Actual source code: owlqn.c

      1: #include <petsctaolinesearch.h>
      2: #include <../src/tao/unconstrained/impls/owlqn/owlqn.h>

      4: #define OWLQN_BFGS                0
      5: #define OWLQN_SCALED_GRADIENT     1
      6: #define OWLQN_GRADIENT            2

      8: static PetscErrorCode ProjDirect_OWLQN(Vec d, Vec g)
      9: {
     10:   const PetscReal *gptr;
     11:   PetscReal       *dptr;
     12:   PetscInt        low,high,low1,high1,i;

     14:   VecGetOwnershipRange(d,&low,&high);
     15:   VecGetOwnershipRange(g,&low1,&high1);

     17:   VecGetArrayRead(g,&gptr);
     18:   VecGetArray(d,&dptr);
     19:   for (i = 0; i < high-low; i++) {
     20:     if (dptr[i] * gptr[i] <= 0.0) {
     21:       dptr[i] = 0.0;
     22:     }
     23:   }
     24:   VecRestoreArray(d,&dptr);
     25:   VecRestoreArrayRead(g,&gptr);
     26:   return 0;
     27: }

     29: static PetscErrorCode ComputePseudoGrad_OWLQN(Vec x, Vec gv, PetscReal lambda)
     30: {
     31:   const PetscReal *xptr;
     32:   PetscReal       *gptr;
     33:   PetscInt        low,high,low1,high1,i;

     35:   VecGetOwnershipRange(x,&low,&high);
     36:   VecGetOwnershipRange(gv,&low1,&high1);

     38:   VecGetArrayRead(x,&xptr);
     39:   VecGetArray(gv,&gptr);
     40:   for (i = 0; i < high-low; i++) {
     41:     if (xptr[i] < 0.0)               gptr[i] = gptr[i] - lambda;
     42:     else if (xptr[i] > 0.0)          gptr[i] = gptr[i] + lambda;
     43:     else if (gptr[i] + lambda < 0.0) gptr[i] = gptr[i] + lambda;
     44:     else if (gptr[i] - lambda > 0.0) gptr[i] = gptr[i] - lambda;
     45:     else                             gptr[i] = 0.0;
     46:   }
     47:   VecRestoreArray(gv,&gptr);
     48:   VecRestoreArrayRead(x,&xptr);
     49:   return 0;
     50: }

     52: static PetscErrorCode TaoSolve_OWLQN(Tao tao)
     53: {
     54:   TAO_OWLQN                    *lmP = (TAO_OWLQN *)tao->data;
     55:   PetscReal                    f, fold, gdx, gnorm;
     56:   PetscReal                    step = 1.0;
     57:   PetscReal                    delta;
     58:   PetscInt                     stepType;
     59:   PetscInt                     iter = 0;
     60:   TaoLineSearchConvergedReason ls_status = TAOLINESEARCH_CONTINUE_ITERATING;

     62:   if (tao->XL || tao->XU || tao->ops->computebounds) {
     63:     PetscInfo(tao,"WARNING: Variable bounds have been set but will be ignored by owlqn algorithm\n");
     64:   }

     66:   /* Check convergence criteria */
     67:   TaoComputeObjectiveAndGradient(tao, tao->solution, &f, tao->gradient);
     68:   VecCopy(tao->gradient, lmP->GV);
     69:   ComputePseudoGrad_OWLQN(tao->solution,lmP->GV,lmP->lambda);
     70:   VecNorm(lmP->GV,NORM_2,&gnorm);

     73:   tao->reason = TAO_CONTINUE_ITERATING;
     74:   TaoLogConvergenceHistory(tao,f,gnorm,0.0,tao->ksp_its);
     75:   TaoMonitor(tao,iter,f,gnorm,0.0,step);
     76:   (*tao->ops->convergencetest)(tao,tao->cnvP);
     77:   if (tao->reason != TAO_CONTINUE_ITERATING) return 0;

