static PetscErrorCode TaoSetFromOptions_SQPCON(Tao tao)
{
PetscErrorCode ierr;
PetscFunctionBegin;
ierr = TaoLineSearchSetFromOptions(tao->linesearch);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
static int TaoSetOptions_BCG(TAO_SOLVER tao, void*solver)
{
TAO_BCG *cg = (TAO_BCG *)solver;
double dtmp;
int info;
char method[20];
PetscTruth flg;
TaoFunctionBegin;
info = OptionsHead("Nonlinear Conjugate Gradient method for unconstrained optimization");CHKERRQ(info);
info = OptionsDouble("-tao_cg_restart","Restart method based on orthogonality of successive gradients","TaoBCGSetRestartTol",cg->eta,&dtmp, &flg);CHKERRQ(info);
if (flg){
info =TaoCGSetRestartTol(tao,dtmp);
}
info = OptionsString("-tao_cg_type [fletcher-reeves,polak-ribiere,polak-ribiere-plus]","Set Nonlinear BCG method","TaoBCGSetMethod","polak-ribiere-plus",method,19,&flg);
CHKERRQ(info);
if (flg){
/*
info = TaoBCGSetMethod(tao, method);CHKERRQ(info);
*/
}
info = TaoLineSearchSetFromOptions(tao);CHKERRQ(info);
info = OptionsTail();CHKERRQ(info);
TaoFunctionReturn(0);
}
static PetscErrorCode TaoSetFromOptions_BQNLS(PetscOptionItems *PetscOptionsObject,Tao tao)
{
TAO_BNK *bnk = (TAO_BNK *)tao->data;
TAO_BQNK *bqnk = (TAO_BQNK*)bnk->ctx;
PetscErrorCode ierr;
KSPType ksp_type;
PetscBool is_spd;
PetscFunctionBegin;
ierr = PetscOptionsHead(PetscOptionsObject,"Quasi-Newton-Krylov method for bound constrained optimization");CHKERRQ(ierr);
ierr = PetscOptionsEList("-tao_bqnls_as_type", "active set estimation method", "", BNK_AS, BNK_AS_TYPES, BNK_AS[bnk->as_type], &bnk->as_type, 0);CHKERRQ(ierr);
ierr = PetscOptionsReal("-tao_bqnls_epsilon", "(developer) tolerance used when computing actual and predicted reduction", "", bnk->epsilon, &bnk->epsilon,NULL);CHKERRQ(ierr);
ierr = PetscOptionsReal("-tao_bqnls_as_tol", "(developer) initial tolerance used when estimating actively bounded variables", "", bnk->as_tol, &bnk->as_tol,NULL);CHKERRQ(ierr);
ierr = PetscOptionsReal("-tao_bqnls_as_step", "(developer) step length used when estimating actively bounded variables", "", bnk->as_step, &bnk->as_step,NULL);CHKERRQ(ierr);
ierr = PetscOptionsInt("-tao_bqnls_max_cg_its", "number of BNCG iterations to take for each Newton step", "", bnk->max_cg_its, &bnk->max_cg_its,NULL);CHKERRQ(ierr);
ierr = PetscOptionsTail();CHKERRQ(ierr);
ierr = TaoSetFromOptions(bnk->bncg);CHKERRQ(ierr);
ierr = TaoLineSearchSetFromOptions(tao->linesearch);CHKERRQ(ierr);
ierr = KSPSetFromOptions(tao->ksp);CHKERRQ(ierr);
ierr = KSPGetType(tao->ksp,&ksp_type);CHKERRQ(ierr);
bnk->is_nash = bnk->is_gltr = bnk->is_stcg = PETSC_FALSE;
ierr = MatSetFromOptions(bqnk->B);CHKERRQ(ierr);
ierr = MatGetOption(bqnk->B, MAT_SPD, &is_spd);CHKERRQ(ierr);
if (!is_spd) SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_INCOMP, "LMVM matrix must be symmetric positive-definite");
