Actual source code: taosolver.c
1: #include <petsc/private/taoimpl.h>
2: #include <petsc/private/snesimpl.h>
4: PetscBool TaoRegisterAllCalled = PETSC_FALSE;
5: PetscFunctionList TaoList = NULL;
7: PetscClassId TAO_CLASSID;
9: PetscLogEvent TAO_Solve;
10: PetscLogEvent TAO_ObjectiveEval;
11: PetscLogEvent TAO_GradientEval;
12: PetscLogEvent TAO_ObjGradEval;
13: PetscLogEvent TAO_HessianEval;
14: PetscLogEvent TAO_JacobianEval;
15: PetscLogEvent TAO_ConstraintsEval;
17: const char *TaoSubSetTypes[] = {"subvec", "mask", "matrixfree", "TaoSubSetType", "TAO_SUBSET_", NULL};
19: struct _n_TaoMonitorDrawCtx {
20: PetscViewer viewer;
21: PetscInt howoften; /* when > 0 uses iteration % howoften, when negative only final solution plotted */
22: };
24: static PetscErrorCode KSPPreSolve_TAOEW_Private(KSP ksp, Vec b, Vec x, void *ctx)
25: {
26: Tao tao = (Tao)ctx;
27: SNES snes_ewdummy = tao->snes_ewdummy;
29: PetscFunctionBegin;
30: if (!snes_ewdummy) PetscFunctionReturn(PETSC_SUCCESS);
31: /* populate snes_ewdummy struct values used in KSPPreSolve_SNESEW */
32: snes_ewdummy->vec_func = b;
33: snes_ewdummy->rtol = tao->gttol;
34: snes_ewdummy->iter = tao->niter;
35: PetscCall(VecNorm(b, NORM_2, &snes_ewdummy->norm));
36: PetscCall(KSPPreSolve_SNESEW(ksp, b, x, snes_ewdummy));
37: snes_ewdummy->vec_func = NULL;
38: PetscFunctionReturn(PETSC_SUCCESS);
39: }
41: static PetscErrorCode KSPPostSolve_TAOEW_Private(KSP ksp, Vec b, Vec x, void *ctx)
42: {
43: Tao tao = (Tao)ctx;
44: SNES snes_ewdummy = tao->snes_ewdummy;
46: PetscFunctionBegin;
47: if (!snes_ewdummy) PetscFunctionReturn(PETSC_SUCCESS);
48: PetscCall(KSPPostSolve_SNESEW(ksp, b, x, snes_ewdummy));
49: PetscFunctionReturn(PETSC_SUCCESS);
50: }
52: static PetscErrorCode TaoSetUpEW_Private(Tao tao)
53: {
54: SNESKSPEW *kctx;
55: const char *ewprefix;
57: PetscFunctionBegin;
58: if (!tao->ksp) PetscFunctionReturn(PETSC_SUCCESS);
59: if (tao->ksp_ewconv) {
60: if (!tao->snes_ewdummy) PetscCall(SNESCreate(PetscObjectComm((PetscObject)tao), &tao->snes_ewdummy));
61: tao->snes_ewdummy->ksp_ewconv = PETSC_TRUE;
62: PetscCall(KSPSetPreSolve(tao->ksp, KSPPreSolve_TAOEW_Private, tao));
63: PetscCall(KSPSetPostSolve(tao->ksp, KSPPostSolve_TAOEW_Private, tao));
65: PetscCall(KSPGetOptionsPrefix(tao->ksp, &ewprefix));
66: kctx = (SNESKSPEW *)tao->snes_ewdummy->kspconvctx;
67: PetscCall(SNESEWSetFromOptions_Private(kctx, PETSC_FALSE, PetscObjectComm((PetscObject)tao), ewprefix));
68: } else PetscCall(SNESDestroy(&tao->snes_ewdummy));
69: PetscFunctionReturn(PETSC_SUCCESS);
70: }
72: /*@
73: TaoParametersInitialize - Sets all the parameters in `tao` to their default value (when `TaoCreate()` was called) if they
74: currently contain default values. Default values are the parameter values when the object's type is set.
76: Collective
78: Input Parameter:
79: . tao - the `Tao` object
81: Level: developer
83: Developer Note:
84: This is called by all the `TaoCreate_XXX()` routines.
86: .seealso: [](ch_snes), `Tao`, `TaoSolve()`, `TaoDestroy()`,
87: `PetscObjectParameterSetDefault()`
88: @*/
89: PetscErrorCode TaoParametersInitialize(Tao tao)
90: {
91: PetscObjectParameterSetDefault(tao, max_it, 10000);
92: PetscObjectParameterSetDefault(tao, max_funcs, PETSC_UNLIMITED);
93: PetscObjectParameterSetDefault(tao, gatol, PetscDefined(USE_REAL_SINGLE) ? 1e-5 : 1e-8);
94: PetscObjectParameterSetDefault(tao, grtol, PetscDefined(USE_REAL_SINGLE) ? 1e-5 : 1e-8);
95: PetscObjectParameterSetDefault(tao, crtol, PetscDefined(USE_REAL_SINGLE) ? 1e-5 : 1e-8);
96: PetscObjectParameterSetDefault(tao, catol, PetscDefined(USE_REAL_SINGLE) ? 1e-5 : 1e-8);
97: PetscObjectParameterSetDefault(tao, gttol, 0.0);
98: PetscObjectParameterSetDefault(tao, steptol, 0.0);
99: PetscObjectParameterSetDefault(tao, fmin, PETSC_NINFINITY);
100: PetscObjectParameterSetDefault(tao, trust0, PETSC_INFINITY);
101: return PETSC_SUCCESS;
102: }
104: /*@
105: TaoCreate - Creates a Tao solver
107: Collective
109: Input Parameter:
110: . comm - MPI communicator
112: Output Parameter:
113: . newtao - the new `Tao` context
115: Options Database Key:
116: . -tao_type - select which method Tao should use
118: Level: beginner
120: .seealso: [](ch_tao), `Tao`, `TaoSolve()`, `TaoDestroy()`, `TaoSetFromOptions()`, `TaoSetType()`
121: @*/
122: PetscErrorCode TaoCreate(MPI_Comm comm, Tao *newtao)
123: {
124: Tao tao;
126: PetscFunctionBegin;
127: PetscAssertPointer(newtao, 2);
128: PetscCall(TaoInitializePackage());
129: PetscCall(TaoLineSearchInitializePackage());
131: PetscCall(PetscHeaderCreate(tao, TAO_CLASSID, "Tao", "Optimization solver", "Tao", comm, TaoDestroy, TaoView));
132: tao->ops->convergencetest = TaoDefaultConvergenceTest;
134: tao->hist_reset = PETSC_TRUE;
135: PetscCall(TaoResetStatistics(tao));
136: *newtao = tao;
137: PetscFunctionReturn(PETSC_SUCCESS);
138: }
140: /*@
141: TaoSolve - Solves an optimization problem min F(x) s.t. l <= x <= u
143: Collective
145: Input Parameter:
146: . tao - the `Tao` context
148: Level: beginner
150: Notes:
151: The user must set up the `Tao` object with calls to `TaoSetSolution()`, `TaoSetObjective()`, `TaoSetGradient()`, and (if using 2nd order method) `TaoSetHessian()`.
153: You should call `TaoGetConvergedReason()` or run with `-tao_converged_reason` to determine if the optimization algorithm actually succeeded or
154: why it failed.
156: .seealso: [](ch_tao), `Tao`, `TaoCreate()`, `TaoSetObjective()`, `TaoSetGradient()`, `TaoSetHessian()`, `TaoGetConvergedReason()`, `TaoSetUp()`
157: @*/
158: PetscErrorCode TaoSolve(Tao tao)
159: {
160: static PetscBool set = PETSC_FALSE;
162: PetscFunctionBegin;
164: PetscCall(PetscCitationsRegister("@TechReport{tao-user-ref,\n"
165: "title = {Toolkit for Advanced Optimization (TAO) Users Manual},\n"
166: "author = {Todd Munson and Jason Sarich and Stefan Wild and Steve Benson and Lois Curfman McInnes},\n"
167: "Institution = {Argonne National Laboratory},\n"
168: "Year = 2014,\n"
169: "Number = {ANL/MCS-TM-322 - Revision 3.5},\n"
170: "url = {https://www.mcs.anl.gov/research/projects/tao/}\n}\n",
171: &set));
172: tao->header_printed = PETSC_FALSE;
173: PetscCall(TaoSetUp(tao));
174: PetscCall(TaoResetStatistics(tao));
175: if (tao->linesearch) PetscCall(TaoLineSearchReset(tao->linesearch));
177: PetscCall(PetscLogEventBegin(TAO_Solve, tao, 0, 0, 0));
178: PetscTryTypeMethod(tao, solve);
179: PetscCall(PetscLogEventEnd(TAO_Solve, tao, 0, 0, 0));
181: PetscCall(VecViewFromOptions(tao->solution, (PetscObject)tao, "-tao_view_solution"));
183: tao->ntotalits += tao->niter;
185: if (tao->printreason) {
186: PetscViewer viewer = PETSC_VIEWER_STDOUT_(((PetscObject)tao)->comm);
188: PetscCall(PetscViewerASCIIAddTab(viewer, ((PetscObject)tao)->tablevel));
189: if (tao->reason > 0) {
190: if (((PetscObject)tao)->prefix) {
191: PetscCall(PetscViewerASCIIPrintf(viewer, " TAO %s solve converged due to %s iterations %" PetscInt_FMT "\n", ((PetscObject)tao)->prefix, TaoConvergedReasons[tao->reason], tao->niter));
192: } else {
193: PetscCall(PetscViewerASCIIPrintf(viewer, " TAO solve converged due to %s iterations %" PetscInt_FMT "\n", TaoConvergedReasons[tao->reason], tao->niter));
194: }
195: } else {
196: if (((PetscObject)tao)->prefix) {
197: PetscCall(PetscViewerASCIIPrintf(viewer, " TAO %s solve did not converge due to %s iteration %" PetscInt_FMT "\n", ((PetscObject)tao)->prefix, TaoConvergedReasons[tao->reason], tao->niter));
198: } else {
199: PetscCall(PetscViewerASCIIPrintf(viewer, " TAO solve did not converge due to %s iteration %" PetscInt_FMT "\n", TaoConvergedReasons[tao->reason], tao->niter));
200: }
201: }
202: PetscCall(PetscViewerASCIISubtractTab(viewer, ((PetscObject)tao)->tablevel));
203: }
204: PetscCall(TaoViewFromOptions(tao, NULL, "-tao_view"));
205: PetscFunctionReturn(PETSC_SUCCESS);
206: }
208: /*@
209: TaoSetUp - Sets up the internal data structures for the later use
210: of a Tao solver
212: Collective
214: Input Parameter:
215: . tao - the `Tao` context
217: Level: advanced
219: Note:
220: The user will not need to explicitly call `TaoSetUp()`, as it will
221: automatically be called in `TaoSolve()`. However, if the user
222: desires to call it explicitly, it should come after `TaoCreate()`
223: and any TaoSetSomething() routines, but before `TaoSolve()`.
225: .seealso: [](ch_tao), `Tao`, `TaoCreate()`, `TaoSolve()`
226: @*/
227: PetscErrorCode TaoSetUp(Tao tao)
228: {
229: PetscFunctionBegin;
231: if (tao->setupcalled) PetscFunctionReturn(PETSC_SUCCESS);
232: PetscCall(TaoSetUpEW_Private(tao));
233: PetscCheck(tao->solution, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONGSTATE, "Must call TaoSetSolution");
234: PetscTryTypeMethod(tao, setup);
235: tao->setupcalled = PETSC_TRUE;
236: PetscFunctionReturn(PETSC_SUCCESS);
237: }
239: /*@
240: TaoDestroy - Destroys the `Tao` context that was created with `TaoCreate()`
242: Collective
244: Input Parameter:
245: . tao - the `Tao` context
247: Level: beginner
249: .seealso: [](ch_tao), `Tao`, `TaoCreate()`, `TaoSolve()`
250: @*/
251: PetscErrorCode TaoDestroy(Tao *tao)
252: {
253: PetscFunctionBegin;
254: if (!*tao) PetscFunctionReturn(PETSC_SUCCESS);
256: if (--((PetscObject)*tao)->refct > 0) {
257: *tao = NULL;
258: PetscFunctionReturn(PETSC_SUCCESS);
259: }
261: PetscTryTypeMethod(*tao, destroy);
262: PetscCall(KSPDestroy(&(*tao)->ksp));
263: PetscCall(SNESDestroy(&(*tao)->snes_ewdummy));
264: PetscCall(TaoLineSearchDestroy(&(*tao)->linesearch));
266: if ((*tao)->ops->convergencedestroy) {
267: PetscCall((*(*tao)->ops->convergencedestroy)((*tao)->cnvP));
268: if ((*tao)->jacobian_state_inv) PetscCall(MatDestroy(&(*tao)->jacobian_state_inv));
269: }
270: PetscCall(VecDestroy(&(*tao)->solution));
271: PetscCall(VecDestroy(&(*tao)->gradient));
272: PetscCall(VecDestroy(&(*tao)->ls_res));
274: if ((*tao)->gradient_norm) {
275: PetscCall(PetscObjectDereference((PetscObject)(*tao)->gradient_norm));
276: PetscCall(VecDestroy(&(*tao)->gradient_norm_tmp));
277: }
279: PetscCall(VecDestroy(&(*tao)->XL));
280: PetscCall(VecDestroy(&(*tao)->XU));
281: PetscCall(VecDestroy(&(*tao)->IL));
282: PetscCall(VecDestroy(&(*tao)->IU));
283: PetscCall(VecDestroy(&(*tao)->DE));
284: PetscCall(VecDestroy(&(*tao)->DI));
285: PetscCall(VecDestroy(&(*tao)->constraints));
286: PetscCall(VecDestroy(&(*tao)->constraints_equality));
287: PetscCall(VecDestroy(&(*tao)->constraints_inequality));
288: PetscCall(VecDestroy(&(*tao)->stepdirection));
289: PetscCall(MatDestroy(&(*tao)->hessian_pre));
290: PetscCall(MatDestroy(&(*tao)->hessian));
291: PetscCall(MatDestroy(&(*tao)->ls_jac));
292: PetscCall(MatDestroy(&(*tao)->ls_jac_pre));
293: PetscCall(MatDestroy(&(*tao)->jacobian_pre));
294: PetscCall(MatDestroy(&(*tao)->jacobian));
295: PetscCall(MatDestroy(&(*tao)->jacobian_state_pre));
296: PetscCall(MatDestroy(&(*tao)->jacobian_state));
297: PetscCall(MatDestroy(&(*tao)->jacobian_state_inv));
298: PetscCall(MatDestroy(&(*tao)->jacobian_design));
299: PetscCall(MatDestroy(&(*tao)->jacobian_equality));
300: PetscCall(MatDestroy(&(*tao)->jacobian_equality_pre));
301: PetscCall(MatDestroy(&(*tao)->jacobian_inequality));
302: PetscCall(MatDestroy(&(*tao)->jacobian_inequality_pre));
303: PetscCall(ISDestroy(&(*tao)->state_is));
304: PetscCall(ISDestroy(&(*tao)->design_is));
305: PetscCall(VecDestroy(&(*tao)->res_weights_v));
306: PetscCall(TaoMonitorCancel(*tao));
307: if ((*tao)->hist_malloc) PetscCall(PetscFree4((*tao)->hist_obj, (*tao)->hist_resid, (*tao)->hist_cnorm, (*tao)->hist_lits));
308: if ((*tao)->res_weights_n) {
309: PetscCall(PetscFree((*tao)->res_weights_rows));
310: PetscCall(PetscFree((*tao)->res_weights_cols));
311: PetscCall(PetscFree((*tao)->res_weights_w));
312: }
313: PetscCall(PetscHeaderDestroy(tao));
314: PetscFunctionReturn(PETSC_SUCCESS);
315: }
317: /*@
318: TaoKSPSetUseEW - Sets `SNES` to use Eisenstat-Walker method {cite}`ew96`for computing relative tolerance for linear solvers.
