DELTA*DELTA EVALUE=ZERO NM=N-1 DO 10 I=1,N D(I)=ZERO TD(I)=H(I,I) DO 10 J=1,I 10 H(I,J)=H(J,I) C C Apply Householder transformations to obtain a tridiagonal matrix that C is similar to H, and put the elements of the Householder vectors in C the lower triangular part of H. Further, TD and TN will contain the C diagonal and other nonzero elements of the tridiagonal matrix. C DO 80 K=1,NM KP=K+1 SUM=ZERO IF (KP .LT. N) THEN KPP=KP+1 DO 20 I=KPP,N 20 SUM=SUM+H(I,K)**2 END IF IF (SUM .EQ. ZERO) THEN TN(K)=H(KP,K) H(KP,K)=ZERO ELSE TEMP=H(KP,K) TN(K)=DSIGN(DSQRT(SUM+TEMP*TEMP),TEMP) H(KP,K)=-SUM/(TEMP+TN(K)) TEMP=DSQRT(TWO/(SUM+H(KP,K)**2)) DO 30 I=KP,N W(I)=TEMP*H(I,K) H(I,K)=W(I) 30 Z(I)=TD(I)*W(I) WZ=ZERO DO 50 J=KP,NM JP=J+1 DO 40 I=JP,N Z(I)=Z(I)+H(I,J)*W(J) 40 Z(J)=Z(J)+H(I,J)*W(I) 50 WZ=WZ+W(J)*Z(J) WZ=WZ+W(N)*Z(N) DO 70 J=KP,N TD(J)=TD(J)+W(J)*(WZ*W(J)-TWO*Z(J)) IF (J .LT. N) THEN JP=J+1 DO 60 I=JP,N 60 H(I,J)=H(I,J)-W(I)*Z(J)-W(J)*(Z(I)-WZ*W(I)) END IF 70 CONTINUE END IF 80 CONTINUE C C Form GG by applying the similarity transformation to G. C GSQ=ZERO DO 90 I=1,N GG(I)=G(I) 90 GSQ=GSQ+G(I)**2 GNORM=DSQRT(GSQ) DO 110 K=1,NM KP=K+1 SUM=ZERO DO 100 I=KP,N 100 SUM=SUM+GG(I)*H(I,K) DO 110 I=KP,N 110 GG(I)=GG(I)-SUM*H(I,K) C C Begin the trust region calculation with a tridiagonal matrix by C calculating the norm of H. Then treat the case when H is zero. C HNORM=DABS(TD(1))+DABS(TN(1)) TDMIN=TD(1) TN(N)=ZERO DO 120 I=2,N TEMP=DABS(TN(I-1))+DABS(TD(I))+DABS(TN(I)) HNORM=DMAX1(HNORM,TEMP) 120 TDMIN=DMIN1(TDMIN,TD(I)) IF (HNORM .EQ. ZERO) THEN IF (GNORM .EQ. ZERO) GOTO 400 SCALE=DELTA/GNORM DO 130 I=1,N 130 D(I)=-SCALE*GG(I) GOTO 370 END IF C C Set the initial values of PAR and its bounds. C PARL=DMAX1(ZERO,-TDMIN,GNORM/DELTA-HNORM) PARLEST=PARL PAR=PARL PARU=ZERO PARUEST=ZERO POSDEF=ZERO ITERC=0 C C Calculate the pivots of the Cholesky factorization of (H+PAR*I). C 140 ITERC=ITERC+1 KSAV=0 PIV(1)=TD(1)+PAR K=1 150 IF (PIV(K) .GT. ZERO) THEN PIV(K+1)=TD(K+1)+PAR-TN(K)**2/PIV(K) ELSE IF (PIV(K) .LT. ZERO .OR. TN(K) .NE. ZERO) GOTO 160 KSAV=K PIV(K+1)=TD(K+1)+PAR END IF K=K+1 IF (K .LT. N) GOTO 150 IF (PIV(K) .LT. ZERO) GOTO 160 IF (PIV(K) .EQ. ZERO) KSAV=K C C Branch if all the pivots are positive, allowing for the case when C G is zero. C IF (KSAV .EQ. 0 .AND. GSQ .GT. ZERO) GOTO 230 IF (GSQ .EQ. ZERO) THEN IF (PAR .EQ. ZERO) GOTO 370 PARU=PAR PARUEST=PAR IF (KSAV .EQ. 0) GOTO 190 END IF K=KSAV C C Set D to a direction of nonpositive curvature of the given tridiagonal C matrix, and thus revise PARLEST. C 160 D(K)=ONE IF (DABS(TN(K)) .LE. DABS(PIV(K))) THEN DSQ=ONE DHD=PIV(K) ELSE TEMP=TD(K+1)+PAR IF (TEMP .LE. DABS(PIV(K))) THEN D(K+1)=DSIGN(ONE,-TN(K)) DHD=PIV(K)+TEMP-TWO*DABS(TN(K)) ELSE D(K+1)=-TN(K)/TEMP DHD=PIV(K)+TN(K)*D(K+1) END IF DSQ=ONE+D(K+1)**2 END IF 170 IF (K .GT. 1) THEN K=K-1 IF (TN(K) .NE. ZERO) THEN D(K)=-TN(K)*D(K+1)/PIV(K) DSQ=DSQ+D(K)**2 GOTO 170 END IF DO 180 I=1,K 180 D(I)=ZERO END IF PARL=PAR PARLEST=PAR-DHD/DSQ C C Terminate with D set to a multiple of the current D if the following C test suggests that it suitable to do so. C 190 TEMP=PARUEST IF (GSQ .EQ. ZERO) TEMP=TEMP*(ONE-TOL) IF (PARUEST .GT. ZERO .AND. PARLEST .GE. TEMP) THEN DTG=ZERO DO 200 I=1,N 200 DTG=DTG+D(I)*GG(I) SCALE=-DSIGN(DELTA/DSQRT(DSQ),DTG) DO 210 I=1,N 210 D(I)=SCALE*D(I) GOTO 370 END IF C C Pick the value of PAR for the next iteration. C 220 IF (PARU .EQ. ZERO) THEN PAR=TWO*PARLEST+GNORM/DELTA ELSE PAR=0.5D0*(PARL+PARU) PAR=DMAX1(PAR,PARLEST) END IF IF (PARUEST .GT. ZERO) PAR=DMIN1(PAR,PARUEST) GOTO 140 C C Calculate D for the current PAR in the positive definite case. C 230 W(1)=-GG(1)/PIV(1) DO 240 I=2,N 240 W(I)=(-GG(I)-TN(I-1)*W(I-1))/PIV(I) D(N)=W(N) DO 250 I=NM,1,-1 250 D(I)=W(I)-TN(I)*D(I+1)/PIV(I) C C Branch if a Newton-Raphson step is acceptable. C DSQ=ZERO WSQ=ZERO DO 260 I=1,N DSQ=DSQ+D(I)**2 260 WSQ=WSQ+PIV(I)*W(I)**2 IF (PAR .EQ. ZERO .AND. DSQ .LE. DELSQ) GOTO 320 C C Make the usual test for acceptability of a full trust region step. C DNORM=DSQRT(DSQ) PHI=ONE/DNORM-ONE/DELTA TEMP=TOL*(ONE+PAR*DSQ/WSQ)-DSQ*PHI*PHI IF (TEMP .GE. ZERO) THEN SCALE=DELTA/DNORM DO 270 I=1,N 270 D(I)=SCALE*D(I) GOTO 370 END IF IF (ITERC .GE. 2 .AND. PAR .LE. PARL) GOTO 370 IF (PARU .GT. ZERO .AND. PAR .GE. PARU) GOTO 370 C C Complete the iteration when PHI is negative. C IF (PHI .LT. ZERO) THEN PARLEST=PAR IF (POSDEF. EQ. ONE) THEN IF (PHI .LE. PHIL) GOTO 370 SLOPE=(PHI-PHIL)/(PAR-PARL) PARLEST=PAR-PHI/SLOPE END IF SLOPE=ONE/GNORM IF (PARU .GT. ZERO) SLOPE=(PHIU-PHI)/(PARU-PAR) TEMP=PAR-PHI/SLOPE IF (PARUEST .GT. ZERO) TEMP=DMIN1(TEMP,PARUEST) PARUEST=TEMP POSDEF=ONE PARL=PAR PHIL=PHI GOTO 220 END IF C C If required, calculate Z for the alternative test for convergence. C IF (POSDEF .EQ. ZERO) THEN W(1)=ONE/PIV(1) DO 280 I=2,N TEMP=-TN(I-1)*W(I-1) 280 W(I)=(DSIGN(ONE,TEMP)+TEMP)/PIV(I) Z(N)=W(N) DO 290 I=NM,1,-1 290 Z(I)=W(I)-TN(I)*Z(I+1)/PIV(I) WWSQ=ZERO ZSQ=ZERO DTZ=ZERO DO 300 I=1,N WWSQ=WWSQ+PIV(I)*W(I)**2 ZSQ=ZSQ+Z(I)**2 300 DTZ=DTZ+D(I)*Z(I) C C Apply the alternative test for convergence. C TEMPA=DABS(DELSQ-DSQ) TEMPB=DSQRT(DTZ*DTZ+TEMPA*ZSQ) GAM=TEMPA/(DSIGN(TEMPB,DTZ)+DTZ) TEMP=TOL*(WSQ+PAR*DELSQ)-GAM*GAM*WWSQ IF (TEMP .GE. ZERO) THEN DO 310 I=1,N 310 D(I)=D(I)+GAM*Z(I) GOTO 370 END IF PARLEST=DMAX1(PARLEST,PAR-WWSQ/ZSQ) END IF C C Complete the iteration when PHI is positive. C SLOPE=ONE/GNORM IF (PARU .GT. ZERO) THEN IF (PHI .GE. PHIU) GOTO 370 SLOPE=(PHIU-PHI)/(PARU-PAR) END IF PARLEST=DMAX1(PARLEST,PAR-PHI/SLOPE) PARUEST=PAR IF (POSDEF .EQ. ONE) THEN SLOPE=(PHI-PHIL)/(PAR-PARL) PARUEST=PAR-PHI/SLOPE END IF PARU=PAR PHIU=PHI GOTO 220 C C Set EVALUE to the least eigenvalue of the second derivative matrix if C D is a Newton-Raphson step. SHFMAX will be an upper bound on EVALUE. C 320 SHFMIN=ZERO PIVOT=TD(1) SHFMAX=PIVOT DO 330 K=2,N PIVOT=TD(K)-TN(K-1)**2/PIVOT 330 SHFMAX=DMIN1(SHFMAX,PIVOT) C C Find EVALUE by a bisection method, but occasionally SHFMAX may be C adjusted by the rule of false position. C KSAVE=0 340 SHIFT=0.5D0*(SHFMIN+SHFMAX) K=1 TEMP=TD(1)-SHIFT 350 IF (TEMP .GT. ZERO) THEN PIV(K)=TEMP IF (K .LT. N) THEN TEMP=TD(K+1)-SHIFT-TN(K)**2/TEMP K=K+1 GOTO 350 END IF SHFMIN=SHIFT ELSE IF (K .LT. KSAVE) GOTO 360 IF (K .EQ. KSAVE) THEN IF (PIVKSV .EQ. ZERO) GOTO 360 IF (PIV(K)-TEMP .LT. TEMP-PIVKSV) THEN PIVKSV=TEMP SHFMAX=SHIFT ELSE PIVKSV=ZERO SHFMAX=(SHIFT*PIV(K)-SHFMIN*TEMP)/(PIV(K)-TEMP) END IF ELSE KSAVE=K PIVKSV=TEMP SHFMAX=SHIFT END IF END IF IF (SHFMIN .LE. 0.99D0*SHFMAX) GOTO 340 360 EVALUE=SHFMIN C C Apply the inverse Householder transformations to D. C 370 NM=N-1 DO 390 K=NM,1,-1 KP=K+1 SUM=ZERO DO 380 I=KP,N 380 SUM=SUM+D(I)*H(I,K) DO 390 I=KP,N 390 D(I)=D(I)-SUM*H(I,K) C C Return from the subroutine. C 400 RETURN END minqa/src/bigden.f0000644000176000001440000002301112415434561013577 0ustar ripleyusers SUBROUTINE BIGDEN (N,NPT,XOPT,XPT,BMAT,ZMAT,IDZ,NDIM,KOPT, 1 KNEW,D,W,VLAG,BETA,S,WVEC,PROD) IMPLICIT DOUBLE PRECISION (A-H,O-Z) DIMENSION XOPT(*),XPT(NPT,*),BMAT(NDIM,*),ZMAT(NPT,*),D(*), 1 W(*),VLAG(*),S(*),WVEC(NDIM,*),PROD(NDIM,*) DIMENSION DEN(9),DENEX(9),PAR(9) C C N is the number of variables. C NPT is the number of interpolation equations. C XOPT is the best interpolation point so far. C XPT contains the coordinates of the current interpolation points. C BMAT provides the last N columns of H. C ZMAT and IDZ give a factorization of the first NPT by NPT submatrix of H. C NDIM is the first dimension of BMAT and has the value NPT+N. C KOPT is the index of the optimal interpolation point. C KNEW is the index of the interpolation point that is going to be moved. C D will be set to the step from XOPT to the new point, and on entry it C should be the D that was calculated by the last call of BIGLAG. The C length of the initial D provides a trust region bound on the final D. C W will be set to Wcheck for the final choice of D. C VLAG will be set to Theta*Wcheck+e_b for the final choice of D. C BETA will be set to the value that will occur in the updating formula C when the KNEW-th interpolation point is moved to its new position. C S, WVEC, PROD and the private arrays DEN, DENEX and PAR will be used C for working space. C C D is calculated in a way that should provide a denominator with a large C modulus in the updating formula when the KNEW-th interpolation point is C shifted to the new position XOPT+D. C C Set some constants. C HALF=0.5D0 ONE=1.0D0 QUART=0.25D0 TWO=2.0D0 ZERO=0.0D0 TWOPI=8.0D0*DATAN(ONE) NPTM=NPT-N-1 C C Store the first NPT elements of the KNEW-th column of H in W(N+1) C to W(N+NPT). C DO 10 K=1,NPT 10 W(N+K)=ZERO DO 20 J=1,NPTM TEMP=ZMAT(KNEW,J) IF (J .LT. IDZ) TEMP=-TEMP DO 20 K=1,NPT 20 W(N+K)=W(N+K)+TEMP*ZMAT(K,J) ALPHA=W(N+KNEW) C C The initial search direction D is taken from the last call of BIGLAG, C and the initial S is set below, usually to the direction from X_OPT C to X_KNEW, but a different direction to an interpolation point may C be chosen, in order to prevent S from being nearly parallel to D. C DD=ZERO DS=ZERO SS=ZERO XOPTSQ=ZERO DO 30 I=1,N DD=DD+D(I)**2 S(I)=XPT(KNEW,I)-XOPT(I) DS=DS+D(I)*S(I) SS=SS+S(I)**2 30 XOPTSQ=XOPTSQ+XOPT(I)**2 IF (DS*DS .GT. 0.99D0*DD*SS) THEN KSAV=KNEW DTEST=DS*DS/SS DO 50 K=1,NPT IF (K .NE. KOPT) THEN DSTEMP=ZERO SSTEMP=ZERO DO 40 I=1,N DIFF=XPT(K,I)-XOPT(I) DSTEMP=DSTEMP+D(I)*DIFF 40 SSTEMP=SSTEMP+DIFF*DIFF IF (DSTEMP*DSTEMP/SSTEMP .LT. DTEST) THEN KSAV=K DTEST=DSTEMP*DSTEMP/SSTEMP DS=DSTEMP SS=SSTEMP END IF END IF 50 CONTINUE DO 60 I=1,N 60 S(I)=XPT(KSAV,I)-XOPT(I) END IF SSDEN=DD*SS-DS*DS ITERC=0 DENSAV=ZERO C C Begin the iteration by overwriting S with a vector that has the C required length and direction. C 70 ITERC=ITERC+1 TEMP=ONE/DSQRT(SSDEN) XOPTD=ZERO XOPTS=ZERO DO 80 I=1,N S(I)=TEMP*(DD*S(I)-DS*D(I)) XOPTD=XOPTD+XOPT(I)*D(I) 80 XOPTS=XOPTS+XOPT(I)*S(I) C C Set the coefficients of the first two terms of BETA. C TEMPA=HALF*XOPTD*XOPTD TEMPB=HALF*XOPTS*XOPTS DEN(1)=DD*(XOPTSQ+HALF*DD)+TEMPA+TEMPB DEN(2)=TWO*XOPTD*DD DEN(3)=TWO*XOPTS*DD DEN(4)=TEMPA-TEMPB DEN(5)=XOPTD*XOPTS DO 90 I=6,9 90 DEN(I)=ZERO C C Put the coefficients of Wcheck in WVEC. C DO 110 K=1,NPT TEMPA=ZERO TEMPB=ZERO TEMPC=ZERO DO 100 I=1,N TEMPA=TEMPA+XPT(K,I)*D(I) TEMPB=TEMPB+XPT(K,I)*S(I) 100 TEMPC=TEMPC+XPT(K,I)*XOPT(I) WVEC(K,1)=QUART*(TEMPA*TEMPA+TEMPB*TEMPB) WVEC(K,2)=TEMPA*TEMPC WVEC(K,3)=TEMPB*TEMPC WVEC(K,4)=QUART*(TEMPA*TEMPA-TEMPB*TEMPB) 110 WVEC(K,5)=HALF*TEMPA*TEMPB DO 120 I=1,N IP=I+NPT WVEC(IP,1)=ZERO WVEC(IP,2)=D(I) WVEC(IP,3)=S(I) WVEC(IP,4)=ZERO 120 WVEC(IP,5)=ZERO C C Put the