Actual source code: tron.c

petsc-3.6.1 2015-08-06
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  1: #include <../src/tao/bound/impls/tron/tron.h>
  2: #include <petsc/private/kspimpl.h>
  3: #include <petsc/private/matimpl.h>
  4: #include <../src/tao/matrix/submatfree.h>


  7: /* TRON Routines */
  8: static PetscErrorCode TronGradientProjections(Tao,TAO_TRON*);
  9: /*------------------------------------------------------------*/
 12: static PetscErrorCode TaoDestroy_TRON(Tao tao)
 13: {
 14:   TAO_TRON       *tron = (TAO_TRON *)tao->data;

 18:   VecDestroy(&tron->X_New);
 19:   VecDestroy(&tron->G_New);
 20:   VecDestroy(&tron->Work);
 21:   VecDestroy(&tron->DXFree);
 22:   VecDestroy(&tron->R);
 23:   VecDestroy(&tron->diag);
 24:   VecScatterDestroy(&tron->scatter);
 25:   ISDestroy(&tron->Free_Local);
 26:   MatDestroy(&tron->H_sub);
 27:   MatDestroy(&tron->Hpre_sub);
 28:   PetscFree(tao->data);
 29:   return(0);
 30: }

 32: /*------------------------------------------------------------*/
 35: static PetscErrorCode TaoSetFromOptions_TRON(PetscOptions *PetscOptionsObject,Tao tao)
 36: {
 37:   TAO_TRON       *tron = (TAO_TRON *)tao->data;
 39:   PetscBool      flg;

 42:   PetscOptionsHead(PetscOptionsObject,"Newton Trust Region Method for bound constrained optimization");
 43:   PetscOptionsInt("-tao_tron_maxgpits","maximum number of gradient projections per TRON iterate","TaoSetMaxGPIts",tron->maxgpits,&tron->maxgpits,&flg);
 44:   PetscOptionsTail();
 45:   TaoLineSearchSetFromOptions(tao->linesearch);
 46:   KSPSetFromOptions(tao->ksp);
 47:   return(0);
 48: }

 50: /*------------------------------------------------------------*/
 53: static PetscErrorCode TaoView_TRON(Tao tao, PetscViewer viewer)
 54: {
 55:   TAO_TRON         *tron = (TAO_TRON *)tao->data;
 56:   PetscBool        isascii;
 57:   PetscErrorCode   ierr;

 60:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&isascii);
 61:   if (isascii) {
 62:     PetscViewerASCIIPushTab(viewer);
 63:     PetscViewerASCIIPrintf(viewer,"Total PG its: %D,",tron->total_gp_its);
 64:     PetscViewerASCIIPrintf(viewer,"PG tolerance: %g \n",(double)tron->pg_ftol);
 65:     PetscViewerASCIIPopTab(viewer);
 66:   }
 67:   return(0);
 68: }


 71: /* ---------------------------------------------------------- */
 74: static PetscErrorCode TaoSetup_TRON(Tao tao)
 75: {
 77:   TAO_TRON       *tron = (TAO_TRON *)tao->data;


 81:   /* Allocate some arrays */
 82:   VecDuplicate(tao->solution, &tron->diag);
 83:   VecDuplicate(tao->solution, &tron->X_New);
 84:   VecDuplicate(tao->solution, &tron->G_New);
 85:   VecDuplicate(tao->solution, &tron->Work);
 86:   VecDuplicate(tao->solution, &tao->gradient);
 87:   VecDuplicate(tao->solution, &tao->stepdirection);
 88:   if (!tao->XL) {
 89:       VecDuplicate(tao->solution, &tao->XL);
 90:       VecSet(tao->XL, PETSC_NINFINITY);
 91:   }
 92:   if (!tao->XU) {
 93:       VecDuplicate(tao->solution, &tao->XU);
 94:       VecSet(tao->XU, PETSC_INFINITY);
 95:   }
 96:   return(0);
 97: }



103: static PetscErrorCode TaoSolve_TRON(Tao tao)
104: {
105:   TAO_TRON                     *tron = (TAO_TRON *)tao->data;
106:   PetscErrorCode               ierr;
107:   PetscInt                     its;
108:   TaoConvergedReason           reason = TAO_CONTINUE_ITERATING;
109:   TaoLineSearchConvergedReason ls_reason = TAOLINESEARCH_CONTINUE_ITERATING;
110:   PetscReal                    prered,actred,delta,f,f_new,rhok,gdx,xdiff,stepsize;

