Actual source code: itfunc.c
1: /*
2: Interface KSP routines that the user calls.
3: */
5: #include <petsc/private/kspimpl.h>
6: #include <petsc/private/matimpl.h>
7: #include <petscdm.h>
9: /* number of nested levels of KSPSetUp/Solve(). This is used to determine if KSP_DIVERGED_ITS should be fatal. */
10: static PetscInt level = 0;
12: static inline PetscErrorCode ObjectView(PetscObject obj, PetscViewer viewer, PetscViewerFormat format)
13: {
14: PetscCall(PetscViewerPushFormat(viewer, format));
15: PetscCall(PetscObjectView(obj, viewer));
16: PetscCall(PetscViewerPopFormat(viewer));
17: return PETSC_SUCCESS;
18: }
20: /*@
21: KSPComputeExtremeSingularValues - Computes the extreme singular values
22: for the preconditioned operator. Called after or during `KSPSolve()`.
24: Not Collective
26: Input Parameter:
27: . ksp - iterative context obtained from `KSPCreate()`
29: Output Parameters:
30: + emax - maximum estimated singular value
31: - emin - minimum estimated singular value
33: Options Database Key:
34: . -ksp_view_singularvalues - compute extreme singular values and print when `KSPSolve()` completes.
36: Notes:
37: One must call `KSPSetComputeSingularValues()` before calling `KSPSetUp()`
38: (or use the option -ksp_view_eigenvalues) in order for this routine to work correctly.
40: Many users may just want to use the monitoring routine
41: `KSPMonitorSingularValue()` (which can be set with option -ksp_monitor_singular_value)
42: to print the extreme singular values at each iteration of the linear solve.
44: Estimates of the smallest singular value may be very inaccurate, especially if the Krylov method has not converged.
45: The largest singular value is usually accurate to within a few percent if the method has converged, but is still not
46: intended for eigenanalysis. Consider the excellent package `SLEPc` if accurate values are required.
48: Disable restarts if using KSPGMRES, otherwise this estimate will only be using those iterations after the last
49: restart. See `KSPGMRESSetRestart()` for more details.
51: Level: advanced
53: .seealso: [](chapter_ksp), `KSPSetComputeSingularValues()`, `KSPMonitorSingularValue()`, `KSPComputeEigenvalues()`, `KSP`
54: @*/
55: PetscErrorCode KSPComputeExtremeSingularValues(KSP ksp, PetscReal *emax, PetscReal *emin)
56: {
57: PetscFunctionBegin;
61: PetscCheck(ksp->calc_sings, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONGSTATE, "Singular values not requested before KSPSetUp()");
63: if (ksp->ops->computeextremesingularvalues) PetscUseTypeMethod(ksp, computeextremesingularvalues, emax, emin);
64: else {
65: *emin = -1.0;
66: *emax = -1.0;
67: }
68: PetscFunctionReturn(PETSC_SUCCESS);
69: }
71: /*@
72: KSPComputeEigenvalues - Computes the extreme eigenvalues for the
73: preconditioned operator. Called after or during `KSPSolve()`.
75: Not Collective
77: Input Parameters:
78: + ksp - iterative context obtained from `KSPCreate()`
79: - n - size of arrays r and c. The number of eigenvalues computed (neig) will, in
80: general, be less than this.
82: Output Parameters:
83: + r - real part of computed eigenvalues, provided by user with a dimension of at least n
84: . c - complex part of computed eigenvalues, provided by user with a dimension of at least n
85: - neig - actual number of eigenvalues computed (will be less than or equal to n)
87: Options Database Keys:
88: . -ksp_view_eigenvalues - Prints eigenvalues to stdout
90: Notes:
91: The number of eigenvalues estimated depends on the size of the Krylov space
92: generated during the `KSPSolve()` ; for example, with
93: CG it corresponds to the number of CG iterations, for GMRES it is the number
94: of GMRES iterations SINCE the last restart. Any extra space in r[] and c[]
95: will be ignored.
97: `KSPComputeEigenvalues()` does not usually provide accurate estimates; it is
98: intended only for assistance in understanding the convergence of iterative
99: methods, not for eigenanalysis. For accurate computation of eigenvalues we recommend using
100: the excellent package SLEPc.
