コード例 #1
0
ファイル: bqnls.c プロジェクト: firedrakeproject/petsc
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 = MatSymBrdnSetDelta(bqnk->B, delta);CHKERRQ(ierr);
  ierr = MatLMVMUpdate(bqnk->B, tao->solution, bnk->unprojected_gradient);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}
コード例 #2
0
ファイル: lmvmmat.c プロジェクト: 00liujj/petsc
extern PetscErrorCode MatLMVMSetPrev(Mat M, Vec x, Vec g)
{
  MatLMVMCtx     *ctx;
  PetscErrorCode ierr;
  PetscBool      same;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(x,VEC_CLASSID,2);
  PetscValidHeaderSpecific(g,VEC_CLASSID,3);
  ierr = PetscObjectTypeCompare((PetscObject)M,MATSHELL,&same);CHKERRQ(ierr);
  if (!same) SETERRQ(PETSC_COMM_SELF,1,"Matrix M is not type MatLMVM");
  ierr = MatShellGetContext(M,(void**)&ctx);CHKERRQ(ierr);
  if (ctx->nupdates == 0) {
    ierr = MatLMVMUpdate(M,x,g);CHKERRQ(ierr);
  } else {
    ierr = VecCopy(x,ctx->Xprev);CHKERRQ(ierr);
    ierr = VecCopy(g,ctx->Gprev);CHKERRQ(ierr);
    /*  TODO scaling specific terms */
  }
  PetscFunctionReturn(0);
}
コード例 #3
0
ファイル: owlqn.c プロジェクト: OpenCMISS-Dependencies/petsc
static PetscErrorCode TaoSolve_OWLQN(Tao tao)
{
  TAO_OWLQN                    *lmP = (TAO_OWLQN *)tao->data;
  PetscReal                    f, fold, gdx, gnorm;
  PetscReal                    step = 1.0;
  PetscReal                    delta;
  PetscErrorCode               ierr;
  PetscInt                     stepType;
  PetscInt                     iter = 0;
  TaoConvergedReason           reason = TAO_CONTINUE_ITERATING;
  TaoLineSearchConvergedReason ls_status = TAOLINESEARCH_CONTINUE_ITERATING;

  PetscFunctionBegin;
  if (tao->XL || tao->XU || tao->ops->computebounds) {
    ierr = PetscPrintf(((PetscObject)tao)->comm,"WARNING: Variable bounds have been set but will be ignored by owlqn algorithm\n");CHKERRQ(ierr);
  }

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

  ierr = VecCopy(tao->gradient, lmP->GV);CHKERRQ(ierr);

  ierr = ComputePseudoGrad_OWLQN(tao->solution,lmP->GV,lmP->lambda);CHKERRQ(ierr);

  ierr = VecNorm(lmP->GV,NORM_2,&gnorm);CHKERRQ(ierr);

  if (PetscIsInfOrNanReal(f) || PetscIsInfOrNanReal(gnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN");

  ierr = TaoMonitor(tao, iter, f, gnorm, 0.0, step, &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(lmP->M,delta);CHKERRQ(ierr);

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

  /* Have not converged; continue with Newton method */
  while (reason == TAO_CONTINUE_ITERATING) {
    /* Compute direction */
    ierr = MatLMVMUpdate(lmP->M,tao->solution,tao->gradient);CHKERRQ(ierr);
    ierr = MatLMVMSolve(lmP->M, lmP->GV, lmP->D);CHKERRQ(ierr);

    ierr = ProjDirect_OWLQN(lmP->D,lmP->GV);CHKERRQ(ierr);

    ++lmP->bfgs;

    /* Check for success (descent direction) */
    ierr = VecDot(lmP->D, lmP->GV , &gdx);CHKERRQ(ierr);
    if ((gdx <= 0.0) || PetscIsInfOrNanReal(gdx)) {

      /* Step is not descent or direction produced not a number
         We can assert bfgsUpdates > 1 in this case because
         the first solve produces the scaled gradient direction,
         which is guaranteed to be descent

         Use steepest descent direction (scaled) */
      ++lmP->grad;

      if (f != 0.0) {
        delta = 2.0 * PetscAbsScalar(f) / (gnorm*gnorm);
      } else {
        delta = 2.0 / (gnorm*gnorm);
      }
      ierr = MatLMVMSetDelta(lmP->M, delta);CHKERRQ(ierr);
      ierr = MatLMVMReset(lmP->M);CHKERRQ(ierr);
      ierr = MatLMVMUpdate(lmP->M, tao->solution, tao->gradient);CHKERRQ(ierr);
      ierr = MatLMVMSolve(lmP->M,lmP->GV, lmP->D);CHKERRQ(ierr);

      ierr = ProjDirect_OWLQN(lmP->D,lmP->GV);CHKERRQ(ierr);

      lmP->bfgs = 1;
      ++lmP->sgrad;
      stepType = OWLQN_SCALED_GRADIENT;
    } else {
      if (1 == lmP->bfgs) {
        /* The first BFGS direction is always the scaled gradient */
        ++lmP->sgrad;
        stepType = OWLQN_SCALED_GRADIENT;
      } else {
        ++lmP->bfgs;
        stepType = OWLQN_BFGS;
      }
    }

    ierr = VecScale(lmP->D, -1.0);CHKERRQ(ierr);