113:   tron->pgstepsize=1.0;
114:   tao->trust = tao->trust0;
115:   /*   Project the current point onto the feasible set */
116:   TaoComputeVariableBounds(tao);
117:   VecMedian(tao->XL,tao->solution,tao->XU,tao->solution);
118:   TaoLineSearchSetVariableBounds(tao->linesearch,tao->XL,tao->XU);

120:   TaoComputeObjectiveAndGradient(tao,tao->solution,&tron->f,tao->gradient);
121:   ISDestroy(&tron->Free_Local);

123:   VecWhichBetween(tao->XL,tao->solution,tao->XU,&tron->Free_Local);

125:   /* Project the gradient and calculate the norm */
126:   VecBoundGradientProjection(tao->gradient,tao->solution, tao->XL, tao->XU, tao->gradient);
127:   VecNorm(tao->gradient,NORM_2,&tron->gnorm);

129:   if (PetscIsInfOrNanReal(tron->f) || PetscIsInfOrNanReal(tron->gnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf pr NaN");
130:   if (tao->trust <= 0) {
131:     tao->trust=PetscMax(tron->gnorm*tron->gnorm,1.0);
132:   }

134:   tron->stepsize=tao->trust;
135:   TaoMonitor(tao, tao->niter, tron->f, tron->gnorm, 0.0, tron->stepsize, &reason);
136:   while (reason==TAO_CONTINUE_ITERATING){
137:     tao->ksp_its=0;
138:     TronGradientProjections(tao,tron);
139:     f=tron->f; delta=tao->trust;
140:     tron->n_free_last = tron->n_free;
141:     TaoComputeHessian(tao,tao->solution,tao->hessian,tao->hessian_pre);

143:     ISGetSize(tron->Free_Local, &tron->n_free);

145:     /* If no free variables */
146:     if (tron->n_free == 0) {
147:       actred=0;
148:       PetscInfo(tao,"No free variables in tron iteration.\n");
149:       VecNorm(tao->gradient,NORM_2,&tron->gnorm);
150:       TaoMonitor(tao, tao->niter, tron->f, tron->gnorm, 0.0, delta, &reason);
151:       if (!reason) {
152:         reason = TAO_CONVERGED_STEPTOL;
153:         TaoSetConvergedReason(tao,reason);
154:       }

156:       break;

158:     }
159:     /* use free_local to mask/submat gradient, hessian, stepdirection */
160:     TaoVecGetSubVec(tao->gradient,tron->Free_Local,tao->subset_type,0.0,&tron->R);
161:     TaoVecGetSubVec(tao->gradient,tron->Free_Local,tao->subset_type,0.0,&tron->DXFree);
162:     VecSet(tron->DXFree,0.0);
163:     VecScale(tron->R, -1.0);
164:     TaoMatGetSubMat(tao->hessian, tron->Free_Local, tron->diag, tao->subset_type, &tron->H_sub);
165:     if (tao->hessian == tao->hessian_pre) {
166:       MatDestroy(&tron->Hpre_sub);
167:       PetscObjectReference((PetscObject)(tron->H_sub));
168:       tron->Hpre_sub = tron->H_sub;
169:     } else {
170:       TaoMatGetSubMat(tao->hessian_pre, tron->Free_Local, tron->diag, tao->subset_type,&tron->Hpre_sub);
171:     }
172:     KSPReset(tao->ksp);
173:     KSPSetOperators(tao->ksp, tron->H_sub, tron->Hpre_sub);
174:     while (1) {

176:       /* Approximately solve the reduced linear system */
177:       KSPSTCGSetRadius(tao->ksp,delta);

179:       KSPSolve(tao->ksp, tron->R, tron->DXFree);
180:       KSPGetIterationNumber(tao->ksp,&its);
181:       tao->ksp_its+=its;
182:       tao->ksp_tot_its+=its;
183:       VecSet(tao->stepdirection,0.0);

185:       /* Add dxfree matrix to compute step direction vector */
186:       VecISAXPY(tao->stepdirection,tron->Free_Local,1.0,tron->DXFree);
187:       if (0) {
188:         PetscReal rhs,stepnorm;
189:         VecNorm(tron->R,NORM_2,&rhs);
190:         VecNorm(tron->DXFree,NORM_2,&stepnorm);
191:         PetscPrintf(PETSC_COMM_WORLD,"|rhs|=%g\t|s|=%g\n",(double)rhs,(double)stepnorm);
192:       }


195:       VecDot(tao->gradient, tao->stepdirection, &gdx);
196:       PetscInfo1(tao,"Expected decrease in function value: %14.12e\n",(double)gdx);