320: Logically Collective
322: Input Parameters:
323: + tao - Tao context
324: - flag - `PETSC_TRUE` or `PETSC_FALSE`
326: Level: advanced
328: Note:
329: See `SNESKSPSetUseEW()` for customization details.
331: .seealso: [](ch_tao), `Tao`, `SNESKSPSetUseEW()`
332: @*/
333: PetscErrorCode TaoKSPSetUseEW(Tao tao, PetscBool flag)
334: {
335: PetscFunctionBegin;
338: tao->ksp_ewconv = flag;
339: PetscFunctionReturn(PETSC_SUCCESS);
340: }
342: /*@
343: TaoSetFromOptions - Sets various Tao parameters from the options database
345: Collective
347: Input Parameter:
348: . tao - the `Tao` solver context
350: Options Database Keys:
351: + -tao_type <type> - The algorithm that Tao uses (lmvm, nls, etc.)
352: . -tao_gatol <gatol> - absolute error tolerance for ||gradient||
353: . -tao_grtol <grtol> - relative error tolerance for ||gradient||
354: . -tao_gttol <gttol> - reduction of ||gradient|| relative to initial gradient
355: . -tao_max_it <max> - sets maximum number of iterations
356: . -tao_max_funcs <max> - sets maximum number of function evaluations
357: . -tao_fmin <fmin> - stop if function value reaches fmin
358: . -tao_steptol <tol> - stop if trust region radius less than <tol>
359: . -tao_trust0 <t> - initial trust region radius
360: . -tao_view_solution - view the solution at the end of the optimization process
361: . -tao_monitor - prints function value and residual norm at each iteration
362: . -tao_monitor_short - same as `-tao_monitor`, but truncates very small values
363: . -tao_monitor_constraint_norm - prints objective value, gradient, and constraint norm at each iteration
364: . -tao_monitor_globalization - prints information about the globalization at each iteration
365: . -tao_monitor_solution - prints solution vector at each iteration
366: . -tao_monitor_ls_residual - prints least-squares residual vector at each iteration
367: . -tao_monitor_step - prints step vector at each iteration
368: . -tao_monitor_gradient - prints gradient vector at each iteration
369: . -tao_monitor_solution_draw - graphically view solution vector at each iteration
370: . -tao_monitor_step_draw - graphically view step vector at each iteration
371: . -tao_monitor_gradient_draw - graphically view gradient at each iteration
372: . -tao_monitor_cancel - cancels all monitors (except those set with command line)
373: . -tao_fd_gradient - use gradient computed with finite differences
374: . -tao_fd_hessian - use hessian computed with finite differences
375: . -tao_mf_hessian - use matrix-free Hessian computed with finite differences
376: . -tao_view - prints information about the Tao after solving
377: - -tao_converged_reason - prints the reason Tao stopped iterating
379: Level: beginner
381: Note:
382: To see all options, run your program with the `-help` option or consult the
383: user's manual. Should be called after `TaoCreate()` but before `TaoSolve()`
385: .seealso: [](ch_tao), `Tao`, `TaoCreate()`, `TaoSolve()`
386: @*/
387: PetscErrorCode TaoSetFromOptions(Tao tao)
388: {
389: TaoType default_type = TAOLMVM;
390: char type[256], monfilename[PETSC_MAX_PATH_LEN];
391: PetscViewer monviewer;
392: PetscBool flg, found;
393: MPI_Comm comm;
394: PetscReal catol, crtol, gatol, grtol, gttol;
396: PetscFunctionBegin;
398: PetscCall(PetscObjectGetComm((PetscObject)tao, &comm));
400: if (((PetscObject)tao)->type_name) default_type = ((PetscObject)tao)->type_name;
402: PetscObjectOptionsBegin((PetscObject)tao);
403: /* Check for type from options */
404: PetscCall(PetscOptionsFList("-tao_type", "Tao Solver type", "TaoSetType", TaoList, default_type, type, 256, &flg));
405: if (flg) {
406: PetscCall(TaoSetType(tao, type));
407: } else if (!((PetscObject)tao)->type_name) {
408: PetscCall(TaoSetType(tao, default_type));
409: }
411: /* Tao solvers do not set the prefix, set it here if not yet done
412: We do it after SetType since solver may have been changed */
413: if (tao->linesearch) {
414: const char *prefix;
415: PetscCall(TaoLineSearchGetOptionsPrefix(tao->linesearch, &prefix));
416: if (!prefix) PetscCall(TaoLineSearchSetOptionsPrefix(tao->linesearch, ((PetscObject)tao)->prefix));
417: }
419: catol = tao->catol;
420: crtol = tao->crtol;
421: PetscCall(PetscOptionsReal("-tao_catol", "Stop if constraints violations within", "TaoSetConstraintTolerances", tao->catol, &catol, NULL));
422: PetscCall(PetscOptionsReal("-tao_crtol", "Stop if relative constraint violations within", "TaoSetConstraintTolerances", tao->crtol, &crtol, NULL));
423: PetscCall(TaoSetConstraintTolerances(tao, catol, crtol));
425: gatol = tao->gatol;
426: grtol = tao->grtol;
427: gttol = tao->gttol;
428: PetscCall(PetscOptionsReal("-tao_gatol", "Stop if norm of gradient less than", "TaoSetTolerances", tao->gatol, &gatol, NULL));
429: PetscCall(PetscOptionsReal("-tao_grtol", "Stop if norm of gradient divided by the function value is less than", "TaoSetTolerances", tao->grtol, &grtol, NULL));
430: PetscCall(PetscOptionsReal("-tao_gttol", "Stop if the norm of the gradient is less than the norm of the initial gradient times tol", "TaoSetTolerances", tao->gttol, >tol, NULL));
431: PetscCall(TaoSetTolerances(tao, gatol, grtol, gttol));
433: PetscCall(PetscOptionsInt("-tao_max_it", "Stop if iteration number exceeds", "TaoSetMaximumIterations", tao->max_it, &tao->max_it, &flg));
434: if (flg) PetscCall(TaoSetMaximumIterations(tao, tao->max_it));
436: PetscCall(PetscOptionsInt("-tao_max_funcs", "Stop if number of function evaluations exceeds", "TaoSetMaximumFunctionEvaluations", tao->max_funcs, &tao->max_funcs, &flg));
437: if (flg) PetscCall(TaoSetMaximumFunctionEvaluations(tao, tao->max_funcs));
439: PetscCall(PetscOptionsReal("-tao_fmin", "Stop if function less than", "TaoSetFunctionLowerBound", tao->fmin, &tao->fmin, NULL));
440: PetscCall(PetscOptionsBoundedReal("-tao_steptol", "Stop if step size or trust region radius less than", "", tao->steptol, &tao->steptol, NULL, 0));
441: PetscCall(PetscOptionsReal("-tao_trust0", "Initial trust region radius", "TaoSetInitialTrustRegionRadius", tao->trust0, &tao->trust0, &flg));
442: if (flg) PetscCall(TaoSetInitialTrustRegionRadius(tao, tao->trust0));
444: PetscCall(PetscOptionsDeprecated("-tao_solution_monitor", "-tao_monitor_solution", "3.21", NULL));
445: PetscCall(PetscOptionsDeprecated("-tao_gradient_monitor", "-tao_monitor_gradient", "3.21", NULL));
446: PetscCall(PetscOptionsDeprecated("-tao_stepdirection_monitor", "-tao_monitor_step", "3.21", NULL));
447: PetscCall(PetscOptionsDeprecated("-tao_residual_monitor", "-tao_monitor_residual", "3.21", NULL));
448: PetscCall(PetscOptionsDeprecated("-tao_smonitor", "-tao_monitor_short", "3.21", NULL));
449: PetscCall(PetscOptionsDeprecated("-tao_cmonitor", "-tao_monitor_constraint_norm", "3.21", NULL));
450: PetscCall(PetscOptionsDeprecated("-tao_gmonitor", "-tao_monitor_globalization", "3.21", NULL));
451: PetscCall(PetscOptionsDeprecated("-tao_draw_solution", "-tao_monitor_solution_draw", "3.21", NULL));
452: PetscCall(PetscOptionsDeprecated("-tao_draw_gradient", "-tao_monitor_gradient_draw", "3.21", NULL));
453: PetscCall(PetscOptionsDeprecated("-tao_draw_step", "-tao_monitor_step_draw", "3.21", NULL));
455: PetscCall(PetscOptionsString("-tao_monitor_solution", "View solution vector after each iteration", "TaoMonitorSet", "stdout", monfilename, sizeof(monfilename), &flg));
456: if (flg) {
457: PetscCall(PetscViewerASCIIOpen(comm, monfilename, &monviewer));
458: PetscCall(TaoMonitorSet(tao, TaoMonitorSolution, monviewer, (PetscCtxDestroyFn *)PetscViewerDestroy));
459: }
461: PetscCall(PetscOptionsBool("-tao_converged_reason", "Print reason for Tao converged", "TaoSolve", tao->printreason, &tao->printreason, NULL));
462: PetscCall(PetscOptionsString("-tao_monitor_gradient", "View gradient vector for each iteration", "TaoMonitorSet", "stdout", monfilename, sizeof(monfilename), &flg));
463: if (flg) {
464: PetscCall(PetscViewerASCIIOpen(comm, monfilename, &monviewer));
465: PetscCall(TaoMonitorSet(tao, TaoMonitorGradient, monviewer, (PetscCtxDestroyFn *)PetscViewerDestroy));
466: }
468: PetscCall(PetscOptionsString("-tao_monitor_step", "View step vector after each iteration", "TaoMonitorSet", "stdout", monfilename, sizeof(monfilename), &flg));
469: if (flg) {
470: PetscCall(PetscViewerASCIIOpen(comm, monfilename, &monviewer));
471: PetscCall(TaoMonitorSet(tao, TaoMonitorStep, monviewer, (PetscCtxDestroyFn *)PetscViewerDestroy));
472: }
474: PetscCall(PetscOptionsString("-tao_monitor_residual", "View least-squares residual vector after each iteration", "TaoMonitorSet", "stdout", monfilename, sizeof(monfilename), &flg));
475: if (flg) {
476: PetscCall(PetscViewerASCIIOpen(comm, monfilename, &monviewer));
477: PetscCall(TaoMonitorSet(tao, TaoMonitorResidual, monviewer, (PetscCtxDestroyFn *)PetscViewerDestroy));
478: }
480: PetscCall(PetscOptionsString("-tao_monitor", "Use the default convergence monitor", "TaoMonitorSet", "stdout", monfilename, sizeof(monfilename), &flg));
481: if (flg) {
482: PetscCall(PetscViewerASCIIOpen(comm, monfilename, &monviewer));
483: PetscCall(TaoMonitorSet(tao, TaoMonitorDefault, monviewer, (PetscCtxDestroyFn *)PetscViewerDestroy));
484: }
486: PetscCall(PetscOptionsString("-tao_monitor_globalization", "Use the convergence monitor with extra globalization info", "TaoMonitorSet", "stdout", monfilename, sizeof(monfilename), &flg));
487: if (flg) {
488: PetscCall(PetscViewerASCIIOpen(comm, monfilename, &monviewer));
489: PetscCall(TaoMonitorSet(tao, TaoMonitorGlobalization, monviewer, (PetscCtxDestroyFn *)PetscViewerDestroy));
490: }
492: PetscCall(PetscOptionsString("-tao_monitor_short", "Use the short convergence monitor", "TaoMonitorSet", "stdout", monfilename, sizeof(monfilename), &flg));
493: if (flg) {
494: PetscCall(PetscViewerASCIIOpen(comm, monfilename, &monviewer));
495: PetscCall(TaoMonitorSet(tao, TaoMonitorDefaultShort, monviewer, (PetscCtxDestroyFn *)PetscViewerDestroy));
496: }
498: PetscCall(PetscOptionsString("-tao_monitor_constraint_norm", "Use the default convergence monitor with constraint norm", "TaoMonitorSet", "stdout", monfilename, sizeof(monfilename), &flg));
499: if (flg) {
500: PetscCall(PetscViewerASCIIOpen(comm, monfilename, &monviewer));
501: PetscCall(TaoMonitorSet(tao, TaoMonitorConstraintNorm, monviewer, (PetscCtxDestroyFn *)PetscViewerDestroy));
502: }
504: flg = PETSC_FALSE;
505: PetscCall(PetscOptionsDeprecated("-tao_cancelmonitors", "-tao_monitor_cancel", "3.21", NULL));
506: PetscCall(PetscOptionsBool("-tao_monitor_cancel", "cancel all monitors and call any registered destroy routines", "TaoMonitorCancel", flg, &flg, NULL));
507: if (flg) PetscCall(TaoMonitorCancel(tao));
509: flg = PETSC_FALSE;
510: PetscCall(PetscOptionsBool("-tao_monitor_solution_draw", "Plot solution vector at each iteration", "TaoMonitorSet", flg, &flg, NULL));
511: if (flg) {
512: TaoMonitorDrawCtx drawctx;
513: PetscInt howoften = 1;
514: PetscCall(TaoMonitorDrawCtxCreate(PetscObjectComm((PetscObject)tao), NULL, NULL, PETSC_DECIDE, PETSC_DECIDE, 300, 300, howoften, &drawctx));
515: PetscCall(TaoMonitorSet(tao, TaoMonitorSolutionDraw, drawctx, (PetscCtxDestroyFn *)TaoMonitorDrawCtxDestroy));
516: }
518: flg = PETSC_FALSE;
519: PetscCall(PetscOptionsBool("-tao_monitor_step_draw", "Plots step at each iteration", "TaoMonitorSet", flg, &flg, NULL));
520: if (flg) PetscCall(TaoMonitorSet(tao, TaoMonitorStepDraw, NULL, NULL));
522: flg = PETSC_FALSE;
523: PetscCall(PetscOptionsBool("-tao_monitor_gradient_draw", "plots gradient at each iteration", "TaoMonitorSet", flg, &flg, NULL));
524: if (flg) {
525: TaoMonitorDrawCtx drawctx;
526: PetscInt howoften = 1;
527: PetscCall(TaoMonitorDrawCtxCreate(PetscObjectComm((PetscObject)tao), NULL, NULL, PETSC_DECIDE, PETSC_DECIDE, 300, 300, howoften, &drawctx));
528: PetscCall(TaoMonitorSet(tao, TaoMonitorGradientDraw, drawctx, (PetscCtxDestroyFn *)TaoMonitorDrawCtxDestroy));
529: }
530: flg = PETSC_FALSE;
531: PetscCall(PetscOptionsBool("-tao_fd_gradient", "compute gradient using finite differences", "TaoDefaultComputeGradient", flg, &flg, NULL));
532: if (flg) PetscCall(TaoSetGradient(tao, NULL, TaoDefaultComputeGradient, NULL));
533: flg = PETSC_FALSE;
534: PetscCall(PetscOptionsBool("-tao_fd_hessian", "compute Hessian using finite differences", "TaoDefaultComputeHessian", flg, &flg, NULL));
535: if (flg) {
536: Mat H;
538: PetscCall(MatCreate(PetscObjectComm((PetscObject)tao), &H));
539: PetscCall(MatSetType(H, MATAIJ));
540: PetscCall(TaoSetHessian(tao, H, H, TaoDefaultComputeHessian, NULL));
541: PetscCall(MatDestroy(&H));
542: }
543: flg = PETSC_FALSE;
544: PetscCall(PetscOptionsBool("-tao_mf_hessian", "compute matrix-free Hessian using finite differences", "TaoDefaultComputeHessianMFFD", flg, &flg, NULL));
545: if (flg) {
546: Mat H;
548: PetscCall(MatCreate(PetscObjectComm((PetscObject)tao), &H));
549: PetscCall(TaoSetHessian(tao, H, H, TaoDefaultComputeHessianMFFD, NULL));
550: PetscCall(MatDestroy(&H));
551: }
552: PetscCall(PetscOptionsBool("-tao_recycle_history", "enable recycling/re-using information from the previous TaoSolve() call for some algorithms", "TaoSetRecycleHistory", flg, &flg, &found));
553: if (found) PetscCall(TaoSetRecycleHistory(tao, flg));
554: PetscCall(PetscOptionsEnum("-tao_subset_type", "subset type", "", TaoSubSetTypes, (PetscEnum)tao->subset_type, (PetscEnum *)&tao->subset_type, NULL));
556: if (tao->ksp) {
557: PetscCall(PetscOptionsBool("-tao_ksp_ew", "Use Eisentat-Walker linear system convergence test", "TaoKSPSetUseEW", tao->ksp_ewconv, &tao->ksp_ewconv, NULL));
558: PetscCall(TaoKSPSetUseEW(tao, tao->ksp_ewconv));
559: }
561: PetscTryTypeMethod(tao, setfromoptions, PetscOptionsObject);
563: /* process any options handlers added with PetscObjectAddOptionsHandler() */
564: PetscCall(PetscObjectProcessOptionsHandlers((PetscObject)tao, PetscOptionsObject));
565: PetscOptionsEnd();
567: if (tao->linesearch) PetscCall(TaoLineSearchSetFromOptions(tao->linesearch));
568: PetscFunctionReturn(PETSC_SUCCESS);
569: }
571: /*@
572: TaoViewFromOptions - View a `Tao` object based on values in the options database
574: Collective
576: Input Parameters:
577: + A - the `Tao` context
578: . obj - Optional object that provides the prefix for the options database
579: - name - command line option
581: Level: intermediate
583: .seealso: [](ch_tao), `Tao`, `TaoView`, `PetscObjectViewFromOptions()`, `TaoCreate()`
584: @*/
585: PetscErrorCode TaoViewFromOptions(Tao A, PetscObject obj, const char name[])
586: {
587: PetscFunctionBegin;
589: PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
590: PetscFunctionReturn(PETSC_SUCCESS);
591: }
593: /*@
594: TaoView - Prints information about the `Tao` object
596: Collective
598: Input Parameters:
599: + tao - the `Tao` context
600: - viewer - visualization context
602: Options Database Key:
603: . -tao_view - Calls `TaoView()` at the end of `TaoSolve()`
605: Level: beginner
607: Notes:
608: The available visualization contexts include
609: + `PETSC_VIEWER_STDOUT_SELF` - standard output (default)
610: - `PETSC_VIEWER_STDOUT_WORLD` - synchronized standard
611: output where only the first processor opens
612: the file. All other processors send their
613: data to the first processor to print.
