Actual source code: taocg.c

petsc-3.9.4 2018-09-11
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  1:  #include <petsctaolinesearch.h>
  2:  #include <../src/tao/unconstrained/impls/cg/taocg.h>

  4: #define CG_FletcherReeves       0
  5: #define CG_PolakRibiere         1
  6: #define CG_PolakRibierePlus     2
  7: #define CG_HestenesStiefel      3
  8: #define CG_DaiYuan              4
  9: #define CG_Types                5

 11: static const char *CG_Table[64] = {"fr", "pr", "prp", "hs", "dy"};

 13: static PetscErrorCode TaoSolve_CG(Tao tao)
 14: {
 15:   TAO_CG                       *cgP = (TAO_CG*)tao->data;
 16:   PetscErrorCode               ierr;
 17:   TaoLineSearchConvergedReason ls_status = TAOLINESEARCH_CONTINUE_ITERATING;
 18:   PetscReal                    step=1.0,f,gnorm,gnorm2,delta,gd,ginner,beta;
 19:   PetscReal                    gd_old,gnorm2_old,f_old;

 22:   if (tao->XL || tao->XU || tao->ops->computebounds) {
 23:     PetscPrintf(((PetscObject)tao)->comm,"WARNING: Variable bounds have been set but will be ignored by cg algorithm\n");
 24:   }

 26:   /*  Check convergence criteria */
 27:   TaoComputeObjectiveAndGradient(tao, tao->solution, &f, tao->gradient);
 28:   VecNorm(tao->gradient,NORM_2,&gnorm);
 29:   if (PetscIsInfOrNanReal(f) || PetscIsInfOrNanReal(gnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN");
 30: 
 31:   tao->reason = TAO_CONTINUE_ITERATING;
 32:   TaoLogConvergenceHistory(tao,f,gnorm,0.0,tao->ksp_its);
 33:   TaoMonitor(tao,tao->niter,f,gnorm,0.0,step);
 34:   (*tao->ops->convergencetest)(tao,tao->cnvP);
 35:   if (tao->reason != TAO_CONTINUE_ITERATING) return(0);

 37:   /*  Set initial direction to -gradient */
 38:   VecCopy(tao->gradient, tao->stepdirection);
 39:   VecScale(tao->stepdirection, -1.0);
 40:   gnorm2 = gnorm*gnorm;

 42:   /*  Set initial scaling for the function */
 43:   if (f != 0.0) {
 44:     delta = 2.0*PetscAbsScalar(f) / gnorm2;
 45:     delta = PetscMax(delta,cgP->delta_min);
 46:     delta = PetscMin(delta,cgP->delta_max);
 47:   } else {
 48:     delta = 2.0 / gnorm2;
 49:     delta = PetscMax(delta,cgP->delta_min);
 50:     delta = PetscMin(delta,cgP->delta_max);
 51:   }
 52:   /*  Set counter for gradient and reset steps */
 53:   cgP->ngradsteps = 0;
 54:   cgP->nresetsteps = 0;

 56:   while (1) {
 57:     /*  Save the current gradient information */
 58:     f_old = f;
 59:     gnorm2_old = gnorm2;
 60:     VecCopy(tao->solution, cgP->X_old);
 61:     VecCopy(tao->gradient, cgP->G_old);
 62:     VecDot(tao->gradient, tao->stepdirection, &gd);
 63:     if ((gd >= 0) || PetscIsInfOrNanReal(gd)) {
 64:       ++cgP->ngradsteps;
 65:       if (f != 0.0) {
 66:         delta = 2.0*PetscAbsScalar(f) / gnorm2;
 67:         delta = PetscMax(delta,cgP->delta_min);
 68:         delta = PetscMin(delta,cgP->delta_max);
 69:       } else {
 70:         delta = 2.0 / gnorm2;
 71:         delta = PetscMax(delta,cgP->delta_min);
 72:         delta = PetscMin(delta,cgP->delta_max);
 73:       }

 75:       VecCopy(tao->gradient, tao->stepdirection);
 76:       VecScale(tao->stepdirection, -1.0);
 77:     }

 79:     /*  Search direction for improving point */
 80:     TaoLineSearchSetInitialStepLength(tao->linesearch,delta);
 81:     TaoLineSearchApply(tao->linesearch, tao->solution, &f, tao->gradient, tao->stepdirection, &step, &ls_status);
 82:     TaoAddLineSearchCounts(tao);
 83:     if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER) {
 84:       /*  Linesearch failed */
 85:       /*  Reset factors and use scaled gradient step */
 86:       ++cgP->nresetsteps;
 87:       f = f_old;
 88:       gnorm2 = gnorm2_old;
 89:       VecCopy(cgP->X_old, tao->solution);
 90:       VecCopy(cgP->G_old, tao->gradient);

