Actual source code: lmvm.c
petsc-3.7.1 2016-05-15
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      1: #include <petsctaolinesearch.h>
      2: #include <../src/tao/matrix/lmvmmat.h>
      3: #include <../src/tao/unconstrained/impls/lmvm/lmvm.h>

      5: #define LMVM_BFGS                0
      6: #define LMVM_SCALED_GRADIENT     1
      7: #define LMVM_GRADIENT            2

     11: static PetscErrorCode TaoSolve_LMVM(Tao tao)
     12: {
     13:   TAO_LMVM                     *lmP = (TAO_LMVM *)tao->data;
     14:   PetscReal                    f, fold, gdx, gnorm;
     15:   PetscReal                    step = 1.0;
     16:   PetscReal                    delta;
     17:   PetscErrorCode               ierr;
     18:   PetscInt                     stepType;
     19:   TaoConvergedReason           reason = TAO_CONTINUE_ITERATING;
     20:   TaoLineSearchConvergedReason ls_status = TAOLINESEARCH_CONTINUE_ITERATING;

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

     28:   /*  Check convergence criteria */
     29:   TaoComputeObjectiveAndGradient(tao, tao->solution, &f, tao->gradient);
     30:   TaoGradientNorm(tao, 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 scaling for the function */
     38:   if (f != 0.0) {
     39:     delta = 2.0 * PetscAbsScalar(f) / (gnorm*gnorm);
     40:   } else {
     41:     delta = 2.0 / (gnorm*gnorm);
     42:   }
     43:   MatLMVMSetDelta(lmP->M,delta);

     45:   /*  Set counter for gradient/reset steps */
     46:   lmP->bfgs = 0;
     47:   lmP->sgrad = 0;
     48:   lmP->grad = 0;

     50:   /*  Have not converged; continue with Newton method */
     51:   while (reason == TAO_CONTINUE_ITERATING) {
     52:     /*  Compute direction */
     53:     MatLMVMUpdate(lmP->M,tao->solution,tao->gradient);
     54:     MatLMVMSolve(lmP->M, tao->gradient, lmP->D);
     55:     ++lmP->bfgs;

     57:     /*  Check for success (descent direction) */
     58:     VecDot(lmP->D, tao->gradient, &gdx);
     59:     if ((gdx <= 0.0) || PetscIsInfOrNanReal(gdx)) {
     60:       /* Step is not descent or direction produced not a number
     61:          We can assert bfgsUpdates > 1 in this case because
     62:          the first solve produces the scaled gradient direction,
     63:          which is guaranteed to be descent

     65:          Use steepest descent direction (scaled)
     66:       */

     68:       ++lmP->grad;

     70:       if (f != 0.0) {
     71:         delta = 2.0 * PetscAbsScalar(f) / (gnorm*gnorm);
     72:       } else {
     73:         delta = 2.0 / (gnorm*gnorm);
     74:       }
     75:       MatLMVMSetDelta(lmP->M, delta);
     76:       MatLMVMReset(lmP->M);
     77:       MatLMVMUpdate(lmP->M, tao->solution, tao->gradient);
     78:       MatLMVMSolve(lmP->M,tao->gradient, lmP->D);

     80:       /* On a reset, the direction cannot be not a number; it is a
     81:          scaled gradient step.  No need to check for this condition. */

     83:       lmP->bfgs = 1;
     84:       ++lmP->sgrad;
     85:       stepType = LMVM_SCALED_GRADIENT;
     86:     } else {
     87:       if (1 == lmP->bfgs) {
     88:         /*  The first BFGS direction is always the scaled gradient */
     89:         ++lmP->sgrad;
     90:         stepType = LMVM_SCALED_GRADIENT;
     91:       } else {
     92:         ++lmP->bfgs;
     93:         stepType = LMVM_BFGS;
     94:       }
     95:     }
     96:     VecScale(lmP->D, -1.0);

     98:     /*  Perform the linesearch */
     99:     fold = f;
    100:     VecCopy(tao->solution, lmP->Xold);
    101:     VecCopy(tao->gradient, lmP->Gold);

    103:     TaoLineSearchApply(tao->linesearch, tao->solution, &f, tao->gradient, lmP->D, &step,&ls_status);
    104:     TaoAddLineSearchCounts(tao);

    106:     while (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER && (stepType != LMVM_GRADIENT)) {
    107:       /*  Linesearch failed */
    108:       /*  Reset factors and use scaled gradient step */
    109:       f = fold;
    110:       VecCopy(lmP->Xold, tao->solution);
    111:       VecCopy(lmP->Gold, tao->gradient);

    113:       switch(stepType) {
    114:       case LMVM_BFGS:
    115:         /*  Failed to obtain acceptable iterate with BFGS step */
    116:         /*  Attempt to use the scaled gradient direction */