615: .seealso: [](ch_tao), `Tao`, `PetscViewerASCIIOpen()`
616: @*/
617: PetscErrorCode TaoView(Tao tao, PetscViewer viewer)
618: {
619: PetscBool isascii, isstring;
620: TaoType type;
622: PetscFunctionBegin;
624: if (!viewer) PetscCall(PetscViewerASCIIGetStdout(((PetscObject)tao)->comm, &viewer));
626: PetscCheckSameComm(tao, 1, viewer, 2);
628: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
629: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
630: if (isascii) {
631: PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)tao, viewer));
633: PetscCall(PetscViewerASCIIPushTab(viewer));
634: PetscTryTypeMethod(tao, view, viewer);
635: if (tao->linesearch) PetscCall(TaoLineSearchView(tao->linesearch, viewer));
636: if (tao->ksp) {
637: PetscCall(KSPView(tao->ksp, viewer));
638: PetscCall(PetscViewerASCIIPrintf(viewer, "total KSP iterations: %" PetscInt_FMT "\n", tao->ksp_tot_its));
639: }
641: if (tao->XL || tao->XU) PetscCall(PetscViewerASCIIPrintf(viewer, "Active Set subset type: %s\n", TaoSubSetTypes[tao->subset_type]));
643: PetscCall(PetscViewerASCIIPrintf(viewer, "convergence tolerances: gatol=%g,", (double)tao->gatol));
644: PetscCall(PetscViewerASCIIPrintf(viewer, " grtol=%g,", (double)tao->grtol));
645: PetscCall(PetscViewerASCIIPrintf(viewer, " steptol=%g,", (double)tao->steptol));
646: PetscCall(PetscViewerASCIIPrintf(viewer, " gttol=%g\n", (double)tao->gttol));
647: PetscCall(PetscViewerASCIIPrintf(viewer, "Residual in Function/Gradient:=%g\n", (double)tao->residual));
649: if (tao->constrained) {
650: PetscCall(PetscViewerASCIIPrintf(viewer, "convergence tolerances:"));
651: PetscCall(PetscViewerASCIIPrintf(viewer, " catol=%g,", (double)tao->catol));
652: PetscCall(PetscViewerASCIIPrintf(viewer, " crtol=%g\n", (double)tao->crtol));
653: PetscCall(PetscViewerASCIIPrintf(viewer, "Residual in Constraints:=%g\n", (double)tao->cnorm));
654: }
656: if (tao->trust < tao->steptol) {
657: PetscCall(PetscViewerASCIIPrintf(viewer, "convergence tolerances: steptol=%g\n", (double)tao->steptol));
658: PetscCall(PetscViewerASCIIPrintf(viewer, "Final trust region radius:=%g\n", (double)tao->trust));
659: }
661: if (tao->fmin > -1.e25) PetscCall(PetscViewerASCIIPrintf(viewer, "convergence tolerances: function minimum=%g\n", (double)tao->fmin));
662: PetscCall(PetscViewerASCIIPrintf(viewer, "Objective value=%g\n", (double)tao->fc));
664: PetscCall(PetscViewerASCIIPrintf(viewer, "total number of iterations=%" PetscInt_FMT ", ", tao->niter));
665: PetscCall(PetscViewerASCIIPrintf(viewer, " (max: %" PetscInt_FMT ")\n", tao->max_it));
667: if (tao->nfuncs > 0) {
668: PetscCall(PetscViewerASCIIPrintf(viewer, "total number of function evaluations=%" PetscInt_FMT ",", tao->nfuncs));
669: if (tao->max_funcs == PETSC_UNLIMITED) PetscCall(PetscViewerASCIIPrintf(viewer, " (max: unlimited)\n"));
670: else PetscCall(PetscViewerASCIIPrintf(viewer, " (max: %" PetscInt_FMT ")\n", tao->max_funcs));
671: }
672: if (tao->ngrads > 0) {
673: PetscCall(PetscViewerASCIIPrintf(viewer, "total number of gradient evaluations=%" PetscInt_FMT ",", tao->ngrads));
674: if (tao->max_funcs == PETSC_UNLIMITED) PetscCall(PetscViewerASCIIPrintf(viewer, " (max: unlimited)\n"));
675: else PetscCall(PetscViewerASCIIPrintf(viewer, " (max: %" PetscInt_FMT ")\n", tao->max_funcs));
676: }
677: if (tao->nfuncgrads > 0) {
678: PetscCall(PetscViewerASCIIPrintf(viewer, "total number of function/gradient evaluations=%" PetscInt_FMT ",", tao->nfuncgrads));
679: if (tao->max_funcs == PETSC_UNLIMITED) PetscCall(PetscViewerASCIIPrintf(viewer, " (max: unlimited)\n"));
680: else PetscCall(PetscViewerASCIIPrintf(viewer, " (max: %" PetscInt_FMT ")\n", tao->max_funcs));
681: }
682: if (tao->nhess > 0) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of Hessian evaluations=%" PetscInt_FMT "\n", tao->nhess));
683: if (tao->nconstraints > 0) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of constraint function evaluations=%" PetscInt_FMT "\n", tao->nconstraints));
684: if (tao->njac > 0) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of Jacobian evaluations=%" PetscInt_FMT "\n", tao->njac));
686: if (tao->reason > 0) {
687: PetscCall(PetscViewerASCIIPrintf(viewer, "Solution converged: "));
688: switch (tao->reason) {
689: case TAO_CONVERGED_GATOL:
690: PetscCall(PetscViewerASCIIPrintf(viewer, " ||g(X)|| <= gatol\n"));
691: break;
692: case TAO_CONVERGED_GRTOL:
693: PetscCall(PetscViewerASCIIPrintf(viewer, " ||g(X)||/|f(X)| <= grtol\n"));
694: break;
695: case TAO_CONVERGED_GTTOL:
696: PetscCall(PetscViewerASCIIPrintf(viewer, " ||g(X)||/||g(X0)|| <= gttol\n"));
697: break;
698: case TAO_CONVERGED_STEPTOL:
699: PetscCall(PetscViewerASCIIPrintf(viewer, " Steptol -- step size small\n"));
700: break;
701: case TAO_CONVERGED_MINF:
702: PetscCall(PetscViewerASCIIPrintf(viewer, " Minf -- f < fmin\n"));
703: break;
704: case TAO_CONVERGED_USER:
705: PetscCall(PetscViewerASCIIPrintf(viewer, " User Terminated\n"));
706: break;
707: default:
708: PetscCall(PetscViewerASCIIPrintf(viewer, " %d\n", tao->reason));
709: break;
710: }
711: } else if (tao->reason == TAO_CONTINUE_ITERATING) {
712: PetscCall(PetscViewerASCIIPrintf(viewer, "Solver never run\n"));
713: } else {
714: PetscCall(PetscViewerASCIIPrintf(viewer, "Solver failed: "));
715: switch (tao->reason) {
716: case TAO_DIVERGED_MAXITS:
717: PetscCall(PetscViewerASCIIPrintf(viewer, " Maximum Iterations\n"));
718: break;
719: case TAO_DIVERGED_NAN:
720: PetscCall(PetscViewerASCIIPrintf(viewer, " NAN or Inf encountered\n"));
721: break;
722: case TAO_DIVERGED_MAXFCN:
723: PetscCall(PetscViewerASCIIPrintf(viewer, " Maximum Function Evaluations\n"));
724: break;
725: case TAO_DIVERGED_LS_FAILURE:
726: PetscCall(PetscViewerASCIIPrintf(viewer, " Line Search Failure\n"));
727: break;
728: case TAO_DIVERGED_TR_REDUCTION:
729: PetscCall(PetscViewerASCIIPrintf(viewer, " Trust Region too small\n"));
730: break;
731: case TAO_DIVERGED_USER:
732: PetscCall(PetscViewerASCIIPrintf(viewer, " User Terminated\n"));
733: break;
734: default:
735: PetscCall(PetscViewerASCIIPrintf(viewer, " %d\n", tao->reason));
736: break;
737: }
738: }
739: PetscCall(PetscViewerASCIIPopTab(viewer));
740: } else if (isstring) {
741: PetscCall(TaoGetType(tao, &type));
742: PetscCall(PetscViewerStringSPrintf(viewer, " %-3.3s", type));
743: }
744: PetscFunctionReturn(PETSC_SUCCESS);
745: }
747: /*@
748: TaoSetRecycleHistory - Sets the boolean flag to enable/disable re-using
749: iterate information from the previous `TaoSolve()`. This feature is disabled by
750: default.
752: Logically Collective
754: Input Parameters:
755: + tao - the `Tao` context
756: - recycle - boolean flag
758: Options Database Key:
759: . -tao_recycle_history <true,false> - reuse the history
761: Level: intermediate
763: Notes:
764: For conjugate gradient methods (`TAOBNCG`), this re-uses the latest search direction
765: from the previous `TaoSolve()` call when computing the first search direction in a
766: new solution. By default, CG methods set the first search direction to the
767: negative gradient.
769: For quasi-Newton family of methods (`TAOBQNLS`, `TAOBQNKLS`, `TAOBQNKTR`, `TAOBQNKTL`), this re-uses
770: the accumulated quasi-Newton Hessian approximation from the previous `TaoSolve()`
771: call. By default, QN family of methods reset the initial Hessian approximation to
772: the identity matrix.
774: For any other algorithm, this setting has no effect.
776: .seealso: [](ch_tao), `Tao`, `TaoGetRecycleHistory()`, `TAOBNCG`, `TAOBQNLS`, `TAOBQNKLS`, `TAOBQNKTR`, `TAOBQNKTL`
777: @*/
778: PetscErrorCode TaoSetRecycleHistory(Tao tao, PetscBool recycle)
779: {
780: PetscFunctionBegin;
783: tao->recycle = recycle;
784: PetscFunctionReturn(PETSC_SUCCESS);
785: }
787: /*@
788: TaoGetRecycleHistory - Retrieve the boolean flag for re-using iterate information
789: from the previous `TaoSolve()`. This feature is disabled by default.
791: Logically Collective
793: Input Parameter:
794: . tao - the `Tao` context
796: Output Parameter:
797: . recycle - boolean flag
799: Level: intermediate
801: .seealso: [](ch_tao), `Tao`, `TaoSetRecycleHistory()`, `TAOBNCG`, `TAOBQNLS`, `TAOBQNKLS`, `TAOBQNKTR`, `TAOBQNKTL`
802: @*/
803: PetscErrorCode TaoGetRecycleHistory(Tao tao, PetscBool *recycle)
804: {
805: PetscFunctionBegin;
807: PetscAssertPointer(recycle, 2);
808: *recycle = tao->recycle;
809: PetscFunctionReturn(PETSC_SUCCESS);
810: }
812: /*@
813: TaoSetTolerances - Sets parameters used in `TaoSolve()` convergence tests
815: Logically Collective
817: Input Parameters:
818: + tao - the `Tao` context
819: . gatol - stop if norm of gradient is less than this
820: . grtol - stop if relative norm of gradient is less than this
821: - gttol - stop if norm of gradient is reduced by this factor
823: Options Database Keys:
824: + -tao_gatol <gatol> - Sets gatol
825: . -tao_grtol <grtol> - Sets grtol
826: - -tao_gttol <gttol> - Sets gttol
828: Stopping Criteria\:
829: .vb
830: ||g(X)|| <= gatol
831: ||g(X)|| / |f(X)| <= grtol
832: ||g(X)|| / ||g(X0)|| <= gttol
833: .ve
835: Level: beginner
837: Notes:
838: Use `PETSC_CURRENT` to leave one or more tolerances unchanged.