coefficents of THETA*Wcheck in PROD. C DO 190 JC=1,5 NW=NPT IF (JC .EQ. 2 .OR. JC .EQ. 3) NW=NDIM DO 130 K=1,NPT 130 PROD(K,JC)=ZERO DO 150 J=1,NPTM SUM=ZERO DO 140 K=1,NPT 140 SUM=SUM+ZMAT(K,J)*WVEC(K,JC) IF (J .LT. IDZ) SUM=-SUM DO 150 K=1,NPT 150 PROD(K,JC)=PROD(K,JC)+SUM*ZMAT(K,J) IF (NW .EQ. NDIM) THEN DO 170 K=1,NPT SUM=ZERO DO 160 J=1,N 160 SUM=SUM+BMAT(K,J)*WVEC(NPT+J,JC) 170 PROD(K,JC)=PROD(K,JC)+SUM END IF DO 190 J=1,N SUM=ZERO DO 180 I=1,NW 180 SUM=SUM+BMAT(I,J)*WVEC(I,JC) 190 PROD(NPT+J,JC)=SUM C C Include in DEN the part of BETA that depends on THETA. C DO 210 K=1,NDIM SUM=ZERO DO 200 I=1,5 PAR(I)=HALF*PROD(K,I)*WVEC(K,I) 200 SUM=SUM+PAR(I) DEN(1)=DEN(1)-PAR(1)-SUM TEMPA=PROD(K,1)*WVEC(K,2)+PROD(K,2)*WVEC(K,1) TEMPB=PROD(K,2)*WVEC(K,4)+PROD(K,4)*WVEC(K,2) TEMPC=PROD(K,3)*WVEC(K,5)+PROD(K,5)*WVEC(K,3) DEN(2)=DEN(2)-TEMPA-HALF*(TEMPB+TEMPC) DEN(6)=DEN(6)-HALF*(TEMPB-TEMPC) TEMPA=PROD(K,1)*WVEC(K,3)+PROD(K,3)*WVEC(K,1) TEMPB=PROD(K,2)*WVEC(K,5)+PROD(K,5)*WVEC(K,2) TEMPC=PROD(K,3)*WVEC(K,4)+PROD(K,4)*WVEC(K,3) DEN(3)=DEN(3)-TEMPA-HALF*(TEMPB-TEMPC) DEN(7)=DEN(7)-HALF*(TEMPB+TEMPC) TEMPA=PROD(K,1)*WVEC(K,4)+PROD(K,4)*WVEC(K,1) DEN(4)=DEN(4)-TEMPA-PAR(2)+PAR(3) TEMPA=PROD(K,1)*WVEC(K,5)+PROD(K,5)*WVEC(K,1) TEMPB=PROD(K,2)*WVEC(K,3)+PROD(K,3)*WVEC(K,2) DEN(5)=DEN(5)-TEMPA-HALF*TEMPB DEN(8)=DEN(8)-PAR(4)+PAR(5) TEMPA=PROD(K,4)*WVEC(K,5)+PROD(K,5)*WVEC(K,4) 210 DEN(9)=DEN(9)-HALF*TEMPA C C Extend DEN so that it holds all the coefficients of DENOM. C SUM=ZERO DO 220 I=1,5 PAR(I)=HALF*PROD(KNEW,I)**2 220 SUM=SUM+PAR(I) DENEX(1)=ALPHA*DEN(1)+PAR(1)+SUM TEMPA=TWO*PROD(KNEW,1)*PROD(KNEW,2) TEMPB=PROD(KNEW,2)*PROD(KNEW,4) TEMPC=PROD(KNEW,3)*PROD(KNEW,5) DENEX(2)=ALPHA*DEN(2)+TEMPA+TEMPB+TEMPC DENEX(6)=ALPHA*DEN(6)+TEMPB-TEMPC TEMPA=TWO*PROD(KNEW,1)*PROD(KNEW,3) TEMPB=PROD(KNEW,2)*PROD(KNEW,5) TEMPC=PROD(KNEW,3)*PROD(KNEW,4) DENEX(3)=ALPHA*DEN(3)+TEMPA+TEMPB-TEMPC DENEX(7)=ALPHA*DEN(7)+TEMPB+TEMPC TEMPA=TWO*PROD(KNEW,1)*PROD(KNEW,4) DENEX(4)=ALPHA*DEN(4)+TEMPA+PAR(2)-PAR(3) TEMPA=TWO*PROD(KNEW,1)*PROD(KNEW,5) DENEX(5)=ALPHA*DEN(5)+TEMPA+PROD(KNEW,2)*PROD(KNEW,3) DENEX(8)=ALPHA*DEN(8)+PAR(4)-PAR(5) DENEX(9)=ALPHA*DEN(9)+PROD(KNEW,4)*PROD(KNEW,5) C C Seek the value of the angle that maximizes the modulus of DENOM. C SUM=DENEX(1)+DENEX(2)+DENEX(4)+DENEX(6)+DENEX(8) DENOLD=SUM DENMAX=SUM ISAVE=0 IU=49 TEMP=TWOPI/DFLOAT(IU+1) PAR(1)=ONE DO 250 I=1,IU ANGLE=DFLOAT(I)*TEMP PAR(2)=DCOS(ANGLE) PAR(3)=DSIN(ANGLE) DO 230 J=4,8,2 PAR(J)=PAR(2)*PAR(J-2)-PAR(3)*PAR(J-1) 230 PAR(J+1)=PAR(2)*PAR(J-1)+PAR(3)*PAR(J-2) SUMOLD=SUM SUM=ZERO DO 240 J=1,9 240 SUM=SUM+DENEX(J)*PAR(J) IF (DABS(SUM) .GT. DABS(DENMAX)) THEN DENMAX=SUM ISAVE=I TEMPA=SUMOLD ELSE IF (I .EQ. ISAVE+1) THEN TEMPB=SUM END IF 250 CONTINUE IF (ISAVE .EQ. 0) TEMPA=SUM IF (ISAVE .EQ. IU) TEMPB=DENOLD STEP=ZERO IF (TEMPA .NE. TEMPB) THEN TEMPA=TEMPA-DENMAX TEMPB=TEMPB-DENMAX STEP=HALF*(TEMPA-TEMPB)/(TEMPA+TEMPB) END IF ANGLE=TEMP*(DFLOAT(ISAVE)+STEP) C C Calculate the new parameters of the denominator, the new VLAG vector C and the new D. Then test for convergence. C PAR(2)=DCOS(ANGLE) PAR(3)=DSIN(ANGLE) DO 260 J=4,8,2 PAR(J)=PAR(2)*PAR(J-2)-PAR(3)*PAR(J-1) 260 PAR(J+1)=PAR(2)*PAR(J-1)+PAR(3)*PAR(J-2) BETA=ZERO DENMAX=ZERO DO 270 J=1,9 BETA=BETA+DEN(J)*PAR(J) 270 DENMAX=DENMAX+DENEX(J)*PAR(J) DO 280 K=1,NDIM VLAG(K)=ZERO DO 280 J=1,5 280 VLAG(K)=VLAG(K)+PROD(K,J)*PAR(J) TAU=VLAG(KNEW) DD=ZERO TEMPA=ZERO TEMPB=ZERO DO 290 I=1,N D(I)=PAR(2)*D(I)+PAR(3)*S(I) W(I)=XOPT(I)+D(I) DD=DD+D(I)**2 TEMPA=TEMPA+D(I)*W(I) 290 TEMPB=TEMPB+W(I)*W(I) IF (ITERC .GE. N) GOTO 340 IF (ITERC .GT. 1) DENSAV=DMAX1(DENSAV,DENOLD) IF (DABS(DENMAX) .LE. 1.1D0*DABS(DENSAV)) GOTO 340 DENSAV=DENMAX C C Set S to half the gradient of the denominator with respect to D. C Then branch for the next iteration. C DO 300 I=1,N TEMP=TEMPA*XOPT(I)+TEMPB*D(I)-VLAG(NPT+I) 300 S(I)=TAU*BMAT(KNEW,I)+ALPHA*TEMP DO 320 K=1,NPT SUM=ZERO DO 310 J=1,N 310 SUM=SUM+XPT(K,J)*W(J) TEMP=(TAU*W(N+K)-ALPHA*VLAG(K))*SUM DO 320 I=1,N 320 S(I)=S(I)+TEMP*XPT(K,I) SS=ZERO DS=ZERO DO 330 I=1,N SS=SS+S(I)**2 330 DS=DS+D(I)*S(I) SSDEN=DD*SS-DS*DS IF (SSDEN .GE. 1.0D-8*DD*SS) GOTO 70 C C Set the vector W before the RETURN from the subroutine. C 340 DO 350 K=1,NDIM W(K)=ZERO DO 350 J=1,5 350 W(K)=W(K)+WVEC(K,J)*PAR(J) VLAG(KOPT)=VLAG(KOPT)+ONE RETURN END minqa/src/uobyqb.f0000644000176000001440000003374612415434561013670 0ustar ripleyusersC%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% uobyqb.f %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% SUBROUTINE UOBYQB (N,X,RHOBEG,RHOEND,IPRINT,MAXFUN,NPT,XBASE, 1 XOPT,XNEW,XPT,PQ,PL,H,G,D,VLAG,W,IERR) IMPLICIT DOUBLE PRECISION (A-H,O-Z) CJN Declare IERR INTEGER IERR DIMENSION X(*),XBASE(*),XOPT(*),XNEW(*),XPT(NPT,*),PQ(*), 1 PL(NPT,*),H(N,*),G(*),D(*),VLAG(*),W(*) C C The arguments N, X, RHOBEG, RHOEND, IPRINT and MAXFUN are identical to C the corresponding arguments in SUBROUTINE UOBYQA. C NPT is set by UOBYQA to (N*N+3*N+2)/2 for the above dimension statement. C XBASE will contain a shift of origin that reduces the contributions from C rounding errors to values of the model and Lagrange functions. C XOPT will be set to the displacement from XBASE of the vector of C variables that provides the least calculated F so far. C XNEW will be set to the displacement from XBASE of the vector of C variables for the current calculation of F. C XPT will contain the interpolation point coordinates relative to XBASE. C PQ will contain the parameters of the quadratic model. C PL will contain the parameters of the Lagrange functions. C H will provide the second derivatives that TRSTEP and LAGMAX require. C G will provide the first derivatives that TRSTEP and LAGMAX require. C D is reserved for trial steps from XOPT, except that it will contain C diagonal second derivatives during the initialization procedure. C VLAG will contain the values of the Lagrange functions at a new point X. C The array W will be used for working space. Its length must be at least C max [ 6*N, ( N**2 + 3*N + 2 ) / 2 ]. C C Set some constants. C ONE=1.0D0 TWO=2.0D0 ZERO=0.0D0 HALF=0.5D0 TOL=0.01D0 NNP=N+N+1 NPTM=NPT-1 NFTEST=MAX0(MAXFUN,1) C C Initialization. NF is the number of function calculations so far. C RHO=RHOBEG RHOSQ=RHO*RHO NF=0 DO 10 I=1,N XBASE(I)=X(I) DO 10 K=1,NPT 10 XPT(K,I)=ZERO DO 20 K=1,NPT DO 20 J=1,NPTM 20 PL(K,J)=ZERO C C The branch to label 120 obtains a new value of the objective function C and then there is a branch back to label 50, because the new function C value is needed to form the initial quadratic model. The least function C value so far and its index are noted below. C 30 DO 40 I=1,N 40 X(I)=XBASE(I)+XPT(NF+1,I) GOTO 120 50 IF (NF .EQ. 1) THEN FOPT=F KOPT=NF FBASE=F J=0 JSWITCH=-1 IH=N ELSE IF (F .LT. FOPT) THEN FOPT=F KOPT=NF END IF END IF C C Form the gradient and diagonal second derivatives of the initial C quadratic model and Lagrange functions. C IF (NF .LE. NNP) THEN JSWITCH=-JSWITCH IF (JSWITCH .GT. 0) THEN IF (J .GE. 1) THEN IH=IH+J IF (W(J) .LT. ZERO) THEN D(J)=(FSAVE+F-TWO*FBASE)/RHOSQ PQ(J)=(FSAVE-F)/(TWO*RHO) PL(1,IH)=-TWO/RHOSQ PL(NF-1,J)=HALF/RHO PL(NF-1,IH)=ONE/RHOSQ ELSE PQ(J)=(4.0D0*FSAVE-3.0D0*FBASE-F)/(TWO*RHO) D(J)=(FBASE+F-TWO*FSAVE)/RHOSQ PL(1,J)=-1.5D0/RHO PL(1,IH)=ONE/RHOSQ PL(NF-1,J)=TWO/RHO PL(NF-1,IH)=-TWO/RHOSQ END IF PQ(IH)=D(J) PL(NF,J)=-HALF/RHO PL(NF,IH)=ONE/RHOSQ END IF C C Pick the shift from XBASE to the next initial interpolation point C that provides diagonal second derivatives. C IF (J .LT. N) THEN J=J+1 XPT(NF+1,J)=RHO END IF ELSE FSAVE=F IF (F .LT. FBASE) THEN W(J)=RHO XPT(NF+1,J)=TWO*RHO ELSE W(J)=-RHO XPT(NF+1,J)=-RHO END IF END IF IF (NF .LT. NNP) GOTO 30 C C Form the off-diagonal second derivatives of the initial quadratic model. C IH=N IP=1 IQ=2 END IF IH=IH+1 IF (NF .GT. NNP) THEN TEMP=ONE/(W(IP)*W(IQ)) TEMPA=F-FBASE-W(IP)*PQ(IP)-W(IQ)*PQ(IQ) PQ(IH)=(TEMPA-HALF*RHOSQ*(D(IP)+D(IQ)))*TEMP PL(1,IH)=TEMP IW=IP+IP IF (W(IP) .LT. ZERO) IW=IW+1 PL(IW,IH)=-TEMP IW=IQ+IQ IF (W(IQ) .LT. ZERO) IW=IW+1 PL(IW,IH)=-TEMP PL(NF,IH)=TEMP C C Pick the shift from XBASE to the next initial interpolation point C that provides off-diagonal second derivatives. C IP=IP+1 END IF IF (IP .EQ. IQ) THEN IH=IH+1 IP=1 IQ=IQ+1 END IF IF (NF .LT. NPT) THEN XPT(NF+1,IP)=W(IP) XPT(NF+1,IQ)=W(IQ) GOTO 30 END IF C C Set parameters to begin the iterations for the current RHO. C SIXTHM=ZERO DELTA=RHO 60 TWORSQ=(TWO*RHO)**2 RHOSQ=RHO*RHO C C Form the gradient of the quadratic model at the trust region centre. C 70 KNEW=0 IH=N DO 80 J=1,N XOPT(J)=XPT(KOPT,J) G(J)=PQ(J) DO 80 I=1,J IH=IH+1 G(I)=G(I)+PQ(IH)*XOPT(J) IF (I .LT. J) G(J)=G(J)+PQ(IH)*XOPT(I) 80 H(I,J)=PQ(IH) C C Generate the next trust region step and test its length. Set KNEW C to -1 if the purpose of the next F will be to improve conditioning, C and also calculate a lower bound on the Hessian term of the model Q. C CALL TRSTEP (N,G,H,DELTA,TOL,D,W(1),W(N+1),W(2*N+1),W(3*N+1), 1 W(4*N+1),W(5*N+1),EVALUE) TEMP=ZERO DO 90 I=1,N 90 TEMP=TEMP+D(I)**2 DNORM=DMIN1(DELTA,DSQRT(TEMP)) ERRTOL=-ONE IF (DNORM .LT. HALF*RHO) THEN KNEW=-1 ERRTOL=HALF*EVALUE*RHO*RHO IF (NF .LE. NPT+9) ERRTOL=ZERO GOTO 290 END IF C C Calculate the next value of the objective function. C 100 DO 110 I=1,N XNEW(I)=XOPT(I)+D(I) 110 X(I)=XBASE(I)+XNEW(I) 120 IF (NF .GE. NFTEST) THEN C$$$ IF (IPRINT .GT. 0) PRINT 130 C$$$ 130 FORMAT (/4X,'Return from UOBYQA because CALFUN has been', C$$$ 1 ' called MAXFUN times') C$$$ GOTO 420 CJN Too many function evaluations IERR=390 GOTO 420 END IF NF=NF+1 F = CALFUN (N,X,IPRINT) c$$$ IF (IPRINT .EQ. 3) THEN c$$$ PRINT 70, NF,F,(X(I),I=1,N) c$$$ 70 FORMAT (/4X,'Function number',I6,' F =',1PD18.10, c$$$ 1 ' The corresponding X is:'/(2X,5D15.6)) c$$$ END IF c$$$ CALL minqi3 (IPRINT, F, NF, N, X) IF (NF .LE. NPT) GOTO 50 IF (KNEW .EQ. -1) GOTO 420 C C Use the quadratic model to predict the change in F due to the step D, C and find the values of the Lagrange functions at the new point. C VQUAD=ZERO IH=N DO 150 J=1,N W(J)=D(J) VQUAD=VQUAD+W(J)*PQ(J) DO 150 I=1,J IH=IH+1 W(IH)=D(I)*XNEW(J)+D(J)*XOPT(I) IF (I .EQ. J) W(IH)=HALF*W(IH) 150 VQUAD=VQUAD+W(IH)*PQ(IH) DO 170 K=1,NPT TEMP=ZERO DO 160 J=1,NPTM 160 TEMP=TEMP+W(J)*PL(K,J) 170 VLAG(K)=TEMP VLAG(KOPT)=VLAG(KOPT)+ONE C C Update SIXTHM, which is a lower bound on one sixth of the greatest C third derivative of F. C DIFF=F-FOPT-VQUAD SUM=ZERO DO 190 K=1,NPT TEMP=ZERO DO 180 I=1,N 180 TEMP=TEMP+(XPT(K,I)-XNEW(I))**2 TEMP=DSQRT(TEMP) 190 SUM=SUM+DABS(TEMP*TEMP*TEMP*VLAG(K)) SIXTHM=DMAX1(SIXTHM,DABS(DIFF)/SUM) C