Date: 2021-03-03T22:28:14+00:00

Actual source code: armijo.c
petsc-3.14.5 2021-03-03
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      1: #include <petsc/private/taolinesearchimpl.h>
      2: #include <../src/tao/linesearch/impls/armijo/armijo.h>

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

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

     11: static PetscErrorCode TaoLineSearchDestroy_Armijo(TaoLineSearch ls)
     12: {
     13:   TaoLineSearch_ARMIJO *armP = (TaoLineSearch_ARMIJO *)ls->data;
     14:   PetscErrorCode       ierr;

     17:   PetscFree(armP->memory);
     18:   VecDestroy(&armP->x);
     19:   VecDestroy(&armP->work);
     20:   PetscFree(ls->data);
     21:   return(0);
     22: }

     24: static PetscErrorCode TaoLineSearchReset_Armijo(TaoLineSearch ls)
     25: {
     26:   TaoLineSearch_ARMIJO *armP = (TaoLineSearch_ARMIJO *)ls->data;
     27:   PetscErrorCode       ierr;

     30:   PetscFree(armP->memory);
     31:   armP->memorySetup = PETSC_FALSE;
     32:   return(0);
     33: }

     35: static PetscErrorCode TaoLineSearchSetFromOptions_Armijo(PetscOptionItems *PetscOptionsObject,TaoLineSearch ls)
     36: {
     37:   TaoLineSearch_ARMIJO *armP = (TaoLineSearch_ARMIJO *)ls->data;
     38:   PetscErrorCode       ierr;

     41:   PetscOptionsHead(PetscOptionsObject,"Armijo linesearch options");
     42:   PetscOptionsReal("-tao_ls_armijo_alpha", "initial reference constant", "", armP->alpha, &armP->alpha,NULL);
     43:   PetscOptionsReal("-tao_ls_armijo_beta_inf", "decrease constant one", "", armP->beta_inf, &armP->beta_inf,NULL);
     44:   PetscOptionsReal("-tao_ls_armijo_beta", "decrease constant", "", armP->beta, &armP->beta,NULL);
     45:   PetscOptionsReal("-tao_ls_armijo_sigma", "acceptance constant", "", armP->sigma, &armP->sigma,NULL);
     46:   PetscOptionsInt("-tao_ls_armijo_memory_size", "number of historical elements", "", armP->memorySize, &armP->memorySize,NULL);
     47:   PetscOptionsInt("-tao_ls_armijo_reference_policy", "policy for updating reference value", "", armP->referencePolicy, &armP->referencePolicy,NULL);
     48:   PetscOptionsInt("-tao_ls_armijo_replacement_policy", "policy for updating memory", "", armP->replacementPolicy, &armP->replacementPolicy,NULL);
     49:   PetscOptionsBool("-tao_ls_armijo_nondescending","Use nondescending armijo algorithm","",armP->nondescending,&armP->nondescending,NULL);
     50:   PetscOptionsTail();
     51:   return(0);
     52: }

     54: static PetscErrorCode TaoLineSearchView_Armijo(TaoLineSearch ls, PetscViewer pv)
     55: {
     56:   TaoLineSearch_ARMIJO *armP = (TaoLineSearch_ARMIJO *)ls->data;
     57:   PetscBool            isascii;
     58:   PetscErrorCode       ierr;

     61:   PetscObjectTypeCompare((PetscObject)pv, PETSCVIEWERASCII, &isascii);
     62:   if (isascii) {
     63:     ierr=PetscViewerASCIIPrintf(pv,"  Armijo linesearch",armP->alpha);
     64:     if (armP->nondescending) {
     65:       PetscViewerASCIIPrintf(pv, " (nondescending)");
     66:     }
     67:     if (ls->bounded) {
     68:       PetscViewerASCIIPrintf(pv," (projected)");
     69:     }
     70:     ierr=PetscViewerASCIIPrintf(pv,": alpha=%g beta=%g ",(double)armP->alpha,(double)armP->beta);
     71:     ierr=PetscViewerASCIIPrintf(pv,"sigma=%g ",(double)armP->sigma);
     72:     ierr=PetscViewerASCIIPrintf(pv,"memsize=%D\n",armP->memorySize);
     73:   }
     74:   return(0);
     75: }

     77: /* @ TaoApply_Armijo - This routine performs a linesearch. It
     78:    backtracks until the (nonmonotone) Armijo conditions are satisfied.