     79:   /* Set initial scaling for the function */
     80:   delta = 2.0 * PetscMax(1.0, PetscAbsScalar(f)) / (gnorm*gnorm);
     81:   MatLMVMSetJ0Scale(lmP->M, delta);

     83:   /* Set counter for gradient/reset steps */
     84:   lmP->bfgs = 0;
     85:   lmP->sgrad = 0;
     86:   lmP->grad = 0;

     88:   /* Have not converged; continue with Newton method */
     89:   while (tao->reason == TAO_CONTINUE_ITERATING) {
     90:     /* Call general purpose update function */
     91:     if (tao->ops->update) {
     92:       (*tao->ops->update)(tao, tao->niter, tao->user_update);
     93:     }

     95:     /* Compute direction */
     96:     MatLMVMUpdate(lmP->M,tao->solution,tao->gradient);
     97:     MatSolve(lmP->M, lmP->GV, lmP->D);

     99:     ProjDirect_OWLQN(lmP->D,lmP->GV);

    101:     ++lmP->bfgs;

    103:     /* Check for success (descent direction) */
    104:     VecDot(lmP->D, lmP->GV , &gdx);
    105:     if ((gdx <= 0.0) || PetscIsInfOrNanReal(gdx)) {

    107:       /* Step is not descent or direction produced not a number
    108:          We can assert bfgsUpdates > 1 in this case because
    109:          the first solve produces the scaled gradient direction,
    110:          which is guaranteed to be descent

    112:          Use steepest descent direction (scaled) */
    113:       ++lmP->grad;

    115:       delta = 2.0 * PetscMax(1.0, PetscAbsScalar(f)) / (gnorm*gnorm);
    116:       MatLMVMSetJ0Scale(lmP->M, delta);
    117:       MatLMVMReset(lmP->M, PETSC_FALSE);
    118:       MatLMVMUpdate(lmP->M, tao->solution, tao->gradient);
    119:       MatSolve(lmP->M,lmP->GV, lmP->D);

    121:       ProjDirect_OWLQN(lmP->D,lmP->GV);

    123:       lmP->bfgs = 1;
    124:       ++lmP->sgrad;
    125:       stepType = OWLQN_SCALED_GRADIENT;
    126:     } else {
    127:       if (1 == lmP->bfgs) {
    128:         /* The first BFGS direction is always the scaled gradient */
    129:         ++lmP->sgrad;
    130:         stepType = OWLQN_SCALED_GRADIENT;
    131:       } else {
    132:         ++lmP->bfgs;
    133:         stepType = OWLQN_BFGS;
    134:       }
    135:     }

    137:     VecScale(lmP->D, -1.0);

    139:     /* Perform the linesearch */
    140:     fold = f;
    141:     VecCopy(tao->solution, lmP->Xold);
    142:     VecCopy(tao->gradient, lmP->Gold);

    144:     TaoLineSearchApply(tao->linesearch, tao->solution, &f, lmP->GV, lmP->D, &step,&ls_status);
    145:     TaoAddLineSearchCounts(tao);

    147:     while (((int)ls_status < 0) && (stepType != OWLQN_GRADIENT)) {

    149:       /* Reset factors and use scaled gradient step */
    150:       f = fold;
    151:       VecCopy(lmP->Xold, tao->solution);
    152:       VecCopy(lmP->Gold, tao->gradient);
    153:       VecCopy(tao->gradient, lmP->GV);

    155:       ComputePseudoGrad_OWLQN(tao->solution,lmP->GV,lmP->lambda);

    157:       switch(stepType) {
    158:       case OWLQN_BFGS:
    159:         /* Failed to obtain acceptable iterate with BFGS step
    160:            Attempt to use the scaled gradient direction */

    162:         delta = 2.0 * PetscMax(1.0, PetscAbsScalar(f)) / (gnorm*gnorm);
    163:         MatLMVMSetJ0Scale(lmP->M, delta);
    164:         MatLMVMReset(lmP->M, PETSC_FALSE);
    165:         MatLMVMUpdate(lmP->M, tao->solution, tao->gradient);
    166:         MatSolve(lmP->M, lmP->GV, lmP->D);

