static PetscErrorCode TaoLineSearchApply_GPCG(TaoLineSearch ls, Vec x, PetscReal *f, Vec g, Vec s)
{
TaoLineSearch_GPCG *neP = (TaoLineSearch_GPCG *)ls->data;
PetscErrorCode ierr;
PetscInt i;
PetscBool g_computed=PETSC_FALSE; /* to prevent extra gradient computation */
PetscReal d1,finit,actred,prered,rho, gdx;
PetscFunctionBegin;
/* ls->stepmin - lower bound for step */
/* ls->stepmax - upper bound for step */
/* ls->rtol - relative tolerance for an acceptable step */
/* ls->ftol - tolerance for sufficient decrease condition */
/* ls->gtol - tolerance for curvature condition */
/* ls->nfeval - number of function evaluations */
/* ls->nfeval - number of function/gradient evaluations */
/* ls->max_funcs - maximum number of function evaluations */
ls->reason = TAOLINESEARCH_CONTINUE_ITERATING;
ls->step = ls->initstep;
if (!neP->W2) {
ierr = VecDuplicate(x,&neP->W2);CHKERRQ(ierr);
ierr = VecDuplicate(x,&neP->W1);CHKERRQ(ierr);
ierr = VecDuplicate(x,&neP->Gold);CHKERRQ(ierr);
neP->x = x;
ierr = PetscObjectReference((PetscObject)neP->x);CHKERRQ(ierr);
} else if (x != neP->x) {
ierr = VecDestroy(&neP->x);CHKERRQ(ierr);
ierr = VecDestroy(&neP->W1);CHKERRQ(ierr);
ierr = VecDestroy(&neP->W2);CHKERRQ(ierr);
ierr = VecDestroy(&neP->Gold);CHKERRQ(ierr);
ierr = VecDuplicate(x,&neP->W1);CHKERRQ(ierr);
ierr = VecDuplicate(x,&neP->W2);CHKERRQ(ierr);
ierr = VecDuplicate(x,&neP->Gold);CHKERRQ(ierr);
ierr = PetscObjectDereference((PetscObject)neP->x);CHKERRQ(ierr);
neP->x = x;
ierr = PetscObjectReference((PetscObject)neP->x);CHKERRQ(ierr);
}
ierr = VecDot(g,s,&gdx);CHKERRQ(ierr);
if (gdx > 0) {
ierr = PetscInfo1(ls,"Line search error: search direction is not descent direction. dot(g,s) = %g\n",(double)gdx);CHKERRQ(ierr);
ls->reason = TAOLINESEARCH_FAILED_ASCENT;
PetscFunctionReturn(0);
}
ierr = VecCopy(x,neP->W2);CHKERRQ(ierr);
ierr = VecCopy(g,neP->Gold);CHKERRQ(ierr);
if (ls->bounded) {
/* Compute the smallest steplength that will make one nonbinding variable equal the bound */
ierr = VecStepBoundInfo(x,s,ls->lower,ls->upper,&rho,&actred,&d1);CHKERRQ(ierr);
ls->step = PetscMin(ls->step,d1);
}
rho=0; actred=0;
if (ls->step < 0) {
ierr = PetscInfo1(ls,"Line search error: initial step parameter %g< 0\n",(double)ls->step);CHKERRQ(ierr);
ls->reason = TAOLINESEARCH_HALTED_OTHER;
PetscFunctionReturn(0);
}
/* Initialization */
finit = *f;
for (i=0; i< ls->max_funcs; i++) {
/* Force the step to be within the bounds */
ls->step = PetscMax(ls->step,ls->stepmin);
ls->step = PetscMin(ls->step,ls->stepmax);
ierr = VecCopy(x,neP->W2);CHKERRQ(ierr);
ierr = VecAXPY(neP->W2,ls->step,s);CHKERRQ(ierr);
if (ls->bounded) {
/* Make sure new vector is numerically within bounds */
ierr = VecMedian(neP->W2,ls->lower,ls->upper,neP->W2);CHKERRQ(ierr);
}
/* Gradient is not needed here. Unless there is a separate
gradient routine, compute it here anyway to prevent recomputing at
the end of the line search */
if (ls->hasobjective) {
ierr = TaoLineSearchComputeObjective(ls,neP->W2,f);CHKERRQ(ierr);
g_computed=PETSC_FALSE;
} else if (ls->usegts){
ierr = TaoLineSearchComputeObjectiveAndGTS(ls,neP->W2,f,&gdx);CHKERRQ(ierr);
g_computed=PETSC_FALSE;
} else {
ierr = TaoLineSearchComputeObjectiveAndGradient(ls,neP->W2,f,g);CHKERRQ(ierr);
g_computed=PETSC_TRUE;
}
if (0 == i) {
ls->f_fullstep = *f;
}
actred = *f - finit;
ierr = VecCopy(neP->W2,neP->W1);CHKERRQ(ierr);
ierr = VecAXPY(neP->W1,-1.0,x);CHKERRQ(ierr); /* W1 = W2 - X */
ierr = VecDot(neP->W1,neP->Gold,&prered);CHKERRQ(ierr);
if (fabs(prered)<1.0e-100) prered=1.0e-12;
rho = actred/prered;
/*
If sufficient progress has been obtained, accept the
point. Otherwise, backtrack.
