Date: 2025-03-29T05:58:47+00:00

Actual source code: taosolver_fg.c

      1: #include <petsc/private/taoimpl.h>

      3: /*@
      4:   TaoSetSolution - Sets the vector holding the initial guess for the solve

      6:   Logically Collective

      8:   Input Parameters:
      9: + tao - the `Tao` context
     10: - x0  - the initial guess

     12:   Level: beginner

     14: .seealso: [](ch_tao), `Tao`, `TaoCreate()`, `TaoSolve()`, `TaoGetSolution()`
     15: @*/
     16: PetscErrorCode TaoSetSolution(Tao tao, Vec x0)
     17: {
     18:   PetscFunctionBegin;
     21:   PetscCall(PetscObjectReference((PetscObject)x0));
     22:   PetscCall(VecDestroy(&tao->solution));
     23:   tao->solution = x0;
     24:   PetscFunctionReturn(PETSC_SUCCESS);
     25: }

     27: PetscErrorCode TaoTestGradient(Tao tao, Vec x, Vec g1)
     28: {
     29:   Vec               g2, g3;
     30:   PetscBool         complete_print = PETSC_FALSE, test = PETSC_FALSE;
     31:   PetscReal         hcnorm, fdnorm, hcmax, fdmax, diffmax, diffnorm;
     32:   PetscScalar       dot;
     33:   MPI_Comm          comm;
     34:   PetscViewer       viewer, mviewer;
     35:   PetscViewerFormat format;
     36:   PetscInt          tabs;
     37:   static PetscBool  directionsprinted = PETSC_FALSE;

     39:   PetscFunctionBegin;
     40:   PetscObjectOptionsBegin((PetscObject)tao);
     41:   PetscCall(PetscOptionsName("-tao_test_gradient", "Compare hand-coded and finite difference Gradients", "None", &test));
     42:   PetscCall(PetscOptionsViewer("-tao_test_gradient_view", "View difference between hand-coded and finite difference Gradients element entries", "None", &mviewer, &format, &complete_print));
     43:   PetscOptionsEnd();
     44:   if (!test) {
     45:     if (complete_print) PetscCall(PetscViewerDestroy(&mviewer));
     46:     PetscFunctionReturn(PETSC_SUCCESS);
     47:   }

     49:   PetscCall(PetscObjectGetComm((PetscObject)tao, &comm));
     50:   PetscCall(PetscViewerASCIIGetStdout(comm, &viewer));
     51:   PetscCall(PetscViewerASCIIGetTab(viewer, &tabs));
     52:   PetscCall(PetscViewerASCIISetTab(viewer, ((PetscObject)tao)->tablevel));
     53:   PetscCall(PetscViewerASCIIPrintf(viewer, "  ---------- Testing Gradient -------------\n"));
     54:   if (!complete_print && !directionsprinted) {
     55:     PetscCall(PetscViewerASCIIPrintf(viewer, "  Run with -tao_test_gradient_view and optionally -tao_test_gradient <threshold> to show difference\n"));
     56:     PetscCall(PetscViewerASCIIPrintf(viewer, "    of hand-coded and finite difference gradient entries greater than <threshold>.\n"));
     57:   }
     58:   if (!directionsprinted) {
     59:     PetscCall(PetscViewerASCIIPrintf(viewer, "  Testing hand-coded Gradient, if (for double precision runs) ||G - Gfd||/||G|| is\n"));
     60:     PetscCall(PetscViewerASCIIPrintf(viewer, "    O(1.e-8), the hand-coded Gradient is probably correct.\n"));
     61:     directionsprinted = PETSC_TRUE;
     62:   }
     63:   if (complete_print) PetscCall(PetscViewerPushFormat(mviewer, format));

     65:   PetscCall(VecDuplicate(x, &g2));
     66:   PetscCall(VecDuplicate(x, &g3));

     68:   /* Compute finite difference gradient, assume the gradient is already computed by TaoComputeGradient() and put into g1 */
     69:   PetscCall(TaoDefaultComputeGradient(tao, x, g2, NULL));