     80:    Input Parameters:
     81: +  tao - Tao context
     82: .  X - current iterate (on output X contains new iterate, X + step*S)
     83: .  S - search direction
     84: .  f - merit function evaluated at X
     85: .  G - gradient of merit function evaluated at X
     86: .  W - work vector
     87: -  step - initial estimate of step length

     89:    Output parameters:
     90: +  f - merit function evaluated at new iterate, X + step*S
     91: .  G - gradient of merit function evaluated at new iterate, X + step*S
     92: .  X - new iterate
     93: -  step - final step length

     95: @ */
     96: static PetscErrorCode TaoLineSearchApply_Armijo(TaoLineSearch ls, Vec x, PetscReal *f, Vec g, Vec s)
     97: {
     98:   TaoLineSearch_ARMIJO *armP = (TaoLineSearch_ARMIJO *)ls->data;
     99:   PetscErrorCode       ierr;
    100:   PetscInt             i,its=0;
    101:   PetscReal            fact, ref, gdx;
    102:   PetscInt             idx;
    103:   PetscBool            g_computed=PETSC_FALSE; /* to prevent extra gradient computation */

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

    108:   ls->reason = TAOLINESEARCH_CONTINUE_ITERATING;
    109:   if (!armP->work) {
    110:     VecDuplicate(x,&armP->work);
    111:     armP->x = x;
    112:     PetscObjectReference((PetscObject)armP->x);
    113:   } else if (x != armP->x) {
    114:     /* If x has changed, then recreate work */
    115:     VecDestroy(&armP->work);
    116:     VecDuplicate(x,&armP->work);
    117:     PetscObjectDereference((PetscObject)armP->x);
    118:     armP->x = x;
    119:     PetscObjectReference((PetscObject)armP->x);
    120:   }

    122:   /* Check linesearch parameters */
    123:   if (armP->alpha < 1) {
    124:     PetscInfo1(ls,"Armijo line search error: alpha (%g) < 1\n", (double)armP->alpha);
    125:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    126:   } else if ((armP->beta <= 0) || (armP->beta >= 1)) {
    127:     PetscInfo1(ls,"Armijo line search error: beta (%g) invalid\n", (double)armP->beta);
    128:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    129:   } else if ((armP->beta_inf <= 0) || (armP->beta_inf >= 1)) {
    130:     PetscInfo1(ls,"Armijo line search error: beta_inf (%g) invalid\n", (double)armP->beta_inf);
    131:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    132:   } else if ((armP->sigma <= 0) || (armP->sigma >= 0.5)) {
    133:     PetscInfo1(ls,"Armijo line search error: sigma (%g) invalid\n", (double)armP->sigma);
    134:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    135:   } else if (armP->memorySize < 1) {
    136:     PetscInfo1(ls,"Armijo line search error: memory_size (%D) < 1\n", armP->memorySize);
    137:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    138:   } else if ((armP->referencePolicy != REFERENCE_MAX) && (armP->referencePolicy != REFERENCE_AVE) && (armP->referencePolicy != REFERENCE_MEAN)) {
    139:     PetscInfo(ls,"Armijo line search error: reference_policy invalid\n");
    140:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    141:   } else if ((armP->replacementPolicy != REPLACE_FIFO) && (armP->replacementPolicy != REPLACE_MRU)) {
    142:     PetscInfo(ls,"Armijo line search error: replacement_policy invalid\n");
    143:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    144:   } else if (PetscIsInfOrNanReal(*f)) {
    145:     PetscInfo(ls,"Armijo line search error: initial function inf or nan\n");
    146:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    147:   }

