xref: /petsc/src/tao/bound/impls/blmvm/blmvm.c (revision 5d5277661bc9d5de49c003e79a2b7124bf8a2eb4)

#include <petsctaolinesearch.h>
#include <../src/tao/matrix/lmvmmat.h>
#include <../src/tao/bound/impls/blmvm/blmvm.h>

/*------------------------------------------------------------*/
#undef __FUNCT__
#define __FUNCT__ "TaoSolve_BLMVM"
static PetscErrorCode TaoSolve_BLMVM(Tao tao)
{
  PetscErrorCode               ierr;
  TAO_BLMVM                    *blmP = (TAO_BLMVM *)tao->data;
  TaoConvergedReason           reason = TAO_CONTINUE_ITERATING;
  TaoLineSearchConvergedReason ls_status = TAOLINESEARCH_CONTINUE_ITERATING;
  PetscReal                    f, fold, gdx, gnorm;
  PetscReal                    stepsize = 1.0,delta;

  PetscFunctionBegin;
  /*  Project initial point onto bounds */
  ierr = TaoComputeVariableBounds(tao);CHKERRQ(ierr);
  ierr = VecMedian(tao->XL,tao->solution,tao->XU,tao->solution);CHKERRQ(ierr);
  ierr = TaoLineSearchSetVariableBounds(tao->linesearch,tao->XL,tao->XU);CHKERRQ(ierr);

  /* Check convergence criteria */
  ierr = TaoComputeObjectiveAndGradient(tao, tao->solution,&f,blmP->unprojected_gradient);CHKERRQ(ierr);
  ierr = VecBoundGradientProjection(blmP->unprojected_gradient,tao->solution, tao->XL,tao->XU,tao->gradient);CHKERRQ(ierr);

  ierr = VecNorm(tao->gradient,NORM_2,&gnorm);CHKERRQ(ierr);
  if (PetscIsInfOrNanReal(f) || PetscIsInfOrNanReal(gnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf pr NaN");

  ierr = TaoMonitor(tao, tao->niter, f, gnorm, 0.0, stepsize, &reason);CHKERRQ(ierr);
  if (reason != TAO_CONTINUE_ITERATING) PetscFunctionReturn(0);

  /* Set initial scaling for the function */
  if (f != 0.0) {
    delta = 2.0*PetscAbsScalar(f) / (gnorm*gnorm);
  } else {
    delta = 2.0 / (gnorm*gnorm);
  }
  ierr = MatLMVMSetDelta(blmP->M,delta);CHKERRQ(ierr);

  /* Set counter for gradient/reset steps */
  blmP->grad = 0;
  blmP->reset = 0;

  /* Have not converged; continue with Newton method */
  while (reason == TAO_CONTINUE_ITERATING) {
    /* Compute direction */
    ierr = MatLMVMUpdate(blmP->M, tao->solution, tao->gradient);CHKERRQ(ierr);
    ierr = MatLMVMSolve(blmP->M, blmP->unprojected_gradient, tao->stepdirection);CHKERRQ(ierr);
    ierr = VecBoundGradientProjection(tao->stepdirection,tao->solution,tao->XL,tao->XU,tao->gradient);CHKERRQ(ierr);

    /* Check for success (descent direction) */
    ierr = VecDot(blmP->unprojected_gradient, tao->gradient, &gdx);CHKERRQ(ierr);
    if (gdx <= 0) {
      /* Step is not descent or solve was not successful
         Use steepest descent direction (scaled) */
      ++blmP->grad;

      if (f != 0.0) {
        delta = 2.0*PetscAbsScalar(f) / (gnorm*gnorm);
      } else {
        delta = 2.0 / (gnorm*gnorm);
      }
      ierr = MatLMVMSetDelta(blmP->M,delta);CHKERRQ(ierr);
      ierr = MatLMVMReset(blmP->M);CHKERRQ(ierr);
      ierr = MatLMVMUpdate(blmP->M, tao->solution, blmP->unprojected_gradient);CHKERRQ(ierr);
      ierr = MatLMVMSolve(blmP->M,blmP->unprojected_gradient, tao->stepdirection);CHKERRQ(ierr);
    }
    ierr = VecScale(tao->stepdirection,-1.0);CHKERRQ(ierr);