*/
if (actred > 0) {
ierr = PetscInfo(ls,"Step resulted in ascent, rejecting.\n");CHKERRQ(ierr);
ls->step = (ls->step)/2;
} else if (rho > ls->ftol){
break;
} else{
ls->step = (ls->step)/2;
}
/* Convergence testing */
if (ls->step <= ls->stepmin || ls->step >= ls->stepmax) {
ls->reason = TAOLINESEARCH_HALTED_OTHER;
ierr = PetscInfo(ls,"Rounding errors may prevent further progress. May not be a step satisfying\n");CHKERRQ(ierr);
ierr = PetscInfo(ls,"sufficient decrease and curvature conditions. Tolerances may be too small.\n");CHKERRQ(ierr);
break;
}
if (ls->step == ls->stepmax) {
ierr = PetscInfo1(ls,"Step is at the upper bound, stepmax (%g)\n",(double)ls->stepmax);CHKERRQ(ierr);
ls->reason = TAOLINESEARCH_HALTED_UPPERBOUND;
break;
}
if (ls->step == ls->stepmin) {
ierr = PetscInfo1(ls,"Step is at the lower bound, stepmin (%g)\n",(double)ls->stepmin);CHKERRQ(ierr);
ls->reason = TAOLINESEARCH_HALTED_LOWERBOUND;
break;
}
if ((ls->nfeval+ls->nfgeval) >= ls->max_funcs) {
ierr = PetscInfo2(ls,"Number of line search function evals (%D) > maximum (%D)\n",ls->nfeval+ls->nfgeval,ls->max_funcs);CHKERRQ(ierr);
ls->reason = TAOLINESEARCH_HALTED_MAXFCN;
break;
}
if ((neP->bracket) && (ls->stepmax - ls->stepmin <= ls->rtol*ls->stepmax)){
ierr = PetscInfo1(ls,"Relative width of interval of uncertainty is at most rtol (%g)\n",(double)ls->rtol);CHKERRQ(ierr);
ls->reason = TAOLINESEARCH_HALTED_RTOL;
break;
}
}
ierr = PetscInfo2(ls,"%D function evals in line search, step = %g\n",ls->nfeval+ls->nfgeval,(double)ls->step);CHKERRQ(ierr);
/* set new solution vector and compute gradient if necessary */
ierr = VecCopy(neP->W2, x);CHKERRQ(ierr);
if (ls->reason == TAOLINESEARCH_CONTINUE_ITERATING) {
ls->reason = TAOLINESEARCH_SUCCESS;
}
if (!g_computed) {
ierr = TaoLineSearchComputeGradient(ls,x,g);CHKERRQ(ierr);
}
PetscFunctionReturn(0);
}
static PetscErrorCode TaoLineSearchApply_MT(TaoLineSearch ls, Vec x, PetscReal *f, Vec g, Vec s)
{
PetscErrorCode ierr;
TaoLineSearch_MT *mt;
PetscReal xtrapf = 4.0;
PetscReal finit, width, width1, dginit, fm, fxm, fym, dgm, dgxm, dgym;
PetscReal dgx, dgy, dg, dg2, fx, fy, stx, sty, dgtest;
PetscReal ftest1=0.0, ftest2=0.0;
PetscInt i, stage1,n1,n2,nn1,nn2;
PetscReal bstepmin1, bstepmin2, bstepmax;
PetscBool g_computed=PETSC_FALSE; /* to prevent extra gradient computation */
PetscFunctionBegin;
PetscValidHeaderSpecific(ls,TAOLINESEARCH_CLASSID,1);
PetscValidHeaderSpecific(x,VEC_CLASSID,2);
PetscValidScalarPointer(f,3);
PetscValidHeaderSpecific(g,VEC_CLASSID,4);
PetscValidHeaderSpecific(s,VEC_CLASSID,5);
/* comm,type,size checks are done in interface TaoLineSearchApply */
mt = (TaoLineSearch_MT*)(ls->data);
ls->reason = TAOLINESEARCH_CONTINUE_ITERATING;
/* Check work vector */
if (!mt->work) {
ierr = VecDuplicate(x,&mt->work);CHKERRQ(ierr);
mt->x = x;