    168:         ProjDirect_OWLQN(lmP->D,lmP->GV);

    170:         lmP->bfgs = 1;
    171:         ++lmP->sgrad;
    172:         stepType = OWLQN_SCALED_GRADIENT;
    173:         break;

    175:       case OWLQN_SCALED_GRADIENT:
    176:         /* The scaled gradient step did not produce a new iterate;
    177:            attempt to use the gradient direction.
    178:            Need to make sure we are not using a different diagonal scaling */
    179:         MatLMVMSetJ0Scale(lmP->M, 1.0);
    180:         MatLMVMReset(lmP->M, PETSC_FALSE);
    181:         MatLMVMUpdate(lmP->M, tao->solution, tao->gradient);
    182:         MatSolve(lmP->M, lmP->GV, lmP->D);

    184:         ProjDirect_OWLQN(lmP->D,lmP->GV);

    186:         lmP->bfgs = 1;
    187:         ++lmP->grad;
    188:         stepType = OWLQN_GRADIENT;
    189:         break;
    190:       }
    191:       VecScale(lmP->D, -1.0);

    193:       /* Perform the linesearch */
    194:       TaoLineSearchApply(tao->linesearch, tao->solution, &f, lmP->GV, lmP->D, &step, &ls_status);
    195:       TaoAddLineSearchCounts(tao);
    196:     }

    198:     if ((int)ls_status < 0) {
    199:       /* Failed to find an improving point*/
    200:       f = fold;
    201:       VecCopy(lmP->Xold, tao->solution);
    202:       VecCopy(lmP->Gold, tao->gradient);
    203:       VecCopy(tao->gradient, lmP->GV);
    204:       step = 0.0;
    205:     } else {
    206:       /* a little hack here, because that gv is used to store g */
    207:       VecCopy(lmP->GV, tao->gradient);
    208:     }

    210:     ComputePseudoGrad_OWLQN(tao->solution,lmP->GV,lmP->lambda);

    212:     /* Check for termination */

    214:     VecNorm(lmP->GV,NORM_2,&gnorm);

    216:     iter++;
    217:     TaoLogConvergenceHistory(tao,f,gnorm,0.0,tao->ksp_its);
    218:     TaoMonitor(tao,iter,f,gnorm,0.0,step);
    219:     (*tao->ops->convergencetest)(tao,tao->cnvP);

    221:     if ((int)ls_status < 0) break;
    222:   }
    223:   return 0;
    224: }

    226: static PetscErrorCode TaoSetUp_OWLQN(Tao tao)
    227: {
    228:   TAO_OWLQN      *lmP = (TAO_OWLQN *)tao->data;
    229:   PetscInt       n,N;

    231:   /* Existence of tao->solution checked in TaoSetUp() */
    232:   if (!tao->gradient) VecDuplicate(tao->solution,&tao->gradient);
    233:   if (!tao->stepdirection) VecDuplicate(tao->solution,&tao->stepdirection);
    234:   if (!lmP->D) VecDuplicate(tao->solution,&lmP->D);
    235:   if (!lmP->GV) VecDuplicate(tao->solution,&lmP->GV);
    236:   if (!lmP->Xold) VecDuplicate(tao->solution,&lmP->Xold);
    237:   if (!lmP->Gold) VecDuplicate(tao->solution,&lmP->Gold);

    239:   /* Create matrix for the limited memory approximation */
    240:   VecGetLocalSize(tao->solution,&n);
    241:   VecGetSize(tao->solution,&N);
    242:   MatCreateLMVMBFGS(((PetscObject)tao)->comm,n,N,&lmP->M);
    243:   MatLMVMAllocate(lmP->M,tao->solution,tao->gradient);
    244:   return 0;
    245: }

    247: /* ---------------------------------------------------------- */
    248: static PetscErrorCode TaoDestroy_OWLQN(Tao tao)
    249: {
    250:   TAO_OWLQN      *lmP = (TAO_OWLQN *)tao->data;

