Actual source code: owarmijo.c

petsc-3.11.1 2019-04-17
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  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:   ierr=VecGetOwnershipRange(w,&low,&high);
 21:   ierr=VecGetOwnershipRange(x,&low1,&high1);
 22:   ierr=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:     ierr=PetscViewerASCIIPrintf(pv,"  OWArmijo linesearch",armP->alpha);
 83:     if (armP->nondescending) {
 84:       PetscViewerASCIIPrintf(pv, " (nondescending)");
 85:     }
 86:     ierr=PetscViewerASCIIPrintf(pv,": alpha=%g beta=%g ",(double)armP->alpha,(double)armP->beta);
 87:     ierr=PetscViewerASCIIPrintf(pv,"sigma=%g ",(double)armP->sigma);
 88:     ierr=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:   }
150: 
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;
253: 
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: PETSC_EXTERN PetscErrorCode TaoLineSearchCreate_OWArmijo(TaoLineSearch ls)
307: {
308:   TaoLineSearch_OWARMIJO *armP;
309:   PetscErrorCode         ierr;

313:   PetscNewLog(ls,&armP);

315:   armP->memory = NULL;
316:   armP->alpha = 1.0;
317:   armP->beta = 0.25;
318:   armP->beta_inf = 0.25;
319:   armP->sigma = 1e-4;
320:   armP->memorySize = 1;
321:   armP->referencePolicy = REFERENCE_MAX;
322:   armP->replacementPolicy = REPLACE_MRU;
323:   armP->nondescending=PETSC_FALSE;
324:   ls->data = (void*)armP;
325:   ls->initstep=0.1;
326:   ls->ops->setup=0;
327:   ls->ops->reset=0;
328:   ls->ops->apply=TaoLineSearchApply_OWArmijo;
329:   ls->ops->view = TaoLineSearchView_OWArmijo;
330:   ls->ops->destroy = TaoLineSearchDestroy_OWArmijo;
331:   ls->ops->setfromoptions = TaoLineSearchSetFromOptions_OWArmijo;
332:   return(0);
333: }