C Update FOPT and XOPT if the new F is the least value of the objective C function so far. Then branch if D is not a trust region step. C FSAVE=FOPT IF (F .LT. FOPT) THEN FOPT=F DO 200 I=1,N 200 XOPT(I)=XNEW(I) END IF KSAVE=KNEW IF (KNEW .GT. 0) GOTO 240 C C Pick the next value of DELTA after a trust region step. C IF (VQUAD .GE. ZERO) THEN CJN IF (IPRINT .GT. 0) CALL minqer(2101) C$$$ IF (IPRINT .GT. 0) PRINT 210 C$$$ 210 FORMAT (/4X,'Return from UOBYQA because a trust', C$$$ 1 ' region step has failed to reduce Q') IERR=2101 GOTO 420 END IF RATIO=(F-FSAVE)/VQUAD IF (RATIO .LE. 0.1D0) THEN DELTA=HALF*DNORM ELSE IF (RATIO. LE. 0.7D0) THEN DELTA=DMAX1(HALF*DELTA,DNORM) ELSE DELTA=DMAX1(DELTA,1.25D0*DNORM,DNORM+RHO) END IF IF (DELTA .LE. 1.5D0*RHO) DELTA=RHO C C Set KNEW to the index of the next interpolation point to be deleted. C KTEMP=0 DETRAT=ZERO IF (F .GE. FSAVE) THEN KTEMP=KOPT DETRAT=ONE END IF DO 230 K=1,NPT SUM=ZERO DO 220 I=1,N 220 SUM=SUM+(XPT(K,I)-XOPT(I))**2 TEMP=DABS(VLAG(K)) IF (SUM .GT. RHOSQ) TEMP=TEMP*(SUM/RHOSQ)**1.5D0 IF (TEMP .GT. DETRAT .AND. K .NE. KTEMP) THEN DETRAT=TEMP DDKNEW=SUM KNEW=K END IF 230 CONTINUE IF (KNEW .EQ. 0) GOTO 290 C C Replace the interpolation point that has index KNEW by the point XNEW, C and also update the Lagrange functions and the quadratic model. C 240 DO 250 I=1,N 250 XPT(KNEW,I)=XNEW(I) TEMP=ONE/VLAG(KNEW) DO 260 J=1,NPTM PL(KNEW,J)=TEMP*PL(KNEW,J) 260 PQ(J)=PQ(J)+DIFF*PL(KNEW,J) DO 280 K=1,NPT IF (K .NE. KNEW) THEN TEMP=VLAG(K) DO 270 J=1,NPTM 270 PL(K,J)=PL(K,J)-TEMP*PL(KNEW,J) END IF 280 CONTINUE C C Update KOPT if F is the least calculated value of the objective C function. Then branch for another trust region calculation. The C case KSAVE>0 indicates that a model step has just been taken. C IF (F .LT. FSAVE) THEN KOPT=KNEW GOTO 70 END IF IF (KSAVE .GT. 0) GOTO 70 IF (DNORM .GT. TWO*RHO) GOTO 70 IF (DDKNEW .GT. TWORSQ) GOTO 70 C C Alternatively, find out if the interpolation points are close C enough to the best point so far. C 290 DO 300 K=1,NPT W(K)=ZERO DO 300 I=1,N 300 W(K)=W(K)+(XPT(K,I)-XOPT(I))**2 310 KNEW=-1 DISTEST=TWORSQ DO 320 K=1,NPT IF (W(K) .GT. DISTEST) THEN KNEW=K DISTEST=W(K) END IF 320 CONTINUE C C If a point is sufficiently far away, then set the gradient and Hessian C of its Lagrange function at the centre of the trust region, and find C half the sum of squares of components of the Hessian. C IF (KNEW .GT. 0) THEN IH=N SUMH=ZERO DO 340 J=1,N G(J)=PL(KNEW,J) DO 330 I=1,J IH=IH+1 TEMP=PL(KNEW,IH) G(J)=G(J)+TEMP*XOPT(I) IF (I .LT. J) THEN G(I)=G(I)+TEMP*XOPT(J) SUMH=SUMH+TEMP*TEMP END IF 330 H(I,J)=TEMP 340 SUMH=SUMH+HALF*TEMP*TEMP C C If ERRTOL is positive, test whether to replace the interpolation point C with index KNEW, using a bound on the maximum modulus of its Lagrange C function in the trust region. C IF (ERRTOL .GT. ZERO) THEN W(KNEW)=ZERO SUMG=ZERO DO 350 I=1,N 350 SUMG=SUMG+G(I)**2 ESTIM=RHO*(DSQRT(SUMG)+RHO*DSQRT(HALF*SUMH)) WMULT=SIXTHM*DISTEST**1.5D0 IF (WMULT*ESTIM .LE. ERRTOL) GOTO 310 END IF C C If the KNEW-th point may be replaced, then pick a D that gives a large C value of the modulus of its Lagrange function within the trust region. C Here the vector XNEW is used as temporary working space. C CALL LAGMAX (N,G,H,RHO,D,XNEW,VMAX) IF (ERRTOL .GT. ZERO) THEN IF (WMULT*VMAX .LE. ERRTOL) GOTO 310 END IF GOTO 100 END IF IF (DNORM .GT. RHO) GOTO 70 C C Prepare to reduce RHO by shifting XBASE to the best point so far, C and make the corresponding changes to the gradients of the Lagrange C functions and the quadratic model. C IF (RHO .GT. RHOEND) THEN IH=N DO 380 J=1,N XBASE(J)=XBASE(J)+XOPT(J) DO 360 K=1,NPT 360 XPT(K,J)=XPT(K,J)-XOPT(J) DO 380 I=1,J IH=IH+1 PQ(I)=PQ(I)+PQ(IH)*XOPT(J) IF (I .LT. J) THEN PQ(J)=PQ(J)+PQ(IH)*XOPT(I) DO 370 K=1,NPT 370 PL(K,J)=PL(K,J)+PL(K,IH)*XOPT(I) END IF DO 380 K=1,NPT 380 PL(K,I)=PL(K,I)+PL(K,IH)*XOPT(J) C C Pick the next values of RHO and DELTA. C DELTA=HALF*RHO RATIO=RHO/RHOEND IF (RATIO .LE. 16.0D0) THEN RHO=RHOEND ELSE IF (RATIO .LE. 250.0D0) THEN RHO=DSQRT(RATIO)*RHOEND ELSE RHO=0.1D0*RHO END IF DELTA=DMAX1(DELTA,RHO) IF (IPRINT .GE. 2) THEN CALL minqit(IPRINT, RHO, NF, FOPT, N, XBASE, XOPT) C$$$ IF (IPRINT .GE. 3) PRINT 390 C$$$ 390 FORMAT (5X) C$$$ PRINT 400, RHO,NF C$$$ 400 FORMAT (/4X,'New RHO =',1PD11.4,5X,'Number of', C$$$ 1 ' function values =',I6) C$$$ PRINT 410, FOPT,(XBASE(I),I=1,N) C$$$ 410 FORMAT (4X,'Least value of F =',1PD23.15,9X, C$$$ 1 'The corresponding X is:'/(2X,5D15.6)) END IF GOTO 60 END IF C C Return from the calculation, after another Newton-Raphson step, if C it is too short to have been tried before. C IF (ERRTOL .GE. ZERO) GOTO 100 420 IF (FOPT .LE. F) THEN DO 430 I=1,N 430 X(I)=XBASE(I)+XOPT(I) F=FOPT END IF IF (IPRINT .GE. 1) THEN CALL minqir(IPRINT, F, NF, N, X) C$$$ PRINT 440, NF C$$$ 440 FORMAT (/4X,'At the return from UOBYQA',5X, C$$$ 1 'Number of function values =',I6) C$$$ PRINT 410, F,(X(I),I=1,N) END IF RETURN END minqa/src/lagmax.f0000644000176000001440000001157012415434561013627 0ustar ripleyusersC%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% lagmax.f %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% SUBROUTINE LAGMAX (N,G,H,RHO,D,V,VMAX) IMPLICIT DOUBLE PRECISION (A-H,O-Z) DIMENSION G(*),H(N,*),D(*),V(*) C C N is the number of variables of a quadratic objective function, Q say. C G is the gradient of Q at the origin. C H is the symmetric Hessian matrix of Q. Only the upper triangular and C diagonal parts need be set. C RHO is the trust region radius, and has to be positive. C D will be set to the calculated vector of variables. C The array V will be used for working space. C VMAX will be set to |Q(0)-Q(D)|. C C Calculating the D that maximizes |Q(0)-Q(D)| subject to ||D|| .LEQ. RHO C requires of order N**3 operations, but sometimes it is adequate if C |Q(0)-Q(D)| is within about 0.9 of its greatest possible value. This C subroutine provides such a solution in only of order N**2 operations, C where the claim of accuracy has been tested by numerical experiments. C C Preliminary calculations. C HALF=0.5D0 HALFRT=DSQRT(HALF) ONE=1.0D0 ZERO=0.0D0 C C Pick V such that ||HV|| / ||V|| is large. C HMAX=ZERO DO 20 I=1,N SUM=ZERO DO 10 J=1,N H(J,I)=H(I,J) 10 SUM=SUM+H(I,J)**2 IF (SUM .GT. HMAX) THEN HMAX=SUM K=I END IF 20 CONTINUE DO 30 J=1,N 30 V(J)=H(K,J) C C Set D to a vector in the subspace spanned by V and HV that maximizes C |(D,HD)|/(D,D), except that we set D=HV if V and HV are nearly parallel. C The vector that has the name D at label 60 used to be the vector W. C VSQ=ZERO VHV=ZERO DSQ=ZERO DO 50 I=1,N VSQ=VSQ+V(I)**2 D(I)=ZERO DO 40 J=1,N 40 D(I)=D(I)+H(I,J)*V(J) VHV=VHV+V(I)*D(I) 50 DSQ=DSQ+D(I)**2 IF (VHV*VHV .LE. 0.9999D0*DSQ*VSQ) THEN TEMP=VHV/VSQ WSQ=ZERO DO 60 I=1,N D(I)=D(I)-TEMP*V(I) 60 WSQ=WSQ+D(I)**2 WHW=ZERO RATIO=DSQRT(WSQ/VSQ) DO 80 I=1,N TEMP=ZERO DO 70 J=1,N 70 TEMP=TEMP+H(I,J)*D(J) WHW=WHW+TEMP*D(I) 80 V(I)=RATIO*V(I) VHV=RATIO*RATIO*VHV VHW=RATIO*WSQ TEMP=HALF*(WHW-VHV) TEMP=TEMP+DSIGN(DSQRT(TEMP**2+VHW**2),WHW+VHV) DO 90 I=1,N 90 D(I)=VHW*V(I)+TEMP*D(I) END IF C C We now turn our attention to the subspace spanned by G and D. A multiple C of the current D is returned if that choice seems to be adequate. C GG=ZERO GD=ZERO DD=ZERO DHD=ZERO DO 110 I=1,N GG=GG+G(I)**2 GD=GD+G(I)*D(I) DD=DD+D(I)**2 SUM=ZERO DO 100 J=1,N 100 SUM=SUM+H(I,J)*D(J) 110 DHD=DHD+SUM*D(I) TEMP=GD/GG VV=ZERO SCALE=DSIGN(RHO/DSQRT(DD),GD*DHD) DO 120 I=1,N V(I)=D(I)-TEMP*G(I) VV=VV+V(I)**2 120 D(I)=SCALE*D(I) GNORM=DSQRT(GG) IF (GNORM*DD .LE. 0.5D-2*RHO*DABS(DHD) .OR. 1 VV/DD .LE. 1.0D-4) THEN VMAX=DABS(SCALE*(GD+HALF*SCALE*DHD)) GOTO 170 END IF C C G and V are now orthogonal in the subspace spanned by G and D. Hence C we generate an orthonormal basis of this subspace such that (D,HV) is C negligible or zero, where D and V will be the basis vectors. C GHG=ZERO VHG=ZERO VHV=ZERO DO 140 I=1,N SUM=ZERO SUMV=ZERO DO 130 J=1,N SUM=SUM+H(I,J)*G(J) 130 SUMV=SUMV+H(I,J)*V(J) GHG=GHG+SUM*G(I) VHG=VHG+SUMV*G(I) 140 VHV=VHV+SUMV*V(I) VNORM=DSQRT(VV) GHG=GHG/GG VHG=VHG/(VNORM*GNORM) VHV=VHV/VV IF (DABS(VHG) .LE. 0.01D0*DMAX1(DABS(GHG),DABS(VHV))) THEN VMU=GHG-VHV WCOS=ONE WSIN=ZERO ELSE TEMP=HALF*(GHG-VHV) VMU=TEMP+DSIGN(DSQRT(TEMP**2+VHG**2),TEMP) TEMP=DSQRT(VMU**2+VHG**2) WCOS=VMU/TEMP WSIN=VHG/TEMP END IF TEMPA=WCOS/GNORM TEMPB=WSIN/VNORM TEMPC=WCOS/VNORM TEMPD=WSIN/GNORM DO 150 I=1,N D(I)=TEMPA*G(I)+TEMPB*V(I) 150 V(I)=TEMPC*V(I)-TEMPD*G(I) C C The final D is a multiple of the current D, V, D+V or D-V. We make the C choice from these possibilities that is optimal. C DLIN=WCOS*GNORM/RHO VLIN=-WSIN*GNORM/RHO TEMPA=DABS(DLIN)+HALF*DABS(VMU+VHV) TEMPB=DABS(VLIN)+HALF*DABS(GHG-VMU) TEMPC=HALFRT*(DABS(DLIN)+DABS(VLIN))+0.25D0*DABS(GHG+VHV) IF (TEMPA .GE. TEMPB .AND. TEMPA .GE. TEMPC) THEN TEMPD=DSIGN(RHO,DLIN*(VMU+VHV)) TEMPV=ZERO ELSE IF (TEMPB .GE. TEMPC) THEN TEMPD=ZERO TEMPV=DSIGN(RHO,VLIN*(GHG-VMU)) ELSE TEMPD=DSIGN(HALFRT*RHO,DLIN*(GHG+VHV)) TEMPV=DSIGN(HALFRT*RHO,VLIN*(GHG+VHV)) END IF DO 160 I=1,N 160 D(I)=TEMPD*D(I)+TEMPV*V(I) VMAX=RHO*RHO*DMAX1(TEMPA,TEMPB,TEMPC) 170 RETURN END minqa/src/uobyqa.f0000644000176000001440000000536012415434561013656 0ustar ripleyusersC%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% uobyqa.f %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% SUBROUTINE UOBYQA (N,X,RHOBEG,RHOEND,IPRINT,MAXFUN,W, IERR) IMPLICIT DOUBLE PRECISION (A-H,O-Z) CJN Declare IERR INTEGER IERR DIMENSION X(*),W(*) C C This subroutine seeks the least value of a function of many variables, C by a trust region method that forms quadratic models by interpolation. C The algorithm is described in "UOBYQA: unconstrained optimization by C quadratic approximation" by M.J.D. Powell, Report DAMTP 2000/NA14, C University of Cambridge. The arguments of the subroutine are as follows. C C N must be set to the number of variables and must be at least two. C Initial values of the variables must be set in X(1),X(2),...,X(N). They C will be changed to the values that give the least calculated F. C RHOBEG and RHOEND must be set to the initial and final values of a trust C region radius, so both must be positive with RHOEND<=RHOBEG. Typically C RHOBEG should be about one tenth of the greatest expected change to a C variable, and RHOEND should indicate the accuracy that is required in C the final values of the variables. C The value of IPRINT should be set to 0, 1, 2 or 3, which controls the C amount of printing. Specifically, there is no output if IPRINT=0 and C there is output only at