static PetscErrorCode TronGradientProjections(Tao tao,TAO_TRON *tron)
{
PetscErrorCode ierr;
PetscInt i;
TaoLineSearchConvergedReason ls_reason;
PetscReal actred=-1.0,actred_max=0.0;
PetscReal f_new;
/*
The gradient and function value passed into and out of this
routine should be current and correct.
The free, active, and binding variables should be already identified
*/
PetscFunctionBegin;
if (tron->Free_Local) {
ierr = ISDestroy(&tron->Free_Local);CHKERRQ(ierr);
}
ierr = VecWhichBetween(tao->XL,tao->solution,tao->XU,&tron->Free_Local);CHKERRQ(ierr);
for (i=0;i<tron->maxgpits;i++){
if ( -actred <= (tron->pg_ftol)*actred_max) break;
tron->gp_iterates++; tron->total_gp_its++;
f_new=tron->f;
ierr = VecCopy(tao->gradient, tao->stepdirection);CHKERRQ(ierr);
ierr = VecScale(tao->stepdirection, -1.0);CHKERRQ(ierr);
ierr = TaoLineSearchSetInitialStepLength(tao->linesearch,tron->pgstepsize);CHKERRQ(ierr);
ierr = TaoLineSearchApply(tao->linesearch, tao->solution, &f_new, tao->gradient, tao->stepdirection,
&tron->pgstepsize, &ls_reason);CHKERRQ(ierr);
ierr = TaoAddLineSearchCounts(tao);CHKERRQ(ierr);
/* Update the iterate */
actred = f_new - tron->f;
actred_max = PetscMax(actred_max,-(f_new - tron->f));
tron->f = f_new;
if (tron->Free_Local) {
ierr = ISDestroy(&tron->Free_Local);CHKERRQ(ierr);
}
ierr = VecWhichBetween(tao->XL,tao->solution,tao->XU,&tron->Free_Local);CHKERRQ(ierr);
}
PetscFunctionReturn(0);
}
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);
}
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);
}
static PetscErrorCode TaoSolve_TRON(Tao tao)
{
TAO_TRON *tron = (TAO_TRON *)tao->data;
PetscErrorCode ierr;
PetscInt its;
TaoConvergedReason reason = TAO_CONTINUE_ITERATING;
TaoLineSearchConvergedReason ls_reason = TAOLINESEARCH_CONTINUE_ITERATING;
PetscReal prered,actred,delta,f,f_new,rhok,gdx,xdiff,stepsize;
PetscFunctionBegin;
tron->pgstepsize=1.0;
tao->trust = tao->trust0;
/* Project the current point onto the feasible set */
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);
ierr = TaoComputeObjectiveAndGradient(tao,tao->solution,&tron->f,tao->gradient);CHKERRQ(ierr);
ierr = ISDestroy(&tron->Free_Local);CHKERRQ(ierr);
ierr = VecWhichBetween(tao->XL,tao->solution,tao->XU,&tron->Free_Local);CHKERRQ(ierr);
/* Project the gradient and calculate the norm */
ierr = VecBoundGradientProjection(tao->gradient,tao->solution, tao->XL, tao->XU, tao->gradient);CHKERRQ(ierr);
ierr = VecNorm(tao->gradient,NORM_2,&tron->gnorm);CHKERRQ(ierr);
if (PetscIsInfOrNanReal(tron->f) || PetscIsInfOrNanReal(tron->gnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf pr NaN");
if (tao->trust <= 0) {
tao->trust=PetscMax(tron->gnorm*tron->gnorm,1.0);
}
tron->stepsize=tao->trust;
ierr = TaoMonitor(tao, tao->niter, tron->f, tron->gnorm, 0.0, tron->stepsize, &reason);CHKERRQ(ierr);
while (reason==TAO_CONTINUE_ITERATING){
tao->ksp_its=0;
ierr = TronGradientProjections(tao,tron);CHKERRQ(ierr);
f=tron->f; delta=tao->trust;
tron->n_free_last = tron->n_free;
ierr = TaoComputeHessian(tao,tao->solution,tao->hessian,tao->hessian_pre);CHKERRQ(ierr);
ierr = ISGetSize(tron->Free_Local, &tron->n_free);CHKERRQ(ierr);
/* If no free variables */
if (tron->n_free == 0) {
actred=0;
ierr = PetscInfo(tao,"No free variables in tron iteration.\n");CHKERRQ(ierr);
ierr = VecNorm(tao->gradient,NORM_2,&tron->gnorm);CHKERRQ(ierr);
ierr = TaoMonitor(tao, tao->niter, tron->f, tron->gnorm, 0.0, delta, &reason);CHKERRQ(ierr);
if (!reason) {
reason = TAO_CONVERGED_STEPTOL;
ierr = TaoSetConvergedReason(tao,reason);CHKERRQ(ierr);
}
break;
}
/* use free_local to mask/submat gradient, hessian, stepdirection */
ierr = TaoVecGetSubVec(tao->gradient,tron->Free_Local,tao->subset_type,0.0,&tron->R);CHKERRQ(ierr);