198:       VecCopy(tao->solution, tron->X_New);
199:       VecCopy(tao->gradient, tron->G_New);

201:       stepsize=1.0;f_new=f;

203:       TaoLineSearchSetInitialStepLength(tao->linesearch,1.0);
204:       TaoLineSearchApply(tao->linesearch, tron->X_New, &f_new, tron->G_New, tao->stepdirection,&stepsize,&ls_reason);
205:       TaoAddLineSearchCounts(tao);

207:       MatMult(tao->hessian, tao->stepdirection, tron->Work);
208:       VecAYPX(tron->Work, 0.5, tao->gradient);
209:       VecDot(tao->stepdirection, tron->Work, &prered);
210:       actred = f_new - f;
211:       if (actred<0) {
212:         rhok=PetscAbs(-actred/prered);
213:       } else {
214:         rhok=0.0;
215:       }

217:       /* Compare actual improvement to the quadratic model */
218:       if (rhok > tron->eta1) { /* Accept the point */
219:         /* d = x_new - x */
220:         VecCopy(tron->X_New, tao->stepdirection);
221:         VecAXPY(tao->stepdirection, -1.0, tao->solution);

223:         VecNorm(tao->stepdirection, NORM_2, &xdiff);
224:         xdiff *= stepsize;

226:         /* Adjust trust region size */
227:         if (rhok < tron->eta2 ){
228:           delta = PetscMin(xdiff,delta)*tron->sigma1;
229:         } else if (rhok > tron->eta4 ){
230:           delta= PetscMin(xdiff,delta)*tron->sigma3;
231:         } else if (rhok > tron->eta3 ){
232:           delta=PetscMin(xdiff,delta)*tron->sigma2;
233:         }
234:         VecBoundGradientProjection(tron->G_New,tron->X_New, tao->XL, tao->XU, tao->gradient);
235:         if (tron->Free_Local) {
236:           ISDestroy(&tron->Free_Local);
237:         }
238:         VecWhichBetween(tao->XL, tron->X_New, tao->XU, &tron->Free_Local);
239:         f=f_new;
240:         VecNorm(tao->gradient,NORM_2,&tron->gnorm);
241:         VecCopy(tron->X_New, tao->solution);
242:         VecCopy(tron->G_New, tao->gradient);
243:         break;
244:       }
245:       else if (delta <= 1e-30) {
246:         break;
247:       }
248:       else {
249:         delta /= 4.0;
250:       }
251:     } /* end linear solve loop */


254:     tron->f=f; tron->actred=actred; tao->trust=delta;
255:     tao->niter++;
256:     TaoMonitor(tao, tao->niter, tron->f, tron->gnorm, 0.0, delta, &reason);
257:   }  /* END MAIN LOOP  */

259:   return(0);
260: }


265: static PetscErrorCode TronGradientProjections(Tao tao,TAO_TRON *tron)
266: {
267:   PetscErrorCode                 ierr;
268:   PetscInt                       i;
269:   TaoLineSearchConvergedReason ls_reason;
270:   PetscReal                      actred=-1.0,actred_max=0.0;
271:   PetscReal                      f_new;
272:   /*
273:      The gradient and function value passed into and out of this
274:      routine should be current and correct.

276:      The free, active, and binding variables should be already identified
277:   */
279:   if (tron->Free_Local) {
280:     ISDestroy(&tron->Free_Local);
281:   }
282:   VecWhichBetween(tao->XL,tao->solution,tao->XU,&tron->Free_Local);

284:   for (i=0;i<tron->maxgpits;i++){

286:     if ( -actred <= (tron->pg_ftol)*actred_max) break;

288:     tron->gp_iterates++; tron->total_gp_its++;
289:     f_new=tron->f;

291:     VecCopy(tao->gradient, tao->stepdirection);
292:     VecScale(tao->stepdirection, -1.0);
293:     TaoLineSearchSetInitialStepLength(tao->linesearch,tron->pgstepsize);
294:     TaoLineSearchApply(tao->linesearch, tao->solution, &f_new, tao->gradient, tao->stepdirection,
295:                               &tron->pgstepsize, &ls_reason);
296:     TaoAddLineSearchCounts(tao);


299:     /* Update the iterate */
300:     actred = f_new - tron->f;
301:     actred_max = PetscMax(actred_max,-(f_new - tron->f));
302:     tron->f = f_new;
303:     if (tron->Free_Local) {
304:       ISDestroy(&tron->Free_Local);
305:     }
306:     VecWhichBetween(tao->XL,tao->solution,tao->XU,&tron->Free_Local);
307:   }