     71:   PetscCall(VecNorm(g2, NORM_2, &fdnorm));
     72:   PetscCall(VecNorm(g1, NORM_2, &hcnorm));
     73:   PetscCall(VecNorm(g2, NORM_INFINITY, &fdmax));
     74:   PetscCall(VecNorm(g1, NORM_INFINITY, &hcmax));
     75:   PetscCall(VecDot(g1, g2, &dot));
     76:   PetscCall(VecCopy(g1, g3));
     77:   PetscCall(VecAXPY(g3, -1.0, g2));
     78:   PetscCall(VecNorm(g3, NORM_2, &diffnorm));
     79:   PetscCall(VecNorm(g3, NORM_INFINITY, &diffmax));
     80:   PetscCall(PetscViewerASCIIPrintf(viewer, "  ||Gfd|| %g, ||G|| = %g, angle cosine = (Gfd'G)/||Gfd||||G|| = %g\n", (double)fdnorm, (double)hcnorm, (double)(PetscRealPart(dot) / (fdnorm * hcnorm))));
     81:   PetscCall(PetscViewerASCIIPrintf(viewer, "  2-norm ||G - Gfd||/||G|| = %g, ||G - Gfd|| = %g\n", (double)(diffnorm / PetscMax(hcnorm, fdnorm)), (double)diffnorm));
     82:   PetscCall(PetscViewerASCIIPrintf(viewer, "  max-norm ||G - Gfd||/||G|| = %g, ||G - Gfd|| = %g\n", (double)(diffmax / PetscMax(hcmax, fdmax)), (double)diffmax));

     84:   if (complete_print) {
     85:     PetscCall(PetscViewerASCIIPrintf(viewer, "  Hand-coded gradient ----------\n"));
     86:     PetscCall(VecView(g1, mviewer));
     87:     PetscCall(PetscViewerASCIIPrintf(viewer, "  Finite difference gradient ----------\n"));
     88:     PetscCall(VecView(g2, mviewer));
     89:     PetscCall(PetscViewerASCIIPrintf(viewer, "  Hand-coded minus finite-difference gradient ----------\n"));
     90:     PetscCall(VecView(g3, mviewer));
     91:   }
     92:   PetscCall(VecDestroy(&g2));
     93:   PetscCall(VecDestroy(&g3));

     95:   if (complete_print) {
     96:     PetscCall(PetscViewerPopFormat(mviewer));
     97:     PetscCall(PetscViewerDestroy(&mviewer));
     98:   }
     99:   PetscCall(PetscViewerASCIISetTab(viewer, tabs));
    100:   PetscFunctionReturn(PETSC_SUCCESS);
    101: }

    103: /*@
    104:   TaoComputeGradient - Computes the gradient of the objective function

    106:   Collective

    108:   Input Parameters:
    109: + tao - the `Tao` context
    110: - X   - input vector

    112:   Output Parameter:
    113: . G - gradient vector

    115:   Options Database Keys:
    116: + -tao_test_gradient      - compare the user provided gradient with one compute via finite differences to check for errors
    117: - -tao_test_gradient_view - display the user provided gradient, the finite difference gradient and the difference between them to help users detect the location of errors in the user provided gradient

    119:   Level: developer

    121:   Note:
    122:   `TaoComputeGradient()` is typically used within the implementation of the optimization method,
    123:   so most users would not generally call this routine themselves.