ierr = PetscObjectReference((PetscObject)mt->x);CHKERRQ(ierr);
} else if (x != mt->x) {
ierr = VecDestroy(&mt->work);CHKERRQ(ierr);
ierr = VecDuplicate(x,&mt->work);CHKERRQ(ierr);
ierr = PetscObjectDereference((PetscObject)mt->x);CHKERRQ(ierr);
mt->x = x;
ierr = PetscObjectReference((PetscObject)mt->x);CHKERRQ(ierr);
}
if (ls->bounded) {
/* Compute step length needed to make all variables equal a bound */
/* Compute the smallest steplength that will make one nonbinding variable
equal the bound */
ierr = VecGetLocalSize(ls->upper,&n1);CHKERRQ(ierr);
ierr = VecGetLocalSize(mt->x, &n2);CHKERRQ(ierr);
ierr = VecGetSize(ls->upper,&nn1);CHKERRQ(ierr);
ierr = VecGetSize(mt->x,&nn2);CHKERRQ(ierr);
if (n1 != n2 || nn1 != nn2) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Variable vector not compatible with bounds vector");
ierr = VecScale(s,-1.0);CHKERRQ(ierr);
ierr = VecBoundGradientProjection(s,x,ls->lower,ls->upper,s);CHKERRQ(ierr);
ierr = VecScale(s,-1.0);CHKERRQ(ierr);
ierr = VecStepBoundInfo(x,s,ls->lower,ls->upper,&bstepmin1,&bstepmin2,&bstepmax);CHKERRQ(ierr);
ls->stepmax = PetscMin(bstepmax,1.0e15);
}
ierr = VecDot(g,s,&dginit);CHKERRQ(ierr);
if (PetscIsInfOrNanReal(dginit)) {
ierr = PetscInfo1(ls,"Initial Line Search step * g is Inf or Nan (%g)\n",(double)dginit);CHKERRQ(ierr);
ls->reason=TAOLINESEARCH_FAILED_INFORNAN;
PetscFunctionReturn(0);
}
if (dginit >= 0.0) {
ierr = PetscInfo1(ls,"Initial Line Search step * g is not descent direction (%g)\n",(double)dginit);CHKERRQ(ierr);
ls->reason = TAOLINESEARCH_FAILED_ASCENT;
PetscFunctionReturn(0);
}
/* Initialization */
mt->bracket = 0;
stage1 = 1;
finit = *f;
dgtest = ls->ftol * dginit;
width = ls->stepmax - ls->stepmin;
width1 = width * 2.0;
ierr = VecCopy(x,mt->work);CHKERRQ(ierr);
/* Variable dictionary:
stx, fx, dgx - the step, function, and derivative at the best step
sty, fy, dgy - the step, function, and derivative at the other endpoint
of the interval of uncertainty
step, f, dg - the step, function, and derivative at the current step */
stx = 0.0;
fx = finit;
dgx = dginit;
sty = 0.0;
fy = finit;
dgy = dginit;
ls->step=ls->initstep;
for (i=0; i< ls->max_funcs; i++) {
/* Set min and max steps to correspond to the interval of uncertainty */
if (mt->bracket) {
ls->stepmin = PetscMin(stx,sty);
ls->stepmax = PetscMax(stx,sty);
} else {
ls->stepmin = stx;
ls->stepmax = ls->step + xtrapf * (ls->step - stx);
}
/* Force the step to be within the bounds */
ls->step = PetscMax(ls->step,ls->stepmin);
ls->step = PetscMin(ls->step,ls->stepmax);
/* If an unusual termination is to occur, then let step be the lowest
point obtained thus far */
if ((stx!=0) && (((mt->bracket) && (ls->step <= ls->stepmin || ls->step >= ls->stepmax)) || ((mt->bracket) && (ls->stepmax - ls->stepmin <= ls->rtol * ls->stepmax)) ||
