Date: 2022-03-04T17:39:55+00:00

Actual source code: owarmijo.c

      2: #include <petsc/private/taolinesearchimpl.h>
      3: #include <../src/tao/linesearch/impls/owarmijo/owarmijo.h>

      5: #define REPLACE_FIFO 1
      6: #define REPLACE_MRU  2

      8: #define REFERENCE_MAX  1
      9: #define REFERENCE_AVE  2
     10: #define REFERENCE_MEAN 3

     12: static PetscErrorCode ProjWork_OWLQN(Vec w,Vec x,Vec gv,PetscReal *gdx)
     13: {
     14:   const PetscReal *xptr,*gptr;
     15:   PetscReal       *wptr;
     16:   PetscErrorCode  ierr;
     17:   PetscInt        low,high,low1,high1,low2,high2,i;

     20:   VecGetOwnershipRange(w,&low,&high);
     21:   VecGetOwnershipRange(x,&low1,&high1);
     22:   VecGetOwnershipRange(gv,&low2,&high2);

     24:   *gdx=0.0;
     25:   VecGetArray(w,&wptr);
     26:   VecGetArrayRead(x,&xptr);
     27:   VecGetArrayRead(gv,&gptr);

     29:   for (i=0;i<high-low;i++) {
     30:     if (xptr[i]*wptr[i]<0.0) wptr[i]=0.0;
     31:     *gdx = *gdx + gptr[i]*(wptr[i]-xptr[i]);
     32:   }
     33:   VecRestoreArray(w,&wptr);
     34:   VecRestoreArrayRead(x,&xptr);
     35:   VecRestoreArrayRead(gv,&gptr);
     36:   return(0);
     37: }

     39: static PetscErrorCode TaoLineSearchDestroy_OWArmijo(TaoLineSearch ls)
     40: {
     41:   TaoLineSearch_OWARMIJO *armP = (TaoLineSearch_OWARMIJO *)ls->data;
     42:   PetscErrorCode         ierr;

     45:   PetscFree(armP->memory);
     46:   if (armP->x) {
     47:     PetscObjectDereference((PetscObject)armP->x);
     48:   }
     49:   VecDestroy(&armP->work);
     50:   PetscFree(ls->data);
     51:   return(0);
     52: }

     54: static PetscErrorCode TaoLineSearchSetFromOptions_OWArmijo(PetscOptionItems *PetscOptionsObject,TaoLineSearch ls)
     55: {
     56:   TaoLineSearch_OWARMIJO *armP = (TaoLineSearch_OWARMIJO *)ls->data;
     57:   PetscErrorCode         ierr;

     60:   PetscOptionsHead(PetscOptionsObject,"OWArmijo linesearch options");
     61:   PetscOptionsReal("-tao_ls_OWArmijo_alpha", "initial reference constant", "", armP->alpha, &armP->alpha,NULL);
     62:   PetscOptionsReal("-tao_ls_OWArmijo_beta_inf", "decrease constant one", "", armP->beta_inf, &armP->beta_inf,NULL);
     63:   PetscOptionsReal("-tao_ls_OWArmijo_beta", "decrease constant", "", armP->beta, &armP->beta,NULL);
     64:   PetscOptionsReal("-tao_ls_OWArmijo_sigma", "acceptance constant", "", armP->sigma, &armP->sigma,NULL);
     65:   PetscOptionsInt("-tao_ls_OWArmijo_memory_size", "number of historical elements", "", armP->memorySize, &armP->memorySize,NULL);
     66:   PetscOptionsInt("-tao_ls_OWArmijo_reference_policy", "policy for updating reference value", "", armP->referencePolicy, &armP->referencePolicy,NULL);
     67:   PetscOptionsInt("-tao_ls_OWArmijo_replacement_policy", "policy for updating memory", "", armP->replacementPolicy, &armP->replacementPolicy,NULL);
     68:   PetscOptionsBool("-tao_ls_OWArmijo_nondescending","Use nondescending OWArmijo algorithm","",armP->nondescending,&armP->nondescending,NULL);
     69:   PetscOptionsTail();
     70:   return(0);
     71: }