840: Use `PETSC_DETERMINE` to set one or more tolerances to their values when the `tao`object's type was set
842: Fortran Note:
843: Use `PETSC_CURRENT_REAL` or `PETSC_DETERMINE_REAL`
845: .seealso: [](ch_tao), `Tao`, `TaoConvergedReason`, `TaoGetTolerances()`
846: @*/
847: PetscErrorCode TaoSetTolerances(Tao tao, PetscReal gatol, PetscReal grtol, PetscReal gttol)
848: {
849: PetscFunctionBegin;
855: if (gatol == (PetscReal)PETSC_DETERMINE) {
856: tao->gatol = tao->default_gatol;
857: } else if (gatol != (PetscReal)PETSC_CURRENT) {
858: PetscCheck(gatol >= 0, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_OUTOFRANGE, "Negative gatol not allowed");
859: tao->gatol = gatol;
860: }
862: if (grtol == (PetscReal)PETSC_DETERMINE) {
863: tao->grtol = tao->default_grtol;
864: } else if (grtol != (PetscReal)PETSC_CURRENT) {
865: PetscCheck(grtol >= 0, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_OUTOFRANGE, "Negative grtol not allowed");
866: tao->grtol = grtol;
867: }
869: if (gttol == (PetscReal)PETSC_DETERMINE) {
870: tao->gttol = tao->default_gttol;
871: } else if (gttol != (PetscReal)PETSC_CURRENT) {
872: PetscCheck(gttol >= 0, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_OUTOFRANGE, "Negative gttol not allowed");
873: tao->gttol = gttol;
874: }
875: PetscFunctionReturn(PETSC_SUCCESS);
876: }
878: /*@
879: TaoSetConstraintTolerances - Sets constraint tolerance parameters used in `TaoSolve()` convergence tests
881: Logically Collective
883: Input Parameters:
884: + tao - the `Tao` context
885: . catol - absolute constraint tolerance, constraint norm must be less than `catol` for used for `gatol` convergence criteria
886: - crtol - relative constraint tolerance, constraint norm must be less than `crtol` for used for `gatol`, `gttol` convergence criteria
888: Options Database Keys:
889: + -tao_catol <catol> - Sets catol
890: - -tao_crtol <crtol> - Sets crtol
892: Level: intermediate
894: Notes:
895: Use `PETSC_CURRENT` to leave one or tolerance unchanged.
897: Use `PETSC_DETERMINE` to set one or more tolerances to their values when the `tao` object's type was set
899: Fortran Note:
900: Use `PETSC_CURRENT_REAL` or `PETSC_DETERMINE_REAL`
902: .seealso: [](ch_tao), `Tao`, `TaoConvergedReason`, `TaoGetTolerances()`, `TaoGetConstraintTolerances()`, `TaoSetTolerances()`
903: @*/
904: PetscErrorCode TaoSetConstraintTolerances(Tao tao, PetscReal catol, PetscReal crtol)
905: {
906: PetscFunctionBegin;
911: if (catol == (PetscReal)PETSC_DETERMINE) {
912: tao->catol = tao->default_catol;
913: } else if (catol != (PetscReal)PETSC_CURRENT) {
914: PetscCheck(catol >= 0, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_OUTOFRANGE, "Negative catol not allowed");
915: tao->catol = catol;
916: }
918: if (crtol == (PetscReal)PETSC_DETERMINE) {
919: tao->crtol = tao->default_crtol;
920: } else if (crtol != (PetscReal)PETSC_CURRENT) {
921: PetscCheck(crtol >= 0, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_OUTOFRANGE, "Negative crtol not allowed");
922: tao->crtol = crtol;
923: }
924: PetscFunctionReturn(PETSC_SUCCESS);
925: }
927: /*@
928: TaoGetConstraintTolerances - Gets constraint tolerance parameters used in `TaoSolve()` convergence tests
930: Not Collective
932: Input Parameter:
933: . tao - the `Tao` context
935: Output Parameters:
936: + catol - absolute constraint tolerance, constraint norm must be less than `catol` for used for `gatol` convergence criteria
937: - crtol - relative constraint tolerance, constraint norm must be less than `crtol` for used for `gatol`, `gttol` convergence criteria
939: Level: intermediate
941: .seealso: [](ch_tao), `Tao`, `TaoConvergedReasons`,`TaoGetTolerances()`, `TaoSetTolerances()`, `TaoSetConstraintTolerances()`
942: @*/
943: PetscErrorCode TaoGetConstraintTolerances(Tao tao, PetscReal *catol, PetscReal *crtol)
944: {
945: PetscFunctionBegin;
947: if (catol) *catol = tao->catol;
948: if (crtol) *crtol = tao->crtol;
949: PetscFunctionReturn(PETSC_SUCCESS);
950: }
952: /*@
953: TaoSetFunctionLowerBound - Sets a bound on the solution objective value.
954: When an approximate solution with an objective value below this number
955: has been found, the solver will terminate.
957: Logically Collective
959: Input Parameters:
960: + tao - the Tao solver context
961: - fmin - the tolerance
963: Options Database Key:
964: . -tao_fmin <fmin> - sets the minimum function value
966: Level: intermediate
968: .seealso: [](ch_tao), `Tao`, `TaoConvergedReason`, `TaoSetTolerances()`
969: @*/
970: PetscErrorCode TaoSetFunctionLowerBound(Tao tao, PetscReal fmin)
971: {
972: PetscFunctionBegin;
975: tao->fmin = fmin;
976: PetscFunctionReturn(PETSC_SUCCESS);
977: }
979: /*@
980: TaoGetFunctionLowerBound - Gets the bound on the solution objective value.
981: When an approximate solution with an objective value below this number
982: has been found, the solver will terminate.
984: Not Collective
986: Input Parameter:
987: . tao - the `Tao` solver context
989: Output Parameter:
990: . fmin - the minimum function value
992: Level: intermediate
994: .seealso: [](ch_tao), `Tao`, `TaoConvergedReason`, `TaoSetFunctionLowerBound()`
995: @*/
996: PetscErrorCode TaoGetFunctionLowerBound(Tao tao, PetscReal *fmin)
997: {
998: PetscFunctionBegin;
1000: PetscAssertPointer(fmin, 2);
1001: *fmin = tao->fmin;
1002: PetscFunctionReturn(PETSC_SUCCESS);
1003: }
1005: /*@
1006: TaoSetMaximumFunctionEvaluations - Sets a maximum number of function evaluations allowed for a `TaoSolve()`.
1008: Logically Collective
1010: Input Parameters:
1011: + tao - the `Tao` solver context
1012: - nfcn - the maximum number of function evaluations (>=0), use `PETSC_UNLIMITED` to have no bound
1014: Options Database Key:
1015: . -tao_max_funcs <nfcn> - sets the maximum number of function evaluations
1017: Level: intermediate
1019: Note:
1020: Use `PETSC_DETERMINE` to use the default maximum number of function evaluations that was set when the object type was set.
1022: Developer Note:
1023: Deprecated support for an unlimited number of function evaluations by passing a negative value.
1025: .seealso: [](ch_tao), `Tao`, `TaoSetTolerances()`, `TaoSetMaximumIterations()`
1026: @*/
1027: PetscErrorCode TaoSetMaximumFunctionEvaluations(Tao tao, PetscInt nfcn)
1028: {
1029: PetscFunctionBegin;
1032: if (nfcn == PETSC_DETERMINE) {
1033: tao->max_funcs = tao->default_max_funcs;
1034: } else if (nfcn == PETSC_UNLIMITED || nfcn < 0) {
1035: tao->max_funcs = PETSC_UNLIMITED;
1036: } else {
1037: PetscCheck(nfcn >= 0, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_OUTOFRANGE, "Maximum number of function evaluations must be positive");
1038: tao->max_funcs = nfcn;
1039: }
1040: PetscFunctionReturn(PETSC_SUCCESS);
1041: }
1043: /*@
1044: TaoGetMaximumFunctionEvaluations - Gets a maximum number of function evaluations allowed for a `TaoSolve()`
1046: Logically Collective
1048: Input Parameter:
1049: . tao - the `Tao` solver context
1051: Output Parameter:
1052: . nfcn - the maximum number of function evaluations
1054: Level: intermediate
1056: .seealso: [](ch_tao), `Tao`, `TaoSetMaximumFunctionEvaluations()`, `TaoGetMaximumIterations()`
1057: @*/
1058: PetscErrorCode TaoGetMaximumFunctionEvaluations(Tao tao, PetscInt *nfcn)
1059: {
1060: PetscFunctionBegin;
1062: PetscAssertPointer(nfcn, 2);
1063: *nfcn = tao->max_funcs;
1064: PetscFunctionReturn(PETSC_SUCCESS);
1065: }
1067: /*@
1068: TaoGetCurrentFunctionEvaluations - Get current number of function evaluations used by a `Tao` object
1070: Not Collective
1072: Input Parameter:
1073: . tao - the `Tao` solver context
1075: Output Parameter:
1076: . nfuncs - the current number of function evaluations (maximum between gradient and function evaluations)
1078: Level: intermediate
1080: .seealso: [](ch_tao), `Tao`, `TaoSetMaximumFunctionEvaluations()`, `TaoGetMaximumFunctionEvaluations()`, `TaoGetMaximumIterations()`
1081: @*/
1082: PetscErrorCode TaoGetCurrentFunctionEvaluations(Tao tao, PetscInt *nfuncs)
1083: {
1084: PetscFunctionBegin;
1086: PetscAssertPointer(nfuncs, 2);
1087: *nfuncs = PetscMax(tao->nfuncs, tao->nfuncgrads);
1088: PetscFunctionReturn(PETSC_SUCCESS);
1089: }
1091: /*@
1092: TaoSetMaximumIterations - Sets a maximum number of iterates to be used in `TaoSolve()`
1094: Logically Collective
1096: Input Parameters:
1097: + tao - the `Tao` solver context
1098: - maxits - the maximum number of iterates (>=0), use `PETSC_UNLIMITED` to have no bound
1100: Options Database Key:
1101: . -tao_max_it <its> - sets the maximum number of iterations
1103: Level: intermediate
1105: Note:
1106: Use `PETSC_DETERMINE` to use the default maximum number of iterations that was set when the object's type was set.
1108: Developer Note:
1109: DeprAlso accepts the deprecated negative values to indicate no limit
1111: .seealso: [](ch_tao), `Tao`, `TaoSetTolerances()`, `TaoSetMaximumFunctionEvaluations()`
1112: @*/
1113: PetscErrorCode TaoSetMaximumIterations(Tao tao, PetscInt maxits)
1114: {
1115: PetscFunctionBegin;
1118: if (maxits == PETSC_DETERMINE) {
1119: tao->max_it = tao->default_max_it;
1120: } else if (maxits == PETSC_UNLIMITED) {
1121: tao->max_it = PETSC_INT_MAX;
1122: } else {
1123: PetscCheck(maxits > 0, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_OUTOFRANGE, "Maximum number of iterations must be positive");
1124: tao->max_it = maxits;
1125: }
1126: PetscFunctionReturn(PETSC_SUCCESS);
1127: }
1129: /*@
1130: TaoGetMaximumIterations - Gets a maximum number of iterates that will be used
1132: Not Collective
1134: Input Parameter:
1135: . tao - the `Tao` solver context
1137: Output Parameter:
1138: . maxits - the maximum number of iterates
1140: Level: intermediate
1142: .seealso: [](ch_tao), `Tao`, `TaoSetMaximumIterations()`, `TaoGetMaximumFunctionEvaluations()`
1143: @*/
1144: PetscErrorCode TaoGetMaximumIterations(Tao tao, PetscInt *maxits)
1145: {
1146: PetscFunctionBegin;
1148: PetscAssertPointer(maxits, 2);
1149: *maxits = tao->max_it;
1150: PetscFunctionReturn(PETSC_SUCCESS);
1151: }
1153: /*@
1154: TaoSetInitialTrustRegionRadius - Sets the initial trust region radius.
1156: Logically Collective
1158: Input Parameters:
1159: + tao - a `Tao` optimization solver
1160: - radius - the trust region radius
1162: Options Database Key:
1163: . -tao_trust0 <t0> - sets initial trust region radius
1165: Level: intermediate
1167: Note:
1168: Use `PETSC_DETERMINE` to use the default radius that was set when the object's type was set.
1170: .seealso: [](ch_tao), `Tao`, `TaoGetTrustRegionRadius()`, `TaoSetTrustRegionTolerance()`, `TAONTR`
1171: @*/
1172: PetscErrorCode TaoSetInitialTrustRegionRadius(Tao tao, PetscReal radius)
1173: {
1174: PetscFunctionBegin;
1177: if (radius == PETSC_DETERMINE) {
1178: tao->trust0 = tao->default_trust0;
1179: } else {
1180: PetscCheck(radius > 0, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_OUTOFRANGE, "Radius must be positive");
1181: tao->trust0 = radius;
1182: }
1183: PetscFunctionReturn(PETSC_SUCCESS);
1184: }
1186: /*@
1187: TaoGetInitialTrustRegionRadius - Gets the initial trust region radius.
1189: Not Collective
1191: Input Parameter:
1192: . tao - a `Tao` optimization solver
1194: Output Parameter:
1195: . radius - the trust region radius
1197: Level: intermediate
1199: .seealso: [](ch_tao), `Tao`, `TaoSetInitialTrustRegionRadius()`, `TaoGetCurrentTrustRegionRadius()`, `TAONTR`
1200: @*/
1201: PetscErrorCode TaoGetInitialTrustRegionRadius(Tao tao, PetscReal *radius)
1202: {
1203: PetscFunctionBegin;
1205: PetscAssertPointer(radius, 2);
1206: *radius = tao->trust0;
1207: PetscFunctionReturn(PETSC_SUCCESS);
1208: }
1210: /*@
1211: TaoGetCurrentTrustRegionRadius - Gets the current trust region radius.
1213: Not Collective
1215: Input Parameter:
1216: . tao - a `Tao` optimization solver
1218: Output Parameter:
1219: . radius - the trust region radius
1221: Level: intermediate
1223: .seealso: [](ch_tao), `Tao`, `TaoSetInitialTrustRegionRadius()`, `TaoGetInitialTrustRegionRadius()`, `TAONTR`
1224: @*/
1225: PetscErrorCode TaoGetCurrentTrustRegionRadius(Tao tao, PetscReal *radius)
1226: {
1227: PetscFunctionBegin;
1229: PetscAssertPointer(radius, 2);
1230: *radius = tao->trust;
1231: PetscFunctionReturn(PETSC_SUCCESS);
1232: }
1234: /*@
1235: TaoGetTolerances - gets the current values of some tolerances used for the convergence testing of `TaoSolve()`
1237: Not Collective
1239: Input Parameter:
1240: . tao - the `Tao` context
1242: Output Parameters:
1243: + gatol - stop if norm of gradient is less than this
1244: . grtol - stop if relative norm of gradient is less than this
1245: - gttol - stop if norm of gradient is reduced by a this factor
1247: Level: intermediate
1249: Note:
1250: `NULL` can be used as an argument if not all tolerances values are needed
1252: .seealso: [](ch_tao), `Tao`, `TaoSetTolerances()`
1253: @*/
1254: PetscErrorCode TaoGetTolerances(Tao tao, PetscReal *gatol, PetscReal *grtol, PetscReal *gttol)
1255: {
1256: PetscFunctionBegin;
1258: if (gatol) *gatol = tao->gatol;
1259: if (grtol) *grtol = tao->grtol;
1260: if (gttol) *gttol = tao->gttol;
1261: PetscFunctionReturn(PETSC_SUCCESS);
1262: }
1264: /*@
1265: TaoGetKSP - Gets the linear solver used by the optimization solver.