PetscFunctionReturn(0);
}
static PetscErrorCode TaoSetFromOptions_BLMVM(PetscOptionItems* PetscOptionsObject,Tao tao)
{
PetscErrorCode ierr;
PetscFunctionBegin;
ierr = PetscOptionsHead(PetscOptionsObject,"Limited-memory variable-metric method for bound constrained optimization");CHKERRQ(ierr);
ierr = TaoLineSearchSetFromOptions(tao->linesearch);CHKERRQ(ierr);
ierr = PetscOptionsTail();CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/* ---------------------------------------------------------- */
EXTERN_C_BEGIN
#undef __FUNCT__
#define __FUNCT__ "TaoCreate_ASILS"
PetscErrorCode TaoCreate_ASILS(Tao tao)
{
TAO_SSLS *asls;
PetscErrorCode ierr;
const char *armijo_type = TAOLINESEARCHARMIJO;
PetscFunctionBegin;
ierr = PetscNewLog(tao,&asls);CHKERRQ(ierr);
tao->data = (void*)asls;
tao->ops->solve = TaoSolve_ASILS;
tao->ops->setup = TaoSetUp_ASILS;
tao->ops->view = TaoView_SSLS;
tao->ops->setfromoptions = TaoSetFromOptions_SSLS;
tao->ops->destroy = TaoDestroy_ASILS;
tao->subset_type = TAO_SUBSET_SUBVEC;
asls->delta = 1e-10;
asls->rho = 2.1;
asls->fixed = NULL;
asls->free = NULL;
asls->J_sub = NULL;
asls->Jpre_sub = NULL;
asls->w = NULL;
asls->r1 = NULL;
asls->r2 = NULL;
asls->r3 = NULL;
asls->t1 = NULL;
asls->t2 = NULL;
asls->dxfree = NULL;
asls->identifier = 1e-5;
ierr = TaoLineSearchCreate(((PetscObject)tao)->comm, &tao->linesearch);CHKERRQ(ierr);
ierr = TaoLineSearchSetType(tao->linesearch, armijo_type);CHKERRQ(ierr);
ierr = TaoLineSearchSetFromOptions(tao->linesearch);CHKERRQ(ierr);
ierr = KSPCreate(((PetscObject)tao)->comm, &tao->ksp);CHKERRQ(ierr);
ierr = KSPSetFromOptions(tao->ksp);CHKERRQ(ierr);
tao->max_it = 2000;
tao->max_funcs = 4000;
tao->fatol = 0;
tao->frtol = 0;
tao->gttol = 0;
tao->grtol = 0;
#if defined(PETSC_USE_REAL_SINGLE)
tao->gatol = 1.0e-6;
tao->fmin = 1.0e-4;
#else
tao->gatol = 1.0e-16;
tao->fmin = 1.0e-8;
#endif
PetscFunctionReturn(0);
}
static PetscErrorCode TaoSetFromOptions_OWLQN(Tao tao)
{
TAO_OWLQN *lmP = (TAO_OWLQN *)tao->data;
PetscBool flg;
PetscErrorCode ierr;
PetscFunctionBegin;
ierr = PetscOptionsHead("Orthant-Wise Limited-memory method for Quasi-Newton unconstrained optimization");CHKERRQ(ierr);
ierr = PetscOptionsReal("-tao_owlqn_lambda", "regulariser weight","", 100,&lmP->lambda,&flg); CHKERRQ(ierr);
ierr = PetscOptionsTail();CHKERRQ(ierr);
ierr = TaoLineSearchSetFromOptions(tao->linesearch);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
static PetscErrorCode TaoSetFromOptions_GPCG(Tao tao)
{
TAO_GPCG *gpcg = (TAO_GPCG *)tao->data;
PetscErrorCode ierr;
PetscBool flg;
PetscFunctionBegin;
ierr = PetscOptionsHead("Gradient Projection, Conjugate Gradient method for bound constrained optimization");CHKERRQ(ierr);
ierr=PetscOptionsInt("-gpcg_maxpgits","maximum number of gradient projections per GPCG iterate",0,gpcg->maxgpits,&gpcg->maxgpits,&flg);CHKERRQ(ierr);