     73: static PetscErrorCode TaoLineSearchView_OWArmijo(TaoLineSearch ls, PetscViewer pv)
     74: {
     75:   TaoLineSearch_OWARMIJO *armP = (TaoLineSearch_OWARMIJO *)ls->data;
     76:   PetscBool              isascii;
     77:   PetscErrorCode         ierr;

     80:   PetscObjectTypeCompare((PetscObject)pv, PETSCVIEWERASCII, &isascii);
     81:   if (isascii) {
     82:     PetscViewerASCIIPrintf(pv,"  OWArmijo linesearch",armP->alpha);
     83:     if (armP->nondescending) {
     84:       PetscViewerASCIIPrintf(pv, " (nondescending)");
     85:     }
     86:     PetscViewerASCIIPrintf(pv,": alpha=%g beta=%g ",(double)armP->alpha,(double)armP->beta);
     87:     PetscViewerASCIIPrintf(pv,"sigma=%g ",(double)armP->sigma);
     88:     PetscViewerASCIIPrintf(pv,"memsize=%D\n",armP->memorySize);
     89:   }
     90:   return(0);
     91: }

     93: /* @ TaoApply_OWArmijo - This routine performs a linesearch. It
     94:    backtracks until the (nonmonotone) OWArmijo conditions are satisfied.

     96:    Input Parameters:
     97: +  tao - TAO_SOLVER context
     98: .  X - current iterate (on output X contains new iterate, X + step*S)
     99: .  S - search direction
    100: .  f - merit function evaluated at X
    101: .  G - gradient of merit function evaluated at X
    102: .  W - work vector
    103: -  step - initial estimate of step length

    105:    Output parameters:
    106: +  f - merit function evaluated at new iterate, X + step*S
    107: .  G - gradient of merit function evaluated at new iterate, X + step*S
    108: .  X - new iterate
    109: -  step - final step length

    111:    Info is set to one of:
    112: .   0 - the line search succeeds; the sufficient decrease
    113:    condition and the directional derivative condition hold

    115:    negative number if an input parameter is invalid
    116: -   -1 -  step < 0

    118:    positive number > 1 if the line search otherwise terminates
    119: +    1 -  Step is at the lower bound, stepmin.
    120: @ */
    121: static PetscErrorCode TaoLineSearchApply_OWArmijo(TaoLineSearch ls, Vec x, PetscReal *f, Vec g, Vec s)
    122: {
    123:   TaoLineSearch_OWARMIJO *armP = (TaoLineSearch_OWARMIJO *)ls->data;
    124:   PetscErrorCode         ierr;
    125:   PetscInt               i, its=0;
    126:   PetscReal              fact, ref, gdx;
    127:   PetscInt               idx;
    128:   PetscBool              g_computed=PETSC_FALSE; /* to prevent extra gradient computation */
    129:   Vec                    g_old;
    130:   PetscReal              owlqn_minstep=0.005;
    131:   PetscReal              partgdx;
    132:   MPI_Comm               comm;

    135:   PetscObjectGetComm((PetscObject)ls,&comm);
    136:   fact = 0.0;
    137:   ls->nfeval=0;
    138:   ls->reason = TAOLINESEARCH_CONTINUE_ITERATING;
    139:   if (!armP->work) {
    140:     VecDuplicate(x,&armP->work);
    141:     armP->x = x;
    142:     PetscObjectReference((PetscObject)armP->x);
    143:   } else if (x != armP->x) {
    144:     VecDestroy(&armP->work);
    145:     VecDuplicate(x,&armP->work);
    146:     PetscObjectDereference((PetscObject)armP->x);
    147:     armP->x = x;
    148:     PetscObjectReference((PetscObject)armP->x);
    149:   }

    151:   TaoLineSearchMonitor(ls, 0, *f, 0.0);