    125: .seealso: [](ch_tao), `TaoComputeObjective()`, `TaoComputeObjectiveAndGradient()`, `TaoSetGradient()`
    126: @*/
    127: PetscErrorCode TaoComputeGradient(Tao tao, Vec X, Vec G)
    128: {
    129:   PetscReal dummy;

    131:   PetscFunctionBegin;
    135:   PetscCheckSameComm(tao, 1, X, 2);
    136:   PetscCheckSameComm(tao, 1, G, 3);
    137:   PetscCall(VecLockReadPush(X));
    138:   if (tao->ops->computegradient) {
    139:     PetscCall(PetscLogEventBegin(TAO_GradientEval, tao, X, G, NULL));
    140:     PetscCallBack("Tao callback gradient", (*tao->ops->computegradient)(tao, X, G, tao->user_gradP));
    141:     PetscCall(PetscLogEventEnd(TAO_GradientEval, tao, X, G, NULL));
    142:     tao->ngrads++;
    143:   } else if (tao->ops->computeobjectiveandgradient) {
    144:     PetscCall(PetscLogEventBegin(TAO_ObjGradEval, tao, X, G, NULL));
    145:     PetscCallBack("Tao callback objective/gradient", (*tao->ops->computeobjectiveandgradient)(tao, X, &dummy, G, tao->user_objgradP));
    146:     PetscCall(PetscLogEventEnd(TAO_ObjGradEval, tao, X, G, NULL));
    147:     tao->nfuncgrads++;
    148:   } else SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONGSTATE, "TaoSetGradient() has not been called");
    149:   PetscCall(VecLockReadPop(X));

    151:   PetscCall(TaoTestGradient(tao, X, G));
    152:   PetscFunctionReturn(PETSC_SUCCESS);
    153: }

    155: /*@
    156:   TaoComputeObjective - Computes the objective function value at a given point

    158:   Collective

    160:   Input Parameters:
    161: + tao - the `Tao` context
    162: - X   - input vector

    164:   Output Parameter:
    165: . f - Objective value at X

    167:   Level: developer

    169:   Note:
    170:   `TaoComputeObjective()` is typically used within the implementation of the optimization algorithm
    171:   so most users would not generally call this routine themselves.

    173: .seealso: [](ch_tao), `Tao`, `TaoComputeGradient()`, `TaoComputeObjectiveAndGradient()`, `TaoSetObjective()`
    174: @*/
    175: PetscErrorCode TaoComputeObjective(Tao tao, Vec X, PetscReal *f)
    176: {
    177:   Vec temp;

    179:   PetscFunctionBegin;
    182:   PetscCheckSameComm(tao, 1, X, 2);
    183:   PetscCall(VecLockReadPush(X));
    184:   if (tao->ops->computeobjective) {
    185:     PetscCall(PetscLogEventBegin(TAO_ObjectiveEval, tao, X, NULL, NULL));
    186:     PetscCallBack("Tao callback objective", (*tao->ops->computeobjective)(tao, X, f, tao->user_objP));
    187:     PetscCall(PetscLogEventEnd(TAO_ObjectiveEval, tao, X, NULL, NULL));
    188:     tao->nfuncs++;
    189:   } else if (tao->ops->computeobjectiveandgradient) {
    190:     PetscCall(PetscInfo(tao, "Duplicating variable vector in order to call func/grad routine\n"));
    191:     PetscCall(VecDuplicate(X, &temp));
    192:     PetscCall(PetscLogEventBegin(TAO_ObjGradEval, tao, X, NULL, NULL));
    193:     PetscCallBack("Tao callback objective/gradient", (*tao->ops->computeobjectiveandgradient)(tao, X, f, temp, tao->user_objgradP));
    194:     PetscCall(PetscLogEventEnd(TAO_ObjGradEval, tao, X, NULL, NULL));
    195:     PetscCall(VecDestroy(&temp));
    196:     tao->nfuncgrads++;
    197:   } else SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONGSTATE, "TaoSetObjective() has not been called");
    198:   PetscCall(PetscInfo(tao, "TAO Function evaluation: %20.19e\n", (double)(*f)));
    199:   PetscCall(VecLockReadPop(X));
    200:   PetscFunctionReturn(PETSC_SUCCESS);
    201: }

    203: /*@
    204:   TaoComputeObjectiveAndGradient - Computes the objective function value at a given point

    206:   Collective

    208:   Input Parameters:
    209: + tao - the `Tao` context
    210: - X   - input vector

    212:   Output Parameters:
    213: + f - Objective value at `X`
    214: - G - Gradient vector at `X`

    216:   Level: developer

    218:   Note:
    219:   `TaoComputeObjectiveAndGradient()` is typically used within the implementation of the optimization algorithm,
    220:   so most users would not generally call this routine themselves.