the return if IPRINT=1. Otherwise, each new C value of RHO is printed, with the best vector of variables so far and C the corresponding value of the objective function. Further, each new C value of F with its variables are output if IPRINT=3. C MAXFUN must be set to an upper bound on the number of calls of CALFUN. C The array W will be used for working space. Its length must be at least C ( N**4 + 8*N**3 + 23*N**2 + 42*N + max [ 2*N**2 + 4, 18*N ] ) / 4. CJN Add IERR to tell what the error is to minqer. C IERR gives an error code that is interpreted by minqer interface routine. C and passed back to minqa.R C C SUBROUTINE CALFUN (N,X,F) must be provided by the user. It must set F to C the value of the objective function for the variables X(1),X(2),...,X(N). C C Partition the working space array, so that different parts of it can be C treated separately by the subroutine that performs the main calculation. C NPT=(N*N+3*N+2)/2 IXB=1 IXO=IXB+N IXN=IXO+N IXP=IXN+N IPQ=IXP+N*NPT IPL=IPQ+NPT-1 IH=IPL+(NPT-1)*NPT IG=IH+N*N ID=IG+N IVL=IH IW=ID+N CJN Initialize IERR to 0 (normal exit) IERR=0 CALL UOBYQB (N,X,RHOBEG,RHOEND,IPRINT,MAXFUN,NPT,W(IXB),W(IXO), 1 W(IXN),W(IXP),W(IPQ),W(IPL),W(IH),W(IG),W(ID),W(IVL),W(IW),IERR) RETURN END minqa/src/bobyqa.f0000644000176000001440000001363712415434561013641 0ustar ripleyusers SUBROUTINE BOBYQA (N,NPT,X,XL,XU,RHOBEG,RHOEND,IPRINT, 1 MAXFUN,W,IERR) IMPLICIT DOUBLE PRECISION (A-H,O-Z) DIMENSION X(*),XL(*),XU(*),W(*) C C This subroutine seeks the least value of a function of many variables, C by applying a trust region method that forms quadratic models by C interpolation. There is usually some freedom in the interpolation C conditions, which is taken up by minimizing the Frobenius norm of C the change to the second derivative of the model, beginning with the C zero matrix. The values of the variables are constrained by upper and C lower bounds. The arguments of the subroutine are as follows. C C N must be set to the number of variables and must be at least two. C NPT is the number of interpolation conditions. Its value must be in C the interval [N+2,(N+1)(N+2)/2]. Choices that exceed 2*N+1 are not C recommended. C Initial values of the variables must be set in X(1),X(2),...,X(N). They C will be changed to the values that give the least calculated F. C For I=1,2,...,N, XL(I) and XU(I) must provide the lower and upper C bounds, respectively, on X(I). The construction of quadratic models C requires XL(I) to be strictly less than XU(I) for each I. Further, C the contribution to a model from changes to the I-th variable is C damaged severely by rounding errors if XU(I)-XL(I) is too small. C RHOBEG and RHOEND must be set to the initial and final values of a trust C region radius, so both must be positive with RHOEND no greater than C RHOBEG. Typically, RHOBEG should be about one tenth of the greatest C expected change to a variable, while RHOEND should indicate the C accuracy that is required in the final values of the variables. An C error return occurs if any of the differences XU(I)-XL(I), I=1,...,N, C is less than 2*RHOBEG. C The value of IPRINT should be set to 0, 1, 2 or 3, which controls the C amount of printing. Specifically, there is no output if IPRINT=0 and C there is output only at the return if IPRINT=1. Otherwise, each new C value of RHO is printed, with the best vector of variables so far and C the corresponding value of the objective function. Further, each new C value of F with its variables are output if IPRINT=3. C MAXFUN must be set to an upper bound on the number of calls of CALFUN. C The array W will be used for working space. Its length must be at least C (NPT+5)*(NPT+N)+3*N*(N+5)/2. CJN Add IERR to tell what the error is to minqer. C IERR gives an error code that is interpreted by minqer interface routine. C and passed back to minqa.R C C DOUBLE PRECISION FUNCTION CALFUN (N,X,IP) has to be provided by C the user. It returns the value of the objective function for C the current values of the variables X(1),X(2),...,X(N), which are C generated automatically in a way that satisfies the bounds given C in XL and XU. C C Return if the value of NPT is unacceptable. C C Modified by John Nash to put the setup controls into the R code. NP=N+1 CJ Comment out to END IF as in R code IF (NPT .LT. N+2 .OR. NPT .GT. ((N+2)*NP)/2) THEN CJN CALL minqer(10) c$$$ PRINT 10 c$$$ 10 FORMAT (/4X,'Return from BOBYQA because NPT is not in', c$$$ 1 ' the required interval') c$$$ GO TO 40 IERR = 10 CJN Fail out NPT has unacceptable value GO TO 40 END IF C C Partition the working space array, so that different parts of it can C be treated separately during the calculation of BOBYQB. The partition C requires the first (NPT+2)*(NPT+N)+3*N*(N+5)/2 elements of W plus the C space that is taken by the last array in the argument list of BOBYQB. C 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 CJN 100807 to ensure a defined value for the return code IERR = 0 C C Return if there is insufficient space between the bounds. Modify the C initial X if necessary in order to avoid conflicts between the bounds C and the construction of the first quadratic model. The lower and upper C bounds on moves from the updated X are set now, in the ISL and ISU C partitions of W, in order to provide useful and exact information about C components of X that become within distance RHOBEG from their bounds. C ZERO=0.0D0 DO 30 J=1,N TEMP=XU(J)-XL(J) IF (TEMP .LT. RHOBEG+RHOBEG) THEN CJN CALL minqer(20) c$$$ PRINT 20 c$$$ 20 FORMAT (/4X,'Return from BOBYQA because one of the', c$$$ 1 ' differences XU(I)-XL(I)'/6X,' is less than 2*RHOBEG.') c$$$ GO TO 40 IERR = 20 GOTO 40 END IF JSL=ISL+J-1 JSU=JSL+N W(JSL)=XL(J)-X(J) W(JSU)=XU(J)-X(J) IF (W(JSL) .GE. -RHOBEG) THEN IF (W(JSL) .GE. ZERO) THEN X(J)=XL(J) W(JSL)=ZERO W(JSU)=TEMP ELSE X(J)=XL(J)+RHOBEG W(JSL)=-RHOBEG W(JSU)=DMAX1(XU(J)-X(J),RHOBEG) END IF ELSE IF (W(JSU) .LE. RHOBEG) THEN IF (W(JSU) .LE. ZERO) THEN X(J)=XU(J) W(JSL)=-TEMP W(JSU)=ZERO ELSE X(J)=XU(J)-RHOBEG W(JSL)=DMIN1(XL(J)-X(J),-RHOBEG) W(JSU)=RHOBEG END IF END IF 30 CONTINUE C C Make the call of BOBYQB. C CALL BOBYQB (N,NPT,X,XL,XU,RHOBEG,RHOEND,IPRINT,MAXFUN,W(IXB), 1 W(IXP),W(IFV),W(IXO),W(IGO),W(IHQ),W(IPQ),W(IBMAT),W(IZMAT), 2 NDIM,W(ISL),W(ISU),W(IXN),W(IXA),W(ID),W(IVL),W(IW), IERR) 40 RETURN CJN Added 100807 -- put label 40 END minqa/src/Makevars.win0000644000176000001440000000027411730166327014477 0ustar ripleyusers## -*- mode: makefile; -*- ## Use the R_HOME indirection to support installations of multiple R version PKG_LIBS = $(shell "${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe" -e "Rcpp:::LdFlags()") minqa/src/trsbox.f0000644000176000001440000003105112415434561013673 0ustar ripleyusers SUBROUTINE TRSBOX (N,NPT,XPT,XOPT,GOPT,HQ,PQ,SL,SU,DELTA, 1 XNEW,D,GNEW,XBDI,S,HS,HRED,DSQ,CRVMIN) IMPLICIT DOUBLE PRECISION (A-H,O-Z) DIMENSION XPT(NPT,*),XOPT(*),GOPT(*),HQ(*),PQ(*),SL(*),SU(*), 1 XNEW(*),D(*),GNEW(*),XBDI(*),S(*),HS(*),HRED(*) C C The arguments N, NPT, XPT, XOPT, GOPT, HQ, PQ, SL and SU have the same C meanings as the corresponding arguments of BOBYQB. C DELTA is the trust region radius for the present calculation, which C seeks a small value of the quadratic model within distance DELTA of C XOPT subject to the bounds on the variables. C XNEW will be set to a new vector of variables that is approximately C the one that minimizes the quadratic model within the trust region C subject to the SL and SU constraints on the variables. It satisfies C as equations the bounds that become active during the calculation. C D is the calculated trial step from XOPT, generated iteratively from an C initial value of zero. Thus XNEW is XOPT+D after the final iteration. C GNEW holds the gradient of the quadratic model at XOPT+D. It is updated C when D is updated. C XBDI is a working space vector. For I=1,2,...,N, the element XBDI(I) is C set to -1.0, 0.0, or 1.0, the value being nonzero if and only if the C I-th variable has become fixed at a bound, the bound being SL(I) or C SU(I) in the case XBDI(I)=-1.0 or XBDI(I)=1.0, respectively. This C information is accumulated during the construction of XNEW. C The arrays S, HS and HRED are also used for working space. They hold the C current search direction, and the changes in the gradient of Q along S C and the reduced D, respectively, where the reduced D is the same as D, C except that the components of the fixed variables are zero. C DSQ will be set to the square of the length of XNEW-XOPT. C CRVMIN is set to zero if D reaches the trust region boundary. Otherwise C it is set to the least curvature of H that occurs in the conjugate C gradient searches that are not restricted by any constraints. The C value CRVMIN=-1.0D0 is set, however, if all of these searches are C constrained. C C A version of the truncated conjugate gradient is applied. If a line C search is restricted by a constraint, then the procedure is restarted, C the values of the variables that are at their bounds being fixed. If C the trust region boundary is reached, then further changes may be made C to D, each one being in the two dimensional space that is spanned C by the current D and the gradient of Q at XOPT+D, staying on the trust C region boundary. Termination occurs when the reduction in Q seems to C be close to the greatest reduction that can be achieved. C C Set some constants. C HALF=0.5D0 ONE=1.0D0 ONEMIN=-1.0D0 ZERO=0.0D0 C C The sign of GOPT(I) gives the sign of the change to the I-th variable C that will reduce Q from its value at XOPT. Thus XBDI(I) shows whether C or not to fix the I-th variable at one of its bounds initially, with C NACT being set to the number of fixed variables. D and GNEW are also C set for the first iteration. DELSQ is the upper bound on the sum of C squares of the free variables. QRED is the reduction in Q so far. C ITERC=0 NACT=0 SQSTP=ZERO DO 10 I=1,N XBDI(I)=ZERO IF (XOPT(I) .LE. SL(I)) THEN IF (GOPT(I) .GE. ZERO) XBDI(I)=ONEMIN ELSE IF (XOPT(I) .GE. SU(I)) THEN IF (GOPT(I) .LE. ZERO) XBDI(I)=ONE END IF IF (XBDI(I) .NE. ZERO) NACT=NACT+1 D(I)=ZERO 10 GNEW(I)=GOPT(I) DELSQ=DELTA*DELTA QRED=ZERO CRVMIN=ONEMIN C C Set the next search direction of the conjugate gradient method. It is C the steepest descent direction initially and when the iterations are C restarted because a variable has just been fixed by a bound, and of C course the components of the fixed variables are zero. ITERMAX is an C upper bound on the indices of the conjugate gradient iterations. C 20 BETA=ZERO 30 STEPSQ=ZERO