    /* Perform the linesearch */
    fold = f;
    ierr = VecCopy(tao->solution, lmP->Xold);CHKERRQ(ierr);
    ierr = VecCopy(tao->gradient, lmP->Gold);CHKERRQ(ierr);

    ierr = TaoLineSearchApply(tao->linesearch, tao->solution, &f, lmP->GV, lmP->D, &step,&ls_status);CHKERRQ(ierr);
    ierr = TaoAddLineSearchCounts(tao);CHKERRQ(ierr);

    while (((int)ls_status < 0) && (stepType != OWLQN_GRADIENT)) {

      /* Reset factors and use scaled gradient step */
      f = fold;
      ierr = VecCopy(lmP->Xold, tao->solution);CHKERRQ(ierr);
      ierr = VecCopy(lmP->Gold, tao->gradient);CHKERRQ(ierr);
      ierr = VecCopy(tao->gradient, lmP->GV);CHKERRQ(ierr);

      ierr = ComputePseudoGrad_OWLQN(tao->solution,lmP->GV,lmP->lambda);CHKERRQ(ierr);

      switch(stepType) {
      case OWLQN_BFGS:
        /* Failed to obtain acceptable iterate with BFGS step
           Attempt to use the scaled gradient direction */

        if (f != 0.0) {
          delta = 2.0 * PetscAbsScalar(f) / (gnorm*gnorm);
        } else {
          delta = 2.0 / (gnorm*gnorm);
        }
        ierr = MatLMVMSetDelta(lmP->M, delta);CHKERRQ(ierr);
        ierr = MatLMVMReset(lmP->M);CHKERRQ(ierr);
        ierr = MatLMVMUpdate(lmP->M, tao->solution, tao->gradient);CHKERRQ(ierr);
        ierr = MatLMVMSolve(lmP->M, lmP->GV, lmP->D);CHKERRQ(ierr);

        ierr = ProjDirect_OWLQN(lmP->D,lmP->GV);CHKERRQ(ierr);

        lmP->bfgs = 1;
        ++lmP->sgrad;
        stepType = OWLQN_SCALED_GRADIENT;
        break;

      case OWLQN_SCALED_GRADIENT:
        /* The scaled gradient step did not produce a new iterate;
           attempt to use the gradient direction.
           Need to make sure we are not using a different diagonal scaling */
        ierr = MatLMVMSetDelta(lmP->M, 1.0);CHKERRQ(ierr);
        ierr = MatLMVMReset(lmP->M);CHKERRQ(ierr);
        ierr = MatLMVMUpdate(lmP->M, tao->solution, tao->gradient);CHKERRQ(ierr);
        ierr = MatLMVMSolve(lmP->M, lmP->GV, lmP->D);CHKERRQ(ierr);

        ierr = ProjDirect_OWLQN(lmP->D,lmP->GV);CHKERRQ(ierr);

        lmP->bfgs = 1;
        ++lmP->grad;
        stepType = OWLQN_GRADIENT;
        break;
      }
      ierr = VecScale(lmP->D, -1.0);CHKERRQ(ierr);


      /* Perform the linesearch */
      ierr = TaoLineSearchApply(tao->linesearch, tao->solution, &f, lmP->GV, lmP->D, &step, &ls_status);CHKERRQ(ierr);
      ierr = TaoAddLineSearchCounts(tao);CHKERRQ(ierr);
    }

    if ((int)ls_status < 0) {
      /* Failed to find an improving point*/
      f = fold;
      ierr = VecCopy(lmP->Xold, tao->solution);CHKERRQ(ierr);
      ierr = VecCopy(lmP->Gold, tao->gradient);CHKERRQ(ierr);
      ierr = VecCopy(tao->gradient, lmP->GV);CHKERRQ(ierr);
      step = 0.0;
    } else {
      /* a little hack here, because that gv is used to store g */
      ierr = VecCopy(lmP->GV, tao->gradient);CHKERRQ(ierr);
    }

    ierr = ComputePseudoGrad_OWLQN(tao->solution,lmP->GV,lmP->lambda);CHKERRQ(ierr);

    /* Check for termination */

    ierr = VecNorm(lmP->GV,NORM_2,&gnorm);CHKERRQ(ierr);

    iter++;
    ierr = TaoMonitor(tao,iter,f,gnorm,0.0,step,&reason);CHKERRQ(ierr);

    if ((int)ls_status < 0) break;
  }
  PetscFunctionReturn(0);
}
コード例 #4
0
ファイル: ntr.c プロジェクト: PeiLiu90/petsc
static PetscErrorCode TaoSolve_NTR(Tao tao)
{
  TAO_NTR            *tr = (TAO_NTR *)tao->data;
  PC                 pc;
  KSPConvergedReason ksp_reason;
  TaoConvergedReason reason;
  PetscReal          fmin, ftrial, prered, actred, kappa, sigma, beta;
  PetscReal          tau, tau_1, tau_2, tau_max, tau_min, max_radius;
  PetscReal          f, gnorm;

  PetscReal          delta;
  PetscReal          norm_d;
  PetscErrorCode     ierr;
  PetscInt           iter = 0;
  PetscInt           bfgsUpdates = 0;
  PetscInt           needH;

  PetscInt           i_max = 5;
  PetscInt           j_max = 1;
  PetscInt           i, j, N, n, its;

  PetscFunctionBegin;
  if (tao->XL || tao->XU || tao->ops->computebounds) {
    ierr = PetscPrintf(((PetscObject)tao)->comm,"WARNING: Variable bounds have been set but will be ignored by ntr algorithm\n");CHKERRQ(ierr);
  }

  tao->trust = tao->trust0;

  /* Modify the radius if it is too large or small */
  tao->trust = PetscMax(tao->trust, tr->min_radius);
  tao->trust = PetscMin(tao->trust, tr->max_radius);


  if (NTR_PC_BFGS == tr->pc_type && !tr->M) {
    ierr = VecGetLocalSize(tao->solution,&n);CHKERRQ(ierr);
    ierr = VecGetSize(tao->solution,&N);CHKERRQ(ierr);
    ierr = MatCreateLMVM(((PetscObject)tao)->comm,n,N,&tr->M);CHKERRQ(ierr);
    ierr = MatLMVMAllocateVectors(tr->M,tao->solution);CHKERRQ(ierr);
  }