    153:   /* Check linesearch parameters */
    154:   if (armP->alpha < 1) {
    155:     PetscInfo1(ls,"OWArmijo line search error: alpha (%g) < 1\n", (double)armP->alpha);
    156:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    157:   } else if ((armP->beta <= 0) || (armP->beta >= 1)) {
    158:     PetscInfo1(ls,"OWArmijo line search error: beta (%g) invalid\n", (double)armP->beta);
    159:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    160:   } else if ((armP->beta_inf <= 0) || (armP->beta_inf >= 1)) {
    161:     PetscInfo1(ls,"OWArmijo line search error: beta_inf (%g) invalid\n", (double)armP->beta_inf);
    162:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    163:   } else if ((armP->sigma <= 0) || (armP->sigma >= 0.5)) {
    164:     PetscInfo1(ls,"OWArmijo line search error: sigma (%g) invalid\n", (double)armP->sigma);
    165:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    166:   } else if (armP->memorySize < 1) {
    167:     PetscInfo1(ls,"OWArmijo line search error: memory_size (%D) < 1\n", armP->memorySize);
    168:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    169:   }  else if ((armP->referencePolicy != REFERENCE_MAX) && (armP->referencePolicy != REFERENCE_AVE) && (armP->referencePolicy != REFERENCE_MEAN)) {
    170:     PetscInfo(ls,"OWArmijo line search error: reference_policy invalid\n");
    171:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    172:   } else if ((armP->replacementPolicy != REPLACE_FIFO) && (armP->replacementPolicy != REPLACE_MRU)) {
    173:     PetscInfo(ls,"OWArmijo line search error: replacement_policy invalid\n");
    174:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    175:   } else if (PetscIsInfOrNanReal(*f)) {
    176:     PetscInfo(ls,"OWArmijo line search error: initial function inf or nan\n");
    177:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    178:   }

    180:   if (ls->reason != TAOLINESEARCH_CONTINUE_ITERATING) return(0);

    182:   /* Check to see of the memory has been allocated.  If not, allocate
    183:      the historical array and populate it with the initial function
    184:      values. */
    185:   if (!armP->memory) {
    186:     PetscMalloc1(armP->memorySize, &armP->memory);
    187:   }

    189:   if (!armP->memorySetup) {
    190:     for (i = 0; i < armP->memorySize; i++) {
    191:       armP->memory[i] = armP->alpha*(*f);
    192:     }
    193:     armP->current = 0;
    194:     armP->lastReference = armP->memory[0];
    195:     armP->memorySetup=PETSC_TRUE;
    196:   }

    198:   /* Calculate reference value (MAX) */
    199:   ref = armP->memory[0];
    200:   idx = 0;

    202:   for (i = 1; i < armP->memorySize; i++) {
    203:     if (armP->memory[i] > ref) {
    204:       ref = armP->memory[i];
    205:       idx = i;
    206:     }
    207:   }

    209:   if (armP->referencePolicy == REFERENCE_AVE) {
    210:     ref = 0;
    211:     for (i = 0; i < armP->memorySize; i++) {
    212:       ref += armP->memory[i];
    213:     }
    214:     ref = ref / armP->memorySize;
    215:     ref = PetscMax(ref, armP->memory[armP->current]);
    216:   } else if (armP->referencePolicy == REFERENCE_MEAN) {
    217:     ref = PetscMin(ref, 0.5*(armP->lastReference + armP->memory[armP->current]));
    218:   }

    220:   if (armP->nondescending) {
    221:     fact = armP->sigma;
    222:   }

    224:   VecDuplicate(g,&g_old);
    225:   VecCopy(g,g_old);

    227:   ls->step = ls->initstep;
    228:   while (ls->step >= owlqn_minstep && ls->nfeval < ls->max_funcs) {
    229:     /* Calculate iterate */
    230:     ++its;
    231:     VecCopy(x,armP->work);
    232:     VecAXPY(armP->work,ls->step,s);

    234:     partgdx=0.0;
    235:     ProjWork_OWLQN(armP->work,x,g_old,&partgdx);
    236:     MPIU_Allreduce(&partgdx,&gdx,1,MPIU_REAL,MPIU_SUM,comm);

    238:     /* Check the condition of gdx */
    239:     if (PetscIsInfOrNanReal(gdx)) {
    240:       PetscInfo1(ls,"Initial Line Search step * g is Inf or Nan (%g)\n",(double)gdx);
    241:       ls->reason=TAOLINESEARCH_FAILED_INFORNAN;
    242:       return(0);
    243:     }
    244:     if (gdx >= 0.0) {
    245:       PetscInfo1(ls,"Initial Line Search step is not descent direction (g's=%g)\n",(double)gdx);
    246:       ls->reason = TAOLINESEARCH_FAILED_ASCENT;
    247:       return(0);
    248:     }