((ls->nfeval+ls->nfgeval) >= ls->max_funcs - 1) || (mt->infoc == 0))) {
ls->step = stx;
}
ierr = VecCopy(x,mt->work);CHKERRQ(ierr);
ierr = VecAXPY(mt->work,ls->step,s);CHKERRQ(ierr); /* W = X + step*S */
if (ls->bounded) {
ierr = VecMedian(ls->lower, mt->work, ls->upper, mt->work);CHKERRQ(ierr);
}
if (ls->usegts) {
ierr = TaoLineSearchComputeObjectiveAndGTS(ls,mt->work,f,&dg);CHKERRQ(ierr);
g_computed=PETSC_FALSE;
} else {
ierr = TaoLineSearchComputeObjectiveAndGradient(ls,mt->work,f,g);CHKERRQ(ierr);
g_computed=PETSC_TRUE;
if (ls->bounded) {
ierr = VecDot(g,x,&dg);CHKERRQ(ierr);
ierr = VecDot(g,mt->work,&dg2);CHKERRQ(ierr);
dg = (dg2 - dg)/ls->step;
} else {
ierr = VecDot(g,s,&dg);CHKERRQ(ierr);
}
}
if (0 == i) {
ls->f_fullstep=*f;
}
if (PetscIsInfOrNanReal(*f) || PetscIsInfOrNanReal(dg)) {
/* User provided compute function generated Not-a-Number, assume
domain violation and set function value and directional
derivative to infinity. */
*f = PETSC_INFINITY;
dg = PETSC_INFINITY;
}
ftest1 = finit + ls->step * dgtest;
if (ls->bounded) {
ftest2 = finit + ls->step * dgtest * ls->ftol;
}
/* Convergence testing */
if (((*f - ftest1 <= 1.0e-10 * PetscAbsReal(finit)) && (PetscAbsReal(dg) + ls->gtol*dginit <= 0.0))) {
ierr = PetscInfo(ls, "Line search success: Sufficient decrease and directional deriv conditions hold\n");CHKERRQ(ierr);
ls->reason = TAOLINESEARCH_SUCCESS;
break;
}
/* Check Armijo if beyond the first breakpoint */
if (ls->bounded && (*f <= ftest2) && (ls->step >= bstepmin2)) {
ierr = PetscInfo(ls,"Line search success: Sufficient decrease.\n");CHKERRQ(ierr);
ls->reason = TAOLINESEARCH_SUCCESS;
break;
}
/* Checks for bad cases */
if (((mt->bracket) && (ls->step <= ls->stepmin||ls->step >= ls->stepmax)) || (!mt->infoc)) {
ierr = PetscInfo(ls,"Rounding errors may prevent further progress. May not be a step satisfying\n");CHKERRQ(ierr);
ierr = PetscInfo(ls,"sufficient decrease and curvature conditions. Tolerances may be too small.\n");CHKERRQ(ierr);
ls->reason = TAOLINESEARCH_HALTED_OTHER;
break;
}
if ((ls->step == ls->stepmax) && (*f <= ftest1) && (dg <= dgtest)) {
ierr = PetscInfo1(ls,"Step is at the upper bound, stepmax (%g)\n",(double)ls->stepmax);CHKERRQ(ierr);
ls->reason = TAOLINESEARCH_HALTED_UPPERBOUND;
break;
}
if ((ls->step == ls->stepmin) && (*f >= ftest1) && (dg >= dgtest)) {
ierr = PetscInfo1(ls,"Step is at the lower bound, stepmin (%g)\n",(double)ls->stepmin);CHKERRQ(ierr);
ls->reason = TAOLINESEARCH_HALTED_LOWERBOUND;
break;
}
if ((mt->bracket) && (ls->stepmax - ls->stepmin <= ls->rtol*ls->stepmax)){
ierr = PetscInfo1(ls,"Relative width of interval of uncertainty is at most rtol (%g)\n",(double)ls->rtol);CHKERRQ(ierr);
ls->reason = TAOLINESEARCH_HALTED_RTOL;
break;
}
/* In the first stage, we seek a step for which the modified function
has a nonpositive value and nonnegative derivative */