DO 40 I=1,N IF (XBDI(I) .NE. ZERO) THEN S(I)=ZERO ELSE IF (BETA .EQ. ZERO) THEN S(I)=-GNEW(I) ELSE S(I)=BETA*S(I)-GNEW(I) END IF 40 STEPSQ=STEPSQ+S(I)**2 IF (STEPSQ .EQ. ZERO) GOTO 190 IF (BETA .EQ. ZERO) THEN GREDSQ=STEPSQ ITERMAX=ITERC+N-NACT END IF IF (GREDSQ*DELSQ .LE. 1.0D-4*QRED*QRED) GO TO 190 C C Multiply the search direction by the second derivative matrix of Q and C calculate some scalars for the choice of steplength. Then set BLEN to C the length of the the step to the trust region boundary and STPLEN to C the steplength, ignoring the simple bounds. C GOTO 210 50 RESID=DELSQ DS=ZERO SHS=ZERO DO 60 I=1,N IF (XBDI(I) .EQ. ZERO) THEN RESID=RESID-D(I)**2 DS=DS+S(I)*D(I) SHS=SHS+S(I)*HS(I) END IF 60 CONTINUE IF (RESID .LE. ZERO) GOTO 90 TEMP=DSQRT(STEPSQ*RESID+DS*DS) IF (DS .LT. ZERO) THEN BLEN=(TEMP-DS)/STEPSQ ELSE BLEN=RESID/(TEMP+DS) END IF STPLEN=BLEN IF (SHS .GT. ZERO) THEN STPLEN=DMIN1(BLEN,GREDSQ/SHS) END IF C C Reduce STPLEN if necessary in order to preserve the simple bounds, C letting IACT be the index of the new constrained variable. C IACT=0 DO 70 I=1,N IF (S(I) .NE. ZERO) THEN XSUM=XOPT(I)+D(I) IF (S(I) .GT. ZERO) THEN TEMP=(SU(I)-XSUM)/S(I) ELSE TEMP=(SL(I)-XSUM)/S(I) END IF IF (TEMP .LT. STPLEN) THEN STPLEN=TEMP IACT=I END IF END IF 70 CONTINUE C C Update CRVMIN, GNEW and D. Set SDEC to the decrease that occurs in Q. C SDEC=ZERO IF (STPLEN .GT. ZERO) THEN ITERC=ITERC+1 TEMP=SHS/STEPSQ IF (IACT .EQ. 0 .AND. TEMP .GT. ZERO) THEN CRVMIN=DMIN1(CRVMIN,TEMP) IF (CRVMIN .EQ. ONEMIN) CRVMIN=TEMP END IF GGSAV=GREDSQ GREDSQ=ZERO DO 80 I=1,N GNEW(I)=GNEW(I)+STPLEN*HS(I) IF (XBDI(I) .EQ. ZERO) GREDSQ=GREDSQ+GNEW(I)**2 80 D(I)=D(I)+STPLEN*S(I) SDEC=DMAX1(STPLEN*(GGSAV-HALF*STPLEN*SHS),ZERO) QRED=QRED+SDEC END IF C C Restart the conjugate gradient method if it has hit a new bound. C IF (IACT .GT. 0) THEN NACT=NACT+1 XBDI(IACT)=ONE IF (S(IACT) .LT. ZERO) XBDI(IACT)=ONEMIN DELSQ=DELSQ-D(IACT)**2 IF (DELSQ .LE. ZERO) GOTO 90 GOTO 20 END IF C C If STPLEN is less than BLEN, then either apply another conjugate C gradient iteration or RETURN. C IF (STPLEN .LT. BLEN) THEN IF (ITERC .EQ. ITERMAX) GOTO 190 IF (SDEC .LE. 0.01D0*QRED) GOTO 190 BETA=GREDSQ/GGSAV GOTO 30 END IF 90 CRVMIN=ZERO C C Prepare for the alternative iteration by calculating some scalars C and by multiplying the reduced D by the second derivative matrix of C Q, where S holds the reduced D in the call of GGMULT. C 100 IF (NACT .GE. N-1) GOTO 190 DREDSQ=ZERO DREDG=ZERO GREDSQ=ZERO DO 110 I=1,N IF (XBDI(I) .EQ. ZERO) THEN DREDSQ=DREDSQ+D(I)**2 DREDG=DREDG+D(I)*GNEW(I) GREDSQ=GREDSQ+GNEW(I)**2 S(I)=D(I) ELSE S(I)=ZERO END IF 110 CONTINUE ITCSAV=ITERC GOTO 210 C C Let the search direction S be a linear combination of the reduced D C and the reduced G that is orthogonal to the reduced D. C 120 ITERC=ITERC+1 TEMP=GREDSQ*DREDSQ-DREDG*DREDG IF (TEMP .LE. 1.0D-4*QRED*QRED) GOTO 190 TEMP=DSQRT(TEMP) DO 130 I=1,N IF (XBDI(I) .EQ. ZERO) THEN S(I)=(DREDG*D(I)-DREDSQ*GNEW(I))/TEMP ELSE S(I)=ZERO END IF 130 CONTINUE SREDG=-TEMP C C By considering the simple bounds on the variables, calculate an upper C bound on the tangent of half the angle of the alternative iteration, C namely ANGBD, except that, if already a free variable has reached a C bound, there is a branch back to label 100 after fixing that variable. C ANGBD=ONE IACT=0 DO 140 I=1,N IF (XBDI(I) .EQ. ZERO) THEN TEMPA=XOPT(I)+D(I)-SL(I) TEMPB=SU(I)-XOPT(I)-D(I) IF (TEMPA .LE. ZERO) THEN NACT=NACT+1 XBDI(I)=ONEMIN GOTO 100 ELSE IF (TEMPB .LE. ZERO) THEN NACT=NACT+1 XBDI(I)=ONE GOTO 100 END IF RATIO=ONE SSQ=D(I)**2+S(I)**2 TEMP=SSQ-(XOPT(I)-SL(I))**2 IF (TEMP .GT. ZERO) THEN TEMP=DSQRT(TEMP)-S(I) IF (ANGBD*TEMP .GT. TEMPA) THEN ANGBD=TEMPA/TEMP IACT=I XSAV=ONEMIN END IF END IF TEMP=SSQ-(SU(I)-XOPT(I))**2 IF (TEMP .GT. ZERO) THEN TEMP=DSQRT(TEMP)+S(I) IF (ANGBD*TEMP .GT. TEMPB) THEN ANGBD=TEMPB/TEMP IACT=I XSAV=ONE END IF END IF END IF 140 CONTINUE C C Calculate HHD and some curvatures for the alternative iteration. C GOTO 210 150 SHS=ZERO DHS=ZERO DHD=ZERO DO 160 I=1,N IF (XBDI(I) .EQ. ZERO) THEN SHS=SHS+S(I)*HS(I) DHS=DHS+D(I)*HS(I) DHD=DHD+D(I)*HRED(I) END IF 160 CONTINUE C C Seek the greatest reduction in Q for a range of equally spaced values C of ANGT in [0,ANGBD], where ANGT is the tangent of half the angle of C the alternative iteration. C REDMAX=ZERO ISAV=0 REDSAV=ZERO IU=17.0D0*ANGBD+3.1D0 DO 170 I=1,IU ANGT=ANGBD*DFLOAT(I)/DFLOAT(IU) STH=(ANGT+ANGT)/(ONE+ANGT*ANGT) TEMP=SHS+ANGT*(ANGT*DHD-DHS-DHS) REDNEW=STH*(ANGT*DREDG-SREDG-HALF*STH*TEMP) IF (REDNEW .GT. REDMAX) THEN REDMAX=REDNEW ISAV=I RDPREV=REDSAV ELSE IF (I .EQ. ISAV+1) THEN RDNEXT=REDNEW END IF 170 REDSAV=REDNEW C C Return if the reduction is zero. Otherwise, set the sine and cosine C of the angle of the alternative iteration, and calculate SDEC. C IF (ISAV .EQ. 0) GOTO 190 IF (ISAV .LT. IU) THEN TEMP=(RDNEXT-RDPREV)/(REDMAX+REDMAX-RDPREV-RDNEXT) ANGT=ANGBD*(DFLOAT(ISAV)+HALF*TEMP)/DFLOAT(IU) END IF 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 .LE. ZERO) GOTO 190 C C Update GNEW, D and HRED. If the angle of the alternative iteration C is restricted by a bound on a free variable, that variable is fixed C at the bound. C DREDG=ZERO GREDSQ=ZERO DO 180 I=1,N GNEW(I)=GNEW(I)+(CTH-ONE)*HRED(I)+STH*HS(I) IF (XBDI(I) .EQ. ZERO) THEN D(I)=CTH*D(I)+STH*S(I) DREDG=DREDG+D(I)*GNEW(I) GREDSQ=GREDSQ+GNEW(I)**2 END IF 180 HRED(I)=CTH*HRED(I)+STH*HS(I) QRED=QRED+SDEC IF (IACT .GT. 0 .AND. ISAV .EQ. IU) THEN NACT=NACT+1 XBDI(IACT)=XSAV GOTO 100 END IF C C If SDEC is sufficiently small, then RETURN after setting XNEW to C XOPT+D, giving careful attention to the bounds. C IF (SDEC .GT. 0.01D0*QRED) GOTO 120 190 DSQ=ZERO DO 200 I=1,N XNEW(I)=DMAX1(DMIN1(XOPT(I)+D(I),SU(I)),SL(I)) IF (XBDI(I) .EQ. ONEMIN) XNEW(I)=SL(I) IF (XBDI(I) .EQ. ONE) XNEW(I)=SU(I) D(I)=XNEW(I)-XOPT(I) 200 DSQ=DSQ+D(I)**2 RETURN C The following instructions multiply the current S-vector by the second C derivative matrix of the quadratic model, putting the product in HS. C They are reached from three different parts of the software above and C they can be regarded as an external subroutine. C 210 IH=0 DO 220 J=1,N HS(J)=ZERO DO 220 I=1,J IH=IH+1 IF (I .LT. J) HS(J)=HS(J)+HQ(IH)*S(I) 220 HS(I)=HS(I)+HQ(IH)*S(J) DO 250 K=1,NPT IF (PQ(K) .NE. ZERO) THEN TEMP=ZERO DO 230 J=1,N 230 TEMP=TEMP+XPT(K,J)*S(J) TEMP=TEMP*PQ(K) DO 240 I=1,N 240 HS(I)=HS(I)+TEMP*XPT(K,I) END IF 250 CONTINUE IF (CRVMIN .NE. ZERO) GOTO 50 IF (ITERC .GT. ITCSAV) GOTO 150 DO 260 I=1,N 260 HRED(I)=HS(I) GOTO 120 END minqa/src/biglag.f0000644000176000001440000001156212415434561013604 0ustar ripleyusers SUBROUTINE BIGLAG (N,NPT,XOPT,XPT,BMAT,ZMAT,IDZ,NDIM,KNEW, 1 DELTA,D,ALPHA,HCOL,GC,GD,S,W) IMPLICIT DOUBLE PRECISION (A-H,O-Z) DIMENSION XOPT(*),XPT(NPT,*),BMAT(NDIM,*),ZMAT(NPT,*),D(*), 1 HCOL(*),GC(*),GD(*),S(*),W(*) C C N is the number of variables. C NPT is the number of interpolation equations. C XOPT is the best interpolation point so far. C XPT contains the coordinates of the current interpolation points. C BMAT provides the last N columns of H. C ZMAT and IDZ give a factorization of the first NPT by NPT submatrix of H. C NDIM is the first dimension of BMAT and has the value NPT+N. C KNEW is the index of the interpolation point that is going to be moved. C DELTA is the current trust region bound. C D will be set to the step from XOPT to the new point. C ALPHA will be set to the KNEW-th diagonal element of the H matrix. C HCOL, GC, GD, S and W will be used for working space. C C The step D is calculated in a way that attempts to maximize the modulus C of LFUNC(XOPT+D), subject to the bound ||D|| .LE. DELTA, where LFUNC is C the KNEW-th Lagrange function. C C Set some constants. C HALF=0.5D0 ONE=1.0D0 ZERO=0.0D0 TWOPI=8.0D0*DATAN(ONE) DELSQ=DELTA*DELTA NPTM=NPT-N-1 C C Set the first NPT components of HCOL to the leading elements of the C KNEW-th column of H. C ITERC=0 DO 10 K=1,NPT 10 HCOL(K)=ZERO DO 20 J=1,NPTM TEMP=ZMAT(KNEW,J) IF (J .LT. IDZ) TEMP=-TEMP DO 20 K=1,NPT 20 HCOL(K)=HCOL(K)+TEMP*ZMAT(K,J) ALPHA=HCOL(KNEW) C C Set the unscaled initial direction D. Form the gradient of LFUNC at C XOPT, and multiply D by the second derivative matrix of LFUNC. C DD=ZERO DO 30 I=1,N D(I)=XPT(KNEW,I)-XOPT(I) GC(I)=BMAT(KNEW,I) GD(I)=ZERO 30 DD=DD+D(I)**2 DO 50 K=1,NPT TEMP=ZERO SUM=ZERO DO 40 J=1,N TEMP=TEMP+XPT(K,J)*XOPT(J) 40 SUM=SUM+XPT(K,J)*D(J) TEMP=HCOL(K)*TEMP SUM=HCOL(K)*SUM DO 50 I=1,N GC(I)=GC(I)+TEMP*XPT(K,I) 50 GD(I)=GD(I)+SUM*XPT(K,I) C C Scale D and GD, with a sign change if required. Set S to another C vector in the initial two dimensional subspace. C GG=ZERO SP=ZERO DHD=ZERO DO 60 I=1,N GG=GG+GC(I)**2 SP=SP+D(I)*GC(I) 60 DHD=DHD+D(I)*GD(I) SCALE=DELTA/DSQRT(DD) IF (SP*DHD .LT. ZERO) SCALE=-SCALE TEMP=ZERO IF (SP*SP .GT. 0.99D0*DD*GG) TEMP=ONE TAU=SCALE*(DABS(SP)+HALF*SCALE*DABS(DHD)) IF (GG*DELSQ .LT. 0.01D0*TAU*TAU) TEMP=ONE DO 70 I=1,N D(I)=SCALE*D(I) GD(I)=SCALE*GD(I) 70 S(I)=GC(I)+TEMP*GD(I) C C Begin the iteration by overwriting S with a vector that has the C required length and direction, except that termination occurs if C the given D and S are nearly parallel. C 80 ITERC=ITERC+1 DD=ZERO SP=ZERO SS=ZERO DO 90 I=1,N DD=DD+D(I)**2 SP=SP+D(I)*S(I) 90 SS=SS+S(I)**2 TEMP=DD*SS-SP*SP IF (TEMP .LE. 1.0D-8*DD*SS) GOTO 160 DENOM=DSQRT(TEMP) DO 100 I=1,N S(I)=(DD*S(I)-SP*D(I))/DENOM 100 W(I)=ZERO C C Calculate the coefficients of the objective function on the circle, C beginning with the multiplication of S by the second derivative matrix. C DO 120 K=1,NPT SUM=ZERO DO 110 J=1,N 110 SUM=SUM+XPT(K,J)*S(J) SUM=HCOL(K)*SUM DO 120 I=1,N 120 W(I)=W(I)+SUM*XPT(K,I) CF1=ZERO CF2=ZERO CF3=ZERO CF4=ZERO CF5=ZERO DO 130 I=1,N CF1=CF1+S(I)*W(I) CF2=CF2+D(I)*GC(I) CF3=CF3+S(I)*GC(I) CF4=CF4+D(I)*GD(I) 130 CF5=CF5+S(I)*GD(I) CF1=HALF*CF1 CF4=HALF*CF4-CF1 C C Seek the value of the angle that maximizes the modulus of TAU. C TAUBEG=CF1+CF2+CF4 TAUMAX=TAUBEG TAUOLD=TAUBEG ISAVE=0 IU=49 TEMP=TWOPI/DFLOAT(IU+1) DO 140 I=1,IU ANGLE=DFLOAT(I)*TEMP CTH=DCOS(ANGLE) STH=DSIN(ANGLE) TAU=CF1+(CF2+CF4*CTH)*CTH+(CF3+CF5*CTH)*STH IF (DABS(TAU) .GT. DABS(TAUMAX)) THEN TAUMAX=TAU ISAVE=I TEMPA=TAUOLD ELSE IF (I .EQ. ISAVE+1) THEN TEMPB=TAU END IF 140 TAUOLD=TAU IF (ISAVE .EQ. 0) TEMPA=TAU IF (ISAVE .EQ. IU) TEMPB=TAUBEG STEP=ZERO IF (TEMPA .NE. TEMPB) THEN TEMPA=TEMPA-TAUMAX TEMPB=TEMPB-TAUMAX STEP=HALF*(TEMPA-TEMPB)/(TEMPA+TEMPB) END IF ANGLE=TEMP*(DFLOAT(ISAVE)+STEP) C C Calculate the new D and GD. Then test for convergence. C CTH=DCOS(ANGLE) STH=DSIN(ANGLE) TAU=CF1+(CF2+CF4*CTH)*CTH+(CF3+CF5*CTH)*STH DO 150 I=1,N D(I)=CTH*D(I)+STH*S(I) GD(I)=CTH*GD(I)+STH*W(I) 150 S(I)=GC(I)+GD(I) IF (DABS(TAU) .LE. 1.1D0*DABS(TAUBEG)) GOTO 160 IF (ITERC .LT. N) GOTO 80 160 RETURN END minqa/NAMESPACE0000644000176000001440000000015312274070406012627 0ustar ripleyusersuseDynLib(minqa, .registration = TRUE) import(Rcpp) export(bobyqa, newuoa, uobyqa) S3method(print, minqa) minqa/R/0000755000176000001440000000000012415434561011615 5ustar ripleyusersminqa/R/minqa.R0000644000176000001440000002377012415421233013046 0ustar ripleyusers##' Handle the common arguments to all the minimizers ##' ##' Establish defaults for the elements of the control list and parse ##' the list that was provided. The list is converted to an ##' environment. ##' ##' @param ctrl list of control settings ##' @param n length of the par vector ##' ##' @return an environment containing the control settings commonArgs <- function(par, fn, ctrl, rho) { rho$n <- n <- length(rho$par <- as.double(par)) stopifnot(all(is.finite(par)), is.function(fn), length(formals(fn)) >= 1) rho$.feval. <- integer(1) # function evaluation counter ## We use all possible control settings in the default. ## Extra control settings are ignored. ## cc <- do.call(function(npt = min(n+6L, 2L * n + 1L), rhobeg = NA, ## rhoend = NA, iprint = 0L, maxfun=10000L, ## obstop=TRUE, force.start=FALSE) cc <- do.call(function(npt = min(n+2L, 2L * n), rhobeg = NA, rhoend = NA, iprint = 0L, maxfun=10000L, obstop=TRUE, force.start=FALSE,...) { if (length(list(...))