Date: 2016-07-24T17:15:03+00:00

Actual source code: taosolver_fg.c
petsc-3.7.3 2016-07-24
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      1: #include <petsc/private/taoimpl.h> /*I "petsctao.h" I*/

      5: /*@
      6:   TaoSetInitialVector - Sets the initial guess for the solve

      8:   Logically collective on Tao

     10:   Input Parameters:
     11: + tao - the Tao context
     12: - x0  - the initial guess

     14:   Level: beginner
     15: .seealso: TaoCreate(), TaoSolve()
     16: @*/

     18: PetscErrorCode TaoSetInitialVector(Tao tao, Vec x0)
     19: {

     24:   if (x0) {
     26:     PetscObjectReference((PetscObject)x0);
     27:   }
     28:   VecDestroy(&tao->solution);
     29:   tao->solution = x0;
     30:   return(0);
     31: }

     35: /*@
     36:   TaoComputeGradient - Computes the gradient of the objective function

     38:   Collective on Tao

     40:   Input Parameters:
     41: + tao - the Tao context
     42: - X - input vector

     44:   Output Parameter:
     45: . G - gradient vector

     47:   Notes: TaoComputeGradient() is typically used within minimization implementations,
     48:   so most users would not generally call this routine themselves.

     50:   Level: advanced

     52: .seealso: TaoComputeObjective(), TaoComputeObjectiveAndGradient(), TaoSetGradientRoutine()
     53: @*/
     54: PetscErrorCode TaoComputeGradient(Tao tao, Vec X, Vec G)
     55: {
     57:   PetscReal      dummy;

     65:   if (tao->ops->computegradient) {
     66:     PetscLogEventBegin(Tao_GradientEval,tao,X,G,NULL);
     67:     PetscStackPush("Tao user gradient evaluation routine");
     68:     (*tao->ops->computegradient)(tao,X,G,tao->user_gradP);
     69:     PetscStackPop;
     70:     PetscLogEventEnd(Tao_GradientEval,tao,X,G,NULL);
     71:     tao->ngrads++;
     72:   } else if (tao->ops->computeobjectiveandgradient) {
     73:     PetscLogEventBegin(Tao_ObjGradientEval,tao,X,G,NULL);
     74:     PetscStackPush("Tao user objective/gradient evaluation routine");
     75:     (*tao->ops->computeobjectiveandgradient)(tao,X,&dummy,G,tao->user_objgradP);
     76:     PetscStackPop;
     77:     PetscLogEventEnd(Tao_ObjGradientEval,tao,X,G,NULL);
     78:     tao->nfuncgrads++;
     79:   }  else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"TaoSetGradientRoutine() has not been called");
     80:   return(0);
     81: }

     85: /*@
     86:   TaoComputeObjective - Computes the objective function value at a given point

     88:   Collective on Tao

     90:   Input Parameters:
     91: + tao - the Tao context
     92: - X - input vector

     94:   Output Parameter:
     95: . f - Objective value at X

     97:   Notes: TaoComputeObjective() is typically used within minimization implementations,
     98:   so most users would not generally call this routine themselves.

    100:   Level: advanced

    102: .seealso: TaoComputeGradient(), TaoComputeObjectiveAndGradient(), TaoSetObjectiveRoutine()
    103: @*/
    104: PetscErrorCode TaoComputeObjective(Tao tao, Vec X, PetscReal *f)
    105: {
    107:   Vec            temp;

    113:   if (tao->ops->computeobjective) {
    114:     PetscLogEventBegin(Tao_ObjectiveEval,tao,X,NULL,NULL);
    115:     PetscStackPush("Tao user objective evaluation routine");
    116:     (*tao->ops->computeobjective)(tao,X,f,tao->user_objP);
    117:     PetscStackPop;
    118:     PetscLogEventEnd(Tao_ObjectiveEval,tao,X,NULL,NULL);
    119:     tao->nfuncs++;
    120:   } else if (tao->ops->computeobjectiveandgradient) {
    121:     PetscInfo(tao,"Duplicating variable vector in order to call func/grad routine\n");
    122:     VecDuplicate(X,&temp);
    123:     PetscLogEventBegin(Tao_ObjGradientEval,tao,X,NULL,NULL);
    124:     PetscStackPush("Tao user objective/gradient evaluation routine");
    125:     (*tao->ops->computeobjectiveandgradient)(tao,X,f,temp,tao->user_objgradP);
    126:     PetscStackPop;
    127:     PetscLogEventEnd(Tao_ObjGradientEval,tao,X,NULL,NULL);
    128:     VecDestroy(&temp);
    129:     tao->nfuncgrads++;
    130:   }  else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"TaoSetObjectiveRoutine() has not been called");
    131:   PetscInfo1(tao,"TAO Function evaluation: %14.12e\n",(double)(*f));
    132:   return(0);
    133: }

    137: /*@
    138:   TaoComputeObjectiveAndGradient - Computes the objective function value at a given point

    140:   Collective on Tao

    142:   Input Parameters:
    143: + tao - the Tao context
    144: - X - input vector

    146:   Output Parameter:
    147: + f - Objective value at X
    148: - g - Gradient vector at X

    150:   Notes: TaoComputeObjectiveAndGradient() is typically used within minimization implementations,
    151:   so most users would not generally call this routine themselves.