  /* Check convergence criteria */
  ierr = TaoComputeObjectiveAndGradient(tao, tao->solution, &f, 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 or NaN");
  needH = 1;

  ierr = TaoMonitor(tao, iter, f, gnorm, 0.0, 1.0, &reason);CHKERRQ(ierr);
  if (reason != TAO_CONTINUE_ITERATING) PetscFunctionReturn(0);

  /* Create vectors for the limited memory preconditioner */
  if ((NTR_PC_BFGS == tr->pc_type) &&
      (BFGS_SCALE_BFGS != tr->bfgs_scale_type)) {
    if (!tr->Diag) {
        ierr = VecDuplicate(tao->solution, &tr->Diag);CHKERRQ(ierr);
    }
  }

  switch(tr->ksp_type) {
  case NTR_KSP_NASH:
    ierr = KSPSetType(tao->ksp, KSPNASH);CHKERRQ(ierr);
    if (tao->ksp->ops->setfromoptions) {
      (*tao->ksp->ops->setfromoptions)(tao->ksp);
    }
    break;

  case NTR_KSP_STCG:
    ierr = KSPSetType(tao->ksp, KSPSTCG);CHKERRQ(ierr);
    if (tao->ksp->ops->setfromoptions) {
      (*tao->ksp->ops->setfromoptions)(tao->ksp);
    }
    break;

  default:
    ierr = KSPSetType(tao->ksp, KSPGLTR);CHKERRQ(ierr);
    if (tao->ksp->ops->setfromoptions) {
      (*tao->ksp->ops->setfromoptions)(tao->ksp);
    }
    break;
  }

  /*  Modify the preconditioner to use the bfgs approximation */
  ierr = KSPGetPC(tao->ksp, &pc);CHKERRQ(ierr);
  switch(tr->pc_type) {
  case NTR_PC_NONE:
    ierr = PCSetType(pc, PCNONE);CHKERRQ(ierr);
    if (pc->ops->setfromoptions) {
      (*pc->ops->setfromoptions)(pc);
    }
    break;

  case NTR_PC_AHESS:
    ierr = PCSetType(pc, PCJACOBI);CHKERRQ(ierr);
    if (pc->ops->setfromoptions) {
      (*pc->ops->setfromoptions)(pc);
    }
    ierr = PCJacobiSetUseAbs(pc);CHKERRQ(ierr);
    break;

  case NTR_PC_BFGS:
    ierr = PCSetType(pc, PCSHELL);CHKERRQ(ierr);
    if (pc->ops->setfromoptions) {
      (*pc->ops->setfromoptions)(pc);
    }
    ierr = PCShellSetName(pc, "bfgs");CHKERRQ(ierr);
    ierr = PCShellSetContext(pc, tr->M);CHKERRQ(ierr);
    ierr = PCShellSetApply(pc, MatLMVMSolveShell);CHKERRQ(ierr);
    break;

  default:
    /*  Use the pc method set by pc_type */
    break;
  }

  /*  Initialize trust-region radius */
  switch(tr->init_type) {
  case NTR_INIT_CONSTANT:
    /*  Use the initial radius specified */
    break;

  case NTR_INIT_INTERPOLATION:
    /*  Use the initial radius specified */
    max_radius = 0.0;

    for (j = 0; j < j_max; ++j) {
      fmin = f;
      sigma = 0.0;

      if (needH) {
        ierr = TaoComputeHessian(tao,tao->solution,tao->hessian,tao->hessian_pre);CHKERRQ(ierr);
        needH = 0;
      }

      for (i = 0; i < i_max; ++i) {

        ierr = VecCopy(tao->solution, tr->W);CHKERRQ(ierr);
        ierr = VecAXPY(tr->W, -tao->trust/gnorm, tao->gradient);CHKERRQ(ierr);
        ierr = TaoComputeObjective(tao, tr->W, &ftrial);CHKERRQ(ierr);

        if (PetscIsInfOrNanReal(ftrial)) {
          tau = tr->gamma1_i;
        }
        else {
          if (ftrial < fmin) {
            fmin = ftrial;
            sigma = -tao->trust / gnorm;
          }

          ierr = MatMult(tao->hessian, tao->gradient, tao->stepdirection);CHKERRQ(ierr);
          ierr = VecDot(tao->gradient, tao->stepdirection, &prered);CHKERRQ(ierr);

          prered = tao->trust * (gnorm - 0.5 * tao->trust * prered / (gnorm * gnorm));
          actred = f - ftrial;
          if ((PetscAbsScalar(actred) <= tr->epsilon) &&
              (PetscAbsScalar(prered) <= tr->epsilon)) {
            kappa = 1.0;
          }
          else {
            kappa = actred / prered;
          }

          tau_1 = tr->theta_i * gnorm * tao->trust / (tr->theta_i * gnorm * tao->trust + (1.0 - tr->theta_i) * prered - actred);
          tau_2 = tr->theta_i * gnorm * tao->trust / (tr->theta_i * gnorm * tao->trust - (1.0 + tr->theta_i) * prered + actred);
          tau_min = PetscMin(tau_1, tau_2);
          tau_max = PetscMax(tau_1, tau_2);

          if (PetscAbsScalar(kappa - 1.0) <= tr->mu1_i) {
            /*  Great agreement */
            max_radius = PetscMax(max_radius, tao->trust);