>0) warning("unused control arguments ignored") list(npt = npt, rhobeg = rhobeg, rhoend = rhoend, iprint = iprint, maxfun = maxfun, obstop = obstop, force.start = force.start) }, ctrl) ## Create and populate an environment ctrl <- new.env(parent = emptyenv()) # ctrl environment should not chain lapply(names(cc), function(nm) assign(nm, cc[[nm]], envir = ctrl)) ## Adjust and check npt ctrl$npt <- as.integer(max(n + 2L, min(ctrl$npt, ((n+1L)*(n+2L)) %/% 2L))) if (ctrl$npt > (2 * n + 1)) warning("Setting npt > 2 * length(par) + 1 is not recommended.") ## Check and adjust rhobeg and rhoend if (is.na(ctrl$rhobeg)) ctrl$rhobeg <- min(0.95, 0.2 * max(abs(par))) if (is.na(ctrl$rhoend)) ctrl$rhoend <- 1.0e-6 * ctrl$rhobeg stopifnot(0 < ctrl$rhoend, ctrl$rhoend <= ctrl$rhobeg) ## Check recommended range of maxfun if (ctrl$maxfun < 10 * n^2) warning("maxfun < 10 * length(par)^2 is not recommended.") ctrl } ##' Nonlinear optimization with box constraints ##' ##' Minimize a function of many variables subject to box constraints ##' by a trust region method that forms quadratic models by ##' interpolation, using the BOBYQA code written by Mike Powell. ##' ##' @param par numeric vector of starting parameters (length > 1) ##' @param fn function to be minimized. The first argument must be ##' the parameters. ##' @param lower a numeric vector of lower bounds. If of length 1 it ##' is expanded. ##' @param upper a numeric vector of upper bounds. Also may be scalar. ##' @param control a list of control settings ##' @param ... optional, additional arguments to fn ##' ##' @return a list with S3 class bobyqa ##' bobyqa <- function(par, fn, lower = -Inf, upper = Inf, control = list(), ...) { nn <- names(par) ctrl <- commonArgs(par, fn, control, environment()) n <- length(par) fn1 <- function(x) { # fn1 takes exactly 1 argument names(x) <- nn fn(x, ...) } checkObj <- fn1(par) if(length(checkObj) > 1 || !is.numeric(checkObj)) stop("Objective function must return a single numeric value.") ## check the upper and lower arguments, adjusting if necessary lower <- as.double(lower); upper <- as.double(upper) if (length(lower) == 1) lower <- rep(lower, n) if (length(upper) == 1) upper <- rep(upper, n) stopifnot(length(lower) == n, length(upper) == n, all(lower < upper)) if (any(par < lower | par > upper)) { if (ctrl$obstop) stop("Starting values violate bounds") else { par <- pmax(lower, pmax(par, upper)) warning("Some parameters adjusted to nearest bound") } } rng <- upper - lower if (any(rng < 2 * ctrl$rhobeg)) { warning("All upper - lower must be >= 2*rhobeg. Changing rhobeg") ctrl$rhobeg <- 0.2 * min(rng) } verb <- 1 < (ctrl$iprint <- as.integer(ctrl$iprint)) ## Modifications to par if too close to boundary if (all(is.finite(upper)) && all(is.finite(lower)) && all(par >= lower) && all(par <= upper) ) { if (verb) cat("ctrl$force.start = ", ctrl$force.start,"\n") if (!ctrl$force.start) { i <- rng < ctrl$rhobeg # Jens modification if (any(i)) { par[i] <- lower[i] + ctrl$rhobeg warning("Some parameters adjusted away from lower bound") } i <- rng < ctrl$rhobeg # Jens modification if (any(i)) { par[i] <- upper[i] - ctrl$rhobeg warning("Some parameters adjusted away from upper bound") } } } if (verb) { cat("npt =", ctrl$npt, ", n = ",n,"\n") cat("rhobeg = ", ctrl$rhobeg,", rhoend = ", ctrl$rhoend, "\n") } if(ctrl$iprint > 0) cat("start par. = ", par, "fn = ", checkObj, "\n") retlst<- .Call(bobyqa_cpp, par, lower, upper, ctrl, fn1) # JN 20100810 if (retlst$ierr > 0){ ## cat("ierr = ",retlst$ierr,"\n") ## newuoa allowed ierr in c(10, 20, 320, 390, 430) if (retlst$ierr == 10) { retlst$ierr<-2 retlst$msg<-"bobyqa -- NPT is not in the required interval" } else if (retlst$ierr == 320) { retlst$ierr<-5 retlst$msg<-"bobyqa detected too much cancellation in denominator" } else if (retlst$ierr == 390) { retlst$ierr<-1 retlst$msg<-"bobyqa -- maximum number of function evaluations exceeded" } else if (retlst$ierr == 430) { retlst$ierr<-3 retlst$msg<-"bobyqa -- a trust region step failed to reduce q" } else if (retlst$ierr == 20) { retlst$ierr<-4 retlst$msg<-"bobyqa -- one of the box constraint ranges is too small (< 2*RHOBEG)" } } else { retlst$msg<-"Normal exit from bobyqa" } retlst # return(retlst) } ##' An R interface to the NEWUOA implementation of Powell ##' ##' Minimize a function of many variables by a trust region method ##' that forms quadratic models by interpolation, using the NEWUOA ##' code written by Mike Powell. ##' ##' @param par numeric vector of starting parameters (length > 1) ##' @param fn function to be minimized. The first argument must be ##' the parameters. ##' @param control a list of control settings ##' @param ... optional, additional arguments to fn ##' ##' @return a list with S3 class c("newuoa", "minqa") newuoa <- function(par, fn, control = list(), ...) { nn <- names(par) ctrl <- commonArgs(par + 0, fn, control, environment()) n <- length(par) fn1 <- function(x) { # fn1 takes exactly 1 argument names(x) <- nn fn(x, ...) } checkObj <- fn1(par) if(length(checkObj) > 1 || !is.numeric(checkObj)) stop("Objective function must return a single numeric value.") verb <- 1 < (ctrl$iprint <- as.integer(ctrl$iprint)) if (verb) { cat("npt =", ctrl$npt, ", n = ",n,"\n") cat("rhobeg = ", ctrl$rhobeg,", rhoend = ", ctrl$rhoend, "\n") } if(ctrl$iprint > 0) cat("start par. = ", par, "fn = ", checkObj, "\n") retlst<-.Call(newuoa_cpp, par, ctrl, fn1) # JN 20100810 if (retlst$ierr > 0){ ## cat("ierr = ",retlst$ierr,"\n") ## newuoa allowed ierr in c(10, 320, 390, 3701) if (retlst$ierr == 10) { retlst$ierr<-2 retlst$msg<-"newuoa -- NPT is not in the required interval" } else if (retlst$ierr == 320) { retlst$ierr<-5 retlst$msg<-"newuoa detected too much cancellation in denominator" } else if (retlst$ierr == 390) { retlst$ierr<-1 retlst$msg<-"newuoa -- maximum number of function evaluations exceeded" } else if (retlst$ierr == 3701) { retlst$ierr<-3 retlst$msg<-"newuoa -- a trust region step failed to reduce q" } } else { retlst$msg<-"Normal exit from newuoa" } retlst # return(retlst) } ##' An R interface to the UOBYQA implementation of Powell ##' ##' Minimize a function of many variables by a trust region method ##' that forms quadratic models by interpolation, using the UOBYQA ##' code written by Mike Powell. ##' ##' @param par numeric vector of starting parameters (length > 1) ##' @param fn function to be minimized. The first argument must be ##' the parameters. ##' @param control a list of control settings ##' @param ... optional, additional arguments to fn ##' ##' @return a list with S3 class uobyqa uobyqa <- function(par, fn, control = list(), ...) { nn <- names(par) ctrl <- commonArgs(par + 0, fn, control, environment()) n <- length(par) fn1 <- function(x) { # fn1 takes exactly 1 argument names(x) <- nn fn(x, ...) } checkObj <- fn1(par) if(length(checkObj) > 1 || !is.numeric(checkObj)) stop("Objective function must return a single numeric value.") verb <- 1 < (ctrl$iprint <- as.integer(ctrl$iprint)) if (verb) { cat("npt =", ctrl$npt, ", n = ",n,"\n") cat("rhobeg = ", ctrl$rhobeg,", rhoend = ", ctrl$rhoend, "\n") } if(ctrl$iprint > 0) cat("start par. = ", par, "fn = ", checkObj, "\n") retlst<-.Call(uobyqa_cpp, par, ctrl, fn1) # JN 20100810 if (retlst$ierr > 0){ ## cat("ierr = ",retlst$ierr,"\n") ## uobyqa allowed ierr in c(390, 2101) if (retlst$ierr == 390) { retlst$ierr<-1 retlst$msg<-"uobyqa -- maximum number of function evaluations exceeded" } else if (retlst$ierr == 2101) { retlst$ierr<-3 retlst$msg<-"uobyqa -- a trust region step failed to reduce q" } } else { retlst$msg<-"Normal exit from uobyqa" } retlst # return(retlst) } ##' Print method for minqa objects (S3) ##' ##' @param x an object of class that inherits from minqa ##' @param digits number of significant digits - doesn't seem to be used ##' @param ... optional arguments. None are used. ##' @return invisible(x) - side effect is to print ##' @author Douglas Bates print.minqa <- function(x, digits = max(3, getOption("digits") - 3), ...) { cat("parameter estimates:", toString(x$par), "\n") cat("objective:", toString(x$fval), "\n") cat("number of function evaluations:", toString(x$feval), "\n") invisible(x) } minqa/MD50000644000176000001440000000262112415506656011732 0ustar ripleyusersa8b7dfc755fb04ff55eec51ef9bda279 *ChangeLog 8839975cd2d5270d363d7bb3a6f11cee *DESCRIPTION f01ff73394d9c697a24211f3e54ad145 *NAMESPACE aae64ab96c25bdc2cf71d493cc6c2f99 *R/minqa.R 6349999aed75efa8afce1e9e56db610d *man/bobyqa.Rd 6566309b9f1643a9b397dc3d523989f8 *man/newuoa.Rd 41e3efa2069146b80ee7cb87ff37f676 *man/uobyqa.Rd 9fdd9d176f2dc1bda212c941b50cd9ed *src/Makevars 5d035c0d8a1c02024ebbb6f58bf1ae40 *src/Makevars.win 81597a016bcaeda7a9d4e019ebc47cea *src/altmov.f 92d72eb69029e0b29bb78f6c6d820c9d *src/bigden.f be14aae6e6551ed6390609d374cf32c1 *src/biglag.f aefbee423130990afc1110739a0ef0d5 *src/bobyqa.f 89a1b0e5059f9c63adff118f71c1ca81 *src/bobyqb.f 633f3dab52b4f1dae44000da6c718b3c *src/lagmax.f 41ccca902fb30547ecaa19f253f02624 *src/minqa.cpp 996f27037a11bdd13fd9d40305e949fc *src/newuoa.f 5957b0ebc33332be0ed63445763c6aae *src/newuob.f 034f2ebab5c9e9c50de0aaa2874aa01d *src/prelim.f ed1d266c173640aab9ec0885fd057a69 *src/rescue.f 8fb174cc36f55fcaed26268fdf110196 *src/trsapp.f e69fd0660e9fd29bccda692e8acd7b5f *src/trsbox.f 007dba671ba8754e241a1dd0e70b629f *src/trstep.f 3605fb702fbe0854e93052ce1d76120d *src/uobyqa.f f0ccd74fba06ffe840ffd391c995f664 *src/uobyqb.f afca87f4d228e90d878f5d5dc8b0fc68 *src/update.f 65739d0abef615ddae2fe548fe43627d *src/updatebobyqa.f 00ae60d427ca033c7c86cbc05a8407dd *tests/cyq-minqa.R c0a415919698791a7a48c0e1de5309eb *tests/newuoa.R 9ed3203b424b50983ab420743d8d8205 *tests/rvaltest.R minqa/DESCRIPTION0000644000176000001440000000122712415506656013131 0ustar ripleyusersPackage: minqa Type: Package Title: Derivative-free optimization algorithms by quadratic approximation Version: 1.2.4 Author: Douglas Bates, Katharine M. Mullen, John C. Nash, Ravi Varadhan Maintainer: Katharine M. Mullen Description: Derivative-free optimization by quadratic approximation based on an interface to Fortran implementations by M. J. D. Powell. License: GPL-2 URL: http://optimizer.r-forge.r-project.org Imports: Rcpp (>= 0.9.10) LinkingTo: Rcpp SystemRequirements: GNU make NeedsCompilation: yes Packaged: 2014-10-09 07:29:53 UTC; kmm Repository: CRAN Date/Publication: 2014-10-09 15:29:18 minqa/ChangeLog0000644000176000001440000001325512415432664013176 0ustar ripleyusers2014-10-03 K Mullen * src/minqa.cpp: Changed format of output at return to add labels. This affects output for iprint > 0. * src/minqa.cpp: When rho changes and is printed, added labels to output. This affects output for iprint > 1. * R/minqa.R: Now print start parameter values and obj. function value when iprint > 0. * R/minqa.cpp: If iprint > 3, the objective function value and corresponding variables are output every \code{iprint} evaluations. * man/uobyqa.Rd: iprint updated. * man/newuoa.Rd: iprint updated. * man/bobyqa.Rd: iprint updated. 2014-02-03 K Mullen DESCRIPTION: Rcpp is now in Imports, not Depends. NAMESPACE: 'import(Rcpp)' added. 2012-05-07 K Mullen * R/minqa.R: Added check on length of obj. function, with stop if obj. function does not return a numeric value of length 1. As Hans Borchers pointed out, R would crash if bobyqa was given an objective function that returned a vector with length greater than 1. 2012-03-14 Douglas Bates * src/minqa.cpp: avoid evaluating an Rcpp::Function object and use direct calls to R's eval (which shows up as ::Rf_eval in this code). 2011-09-30 K Mullen * src/minqa.cpp brought in patch contributed by Ben Bolker. 2011-07-14 John Nash * man/uobyqa.Rd: corrected output names in documentation. Added msg. * man/newuoa.Rd: corrected output names in documentation. Added msg. * man/bobyqa.Rd: corrected output names in documentation. Added msg. 2011-07-11 John Nash , Kate Mullen * R/minqa.R: bobyqa bug fixes thanks to Ben Bolker: fixed rhobeg intitialization problem and issue with parscale. 2011-02-28 John Nash * DESCRIPTION (Version): New minor version number * R/minqa.R fix npt to min(n+2, 2*n) in call 2011-02-02 Douglas Bates * DESCRIPTION (Version): New minor version number * src/minqa.cpp (calfun): check for finite value of objective function. 2011-01-27 Douglas Bates * src/minqa.cpp: Expand RCPP_FUNCTION_X macros to make debugging easier. 2010-11-19 Douglas Bates * src/Makevars (PKG_LIBS): Change to the backticks version 2010-11-14 Douglas Bates * tests/newuoa.R: added test for newuoa which should signal an error. * src/minqa.cpp: reverted the removal of the ierr argument in calls to Fortran code. 2010-08-10 John Nash * man/bobyqa.Rd: Added revised codes for error exits and tests for some of these errors. Note that the error for npt (number of interpolation points) out of desired range does not work. It appears the minqa.R code forces a valid setting, and ignores user input. * man/newuoa.Rd: Added revised codes for error exits and tests for some of these errors. Note that the error for npt (number of interpolation points) out of desired range does not work. It appears the minqa.R code forces a valid setting, and ignores user input. * man/uobyqa.Rd: Added revised codes for error exits and tests for some of these errors. * src/uobyq*.f: Put in IERR exits and suppressed minqer() call. Also put IERR in calling sequence. * src/newuo*.f: Put in IERR exits and suppressed minqer() call. Also put IERR in calling sequence. * src/minqa.cpp: Put in IERR for newuoa and uobyqa. This involves several changes, including * and & modifiers to variables. 2010-08-09 Douglas Bates * man/bobyqa.Rd: Add a check on the error code. (Note: the R code should be modified to look for the error code in the returned value and produce an appropriate error message.) * src/minqa.cpp: pass ierr in call to rval from bobyqa. (Had it defined previously but forgot to pass it.) * tests/rvaltest.R,src/bobyq*.f,src/minqa.cpp,man/bobyqa.Rd: Modify Fortran sources to return an error code instead of Fortran I/O (John Nash) and incorporate error code in interface routine. Modify tests accordingly. (Note: other optimizers not yet modified.) 2010-06-18 Douglas Bates * src/minqa.cpp: "fval" component of returned list is now directly the function evaluation and not conversion to double and back to NumericVector. In particular, attributes are not stripped. * tests/rvaltest.R: Modify test to ignore attributes (including the dimensions). * DESCRIPTION: New release. 2010-03-14 Douglas Bates * src/*.f: Cleaned up more PRINT statements. Changed CALL CALFUN to F = CALFUN(). * src/minqa.cpp: Moved the C++ code to this file name (previously was bobyqa_cpp.cpp). Re-arranged order of functions and added documentation. Changed CALFUN to a function incorporating the minqi3 output. * man/*.Rd: Updates and modifications to examples * R/minimizers.R: Added print methods for each of the class 2010-03-13 Douglas Bates * src/main.f: removed - R packages should not contain a main program * src/: Removed C functions; Changed to C++ with Rcpp; Added Makevars. * R/: inlined the foo.control functions, amalgamated sources, switched to C++ .Call functions. * man/: Removed docs for newly inlined functions. * DESCRIPTION: New version * NAMESPACE: UseDynLibs to access names of functions for .Call 2010-03-11 Douglas Bates * R/bobyqa.R, src/bobyqa_c.c: Change default argument handling and interface between R and C code. Remove bobyqa.control R function. * man/bobyqa.Rd: Update to new organization. Fix R CMD check problems. minqa/man/0000755000176000001440000000000012415434561012167 5ustar ripleyusersminqa/man/bobyqa.Rd0000644000176000001440000001352012415432540013727 0ustar ripleyusers\name{bobyqa} \alias{bobyqa} \encoding{UTF-8} \title{An R interface to the bobyqa implementation of Powell} \description{ The purpose of \code{bobyqa} is to minimize a function of many variables by a trust region method that forms quadratic models by interpolation. Box constraints (bounds) on the parameters are permitted. } \usage{ bobyqa(par, fn, lower = -Inf, upper = Inf, control = list(), \dots) } \arguments{ \item{par}{A numeric vector of starting estimates of the parameters of the objective function.} \item{fn}{A function that returns the value of the objective at the supplied set of parameters \code{par} using auxiliary data in \dots. The first argument of \code{fn} must be \code{par}. } \item{lower}{A numeric vector of lower bounds on the parameters. If the length is 1 the single lower bound is applied to all parameters.} \item{upper}{A numeric vector of upper bounds on the parameters. If the length is 1 the single upper bound is applied to all parameters.} \item{control}{ An optional list of control settings. See the details section for the names of the settable control values and their effect.} \item{\dots}{Further arguments to be passed to \code{fn}.} } \details{ The function \code{fn} must return a scalar numeric value. The \code{control} argument is a list. Possible named values in the list and their defaults are: \describe{ \item{npt}{ The number of points used to approximate the objective function via a quadratic approximation. The value of npt must be in the interval \eqn{[n+2,(n+1)(n+2)/2]} where \eqn{n} is the number of parameters in \code{par}. Choices that exceed \eqn{2*n+1} are not recommended. If not defined, it will be set to \eqn{\min(n * 2, n+2)}{min(n * 2, n+2)}. } \item{rhobeg}{ \code{rhobeg} and \code{rhoend} must be set to the initial and final values of a trust region radius, so both must be positive with \code{0 < rhoend < rhobeg}. Typically \code{rhobeg} should be about one tenth of the greatest expected change to a variable. If the user does not provide a value, this will be set to \code{min(0.95, 0.2 * max(abs(par)))}. Note also that smallest difference \code{abs(upper-lower)} should be greater than or equal to \code{rhobeg*2}. If this is not the case then \code{rhobeg} will be adjusted. } \item{rhoend}{ The smallest value of the trust region radius that is allowed. If not defined, then 1e-6 times the value set for \code{rhobeg} will be used. } \item{iprint}{ The value of \code{iprint} should be set to an integer value in \code{0, 1, 2, 3, ...}, which controls the amount of printing. Specifically, there is no output if \code{iprint=0} and there is output only at the start and the return if \code{iprint=1}. Otherwise, each new value of \code{rho} is printed, with the best vector of variables so far and the corresponding value of the objective function. Further, each new value of the objective function with its variables are output if \code{iprint=3}. If \code{iprint > 3}, the objective function value and corresponding variables are output every \code{iprint} evaluations. Default value is \code{0}. } \item{maxfun}{ The maximum allowed number of function evaluations. If this is exceeded, the method will terminate. } } } \value{ A list with components: \item{par}{The best set of parameters found.} \item{fval}{The value of the objective at the best set of parameters found.} \item{feval}{The number of function evaluations used.} \item{ierr}{An integer error code. A value of zero indicates success. Other values are \describe{ \item{1}{maximum number of function evaluations exceeded} \item{2}{NPT, the number of approximation points, is not in the required interval} \item{3}{a trust region step failed to reduce q (Consult Powell for explanation.)