ierr = PetscOptionsTail();CHKERRQ(ierr);
ierr = TaoLineSearchSetFromOptions(tao->linesearch);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
static PetscErrorCode TaoSetFromOptions_TRON(PetscOptionItems *PetscOptionsObject,Tao tao)
{
TAO_TRON *tron = (TAO_TRON *)tao->data;
PetscErrorCode ierr;
PetscBool flg;
PetscFunctionBegin;
ierr = PetscOptionsHead(PetscOptionsObject,"Newton Trust Region Method for bound constrained optimization");CHKERRQ(ierr);
ierr = PetscOptionsInt("-tao_tron_maxgpits","maximum number of gradient projections per TRON iterate","TaoSetMaxGPIts",tron->maxgpits,&tron->maxgpits,&flg);CHKERRQ(ierr);
ierr = PetscOptionsTail();CHKERRQ(ierr);
ierr = TaoLineSearchSetFromOptions(tao->linesearch);CHKERRQ(ierr);
ierr = KSPSetFromOptions(tao->ksp);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
PetscErrorCode TaoSetFromOptions_SSLS(PetscOptionItems *PetscOptionsObject,Tao tao)
{
TAO_SSLS *ssls = (TAO_SSLS *)tao->data;
PetscErrorCode ierr;
PetscFunctionBegin;
ierr = PetscOptionsHead(PetscOptionsObject,"Semismooth method with a linesearch for complementarity problems");CHKERRQ(ierr);
ierr = PetscOptionsReal("-ssls_delta", "descent test fraction", "",ssls->delta, &ssls->delta, NULL);CHKERRQ(ierr);
ierr = PetscOptionsReal("-ssls_rho", "descent test power", "",ssls->rho, &ssls->rho, NULL);CHKERRQ(ierr);
ierr = TaoLineSearchSetFromOptions(tao->linesearch);CHKERRQ(ierr);
ierr = KSPSetFromOptions(tao->ksp);CHKERRQ(ierr);
ierr = PetscOptionsTail();CHKERRQ(ierr);
PetscFunctionReturn(0);
}
static int TaoSetOptions_BNLS(TAO_SOLVER tao, void*solver)
{
int info;
TaoInt ival;
TaoTruth flg;
TaoFunctionBegin;
info = TaoOptionsHead("Newton Line Search Method for bound constrained optimization");CHKERRQ(info);
info = TaoOptionInt("-redistribute","Redistribute Free variables (> 1 processors, only)","TaoPetscISType",1,&ival,&flg); CHKERRQ(info);
info = TaoOptionName("-submatrixfree","Mask full matrix instead of extract submatrices","TaoPetscISType",&flg); CHKERRQ(info);
info = TaoOptionsTail();CHKERRQ(info);
info = TaoLineSearchSetFromOptions(tao);CHKERRQ(info);
TaoFunctionReturn(0);
}
EXTERN_C_BEGIN
#undef __FUNCT__
#define __FUNCT__ "TaoCreate_SSFLS"
PetscErrorCode TaoCreate_SSFLS(Tao tao)
{
TAO_SSLS *ssls;
PetscErrorCode ierr;
const char *armijo_type = TAOLINESEARCHARMIJO;
PetscFunctionBegin;
ierr = PetscNewLog(tao,&ssls);CHKERRQ(ierr);
tao->data = (void*)ssls;
tao->ops->solve=TaoSolve_SSFLS;
tao->ops->setup=TaoSetUp_SSFLS;
tao->ops->view=TaoView_SSLS;
tao->ops->setfromoptions=TaoSetFromOptions_SSLS;
tao->ops->destroy = TaoDestroy_SSFLS;
ssls->delta = 1e-10;
ssls->rho = 2.1;
ierr = TaoLineSearchCreate(((PetscObject)tao)->comm,&tao->linesearch);CHKERRQ(ierr);
ierr = TaoLineSearchSetType(tao->linesearch,armijo_type);CHKERRQ(ierr);