102: One must call `KSPSetComputeEigenvalues()` before calling `KSPSetUp()`
103: in order for this routine to work correctly.
105: Many users may just want to use the monitoring routine
106: `KSPMonitorSingularValue()` (which can be set with option -ksp_monitor_singular_value)
107: to print the singular values at each iteration of the linear solve.
109: `KSPComputeRitz()` provides estimates for both the eigenvalues and their corresponding eigenvectors.
111: Level: advanced
113: .seealso: [](chapter_ksp), `KSPSetComputeEigenvalues()`, `KSPSetComputeSingularValues()`, `KSPMonitorSingularValue()`, `KSPComputeExtremeSingularValues()`, `KSP`, `KSPComputeRitz()`
114: @*/
115: PetscErrorCode KSPComputeEigenvalues(KSP ksp, PetscInt n, PetscReal r[], PetscReal c[], PetscInt *neig)
116: {
117: PetscFunctionBegin;
121: PetscCheck(n >= 0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Requested < 0 Eigenvalues");
123: PetscCheck(ksp->calc_sings, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONGSTATE, "Eigenvalues not requested before KSPSetUp()");
125: if (n && ksp->ops->computeeigenvalues) PetscUseTypeMethod(ksp, computeeigenvalues, n, r, c, neig);
126: else *neig = 0;
127: PetscFunctionReturn(PETSC_SUCCESS);
128: }
130: /*@
131: KSPComputeRitz - Computes the Ritz or harmonic Ritz pairs associated with the
132: smallest or largest in modulus, for the preconditioned operator.
134: Not Collective
136: Input Parameters:
137: + ksp - iterative context obtained from `KSPCreate()`
138: . ritz - `PETSC_TRUE` or `PETSC_FALSE` for Ritz pairs or harmonic Ritz pairs, respectively
139: - small - `PETSC_TRUE` or `PETSC_FALSE` for smallest or largest (harmonic) Ritz values, respectively
141: Output Parameters:
142: + nrit - On input number of (harmonic) Ritz pairs to compute; on output, actual number of computed (harmonic) Ritz pairs
143: . S - an array of the Ritz vectors, pass in an array of vectors of size nrit
144: . tetar - real part of the Ritz values, pass in an array of size nrit
145: - tetai - imaginary part of the Ritz values, pass in an array of size nrit
147: Notes:
148: This only works with a `KSPType` of `KSPGMRES`.
150: One must call `KSPSetComputeRitz()` before calling `KSPSetUp()` in order for this routine to work correctly.
152: This routine must be called after `KSPSolve()`.
154: In GMRES, the (harmonic) Ritz pairs are computed from the Hessenberg matrix obtained during
155: the last complete cycle of the GMRES solve, or during the partial cycle if the solve ended before
156: a restart (that is a complete GMRES cycle was never achieved).
158: The number of actual (harmonic) Ritz pairs computed is less than or equal to the restart
159: parameter for GMRES if a complete cycle has been performed or less or equal to the number of GMRES
160: iterations.
162: `KSPComputeEigenvalues()` provides estimates for only the eigenvalues (Ritz values).
164: For real matrices, the (harmonic) Ritz pairs can be complex-valued. In such a case,
165: the routine selects the complex (harmonic) Ritz value and its conjugate, and two successive entries of the
166: vectors S are equal to the real and the imaginary parts of the associated vectors.
167: When PETSc has been built with complex scalars, the real and imaginary parts of the Ritz
168: values are still returned in tetar and tetai, as is done in `KSPComputeEigenvalues()`, but
169: the Ritz vectors S are complex.
171: The (harmonic) Ritz pairs are given in order of increasing (harmonic) Ritz values in modulus.
173: The Ritz pairs do not necessarily accurately reflect the eigenvalues and eigenvectors of the operator, consider the
174: excellent package `SLEPc` if accurate values are required.