            if (tau_max < 1.0) {
              tau = tr->gamma3_i;
            }
            else if (tau_max > tr->gamma4_i) {
              tau = tr->gamma4_i;
            }
            else {
              tau = tau_max;
            }
          }
          else if (PetscAbsScalar(kappa - 1.0) <= tr->mu2_i) {
            /*  Good agreement */
            max_radius = PetscMax(max_radius, tao->trust);

            if (tau_max < tr->gamma2_i) {
              tau = tr->gamma2_i;
            }
            else if (tau_max > tr->gamma3_i) {
              tau = tr->gamma3_i;
            }
            else {
              tau = tau_max;
            }
          }
          else {
            /*  Not good agreement */
            if (tau_min > 1.0) {
              tau = tr->gamma2_i;
            }
            else if (tau_max < tr->gamma1_i) {
              tau = tr->gamma1_i;
            }
            else if ((tau_min < tr->gamma1_i) && (tau_max >= 1.0)) {
              tau = tr->gamma1_i;
            }
            else if ((tau_1 >= tr->gamma1_i) && (tau_1 < 1.0) &&
                     ((tau_2 < tr->gamma1_i) || (tau_2 >= 1.0))) {
              tau = tau_1;
            }
            else if ((tau_2 >= tr->gamma1_i) && (tau_2 < 1.0) &&
                     ((tau_1 < tr->gamma1_i) || (tau_2 >= 1.0))) {
              tau = tau_2;
            }
            else {
              tau = tau_max;
            }
          }
        }
        tao->trust = tau * tao->trust;
      }

      if (fmin < f) {
        f = fmin;
        ierr = VecAXPY(tao->solution, sigma, tao->gradient);CHKERRQ(ierr);
        ierr = TaoComputeGradient(tao,tao->solution, 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 or NaN");
        needH = 1;

        ierr = TaoMonitor(tao, iter, f, gnorm, 0.0, 1.0, &reason);CHKERRQ(ierr);
        if (reason != TAO_CONTINUE_ITERATING) {
          PetscFunctionReturn(0);
        }
      }
    }
    tao->trust = PetscMax(tao->trust, max_radius);

    /*  Modify the radius if it is too large or small */
    tao->trust = PetscMax(tao->trust, tr->min_radius);
    tao->trust = PetscMin(tao->trust, tr->max_radius);
    break;

  default:
    /*  Norm of the first direction will initialize radius */
    tao->trust = 0.0;
    break;
  }

  /* Set initial scaling for the BFGS preconditioner
     This step is done after computing the initial trust-region radius
     since the function value may have decreased */
  if (NTR_PC_BFGS == tr->pc_type) {
    if (f != 0.0) {
      delta = 2.0 * PetscAbsScalar(f) / (gnorm*gnorm);
    }
    else {
      delta = 2.0 / (gnorm*gnorm);
    }
    ierr = MatLMVMSetDelta(tr->M,delta);CHKERRQ(ierr);
  }

  /* Have not converged; continue with Newton method */
  while (reason == TAO_CONTINUE_ITERATING) {
    ++iter;
    tao->ksp_its=0;
    /* Compute the Hessian */
    if (needH) {
      ierr = TaoComputeHessian(tao,tao->solution,tao->hessian,tao->hessian_pre);CHKERRQ(ierr);
      needH = 0;
    }

    if (NTR_PC_BFGS == tr->pc_type) {
      if (BFGS_SCALE_AHESS == tr->bfgs_scale_type) {
        /* Obtain diagonal for the bfgs preconditioner */
        ierr = MatGetDiagonal(tao->hessian, tr->Diag);CHKERRQ(ierr);
        ierr = VecAbs(tr->Diag);CHKERRQ(ierr);
        ierr = VecReciprocal(tr->Diag);CHKERRQ(ierr);
        ierr = MatLMVMSetScale(tr->M,tr->Diag);CHKERRQ(ierr);
      }

      /* Update the limited memory preconditioner */
      ierr = MatLMVMUpdate(tr->M, tao->solution, tao->gradient);CHKERRQ(ierr);
      ++bfgsUpdates;
    }

    while (reason == TAO_CONTINUE_ITERATING) {
      ierr = KSPSetOperators(tao->ksp, tao->hessian, tao->hessian_pre);CHKERRQ(ierr);

      /* Solve the trust region subproblem */
      if (NTR_KSP_NASH == tr->ksp_type) {
        ierr = KSPNASHSetRadius(tao->ksp,tao->trust);CHKERRQ(ierr);
        ierr = KSPSolve(tao->ksp, tao->gradient, tao->stepdirection);CHKERRQ(ierr);
        ierr = KSPGetIterationNumber(tao->ksp,&its);CHKERRQ(ierr);
        tao->ksp_its+=its;
        tao->ksp_tot_its+=its;
        ierr = KSPNASHGetNormD(tao->ksp, &norm_d);CHKERRQ(ierr);
      } else if (NTR_KSP_STCG == tr->ksp_type) {
        ierr = KSPSTCGSetRadius(tao->ksp,tao->trust);CHKERRQ(ierr);
        ierr = KSPSolve(tao->ksp, tao->gradient, tao->stepdirection);CHKERRQ(ierr);
        ierr = KSPGetIterationNumber(tao->ksp,&its);CHKERRQ(ierr);
        tao->ksp_its+=its;
        tao->ksp_tot_its+=its;
        ierr = KSPSTCGGetNormD(tao->ksp, &norm_d);CHKERRQ(ierr);
      } else { /* NTR_KSP_GLTR */
        ierr = KSPGLTRSetRadius(tao->ksp,tao->trust);CHKERRQ(ierr);
        ierr = KSPSolve(tao->ksp, tao->gradient, tao->stepdirection);CHKERRQ(ierr);
        ierr = KSPGetIterationNumber(tao->ksp,&its);CHKERRQ(ierr);
        tao->ksp_its+=its;
        tao->ksp_tot_its+=its;
        ierr = KSPGLTRGetNormD(tao->ksp, &norm_d);CHKERRQ(ierr);
      }

      if (0.0 == tao->trust) {
        /* Radius was uninitialized; use the norm of the direction */
        if (norm_d > 0.0) {
          tao->trust = norm_d;