ierr = TaoLineSearchSetFromOptions(tao->linesearch);
/* Linesearch objective and objectivegradient routines are set in solve routine */
ierr = KSPCreate(((PetscObject)tao)->comm,&tao->ksp);CHKERRQ(ierr);
tao->max_it = 2000;
tao->max_funcs = 4000;
tao->fatol = 0;
tao->frtol = 0;
tao->grtol=0;
tao->grtol=0;
#if defined(PETSC_USE_REAL_SINGLE)
tao->gatol = 1.0e-6;
tao->fmin = 1.0e-4;
#else
tao->gatol = 1.0e-16;
tao->fmin = 1.0e-8;
#endif
PetscFunctionReturn(0);
}
PETSC_EXTERN PetscErrorCode TaoCreate_SSFLS(Tao tao)
{
TAO_SSLS *ssls;
PetscErrorCode ierr;
const char *armijo_type = TAOLINESEARCHARMIJO;
PetscFunctionBegin;
ierr = PetscNewLog(tao,&ssls);CHKERRQ(ierr);
tao->data = (void*)ssls;
tao->ops->solve=TaoSolve_SSFLS;
tao->ops->setup=TaoSetUp_SSFLS;
tao->ops->view=TaoView_SSLS;
tao->ops->setfromoptions = TaoSetFromOptions_SSLS;
tao->ops->destroy = TaoDestroy_SSFLS;
ssls->delta = 1e-10;
ssls->rho = 2.1;
ierr = TaoLineSearchCreate(((PetscObject)tao)->comm,&tao->linesearch);CHKERRQ(ierr);
ierr = TaoLineSearchSetType(tao->linesearch,armijo_type);CHKERRQ(ierr);
ierr = TaoLineSearchSetOptionsPrefix(tao->linesearch,tao->hdr.prefix);CHKERRQ(ierr);
ierr = TaoLineSearchSetFromOptions(tao->linesearch);CHKERRQ(ierr);
/* Linesearch objective and objectivegradient routines are set in solve routine */
ierr = KSPCreate(((PetscObject)tao)->comm,&tao->ksp);CHKERRQ(ierr);
ierr = KSPSetOptionsPrefix(tao->ksp,tao->hdr.prefix);CHKERRQ(ierr);
/* Override default settings (unless already changed) */
if (!tao->max_it_changed) tao->max_it = 2000;
if (!tao->max_funcs_changed) tao->max_funcs = 4000;
if (!tao->fatol_changed) tao->fatol = 0;
if (!tao->frtol_changed) tao->frtol = 0;
if (!tao->gttol_changed) tao->gttol = 0;
if (!tao->grtol_changed) tao->grtol = 0;
#if defined(PETSC_USE_REAL_SINGLE)
if (!tao->gatol_changed) tao->gatol = 1.0e-6;
if (!tao->fmin_changed) tao->fmin = 1.0e-4;
#else
if (!tao->gatol_changed) tao->gatol = 1.0e-16;
if (!tao->fmin_changed) tao->fmin = 1.0e-8;
#endif
PetscFunctionReturn(0);
}
static int TaoSetFromOptions_GPCG(TAO_SOLVER tao, void*solver)
{
TAO_GPCG *gpcg = (TAO_GPCG *)solver;
int info;
TaoInt ival;
TaoTruth flg;
TaoFunctionBegin;
info = TaoOptionsHead("Gradient Projection, Conjugate Gradient method for bound constrained optimization");CHKERRQ(info);
info=TaoOptionInt("-gpcg_maxpgits","maximum number of gradient projections per GPCG iterate",0,gpcg->maxgpits,&gpcg->maxgpits,&flg);
CHKERRQ(info);
info = TaoOptionInt("-redistribute","Redistribute Free variables (> 1 processors, only)","TaoPetscISType",1,&ival,&flg); CHKERRQ(info);
info = TaoOptionName("-submatrixfree","Mask full matrix instead of extract submatrices","TaoPetscISType",&flg); CHKERRQ(info);
info = TaoOptionsTail();CHKERRQ(info);
info=TaoLineSearchSetFromOptions(tao);CHKERRQ(info);
TaoFunctionReturn(0);
}