1267: Not Collective
1269: Input Parameter:
1270: . tao - the `Tao` solver
1272: Output Parameter:
1273: . ksp - the `KSP` linear solver used in the optimization solver
1275: Level: intermediate
1277: .seealso: [](ch_tao), `Tao`, `KSP`
1278: @*/
1279: PetscErrorCode TaoGetKSP(Tao tao, KSP *ksp)
1280: {
1281: PetscFunctionBegin;
1283: PetscAssertPointer(ksp, 2);
1284: *ksp = tao->ksp;
1285: PetscFunctionReturn(PETSC_SUCCESS);
1286: }
1288: /*@
1289: TaoGetLinearSolveIterations - Gets the total number of linear iterations
1290: used by the `Tao` solver
1292: Not Collective
1294: Input Parameter:
1295: . tao - the `Tao` context
1297: Output Parameter:
1298: . lits - number of linear iterations
1300: Level: intermediate
1302: Note:
1303: This counter is reset to zero for each successive call to `TaoSolve()`
1305: .seealso: [](ch_tao), `Tao`, `TaoGetKSP()`
1306: @*/
1307: PetscErrorCode TaoGetLinearSolveIterations(Tao tao, PetscInt *lits)
1308: {
1309: PetscFunctionBegin;
1311: PetscAssertPointer(lits, 2);
1312: *lits = tao->ksp_tot_its;
1313: PetscFunctionReturn(PETSC_SUCCESS);
1314: }
1316: /*@
1317: TaoGetLineSearch - Gets the line search used by the optimization solver.
1319: Not Collective
1321: Input Parameter:
1322: . tao - the `Tao` solver
1324: Output Parameter:
1325: . ls - the line search used in the optimization solver
1327: Level: intermediate
1329: .seealso: [](ch_tao), `Tao`, `TaoLineSearch`, `TaoLineSearchType`
1330: @*/
1331: PetscErrorCode TaoGetLineSearch(Tao tao, TaoLineSearch *ls)
1332: {
1333: PetscFunctionBegin;
1335: PetscAssertPointer(ls, 2);
1336: *ls = tao->linesearch;
1337: PetscFunctionReturn(PETSC_SUCCESS);
1338: }
1340: /*@
1341: TaoAddLineSearchCounts - Adds the number of function evaluations spent
1342: in the line search to the running total.
1344: Input Parameters:
1345: . tao - the `Tao` solver
1347: Level: developer
1349: .seealso: [](ch_tao), `Tao`, `TaoGetLineSearch()`, `TaoLineSearchApply()`
1350: @*/
1351: PetscErrorCode TaoAddLineSearchCounts(Tao tao)
1352: {
1353: PetscBool flg;
1354: PetscInt nfeval, ngeval, nfgeval;
1356: PetscFunctionBegin;
1358: if (tao->linesearch) {
1359: PetscCall(TaoLineSearchIsUsingTaoRoutines(tao->linesearch, &flg));
1360: if (!flg) {
1361: PetscCall(TaoLineSearchGetNumberFunctionEvaluations(tao->linesearch, &nfeval, &ngeval, &nfgeval));
1362: tao->nfuncs += nfeval;
1363: tao->ngrads += ngeval;
1364: tao->nfuncgrads += nfgeval;
1365: }
1366: }
1367: PetscFunctionReturn(PETSC_SUCCESS);
1368: }
1370: /*@
1371: TaoGetSolution - Returns the vector with the current solution from the `Tao` object
1373: Not Collective
1375: Input Parameter:
1376: . tao - the `Tao` context
1378: Output Parameter:
1379: . X - the current solution
1381: Level: intermediate
1383: Note:
1384: The returned vector will be the same object that was passed into `TaoSetSolution()`
1386: .seealso: [](ch_tao), `Tao`, `TaoSetSolution()`, `TaoSolve()`
1387: @*/
1388: PetscErrorCode TaoGetSolution(Tao tao, Vec *X)
1389: {
1390: PetscFunctionBegin;
1392: PetscAssertPointer(X, 2);
1393: *X = tao->solution;
1394: PetscFunctionReturn(PETSC_SUCCESS);
1395: }
1397: /*@
1398: TaoResetStatistics - Initialize the statistics collected by the `Tao` object.
1399: These statistics include the iteration number, residual norms, and convergence status.
1400: This routine gets called before solving each optimization problem.
1402: Collective
1404: Input Parameter:
1405: . tao - the `Tao` context
1407: Level: developer
1409: .seealso: [](ch_tao), `Tao`, `TaoCreate()`, `TaoSolve()`
1410: @*/
1411: PetscErrorCode TaoResetStatistics(Tao tao)
1412: {
1413: PetscFunctionBegin;
1415: tao->niter = 0;
1416: tao->nfuncs = 0;
1417: tao->nfuncgrads = 0;
1418: tao->ngrads = 0;
1419: tao->nhess = 0;
1420: tao->njac = 0;
1421: tao->nconstraints = 0;
1422: tao->ksp_its = 0;
1423: tao->ksp_tot_its = 0;
1424: tao->reason = TAO_CONTINUE_ITERATING;
1425: tao->residual = 0.0;
1426: tao->cnorm = 0.0;
1427: tao->step = 0.0;
1428: tao->lsflag = PETSC_FALSE;
1429: if (tao->hist_reset) tao->hist_len = 0;
1430: PetscFunctionReturn(PETSC_SUCCESS);
1431: }
1433: /*@C
1434: TaoSetUpdate - Sets the general-purpose update function called
1435: at the beginning of every iteration of the optimization algorithm. Called after the new solution and the gradient
1436: is determined, but before the Hessian is computed (if applicable).
1438: Logically Collective
1440: Input Parameters:
1441: + tao - The `Tao` solver
1442: . func - The function
1443: - ctx - The update function context
1445: Calling sequence of `func`:
1446: + tao - The optimizer context
1447: . it - The current iteration index
1448: - ctx - The update context
1450: Level: advanced
1452: Notes:
1453: Users can modify the gradient direction or any other vector associated to the specific solver used.
1454: The objective function value is always recomputed after a call to the update hook.
1456: .seealso: [](ch_tao), `Tao`, `TaoSolve()`
1457: @*/
1458: PetscErrorCode TaoSetUpdate(Tao tao, PetscErrorCode (*func)(Tao tao, PetscInt it, void *ctx), void *ctx)
1459: {
1460: PetscFunctionBegin;
1462: tao->ops->update = func;
1463: tao->user_update = ctx;
1464: PetscFunctionReturn(PETSC_SUCCESS);
1465: }
1467: /*@C
1468: TaoSetConvergenceTest - Sets the function that is to be used to test
1469: for convergence of the iterative minimization solution. The new convergence
1470: testing routine will replace Tao's default convergence test.
1472: Logically Collective
1474: Input Parameters:
1475: + tao - the `Tao` object
1476: . conv - the routine to test for convergence
1477: - ctx - [optional] context for private data for the convergence routine
1478: (may be `NULL`)
1480: Calling sequence of `conv`:
1481: + tao - the `Tao` object
1482: - ctx - [optional] convergence context
1484: Level: advanced
1486: Note:
1487: The new convergence testing routine should call `TaoSetConvergedReason()`.
1489: .seealso: [](ch_tao), `Tao`, `TaoSolve()`, `TaoSetConvergedReason()`, `TaoGetSolutionStatus()`, `TaoGetTolerances()`, `TaoMonitorSet()`
1490: @*/
1491: PetscErrorCode TaoSetConvergenceTest(Tao tao, PetscErrorCode (*conv)(Tao, void *), void *ctx)
1492: {
1493: PetscFunctionBegin;
1495: tao->ops->convergencetest = conv;
1496: tao->cnvP = ctx;
1497: PetscFunctionReturn(PETSC_SUCCESS);
1498: }
1500: /*@C
1501: TaoMonitorSet - Sets an additional function that is to be used at every
1502: iteration of the solver to display the iteration's
1503: progress.
1505: Logically Collective
1507: Input Parameters:
1508: + tao - the `Tao` solver context
1509: . func - monitoring routine
1510: . ctx - [optional] user-defined context for private data for the monitor routine (may be `NULL`)
1511: - dest - [optional] function to destroy the context when the `Tao` is destroyed, see `PetscCtxDestroyFn` for the calling sequence
1513: Calling sequence of `func`:
1514: + tao - the `Tao` solver context
1515: - ctx - [optional] monitoring context
1517: Level: intermediate
1519: Notes:
1520: See `TaoSetFromOptions()` for a monitoring options.
1522: Several different monitoring routines may be set by calling
1523: `TaoMonitorSet()` multiple times; all will be called in the
1524: order in which they were set.
1526: Fortran Notes:
1527: Only one monitor function may be set
1529: .seealso: [](ch_tao), `Tao`, `TaoSolve()`, `TaoMonitorDefault()`, `TaoMonitorCancel()`, `TaoSetDestroyRoutine()`, `TaoView()`, `PetscCtxDestroyFn`
1530: @*/
1531: PetscErrorCode TaoMonitorSet(Tao tao, PetscErrorCode (*func)(Tao, void *), void *ctx, PetscCtxDestroyFn *dest)
1532: {
1533: PetscFunctionBegin;
1535: PetscCheck(tao->numbermonitors < MAXTAOMONITORS, PetscObjectComm((PetscObject)tao), PETSC_ERR_SUP, "Cannot attach another monitor -- max=%d", MAXTAOMONITORS);
1536: for (PetscInt i = 0; i < tao->numbermonitors; i++) {
1537: PetscBool identical;
1539: PetscCall(PetscMonitorCompare((PetscErrorCode (*)(void))(PetscVoidFn *)func, ctx, dest, (PetscErrorCode (*)(void))(PetscVoidFn *)tao->monitor[i], tao->monitorcontext[i], tao->monitordestroy[i], &identical));
1540: if (identical) PetscFunctionReturn(PETSC_SUCCESS);
1541: }
1542: tao->monitor[tao->numbermonitors] = func;
1543: tao->monitorcontext[tao->numbermonitors] = ctx;
1544: tao->monitordestroy[tao->numbermonitors] = dest;
1545: ++tao->numbermonitors;
1546: PetscFunctionReturn(PETSC_SUCCESS);
1547: }
1549: /*@
1550: TaoMonitorCancel - Clears all the monitor functions for a `Tao` object.
1552: Logically Collective
1554: Input Parameter:
1555: . tao - the `Tao` solver context
1557: Options Database Key:
1558: . -tao_monitor_cancel - cancels all monitors that have been hardwired
1559: into a code by calls to `TaoMonitorSet()`, but does not cancel those
1560: set via the options database
1562: Level: advanced
1564: Note:
1565: There is no way to clear one specific monitor from a `Tao` object.
1567: .seealso: [](ch_tao), `Tao`, `TaoMonitorDefault()`, `TaoMonitorSet()`
1568: @*/
1569: PetscErrorCode TaoMonitorCancel(Tao tao)
1570: {
1571: PetscInt i;
1573: PetscFunctionBegin;
1575: for (i = 0; i < tao->numbermonitors; i++) {
1576: if (tao->monitordestroy[i]) PetscCall((*tao->monitordestroy[i])(&tao->monitorcontext[i]));
1577: }
1578: tao->numbermonitors = 0;
1579: PetscFunctionReturn(PETSC_SUCCESS);
1580: }
1582: /*@
1583: TaoMonitorDefault - Default routine for monitoring progress of `TaoSolve()`
1585: Collective
1587: Input Parameters:
1588: + tao - the `Tao` context
1589: - ctx - `PetscViewer` context or `NULL`
1591: Options Database Key:
1592: . -tao_monitor - turn on default monitoring
1594: Level: advanced
1596: Note:
1597: This monitor prints the function value and gradient
1598: norm at each iteration.
1600: .seealso: [](ch_tao), `Tao`, `TaoMonitorDefaultShort()`, `TaoMonitorSet()`
1601: @*/
1602: PetscErrorCode TaoMonitorDefault(Tao tao, void *ctx)
1603: {
1604: PetscInt its, tabs;
1605: PetscReal fct, gnorm;
1606: PetscViewer viewer = (PetscViewer)ctx;
1608: PetscFunctionBegin;
1611: its = tao->niter;
1612: fct = tao->fc;
1613: gnorm = tao->residual;
1614: PetscCall(PetscViewerASCIIGetTab(viewer, &tabs));
1615: PetscCall(PetscViewerASCIISetTab(viewer, ((PetscObject)tao)->tablevel));
1616: if (its == 0 && ((PetscObject)tao)->prefix && !tao->header_printed) {
1617: PetscCall(PetscViewerASCIIPrintf(viewer, " Iteration information for %s solve.\n", ((PetscObject)tao)->prefix));
1618: tao->header_printed = PETSC_TRUE;
1619: }
1620: PetscCall(PetscViewerASCIIPrintf(viewer, "%3" PetscInt_FMT " TAO,", its));
1621: PetscCall(PetscViewerASCIIPrintf(viewer, " Function value: %g,", (double)fct));
1622: if (gnorm >= PETSC_INFINITY) {
1623: PetscCall(PetscViewerASCIIPrintf(viewer, " Residual: Inf \n"));
1624: } else {
1625: PetscCall(PetscViewerASCIIPrintf(viewer, " Residual: %g \n", (double)gnorm));
1626: }
1627: PetscCall(PetscViewerASCIISetTab(viewer, tabs));
1628: PetscFunctionReturn(PETSC_SUCCESS);
1629: }
1631: /*@
1632: TaoMonitorGlobalization - Default routine for monitoring progress of `TaoSolve()` with extra detail on the globalization method.
1634: Collective
1636: Input Parameters:
1637: + tao - the `Tao` context
1638: - ctx - `PetscViewer` context or `NULL`
1640: Options Database Key:
1641: . -tao_monitor_globalization - turn on monitoring with globalization information
1643: Level: advanced
1645: Note:
1646: This monitor prints the function value and gradient norm at each
1647: iteration, as well as the step size and trust radius. Note that the
1648: step size and trust radius may be the same for some algorithms.
1650: .seealso: [](ch_tao), `Tao`, `TaoMonitorDefaultShort()`, `TaoMonitorSet()`
1651: @*/
1652: PetscErrorCode TaoMonitorGlobalization(Tao tao, void *ctx)
1653: {
1654: PetscInt its, tabs;
1655: PetscReal fct, gnorm, stp, tr;
1656: PetscViewer viewer = (PetscViewer)ctx;
1658: PetscFunctionBegin;
1661: its = tao->niter;
1662: fct = tao->fc;
1663: gnorm = tao->residual;
1664: stp = tao->step;
1665: tr = tao->trust;
1666: PetscCall(PetscViewerASCIIGetTab(viewer, &tabs));
1667: PetscCall(PetscViewerASCIISetTab(viewer, ((PetscObject)tao)->tablevel));
1668: if (its == 0 && ((PetscObject)tao)->prefix && !tao->header_printed) {
1669: PetscCall(PetscViewerASCIIPrintf(viewer, " Iteration information for %s solve.\n", ((PetscObject)tao)->prefix));
1670: tao->header_printed = PETSC_TRUE;
1671: }
1672: PetscCall(PetscViewerASCIIPrintf(viewer, "%3" PetscInt_FMT " TAO,", its));
1673: PetscCall(PetscViewerASCIIPrintf(viewer, " Function value: %g,", (double)fct));
1674: if (gnorm >= PETSC_INFINITY) {
1675: PetscCall(PetscViewerASCIIPrintf(viewer, " Residual: Inf,"));
1676: } else {
1677: PetscCall(PetscViewerASCIIPrintf(viewer, " Residual: %g,", (double)gnorm));
1678: }
1679: PetscCall(PetscViewerASCIIPrintf(viewer, " Step: %g, Trust: %g\n", (double)stp, (double)tr));
1680: PetscCall(PetscViewerASCIISetTab(viewer, tabs));
1681: PetscFunctionReturn(PETSC_SUCCESS);
1682: }
1684: /*@
1685: TaoMonitorDefaultShort - Routine for monitoring progress of `TaoSolve()` that displays fewer digits than `TaoMonitorDefault()`
1687: Collective
1689: Input Parameters:
1690: + tao - the `Tao` context
1691: - ctx - `PetscViewer` context of type `PETSCVIEWERASCII`
1693: Options Database Key:
1694: . -tao_monitor_short - turn on default short monitoring
1696: Level: advanced
1698: Note:
1699: Same as `TaoMonitorDefault()` except
1700: it prints fewer digits of the residual as the residual gets smaller.