if ((stage1) && (*f <= ftest1) && (dg >= dginit * PetscMin(ls->ftol, ls->gtol))) {
stage1 = 0;
}
/* A modified function is used to predict the step only if we
have not obtained a step for which the modified function has a
nonpositive function value and nonnegative derivative, and if a
lower function value has been obtained but the decrease is not
sufficient */
if ((stage1) && (*f <= fx) && (*f > ftest1)) {
fm = *f - ls->step * dgtest; /* Define modified function */
fxm = fx - stx * dgtest; /* and derivatives */
fym = fy - sty * dgtest;
dgm = dg - dgtest;
dgxm = dgx - dgtest;
dgym = dgy - dgtest;
/* if (dgxm * (ls->step - stx) >= 0.0) */
/* Update the interval of uncertainty and compute the new step */
ierr = Tao_mcstep(ls,&stx,&fxm,&dgxm,&sty,&fym,&dgym,&ls->step,&fm,&dgm);CHKERRQ(ierr);
fx = fxm + stx * dgtest; /* Reset the function and */
fy = fym + sty * dgtest; /* gradient values */
dgx = dgxm + dgtest;
dgy = dgym + dgtest;
} else {
/* Update the interval of uncertainty and compute the new step */
ierr = Tao_mcstep(ls,&stx,&fx,&dgx,&sty,&fy,&dgy,&ls->step,f,&dg);CHKERRQ(ierr);
}
/* Force a sufficient decrease in the interval of uncertainty */
if (mt->bracket) {
if (PetscAbsReal(sty - stx) >= 0.66 * width1) ls->step = stx + 0.5*(sty - stx);
width1 = width;
width = PetscAbsReal(sty - stx);
}
}
if ((ls->nfeval+ls->nfgeval) > ls->max_funcs) {
ierr = PetscInfo2(ls,"Number of line search function evals (%D) > maximum (%D)\n",(ls->nfeval+ls->nfgeval),ls->max_funcs);CHKERRQ(ierr);
ls->reason = TAOLINESEARCH_HALTED_MAXFCN;
}
/* Finish computations */
ierr = PetscInfo2(ls,"%D function evals in line search, step = %g\n",(ls->nfeval+ls->nfgeval),(double)ls->step);CHKERRQ(ierr);
/* Set new solution vector and compute gradient if needed */
ierr = VecCopy(mt->work,x);CHKERRQ(ierr);
if (!g_computed) {
ierr = TaoLineSearchComputeGradient(ls,mt->work,g);CHKERRQ(ierr);
}
PetscFunctionReturn(0);
}
/* @ TaoApply_OWArmijo - This routine performs a linesearch. It
backtracks until the (nonmonotone) OWArmijo conditions are satisfied.
Input Parameters:
+ tao - TAO_SOLVER context
. X - current iterate (on output X contains new iterate, X + step*S)
. S - search direction
. f - merit function evaluated at X
. G - gradient of merit function evaluated at X
. W - work vector
- step - initial estimate of step length
Output parameters:
+ f - merit function evaluated at new iterate, X + step*S
. G - gradient of merit function evaluated at new iterate, X + step*S
. X - new iterate
- step - final step length
Info is set to one of:
. 0 - the line search succeeds; the sufficient decrease
condition and the directional derivative condition hold
negative number if an input parameter is invalid
- -1 - step < 0
positive number > 1 if the line search otherwise terminates
+ 1 - Step is at the lower bound, stepmin.