    118:         if (f != 0.0) {
    119:           delta = 2.0 * PetscAbsScalar(f) / (gnorm*gnorm);
    120:         } else {
    121:           delta = 2.0 / (gnorm*gnorm);
    122:         }
    123:         MatLMVMSetDelta(lmP->M, delta);
    124:         MatLMVMReset(lmP->M);
    125:         MatLMVMUpdate(lmP->M, tao->solution, tao->gradient);
    126:         MatLMVMSolve(lmP->M, tao->gradient, lmP->D);

    128:         /* On a reset, the direction cannot be not a number; it is a
    129:            scaled gradient step.  No need to check for this condition. */

    131:         lmP->bfgs = 1;
    132:         ++lmP->sgrad;
    133:         stepType = LMVM_SCALED_GRADIENT;
    134:         break;

    136:       case LMVM_SCALED_GRADIENT:
    137:         /* The scaled gradient step did not produce a new iterate;
    138:            attempt to use the gradient direction.
    139:            Need to make sure we are not using a different diagonal scaling */
    140:         MatLMVMSetDelta(lmP->M, 1.0);
    141:         MatLMVMReset(lmP->M);
    142:         MatLMVMUpdate(lmP->M, tao->solution, tao->gradient);
    143:         MatLMVMSolve(lmP->M, tao->gradient, lmP->D);

    145:         lmP->bfgs = 1;
    146:         ++lmP->grad;
    147:         stepType = LMVM_GRADIENT;
    148:         break;
    149:       }
    150:       VecScale(lmP->D, -1.0);

    152:       /*  Perform the linesearch */
    153:       TaoLineSearchApply(tao->linesearch, tao->solution, &f, tao->gradient, lmP->D, &step, &ls_status);
    154:       TaoAddLineSearchCounts(tao);
    155:     }

    157:     if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER) {
    158:       /*  Failed to find an improving point */
    159:       f = fold;
    160:       VecCopy(lmP->Xold, tao->solution);
    161:       VecCopy(lmP->Gold, tao->gradient);
    162:       step = 0.0;
    163:       reason = TAO_DIVERGED_LS_FAILURE;
    164:       tao->reason = TAO_DIVERGED_LS_FAILURE;
    165:     }

    167:     /*  Check for termination */
    168:     TaoGradientNorm(tao, tao->gradient,NORM_2,&gnorm);

    170:     tao->niter++;
    171:     TaoMonitor(tao,tao->niter,f,gnorm,0.0,step,&reason);
    172:   }
    173:   return(0);
    174: }

    178: static PetscErrorCode TaoSetUp_LMVM(Tao tao)
    179: {
    180:   TAO_LMVM       *lmP = (TAO_LMVM *)tao->data;
    181:   PetscInt       n,N;
    183:   KSP            H0ksp;

    186:   /* Existence of tao->solution checked in TaoSetUp() */
    187:   if (!tao->gradient) {VecDuplicate(tao->solution,&tao->gradient);  }
    188:   if (!tao->stepdirection) {VecDuplicate(tao->solution,&tao->stepdirection);  }
    189:   if (!lmP->D) {VecDuplicate(tao->solution,&lmP->D);  }
    190:   if (!lmP->Xold) {VecDuplicate(tao->solution,&lmP->Xold);  }
    191:   if (!lmP->Gold) {VecDuplicate(tao->solution,&lmP->Gold);  }

    193:   /*  Create matrix for the limited memory approximation */
    194:   VecGetLocalSize(tao->solution,&n);
    195:   VecGetSize(tao->solution,&N);
    196:   MatCreateLMVM(((PetscObject)tao)->comm,n,N,&lmP->M);
    197:   MatLMVMAllocateVectors(lmP->M,tao->solution);

    199:   /* If the user has set a matrix to solve as the initial H0, set the options prefix here, and set up the KSP */
    200:   if (lmP->H0) {
    201:     const char *prefix;
    202:     PC H0pc;

    204:     MatLMVMSetH0(lmP->M, lmP->H0);
    205:     MatLMVMGetH0KSP(lmP->M, &H0ksp);

    207:     TaoGetOptionsPrefix(tao, &prefix);
    208:     KSPSetOptionsPrefix(H0ksp, prefix);
    209:     PetscObjectAppendOptionsPrefix((PetscObject)H0ksp, "tao_h0_");
    210:     KSPGetPC(H0ksp, &H0pc);
    211:     PetscObjectAppendOptionsPrefix((PetscObject)H0pc,  "tao_h0_");

    213:     KSPSetFromOptions(H0ksp);
    214:     KSPSetUp(H0ksp);
    215:   }

    217:   return(0);
    218: }

    220: /* ---------------------------------------------------------- */
    223: static PetscErrorCode TaoDestroy_LMVM(Tao tao)
    224: {
    225:   TAO_LMVM       *lmP = (TAO_LMVM *)tao->data;