1701: This is because the later digits are meaningless and are often
1702: different on different machines; by using this routine different
1703: machines will usually generate the same output.
1705: .seealso: [](ch_tao), `Tao`, `TaoMonitorDefault()`, `TaoMonitorSet()`
1706: @*/
1707: PetscErrorCode TaoMonitorDefaultShort(Tao tao, void *ctx)
1708: {
1709: PetscInt its, tabs;
1710: PetscReal fct, gnorm;
1711: PetscViewer viewer = (PetscViewer)ctx;
1713: PetscFunctionBegin;
1716: its = tao->niter;
1717: fct = tao->fc;
1718: gnorm = tao->residual;
1719: PetscCall(PetscViewerASCIIGetTab(viewer, &tabs));
1720: PetscCall(PetscViewerASCIISetTab(viewer, ((PetscObject)tao)->tablevel));
1721: PetscCall(PetscViewerASCIIPrintf(viewer, "iter = %3" PetscInt_FMT ",", its));
1722: PetscCall(PetscViewerASCIIPrintf(viewer, " Function value %g,", (double)fct));
1723: if (gnorm >= PETSC_INFINITY) {
1724: PetscCall(PetscViewerASCIIPrintf(viewer, " Residual: Inf \n"));
1725: } else if (gnorm > 1.e-6) {
1726: PetscCall(PetscViewerASCIIPrintf(viewer, " Residual: %g \n", (double)gnorm));
1727: } else if (gnorm > 1.e-11) {
1728: PetscCall(PetscViewerASCIIPrintf(viewer, " Residual: < 1.0e-6 \n"));
1729: } else {
1730: PetscCall(PetscViewerASCIIPrintf(viewer, " Residual: < 1.0e-11 \n"));
1731: }
1732: PetscCall(PetscViewerASCIISetTab(viewer, tabs));
1733: PetscFunctionReturn(PETSC_SUCCESS);
1734: }
1736: /*@
1737: TaoMonitorConstraintNorm - same as `TaoMonitorDefault()` except
1738: it prints the norm of the constraint function.
1740: Collective
1742: Input Parameters:
1743: + tao - the `Tao` context
1744: - ctx - `PetscViewer` context or `NULL`
1746: Options Database Key:
1747: . -tao_monitor_constraint_norm - monitor the constraints
1749: Level: advanced
1751: .seealso: [](ch_tao), `Tao`, `TaoMonitorDefault()`, `TaoMonitorSet()`
1752: @*/
1753: PetscErrorCode TaoMonitorConstraintNorm(Tao tao, void *ctx)
1754: {
1755: PetscInt its, tabs;
1756: PetscReal fct, gnorm;
1757: PetscViewer viewer = (PetscViewer)ctx;
1759: PetscFunctionBegin;
1762: its = tao->niter;
1763: fct = tao->fc;
1764: gnorm = tao->residual;
1765: PetscCall(PetscViewerASCIIGetTab(viewer, &tabs));
1766: PetscCall(PetscViewerASCIISetTab(viewer, ((PetscObject)tao)->tablevel));
1767: PetscCall(PetscViewerASCIIPrintf(viewer, "iter = %" PetscInt_FMT ",", its));
1768: PetscCall(PetscViewerASCIIPrintf(viewer, " Function value: %g,", (double)fct));
1769: PetscCall(PetscViewerASCIIPrintf(viewer, " Residual: %g ", (double)gnorm));
1770: PetscCall(PetscViewerASCIIPrintf(viewer, " Constraint: %g \n", (double)tao->cnorm));
1771: PetscCall(PetscViewerASCIISetTab(viewer, tabs));
1772: PetscFunctionReturn(PETSC_SUCCESS);
1773: }
1775: /*@C
1776: TaoMonitorSolution - Views the solution at each iteration of `TaoSolve()`
1778: Collective
1780: Input Parameters:
1781: + tao - the `Tao` context
1782: - ctx - `PetscViewer` context or `NULL`
1784: Options Database Key:
1785: . -tao_monitor_solution - view the solution
1787: Level: advanced
1789: .seealso: [](ch_tao), `Tao`, `TaoMonitorDefaultShort()`, `TaoMonitorSet()`
1790: @*/
1791: PetscErrorCode TaoMonitorSolution(Tao tao, void *ctx)
1792: {
1793: PetscViewer viewer = (PetscViewer)ctx;
1795: PetscFunctionBegin;
1798: PetscCall(VecView(tao->solution, viewer));
1799: PetscFunctionReturn(PETSC_SUCCESS);
1800: }
1802: /*@C
1803: TaoMonitorGradient - Views the gradient at each iteration of `TaoSolve()`
1805: Collective
1807: Input Parameters:
1808: + tao - the `Tao` context
1809: - ctx - `PetscViewer` context or `NULL`
1811: Options Database Key:
1812: . -tao_monitor_gradient - view the gradient at each iteration
1814: Level: advanced
1816: .seealso: [](ch_tao), `Tao`, `TaoMonitorDefaultShort()`, `TaoMonitorSet()`
1817: @*/
1818: PetscErrorCode TaoMonitorGradient(Tao tao, void *ctx)
1819: {
1820: PetscViewer viewer = (PetscViewer)ctx;
1822: PetscFunctionBegin;
1825: PetscCall(VecView(tao->gradient, viewer));
1826: PetscFunctionReturn(PETSC_SUCCESS);
1827: }
1829: /*@C
1830: TaoMonitorStep - Views the step-direction at each iteration of `TaoSolve()`
1832: Collective
1834: Input Parameters:
1835: + tao - the `Tao` context
1836: - ctx - `PetscViewer` context or `NULL`
1838: Options Database Key:
1839: . -tao_monitor_step - view the step vector at each iteration
1841: Level: advanced
1843: .seealso: [](ch_tao), `Tao`, `TaoMonitorDefaultShort()`, `TaoMonitorSet()`
1844: @*/
1845: PetscErrorCode TaoMonitorStep(Tao tao, void *ctx)
1846: {
1847: PetscViewer viewer = (PetscViewer)ctx;
1849: PetscFunctionBegin;
1852: PetscCall(VecView(tao->stepdirection, viewer));
1853: PetscFunctionReturn(PETSC_SUCCESS);
1854: }
1856: /*@C
1857: TaoMonitorSolutionDraw - Plots the solution at each iteration of `TaoSolve()`
1859: Collective
1861: Input Parameters:
1862: + tao - the `Tao` context
1863: - ctx - `TaoMonitorDraw` context
1865: Options Database Key:
1866: . -tao_monitor_solution_draw - draw the solution at each iteration
1868: Level: advanced
1870: Note:
1871: The context created by `TaoMonitorDrawCtxCreate()`, along with `TaoMonitorSolutionDraw()`, and `TaoMonitorDrawCtxDestroy()`
1872: are passed to `TaoMonitorSet()` to monitor the solution graphically.
1874: .seealso: [](ch_tao), `Tao`, `TaoMonitorSolution()`, `TaoMonitorSet()`, `TaoMonitorGradientDraw()`, `TaoMonitorDrawCtxCreate()`,
1875: `TaoMonitorDrawCtxDestroy()`
1876: @*/
1877: PetscErrorCode TaoMonitorSolutionDraw(Tao tao, void *ctx)
1878: {
1879: TaoMonitorDrawCtx ictx = (TaoMonitorDrawCtx)ctx;
1881: PetscFunctionBegin;
1883: if (!(((ictx->howoften > 0) && (!(tao->niter % ictx->howoften))) || ((ictx->howoften == -1) && tao->reason))) PetscFunctionReturn(PETSC_SUCCESS);
1884: PetscCall(VecView(tao->solution, ictx->viewer));
1885: PetscFunctionReturn(PETSC_SUCCESS);
1886: }
1888: /*@C
1889: TaoMonitorGradientDraw - Plots the gradient at each iteration of `TaoSolve()`
1891: Collective
1893: Input Parameters:
1894: + tao - the `Tao` context
1895: - ctx - `PetscViewer` context
1897: Options Database Key:
1898: . -tao_monitor_gradient_draw - draw the gradient at each iteration
1900: Level: advanced
1902: .seealso: [](ch_tao), `Tao`, `TaoMonitorGradient()`, `TaoMonitorSet()`, `TaoMonitorSolutionDraw()`
1903: @*/
1904: PetscErrorCode TaoMonitorGradientDraw(Tao tao, void *ctx)
1905: {
1906: TaoMonitorDrawCtx ictx = (TaoMonitorDrawCtx)ctx;
1908: PetscFunctionBegin;
1910: if (!(((ictx->howoften > 0) && (!(tao->niter % ictx->howoften))) || ((ictx->howoften == -1) && tao->reason))) PetscFunctionReturn(PETSC_SUCCESS);
1911: PetscCall(VecView(tao->gradient, ictx->viewer));
1912: PetscFunctionReturn(PETSC_SUCCESS);
1913: }
1915: /*@C
1916: TaoMonitorStepDraw - Plots the step direction at each iteration of `TaoSolve()`
1918: Collective
1920: Input Parameters:
1921: + tao - the `Tao` context
1922: - ctx - the `PetscViewer` context
1924: Options Database Key:
1925: . -tao_monitor_step_draw - draw the step direction at each iteration
1927: Level: advanced
1929: .seealso: [](ch_tao), `Tao`, `TaoMonitorSet()`, `TaoMonitorSolutionDraw`
1930: @*/
1931: PetscErrorCode TaoMonitorStepDraw(Tao tao, void *ctx)
1932: {
1933: PetscViewer viewer = (PetscViewer)ctx;
1935: PetscFunctionBegin;
1938: PetscCall(VecView(tao->stepdirection, viewer));
1939: PetscFunctionReturn(PETSC_SUCCESS);
1940: }
1942: /*@C
1943: TaoMonitorResidual - Views the least-squares residual at each iteration of `TaoSolve()`
1945: Collective
1947: Input Parameters:
1948: + tao - the `Tao` context
1949: - ctx - the `PetscViewer` context or `NULL`
1951: Options Database Key:
1952: . -tao_monitor_ls_residual - view the residual at each iteration
1954: Level: advanced
1956: .seealso: [](ch_tao), `Tao`, `TaoMonitorDefaultShort()`, `TaoMonitorSet()`
1957: @*/
1958: PetscErrorCode TaoMonitorResidual(Tao tao, void *ctx)
1959: {
1960: PetscViewer viewer = (PetscViewer)ctx;
1962: PetscFunctionBegin;
1965: PetscCall(VecView(tao->ls_res, viewer));
1966: PetscFunctionReturn(PETSC_SUCCESS);
1967: }
1969: /*@
1970: TaoDefaultConvergenceTest - Determines whether the solver should continue iterating
1971: or terminate.
1973: Collective
1975: Input Parameters:
1976: + tao - the `Tao` context
1977: - dummy - unused dummy context
1979: Level: developer
1981: Notes:
1982: This routine checks the residual in the optimality conditions, the
1983: relative residual in the optimity conditions, the number of function
1984: evaluations, and the function value to test convergence. Some
1985: solvers may use different convergence routines.
1987: .seealso: [](ch_tao), `Tao`, `TaoSetTolerances()`, `TaoGetConvergedReason()`, `TaoSetConvergedReason()`
1988: @*/
1989: PetscErrorCode TaoDefaultConvergenceTest(Tao tao, void *dummy)
1990: {
1991: PetscInt niter = tao->niter, nfuncs = PetscMax(tao->nfuncs, tao->nfuncgrads);
1992: PetscInt max_funcs = tao->max_funcs;
1993: PetscReal gnorm = tao->residual, gnorm0 = tao->gnorm0;
1994: PetscReal f = tao->fc, steptol = tao->steptol, trradius = tao->step;
1995: PetscReal gatol = tao->gatol, grtol = tao->grtol, gttol = tao->gttol;
1996: PetscReal catol = tao->catol, crtol = tao->crtol;
1997: PetscReal fmin = tao->fmin, cnorm = tao->cnorm;
1998: TaoConvergedReason reason = tao->reason;
2000: PetscFunctionBegin;
2002: if (reason != TAO_CONTINUE_ITERATING) PetscFunctionReturn(PETSC_SUCCESS);
2004: if (PetscIsInfOrNanReal(f)) {
2005: PetscCall(PetscInfo(tao, "Failed to converged, function value is Inf or NaN\n"));
2006: reason = TAO_DIVERGED_NAN;
2007: } else if (f <= fmin && cnorm <= catol) {
2008: PetscCall(PetscInfo(tao, "Converged due to function value %g < minimum function value %g\n", (double)f, (double)fmin));
2009: reason = TAO_CONVERGED_MINF;
2010: } else if (gnorm <= gatol && cnorm <= catol) {
2011: PetscCall(PetscInfo(tao, "Converged due to residual norm ||g(X)||=%g < %g\n", (double)gnorm, (double)gatol));
2012: reason = TAO_CONVERGED_GATOL;
2013: } else if (f != 0 && PetscAbsReal(gnorm / f) <= grtol && cnorm <= crtol) {
2014: PetscCall(PetscInfo(tao, "Converged due to residual ||g(X)||/|f(X)| =%g < %g\n", (double)(gnorm / f), (double)grtol));
2015: reason = TAO_CONVERGED_GRTOL;
2016: } else if (gnorm0 != 0 && ((gttol == 0 && gnorm == 0) || gnorm / gnorm0 < gttol) && cnorm <= crtol) {
2017: PetscCall(PetscInfo(tao, "Converged due to relative residual norm ||g(X)||/||g(X0)|| = %g < %g\n", (double)(gnorm / gnorm0), (double)gttol));
2018: reason = TAO_CONVERGED_GTTOL;
2019: } else if (max_funcs != PETSC_UNLIMITED && nfuncs > max_funcs) {
2020: PetscCall(PetscInfo(tao, "Exceeded maximum number of function evaluations: %" PetscInt_FMT " > %" PetscInt_FMT "\n", nfuncs, max_funcs));
2021: reason = TAO_DIVERGED_MAXFCN;
2022: } else if (tao->lsflag != 0) {
2023: PetscCall(PetscInfo(tao, "Tao Line Search failure.\n"));
2024: reason = TAO_DIVERGED_LS_FAILURE;
2025: } else if (trradius < steptol && niter > 0) {
2026: PetscCall(PetscInfo(tao, "Trust region/step size too small: %g < %g\n", (double)trradius, (double)steptol));
2027: reason = TAO_CONVERGED_STEPTOL;
2028: } else if (niter >= tao->max_it) {
2029: PetscCall(PetscInfo(tao, "Exceeded maximum number of iterations: %" PetscInt_FMT " > %" PetscInt_FMT "\n", niter, tao->max_it));
2030: reason = TAO_DIVERGED_MAXITS;
2031: } else {
2032: reason = TAO_CONTINUE_ITERATING;
2033: }
2034: tao->reason = reason;
2035: PetscFunctionReturn(PETSC_SUCCESS);
2036: }
2038: /*@
2039: TaoSetOptionsPrefix - Sets the prefix used for searching for all
2040: Tao options in the database.