    222: .seealso: [](ch_tao), `TaoComputeGradient()`, `TaoSetObjective()`
    223: @*/
    224: PetscErrorCode TaoComputeObjectiveAndGradient(Tao tao, Vec X, PetscReal *f, Vec G)
    225: {
    226:   PetscFunctionBegin;
    230:   PetscCheckSameComm(tao, 1, X, 2);
    231:   PetscCheckSameComm(tao, 1, G, 4);
    232:   PetscCall(VecLockReadPush(X));
    233:   if (tao->ops->computeobjectiveandgradient) {
    234:     PetscCall(PetscLogEventBegin(TAO_ObjGradEval, tao, X, G, NULL));
    235:     if (tao->ops->computegradient == TaoDefaultComputeGradient) {
    236:       PetscCall(TaoComputeObjective(tao, X, f));
    237:       PetscCall(TaoDefaultComputeGradient(tao, X, G, NULL));
    238:     } else PetscCallBack("Tao callback objective/gradient", (*tao->ops->computeobjectiveandgradient)(tao, X, f, G, tao->user_objgradP));
    239:     PetscCall(PetscLogEventEnd(TAO_ObjGradEval, tao, X, G, NULL));
    240:     tao->nfuncgrads++;
    241:   } else if (tao->ops->computeobjective && tao->ops->computegradient) {
    242:     PetscCall(PetscLogEventBegin(TAO_ObjectiveEval, tao, X, NULL, NULL));
    243:     PetscCallBack("Tao callback objective", (*tao->ops->computeobjective)(tao, X, f, tao->user_objP));
    244:     PetscCall(PetscLogEventEnd(TAO_ObjectiveEval, tao, X, NULL, NULL));
    245:     tao->nfuncs++;
    246:     PetscCall(PetscLogEventBegin(TAO_GradientEval, tao, X, G, NULL));
    247:     PetscCallBack("Tao callback gradient", (*tao->ops->computegradient)(tao, X, G, tao->user_gradP));
    248:     PetscCall(PetscLogEventEnd(TAO_GradientEval, tao, X, G, NULL));
    249:     tao->ngrads++;
    250:   } else SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONGSTATE, "TaoSetObjective() or TaoSetGradient() not set");
    251:   PetscCall(PetscInfo(tao, "TAO Function evaluation: %20.19e\n", (double)(*f)));
    252:   PetscCall(VecLockReadPop(X));

    254:   PetscCall(TaoTestGradient(tao, X, G));
    255:   PetscFunctionReturn(PETSC_SUCCESS);
    256: }

    258: /*@C
    259:   TaoSetObjective - Sets the function evaluation routine for minimization

    261:   Logically Collective

    263:   Input Parameters:
    264: + tao  - the `Tao` context
    265: . func - the objective function
    266: - ctx  - [optional] user-defined context for private data for the function evaluation
    267:         routine (may be `NULL`)

    269:   Calling sequence of `func`:
    270: + tao - the optimizer
    271: . x   - input vector
    272: . f   - function value
    273: - ctx - [optional] user-defined function context

    275:   Level: beginner

    277: .seealso: [](ch_tao), `TaoSetGradient()`, `TaoSetHessian()`, `TaoSetObjectiveAndGradient()`, `TaoGetObjective()`
    278: @*/
    279: PetscErrorCode TaoSetObjective(Tao tao, PetscErrorCode (*func)(Tao tao, Vec x, PetscReal *f, void *ctx), void *ctx)
    280: {
    281:   PetscFunctionBegin;
    283:   if (ctx) tao->user_objP = ctx;
    284:   if (func) tao->ops->computeobjective = func;
    285:   PetscFunctionReturn(PETSC_SUCCESS);
    286: }