} \item{4}{one of the box constraint ranges is too small (< 2*RHOBEG)} \item{5}{bobyqa detected too much cancellation in denominator (We have not fully understood Powell's code to explain this.)} } } \item{msg}{A message describing the outcome of UOBYQA} } \references{ M. J. D. Powell (2007) "Developments of NEWUOA for unconstrained minimization without derivatives", Cambridge University, Department of Applied Mathematics and Theoretical Physics, Numerical Analysis Group, Report NA2007/05, \url{http://www.damtp.cam.ac.uk/user/na/NA_papers/NA2007_05.pdf}. M. J. D. Powell (2009), "The BOBYQA algorithm for bound constrained optimization without derivatives", Report No. DAMTP 2009/NA06, Centre for Mathematical Sciences, University of Cambridge, UK. \url{http://www.damtp.cam.ac.uk/user/na/NA_papers/NA2009_06.pdf}. Description was taken from comments in the Fortran code of M. J. D. Powell on which \pkg{minqa} is based. } \seealso{\code{\link{optim}}, \code{\link{nlminb}}} \examples{ fr <- function(x) { ## Rosenbrock Banana function 100 * (x[2] - x[1]^2)^2 + (1 - x[1])^2 } (x1 <- bobyqa(c(1, 2), fr, lower = c(0, 0), upper = c(4, 4))) ## => optimum at c(1, 1) with fval = 0 str(x1) # see that the error code and msg are returned # check the error exits # too many iterations x1e<-bobyqa(c(1, 2), fr, lower = c(0, 0), upper = c(4, 4), control = list(maxfun=50)) str(x1e) # Throw an error because bounds too tight x1b<-bobyqa(c(4,4), fr, lower = c(0, 3.9999999), upper = c(4, 4)) str(x1b) # Throw an error because npt is too small -- does NOT work as of 2010-8-10 as # minqa.R seems to force a reset. x1n<-bobyqa(c(2,2), fr, lower = c(0, 0), upper = c(4, 4), control=list(npt=1)) str(x1n) # To add if we can find them -- examples of ierr = 3 and ierr = 5. } \keyword{nonlinear} \keyword{optimize} minqa/man/newuoa.Rd0000644000176000001440000001203012415432624013746 0ustar ripleyusers\name{newuoa} \alias{newuoa} \encoding{UTF-8} \title{An R interface to the NEWUOA implementation of Powell} \description{ The purpose of \code{newuoa} is to minimize a function of many variables by a trust region method that forms quadratic models by interpolation. } \usage{ newuoa(par, fn, control = list(), \dots) } \arguments{ \item{par}{A numeric vector of starting estimates. } \item{fn}{A function that returns the value of the objective at the supplied set of parameters \code{par} using auxiliary data in \dots. The first argument of \code{fn} must be \code{par}. } \item{control}{ An optional list of control settings. See the details section for the names of the settable control values and their effect. } \item{\dots}{Further arguments to be passed to \code{fn}.} } \details{ Functions \code{fn} must return a numeric value. The \code{control} argument is a list; possible named values in the list and their defaults are: \describe{ \item{npt}{ The number of points used to approximate the objective function via a quadratic approximation. The value of npt must be in the interval \eqn{[n+2,(n+1)(n+2)/2]} where \eqn{n} is the number of parameters in \code{par}. Choices that exceed \eqn{2*n+1} are not recommended. If not defined, it will be set to \eqn{\min(n * 2, n+2)}{min(n * 2, n+2)}. } \item{rhobeg}{ \code{rhobeg} and \code{rhoend} must be set to the initial and final values of a trust region radius, so both must be positive with \code{0 < rhoend < rhobeg}. Typically \code{rhobeg} should be about one tenth of the greatest expected change to a variable. If the user does not provide a value, this will be set to \code{max(par) / 2)} } \item{rhoend}{ The smallest value of the trust region radius that is allowed. If not defined, then 1e-6 times the value set for \code{rhobeg} will be used. } \item{iprint}{ The value of \code{iprint} should be set to an integer value in \code{0, 1, 2, 3, ...}, which controls the amount of printing. Specifically, there is no output if \code{iprint=0} and there is output only at the start and the return if \code{iprint=1}. Otherwise, each new value of \code{rho} is printed, with the best vector of variables so far and the corresponding value of the objective function. Further, each new value of the objective function with its variables are output if \code{iprint=3}. If \code{iprint > 3}, the objective function value and corresponding variables are output every \code{iprint} evaluations. Default value is \code{0}. } \item{maxfun}{ The maximum allowed number of function evaluations. If this is exceeded, the method will terminate. } } } \value{ A list with components: \item{par}{The best set of parameters found.} \item{fval}{The value of the objective at the best set of parameters found.} \item{feval}{Number of function evaluations to determine the optimum} \item{ierr}{An integer error code. A value of zero indicates success. Other values (consistent with BOBYQA values) are \describe{ \item{1}{maximum number of function evaluations exceeded} \item{2}{NPT, the number of approximation points, is not in the required interval} \item{3}{a trust region step failed to reduce q (Consult Powell for explanation.)} \item{5}{newuoa detected too much cancellation in denominator (We have not fully understood Powell's code to explain this.)} } } \item{msg}{A message describing the outcome of UOBYQA} } \references{ M. J. D. Powell, "The NEWUOA software for unconstrained optimization without derivatives", in \bold{Large-Scale Nonlinear Optimization}, Series: Nonconvex Optimization and Its Applications , Vol. 83, Di Pillo, Gianni; Roma, Massimo (Eds.) 2006, New York: Springer US. M. J. D. Powell, "Developments of NEWUOA for minimization without derivatives" IMA Journal of Numerical Analysis, 2008; 28: 649-664. M. J. D. Powell (2007) "Developments of NEWUOA for unconstrained minimization without derivatives" Cambridge University, Department of Applied Mathematics and Theoretical Physics, Numerical Analysis Group, Report NA2007/05, \url{http://www.damtp.cam.ac.uk/user/na/NA_papers/NA2007_05.pdf}. Description was taken from comments in the Fortran code of M. J. D. Powell on which \pkg{minqa} is based. } \seealso{\code{\link{optim}}, \code{\link{nlminb}}} \examples{ fr <- function(x) { ## Rosenbrock Banana function 100 * (x[2] - x[1]^2)^2 + (1 - x[1])^2 } (x2 <- newuoa(c(1, 2), fr)) ## => optimum at c(1, 1) with fval = 0 # check the error exits # too many iterations x2e<-newuoa(c(1, 2), fr, control = list(maxfun=50)) str(x2e) # Throw an error because npt is too small -- does NOT work as of 2010-8-10 as # minqa.R seems to force a reset. x2n<-newuoa(c(2,2), fr, control=list(npt=1)) str(x2n) # To add if we can find them -- examples of ierr = 3 and ierr = 5. } \keyword{nonlinear} \keyword{optimize} minqa/man/uobyqa.Rd0000644000176000001440000001016112415432574013757 0ustar ripleyusers\name{uobyqa} \alias{uobyqa} \encoding{UTF-8} \title{An R interface to the uobyqa implementation of Powell} \description{ The purpose of \code{uobyqa} is to minimize a function of many variables by a trust region method that forms quadratic models by interpolation. } \usage{ uobyqa(par, fn, control = list(), \dots) } \arguments{ \item{par}{A numeric vector of starting estimates. } \item{fn}{A function that returns the value of the objective at the supplied set of parameters \code{par} using auxiliary data in \dots. The first argument of \code{fn} must be \code{par}. } \item{control}{ An optional list of control settings. See the details section for the names of the settable control values and their effect. } \item{\dots}{Further arguments to be passed to \code{fn}.} } \details{ Functions \code{fn} must return a numeric value. The \code{control} argument is a list. Possible named values in the list and their defaults are: \describe{ \item{rhobeg}{ \code{rhobeg} and \code{rhoend} must be set to the initial and final values of a trust region radius, so both must be positive with \code{0 < rhoend < rhobeg}. Typically \code{rhobeg} should be about one tenth of the greatest expected change to a variable. } \item{rhoend}{ The smallest value of the trust region radius that is allowed. If not defined, then 1e-6 times the value set for \code{rhobeg} will be used. } \item{iprint}{ The value of \code{iprint} should be set to an integer value in \code{0, 1, 2, 3, ...}, which controls the amount of printing. Specifically, there is no output if \code{iprint=0} and there is output only at the start and the return if \code{iprint=1}. Otherwise, each new value of \code{rho} is printed, with the best vector of variables so far and the corresponding value of the objective function. Further, each new value of the objective function with its variables are output if \code{iprint=3}. If \code{iprint > 3}, the objective function value and corresponding variables are output every \code{iprint} evaluations. Default value is \code{0}. } \item{maxfun}{ The maximum allowed number of function evaluations. If this is exceeded, the method will terminate. } } Powell's Fortran code has been slightly modified (thanks to Doug Bates for help on this) to avoid use of PRINT statements. Output is now via calls to C routines set up to work with the routines BOBYQA, NEWUOA and UOBYQA. } \value{ A list with components: \item{par}{The best set of parameters found.} \item{fval}{The value of the objective at the best set of parameters found.} \item{feval}{The number of function evaluations used.} \item{ierr}{An integer error code. A value of zero indicates success. Other values (consistent with BOBYQA values) are \describe{ \item{1}{maximum number of function evaluations exceeded} \item{3}{a trust region step failed to reduce q (Consult Powell for explanation.)} } } \item{msg}{A message describing the outcome of UOBYQA} } \references{ M. J. D. Powell, "The uobyqa software for unconstrained optimization without derivatives", in \bold{Large-Scale Nonlinear Optimization}, Series: Nonconvex Optimization and Its Applications , Vol. 83, Di Pillo, Gianni; Roma, Massimo (Eds.) 2006, New York: Springer US. M. J. D. Powell, "Developments of uobyqa for minimization without derivatives", IMA Journal of Numerical Analysis, 2008; 28: 649-664. Description was taken from comments in the Fortran code of M. J. D. Powell on which \pkg{minqa} is based. } \seealso{\code{\link{optim}}, \code{\link{nlminb}}} \examples{ fr <- function(x) { ## Rosenbrock Banana function 100 * (x[2] - x[1]^2)^2 + (1 - x[1])^2 } (x3 <- uobyqa(c(1, 2), fr)) ## => optimum at c(1, 1) with fval = 0 # check the error exits # too many iterations x3e<-uobyqa(c(1, 2), fr, control = list(maxfun=50)) str(x3e) # To add if we can find them -- examples of ierr = 3. } \keyword{nonlinear} \keyword{optimize}