          /* Modify the radius if it is too large or small */
          tao->trust = PetscMax(tao->trust, tr->min_radius);
          tao->trust = PetscMin(tao->trust, tr->max_radius);
        }
        else {
          /* The direction was bad; set radius to default value and re-solve
             the trust-region subproblem to get a direction */
          tao->trust = tao->trust0;

          /* Modify the radius if it is too large or small */
          tao->trust = PetscMax(tao->trust, tr->min_radius);
          tao->trust = PetscMin(tao->trust, tr->max_radius);

          if (NTR_KSP_NASH == tr->ksp_type) {
            ierr = KSPNASHSetRadius(tao->ksp,tao->trust);CHKERRQ(ierr);
            ierr = KSPSolve(tao->ksp, tao->gradient, tao->stepdirection);CHKERRQ(ierr);
            ierr = KSPGetIterationNumber(tao->ksp,&its);CHKERRQ(ierr);
            tao->ksp_its+=its;
            tao->ksp_tot_its+=its;
            ierr = KSPNASHGetNormD(tao->ksp, &norm_d);CHKERRQ(ierr);
          } else if (NTR_KSP_STCG == tr->ksp_type) {
            ierr = KSPSTCGSetRadius(tao->ksp,tao->trust);CHKERRQ(ierr);
            ierr = KSPSolve(tao->ksp, tao->gradient, tao->stepdirection);CHKERRQ(ierr);
            ierr = KSPGetIterationNumber(tao->ksp,&its);CHKERRQ(ierr);
            tao->ksp_its+=its;
            tao->ksp_tot_its+=its;
            ierr = KSPSTCGGetNormD(tao->ksp, &norm_d);CHKERRQ(ierr);
          } else { /* NTR_KSP_GLTR */
            ierr = KSPGLTRSetRadius(tao->ksp,tao->trust);CHKERRQ(ierr);
            ierr = KSPSolve(tao->ksp, tao->gradient, tao->stepdirection);CHKERRQ(ierr);
            ierr = KSPGetIterationNumber(tao->ksp,&its);CHKERRQ(ierr);
            tao->ksp_its+=its;
            tao->ksp_tot_its+=its;
            ierr = KSPGLTRGetNormD(tao->ksp, &norm_d);CHKERRQ(ierr);
          }

          if (norm_d == 0.0) SETERRQ(PETSC_COMM_SELF,1, "Initial direction zero");
        }
      }
      ierr = VecScale(tao->stepdirection, -1.0);CHKERRQ(ierr);
      ierr = KSPGetConvergedReason(tao->ksp, &ksp_reason);CHKERRQ(ierr);
      if ((KSP_DIVERGED_INDEFINITE_PC == ksp_reason) &&
          (NTR_PC_BFGS == tr->pc_type) && (bfgsUpdates > 1)) {
        /* Preconditioner is numerically indefinite; reset the
           approximate if using BFGS preconditioning. */

        if (f != 0.0) {
          delta = 2.0 * PetscAbsScalar(f) / (gnorm*gnorm);
        }
        else {
          delta = 2.0 / (gnorm*gnorm);
        }
        ierr = MatLMVMSetDelta(tr->M, delta);CHKERRQ(ierr);
        ierr = MatLMVMReset(tr->M);CHKERRQ(ierr);
        ierr = MatLMVMUpdate(tr->M, tao->solution, tao->gradient);CHKERRQ(ierr);
        bfgsUpdates = 1;
      }

      if (NTR_UPDATE_REDUCTION == tr->update_type) {
        /* Get predicted reduction */
        if (NTR_KSP_NASH == tr->ksp_type) {
          ierr = KSPNASHGetObjFcn(tao->ksp,&prered);CHKERRQ(ierr);
        } else if (NTR_KSP_STCG == tr->ksp_type) {
          ierr = KSPSTCGGetObjFcn(tao->ksp,&prered);CHKERRQ(ierr);
        } else { /* gltr */
          ierr = KSPGLTRGetObjFcn(tao->ksp,&prered);CHKERRQ(ierr);
        }

        if (prered >= 0.0) {
          /* The predicted reduction has the wrong sign.  This cannot
             happen in infinite precision arithmetic.  Step should
             be rejected! */
          tao->trust = tr->alpha1 * PetscMin(tao->trust, norm_d);
        }
        else {
          /* Compute trial step and function value */
          ierr = VecCopy(tao->solution,tr->W);CHKERRQ(ierr);
          ierr = VecAXPY(tr->W, 1.0, tao->stepdirection);CHKERRQ(ierr);
          ierr = TaoComputeObjective(tao, tr->W, &ftrial);CHKERRQ(ierr);

          if (PetscIsInfOrNanReal(ftrial)) {
            tao->trust = tr->alpha1 * PetscMin(tao->trust, norm_d);
          } else {
            /* Compute and actual reduction */
            actred = f - ftrial;
            prered = -prered;
            if ((PetscAbsScalar(actred) <= tr->epsilon) &&
                (PetscAbsScalar(prered) <= tr->epsilon)) {
              kappa = 1.0;
            }
            else {
              kappa = actred / prered;
            }