309:   return(0);
310: }

314: static PetscErrorCode TaoComputeDual_TRON(Tao tao, Vec DXL, Vec DXU) {

316:   TAO_TRON       *tron = (TAO_TRON *)tao->data;

323:   if (!tron->Work || !tao->gradient) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Dual variables don't exist yet or no longer exist.\n");

325:   VecBoundGradientProjection(tao->gradient,tao->solution,tao->XL,tao->XU,tron->Work);
326:   VecCopy(tron->Work,DXL);
327:   VecAXPY(DXL,-1.0,tao->gradient);
328:   VecSet(DXU,0.0);
329:   VecPointwiseMax(DXL,DXL,DXU);

331:   VecCopy(tao->gradient,DXU);
332:   VecAXPY(DXU,-1.0,tron->Work);
333:   VecSet(tron->Work,0.0);
334:   VecPointwiseMin(DXU,tron->Work,DXU);
335:   return(0);
336: }

338: /*------------------------------------------------------------*/
339: /*MC
340:   TAOTRON - The TRON algorithm is an active-set Newton trust region method
341:   for bound-constrained minimization.

343:   Options Database Keys:
344: + -tao_tron_maxgpits - maximum number of gradient projections per TRON iterate
345: - -tao_subset_type - "subvec","mask","matrix-free", strategies for handling active-sets

347:   Level: beginner
348: M*/
351: PETSC_EXTERN PetscErrorCode TaoCreate_TRON(Tao tao)
352: {
353:   TAO_TRON       *tron;
355:   const char     *morethuente_type = TAOLINESEARCHMT;

358:   tao->ops->setup = TaoSetup_TRON;
359:   tao->ops->solve = TaoSolve_TRON;
360:   tao->ops->view = TaoView_TRON;
361:   tao->ops->setfromoptions = TaoSetFromOptions_TRON;
362:   tao->ops->destroy = TaoDestroy_TRON;
363:   tao->ops->computedual = TaoComputeDual_TRON;

365:   PetscNewLog(tao,&tron);
366:   tao->data = (void*)tron;

368:   /* Override default settings (unless already changed) */
369:   if (!tao->max_it_changed) tao->max_it = 50;

371: #if defined(PETSC_USE_REAL_SINGLE)
372:   if (!tao->fatol_changed) tao->fatol = 1e-6;
373:   if (!tao->frtol_changed) tao->frtol = 1e-6;
374:   if (!tao->steptol_changed) tao->steptol = 1.0e-6;
375: #else
376:   if (!tao->fatol_changed) tao->fatol = 1e-12;
377:   if (!tao->frtol_changed) tao->frtol = 1e-12;
378:   if (!tao->steptol_changed) tao->steptol = 1.0e-12;
379: #endif

381:   if (!tao->trust0_changed) tao->trust0 = 1.0;

383:   /* Initialize pointers and variables */
384:   tron->n            = 0;
385:   tron->maxgpits     = 3;
386:   tron->pg_ftol      = 0.001;

388:   tron->eta1         = 1.0e-4;
389:   tron->eta2         = 0.25;
390:   tron->eta3         = 0.50;
391:   tron->eta4         = 0.90;

393:   tron->sigma1       = 0.5;
394:   tron->sigma2       = 2.0;
395:   tron->sigma3       = 4.0;

397:   tron->gp_iterates  = 0; /* Cumulative number */
398:   tron->total_gp_its = 0;
399:   tron->n_free       = 0;

401:   tron->DXFree=NULL;
402:   tron->R=NULL;
403:   tron->X_New=NULL;
404:   tron->G_New=NULL;
405:   tron->Work=NULL;
406:   tron->Free_Local=NULL;
407:   tron->H_sub=NULL;
408:   tron->Hpre_sub=NULL;
409:   tao->subset_type = TAO_SUBSET_SUBVEC;

411:   TaoLineSearchCreate(((PetscObject)tao)->comm, &tao->linesearch);
412:   TaoLineSearchSetType(tao->linesearch,morethuente_type);
413:   TaoLineSearchUseTaoRoutines(tao->linesearch,tao);
414:   TaoLineSearchSetOptionsPrefix(tao->linesearch,tao->hdr.prefix);

416:   KSPCreate(((PetscObject)tao)->comm, &tao->ksp);
417:   KSPSetOptionsPrefix(tao->ksp, tao->hdr.prefix);
418:   KSPSetType(tao->ksp,KSPSTCG);
419:   return(0);
420: }