    252:   if (tao->setupcalled) {
    253:     VecDestroy(&lmP->Xold);
    254:     VecDestroy(&lmP->Gold);
    255:     VecDestroy(&lmP->D);
    256:     MatDestroy(&lmP->M);
    257:     VecDestroy(&lmP->GV);
    258:   }
    259:   PetscFree(tao->data);
    260:   return 0;
    261: }

    263: /*------------------------------------------------------------*/
    264: static PetscErrorCode TaoSetFromOptions_OWLQN(PetscOptionItems *PetscOptionsObject,Tao tao)
    265: {
    266:   TAO_OWLQN      *lmP = (TAO_OWLQN *)tao->data;

    268:   PetscOptionsHead(PetscOptionsObject,"Orthant-Wise Limited-memory method for Quasi-Newton unconstrained optimization");
    269:   PetscOptionsReal("-tao_owlqn_lambda", "regulariser weight","", 100,&lmP->lambda,NULL);
    270:   PetscOptionsTail();
    271:   TaoLineSearchSetFromOptions(tao->linesearch);
    272:   return 0;
    273: }

    275: /*------------------------------------------------------------*/
    276: static PetscErrorCode TaoView_OWLQN(Tao tao, PetscViewer viewer)
    277: {
    278:   TAO_OWLQN      *lm = (TAO_OWLQN *)tao->data;
    279:   PetscBool      isascii;

    281:   PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii);
    282:   if (isascii) {
    283:     PetscViewerASCIIPushTab(viewer);
    284:     PetscViewerASCIIPrintf(viewer, "BFGS steps: %D\n", lm->bfgs);
    285:     PetscViewerASCIIPrintf(viewer, "Scaled gradient steps: %D\n", lm->sgrad);
    286:     PetscViewerASCIIPrintf(viewer, "Gradient steps: %D\n", lm->grad);
    287:     PetscViewerASCIIPopTab(viewer);
    288:   }
    289:   return 0;
    290: }

    292: /* ---------------------------------------------------------- */
    293: /*MC
    294:   TAOOWLQN - orthant-wise limited memory quasi-newton algorithm

    296: . - tao_owlqn_lambda - regulariser weight

    298:   Level: beginner
    299: M*/

    301: PETSC_EXTERN PetscErrorCode TaoCreate_OWLQN(Tao tao)
    302: {
    303:   TAO_OWLQN      *lmP;
    304:   const char     *owarmijo_type = TAOLINESEARCHOWARMIJO;

    306:   tao->ops->setup = TaoSetUp_OWLQN;
    307:   tao->ops->solve = TaoSolve_OWLQN;
    308:   tao->ops->view = TaoView_OWLQN;
    309:   tao->ops->setfromoptions = TaoSetFromOptions_OWLQN;
    310:   tao->ops->destroy = TaoDestroy_OWLQN;

    312:   PetscNewLog(tao,&lmP);
    313:   lmP->D = NULL;
    314:   lmP->M = NULL;
    315:   lmP->GV = NULL;
    316:   lmP->Xold = NULL;
    317:   lmP->Gold = NULL;
    318:   lmP->lambda = 1.0;

    320:   tao->data = (void*)lmP;
    321:   /* Override default settings (unless already changed) */
    322:   if (!tao->max_it_changed) tao->max_it = 2000;
    323:   if (!tao->max_funcs_changed) tao->max_funcs = 4000;

    325:   TaoLineSearchCreate(((PetscObject)tao)->comm,&tao->linesearch);
    326:   PetscObjectIncrementTabLevel((PetscObject)tao->linesearch, (PetscObject)tao, 1);
    327:   TaoLineSearchSetType(tao->linesearch,owarmijo_type);
    328:   TaoLineSearchUseTaoRoutines(tao->linesearch,tao);
    329:   TaoLineSearchSetOptionsPrefix(tao->linesearch,tao->hdr.prefix);
    330:   return 0;
    331: }