ierr = TaoVecGetSubVec(tao->gradient,tron->Free_Local,tao->subset_type,0.0,&tron->DXFree);CHKERRQ(ierr);
ierr = VecSet(tron->DXFree,0.0);CHKERRQ(ierr);
ierr = VecScale(tron->R, -1.0);CHKERRQ(ierr);
ierr = TaoMatGetSubMat(tao->hessian, tron->Free_Local, tron->diag, tao->subset_type, &tron->H_sub);CHKERRQ(ierr);
if (tao->hessian == tao->hessian_pre) {
ierr = MatDestroy(&tron->Hpre_sub);CHKERRQ(ierr);
ierr = PetscObjectReference((PetscObject)(tron->H_sub));CHKERRQ(ierr);
tron->Hpre_sub = tron->H_sub;
} else {
ierr = TaoMatGetSubMat(tao->hessian_pre, tron->Free_Local, tron->diag, tao->subset_type,&tron->Hpre_sub);CHKERRQ(ierr);
}
ierr = KSPReset(tao->ksp);CHKERRQ(ierr);
ierr = KSPSetOperators(tao->ksp, tron->H_sub, tron->Hpre_sub);CHKERRQ(ierr);
while (1) {
/* Approximately solve the reduced linear system */
ierr = KSPSTCGSetRadius(tao->ksp,delta);CHKERRQ(ierr);
ierr = KSPSolve(tao->ksp, tron->R, tron->DXFree);CHKERRQ(ierr);
ierr = KSPGetIterationNumber(tao->ksp,&its);CHKERRQ(ierr);
tao->ksp_its+=its;
tao->ksp_tot_its+=its;
ierr = VecSet(tao->stepdirection,0.0);CHKERRQ(ierr);
/* Add dxfree matrix to compute step direction vector */
ierr = VecISAXPY(tao->stepdirection,tron->Free_Local,1.0,tron->DXFree);CHKERRQ(ierr);
if (0) {
PetscReal rhs,stepnorm;
ierr = VecNorm(tron->R,NORM_2,&rhs);CHKERRQ(ierr);
ierr = VecNorm(tron->DXFree,NORM_2,&stepnorm);CHKERRQ(ierr);
ierr = PetscPrintf(PETSC_COMM_WORLD,"|rhs|=%g\t|s|=%g\n",(double)rhs,(double)stepnorm);CHKERRQ(ierr);
}
ierr = VecDot(tao->gradient, tao->stepdirection, &gdx);CHKERRQ(ierr);
ierr = PetscInfo1(tao,"Expected decrease in function value: %14.12e\n",(double)gdx);CHKERRQ(ierr);
ierr = VecCopy(tao->solution, tron->X_New);CHKERRQ(ierr);
ierr = VecCopy(tao->gradient, tron->G_New);CHKERRQ(ierr);
stepsize=1.0;f_new=f;
ierr = TaoLineSearchSetInitialStepLength(tao->linesearch,1.0);CHKERRQ(ierr);
ierr = TaoLineSearchApply(tao->linesearch, tron->X_New, &f_new, tron->G_New, tao->stepdirection,&stepsize,&ls_reason);CHKERRQ(ierr);CHKERRQ(ierr);
ierr = TaoAddLineSearchCounts(tao);CHKERRQ(ierr);
ierr = MatMult(tao->hessian, tao->stepdirection, tron->Work);CHKERRQ(ierr);
ierr = VecAYPX(tron->Work, 0.5, tao->gradient);CHKERRQ(ierr);
ierr = VecDot(tao->stepdirection, tron->Work, &prered);CHKERRQ(ierr);
actred = f_new - f;
if (actred<0) {
rhok=PetscAbs(-actred/prered);
} else {
rhok=0.0;
}
/* Compare actual improvement to the quadratic model */
if (rhok > tron->eta1) { /* Accept the point */
/* d = x_new - x */
ierr = VecCopy(tron->X_New, tao->stepdirection);CHKERRQ(ierr);
ierr = VecAXPY(tao->stepdirection, -1.0, tao->solution);CHKERRQ(ierr);
ierr = VecNorm(tao->stepdirection, NORM_2, &xdiff);CHKERRQ(ierr);
xdiff *= stepsize;
/* Adjust trust region size */
if (rhok < tron->eta2 ){
delta = PetscMin(xdiff,delta)*tron->sigma1;
} else if (rhok > tron->eta4 ){
delta= PetscMin(xdiff,delta)*tron->sigma3;
} else if (rhok > tron->eta3 ){
delta=PetscMin(xdiff,delta)*tron->sigma2;
}
ierr = VecBoundGradientProjection(tron->G_New,tron->X_New, tao->XL, tao->XU, tao->gradient);CHKERRQ(ierr);
if (tron->Free_Local) {
ierr = ISDestroy(&tron->Free_Local);CHKERRQ(ierr);
}
ierr = VecWhichBetween(tao->XL, tron->X_New, tao->XU, &tron->Free_Local);CHKERRQ(ierr);
f=f_new;
ierr = VecNorm(tao->gradient,NORM_2,&tron->gnorm);CHKERRQ(ierr);
ierr = VecCopy(tron->X_New, tao->solution);CHKERRQ(ierr);
ierr = VecCopy(tron->G_New, tao->gradient);CHKERRQ(ierr);
break;
}
else if (delta <= 1e-30) {
break;
}
else {
delta /= 4.0;
}
} /* end linear solve loop */
tron->f=f; tron->actred=actred; tao->trust=delta;
tao->niter++;
ierr = TaoMonitor(tao, tao->niter, tron->f, tron->gnorm, 0.0, delta, &reason);CHKERRQ(ierr);
} /* END MAIN LOOP */
PetscFunctionReturn(0);
}
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);
}