            /* Accept or reject the step and update radius */
            if (kappa < tr->eta1) {
              /* Reject the step */
              tao->trust = tr->alpha1 * PetscMin(tao->trust, norm_d);
            }
            else {
              /* Accept the step */
              if (kappa < tr->eta2) {
                /* Marginal bad step */
                tao->trust = tr->alpha2 * PetscMin(tao->trust, norm_d);
              }
              else if (kappa < tr->eta3) {
                /* Reasonable step */
                tao->trust = tr->alpha3 * tao->trust;
              }
              else if (kappa < tr->eta4) {
                /* Good step */
                tao->trust = PetscMax(tr->alpha4 * norm_d, tao->trust);
              }
              else {
                /* Very good step */
                tao->trust = PetscMax(tr->alpha5 * norm_d, tao->trust);
              }
              break;
            }
          }
        }
      }
      else {
        /* Get predicted reduction */
        if (NTR_KSP_NASH == tr->ksp_type) {
          ierr = KSPNASHGetObjFcn(tao->ksp,&prered);CHKERRQ(ierr);
        } else if (NTR_KSP_STCG == tr->ksp_type) {
          ierr = KSPSTCGGetObjFcn(tao->ksp,&prered);CHKERRQ(ierr);
        } else { /* gltr */
          ierr = KSPGLTRGetObjFcn(tao->ksp,&prered);CHKERRQ(ierr);
        }

        if (prered >= 0.0) {
          /* The predicted reduction has the wrong sign.  This cannot
             happen in infinite precision arithmetic.  Step should
             be rejected! */
          tao->trust = tr->gamma1 * PetscMin(tao->trust, norm_d);
        }
        else {
          ierr = VecCopy(tao->solution, tr->W);CHKERRQ(ierr);
          ierr = VecAXPY(tr->W, 1.0, tao->stepdirection);CHKERRQ(ierr);
          ierr = TaoComputeObjective(tao, tr->W, &ftrial);CHKERRQ(ierr);
          if (PetscIsInfOrNanReal(ftrial)) {
            tao->trust = tr->gamma1 * PetscMin(tao->trust, norm_d);
          }
          else {
            ierr = VecDot(tao->gradient, tao->stepdirection, &beta);CHKERRQ(ierr);
            actred = f - ftrial;
            prered = -prered;
            if ((PetscAbsScalar(actred) <= tr->epsilon) &&
                (PetscAbsScalar(prered) <= tr->epsilon)) {
              kappa = 1.0;
            }
            else {
              kappa = actred / prered;
            }

            tau_1 = tr->theta * beta / (tr->theta * beta - (1.0 - tr->theta) * prered + actred);
            tau_2 = tr->theta * beta / (tr->theta * beta + (1.0 + tr->theta) * prered - actred);
            tau_min = PetscMin(tau_1, tau_2);
            tau_max = PetscMax(tau_1, tau_2);

            if (kappa >= 1.0 - tr->mu1) {
              /* Great agreement; accept step and update radius */
              if (tau_max < 1.0) {
                tao->trust = PetscMax(tao->trust, tr->gamma3 * norm_d);
              }
              else if (tau_max > tr->gamma4) {
                tao->trust = PetscMax(tao->trust, tr->gamma4 * norm_d);
              }
              else {
                tao->trust = PetscMax(tao->trust, tau_max * norm_d);
              }
              break;
            }
            else if (kappa >= 1.0 - tr->mu2) {
              /* Good agreement */

              if (tau_max < tr->gamma2) {
                tao->trust = tr->gamma2 * PetscMin(tao->trust, norm_d);
              }
              else if (tau_max > tr->gamma3) {
                tao->trust = PetscMax(tao->trust, tr->gamma3 * norm_d);
              }
              else if (tau_max < 1.0) {
                tao->trust = tau_max * PetscMin(tao->trust, norm_d);
              }
              else {
                tao->trust = PetscMax(tao->trust, tau_max * norm_d);
              }
              break;
            }
            else {
              /* Not good agreement */
              if (tau_min > 1.0) {
                tao->trust = tr->gamma2 * PetscMin(tao->trust, norm_d);
              }
              else if (tau_max < tr->gamma1) {
                tao->trust = tr->gamma1 * PetscMin(tao->trust, norm_d);
              }
              else if ((tau_min < tr->gamma1) && (tau_max >= 1.0)) {
                tao->trust = tr->gamma1 * PetscMin(tao->trust, norm_d);
              }
              else if ((tau_1 >= tr->gamma1) && (tau_1 < 1.0) &&
                       ((tau_2 < tr->gamma1) || (tau_2 >= 1.0))) {
                tao->trust = tau_1 * PetscMin(tao->trust, norm_d);
              }
              else if ((tau_2 >= tr->gamma1) && (tau_2 < 1.0) &&
                       ((tau_1 < tr->gamma1) || (tau_2 >= 1.0))) {
                tao->trust = tau_2 * PetscMin(tao->trust, norm_d);
              }
              else {
                tao->trust = tau_max * PetscMin(tao->trust, norm_d);
              }
            }
          }
        }
      }

      /* The step computed was not good and the radius was decreased.
         Monitor the radius to terminate. */
      ierr = TaoMonitor(tao, iter, f, gnorm, 0.0, tao->trust, &reason);CHKERRQ(ierr);
    }

    /* The radius may have been increased; modify if it is too large */
    tao->trust = PetscMin(tao->trust, tr->max_radius);

    if (reason == TAO_CONTINUE_ITERATING) {
      ierr = VecCopy(tr->W, tao->solution);CHKERRQ(ierr);
      f = ftrial;
      ierr = TaoComputeGradient(tao, tao->solution, tao->gradient);
      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 or NaN");
      needH = 1;
      ierr = TaoMonitor(tao, iter, f, gnorm, 0.0, tao->trust, &reason);CHKERRQ(ierr);
    }
  }
  PetscFunctionReturn(0);
}
コード例 #5
0
ファイル: sqpcon.c プロジェクト: 00liujj/petsc
static PetscErrorCode TaoSolve_SQPCON(Tao tao)
{
  TAO_SQPCON                   *sqpconP = (TAO_SQPCON*)tao->data;
  PetscInt                     iter=0;
  TaoConvergedReason           reason = TAO_CONTINUE_ITERATING;
  TaoLineSearchConvergedReason ls_reason = TAOLINESEARCH_CONTINUE_ITERATING;
  PetscReal                    step=1.0,f,fm, fold;
  PetscReal                    cnorm, mnorm;
  PetscBool                    use_update=PETSC_TRUE; /*  don't update Q if line search failed */
  PetscErrorCode               ierr;