2042: Logically Collective
2044: Input Parameters:
2045: + tao - the `Tao` context
2046: - p - the prefix string to prepend to all Tao option requests
2048: Level: advanced
2050: Notes:
2051: A hyphen (-) must NOT be given at the beginning of the prefix name.
2052: The first character of all runtime options is AUTOMATICALLY the hyphen.
2054: For example, to distinguish between the runtime options for two
2055: different Tao solvers, one could call
2056: .vb
2057: TaoSetOptionsPrefix(tao1,"sys1_")
2058: TaoSetOptionsPrefix(tao2,"sys2_")
2059: .ve
2061: This would enable use of different options for each system, such as
2062: .vb
2063: -sys1_tao_method blmvm -sys1_tao_grtol 1.e-3
2064: -sys2_tao_method lmvm -sys2_tao_grtol 1.e-4
2065: .ve
2067: .seealso: [](ch_tao), `Tao`, `TaoSetFromOptions()`, `TaoAppendOptionsPrefix()`, `TaoGetOptionsPrefix()`
2068: @*/
2069: PetscErrorCode TaoSetOptionsPrefix(Tao tao, const char p[])
2070: {
2071: PetscFunctionBegin;
2073: PetscCall(PetscObjectSetOptionsPrefix((PetscObject)tao, p));
2074: if (tao->linesearch) PetscCall(TaoLineSearchSetOptionsPrefix(tao->linesearch, p));
2075: if (tao->ksp) PetscCall(KSPSetOptionsPrefix(tao->ksp, p));
2076: PetscFunctionReturn(PETSC_SUCCESS);
2077: }
2079: /*@
2080: TaoAppendOptionsPrefix - Appends to the prefix used for searching for all Tao options in the database.
2082: Logically Collective
2084: Input Parameters:
2085: + tao - the `Tao` solver context
2086: - p - the prefix string to prepend to all `Tao` option requests
2088: Level: advanced
2090: Note:
2091: A hyphen (-) must NOT be given at the beginning of the prefix name.
2092: The first character of all runtime options is automatically the hyphen.
2094: .seealso: [](ch_tao), `Tao`, `TaoSetFromOptions()`, `TaoSetOptionsPrefix()`, `TaoGetOptionsPrefix()`
2095: @*/
2096: PetscErrorCode TaoAppendOptionsPrefix(Tao tao, const char p[])
2097: {
2098: PetscFunctionBegin;
2100: PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)tao, p));
2101: if (tao->linesearch) PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)tao->linesearch, p));
2102: if (tao->ksp) PetscCall(KSPAppendOptionsPrefix(tao->ksp, p));
2103: PetscFunctionReturn(PETSC_SUCCESS);
2104: }
2106: /*@
2107: TaoGetOptionsPrefix - Gets the prefix used for searching for all
2108: Tao options in the database
2110: Not Collective
2112: Input Parameter:
2113: . tao - the `Tao` context
2115: Output Parameter:
2116: . p - pointer to the prefix string used is returned
2118: Level: advanced
2120: .seealso: [](ch_tao), `Tao`, `TaoSetFromOptions()`, `TaoSetOptionsPrefix()`, `TaoAppendOptionsPrefix()`
2121: @*/
2122: PetscErrorCode TaoGetOptionsPrefix(Tao tao, const char *p[])
2123: {
2124: PetscFunctionBegin;
2126: PetscCall(PetscObjectGetOptionsPrefix((PetscObject)tao, p));
2127: PetscFunctionReturn(PETSC_SUCCESS);
2128: }
2130: /*@
2131: TaoSetType - Sets the `TaoType` for the minimization solver.
2133: Collective
2135: Input Parameters:
2136: + tao - the `Tao` solver context
2137: - type - a known method
2139: Options Database Key:
2140: . -tao_type <type> - Sets the method; use -help for a list
2141: of available methods (for instance, "-tao_type lmvm" or "-tao_type tron")
2143: Level: intermediate
2145: .seealso: [](ch_tao), `Tao`, `TaoCreate()`, `TaoGetType()`, `TaoType`
2146: @*/
2147: PetscErrorCode TaoSetType(Tao tao, TaoType type)
2148: {
2149: PetscErrorCode (*create_xxx)(Tao);
2150: PetscBool issame;
2152: PetscFunctionBegin;
2155: PetscCall(PetscObjectTypeCompare((PetscObject)tao, type, &issame));
2156: if (issame) PetscFunctionReturn(PETSC_SUCCESS);
2158: PetscCall(PetscFunctionListFind(TaoList, type, &create_xxx));
2159: PetscCheck(create_xxx, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_UNKNOWN_TYPE, "Unable to find requested Tao type %s", type);
2161: /* Destroy the existing solver information */
2162: PetscTryTypeMethod(tao, destroy);
2163: PetscCall(KSPDestroy(&tao->ksp));
2164: PetscCall(TaoLineSearchDestroy(&tao->linesearch));
2166: /* Reinitialize type-specific function pointers in TaoOps structure */
2167: tao->ops->setup = NULL;
2168: tao->ops->computedual = NULL;
2169: tao->ops->solve = NULL;
2170: tao->ops->view = NULL;
2171: tao->ops->setfromoptions = NULL;
2172: tao->ops->destroy = NULL;
2174: tao->setupcalled = PETSC_FALSE;
2176: PetscCall((*create_xxx)(tao));
2177: PetscCall(PetscObjectChangeTypeName((PetscObject)tao, type));
2178: PetscFunctionReturn(PETSC_SUCCESS);
2179: }
2181: /*@C
2182: TaoRegister - Adds a method to the Tao package for minimization.
2184: Not Collective, No Fortran Support
2186: Input Parameters:
2187: + sname - name of a new user-defined solver
2188: - func - routine to Create method context
2190: Example Usage:
2191: .vb
2192: TaoRegister("my_solver", MySolverCreate);
2193: .ve
2195: Then, your solver can be chosen with the procedural interface via
2196: .vb
2197: TaoSetType(tao, "my_solver")
2198: .ve
2199: or at runtime via the option
2200: .vb
2201: -tao_type my_solver
2202: .ve
2204: Level: advanced
2206: Note:
2207: `TaoRegister()` may be called multiple times to add several user-defined solvers.
2209: .seealso: [](ch_tao), `Tao`, `TaoSetType()`, `TaoRegisterAll()`, `TaoRegisterDestroy()`
2210: @*/
2211: PetscErrorCode TaoRegister(const char sname[], PetscErrorCode (*func)(Tao))
2212: {
2213: PetscFunctionBegin;
2214: PetscCall(TaoInitializePackage());
2215: PetscCall(PetscFunctionListAdd(&TaoList, sname, func));
2216: PetscFunctionReturn(PETSC_SUCCESS);
2217: }
2219: /*@C
2220: TaoRegisterDestroy - Frees the list of minimization solvers that were
2221: registered by `TaoRegister()`.
2223: Not Collective
2225: Level: advanced
2227: .seealso: [](ch_tao), `Tao`, `TaoRegisterAll()`, `TaoRegister()`
2228: @*/
2229: PetscErrorCode TaoRegisterDestroy(void)
2230: {
2231: PetscFunctionBegin;
2232: PetscCall(PetscFunctionListDestroy(&TaoList));
2233: TaoRegisterAllCalled = PETSC_FALSE;
2234: PetscFunctionReturn(PETSC_SUCCESS);
2235: }
2237: /*@
2238: TaoGetIterationNumber - Gets the number of `TaoSolve()` iterations completed
2239: at this time.
2241: Not Collective
2243: Input Parameter:
2244: . tao - the `Tao` context
2246: Output Parameter:
2247: . iter - iteration number
2249: Notes:
2250: For example, during the computation of iteration 2 this would return 1.
2252: Level: intermediate
2254: .seealso: [](ch_tao), `Tao`, `TaoGetLinearSolveIterations()`, `TaoGetResidualNorm()`, `TaoGetObjective()`
2255: @*/
2256: PetscErrorCode TaoGetIterationNumber(Tao tao, PetscInt *iter)
2257: {
2258: PetscFunctionBegin;
2260: PetscAssertPointer(iter, 2);
2261: *iter = tao->niter;
2262: PetscFunctionReturn(PETSC_SUCCESS);
2263: }
2265: /*@
2266: TaoGetResidualNorm - Gets the current value of the norm of the residual (gradient)
2267: at this time.
2269: Not Collective
2271: Input Parameter:
2272: . tao - the `Tao` context
2274: Output Parameter:
2275: . value - the current value
2277: Level: intermediate
2279: Developer Notes:
2280: This is the 2-norm of the residual, we cannot use `TaoGetGradientNorm()` because that has
2281: a different meaning. For some reason `Tao` sometimes calls the gradient the residual.
2283: .seealso: [](ch_tao), `Tao`, `TaoGetLinearSolveIterations()`, `TaoGetIterationNumber()`, `TaoGetObjective()`
2284: @*/
2285: PetscErrorCode TaoGetResidualNorm(Tao tao, PetscReal *value)
2286: {
2287: PetscFunctionBegin;
2289: PetscAssertPointer(value, 2);
2290: *value = tao->residual;
2291: PetscFunctionReturn(PETSC_SUCCESS);
2292: }
2294: /*@
2295: TaoSetIterationNumber - Sets the current iteration number.
2297: Logically Collective
2299: Input Parameters:
2300: + tao - the `Tao` context
2301: - iter - iteration number
2303: Level: developer
2305: .seealso: [](ch_tao), `Tao`, `TaoGetLinearSolveIterations()`
2306: @*/
2307: PetscErrorCode TaoSetIterationNumber(Tao tao, PetscInt iter)
2308: {
2309: PetscFunctionBegin;
2312: PetscCall(PetscObjectSAWsTakeAccess((PetscObject)tao));
2313: tao->niter = iter;
2314: PetscCall(PetscObjectSAWsGrantAccess((PetscObject)tao));
2315: PetscFunctionReturn(PETSC_SUCCESS);
2316: }
2318: /*@
2319: TaoGetTotalIterationNumber - Gets the total number of `TaoSolve()` iterations
2320: completed. This number keeps accumulating if multiple solves
2321: are called with the `Tao` object.
2323: Not Collective
2325: Input Parameter:
2326: . tao - the `Tao` context
2328: Output Parameter:
2329: . iter - number of iterations
2331: Level: intermediate
2333: Note:
2334: The total iteration count is updated after each solve, if there is a current
2335: `TaoSolve()` in progress then those iterations are not included in the count
2337: .seealso: [](ch_tao), `Tao`, `TaoGetLinearSolveIterations()`
2338: @*/
2339: PetscErrorCode TaoGetTotalIterationNumber(Tao tao, PetscInt *iter)
2340: {
2341: PetscFunctionBegin;
2343: PetscAssertPointer(iter, 2);
2344: *iter = tao->ntotalits;
2345: PetscFunctionReturn(PETSC_SUCCESS);
2346: }
2348: /*@
2349: TaoSetTotalIterationNumber - Sets the current total iteration number.
2351: Logically Collective
2353: Input Parameters:
2354: + tao - the `Tao` context
2355: - iter - the iteration number
2357: Level: developer
2359: .seealso: [](ch_tao), `Tao`, `TaoGetLinearSolveIterations()`
2360: @*/
2361: PetscErrorCode TaoSetTotalIterationNumber(Tao tao, PetscInt iter)
2362: {
2363: PetscFunctionBegin;
2366: PetscCall(PetscObjectSAWsTakeAccess((PetscObject)tao));
2367: tao->ntotalits = iter;
2368: PetscCall(PetscObjectSAWsGrantAccess((PetscObject)tao));
2369: PetscFunctionReturn(PETSC_SUCCESS);
2370: }
2372: /*@
2373: TaoSetConvergedReason - Sets the termination flag on a `Tao` object
2375: Logically Collective
2377: Input Parameters:
2378: + tao - the `Tao` context
2379: - reason - the `TaoConvergedReason`
2381: Level: intermediate
2383: .seealso: [](ch_tao), `Tao`, `TaoConvergedReason`
2384: @*/
2385: PetscErrorCode TaoSetConvergedReason(Tao tao, TaoConvergedReason reason)
2386: {
2387: PetscFunctionBegin;
2390: tao->reason = reason;
2391: PetscFunctionReturn(PETSC_SUCCESS);
2392: }
2394: /*@
2395: TaoGetConvergedReason - Gets the reason the `TaoSolve()` was stopped.
2397: Not Collective
2399: Input Parameter:
2400: . tao - the `Tao` solver context
2402: Output Parameter:
2403: . reason - value of `TaoConvergedReason`
2405: Level: intermediate
2407: .seealso: [](ch_tao), `Tao`, `TaoConvergedReason`, `TaoSetConvergenceTest()`, `TaoSetTolerances()`
2408: @*/
2409: PetscErrorCode TaoGetConvergedReason(Tao tao, TaoConvergedReason *reason)
2410: {
2411: PetscFunctionBegin;
2413: PetscAssertPointer(reason, 2);
2414: *reason = tao->reason;
2415: PetscFunctionReturn(PETSC_SUCCESS);
2416: }
2418: /*@
2419: TaoGetSolutionStatus - Get the current iterate, objective value,
2420: residual, infeasibility, and termination from a `Tao` object
2422: Not Collective
2424: Input Parameter:
2425: . tao - the `Tao` context
2427: Output Parameters:
2428: + its - the current iterate number (>=0)
2429: . f - the current function value
2430: . gnorm - the square of the gradient norm, duality gap, or other measure indicating distance from optimality.
2431: . cnorm - the infeasibility of the current solution with regard to the constraints.
2432: . xdiff - the step length or trust region radius of the most recent iterate.
2433: - reason - The termination reason, which can equal `TAO_CONTINUE_ITERATING`
2435: Level: intermediate
2437: Notes:
2438: Tao returns the values set by the solvers in the routine `TaoMonitor()`.
2440: If any of the output arguments are set to `NULL`, no corresponding value will be returned.
2442: .seealso: [](ch_tao), `TaoMonitor()`, `TaoGetConvergedReason()`
2443: @*/
2444: PetscErrorCode TaoGetSolutionStatus(Tao tao, PetscInt *its, PetscReal *f, PetscReal *gnorm, PetscReal *cnorm, PetscReal *xdiff, TaoConvergedReason *reason)
2445: {
2446: PetscFunctionBegin;
2448: if (its) *its = tao->niter;
2449: if (f) *f = tao->fc;
2450: if (gnorm) *gnorm = tao->residual;
2451: if (cnorm) *cnorm = tao->cnorm;
2452: if (reason) *reason = tao->reason;
2453: if (xdiff) *xdiff = tao->step;
2454: PetscFunctionReturn(PETSC_SUCCESS);
2455: }
2457: /*@
2458: TaoGetType - Gets the current `TaoType` being used in the `Tao` object
2460: Not Collective
2462: Input Parameter:
2463: . tao - the `Tao` solver context
2465: Output Parameter:
2466: . type - the `TaoType`
2468: Level: intermediate
2470: .seealso: [](ch_tao), `Tao`, `TaoType`, `TaoSetType()`
2471: @*/
2472: PetscErrorCode TaoGetType(Tao tao, TaoType *type)
2473: {
2474: PetscFunctionBegin;
2476: PetscAssertPointer(type, 2);
2477: *type = ((PetscObject)tao)->type_name;
2478: PetscFunctionReturn(PETSC_SUCCESS);
2479: }
2481: /*@C
2482: TaoMonitor - Monitor the solver and the current solution. This
2483: routine will record the iteration number and residual statistics,
2484: and call any monitors specified by the user.
