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:   PetscInt        low,high,low1,high1,low2,high2,i;

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

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

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

     37: static PetscErrorCode TaoLineSearchDestroy_OWArmijo(TaoLineSearch ls)
     38: {
     39:   TaoLineSearch_OWARMIJO *armP = (TaoLineSearch_OWARMIJO *)ls->data;

     41:   PetscFree(armP->memory);
     42:   if (armP->x) {
     43:     PetscObjectDereference((PetscObject)armP->x);
     44:   }
     45:   VecDestroy(&armP->work);
     46:   PetscFree(ls->data);
     47:   return 0;
     48: }

     50: static PetscErrorCode TaoLineSearchSetFromOptions_OWArmijo(PetscOptionItems *PetscOptionsObject,TaoLineSearch ls)
     51: {
     52:   TaoLineSearch_OWARMIJO *armP = (TaoLineSearch_OWARMIJO *)ls->data;

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

     67: static PetscErrorCode TaoLineSearchView_OWArmijo(TaoLineSearch ls, PetscViewer pv)
     68: {
     69:   TaoLineSearch_OWARMIJO *armP = (TaoLineSearch_OWARMIJO *)ls->data;
     70:   PetscBool              isascii;

     72:   PetscObjectTypeCompare((PetscObject)pv, PETSCVIEWERASCII, &isascii);
     73:   if (isascii) {
     74:     PetscViewerASCIIPrintf(pv,"  OWArmijo linesearch",armP->alpha);
     75:     if (armP->nondescending) {
     76:       PetscViewerASCIIPrintf(pv, " (nondescending)");
     77:     }
     78:     PetscViewerASCIIPrintf(pv,": alpha=%g beta=%g ",(double)armP->alpha,(double)armP->beta);
     79:     PetscViewerASCIIPrintf(pv,"sigma=%g ",(double)armP->sigma);
     80:     PetscViewerASCIIPrintf(pv,"memsize=%D\n",armP->memorySize);
     81:   }
     82:   return 0;
     83: }

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

     88:    Input Parameters:
     89: +  tao - TAO_SOLVER context
     90: .  X - current iterate (on output X contains new iterate, X + step*S)
     91: .  S - search direction
     92: .  f - merit function evaluated at X
     93: .  G - gradient of merit function evaluated at X
     94: .  W - work vector
     95: -  step - initial estimate of step length

     97:    Output parameters:
     98: +  f - merit function evaluated at new iterate, X + step*S
     99: .  G - gradient of merit function evaluated at new iterate, X + step*S
    100: .  X - new iterate
    101: -  step - final step length

    103:    Info is set to one of:
    104: .   0 - the line search succeeds; the sufficient decrease
    105:    condition and the directional derivative condition hold

    107:    negative number if an input parameter is invalid
    108: -   -1 -  step < 0

    110:    positive number > 1 if the line search otherwise terminates
    111: +    1 -  Step is at the lower bound, stepmin.
    112: @ */
    113: static PetscErrorCode TaoLineSearchApply_OWArmijo(TaoLineSearch ls, Vec x, PetscReal *f, Vec g, Vec s)
    114: {
    115:   TaoLineSearch_OWARMIJO *armP = (TaoLineSearch_OWARMIJO *)ls->data;
    116:   PetscInt               i, its=0;
    117:   PetscReal              fact, ref, gdx;
    118:   PetscInt               idx;
    119:   PetscBool              g_computed=PETSC_FALSE; /* to prevent extra gradient computation */
    120:   Vec                    g_old;
    121:   PetscReal              owlqn_minstep=0.005;
    122:   PetscReal              partgdx;
    123:   MPI_Comm               comm;

    125:   PetscObjectGetComm((PetscObject)ls,&comm);
    126:   fact = 0.0;
    127:   ls->nfeval=0;
    128:   ls->reason = TAOLINESEARCH_CONTINUE_ITERATING;
    129:   if (!armP->work) {
    130:     VecDuplicate(x,&armP->work);
    131:     armP->x = x;
    132:     PetscObjectReference((PetscObject)armP->x);
    133:   } else if (x != armP->x) {
    134:     VecDestroy(&armP->work);
    135:     VecDuplicate(x,&armP->work);
    136:     PetscObjectDereference((PetscObject)armP->x);
    137:     armP->x = x;
    138:     PetscObjectReference((PetscObject)armP->x);
    139:   }