  PetscFunctionBegin;
  /* Scatter to U,V */
  ierr = VecScatterBegin(sqpconP->state_scatter, tao->solution, sqpconP->U, INSERT_VALUES, SCATTER_FORWARD);CHKERRQ(ierr);
  ierr = VecScatterEnd(sqpconP->state_scatter, tao->solution, sqpconP->U, INSERT_VALUES, SCATTER_FORWARD);CHKERRQ(ierr);
  ierr = VecScatterBegin(sqpconP->design_scatter, tao->solution, sqpconP->V, INSERT_VALUES, SCATTER_FORWARD);CHKERRQ(ierr);
  ierr = VecScatterEnd(sqpconP->design_scatter, tao->solution, sqpconP->V, INSERT_VALUES, SCATTER_FORWARD);CHKERRQ(ierr);

  /* Evaluate Function, Gradient, Constraints, and Jacobian */
  ierr = TaoComputeObjectiveAndGradient(tao,tao->solution,&f,tao->gradient);CHKERRQ(ierr);
  ierr = TaoComputeConstraints(tao,tao->solution, tao->constraints);CHKERRQ(ierr);
  ierr = TaoComputeJacobianState(tao,tao->solution, &tao->jacobian_state, &tao->jacobian_state_pre, &tao->jacobian_state_inv, &sqpconP->statematflag);CHKERRQ(ierr);
  ierr = TaoComputeJacobianDesign(tao,tao->solution, &tao->jacobian_design, &tao->jacobian_design_pre, &sqpconP->statematflag);CHKERRQ(ierr);

  /* Scatter gradient to GU,GV */
  ierr = VecScatterBegin(sqpconP->state_scatter, tao->gradient, sqpconP->GU, INSERT_VALUES, SCATTER_FORWARD);CHKERRQ(ierr);
  ierr = VecScatterEnd(sqpconP->state_scatter, tao->gradient, sqpconP->GU, INSERT_VALUES, SCATTER_FORWARD);CHKERRQ(ierr);
  ierr = VecScatterBegin(sqpconP->design_scatter, tao->gradient, sqpconP->GV, INSERT_VALUES, SCATTER_FORWARD);CHKERRQ(ierr);
  ierr = VecScatterEnd(sqpconP->design_scatter, tao->gradient, sqpconP->GV, INSERT_VALUES, SCATTER_FORWARD);CHKERRQ(ierr);
  ierr = VecNorm(tao->gradient, NORM_2, &mnorm);CHKERRQ(ierr);

  /* Evaluate constraint norm */
  ierr = VecNorm(tao->constraints, NORM_2, &cnorm);CHKERRQ(ierr);

  /* Monitor convergence */
  ierr = TaoMonitor(tao, iter,f,mnorm,cnorm,step,&reason);CHKERRQ(ierr);

  while (reason == TAO_CONTINUE_ITERATING) {
    /* Solve tbar = -A\t (t is constraints vector) */
    ierr = MatMult(tao->jacobian_state_inv, tao->constraints, sqpconP->Tbar);CHKERRQ(ierr);
    ierr = VecScale(sqpconP->Tbar, -1.0);CHKERRQ(ierr);

    /* aqwac =  A'\(Q*Tbar + c) */
    if (iter > 0) {
      ierr = MatMult(sqpconP->Q,sqpconP->Tbar,sqpconP->WV);CHKERRQ(ierr);
    } else {
      ierr = VecCopy(sqpconP->Tbar, sqpconP->WV);CHKERRQ(ierr);
    }
    ierr = VecAXPY(sqpconP->WV,1.0,sqpconP->GU);CHKERRQ(ierr);

    ierr = MatMultTranspose(tao->jacobian_state_inv, sqpconP->WV, sqpconP->aqwac);CHKERRQ(ierr);

    /* Reduced Gradient dbar = d -  B^t * aqwac */
    ierr = MatMultTranspose(tao->jacobian_design,sqpconP->aqwac, sqpconP->dbar);CHKERRQ(ierr);
    ierr = VecScale(sqpconP->dbar, -1.0);CHKERRQ(ierr);
    ierr = VecAXPY(sqpconP->dbar,1.0,sqpconP->GV);CHKERRQ(ierr);

    /* update reduced hessian */
    ierr = MatLMVMUpdate(sqpconP->R, sqpconP->V, sqpconP->dbar);CHKERRQ(ierr);

    /* Solve R*dv = -dbar using approx. hessian */
    ierr = MatLMVMSolve(sqpconP->R, sqpconP->dbar, sqpconP->DV);CHKERRQ(ierr);
    ierr = VecScale(sqpconP->DV, -1.0);CHKERRQ(ierr);

    /* Backsolve for u =  A\(g - B*dv)  = tbar - A\(B*dv)*/
    ierr = MatMult(tao->jacobian_design, sqpconP->DV, sqpconP->WL);CHKERRQ(ierr);
    ierr = MatMult(tao->jacobian_state_inv, sqpconP->WL, sqpconP->DU);CHKERRQ(ierr);
    ierr = VecScale(sqpconP->DU, -1.0);CHKERRQ(ierr);
    ierr = VecAXPY(sqpconP->DU, 1.0, sqpconP->Tbar);CHKERRQ(ierr);