    153:   Level: advanced

    155: .seealso: TaoComputeGradient(), TaoComputeObjectiveAndGradient(), TaoSetObjectiveRoutine()
    156: @*/
    157: PetscErrorCode TaoComputeObjectiveAndGradient(Tao tao, Vec X, PetscReal *f, Vec G)
    158: {

    167:   if (tao->ops->computeobjectiveandgradient) {
    168:     PetscLogEventBegin(Tao_ObjGradientEval,tao,X,G,NULL);
    169:     PetscStackPush("Tao user objective/gradient evaluation routine");
    170:     (*tao->ops->computeobjectiveandgradient)(tao,X,f,G,tao->user_objgradP);
    171:     PetscStackPop;
    172:     if (tao->ops->computegradient == TaoDefaultComputeGradient) {
    173:       /* Overwrite gradient with finite difference gradient */
    174:       TaoDefaultComputeGradient(tao,X,G,tao->user_objgradP);
    175:     }
    176:     PetscLogEventEnd(Tao_ObjGradientEval,tao,X,G,NULL);
    177:     tao->nfuncgrads++;
    178:   } else if (tao->ops->computeobjective && tao->ops->computegradient) {
    179:     PetscLogEventBegin(Tao_ObjectiveEval,tao,X,NULL,NULL);
    180:     PetscStackPush("Tao user objective evaluation routine");
    181:     (*tao->ops->computeobjective)(tao,X,f,tao->user_objP);
    182:     PetscStackPop;
    183:     PetscLogEventEnd(Tao_ObjectiveEval,tao,X,NULL,NULL);
    184:     tao->nfuncs++;
    185:     PetscLogEventBegin(Tao_GradientEval,tao,X,G,NULL);
    186:     PetscStackPush("Tao user gradient evaluation routine");
    187:     (*tao->ops->computegradient)(tao,X,G,tao->user_gradP);
    188:     PetscStackPop;
    189:     PetscLogEventEnd(Tao_GradientEval,tao,X,G,NULL);
    190:     tao->ngrads++;
    191:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"TaoSetObjectiveRoutine() or TaoSetGradientRoutine() not set");
    192:   PetscInfo1(tao,"TAO Function evaluation: %14.12e\n",(double)(*f));
    193:   return(0);
    194: }

    198: /*@C
    199:   TaoSetObjectiveRoutine - Sets the function evaluation routine for minimization

    201:   Logically collective on Tao

    203:   Input Parameter:
    204: + tao - the Tao context
    205: . func - the objective function
    206: - ctx - [optional] user-defined context for private data for the function evaluation
    207:         routine (may be NULL)

    209:   Calling sequence of func:
    210: $      func (Tao tao, Vec x, PetscReal *f, void *ctx);

    212: + x - input vector
    213: . f - function value
    214: - ctx - [optional] user-defined function context

    216:   Level: beginner

    218: .seealso: TaoSetGradientRoutine(), TaoSetHessianRoutine() TaoSetObjectiveAndGradientRoutine()
    219: @*/
    220: PetscErrorCode TaoSetObjectiveRoutine(Tao tao, PetscErrorCode (*func)(Tao, Vec, PetscReal*,void*),void *ctx)
    221: {
    224:   tao->user_objP = ctx;
    225:   tao->ops->computeobjective = func;
    226:   return(0);
    227: }

    231: /*@C
    232:   TaoSetSeparableObjectiveRoutine - Sets the function evaluation routine for least-square applications

    234:   Logically collective on Tao

    236:   Input Parameter:
    237: + tao - the Tao context
    238: . func - the objective function evaluation routine
    239: - ctx - [optional] user-defined context for private data for the function evaluation
    240:         routine (may be NULL)

    242:   Calling sequence of func:
    243: $      func (Tao tao, Vec x, Vec f, void *ctx);

    245: + x - input vector
    246: . f - function value vector
    247: - ctx - [optional] user-defined function context