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

    143:   /* Check linesearch parameters */
    144:   if (armP->alpha < 1) {
    145:     PetscInfo(ls,"OWArmijo line search error: alpha (%g) < 1\n", (double)armP->alpha);
    146:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    147:   } else if ((armP->beta <= 0) || (armP->beta >= 1)) {
    148:     PetscInfo(ls,"OWArmijo line search error: beta (%g) invalid\n", (double)armP->beta);
    149:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    150:   } else if ((armP->beta_inf <= 0) || (armP->beta_inf >= 1)) {
    151:     PetscInfo(ls,"OWArmijo line search error: beta_inf (%g) invalid\n", (double)armP->beta_inf);
    152:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    153:   } else if ((armP->sigma <= 0) || (armP->sigma >= 0.5)) {
    154:     PetscInfo(ls,"OWArmijo line search error: sigma (%g) invalid\n", (double)armP->sigma);
    155:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    156:   } else if (armP->memorySize < 1) {
    157:     PetscInfo(ls,"OWArmijo line search error: memory_size (%D) < 1\n", armP->memorySize);
    158:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    159:   }  else if ((armP->referencePolicy != REFERENCE_MAX) && (armP->referencePolicy != REFERENCE_AVE) && (armP->referencePolicy != REFERENCE_MEAN)) {
    160:     PetscInfo(ls,"OWArmijo line search error: reference_policy invalid\n");
    161:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    162:   } else if ((armP->replacementPolicy != REPLACE_FIFO) && (armP->replacementPolicy != REPLACE_MRU)) {
    163:     PetscInfo(ls,"OWArmijo line search error: replacement_policy invalid\n");
    164:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    165:   } else if (PetscIsInfOrNanReal(*f)) {
    166:     PetscInfo(ls,"OWArmijo line search error: initial function inf or nan\n");
    167:     ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
    168:   }

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

    172:   /* Check to see of the memory has been allocated.  If not, allocate
    173:      the historical array and populate it with the initial function
    174:      values. */
    175:   if (!armP->memory) {
    176:     PetscMalloc1(armP->memorySize, &armP->memory);
    177:   }

    179:   if (!armP->memorySetup) {
    180:     for (i = 0; i < armP->memorySize; i++) {
    181:       armP->memory[i] = armP->alpha*(*f);
    182:     }
    183:     armP->current = 0;
    184:     armP->lastReference = armP->memory[0];
    185:     armP->memorySetup=PETSC_TRUE;
    186:   }

    188:   /* Calculate reference value (MAX) */
    189:   ref = armP->memory[0];
    190:   idx = 0;

    192:   for (i = 1; i < armP->memorySize; i++) {
    193:     if (armP->memory[i] > ref) {
    194:       ref = armP->memory[i];
    195:       idx = i;
    196:     }
    197:   }

    199:   if (armP->referencePolicy == REFERENCE_AVE) {
    200:     ref = 0;
    201:     for (i = 0; i < armP->memorySize; i++) {
    202:       ref += armP->memory[i];
    203:     }
    204:     ref = ref / armP->memorySize;
    205:     ref = PetscMax(ref, armP->memory[armP->current]);
    206:   } else if (armP->referencePolicy == REFERENCE_MEAN) {
    207:     ref = PetscMin(ref, 0.5*(armP->lastReference + armP->memory[armP->current]));
    208:   }

    210:   if (armP->nondescending) {
    211:     fact = armP->sigma;
    212:   }

    214:   VecDuplicate(g,&g_old);
    215:   VecCopy(g,g_old);

    217:   ls->step = ls->initstep;
    218:   while (ls->step >= owlqn_minstep && ls->nfeval < ls->max_funcs) {
    219:     /* Calculate iterate */
    220:     ++its;
    221:     VecCopy(x,armP->work);
    222:     VecAXPY(armP->work,ls->step,s);

    224:     partgdx=0.0;
    225:     ProjWork_OWLQN(armP->work,x,g_old,&partgdx);
    226:     MPIU_Allreduce(&partgdx,&gdx,1,MPIU_REAL,MPIU_SUM,comm);

