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: }