    /* Perform the linesearch */
    fold = f;
    ierr = VecCopy(tao->solution, blmP->Xold);CHKERRQ(ierr);
    ierr = VecCopy(blmP->unprojected_gradient, blmP->Gold);CHKERRQ(ierr);
    ierr = TaoLineSearchSetInitialStepLength(tao->linesearch,1.0);CHKERRQ(ierr);
    ierr = TaoLineSearchApply(tao->linesearch, tao->solution, &f, blmP->unprojected_gradient, tao->stepdirection, &stepsize, &ls_status);CHKERRQ(ierr);
    ierr = TaoAddLineSearchCounts(tao);CHKERRQ(ierr);

    if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER) {
      /* Linesearch failed
         Reset factors and use scaled (projected) gradient step */
      ++blmP->reset;

      f = fold;
      ierr = VecCopy(blmP->Xold, tao->solution);CHKERRQ(ierr);
      ierr = VecCopy(blmP->Gold, blmP->unprojected_gradient);CHKERRQ(ierr);

      if (f != 0.0) {
        delta = 2.0* PetscAbsScalar(f) / (gnorm*gnorm);
      } else {
        delta = 2.0/ (gnorm*gnorm);
      }
      ierr = MatLMVMSetDelta(blmP->M,delta);CHKERRQ(ierr);
      ierr = MatLMVMReset(blmP->M);CHKERRQ(ierr);
      ierr = MatLMVMUpdate(blmP->M, tao->solution, blmP->unprojected_gradient);CHKERRQ(ierr);
      ierr = MatLMVMSolve(blmP->M, blmP->unprojected_gradient, tao->stepdirection);CHKERRQ(ierr);
      ierr = VecScale(tao->stepdirection, -1.0);CHKERRQ(ierr);

      /* This may be incorrect; linesearch has values fo stepmax and stepmin
         that should be reset. */
      ierr = TaoLineSearchSetInitialStepLength(tao->linesearch,1.0);CHKERRQ(ierr);
      ierr = TaoLineSearchApply(tao->linesearch,tao->solution,&f, blmP->unprojected_gradient, tao->stepdirection,  &stepsize, &ls_status);CHKERRQ(ierr);
      ierr = TaoAddLineSearchCounts(tao);CHKERRQ(ierr);

      if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER) {
        tao->reason = TAO_DIVERGED_LS_FAILURE;
        break;
      }
    }

    /* Check for converged */
    ierr = VecBoundGradientProjection(blmP->unprojected_gradient, tao->solution, tao->XL, tao->XU, tao->gradient);CHKERRQ(ierr);
    ierr = VecNorm(tao->gradient, NORM_2, &gnorm);CHKERRQ(ierr);


    if (PetscIsInfOrNanReal(f) || PetscIsInfOrNanReal(gnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Not-a-Number");
    tao->niter++;
    ierr = TaoMonitor(tao, tao->niter, f, gnorm, 0.0, stepsize, &reason);CHKERRQ(ierr);
  }
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "TaoSetup_BLMVM"
static PetscErrorCode TaoSetup_BLMVM(Tao tao)
{
  TAO_BLMVM      *blmP = (TAO_BLMVM *)tao->data;
  PetscInt       n,N;
  PetscErrorCode ierr;

  PetscFunctionBegin;
  /* Existence of tao->solution checked in TaoSetup() */
  ierr = VecDuplicate(tao->solution,&blmP->Xold);CHKERRQ(ierr);
  ierr = VecDuplicate(tao->solution,&blmP->Gold);CHKERRQ(ierr);
  ierr = VecDuplicate(tao->solution, &blmP->unprojected_gradient);CHKERRQ(ierr);

  if (!tao->stepdirection) {
    ierr = VecDuplicate(tao->solution, &tao->stepdirection);CHKERRQ(ierr);
  }
  if (!tao->gradient) {
    ierr = VecDuplicate(tao->solution,&tao->gradient);CHKERRQ(ierr);
  }
  if (!tao->XL) {
    ierr = VecDuplicate(tao->solution,&tao->XL);CHKERRQ(ierr);
    ierr = VecSet(tao->XL,PETSC_NINFINITY);CHKERRQ(ierr);
  }
  if (!tao->XU) {
    ierr = VecDuplicate(tao->solution,&tao->XU);CHKERRQ(ierr);
    ierr = VecSet(tao->XU,PETSC_INFINITY);CHKERRQ(ierr);
  }
  /* Create matrix for the limited memory approximation */
  ierr = VecGetLocalSize(tao->solution,&n);CHKERRQ(ierr);
  ierr = VecGetSize(tao->solution,&N);CHKERRQ(ierr);
  ierr = MatCreateLMVM(((PetscObject)tao)->comm,n,N,&blmP->M);CHKERRQ(ierr);
  ierr = MatLMVMAllocateVectors(blmP->M,tao->solution);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

/* ---------------------------------------------------------- */
#undef __FUNCT__
#define __FUNCT__ "TaoDestroy_BLMVM"
static PetscErrorCode TaoDestroy_BLMVM(Tao tao)
{
  TAO_BLMVM      *blmP = (TAO_BLMVM *)tao->data;
  PetscErrorCode ierr;

  PetscFunctionBegin;
  if (tao->setupcalled) {
    ierr = MatDestroy(&blmP->M);CHKERRQ(ierr);
    ierr = VecDestroy(&blmP->unprojected_gradient);CHKERRQ(ierr);
    ierr = VecDestroy(&blmP->Xold);CHKERRQ(ierr);
    ierr = VecDestroy(&blmP->Gold);CHKERRQ(ierr);
  }
  ierr = PetscFree(tao->data);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

