xref: /petsc/src/tao/bound/impls/blmvm/blmvm.c (revision 2ec5c1acdc8744b9aa587f7e7afc34821d593ce8)

#include <petsctaolinesearch.h>
#include <../src/tao/unconstrained/impls/lmvm/lmvm.h>
#include <../src/tao/bound/impls/blmvm/blmvm.h>

/*------------------------------------------------------------*/
static PetscErrorCode TaoSolve_BLMVM(Tao tao)
{
  PetscErrorCode               ierr;
  TAO_BLMVM                    *blmP = (TAO_BLMVM *)tao->data;
  TaoLineSearchConvergedReason ls_status = TAOLINESEARCH_CONTINUE_ITERATING;
  PetscReal                    f, fold, gdx, gnorm;
  PetscReal                    stepsize = 1.0,delta;
  PetscInt                     stepType = BLMVM_STEP_GRAD;

  PetscFunctionBegin;
  /*  Project initial point onto bounds */
  ierr = TaoComputeVariableBounds(tao);CHKERRQ(ierr);
  ierr = VecMedian(tao->XL,tao->solution,tao->XU,tao->solution);CHKERRQ(ierr);
  if (tao->bounded) {
    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 = TaoGradientNorm(tao, tao->gradient,NORM_2,&gnorm);CHKERRQ(ierr);
  if (PetscIsInfOrNanReal(f) || PetscIsInfOrNanReal(gnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN");

  tao->reason = TAO_CONTINUE_ITERATING;
  ierr = TaoLogConvergenceHistory(tao,f,gnorm,0.0,tao->ksp_its);CHKERRQ(ierr);
  ierr = TaoMonitor(tao,tao->niter,f,gnorm,0.0,stepsize);CHKERRQ(ierr);
  ierr = (*tao->ops->convergencetest)(tao,tao->cnvP);CHKERRQ(ierr);
  if (tao->reason != TAO_CONTINUE_ITERATING) PetscFunctionReturn(0);

  /* Set initial scaling for the function */
  if (blmP->Mscale) {
    if (f != 0.0) {
      delta = 2.0*PetscAbsScalar(f) / (gnorm*gnorm);
    } else {
      delta = 2.0 / (gnorm*gnorm);
    }
    ierr = MatLMVMSetJ0Scale(blmP->Mscale, 1.0/delta);CHKERRQ(ierr);
  }
  ierr = MatLMVMReset(blmP->M, PETSC_FALSE);CHKERRQ(ierr);

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

  /* Have not converged; continue with Newton method */
  while (tao->reason == TAO_CONTINUE_ITERATING) {
    /* Compute direction */
    ierr = MatLMVMUpdate(blmP->M, tao->solution, tao->gradient);CHKERRQ(ierr);
    ierr = MatSolve(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) */
      stepType = BLMVM_STEP_GRAD;

      if (blmP->Mscale) {
        if (f != 0.0) {
          delta = 2.0*PetscAbsScalar(f) / (gnorm*gnorm);
        } else {
          delta = 2.0 / (gnorm*gnorm);
        }
        ierr = MatLMVMSetJ0Scale(blmP->Mscale, 1.0/delta);CHKERRQ(ierr);
      }
      ierr = MatLMVMReset(blmP->M, PETSC_FALSE);CHKERRQ(ierr);
      ierr = MatLMVMUpdate(blmP->M, tao->solution, blmP->unprojected_gradient);CHKERRQ(ierr);
      ierr = MatSolve(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 && stepType != BLMVM_STEP_GRAD) {
      /* Linesearch failed
         Reset factors and use scaled (projected) gradient step */
      f = fold;
      ierr = VecCopy(blmP->Xold, tao->solution);CHKERRQ(ierr);
      ierr = VecCopy(blmP->Gold, blmP->unprojected_gradient);CHKERRQ(ierr);

      if (blmP->Mscale) {
        if (f != 0.0) {
          delta = 2.0*PetscAbsScalar(f) / (gnorm*gnorm);
        } else {
          delta = 2.0 / (gnorm*gnorm);
        }
        ierr = MatLMVMSetJ0Scale(blmP->Mscale, 1.0/delta);CHKERRQ(ierr);
      }
      ierr = MatLMVMReset(blmP->M, PETSC_FALSE);CHKERRQ(ierr);
      ierr = MatLMVMUpdate(blmP->M, tao->solution, blmP->unprojected_gradient);CHKERRQ(ierr);
      ierr = MatSolve(blmP->M, blmP->unprojected_gradient, tao->stepdirection);CHKERRQ(ierr);
      ierr = VecScale(tao->stepdirection, -1.0);CHKERRQ(ierr);