    149:   if (ls->reason != TAOLINESEARCH_CONTINUE_ITERATING) {
    150:     return(0);
    151:   }

    153:   /* Check to see of the memory has been allocated.  If not, allocate
    154:      the historical array and populate it with the initial function
    155:      values. */
    156:   if (!armP->memory) {
    157:     PetscMalloc1(armP->memorySize, &armP->memory);
    158:   }

    160:   if (!armP->memorySetup) {
    161:     for (i = 0; i < armP->memorySize; i++) {
    162:       armP->memory[i] = armP->alpha*(*f);
    163:     }

    165:     armP->current = 0;
    166:     armP->lastReference = armP->memory[0];
    167:     armP->memorySetup=PETSC_TRUE;
    168:   }

    170:   /* Calculate reference value (MAX) */
    171:   ref = armP->memory[0];
    172:   idx = 0;

    174:   for (i = 1; i < armP->memorySize; i++) {
    175:     if (armP->memory[i] > ref) {
    176:       ref = armP->memory[i];
    177:       idx = i;
    178:     }
    179:   }

    181:   if (armP->referencePolicy == REFERENCE_AVE) {
    182:     ref = 0;
    183:     for (i = 0; i < armP->memorySize; i++) {
    184:       ref += armP->memory[i];
    185:     }
    186:     ref = ref / armP->memorySize;
    187:     ref = PetscMax(ref, armP->memory[armP->current]);
    188:   } else if (armP->referencePolicy == REFERENCE_MEAN) {
    189:     ref = PetscMin(ref, 0.5*(armP->lastReference + armP->memory[armP->current]));
    190:   }
    191:   VecDot(g,s,&gdx);

    193:   if (PetscIsInfOrNanReal(gdx)) {
    194:     PetscInfo1(ls,"Initial Line Search step * g is Inf or Nan (%g)\n",(double)gdx);
    195:     ls->reason=TAOLINESEARCH_FAILED_INFORNAN;
    196:     return(0);
    197:   }
    198:   if (gdx >= 0.0) {
    199:     PetscInfo1(ls,"Initial Line Search step is not descent direction (g's=%g)\n",(double)gdx);
    200:     ls->reason = TAOLINESEARCH_FAILED_ASCENT;
    201:     return(0);
    202:   }

    204:   if (armP->nondescending) {
    205:     fact = armP->sigma;
    206:   } else {
    207:     fact = armP->sigma * gdx;
    208:   }
    209:   ls->step = ls->initstep;
    210:   while (ls->step >= ls->stepmin && (ls->nfeval+ls->nfgeval) < ls->max_funcs) {
    211:     /* Calculate iterate */
    212:     ++its;
    213:     VecCopy(x,armP->work);
    214:     VecAXPY(armP->work,ls->step,s);
    215:     if (ls->bounded) {
    216:       VecMedian(ls->lower,armP->work,ls->upper,armP->work);
    217:     }

    219:     /* Calculate function at new iterate */
    220:     if (ls->hasobjective) {
    221:       TaoLineSearchComputeObjective(ls,armP->work,f);
    222:       g_computed=PETSC_FALSE;
    223:     } else if (ls->usegts) {
    224:       TaoLineSearchComputeObjectiveAndGTS(ls,armP->work,f,&gdx);
    225:       g_computed=PETSC_FALSE;
    226:     } else {
    227:       TaoLineSearchComputeObjectiveAndGradient(ls,armP->work,f,g);
    228:       g_computed=PETSC_TRUE;
    229:     }
    230:     if (ls->step == ls->initstep) {
    231:       ls->f_fullstep = *f;
    232:     }