    288: /*@C
    289:   TaoGetObjective - Gets the function evaluation routine for the function to be minimized

    291:   Not Collective

    293:   Input Parameter:
    294: . tao - the `Tao` context

    296:   Output Parameters:
    297: + func - the objective function
    298: - ctx  - the user-defined context for private data for the function evaluation

    300:   Calling sequence of `func`:
    301: + tao - the optimizer
    302: . x   - input vector
    303: . f   - function value
    304: - ctx - [optional] user-defined function context

    306:   Level: beginner

    308: .seealso: [](ch_tao), `Tao`, `TaoSetGradient()`, `TaoSetHessian()`, `TaoSetObjective()`
    309: @*/
    310: PetscErrorCode TaoGetObjective(Tao tao, PetscErrorCode (**func)(Tao tao, Vec x, PetscReal *f, void *ctx), void **ctx)
    311: {
    312:   PetscFunctionBegin;
    314:   if (func) *func = tao->ops->computeobjective;
    315:   if (ctx) *ctx = tao->user_objP;
    316:   PetscFunctionReturn(PETSC_SUCCESS);
    317: }

    319: /*@C
    320:   TaoSetResidualRoutine - Sets the residual evaluation routine for least-square applications

    322:   Logically Collective

    324:   Input Parameters:
    325: + tao  - the `Tao` context
    326: . res  - the residual vector
    327: . func - the residual evaluation routine
    328: - ctx  - [optional] user-defined context for private data for the function evaluation
    329:          routine (may be `NULL`)

    331:   Calling sequence of `func`:
    332: + tao - the optimizer
    333: . x   - input vector
    334: . res - function value vector
    335: - ctx - [optional] user-defined function context

    337:   Level: beginner

    339: .seealso: [](ch_tao), `Tao`, `TaoSetObjective()`, `TaoSetJacobianRoutine()`
    340: @*/
    341: PetscErrorCode TaoSetResidualRoutine(Tao tao, Vec res, PetscErrorCode (*func)(Tao tao, Vec x, Vec res, void *ctx), void *ctx)
    342: {
    343:   PetscFunctionBegin;
    346:   PetscCall(PetscObjectReference((PetscObject)res));
    347:   if (tao->ls_res) PetscCall(VecDestroy(&tao->ls_res));
    348:   tao->ls_res               = res;
    349:   tao->user_lsresP          = ctx;
    350:   tao->ops->computeresidual = func;
    351:   PetscFunctionReturn(PETSC_SUCCESS);
    352: }

    354: /*@
    355:   TaoSetResidualWeights - Give weights for the residual values. A vector can be used if only diagonal terms are used, otherwise a matrix can be give.

    357:   Collective

    359:   Input Parameters:
    360: + tao     - the `Tao` context
    361: . sigma_v - vector of weights (diagonal terms only)
    362: . n       - the number of weights (if using off-diagonal)
    363: . rows    - index list of rows for `sigma_v`
    364: . cols    - index list of columns for `sigma_v`
    365: - vals    - array of weights

    367:   Level: intermediate

    369:   Notes:
    370:   If this function is not provided, or if `sigma_v` and `vals` are both `NULL`, then the
    371:   identity matrix will be used for weights.

    373:   Either `sigma_v` or `vals` should be `NULL`

    375: .seealso: [](ch_tao), `Tao`, `TaoSetResidualRoutine()`
    376: @*/
    377: PetscErrorCode TaoSetResidualWeights(Tao tao, Vec sigma_v, PetscInt n, PetscInt *rows, PetscInt *cols, PetscReal *vals)
    378: {
    379:   PetscInt i;