      /* This may be incorrect; linesearch has values for 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 = TaoGradientNorm(tao, 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 = TaoLogConvergenceHistory(tao,f,gnorm,0.0,tao->ksp_its);CHKERRQ(ierr);
    ierr = TaoMonitor(tao,tao->niter,f,gnorm,0.0,stepsize);CHKERRQ(ierr);
    ierr = (*tao->ops->convergencetest)(tao,tao->cnvP);CHKERRQ(ierr);
  }
  PetscFunctionReturn(0);
}

static PetscErrorCode TaoSetup_BLMVM(Tao tao)
{
  TAO_BLMVM      *blmP = (TAO_BLMVM *)tao->data;
  const char     *prefix;
  PetscErrorCode ierr;

  PetscFunctionBegin;
  if (!blmP->Xold) {
    ierr = VecDuplicate(tao->solution,&blmP->Xold);CHKERRQ(ierr);
  }
  if (!blmP->Gold) {
    ierr = VecDuplicate(tao->solution,&blmP->Gold);CHKERRQ(ierr);
  }
  if (!blmP->unprojected_gradient) {
    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);
  }
  /* Create matrix for the limited memory approximation */
  ierr = MatLMVMAllocate(blmP->M, tao->solution, blmP->unprojected_gradient);CHKERRQ(ierr);

  /* If the user has set a matrix to solve as the initial H0, set the options prefix here, and set up the KSP */
  if (blmP->H0) {
    ierr = MatLMVMSetJ0(blmP->M, blmP->H0);CHKERRQ(ierr);
  } else if (!blmP->no_scale) {
    ierr = MatCreate(((PetscObject)tao)->comm, &blmP->Mscale);CHKERRQ(ierr);
    ierr = MatSetType(blmP->Mscale, MATLMVMDIAGBRDN);CHKERRQ(ierr);
    ierr = TaoGetOptionsPrefix(tao, &prefix);CHKERRQ(ierr);
    ierr = MatSetOptionsPrefix(blmP->Mscale, prefix);CHKERRQ(ierr);
    ierr = MatAppendOptionsPrefix(blmP->Mscale, "tao_blmvm_scale_");CHKERRQ(ierr);
    ierr = MatSetFromOptions(blmP->Mscale);CHKERRQ(ierr);
    ierr = MatLMVMAllocate(blmP->Mscale, tao->solution, blmP->unprojected_gradient);CHKERRQ(ierr);
    ierr = MatLMVMSetJ0(blmP->M, blmP->Mscale);CHKERRQ(ierr);
  }
  PetscFunctionReturn(0);
}

/* ---------------------------------------------------------- */
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);
    if (blmP->Mscale) {
      ierr = MatDestroy(&blmP->Mscale);CHKERRQ(ierr);
    }
    ierr = VecDestroy(&blmP->unprojected_gradient);CHKERRQ(ierr);
    ierr = VecDestroy(&blmP->Xold);CHKERRQ(ierr);
    ierr = VecDestroy(&blmP->Gold);CHKERRQ(ierr);
  }

  if (blmP->H0) {
    PetscObjectDereference((PetscObject)blmP->H0);
  }

  ierr = PetscFree(tao->data);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

/*------------------------------------------------------------*/
static PetscErrorCode TaoSetFromOptions_BLMVM(PetscOptionItems* PetscOptionsObject,Tao tao)
{
  TAO_BLMVM      *blmP = (TAO_BLMVM *)tao->data;
  PetscErrorCode ierr;

  PetscFunctionBegin;
  ierr = PetscOptionsHead(PetscOptionsObject,"Limited-memory variable-metric method for bound constrained optimization");CHKERRQ(ierr);
  ierr = PetscOptionsBool("-tao_blmvm_no_scale","(developer) disable the diagonal Broyden scaling of the QN approximation","",blmP->no_scale,&blmP->no_scale,NULL);CHKERRQ(ierr);
  ierr = PetscOptionsTail();CHKERRQ(ierr);
  ierr = TaoLineSearchSetFromOptions(tao->linesearch);CHKERRQ(ierr);
  ierr = MatSetFromOptions(blmP->M);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}


/*------------------------------------------------------------*/
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 = PetscViewerASCIIPrintf(viewer, "Gradient steps: %D\n", lmP->grad);CHKERRQ(ierr);
  }
  PetscFunctionReturn(0);
}