    249:   Level: beginner

    251: .seealso: TaoSetObjectiveRoutine(), TaoSetJacobianRoutine()
    252: @*/
    253: PetscErrorCode TaoSetSeparableObjectiveRoutine(Tao tao, Vec sepobj, PetscErrorCode (*func)(Tao, Vec, Vec, void*),void *ctx)
    254: {
    258:   tao->user_sepobjP = ctx;
    259:   tao->sep_objective = sepobj;
    260:   tao->ops->computeseparableobjective = func;
    261:   return(0);
    262: }

    266: /*@
    267:   TaoSetSeparableObjectiveWeights - Give weights for the separable objective values. A vector can be used if only diagonal terms are used, otherwise a matrix can be give. If this function is not used, or if sigma_v and sigma_w are both NULL, then the default identity matrix will be used for weights.

    269:   Collective on Tao

    271:   Input Parameters:
    272: + tao - the Tao context
    273: . sigma_v - vector of weights (diagonal terms only)
    274: . n       - the number of weights (if using off-diagonal)
    275: . rows    - index list of rows for sigma_w
    276: . cols    - index list of columns for sigma_w
    277: - vals - array of weights

    281:   Note: Either sigma_v or sigma_w (or both) should be NULL

    283:   Level: intermediate

    285: .seealso: TaoSetSeparableObjectiveRoutine()
    286: @*/
    287: PetscErrorCode TaoSetSeparableObjectiveWeights(Tao tao, Vec sigma_v, PetscInt n, PetscInt *rows, PetscInt *cols, PetscReal *vals)
    288: {
    290:   PetscInt       i;
    293:   VecDestroy(&tao->sep_weights_v);
    294:   tao->sep_weights_v=sigma_v;
    295:   if (sigma_v) {
    296:     PetscObjectReference((PetscObject)sigma_v);
    297:   }
    298:   if (vals) {
    299:     if (tao->sep_weights_n) {
    300:       PetscFree(tao->sep_weights_rows);
    301:       PetscFree(tao->sep_weights_cols);
    302:       PetscFree(tao->sep_weights_w);
    303:     }
    304:     PetscMalloc1(n,&tao->sep_weights_rows);
    305:     PetscMalloc1(n,&tao->sep_weights_cols);
    306:     PetscMalloc1(n,&tao->sep_weights_w);
    307:     tao->sep_weights_n=n;
    308:     for (i=0;i<n;i++) {
    309:       tao->sep_weights_rows[i]=rows[i];
    310:       tao->sep_weights_cols[i]=cols[i];
    311:       tao->sep_weights_w[i]=vals[i];
    312:     }
    313:   } else {
    314:     tao->sep_weights_n=0;
    315:     tao->sep_weights_rows=0;
    316:     tao->sep_weights_cols=0;
    317:   }
    318:   return(0);
    319: }
    322: /*@
    323:   TaoComputeSeparableObjective - Computes a separable objective function vector at a given point (for least-square applications)

    325:   Collective on Tao

    327:   Input Parameters:
    328: + tao - the Tao context
    329: - X - input vector

    331:   Output Parameter:
    332: . f - Objective vector at X

    334:   Notes: TaoComputeSeparableObjective() is typically used within minimization implementations,
    335:   so most users would not generally call this routine themselves.

    337:   Level: advanced

    339: .seealso: TaoSetSeparableObjectiveRoutine()
    340: @*/
    341: PetscErrorCode TaoComputeSeparableObjective(Tao tao, Vec X, Vec F)
    342: {

    351:   if (tao->ops->computeseparableobjective) {
    352:     PetscLogEventBegin(Tao_ObjectiveEval,tao,X,NULL,NULL);
    353:     PetscStackPush("Tao user separable objective evaluation routine");
    354:     (*tao->ops->computeseparableobjective)(tao,X,F,tao->user_sepobjP);
    355:     PetscStackPop;
    356:     PetscLogEventEnd(Tao_ObjectiveEval,tao,X,NULL,NULL);
    357:     tao->nfuncs++;
    358:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"TaoSetSeparableObjectiveRoutine() has not been called");
    359:   PetscInfo(tao,"TAO separable function evaluation.\n");
    360:   return(0);
    361: }

    365: /*@C
    366:   TaoSetGradientRoutine - Sets the gradient evaluation routine for minimization

    368:   Logically collective on Tao

    370:   Input Parameter:
    371: + tao - the Tao context
    372: . func - the gradient function
    373: - ctx - [optional] user-defined context for private data for the gradient evaluation
    374:         routine (may be NULL)

    376:   Calling sequence of func:
    377: $      func (Tao tao, Vec x, Vec g, void *ctx);