    381:   PetscFunctionBegin;
    384:   PetscCall(PetscObjectReference((PetscObject)sigma_v));
    385:   PetscCall(VecDestroy(&tao->res_weights_v));
    386:   tao->res_weights_v = sigma_v;
    387:   if (vals) {
    388:     PetscCall(PetscFree(tao->res_weights_rows));
    389:     PetscCall(PetscFree(tao->res_weights_cols));
    390:     PetscCall(PetscFree(tao->res_weights_w));
    391:     PetscCall(PetscMalloc1(n, &tao->res_weights_rows));
    392:     PetscCall(PetscMalloc1(n, &tao->res_weights_cols));
    393:     PetscCall(PetscMalloc1(n, &tao->res_weights_w));
    394:     tao->res_weights_n = n;
    395:     for (i = 0; i < n; i++) {
    396:       tao->res_weights_rows[i] = rows[i];
    397:       tao->res_weights_cols[i] = cols[i];
    398:       tao->res_weights_w[i]    = vals[i];
    399:     }
    400:   } else {
    401:     tao->res_weights_n    = 0;
    402:     tao->res_weights_rows = NULL;
    403:     tao->res_weights_cols = NULL;
    404:   }
    405:   PetscFunctionReturn(PETSC_SUCCESS);
    406: }

    408: /*@
    409:   TaoComputeResidual - Computes a least-squares residual vector at a given point

    411:   Collective

    413:   Input Parameters:
    414: + tao - the `Tao` context
    415: - X   - input vector

    417:   Output Parameter:
    418: . F - Objective vector at `X`

    420:   Level: advanced

    422:   Notes:
    423:   `TaoComputeResidual()` is typically used within the implementation of the optimization algorithm,
    424:   so most users would not generally call this routine themselves.

    426: .seealso: [](ch_tao), `Tao`, `TaoSetResidualRoutine()`
    427: @*/
    428: PetscErrorCode TaoComputeResidual(Tao tao, Vec X, Vec F)
    429: {
    430:   PetscFunctionBegin;
    434:   PetscCheckSameComm(tao, 1, X, 2);
    435:   PetscCheckSameComm(tao, 1, F, 3);
    436:   if (tao->ops->computeresidual) {
    437:     PetscCall(PetscLogEventBegin(TAO_ObjectiveEval, tao, X, NULL, NULL));
    438:     PetscCallBack("Tao callback least-squares residual", (*tao->ops->computeresidual)(tao, X, F, tao->user_lsresP));
    439:     PetscCall(PetscLogEventEnd(TAO_ObjectiveEval, tao, X, NULL, NULL));
    440:     tao->nfuncs++;
    441:   } else SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONGSTATE, "TaoSetResidualRoutine() has not been called");
    442:   PetscCall(PetscInfo(tao, "TAO least-squares residual evaluation.\n"));
    443:   PetscFunctionReturn(PETSC_SUCCESS);
    444: }

    446: /*@C
    447:   TaoSetGradient - Sets the gradient evaluation routine for the function to be optimized

    449:   Logically Collective

    451:   Input Parameters:
    452: + tao  - the `Tao` context
    453: . g    - [optional] the vector to internally hold the gradient computation
    454: . func - the gradient function
    455: - ctx  - [optional] user-defined context for private data for the gradient evaluation
    456:         routine (may be `NULL`)

    458:   Calling sequence of `func`:
    459: + tao - the optimization solver
    460: . x   - input vector
    461: . g   - gradient value (output)
    462: - ctx - [optional] user-defined function context

    464:   Level: beginner

    466: .seealso: [](ch_tao), `Tao`, `TaoSolve()`, `TaoSetObjective()`, `TaoSetHessian()`, `TaoSetObjectiveAndGradient()`, `TaoGetGradient()`
    467: @*/
    468: PetscErrorCode TaoSetGradient(Tao tao, Vec g, PetscErrorCode (*func)(Tao tao, Vec x, Vec g, void *ctx), void *ctx)
    469: {
    470:   PetscFunctionBegin;
    472:   if (g) {
    474:     PetscCheckSameComm(tao, 1, g, 2);
    475:     PetscCall(PetscObjectReference((PetscObject)g));
    476:     PetscCall(VecDestroy(&tao->gradient));
    477:     tao->gradient = g;
    478:   }
    479:   if (func) tao->ops->computegradient = func;
    480:   if (ctx) tao->user_gradP = ctx;
    481:   PetscFunctionReturn(PETSC_SUCCESS);
    482: }