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*/
PETSC_EXTERN PetscErrorCode TaoCreate_BLMVM(Tao tao)
{
  TAO_BLMVM      *blmP;
  const char     *prefix;
  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);
  blmP->H0 = NULL;
  blmP->no_scale = PETSC_FALSE;
  tao->data = (void*)blmP;

  /* Override default settings (unless already changed) */
  if (!tao->max_it_changed) tao->max_it = 2000;
  if (!tao->max_funcs_changed) tao->max_funcs = 4000;

  ierr = TaoLineSearchCreate(((PetscObject)tao)->comm, &tao->linesearch);CHKERRQ(ierr);
  ierr = PetscObjectIncrementTabLevel((PetscObject)tao->linesearch, (PetscObject)tao, 1);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);

  ierr = MatCreate(((PetscObject)tao)->comm, &blmP->M);CHKERRQ(ierr);
  ierr = MatSetType(blmP->M, MATLMVMBFGS);CHKERRQ(ierr);
  ierr = TaoGetOptionsPrefix(tao, &prefix);CHKERRQ(ierr);
  ierr = MatSetOptionsPrefix(blmP->M, prefix);CHKERRQ(ierr);
  ierr = MatAppendOptionsPrefix(blmP->M, "tao_blmvm_");CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

PETSC_EXTERN PetscErrorCode TaoBLMVMSetH0(Tao tao, Mat H0)
{
  TAO_LMVM       *lmP;
  TAO_BLMVM      *blmP;
  TaoType        type;
  PetscBool      is_lmvm, is_blmvm;
  PetscErrorCode ierr;

  ierr = TaoGetType(tao, &type);CHKERRQ(ierr);
  ierr = PetscStrcmp(type, TAOLMVM,  &is_lmvm);CHKERRQ(ierr);
  ierr = PetscStrcmp(type, TAOBLMVM, &is_blmvm);CHKERRQ(ierr);

  if (is_lmvm) {
    lmP = (TAO_LMVM *)tao->data;
    ierr = PetscObjectReference((PetscObject)H0);CHKERRQ(ierr);
    lmP->H0 = H0;
  } else if (is_blmvm) {
    blmP = (TAO_BLMVM *)tao->data;
    ierr = PetscObjectReference((PetscObject)H0);CHKERRQ(ierr);
    blmP->H0 = H0;
  } else SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONGSTATE, "This routine applies to TAO_LMVM and TAO_BLMVM.");
  PetscFunctionReturn(0);
}

PETSC_EXTERN PetscErrorCode TaoBLMVMGetH0(Tao tao, Mat *H0)
{
  TAO_LMVM       *lmP;
  TAO_BLMVM      *blmP;
  TaoType        type;
  PetscBool      is_lmvm, is_blmvm;
  Mat            M;

  PetscErrorCode ierr;

  ierr = TaoGetType(tao, &type);CHKERRQ(ierr);
  ierr = PetscStrcmp(type, TAOLMVM,  &is_lmvm);CHKERRQ(ierr);
  ierr = PetscStrcmp(type, TAOBLMVM, &is_blmvm);CHKERRQ(ierr);

  if (is_lmvm) {
    lmP = (TAO_LMVM *)tao->data;
    M = lmP->M;
  } else if (is_blmvm) {
    blmP = (TAO_BLMVM *)tao->data;
    M = blmP->M;
  } else SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONGSTATE, "This routine applies to TAO_LMVM and TAO_BLMVM.");
  ierr = MatLMVMGetJ0(M, H0);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

PETSC_EXTERN PetscErrorCode TaoBLMVMGetH0KSP(Tao tao, KSP *ksp)
{
  TAO_LMVM       *lmP;
  TAO_BLMVM      *blmP;
  TaoType        type;
  PetscBool      is_lmvm, is_blmvm;
  Mat            M;
  PetscErrorCode ierr;

  ierr = TaoGetType(tao, &type);CHKERRQ(ierr);
  ierr = PetscStrcmp(type, TAOLMVM,  &is_lmvm);CHKERRQ(ierr);
  ierr = PetscStrcmp(type, TAOBLMVM, &is_blmvm);CHKERRQ(ierr);

  if (is_lmvm) {
    lmP = (TAO_LMVM *)tao->data;
    M = lmP->M;
  } else if (is_blmvm) {
    blmP = (TAO_BLMVM *)tao->data;
    M = blmP->M;
  } else SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONGSTATE, "This routine applies to TAO_LMVM and TAO_BLMVM.");
  ierr = MatLMVMGetJ0KSP(M, ksp);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}