 92:       if (f != 0.0) {
 93:         delta = 2.0*PetscAbsScalar(f) / gnorm2;
 94:         delta = PetscMax(delta,cgP->delta_min);
 95:         delta = PetscMin(delta,cgP->delta_max);
 96:       } else {
 97:         delta = 2.0 / gnorm2;
 98:         delta = PetscMax(delta,cgP->delta_min);
 99:         delta = PetscMin(delta,cgP->delta_max);
100:       }

102:       VecCopy(tao->gradient, tao->stepdirection);
103:       VecScale(tao->stepdirection, -1.0);

105:       TaoLineSearchSetInitialStepLength(tao->linesearch,delta);
106:       TaoLineSearchApply(tao->linesearch, tao->solution, &f, tao->gradient, tao->stepdirection, &step, &ls_status);
107:       TaoAddLineSearchCounts(tao);

109:       if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER) {
110:         /*  Linesearch failed again */
111:         /*  switch to unscaled gradient */
112:         f = f_old;
113:         VecCopy(cgP->X_old, tao->solution);
114:         VecCopy(cgP->G_old, tao->gradient);
115:         delta = 1.0;
116:         VecCopy(tao->solution, tao->stepdirection);
117:         VecScale(tao->stepdirection, -1.0);

119:         TaoLineSearchSetInitialStepLength(tao->linesearch,delta);
120:         TaoLineSearchApply(tao->linesearch, tao->solution, &f, tao->gradient, tao->stepdirection, &step, &ls_status);
121:         TaoAddLineSearchCounts(tao);
122:         if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER) {

124:           /*  Line search failed for last time -- give up */
125:           f = f_old;
126:           VecCopy(cgP->X_old, tao->solution);
127:           VecCopy(cgP->G_old, tao->gradient);
128:           step = 0.0;
129:           tao->reason = TAO_DIVERGED_LS_FAILURE;
130:         }
131:       }
132:     }

134:     /*  Check for bad value */
135:     VecNorm(tao->gradient,NORM_2,&gnorm);
136:     if (PetscIsInfOrNanReal(f) || PetscIsInfOrNanReal(gnorm)) SETERRQ(PETSC_COMM_SELF,1,"User-provided compute function generated Inf or NaN");

138:     /*  Check for termination */
139:     gnorm2 =gnorm * gnorm;
140:     tao->niter++;
141:     TaoLogConvergenceHistory(tao,f,gnorm,0.0,tao->ksp_its);
142:     TaoMonitor(tao,tao->niter,f,gnorm,0.0,step);
143:     (*tao->ops->convergencetest)(tao,tao->cnvP);
144:     if (tao->reason != TAO_CONTINUE_ITERATING) {
145:       break;
146:     }

148:     /*  Check for restart condition */
149:     VecDot(tao->gradient, cgP->G_old, &ginner);
150:     if (PetscAbsScalar(ginner) >= cgP->eta * gnorm2) {
151:       /*  Gradients far from orthognal; use steepest descent direction */
152:       beta = 0.0;
153:     } else {
154:       /*  Gradients close to orthogonal; use conjugate gradient formula */
155:       switch (cgP->cg_type) {
156:       case CG_FletcherReeves:
157:         beta = gnorm2 / gnorm2_old;
158:         break;

160:       case CG_PolakRibiere:
161:         beta = (gnorm2 - ginner) / gnorm2_old;
162:         break;

164:       case CG_PolakRibierePlus:
165:         beta = PetscMax((gnorm2-ginner)/gnorm2_old, 0.0);
166:         break;

168:       case CG_HestenesStiefel:
169:         VecDot(tao->gradient, tao->stepdirection, &gd);
170:         VecDot(cgP->G_old, tao->stepdirection, &gd_old);
171:         beta = (gnorm2 - ginner) / (gd - gd_old);
172:         break;

174:       case CG_DaiYuan:
175:         VecDot(tao->gradient, tao->stepdirection, &gd);
176:         VecDot(cgP->G_old, tao->stepdirection, &gd_old);
177:         beta = gnorm2 / (gd - gd_old);
178:         break;

180:       default:
181:         beta = 0.0;
182:         break;
183:       }
184:     }

186:     /*  Compute the direction d=-g + beta*d */
187:     VecAXPBY(tao->stepdirection, -1.0, beta, tao->gradient);

189:     /*  update initial steplength choice */
190:     delta = 1.0;
191:     delta = PetscMax(delta, cgP->delta_min);
192:     delta = PetscMin(delta, cgP->delta_max);
193:   }
194:   return(0);
195: }

197: static PetscErrorCode TaoSetUp_CG(Tao tao)
198: {
199:   TAO_CG         *cgP = (TAO_CG*)tao->data;

203:   if (!tao->gradient) {VecDuplicate(tao->solution,&tao->gradient);}
204:   if (!tao->stepdirection) {VecDuplicate(tao->solution,&tao->stepdirection); }
205:   if (!cgP->X_old) {VecDuplicate(tao->solution,&cgP->X_old);}
206:   if (!cgP->G_old) {VecDuplicate(tao->gradient,&cgP->G_old); }
207:   return(0);
208: }