    /* Assemble Big D */
    ierr = VecScatterBegin(sqpconP->state_scatter, sqpconP->DU, tao->stepdirection, INSERT_VALUES, SCATTER_REVERSE);CHKERRQ(ierr);
    ierr = VecScatterEnd(sqpconP->state_scatter, sqpconP->DU, tao->stepdirection, INSERT_VALUES, SCATTER_REVERSE);CHKERRQ(ierr);
    ierr = VecScatterBegin(sqpconP->design_scatter, sqpconP->DV, tao->stepdirection, INSERT_VALUES, SCATTER_REVERSE);CHKERRQ(ierr);
    ierr = VecScatterEnd(sqpconP->design_scatter, sqpconP->DV, tao->stepdirection, INSERT_VALUES, SCATTER_REVERSE);CHKERRQ(ierr);

    /* Perform Line Search */
    ierr = VecCopy(tao->solution, sqpconP->Xold);CHKERRQ(ierr);
    ierr = VecCopy(tao->gradient, sqpconP->Gold);CHKERRQ(ierr);
    fold = f;
    ierr = TaoLineSearchComputeObjectiveAndGradient(tao->linesearch,tao->solution,&fm,sqpconP->GL);CHKERRQ(ierr);
    ierr = TaoLineSearchSetInitialStepLength(tao->linesearch,1.0);
    ierr = TaoLineSearchApply(tao->linesearch, tao->solution, &fm, sqpconP->GL, tao->stepdirection,&step, &ls_reason);CHKERRQ(ierr);
    ierr = TaoAddLineSearchCounts(tao);CHKERRQ(ierr);
    if (ls_reason < 0) {
      ierr = VecCopy(sqpconP->Xold, tao->solution);
      ierr = VecCopy(sqpconP->Gold, tao->gradient);
      f = fold;
      ierr = VecAXPY(tao->solution, 1.0, tao->stepdirection);CHKERRQ(ierr);
      ierr = PetscInfo(tao,"Line Search Failed, using full step.");CHKERRQ(ierr);
      use_update=PETSC_FALSE;
    } else {
      use_update = PETSC_TRUE;
    }

    /* Scatter X to U,V */
    ierr = VecScatterBegin(sqpconP->state_scatter, tao->solution, sqpconP->U, INSERT_VALUES, SCATTER_FORWARD);CHKERRQ(ierr);
    ierr = VecScatterEnd(sqpconP->state_scatter, tao->solution, sqpconP->U, INSERT_VALUES, SCATTER_FORWARD);CHKERRQ(ierr);
    ierr = VecScatterBegin(sqpconP->design_scatter, tao->solution, sqpconP->V, INSERT_VALUES, SCATTER_FORWARD);CHKERRQ(ierr);
    ierr = VecScatterEnd(sqpconP->design_scatter, tao->solution, sqpconP->V, INSERT_VALUES, SCATTER_FORWARD);CHKERRQ(ierr);


    /* Evaluate Function, Gradient, Constraints, and Jacobian */
    ierr = TaoComputeObjectiveAndGradient(tao,tao->solution,&f,tao->gradient);CHKERRQ(ierr);
    ierr = TaoComputeConstraints(tao,tao->solution, tao->constraints);CHKERRQ(ierr);
    ierr = TaoComputeJacobianState(tao,tao->solution, &tao->jacobian_state, &tao->jacobian_state_pre, &tao->jacobian_state_inv, &sqpconP->statematflag);CHKERRQ(ierr);
    ierr = TaoComputeJacobianDesign(tao,tao->solution, &tao->jacobian_design, &tao->jacobian_design_pre, &sqpconP->designmatflag);CHKERRQ(ierr);

    /* Scatter gradient to GU,GV */
    ierr = VecScatterBegin(sqpconP->state_scatter, tao->gradient, sqpconP->GU, INSERT_VALUES, SCATTER_FORWARD);CHKERRQ(ierr);
    ierr = VecScatterEnd(sqpconP->state_scatter, tao->gradient, sqpconP->GU, INSERT_VALUES, SCATTER_FORWARD);CHKERRQ(ierr);
    ierr = VecScatterBegin(sqpconP->design_scatter, tao->gradient, sqpconP->GV, INSERT_VALUES, SCATTER_FORWARD);CHKERRQ(ierr);
    ierr = VecScatterEnd(sqpconP->design_scatter, tao->gradient, sqpconP->GV, INSERT_VALUES, SCATTER_FORWARD);CHKERRQ(ierr);

    /* Update approx to hessian of the Lagrangian wrt state (Q)
          with u_k+1, gu_k+1 */
    if (use_update) {
      ierr = MatApproxUpdate(sqpconP->Q,sqpconP->U,sqpconP->GU);CHKERRQ(ierr);
    }
    ierr = VecNorm(sqpconP->GL, NORM_2, &mnorm);CHKERRQ(ierr);

    /* Evaluate constraint norm */
    ierr = VecNorm(tao->constraints, NORM_2, &cnorm);CHKERRQ(ierr);

    /* Monitor convergence */
    iter++;
    ierr = TaoMonitor(tao, iter,f,mnorm,cnorm,step,&reason);CHKERRQ(ierr);
  }
  PetscFunctionReturn(0);
}
コード例 #6
0
ファイル: blmvm.c プロジェクト: masa-ito/PETScToPoisson
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 = 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 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 = 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 = TaoMonitor(tao, tao->niter, f, gnorm, 0.0, stepsize, &reason);CHKERRQ(ierr);
  }
  PetscFunctionReturn(0);
}