Пример #1
0
PetscErrorCode MatMult_SMF(Mat mat,Vec a,Vec y)
{
  MatSubMatFreeCtx ctx;
  PetscErrorCode   ierr;

  PetscFunctionBegin;
  ierr = MatShellGetContext(mat,(void **)&ctx);CHKERRQ(ierr);
  ierr = VecCopy(a,ctx->VR);CHKERRQ(ierr);
  ierr = VecISSet(ctx->VR,ctx->Cols,0.0);CHKERRQ(ierr);
  ierr = MatMult(ctx->A,ctx->VR,y);CHKERRQ(ierr);
  ierr = VecISSet(y,ctx->Rows,0.0);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}
Пример #2
0
Файл: bntr.c Проект: petsc/petsc
PetscErrorCode TaoSolve_BNTR(Tao tao)
{
  PetscErrorCode               ierr;
  TAO_BNK                      *bnk = (TAO_BNK *)tao->data;
  KSPConvergedReason           ksp_reason;

  PetscReal                    oldTrust, prered, actred, steplen, resnorm;
  PetscBool                    cgTerminate, needH = PETSC_TRUE, stepAccepted, shift = PETSC_FALSE;
  PetscInt                     stepType, nDiff;
  
  PetscFunctionBegin;
  /* Initialize the preconditioner, KSP solver and trust radius/line search */
  tao->reason = TAO_CONTINUE_ITERATING;
  ierr = TaoBNKInitialize(tao, bnk->init_type, &needH);CHKERRQ(ierr);
  if (tao->reason != TAO_CONTINUE_ITERATING) PetscFunctionReturn(0);

  /* Have not converged; continue with Newton method */
  while (tao->reason == TAO_CONTINUE_ITERATING) {
    /* Call general purpose update function */
    if (tao->ops->update) {
      ierr = (*tao->ops->update)(tao, tao->niter, tao->user_update);CHKERRQ(ierr);
    }
    ++tao->niter;
    
    if (needH && bnk->inactive_idx) { 
      /* Take BNCG steps (if enabled) to trade-off Hessian evaluations for more gradient evaluations */
      ierr = TaoBNKTakeCGSteps(tao, &cgTerminate);CHKERRQ(ierr);
      if (cgTerminate) {
        tao->reason = bnk->bncg->reason;
        PetscFunctionReturn(0);
      }
      /* Compute the hessian and update the BFGS preconditioner at the new iterate */
      ierr = (*bnk->computehessian)(tao);CHKERRQ(ierr);
      needH = PETSC_FALSE;
    }
    
    /* Store current solution before it changes */
    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);
    
    /* Enter into trust region loops */
    stepAccepted = PETSC_FALSE;
    while (!stepAccepted && tao->reason == TAO_CONTINUE_ITERATING) {
      tao->ksp_its=0;
      
      /* Use the common BNK kernel to compute the Newton step (for inactive variables only) */
      ierr = (*bnk->computestep)(tao, shift, &ksp_reason, &stepType);CHKERRQ(ierr);

      /* Temporarily accept the step and project it into the bounds */
      ierr = VecAXPY(tao->solution, 1.0, tao->stepdirection);CHKERRQ(ierr);
      ierr = TaoBoundSolution(tao->solution, tao->XL,tao->XU, 0.0, &nDiff, tao->solution);CHKERRQ(ierr);

      /* Check if the projection changed the step direction */
      if (nDiff > 0) {
        /* Projection changed the step, so we have to recompute the step and 
           the predicted reduction. Leave the trust radius unchanged. */
        ierr = VecCopy(tao->solution, tao->stepdirection);CHKERRQ(ierr);
        ierr = VecAXPY(tao->stepdirection, -1.0, bnk->Xold);CHKERRQ(ierr);
        ierr = TaoBNKRecomputePred(tao, tao->stepdirection, &prered);CHKERRQ(ierr);
      } else {
        /* Step did not change, so we can just recover the pre-computed prediction */
        ierr = KSPCGGetObjFcn(tao->ksp, &prered);CHKERRQ(ierr);
      }
      prered = -prered;

      /* Compute the actual reduction and update the trust radius */
      ierr = TaoComputeObjective(tao, tao->solution, &bnk->f);CHKERRQ(ierr);
      if (PetscIsInfOrNanReal(bnk->f)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN");
      actred = bnk->fold - bnk->f;
      oldTrust = tao->trust;
      ierr = TaoBNKUpdateTrustRadius(tao, prered, actred, bnk->update_type, stepType, &stepAccepted);CHKERRQ(ierr);

      if (stepAccepted) {
        /* Step is good, evaluate the gradient and flip the need-Hessian switch */
        steplen = 1.0;
        needH = PETSC_TRUE;
        ++bnk->newt;
        ierr = TaoComputeGradient(tao, tao->solution, bnk->unprojected_gradient);CHKERRQ(ierr);
        ierr = TaoBNKEstimateActiveSet(tao, bnk->as_type);CHKERRQ(ierr);
        ierr = VecCopy(bnk->unprojected_gradient, tao->gradient);CHKERRQ(ierr);
        ierr = VecISSet(tao->gradient, bnk->active_idx, 0.0);CHKERRQ(ierr);
        ierr = TaoGradientNorm(tao, tao->gradient, NORM_2, &bnk->gnorm);CHKERRQ(ierr);
      } else {
        /* Step is bad, revert old solution and re-solve with new radius*/
        steplen = 0.0;
        needH = PETSC_FALSE;
        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 change the radius anymore so just terminate */
          tao->reason = TAO_DIVERGED_TR_REDUCTION;
        }
      }

      /*  Check for termination */
      ierr = VecFischer(tao->solution, bnk->unprojected_gradient, tao->XL, tao->XU, bnk->W);CHKERRQ(ierr);
      ierr = VecNorm(bnk->W, NORM_2, &resnorm);CHKERRQ(ierr);
      if (PetscIsInfOrNanReal(resnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN");
      ierr = TaoLogConvergenceHistory(tao, bnk->f, resnorm, 0.0, tao->ksp_its);CHKERRQ(ierr);
      ierr = TaoMonitor(tao, tao->niter, bnk->f, resnorm, 0.0, steplen);CHKERRQ(ierr);
      ierr = (*tao->ops->convergencetest)(tao, tao->cnvP);CHKERRQ(ierr);
    }
  }
  PetscFunctionReturn(0);
}
Пример #3
0
PETSC_EXTERN void PETSC_STDCALL  vecisset_(Vec V,IS S,PetscScalar *c, int *__ierr ){
*__ierr = VecISSet(
	(Vec)PetscToPointer((V) ),
	(IS)PetscToPointer((S) ),*c);
}