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

    236:     if (PetscIsInfOrNanReal(*f)) {
    237:       ls->step *= armP->beta_inf;
    238:     } else {
    239:       /* Check descent condition */
    240:       if (armP->nondescending && *f <= ref - ls->step*fact*ref)
    241:         break;
    242:       if (!armP->nondescending && *f <= ref + ls->step*fact) {
    243:         break;
    244:       }

    246:       ls->step *= armP->beta;
    247:     }
    248:   }

    250:   /* Check termination */
    251:   if (PetscIsInfOrNanReal(*f)) {
    252:     PetscInfo(ls, "Function is inf or nan.\n");
    253:     ls->reason = TAOLINESEARCH_FAILED_INFORNAN;
    254:   } else if (ls->step < ls->stepmin) {
    255:     PetscInfo(ls, "Step length is below tolerance.\n");
    256:     ls->reason = TAOLINESEARCH_HALTED_RTOL;
    257:   } else if ((ls->nfeval+ls->nfgeval) >= ls->max_funcs) {
    258:     PetscInfo2(ls, "Number of line search function evals (%D) > maximum allowed (%D)\n",ls->nfeval+ls->nfgeval, ls->max_funcs);
    259:     ls->reason = TAOLINESEARCH_HALTED_MAXFCN;
    260:   }
    261:   if (ls->reason) {
    262:     return(0);
    263:   }

    265:   /* Successful termination, update memory */
    266:   ls->reason = TAOLINESEARCH_SUCCESS;
    267:   armP->lastReference = ref;
    268:   if (armP->replacementPolicy == REPLACE_FIFO) {
    269:     armP->memory[armP->current++] = *f;
    270:     if (armP->current >= armP->memorySize) {
    271:       armP->current = 0;
    272:     }
    273:   } else {
    274:     armP->current = idx;
    275:     armP->memory[idx] = *f;
    276:   }

    278:   /* Update iterate and compute gradient */
    279:   VecCopy(armP->work,x);
    280:   if (!g_computed) {
    281:     TaoLineSearchComputeGradient(ls, x, g);
    282:   }
    283:   PetscInfo2(ls, "%D function evals in line search, step = %g\n",ls->nfeval, (double)ls->step);
    284:   return(0);
    285: }

    287: /*MC
    288:    TAOLINESEARCHARMIJO - Backtracking line-search that satisfies only the (nonmonotone) Armijo condition
    289:    (i.e., sufficient decrease).

    291:    Armijo line-search type can be selected with "-tao_ls_type armijo".

    293:    Level: developer

    295: .seealso: TaoLineSearchCreate(), TaoLineSearchSetType(), TaoLineSearchApply()

    297: .keywords: Tao, linesearch
    298: M*/
    299: PETSC_EXTERN PetscErrorCode TaoLineSearchCreate_Armijo(TaoLineSearch ls)
    300: {
    301:   TaoLineSearch_ARMIJO *armP;
    302:   PetscErrorCode       ierr;

    306:   PetscNewLog(ls,&armP);

    308:   armP->memory = NULL;
    309:   armP->alpha = 1.0;
    310:   armP->beta = 0.5;
    311:   armP->beta_inf = 0.5;
    312:   armP->sigma = 1e-4;
    313:   armP->memorySize = 1;
    314:   armP->referencePolicy = REFERENCE_MAX;
    315:   armP->replacementPolicy = REPLACE_MRU;
    316:   armP->nondescending=PETSC_FALSE;
    317:   ls->data = (void*)armP;
    318:   ls->initstep=1.0;
    319:   ls->ops->setup = NULL;
    320:   ls->ops->apply = TaoLineSearchApply_Armijo;
    321:   ls->ops->view = TaoLineSearchView_Armijo;
    322:   ls->ops->destroy = TaoLineSearchDestroy_Armijo;
    323:   ls->ops->reset = TaoLineSearchReset_Armijo;
    324:   ls->ops->setfromoptions = TaoLineSearchSetFromOptions_Armijo;
    325:   return(0);
    326: }