2486: Input Parameters:
2487: + tao - the `Tao` context
2488: . its - the current iterate number (>=0)
2489: . f - the current objective function value
2490: . res - the gradient norm, square root of the duality gap, or other measure indicating distance from optimality. This measure will be recorded and
2491: used for some termination tests.
2492: . cnorm - the infeasibility of the current solution with regard to the constraints.
2493: - steplength - multiple of the step direction added to the previous iterate.
2495: Options Database Key:
2496: . -tao_monitor - Use the default monitor, which prints statistics to standard output
2498: Level: developer
2500: .seealso: [](ch_tao), `Tao`, `TaoGetConvergedReason()`, `TaoMonitorDefault()`, `TaoMonitorSet()`
2501: @*/
2502: PetscErrorCode TaoMonitor(Tao tao, PetscInt its, PetscReal f, PetscReal res, PetscReal cnorm, PetscReal steplength)
2503: {
2504: PetscInt i;
2506: PetscFunctionBegin;
2508: tao->fc = f;
2509: tao->residual = res;
2510: tao->cnorm = cnorm;
2511: tao->step = steplength;
2512: if (!its) {
2513: tao->cnorm0 = cnorm;
2514: tao->gnorm0 = res;
2515: }
2516: PetscCall(VecLockReadPush(tao->solution));
2517: for (i = 0; i < tao->numbermonitors; i++) PetscCall((*tao->monitor[i])(tao, tao->monitorcontext[i]));
2518: PetscCall(VecLockReadPop(tao->solution));
2519: PetscFunctionReturn(PETSC_SUCCESS);
2520: }
2522: /*@
2523: TaoSetConvergenceHistory - Sets the array used to hold the convergence history.
2525: Logically Collective
2527: Input Parameters:
2528: + tao - the `Tao` solver context
2529: . obj - array to hold objective value history
2530: . resid - array to hold residual history
2531: . cnorm - array to hold constraint violation history
2532: . lits - integer array holds the number of linear iterations for each Tao iteration
2533: . na - size of `obj`, `resid`, and `cnorm`
2534: - reset - `PETSC_TRUE` indicates each new minimization resets the history counter to zero,
2535: else it continues storing new values for new minimizations after the old ones
2537: Level: intermediate
2539: Notes:
2540: If set, `Tao` will fill the given arrays with the indicated
2541: information at each iteration. If 'obj','resid','cnorm','lits' are
2542: *all* `NULL` then space (using size `na`, or 1000 if `na` is `PETSC_DECIDE`) is allocated for the history.
2543: If not all are `NULL`, then only the non-`NULL` information categories
2544: will be stored, the others will be ignored.
2546: Any convergence information after iteration number 'na' will not be stored.
2548: This routine is useful, e.g., when running a code for purposes
2549: of accurate performance monitoring, when no I/O should be done
2550: during the section of code that is being timed.
2552: .seealso: [](ch_tao), `TaoGetConvergenceHistory()`
2553: @*/
2554: PetscErrorCode TaoSetConvergenceHistory(Tao tao, PetscReal obj[], PetscReal resid[], PetscReal cnorm[], PetscInt lits[], PetscInt na, PetscBool reset)
2555: {
2556: PetscFunctionBegin;
2558: if (obj) PetscAssertPointer(obj, 2);
2559: if (resid) PetscAssertPointer(resid, 3);
2560: if (cnorm) PetscAssertPointer(cnorm, 4);
2561: if (lits) PetscAssertPointer(lits, 5);
2563: if (na == PETSC_DECIDE || na == PETSC_CURRENT) na = 1000;
2564: if (!obj && !resid && !cnorm && !lits) {
2565: PetscCall(PetscCalloc4(na, &obj, na, &resid, na, &cnorm, na, &lits));
2566: tao->hist_malloc = PETSC_TRUE;
2567: }
2569: tao->hist_obj = obj;
2570: tao->hist_resid = resid;
2571: tao->hist_cnorm = cnorm;
2572: tao->hist_lits = lits;
2573: tao->hist_max = na;
2574: tao->hist_reset = reset;
2575: tao->hist_len = 0;
2576: PetscFunctionReturn(PETSC_SUCCESS);
2577: }
2579: /*@C
2580: TaoGetConvergenceHistory - Gets the arrays used that hold the convergence history.
2582: Collective
2584: Input Parameter:
2585: . tao - the `Tao` context
2587: Output Parameters:
2588: + obj - array used to hold objective value history
2589: . resid - array used to hold residual history
2590: . cnorm - array used to hold constraint violation history
2591: . lits - integer array used to hold linear solver iteration count
2592: - nhist - size of `obj`, `resid`, `cnorm`, and `lits`
2594: Level: advanced
2596: Notes:
2597: This routine must be preceded by calls to `TaoSetConvergenceHistory()`
2598: and `TaoSolve()`, otherwise it returns useless information.
2600: This routine is useful, e.g., when running a code for purposes
2601: of accurate performance monitoring, when no I/O should be done
2602: during the section of code that is being timed.
2604: Fortran Notes:
2605: The calling sequence is
2606: .vb
2607: call TaoGetConvergenceHistory(Tao tao, PetscInt nhist, PetscErrorCode ierr)
2608: .ve
2609: In other words this gets the current number of entries in the history. Access the history through the array you passed to `TaoSetConvergenceHistory()`
2611: .seealso: [](ch_tao), `Tao`, `TaoSolve()`, `TaoSetConvergenceHistory()`
2612: @*/
2613: PetscErrorCode TaoGetConvergenceHistory(Tao tao, PetscReal **obj, PetscReal **resid, PetscReal **cnorm, PetscInt **lits, PetscInt *nhist)
2614: {
2615: PetscFunctionBegin;
2617: if (obj) *obj = tao->hist_obj;
2618: if (cnorm) *cnorm = tao->hist_cnorm;
2619: if (resid) *resid = tao->hist_resid;
2620: if (lits) *lits = tao->hist_lits;
2621: if (nhist) *nhist = tao->hist_len;
2622: PetscFunctionReturn(PETSC_SUCCESS);
2623: }
2625: /*@
2626: TaoSetApplicationContext - Sets the optional user-defined context for a `Tao` solver that can be accessed later, for example in the
2627: `Tao` callback functions with `TaoGetApplicationContext()`
2629: Logically Collective
2631: Input Parameters:
2632: + tao - the `Tao` context
2633: - ctx - the user context
2635: Level: intermediate
2637: Fortran Note:
2638: This only works when `ctx` is a Fortran derived type (it cannot be a `PetscObject`), we recommend writing a Fortran interface definition for this
2639: function that tells the Fortran compiler the derived data type that is passed in as the `ctx` argument. See `TaoGetApplicationContext()` for
2640: an example.
2642: .seealso: [](ch_tao), `Tao`, `TaoGetApplicationContext()`
2643: @*/
2644: PetscErrorCode TaoSetApplicationContext(Tao tao, void *ctx)
2645: {
2646: PetscFunctionBegin;
2648: tao->ctx = ctx;
2649: PetscFunctionReturn(PETSC_SUCCESS);
2650: }
2652: /*@
2653: TaoGetApplicationContext - Gets the user-defined context for a `Tao` solver provided with `TaoSetApplicationContext()`
2655: Not Collective
2657: Input Parameter:
2658: . tao - the `Tao` context
2660: Output Parameter:
2661: . ctx - a pointer to the user context
2663: Level: intermediate
2665: Fortran Notes:
2666: This only works when the context is a Fortran derived type (it cannot be a `PetscObject`) and you **must** write a Fortran interface definition for this
2667: function that tells the Fortran compiler the derived data type that is returned as the `ctx` argument. For example,
2668: .vb
2669: Interface TaoGetApplicationContext
2670: Subroutine TaoGetApplicationContext(tao,ctx,ierr)
2671: #include <petsc/finclude/petsctao.h>
2672: use petsctao
2673: Tao tao
2674: type(tUsertype), pointer :: ctx
2675: PetscErrorCode ierr
2676: End Subroutine
2677: End Interface TaoGetApplicationContext
2678: .ve
2680: The prototype for `ctx` must be
2681: .vb
2682: type(tUsertype), pointer :: ctx
2683: .ve
2685: .seealso: [](ch_tao), `Tao`, `TaoSetApplicationContext()`
2686: @*/
2687: PetscErrorCode TaoGetApplicationContext(Tao tao, PeCtx ctx)
2688: {
2689: PetscFunctionBegin;
2691: PetscAssertPointer(ctx, 2);
2692: *(void **)ctx = tao->ctx;
2693: PetscFunctionReturn(PETSC_SUCCESS);
2694: }
2696: /*@
2697: TaoSetGradientNorm - Sets the matrix used to define the norm that measures the size of the gradient in some of the `Tao` algorithms
2699: Collective
2701: Input Parameters:
2702: + tao - the `Tao` context
2703: - M - matrix that defines the norm
2705: Level: beginner
2707: .seealso: [](ch_tao), `Tao`, `TaoGetGradientNorm()`, `TaoGradientNorm()`
2708: @*/
2709: PetscErrorCode TaoSetGradientNorm(Tao tao, Mat M)
2710: {
2711: PetscFunctionBegin;
2714: PetscCall(PetscObjectReference((PetscObject)M));
2715: PetscCall(MatDestroy(&tao->gradient_norm));
2716: PetscCall(VecDestroy(&tao->gradient_norm_tmp));
2717: tao->gradient_norm = M;
2718: PetscCall(MatCreateVecs(M, NULL, &tao->gradient_norm_tmp));
2719: PetscFunctionReturn(PETSC_SUCCESS);
2720: }
2722: /*@
2723: TaoGetGradientNorm - Returns the matrix used to define the norm used for measuring the size of the gradient in some of the `Tao` algorithms
2725: Not Collective
2727: Input Parameter:
2728: . tao - the `Tao` context
2730: Output Parameter:
2731: . M - gradient norm
2733: Level: beginner
2735: .seealso: [](ch_tao), `Tao`, `TaoSetGradientNorm()`, `TaoGradientNorm()`
2736: @*/
2737: PetscErrorCode TaoGetGradientNorm(Tao tao, Mat *M)
2738: {
2739: PetscFunctionBegin;
2741: PetscAssertPointer(M, 2);
2742: *M = tao->gradient_norm;
2743: PetscFunctionReturn(PETSC_SUCCESS);
2744: }
2746: /*@
2747: TaoGradientNorm - Compute the norm using the `NormType`, the user has selected
2749: Collective
2751: Input Parameters:
2752: + tao - the `Tao` context
2753: . gradient - the gradient
2754: - type - the norm type
2756: Output Parameter:
2757: . gnorm - the gradient norm
2759: Level: advanced
2761: Note:
2762: If `TaoSetGradientNorm()` has been set and `type` is `NORM_2` then the norm provided with `TaoSetGradientNorm()` is used.
2764: Developer Notes:
2765: Should be named `TaoComputeGradientNorm()`.
2767: The usage is a bit confusing, with `TaoSetGradientNorm()` plus `NORM_2` resulting in the computation of the user provided
2768: norm, perhaps a refactorization is in order.
2770: .seealso: [](ch_tao), `Tao`, `TaoSetGradientNorm()`, `TaoGetGradientNorm()`
2771: @*/
2772: PetscErrorCode TaoGradientNorm(Tao tao, Vec gradient, NormType type, PetscReal *gnorm)
2773: {
2774: PetscFunctionBegin;
2778: PetscAssertPointer(gnorm, 4);
2779: if (tao->gradient_norm) {
2780: PetscScalar gnorms;
2782: PetscCheck(type == NORM_2, PetscObjectComm((PetscObject)gradient), PETSC_ERR_ARG_WRONG, "Norm type must be NORM_2 if an inner product for the gradient norm is set.");
2783: PetscCall(MatMult(tao->gradient_norm, gradient, tao->gradient_norm_tmp));
2784: PetscCall(VecDot(gradient, tao->gradient_norm_tmp, &gnorms));
2785: *gnorm = PetscRealPart(PetscSqrtScalar(gnorms));
2786: } else {
2787: PetscCall(VecNorm(gradient, type, gnorm));
2788: }
2789: PetscFunctionReturn(PETSC_SUCCESS);
2790: }
2792: /*@C
2793: TaoMonitorDrawCtxCreate - Creates the monitor context for `TaoMonitorSolutionDraw()`
2795: Collective
2797: Input Parameters:
2798: + comm - the communicator to share the context
2799: . host - the name of the X Windows host that will display the monitor
2800: . label - the label to put at the top of the display window
2801: . x - the horizontal coordinate of the lower left corner of the window to open
2802: . y - the vertical coordinate of the lower left corner of the window to open
2803: . m - the width of the window
2804: . n - the height of the window
2805: - howoften - how many `Tao` iterations between displaying the monitor information
2807: Output Parameter:
2808: . ctx - the monitor context
2810: Options Database Keys:
2811: + -tao_monitor_solution_draw - use `TaoMonitorSolutionDraw()` to monitor the solution
2812: - -tao_draw_solution_initial - show initial guess as well as current solution
2814: Level: intermediate
2816: Note:
2817: The context this creates, along with `TaoMonitorSolutionDraw()`, and `TaoMonitorDrawCtxDestroy()`
2818: are passed to `TaoMonitorSet()`.
2820: .seealso: [](ch_tao), `Tao`, `TaoMonitorSet()`, `TaoMonitorDefault()`, `VecView()`, `TaoMonitorDrawCtx()`
2821: @*/
2822: PetscErrorCode TaoMonitorDrawCtxCreate(MPI_Comm comm, const char host[], const char label[], int x, int y, int m, int n, PetscInt howoften, TaoMonitorDrawCtx *ctx)
2823: {
2824: PetscFunctionBegin;
2825: PetscCall(PetscNew(ctx));
2826: PetscCall(PetscViewerDrawOpen(comm, host, label, x, y, m, n, &(*ctx)->viewer));
2827: PetscCall(PetscViewerSetFromOptions((*ctx)->viewer));
2828: (*ctx)->howoften = howoften;
2829: PetscFunctionReturn(PETSC_SUCCESS);
2830: }
2832: /*@C
2833: TaoMonitorDrawCtxDestroy - Destroys the monitor context for `TaoMonitorSolutionDraw()`
2835: Collective
2837: Input Parameter:
2838: . ictx - the monitor context
2840: Level: intermediate
2842: Note:
2843: This is passed to `TaoMonitorSet()` as the final argument, along with `TaoMonitorSolutionDraw()`, and the context
2844: obtained with `TaoMonitorDrawCtxCreate()`.
2846: .seealso: [](ch_tao), `Tao`, `TaoMonitorSet()`, `TaoMonitorDefault()`, `VecView()`, `TaoMonitorSolutionDraw()`
2847: @*/
2848: PetscErrorCode TaoMonitorDrawCtxDestroy(TaoMonitorDrawCtx *ictx)
2849: {
2850: PetscFunctionBegin;
2851: PetscCall(PetscViewerDestroy(&(*ictx)->viewer));
2852: PetscCall(PetscFree(*ictx));
2853: PetscFunctionReturn(PETSC_SUCCESS);
2854: }