    379: + x - input vector
    380: . g - gradient value (output)
    381: - ctx - [optional] user-defined function context

    383:   Level: beginner

    385: .seealso: TaoSetObjectiveRoutine(), TaoSetHessianRoutine() TaoSetObjectiveAndGradientRoutine()
    386: @*/
    387: PetscErrorCode TaoSetGradientRoutine(Tao tao,  PetscErrorCode (*func)(Tao, Vec, Vec, void*),void *ctx)
    388: {
    391:   tao->user_gradP = ctx;
    392:   tao->ops->computegradient = func;
    393:   return(0);
    394: }

    399: /*@C
    400:   TaoSetObjectiveAndGradientRoutine - Sets a combined objective function and gradient evaluation routine for minimization

    402:   Logically collective on Tao

    404:   Input Parameter:
    405: + tao - the Tao context
    406: . func - the gradient function
    407: - ctx - [optional] user-defined context for private data for the gradient evaluation
    408:         routine (may be NULL)

    410:   Calling sequence of func:
    411: $      func (Tao tao, Vec x, PetscReal *f, Vec g, void *ctx);

    413: + x - input vector
    414: . f - objective value (output)
    415: . g - gradient value (output)
    416: - ctx - [optional] user-defined function context

    418:   Level: beginner

    420: .seealso: TaoSetObjectiveRoutine(), TaoSetHessianRoutine() TaoSetObjectiveAndGradientRoutine()
    421: @*/
    422: PetscErrorCode TaoSetObjectiveAndGradientRoutine(Tao tao, PetscErrorCode (*func)(Tao, Vec, PetscReal *, Vec, void*), void *ctx)
    423: {
    426:   tao->user_objgradP = ctx;
    427:   tao->ops->computeobjectiveandgradient = func;
    428:   return(0);
    429: }

    433: /*@
    434:   TaoIsObjectiveDefined -- Checks to see if the user has
    435:   declared an objective-only routine.  Useful for determining when
    436:   it is appropriate to call TaoComputeObjective() or
    437:   TaoComputeObjectiveAndGradient()

    439:   Collective on Tao

    441:   Input Parameter:
    442: + tao - the Tao context
    443: - ctx - PETSC_TRUE if objective function routine is set by user,
    444:         PETSC_FALSE otherwise
    445:   Level: developer

    447: .seealso: TaoSetObjectiveRoutine(), TaoIsGradientDefined(), TaoIsObjectiveAndGradientDefined()
    448: @*/
    449: PetscErrorCode TaoIsObjectiveDefined(Tao tao, PetscBool *flg)
    450: {
    453:   if (tao->ops->computeobjective == 0) *flg = PETSC_FALSE;
    454:   else *flg = PETSC_TRUE;
    455:   return(0);
    456: }

    460: /*@
    461:   TaoIsGradientDefined -- Checks to see if the user has
    462:   declared an objective-only routine.  Useful for determining when
    463:   it is appropriate to call TaoComputeGradient() or
    464:   TaoComputeGradientAndGradient()

    466:   Not Collective

    468:   Input Parameter:
    469: + tao - the Tao context
    470: - ctx - PETSC_TRUE if gradient routine is set by user, PETSC_FALSE otherwise
    471:   Level: developer

    473: .seealso: TaoSetGradientRoutine(), TaoIsObjectiveDefined(), TaoIsObjectiveAndGradientDefined()
    474: @*/
    475: PetscErrorCode TaoIsGradientDefined(Tao tao, PetscBool *flg)
    476: {
    479:   if (tao->ops->computegradient == 0) *flg = PETSC_FALSE;
    480:   else *flg = PETSC_TRUE;
    481:   return(0);
    482: }

    487: /*@
    488:   TaoIsObjectiveAndGradientDefined -- Checks to see if the user has
    489:   declared a joint objective/gradient routine.  Useful for determining when
    490:   it is appropriate to call TaoComputeObjective() or
    491:   TaoComputeObjectiveAndGradient()

    493:   Not Collective

    495:   Input Parameter:
    496: + tao - the Tao context
    497: - ctx - PETSC_TRUE if objective/gradient routine is set by user, PETSC_FALSE otherwise
    498:   Level: developer

    500: .seealso: TaoSetObjectiveAndGradientRoutine(), TaoIsObjectiveDefined(), TaoIsGradientDefined()
    501: @*/
    502: PetscErrorCode TaoIsObjectiveAndGradientDefined(Tao tao, PetscBool *flg)
    503: {
    506:   if (tao->ops->computeobjectiveandgradient == 0) *flg = PETSC_FALSE;
    507:   else *flg = PETSC_TRUE;
    508:   return(0);
    509: }