xref: /petsc/src/tao/bound/impls/bnk/bntr.c (revision fed79b8e9c1768c470b4d2001044b5840bd3f132)

#include <../src/tao/bound/impls/bnk/bnk.h>
#include <petscksp.h>

/*
 Implements Newton's Method with a trust region approach for solving
 bound constrained minimization problems.

 The method can shift the Hessian matrix. The shifting procedure is
 adapted from the PATH algorithm for solving complementarity
 problems.

 The linear system solve should be done with a conjugate gradient
 method, although any method can be used.
*/

static PetscErrorCode TaoSolve_BNTR(Tao tao)
{
  PetscErrorCode               ierr;
  TAO_BNK                      *bnk = (TAO_BNK *)tao->data;

  PetscReal                    oldTrust;
  PetscBool                    stepAccepted = PETSC_TRUE;
  PetscInt                     stepType;

  PetscFunctionBegin;
  /*   Project the current point onto the feasible set */
  ierr = TaoComputeVariableBounds(tao);CHKERRQ(ierr);
  ierr = TaoLineSearchSetVariableBounds(tao->linesearch,tao->XL,tao->XU);CHKERRQ(ierr);

  /* Project the initial point onto the feasible region */
  ierr = VecMedian(tao->XL,tao->solution,tao->XU,tao->solution);CHKERRQ(ierr);

  /* Check convergence criteria */
  ierr = TaoComputeObjectiveAndGradient(tao, tao->solution, &bnk->f, bnk->unprojected_gradient);CHKERRQ(ierr);
  ierr = VecBoundGradientProjection(bnk->unprojected_gradient,tao->solution,tao->XL,tao->XU,tao->gradient);CHKERRQ(ierr);
  ierr = TaoGradientNorm(tao, tao->gradient,NORM_2,&bnk->gnorm);CHKERRQ(ierr);
  if (PetscIsInfOrNanReal(bnk->f) || PetscIsInfOrNanReal(bnk->gnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN");

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

  /* Initialize the preconditioner and trust radius */
  ierr = TaoBNKInitialize(tao);CHKERRQ(ierr);

  /* Have not converged; continue with Newton method */
  while (tao->reason == TAO_CONTINUE_ITERATING) {
    if (stepAccepted) {
      tao->niter++;
      tao->ksp_its=0;
      /* Compute the Hessian */
      ierr = TaoComputeHessian(tao,tao->solution,tao->hessian,tao->hessian_pre);CHKERRQ(ierr);
      /* Update the BFGS preconditioner */
      if (BNK_PC_BFGS == bnk->pc_type) {
        if (BFGS_SCALE_PHESS == bnk->bfgs_scale_type) {
          /* Obtain diagonal for the bfgs preconditioner  */
          ierr = MatGetDiagonal(tao->hessian, bnk->Diag);CHKERRQ(ierr);
          ierr = VecAbs(bnk->Diag);CHKERRQ(ierr);
          ierr = VecReciprocal(bnk->Diag);CHKERRQ(ierr);
          ierr = MatLMVMSetScale(bnk->M,bnk->Diag);CHKERRQ(ierr);
        }
        /* Update the limited memory preconditioner and get existing # of updates */
        ierr = MatLMVMUpdate(bnk->M, tao->solution, bnk->unprojected_gradient);CHKERRQ(ierr);
      }
    }

    /* Use the common BNK kernel to compute the raw Newton step */
    ierr = TaoBNKComputeStep(tao, PETSC_FALSE, &stepType);CHKERRQ(ierr);

    /* Store current solution before it changes */
    oldTrust = tao->trust;
    bnk->fold = bnk->f;
    ierr = VecCopy(tao->solution, bnk->Xold);CHKERRQ(ierr);
    ierr = VecCopy(tao->gradient, bnk->Gold);CHKERRQ(ierr);
    ierr = VecCopy(bnk->unprojected_gradient, bnk->unprojected_gradient_old);CHKERRQ(ierr);

    /* Test the new step for acceptance */
    ierr = VecAXPY(tao->solution, 1.0, tao->stepdirection);CHKERRQ(ierr);
    ierr = TaoComputeObjective(tao, tao->solution, &bnk->f);CHKERRQ(ierr);
    ierr = TaoBNKUpdateTrustRadius(tao, bnk->fold, bnk->f, stepType, &stepAccepted);CHKERRQ(ierr);

    if (stepAccepted) {
      /* Step is good, evaluate gradient and project it */
      ierr = TaoComputeGradient(tao, tao->solution, bnk->unprojected_gradient);CHKERRQ(ierr);
      ierr = VecBoundGradientProjection(bnk->unprojected_gradient,tao->solution,tao->XL,tao->XU,tao->gradient);CHKERRQ(ierr);
    } else {
      /* Step is bad, revert old solution and re-solve with new radius*/
      bnk->f = bnk->fold;
      ierr = VecCopy(bnk->Xold, tao->solution);CHKERRQ(ierr);
      ierr = VecCopy(bnk->Gold, tao->gradient);CHKERRQ(ierr);
      ierr = VecCopy(bnk->unprojected_gradient_old, bnk->unprojected_gradient);CHKERRQ(ierr);
      if (oldTrust == tao->trust) {
        /* Can't shrink trust radius any further, so we have to terminate */
        tao->reason = TAO_DIVERGED_TR_REDUCTION;
      }
    }

    /*  Check for termination */
    ierr = TaoGradientNorm(tao, tao->gradient,NORM_2,&bnk->gnorm);CHKERRQ(ierr);
    if (PetscIsInfOrNanReal(bnk->f) || PetscIsInfOrNanReal(bnk->gnorm)) SETERRQ(PETSC_COMM_SELF,1,"User provided compute function generated Not-a-Number");
    ierr = TaoLogConvergenceHistory(tao,bnk->f,bnk->gnorm,0.0,tao->ksp_its);CHKERRQ(ierr);
    ierr = TaoMonitor(tao,tao->niter,bnk->f,bnk->gnorm,0.0,tao->trust);CHKERRQ(ierr);
    ierr = (*tao->ops->convergencetest)(tao,tao->cnvP);CHKERRQ(ierr);
  }
  PetscFunctionReturn(0);
}

PETSC_EXTERN PetscErrorCode TaoCreate_BNTR(Tao tao)
{
  TAO_BNK        *bnk;
  PetscErrorCode ierr;

  PetscFunctionBegin;
  ierr = TaoCreate_BNK(tao);CHKERRQ(ierr);
  tao->ops->solve=TaoSolve_BNTR;

  bnk = (TAO_BNK *)tao->data;
  bnk->update_type = BNK_UPDATE_REDUCTION;
  bnk->sval = 0.0;
  PetscFunctionReturn(0);
}