    484: /*@C
    485:   TaoGetGradient - Gets the gradient evaluation routine for the function being optimized

    487:   Not Collective

    489:   Input Parameter:
    490: . tao - the `Tao` context

    492:   Output Parameters:
    493: + g    - the vector to internally hold the gradient computation
    494: . func - the gradient function
    495: - ctx  - user-defined context for private data for the gradient evaluation routine

    497:   Calling sequence of `func`:
    498: + tao - the optimizer
    499: . x   - input vector
    500: . g   - gradient value (output)
    501: - ctx - [optional] user-defined function context

    503:   Level: beginner

    505: .seealso: [](ch_tao), `Tao`, `TaoSetObjective()`, `TaoSetHessian()`, `TaoSetObjectiveAndGradient()`, `TaoSetGradient()`
    506: @*/
    507: PetscErrorCode TaoGetGradient(Tao tao, Vec *g, PetscErrorCode (**func)(Tao tao, Vec x, Vec g, void *ctx), void **ctx)
    508: {
    509:   PetscFunctionBegin;
    511:   if (g) *g = tao->gradient;
    512:   if (func) *func = tao->ops->computegradient;
    513:   if (ctx) *ctx = tao->user_gradP;
    514:   PetscFunctionReturn(PETSC_SUCCESS);
    515: }

    517: /*@C
    518:   TaoSetObjectiveAndGradient - Sets a combined objective function and gradient evaluation routine for the function to be optimized

    520:   Logically Collective

    522:   Input Parameters:
    523: + tao  - the `Tao` context
    524: . g    - [optional] the vector to internally hold the gradient computation
    525: . func - the gradient function
    526: - ctx  - [optional] user-defined context for private data for the gradient evaluation
    527:         routine (may be `NULL`)

    529:   Calling sequence of `func`:
    530: + tao - the optimization object
    531: . x   - input vector
    532: . f   - objective value (output)
    533: . g   - gradient value (output)
    534: - ctx - [optional] user-defined function context

    536:   Level: beginner

    538:   Note:
    539:   For some optimization methods using a combined function can be more eifficient.

    541: .seealso: [](ch_tao), `Tao`, `TaoSolve()`, `TaoSetObjective()`, `TaoSetHessian()`, `TaoSetGradient()`, `TaoGetObjectiveAndGradient()`
    542: @*/
    543: PetscErrorCode TaoSetObjectiveAndGradient(Tao tao, Vec g, PetscErrorCode (*func)(Tao tao, Vec x, PetscReal *f, Vec g, void *ctx), void *ctx)
    544: {
    545:   PetscFunctionBegin;
    547:   if (g) {
    549:     PetscCheckSameComm(tao, 1, g, 2);
    550:     PetscCall(PetscObjectReference((PetscObject)g));
    551:     PetscCall(VecDestroy(&tao->gradient));
    552:     tao->gradient = g;
    553:   }
    554:   if (ctx) tao->user_objgradP = ctx;
    555:   if (func) tao->ops->computeobjectiveandgradient = func;
    556:   PetscFunctionReturn(PETSC_SUCCESS);
    557: }

    559: /*@C
    560:   TaoGetObjectiveAndGradient - Gets the combined objective function and gradient evaluation routine for the function to be optimized

    562:   Not Collective

    564:   Input Parameter:
    565: . tao - the `Tao` context

    567:   Output Parameters:
    568: + g    - the vector to internally hold the gradient computation
    569: . func - the gradient function
    570: - ctx  - user-defined context for private data for the gradient evaluation routine

    572:   Calling sequence of `func`:
    573: + tao - the optimizer
    574: . x   - input vector
    575: . f   - objective value (output)
    576: . g   - gradient value (output)
    577: - ctx - [optional] user-defined function context