    229:   if (tao->setupcalled) {
    230:     VecDestroy(&lmP->Xold);
    231:     VecDestroy(&lmP->Gold);
    232:     VecDestroy(&lmP->D);
    233:     MatDestroy(&lmP->M);
    234:   }

    236:   if (lmP->H0) {
    237:     PetscObjectDereference((PetscObject)lmP->H0);
    238:   }

    240:   PetscFree(tao->data);

    242:   return(0);
    243: }

    245: /*------------------------------------------------------------*/
    248: static PetscErrorCode TaoSetFromOptions_LMVM(PetscOptionItems *PetscOptionsObject,Tao tao)
    249: {

    253:   PetscOptionsHead(PetscOptionsObject,"Limited-memory variable-metric method for unconstrained optimization");
    254:   TaoLineSearchSetFromOptions(tao->linesearch);
    255:   PetscOptionsTail();
    256:   return(0);
    257: }

    259: /*------------------------------------------------------------*/
    262: static PetscErrorCode TaoView_LMVM(Tao tao, PetscViewer viewer)
    263: {
    264:   TAO_LMVM       *lm = (TAO_LMVM *)tao->data;
    265:   PetscBool      isascii;

    269:   PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii);
    270:   if (isascii) {
    271:     PetscViewerASCIIPushTab(viewer);
    272:     PetscViewerASCIIPrintf(viewer, "BFGS steps: %D\n", lm->bfgs);
    273:     PetscViewerASCIIPrintf(viewer, "Scaled gradient steps: %D\n", lm->sgrad);
    274:     PetscViewerASCIIPrintf(viewer, "Gradient steps: %D\n", lm->grad);
    275:     PetscViewerASCIIPopTab(viewer);
    276:   }
    277:   return(0);
    278: }

    280: /* ---------------------------------------------------------- */

    282: /*MC
    283:      TAOLMVM - Limited Memory Variable Metric method is a quasi-Newton
    284:      optimization solver for unconstrained minimization. It solves
    285:      the Newton step
    286:               Hkdk = - gk

    288:      using an approximation Bk in place of Hk, where Bk is composed using
    289:      the BFGS update formula. A More-Thuente line search is then used
    290:      to computed the steplength in the dk direction
    291:   Options Database Keys:
    292: +     -tao_lmm_vectors - number of vectors to use for approximation
    293: .     -tao_lmm_scale_type - "none","scalar","broyden"
    294: .     -tao_lmm_limit_type - "none","average","relative","absolute"
    295: .     -tao_lmm_rescale_type - "none","scalar","gl"
    296: .     -tao_lmm_limit_mu - mu limiting factor
    297: .     -tao_lmm_limit_nu - nu limiting factor
    298: .     -tao_lmm_delta_min - minimum delta value
    299: .     -tao_lmm_delta_max - maximum delta value
    300: .     -tao_lmm_broyden_phi - phi factor for Broyden scaling
    301: .     -tao_lmm_scalar_alpha - alpha factor for scalar scaling
    302: .     -tao_lmm_rescale_alpha - alpha factor for rescaling diagonal
    303: .     -tao_lmm_rescale_beta - beta factor for rescaling diagonal
    304: .     -tao_lmm_scalar_history - amount of history for scalar scaling
    305: .     -tao_lmm_rescale_history - amount of history for rescaling diagonal
    306: -     -tao_lmm_eps - rejection tolerance

    308:   Level: beginner
    309: M*/

    313: PETSC_EXTERN PetscErrorCode TaoCreate_LMVM(Tao tao)
    314: {
    315:   TAO_LMVM       *lmP;
    316:   const char     *morethuente_type = TAOLINESEARCHMT;

    320:   tao->ops->setup = TaoSetUp_LMVM;
    321:   tao->ops->solve = TaoSolve_LMVM;
    322:   tao->ops->view = TaoView_LMVM;
    323:   tao->ops->setfromoptions = TaoSetFromOptions_LMVM;
    324:   tao->ops->destroy = TaoDestroy_LMVM;

    326:   PetscNewLog(tao,&lmP);
    327:   lmP->D = 0;
    328:   lmP->M = 0;
    329:   lmP->Xold = 0;
    330:   lmP->Gold = 0;
    331:   lmP->H0   = NULL;

    333:   tao->data = (void*)lmP;
    334:   /* Override default settings (unless already changed) */
    335:   if (!tao->max_it_changed) tao->max_it = 2000;
    336:   if (!tao->max_funcs_changed) tao->max_funcs = 4000;

    338:   TaoLineSearchCreate(((PetscObject)tao)->comm,&tao->linesearch);
    339:   TaoLineSearchSetType(tao->linesearch,morethuente_type);
    340:   TaoLineSearchUseTaoRoutines(tao->linesearch,tao);
    341:   TaoLineSearchSetOptionsPrefix(tao->linesearch,tao->hdr.prefix);
    342:   return(0);
    343: }