Actual source code: taocg.c
petsc-3.7.1 2016-05-15
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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"};

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

     25:    if (tao->XL || tao->XU || tao->ops->computebounds) {
     26:      PetscPrintf(((PetscObject)tao)->comm,"WARNING: Variable bounds have been set but will be ignored by cg algorithm\n");
     27:    }

     29:    /*  Check convergence criteria */
     30:    TaoComputeObjectiveAndGradient(tao, tao->solution, &f, tao->gradient);
     31:    VecNorm(tao->gradient,NORM_2,&gnorm);
     32:    if (PetscIsInfOrNanReal(f) || PetscIsInfOrNanReal(gnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN");

     34:    TaoMonitor(tao, tao->niter, f, gnorm, 0.0, step, &reason);
     35:    if (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:            reason = TAO_DIVERGED_LS_FAILURE;
    130:            tao->reason = TAO_DIVERGED_LS_FAILURE;
    131:          }
    132:        }
    133:      }

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

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

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

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

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

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

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

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

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

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

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

    204:    if (!tao->gradient) {VecDuplicate(tao->solution,&tao->gradient);}
    205:    if (!tao->stepdirection) {VecDuplicate(tao->solution,&tao->stepdirection); }
    206:    if (!cgP->X_old) {VecDuplicate(tao->solution,&cgP->X_old);}
    207:    if (!cgP->G_old) {VecDuplicate(tao->gradient,&cgP->G_old); }
    208:     return(0);
    209:  }

    213:  static PetscErrorCode TaoDestroy_CG(Tao tao)
    214:  {
    215:    TAO_CG         *cgP = (TAO_CG*) tao->data;

    219:    if (tao->setupcalled) {
    220:      VecDestroy(&cgP->X_old);
    221:      VecDestroy(&cgP->G_old);
    222:    }
    223:    TaoLineSearchDestroy(&tao->linesearch);
    224:    PetscFree(tao->data);
    225:    return(0);
    226:  }

    230: static PetscErrorCode TaoSetFromOptions_CG(PetscOptionItems *PetscOptionsObject,Tao tao)
    231:  {
    232:     TAO_CG         *cgP = (TAO_CG*)tao->data;

    236:     TaoLineSearchSetFromOptions(tao->linesearch);
    237:     PetscOptionsHead(PetscOptionsObject,"Nonlinear Conjugate Gradient method for unconstrained optimization");
    238:     PetscOptionsReal("-tao_cg_eta","restart tolerance", "", cgP->eta,&cgP->eta,NULL);
    239:     PetscOptionsEList("-tao_cg_type","cg formula", "", CG_Table, CG_Types, CG_Table[cgP->cg_type], &cgP->cg_type,NULL);
    240:     PetscOptionsReal("-tao_cg_delta_min","minimum delta value", "", cgP->delta_min,&cgP->delta_min,NULL);
    241:     PetscOptionsReal("-tao_cg_delta_max","maximum delta value", "", cgP->delta_max,&cgP->delta_max,NULL);
    242:    PetscOptionsTail();
    243:    return(0);
    244: }

    248: static PetscErrorCode TaoView_CG(Tao tao, PetscViewer viewer)
    249: {
    250:   PetscBool      isascii;
    251:   TAO_CG         *cgP = (TAO_CG*)tao->data;

    255:   PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii);
    256:   if (isascii) {
    257:     PetscViewerASCIIPushTab(viewer);
    258:     PetscViewerASCIIPrintf(viewer, "CG Type: %s\n", CG_Table[cgP->cg_type]);
    259:     PetscViewerASCIIPrintf(viewer, "Gradient steps: %D\n", cgP->ngradsteps);
    260:     ierr= PetscViewerASCIIPrintf(viewer, "Reset steps: %D\n", cgP->nresetsteps);
    261:     PetscViewerASCIIPopTab(viewer);
    262:   }
    263:   return(0);
    264: }

    266: /*MC
    267:      TAOCG -   Nonlinear conjugate gradient method is an extension of the
    268: nonlinear conjugate gradient solver for nonlinear optimization.

    270:    Options Database Keys:
    271: +      -tao_cg_eta <r> - restart tolerance
    272: .      -tao_cg_type <taocg_type> - cg formula
    273: .      -tao_cg_delta_min <r> - minimum delta value
    274: -      -tao_cg_delta_max <r> - maximum delta value

    276:   Notes:
    277:      CG formulas are:
    278:          "fr" - Fletcher-Reeves
    279:          "pr" - Polak-Ribiere
    280:          "prp" - Polak-Ribiere-Plus
    281:          "hs" - Hestenes-Steifel
    282:          "dy" - Dai-Yuan
    283:   Level: beginner
    284: M*/

    289: PETSC_EXTERN PetscErrorCode TaoCreate_CG(Tao tao)
    290: {
    291:   TAO_CG         *cgP;
    292:   const char     *morethuente_type = TAOLINESEARCHMT;

    296:   tao->ops->setup = TaoSetUp_CG;
    297:   tao->ops->solve = TaoSolve_CG;
    298:   tao->ops->view = TaoView_CG;
    299:   tao->ops->setfromoptions = TaoSetFromOptions_CG;
    300:   tao->ops->destroy = TaoDestroy_CG;

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

    306:   /*  Note: nondefault values should be used for nonlinear conjugate gradient  */
    307:   /*  method.  In particular, gtol should be less that 0.5; the value used in  */
    308:   /*  Nocedal and Wright is 0.10.  We use the default values for the  */
    309:   /*  linesearch because it seems to work better. */
    310:   TaoLineSearchCreate(((PetscObject)tao)->comm, &tao->linesearch);
    311:   TaoLineSearchSetType(tao->linesearch, morethuente_type);
    312:   TaoLineSearchUseTaoRoutines(tao->linesearch, tao);
    313:   TaoLineSearchSetOptionsPrefix(tao->linesearch,tao->hdr.prefix);

    315:   PetscNewLog(tao,&cgP);
    316:   tao->data = (void*)cgP;
    317:   cgP->eta = 0.1;
    318:   cgP->delta_min = 1e-7;
    319:   cgP->delta_max = 100;
    320:   cgP->cg_type = CG_PolakRibierePlus;
    321:   return(0);
    322: }