    250:     /* Calculate function at new iterate */
    251:     TaoLineSearchComputeObjectiveAndGradient(ls,armP->work,f,g);
    252:     g_computed=PETSC_TRUE;

    254:     TaoLineSearchMonitor(ls, its, *f, ls->step);

    256:     if (ls->step == ls->initstep) {
    257:       ls->f_fullstep = *f;
    258:     }

    260:     if (PetscIsInfOrNanReal(*f)) {
    261:       ls->step *= armP->beta_inf;
    262:     } else {
    263:       /* Check descent condition */
    264:       if (armP->nondescending && *f <= ref - ls->step*fact*ref) break;
    265:       if (!armP->nondescending && *f <= ref + armP->sigma * gdx) break;
    266:       ls->step *= armP->beta;
    267:     }
    268:   }
    269:   VecDestroy(&g_old);

    271:   /* Check termination */
    272:   if (PetscIsInfOrNanReal(*f)) {
    273:     PetscInfo(ls, "Function is inf or nan.\n");
    274:     ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
    275:   } else if (ls->step < owlqn_minstep) {
    276:     PetscInfo(ls, "Step length is below tolerance.\n");
    277:     ls->reason = TAOLINESEARCH_HALTED_RTOL;
    278:   } else if (ls->nfeval >= ls->max_funcs) {
    279:     PetscInfo2(ls, "Number of line search function evals (%D) > maximum allowed (%D)\n",ls->nfeval, ls->max_funcs);
    280:     ls->reason = TAOLINESEARCH_HALTED_MAXFCN;
    281:   }
    282:   if (ls->reason) return(0);

    284:   /* Successful termination, update memory */
    285:   ls->reason = TAOLINESEARCH_SUCCESS;
    286:   armP->lastReference = ref;
    287:   if (armP->replacementPolicy == REPLACE_FIFO) {
    288:     armP->memory[armP->current++] = *f;
    289:     if (armP->current >= armP->memorySize) {
    290:       armP->current = 0;
    291:     }
    292:   } else {
    293:     armP->current = idx;
    294:     armP->memory[idx] = *f;
    295:   }

    297:   /* Update iterate and compute gradient */
    298:   VecCopy(armP->work,x);
    299:   if (!g_computed) {
    300:     TaoLineSearchComputeGradient(ls, x, g);
    301:   }
    302:   PetscInfo2(ls, "%D function evals in line search, step = %10.4f\n",ls->nfeval, (double)ls->step);
    303:   return(0);
    304: }

    306: /*MC
    307:    TAOLINESEARCHOWARMIJO - Special line-search type for the Orthant-Wise Limited Quasi-Newton (TAOOWLQN) algorithm.
    308:    Should not be used with any other algorithm.

    310:    Level: developer

    312: .keywords: Tao, linesearch
    313: M*/
    314: PETSC_EXTERN PetscErrorCode TaoLineSearchCreate_OWArmijo(TaoLineSearch ls)
    315: {
    316:   TaoLineSearch_OWARMIJO *armP;
    317:   PetscErrorCode         ierr;

    321:   PetscNewLog(ls,&armP);

    323:   armP->memory = NULL;
    324:   armP->alpha = 1.0;
    325:   armP->beta = 0.25;
    326:   armP->beta_inf = 0.25;
    327:   armP->sigma = 1e-4;
    328:   armP->memorySize = 1;
    329:   armP->referencePolicy = REFERENCE_MAX;
    330:   armP->replacementPolicy = REPLACE_MRU;
    331:   armP->nondescending=PETSC_FALSE;
    332:   ls->data = (void*)armP;
    333:   ls->initstep = 0.1;
    334:   ls->ops->monitor = NULL;
    335:   ls->ops->setup = NULL;
    336:   ls->ops->reset = NULL;
    337:   ls->ops->apply = TaoLineSearchApply_OWArmijo;
    338:   ls->ops->view = TaoLineSearchView_OWArmijo;
    339:   ls->ops->destroy = TaoLineSearchDestroy_OWArmijo;
    340:   ls->ops->setfromoptions = TaoLineSearchSetFromOptions_OWArmijo;
    341:   return(0);
    342: }