    579:   Level: beginner

    581: .seealso: [](ch_tao), `Tao`, `TaoSolve()`, `TaoSetObjective()`, `TaoSetGradient()`, `TaoSetHessian()`, `TaoSetObjectiveAndGradient()`
    582: @*/
    583: PetscErrorCode TaoGetObjectiveAndGradient(Tao tao, Vec *g, PetscErrorCode (**func)(Tao tao, Vec x, PetscReal *f, Vec g, void *ctx), void **ctx)
    584: {
    585:   PetscFunctionBegin;
    587:   if (g) *g = tao->gradient;
    588:   if (func) *func = tao->ops->computeobjectiveandgradient;
    589:   if (ctx) *ctx = tao->user_objgradP;
    590:   PetscFunctionReturn(PETSC_SUCCESS);
    591: }

    593: /*@
    594:   TaoIsObjectiveDefined - Checks to see if the user has
    595:   declared an objective-only routine.  Useful for determining when
    596:   it is appropriate to call `TaoComputeObjective()` or
    597:   `TaoComputeObjectiveAndGradient()`

    599:   Not Collective

    601:   Input Parameter:
    602: . tao - the `Tao` context

    604:   Output Parameter:
    605: . flg - `PETSC_TRUE` if function routine is set by user, `PETSC_FALSE` otherwise

    607:   Level: developer

    609: .seealso: [](ch_tao), `Tao`, `TaoSetObjective()`, `TaoIsGradientDefined()`, `TaoIsObjectiveAndGradientDefined()`
    610: @*/
    611: PetscErrorCode TaoIsObjectiveDefined(Tao tao, PetscBool *flg)
    612: {
    613:   PetscFunctionBegin;
    615:   if (tao->ops->computeobjective == NULL) *flg = PETSC_FALSE;
    616:   else *flg = PETSC_TRUE;
    617:   PetscFunctionReturn(PETSC_SUCCESS);
    618: }

    620: /*@
    621:   TaoIsGradientDefined - Checks to see if the user has
    622:   declared an objective-only routine.  Useful for determining when
    623:   it is appropriate to call `TaoComputeGradient()` or
    624:   `TaoComputeGradientAndGradient()`

    626:   Not Collective

    628:   Input Parameter:
    629: . tao - the `Tao` context

    631:   Output Parameter:
    632: . flg - `PETSC_TRUE` if function routine is set by user, `PETSC_FALSE` otherwise

    634:   Level: developer

    636: .seealso: [](ch_tao), `TaoSetGradient()`, `TaoIsObjectiveDefined()`, `TaoIsObjectiveAndGradientDefined()`
    637: @*/
    638: PetscErrorCode TaoIsGradientDefined(Tao tao, PetscBool *flg)
    639: {
    640:   PetscFunctionBegin;
    642:   if (tao->ops->computegradient == NULL) *flg = PETSC_FALSE;
    643:   else *flg = PETSC_TRUE;
    644:   PetscFunctionReturn(PETSC_SUCCESS);
    645: }

    647: /*@
    648:   TaoIsObjectiveAndGradientDefined - Checks to see if the user has
    649:   declared a joint objective/gradient routine.  Useful for determining when
    650:   it is appropriate to call `TaoComputeObjective()` or
    651:   `TaoComputeObjectiveAndGradient()`

    653:   Not Collective

    655:   Input Parameter:
    656: . tao - the `Tao` context

    658:   Output Parameter:
    659: . flg - `PETSC_TRUE` if function routine is set by user, `PETSC_FALSE` otherwise

    661:   Level: developer

    663: .seealso: [](ch_tao), `TaoSetObjectiveAndGradient()`, `TaoIsObjectiveDefined()`, `TaoIsGradientDefined()`
    664: @*/
    665: PetscErrorCode TaoIsObjectiveAndGradientDefined(Tao tao, PetscBool *flg)
    666: {
    667:   PetscFunctionBegin;
    669:   if (tao->ops->computeobjectiveandgradient == NULL) *flg = PETSC_FALSE;
    670:   else *flg = PETSC_TRUE;
    671:   PetscFunctionReturn(PETSC_SUCCESS);
    672: }