210: static PetscErrorCode TaoDestroy_CG(Tao tao)
211: {
212:   TAO_CG         *cgP = (TAO_CG*) tao->data;

216:   if (tao->setupcalled) {
217:     VecDestroy(&cgP->X_old);
218:     VecDestroy(&cgP->G_old);
219:   }
220:   TaoLineSearchDestroy(&tao->linesearch);
221:   PetscFree(tao->data);
222:   return(0);
223: }

225: static PetscErrorCode TaoSetFromOptions_CG(PetscOptionItems *PetscOptionsObject,Tao tao)
226:  {
227:     TAO_CG         *cgP = (TAO_CG*)tao->data;

231:     TaoLineSearchSetFromOptions(tao->linesearch);
232:     PetscOptionsHead(PetscOptionsObject,"Nonlinear Conjugate Gradient method for unconstrained optimization");
233:     PetscOptionsReal("-tao_cg_eta","restart tolerance", "", cgP->eta,&cgP->eta,NULL);
234:     PetscOptionsEList("-tao_cg_type","cg formula", "", CG_Table, CG_Types, CG_Table[cgP->cg_type], &cgP->cg_type,NULL);
235:     PetscOptionsReal("-tao_cg_delta_min","minimum delta value", "", cgP->delta_min,&cgP->delta_min,NULL);
236:     PetscOptionsReal("-tao_cg_delta_max","maximum delta value", "", cgP->delta_max,&cgP->delta_max,NULL);
237:    PetscOptionsTail();
238:    return(0);
239: }

241: static PetscErrorCode TaoView_CG(Tao tao, PetscViewer viewer)
242: {
243:   PetscBool      isascii;
244:   TAO_CG         *cgP = (TAO_CG*)tao->data;

248:   PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii);
249:   if (isascii) {
250:     PetscViewerASCIIPushTab(viewer);
251:     PetscViewerASCIIPrintf(viewer, "CG Type: %s\n", CG_Table[cgP->cg_type]);
252:     PetscViewerASCIIPrintf(viewer, "Gradient steps: %D\n", cgP->ngradsteps);
253:     ierr= PetscViewerASCIIPrintf(viewer, "Reset steps: %D\n", cgP->nresetsteps);
254:     PetscViewerASCIIPopTab(viewer);
255:   }
256:   return(0);
257: }

259: /*MC
260:      TAOCG -   Nonlinear conjugate gradient method is an extension of the
261: nonlinear conjugate gradient solver for nonlinear optimization.

263:    Options Database Keys:
264: +      -tao_cg_eta <r> - restart tolerance
265: .      -tao_cg_type <taocg_type> - cg formula
266: .      -tao_cg_delta_min <r> - minimum delta value
267: -      -tao_cg_delta_max <r> - maximum delta value

269:   Notes:
270:      CG formulas are:
271:          "fr" - Fletcher-Reeves
272:          "pr" - Polak-Ribiere
273:          "prp" - Polak-Ribiere-Plus
274:          "hs" - Hestenes-Steifel
275:          "dy" - Dai-Yuan
276:   Level: beginner
277: M*/


280: PETSC_EXTERN PetscErrorCode TaoCreate_CG(Tao tao)
281: {
282:   TAO_CG         *cgP;
283:   const char     *morethuente_type = TAOLINESEARCHMT;

287:   tao->ops->setup = TaoSetUp_CG;
288:   tao->ops->solve = TaoSolve_CG;
289:   tao->ops->view = TaoView_CG;
290:   tao->ops->setfromoptions = TaoSetFromOptions_CG;
291:   tao->ops->destroy = TaoDestroy_CG;

293:   /* Override default settings (unless already changed) */
294:   if (!tao->max_it_changed) tao->max_it = 2000;
295:   if (!tao->max_funcs_changed) tao->max_funcs = 4000;

297:   /*  Note: nondefault values should be used for nonlinear conjugate gradient  */
298:   /*  method.  In particular, gtol should be less that 0.5; the value used in  */
299:   /*  Nocedal and Wright is 0.10.  We use the default values for the  */
300:   /*  linesearch because it seems to work better. */
301:   TaoLineSearchCreate(((PetscObject)tao)->comm, &tao->linesearch);
302:   PetscObjectIncrementTabLevel((PetscObject)tao->linesearch, (PetscObject)tao, 1);
303:   TaoLineSearchSetType(tao->linesearch, morethuente_type);
304:   TaoLineSearchUseTaoRoutines(tao->linesearch, tao);
305:   TaoLineSearchSetOptionsPrefix(tao->linesearch,tao->hdr.prefix);

307:   PetscNewLog(tao,&cgP);
308:   tao->data = (void*)cgP;
309:   cgP->eta = 0.1;
310:   cgP->delta_min = 1e-7;
311:   cgP->delta_max = 100;
312:   cgP->cg_type = CG_PolakRibierePlus;
313:   return(0);
314: }