/*------------------------------------------------------------*/
#undef __FUNCT__
#define __FUNCT__ "TaoSetFromOptions_BLMVM"
static PetscErrorCode TaoSetFromOptions_BLMVM(PetscOptions* 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);
}


/*------------------------------------------------------------*/
#undef __FUNCT__
#define __FUNCT__ "TaoView_BLMVM"
static int TaoView_BLMVM(Tao tao, PetscViewer viewer)
{
  TAO_BLMVM      *lmP = (TAO_BLMVM *)tao->data;
  PetscBool      isascii;
  PetscErrorCode ierr;

  PetscFunctionBegin;
  ierr = PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii);CHKERRQ(ierr);
  if (isascii) {
    ierr = PetscViewerASCIIPushTab(viewer);CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer, "Gradient steps: %D\n", lmP->grad);CHKERRQ(ierr);
    ierr = PetscViewerASCIIPopTab(viewer);CHKERRQ(ierr);
  }
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "TaoComputeDual_BLMVM"
static PetscErrorCode TaoComputeDual_BLMVM(Tao tao, Vec DXL, Vec DXU)
{
  TAO_BLMVM      *blm = (TAO_BLMVM *) tao->data;
  PetscErrorCode ierr;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(tao,TAO_CLASSID,1);
  PetscValidHeaderSpecific(DXL,VEC_CLASSID,2);
  PetscValidHeaderSpecific(DXU,VEC_CLASSID,3);
  if (!tao->gradient || !blm->unprojected_gradient) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Dual variables don't exist yet or no longer exist.\n");

  ierr = VecCopy(tao->gradient,DXL);CHKERRQ(ierr);
  ierr = VecAXPY(DXL,-1.0,blm->unprojected_gradient);CHKERRQ(ierr);
  ierr = VecSet(DXU,0.0);CHKERRQ(ierr);
  ierr = VecPointwiseMax(DXL,DXL,DXU);CHKERRQ(ierr);

  ierr = VecCopy(blm->unprojected_gradient,DXU);CHKERRQ(ierr);
  ierr = VecAXPY(DXU,-1.0,tao->gradient);CHKERRQ(ierr);
  ierr = VecAXPY(DXU,1.0,DXL);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

/* ---------------------------------------------------------- */
/*MC
  TAOBLMVM - Bounded limited memory variable metric is a quasi-Newton method
         for nonlinear minimization with bound constraints. It is an extension
         of TAOLMVM

  Options Database Keys:
+     -tao_lmm_vectors - number of vectors to use for approximation
.     -tao_lmm_scale_type - "none","scalar","broyden"
.     -tao_lmm_limit_type - "none","average","relative","absolute"
.     -tao_lmm_rescale_type - "none","scalar","gl"
.     -tao_lmm_limit_mu - mu limiting factor
.     -tao_lmm_limit_nu - nu limiting factor
.     -tao_lmm_delta_min - minimum delta value
.     -tao_lmm_delta_max - maximum delta value
.     -tao_lmm_broyden_phi - phi factor for Broyden scaling
.     -tao_lmm_scalar_alpha - alpha factor for scalar scaling
.     -tao_lmm_rescale_alpha - alpha factor for rescaling diagonal
.     -tao_lmm_rescale_beta - beta factor for rescaling diagonal
.     -tao_lmm_scalar_history - amount of history for scalar scaling
.     -tao_lmm_rescale_history - amount of history for rescaling diagonal
-     -tao_lmm_eps - rejection tolerance

  Level: beginner
M*/
#undef __FUNCT__
#define __FUNCT__ "TaoCreate_BLMVM"
PETSC_EXTERN PetscErrorCode TaoCreate_BLMVM(Tao tao)
{
  TAO_BLMVM      *blmP;
  const char     *morethuente_type = TAOLINESEARCHMT;
  PetscErrorCode ierr;

  PetscFunctionBegin;
  tao->ops->setup = TaoSetup_BLMVM;
  tao->ops->solve = TaoSolve_BLMVM;
  tao->ops->view = TaoView_BLMVM;
  tao->ops->setfromoptions = TaoSetFromOptions_BLMVM;
  tao->ops->destroy = TaoDestroy_BLMVM;
  tao->ops->computedual = TaoComputeDual_BLMVM;

  ierr = PetscNewLog(tao,&blmP);CHKERRQ(ierr);
  tao->data = (void*)blmP;
  tao->max_it = 2000;
  tao->max_funcs = 4000;
  tao->fatol = 1e-4;
  tao->frtol = 1e-4;

  ierr = TaoLineSearchCreate(((PetscObject)tao)->comm, &tao->linesearch);CHKERRQ(ierr);
  ierr = TaoLineSearchSetType(tao->linesearch, morethuente_type);CHKERRQ(ierr);
  ierr = TaoLineSearchUseTaoRoutines(tao->linesearch,tao);CHKERRQ(ierr);
  ierr = TaoLineSearchSetOptionsPrefix(tao->linesearch,tao->hdr.prefix);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}