xref: /petsc/src/tao/bound/impls/bqnls/bqnls.c (revision 8ebe3e4e9e00d86ece2e9fcd0cc84910b0ad437c)

#include <../src/tao/bound/impls/bqnk/bqnk.h>

static const char *BNK_AS[64] = {"none", "bertsekas"};

static PetscErrorCode TaoBQNLSComputeHessian(Tao tao)
{
  TAO_BNK        *bnk = (TAO_BNK *)tao->data;
  TAO_BQNK       *bqnk = (TAO_BQNK*)bnk->ctx;
  PetscErrorCode ierr;
  PetscReal      gnorm2, delta;

  PetscFunctionBegin;
  /* Compute the initial scaling and update the approximation */
  gnorm2 = bnk->gnorm*bnk->gnorm;
  if (gnorm2 == 0.0) gnorm2 = PETSC_MACHINE_EPSILON;
  if (bnk->f == 0.0) delta = 2.0 / gnorm2;
  else delta = 2.0 * PetscAbsScalar(bnk->f) / gnorm2;
  ierr = MatLMVMSymBroydenSetDelta(bqnk->B, delta);CHKERRQ(ierr);
  ierr = MatLMVMUpdate(bqnk->B, tao->solution, bnk->unprojected_gradient);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

static PetscErrorCode TaoBQNLSComputeStep(Tao tao, PetscBool shift, KSPConvergedReason *ksp_reason, PetscInt *step_type)
{
  TAO_BNK        *bnk = (TAO_BNK *)tao->data;
  TAO_BQNK       *bqnk = (TAO_BQNK*)bnk->ctx;
  PetscErrorCode ierr;
  PetscInt       nupdates;

  PetscFunctionBegin;
  ierr = MatSolve(bqnk->B, tao->gradient, tao->stepdirection);CHKERRQ(ierr);
  ierr = VecScale(tao->stepdirection, -1.0);CHKERRQ(ierr);
  ierr = TaoBNKBoundStep(tao, bnk->as_type, tao->stepdirection);CHKERRQ(ierr);
  *ksp_reason = KSP_CONVERGED_ATOL;
  ierr = MatLMVMGetUpdateCount(bqnk->B, &nupdates);CHKERRQ(ierr);
  if (nupdates == 0) *step_type = BNK_SCALED_GRADIENT;
  else *step_type = BNK_BFGS;
  PetscFunctionReturn(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;
  PetscBool      is_spd;

  PetscFunctionBegin;
  ierr = PetscOptionsHead(PetscOptionsObject,"Quasi-Newton-Krylov method for bound constrained optimization");CHKERRQ(ierr);
  ierr = PetscOptionsEList("-tao_bnk_as_type", "active set estimation method", "", BNK_AS, BNK_AS_TYPES, BNK_AS[bnk->as_type], &bnk->as_type, NULL);CHKERRQ(ierr);
  ierr = PetscOptionsReal("-tao_bnk_epsilon", "(developer) tolerance used when computing actual and predicted reduction", "", bnk->epsilon, &bnk->epsilon,NULL);CHKERRQ(ierr);
  ierr = PetscOptionsReal("-tao_bnk_as_tol", "(developer) initial tolerance used when estimating actively bounded variables", "", bnk->as_tol, &bnk->as_tol,NULL);CHKERRQ(ierr);
  ierr = PetscOptionsReal("-tao_bnk_as_step", "(developer) step length used when estimating actively bounded variables", "", bnk->as_step, &bnk->as_step,NULL);CHKERRQ(ierr);
  ierr = PetscOptionsInt("-tao_bnk_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 = TaoSetOptionsPrefix(bnk->bncg,((PetscObject)(tao))->prefix);CHKERRQ(ierr);
  ierr = TaoAppendOptionsPrefix(bnk->bncg,"tao_bnk_");CHKERRQ(ierr);
  ierr = TaoSetFromOptions(bnk->bncg);CHKERRQ(ierr);

  ierr = MatSetOptionsPrefix(bqnk->B, ((PetscObject)tao)->prefix);CHKERRQ(ierr);
  ierr = MatAppendOptionsPrefix(bqnk->B, "tao_bqnls_");CHKERRQ(ierr);
  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);
}

/*MC
  TAOBQNLS - Bounded Quasi-Newton Line Search method for nonlinear minimization with bound
             constraints. This method approximates the action of the inverse-Hessian with a
             limited memory quasi-Newton formula. The quasi-Newton matrix and its options are
             accessible via the prefix `-tao_bqnls_`

  Option Database Keys:
+ -tao_bnk_max_cg_its - maximum number of bounded conjugate-gradient iterations taken in each Newton loop
. -tao_bnk_as_type - active-set estimation method ("none", "bertsekas")
. -tao_bnk_epsilon - (developer) tolerance for small pred/actual ratios that trigger automatic step acceptance
. -tao_bnk_as_tol - (developer) initial tolerance used in estimating bounded active variables (-as_type bertsekas)
. -tao_bnk_as_step - (developer) trial step length used in estimating bounded active variables (-as_type bertsekas)

  Level: beginner
.seealso: TAOBNK
M*/
PETSC_EXTERN PetscErrorCode TaoCreate_BQNLS(Tao tao)
{
  TAO_BNK        *bnk;
  TAO_BQNK       *bqnk;
  PetscErrorCode ierr;

  PetscFunctionBegin;
  ierr = TaoCreate_BQNK(tao);CHKERRQ(ierr);
  tao->ops->setfromoptions = TaoSetFromOptions_BQNLS;

  bnk = (TAO_BNK*)tao->data;
  bnk->update_type = BNK_UPDATE_STEP;
  bnk->computehessian = TaoBQNLSComputeHessian;
  bnk->computestep = TaoBQNLSComputeStep;

  bqnk = (TAO_BQNK*)bnk->ctx;
  bqnk->solve = TaoSolve_BNLS;
  ierr = MatSetType(bqnk->B, MATLMVMBFGS);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}