@ */
static PetscErrorCode TaoLineSearchApply_OWArmijo(TaoLineSearch ls, Vec x, PetscReal *f, Vec g, Vec s)
{
TaoLineSearch_OWARMIJO *armP = (TaoLineSearch_OWARMIJO *)ls->data;
PetscErrorCode ierr;
PetscInt i;
PetscReal fact, ref, gdx;
PetscInt idx;
PetscBool g_computed=PETSC_FALSE; /* to prevent extra gradient computation */
Vec g_old;
PetscReal owlqn_minstep=0.005;
PetscReal partgdx;
MPI_Comm comm;
PetscFunctionBegin;
ierr = PetscObjectGetComm((PetscObject)ls,&comm);CHKERRQ(ierr);
fact = 0.0;
ls->nfeval=0;
ls->reason = TAOLINESEARCH_CONTINUE_ITERATING;
if (!armP->work) {
ierr = VecDuplicate(x,&armP->work);CHKERRQ(ierr);
armP->x = x;
ierr = PetscObjectReference((PetscObject)armP->x);CHKERRQ(ierr);
} else if (x != armP->x) {
ierr = VecDestroy(&armP->work);CHKERRQ(ierr);
ierr = VecDuplicate(x,&armP->work);CHKERRQ(ierr);
ierr = PetscObjectDereference((PetscObject)armP->x);CHKERRQ(ierr);
armP->x = x;
ierr = PetscObjectReference((PetscObject)armP->x);CHKERRQ(ierr);
}
/* Check linesearch parameters */
if (armP->alpha < 1) {
ierr = PetscInfo1(ls,"OWArmijo line search error: alpha (%g) < 1\n", (double)armP->alpha);CHKERRQ(ierr);
ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
} else if ((armP->beta <= 0) || (armP->beta >= 1)) {
ierr = PetscInfo1(ls,"OWArmijo line search error: beta (%g) invalid\n", (double)armP->beta);CHKERRQ(ierr);
ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
} else if ((armP->beta_inf <= 0) || (armP->beta_inf >= 1)) {
ierr = PetscInfo1(ls,"OWArmijo line search error: beta_inf (%g) invalid\n", (double)armP->beta_inf);CHKERRQ(ierr);
ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
} else if ((armP->sigma <= 0) || (armP->sigma >= 0.5)) {
ierr = PetscInfo1(ls,"OWArmijo line search error: sigma (%g) invalid\n", (double)armP->sigma);CHKERRQ(ierr);
ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
} else if (armP->memorySize < 1) {
ierr = PetscInfo1(ls,"OWArmijo line search error: memory_size (%D) < 1\n", armP->memorySize);CHKERRQ(ierr);
ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
} else if ((armP->referencePolicy != REFERENCE_MAX) && (armP->referencePolicy != REFERENCE_AVE) && (armP->referencePolicy != REFERENCE_MEAN)) {
ierr = PetscInfo(ls,"OWArmijo line search error: reference_policy invalid\n");CHKERRQ(ierr);
ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
} else if ((armP->replacementPolicy != REPLACE_FIFO) && (armP->replacementPolicy != REPLACE_MRU)) {
ierr = PetscInfo(ls,"OWArmijo line search error: replacement_policy invalid\n");CHKERRQ(ierr);
ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
} else if (PetscIsInfOrNanReal(*f)) {
ierr = PetscInfo(ls,"OWArmijo line search error: initial function inf or nan\n");CHKERRQ(ierr);
ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
}
if (ls->reason != TAOLINESEARCH_CONTINUE_ITERATING) PetscFunctionReturn(0);
/* Check to see of the memory has been allocated. If not, allocate
the historical array and populate it with the initial function
values. */
if (!armP->memory) {
ierr = PetscMalloc1(armP->memorySize, &armP->memory );CHKERRQ(ierr);
}
if (!armP->memorySetup) {
for (i = 0; i < armP->memorySize; i++) {
armP->memory[i] = armP->alpha*(*f);
}