    228:     /* Check the condition of gdx */
    229:     if (PetscIsInfOrNanReal(gdx)) {
    230:       PetscInfo(ls,"Initial Line Search step * g is Inf or Nan (%g)\n",(double)gdx);
    231:       ls->reason=TAOLINESEARCH_FAILED_INFORNAN;
    232:       return 0;
    233:     }
    234:     if (gdx >= 0.0) {
    235:       PetscInfo(ls,"Initial Line Search step is not descent direction (g's=%g)\n",(double)gdx);
    236:       ls->reason = TAOLINESEARCH_FAILED_ASCENT;
    237:       return 0;
    238:     }

    240:     /* Calculate function at new iterate */
    241:     TaoLineSearchComputeObjectiveAndGradient(ls,armP->work,f,g);
    242:     g_computed=PETSC_TRUE;

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

    246:     if (ls->step == ls->initstep) {
    247:       ls->f_fullstep = *f;
    248:     }

    250:     if (PetscIsInfOrNanReal(*f)) {
    251:       ls->step *= armP->beta_inf;
    252:     } else {
    253:       /* Check descent condition */
    254:       if (armP->nondescending && *f <= ref - ls->step*fact*ref) break;
    255:       if (!armP->nondescending && *f <= ref + armP->sigma * gdx) break;
    256:       ls->step *= armP->beta;
    257:     }
    258:   }
    259:   VecDestroy(&g_old);

    261:   /* Check termination */
    262:   if (PetscIsInfOrNanReal(*f)) {
    263:     PetscInfo(ls, "Function is inf or nan.\n");
    264:     ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
    265:   } else if (ls->step < owlqn_minstep) {
    266:     PetscInfo(ls, "Step length is below tolerance.\n");
    267:     ls->reason = TAOLINESEARCH_HALTED_RTOL;
    268:   } else if (ls->nfeval >= ls->max_funcs) {
    269:     PetscInfo(ls, "Number of line search function evals (%D) > maximum allowed (%D)\n",ls->nfeval, ls->max_funcs);
    270:     ls->reason = TAOLINESEARCH_HALTED_MAXFCN;
    271:   }
    272:   if (ls->reason) return 0;

    274:   /* Successful termination, update memory */
    275:   ls->reason = TAOLINESEARCH_SUCCESS;
    276:   armP->lastReference = ref;
    277:   if (armP->replacementPolicy == REPLACE_FIFO) {
    278:     armP->memory[armP->current++] = *f;
    279:     if (armP->current >= armP->memorySize) {
    280:       armP->current = 0;
    281:     }
    282:   } else {
    283:     armP->current = idx;
    284:     armP->memory[idx] = *f;
    285:   }

    287:   /* Update iterate and compute gradient */
    288:   VecCopy(armP->work,x);
    289:   if (!g_computed) {
    290:     TaoLineSearchComputeGradient(ls, x, g);
    291:   }
    292:   PetscInfo(ls, "%D function evals in line search, step = %10.4f\n",ls->nfeval, (double)ls->step);
    293:   return 0;
    294: }

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

    300:    Level: developer

    302: .keywords: Tao, linesearch
    303: M*/
    304: PETSC_EXTERN PetscErrorCode TaoLineSearchCreate_OWArmijo(TaoLineSearch ls)
    305: {
    306:   TaoLineSearch_OWARMIJO *armP;

    309:   PetscNewLog(ls,&armP);

    311:   armP->memory = NULL;
    312:   armP->alpha = 1.0;
    313:   armP->beta = 0.25;
    314:   armP->beta_inf = 0.25;
    315:   armP->sigma = 1e-4;
    316:   armP->memorySize = 1;
    317:   armP->referencePolicy = REFERENCE_MAX;
    318:   armP->replacementPolicy = REPLACE_MRU;
    319:   armP->nondescending=PETSC_FALSE;
    320:   ls->data = (void*)armP;
    321:   ls->initstep = 0.1;
    322:   ls->ops->monitor = NULL;
    323:   ls->ops->setup = NULL;
    324:   ls->ops->reset = NULL;
    325:   ls->ops->apply = TaoLineSearchApply_OWArmijo;
    326:   ls->ops->view = TaoLineSearchView_OWArmijo;
    327:   ls->ops->destroy = TaoLineSearchDestroy_OWArmijo;
    328:   ls->ops->setfromoptions = TaoLineSearchSetFromOptions_OWArmijo;
    329:   return 0;
    330: }