armP->current = 0;
armP->lastReference = armP->memory[0];
armP->memorySetup=PETSC_TRUE;
}
/* Calculate reference value (MAX) */
ref = armP->memory[0];
idx = 0;
for (i = 1; i < armP->memorySize; i++) {
if (armP->memory[i] > ref) {
ref = armP->memory[i];
idx = i;
}
}
if (armP->referencePolicy == REFERENCE_AVE) {
ref = 0;
for (i = 0; i < armP->memorySize; i++) {
ref += armP->memory[i];
}
ref = ref / armP->memorySize;
ref = PetscMax(ref, armP->memory[armP->current]);
} else if (armP->referencePolicy == REFERENCE_MEAN) {
ref = PetscMin(ref, 0.5*(armP->lastReference + armP->memory[armP->current]));
}
if (armP->nondescending) {
fact = armP->sigma;
}
ierr = VecDuplicate(g,&g_old);CHKERRQ(ierr);
ierr = VecCopy(g,g_old);CHKERRQ(ierr);
ls->step = ls->initstep;
while (ls->step >= owlqn_minstep && ls->nfeval < ls->max_funcs) {
/* Calculate iterate */
ierr = VecCopy(x,armP->work);CHKERRQ(ierr);
ierr = VecAXPY(armP->work,ls->step,s);CHKERRQ(ierr);
partgdx=0.0;
ierr = ProjWork_OWLQN(armP->work,x,g_old,&partgdx);
ierr = MPI_Allreduce(&partgdx,&gdx,1,MPIU_REAL,MPIU_SUM,comm);CHKERRQ(ierr);
/* Check the condition of gdx */
if (PetscIsInfOrNanReal(gdx)) {
ierr = PetscInfo1(ls,"Initial Line Search step * g is Inf or Nan (%g)\n",(double)gdx);CHKERRQ(ierr);
ls->reason=TAOLINESEARCH_FAILED_INFORNAN;
PetscFunctionReturn(0);
}
if (gdx >= 0.0) {
ierr = PetscInfo1(ls,"Initial Line Search step is not descent direction (g's=%g)\n",(double)gdx);CHKERRQ(ierr);
ls->reason = TAOLINESEARCH_FAILED_ASCENT;
PetscFunctionReturn(0);
}
/* Calculate function at new iterate */
ierr = TaoLineSearchComputeObjectiveAndGradient(ls,armP->work,f,g);CHKERRQ(ierr);
g_computed=PETSC_TRUE;
if (ls->step == ls->initstep) {
ls->f_fullstep = *f;
}
if (PetscIsInfOrNanReal(*f)) {
ls->step *= armP->beta_inf;
} else {
/* Check descent condition */
if (armP->nondescending && *f <= ref - ls->step*fact*ref) break;
if (!armP->nondescending && *f <= ref + armP->sigma * gdx) break;
ls->step *= armP->beta;
}
}
ierr = VecDestroy(&g_old);CHKERRQ(ierr);
/* Check termination */
if (PetscIsInfOrNanReal(*f)) {
ierr = PetscInfo(ls, "Function is inf or nan.\n");CHKERRQ(ierr);
ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
} else if (ls->step < owlqn_minstep) {
ierr = PetscInfo(ls, "Step length is below tolerance.\n");CHKERRQ(ierr);
ls->reason = TAOLINESEARCH_HALTED_RTOL;
} else if (ls->nfeval >= ls->max_funcs) {
ierr = PetscInfo2(ls, "Number of line search function evals (%D) > maximum allowed (%D)\n",ls->nfeval, ls->max_funcs);CHKERRQ(ierr);
ls->reason = TAOLINESEARCH_HALTED_MAXFCN;
}
if (ls->reason) PetscFunctionReturn(0);
/* Successful termination, update memory */
armP->lastReference = ref;
if (armP->replacementPolicy == REPLACE_FIFO) {
armP->memory[armP->current++] = *f;
if (armP->current >= armP->memorySize) {
armP->current = 0;
}
} else {
armP->current = idx;
armP->memory[idx] = *f;
}
/* Update iterate and compute gradient */
ierr = VecCopy(armP->work,x);CHKERRQ(ierr);
if (!g_computed) {
ierr = TaoLineSearchComputeGradient(ls, x, g);CHKERRQ(ierr);
}
ierr = PetscInfo2(ls, "%D function evals in line search, step = %10.4f\n",ls->nfeval, (double)ls->step);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