176: Level: advanced
178: .seealso: [](chapter_ksp), `KSPSetComputeRitz()`, `KSP`, `KSPGMRES`, `KSPComputeEigenvalues()`, `KSPSetComputeSingularValues()`, `KSPMonitorSingularValue()`
179: @*/
180: PetscErrorCode KSPComputeRitz(KSP ksp, PetscBool ritz, PetscBool small, PetscInt *nrit, Vec S[], PetscReal tetar[], PetscReal tetai[])
181: {
182: PetscFunctionBegin;
184: PetscCheck(ksp->calc_ritz, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONGSTATE, "Ritz pairs not requested before KSPSetUp()");
185: PetscTryTypeMethod(ksp, computeritz, ritz, small, nrit, S, tetar, tetai);
186: PetscFunctionReturn(PETSC_SUCCESS);
187: }
188: /*@
189: KSPSetUpOnBlocks - Sets up the preconditioner for each block in
190: the block Jacobi, block Gauss-Seidel, and overlapping Schwarz
191: methods.
193: Collective
195: Input Parameter:
196: . ksp - the `KSP` context
198: Notes:
199: `KSPSetUpOnBlocks()` is a routine that the user can optionally call for
200: more precise profiling (via -log_view) of the setup phase for these
201: block preconditioners. If the user does not call `KSPSetUpOnBlocks()`,
202: it will automatically be called from within `KSPSolve()`.
204: Calling `KSPSetUpOnBlocks()` is the same as calling `PCSetUpOnBlocks()`
205: on the PC context within the `KSP` context.
207: Level: advanced
209: .seealso: [](chapter_ksp), `PCSetUpOnBlocks()`, `KSPSetUp()`, `PCSetUp()`, `KSP`
210: @*/
211: PetscErrorCode KSPSetUpOnBlocks(KSP ksp)
212: {
213: PC pc;
214: PCFailedReason pcreason;
216: PetscFunctionBegin;
218: level++;
219: PetscCall(KSPGetPC(ksp, &pc));
220: PetscCall(PCSetUpOnBlocks(pc));
221: PetscCall(PCGetFailedReasonRank(pc, &pcreason));
222: level--;
223: /*
224: This is tricky since only a subset of MPI ranks may set this; each KSPSolve_*() is responsible for checking
225: this flag and initializing an appropriate vector with VecSetInf() so that the first norm computation can
226: produce a result at KSPCheckNorm() thus communicating the known problem to all MPI ranks so they may
227: terminate the Krylov solve. For many KSP implementations this is handled within KSPInitialResidual()
228: */
229: if (pcreason) ksp->reason = KSP_DIVERGED_PC_FAILED;
230: PetscFunctionReturn(PETSC_SUCCESS);
231: }
233: /*@
234: KSPSetReusePreconditioner - reuse the current preconditioner, do not construct a new one even if the operator changes
236: Collective
238: Input Parameters:
239: + ksp - iterative context obtained from `KSPCreate()`
240: - flag - `PETSC_TRUE` to reuse the current preconditioner
242: Level: intermediate
244: .seealso: [](chapter_ksp), `KSPCreate()`, `KSPSolve()`, `KSPDestroy()`, `PCSetReusePreconditioner()`, `KSP`
245: @*/
246: PetscErrorCode KSPSetReusePreconditioner(KSP ksp, PetscBool flag)
247: {
248: PC pc;
250: PetscFunctionBegin;
252: PetscCall(KSPGetPC(ksp, &pc));
253: PetscCall(PCSetReusePreconditioner(pc, flag));
254: PetscFunctionReturn(PETSC_SUCCESS);
255: }
257: /*@
258: KSPGetReusePreconditioner - Determines if the `KSP` reuses the current preconditioner even if the operator in the preconditioner has changed.
260: Collective
262: Input Parameter:
263: . ksp - iterative context obtained from `KSPCreate()`
265: Output Parameter:
266: . flag - the boolean flag
268: Level: intermediate
270: .seealso: [](chapter_ksp), `KSPCreate()`, `KSPSolve()`, `KSPDestroy()`, `KSPSetReusePreconditioner()`, `KSP`
271: @*/
272: PetscErrorCode KSPGetReusePreconditioner(KSP ksp, PetscBool *flag)
273: {
274: PetscFunctionBegin;
277: *flag = PETSC_FALSE;
278: if (ksp->pc) PetscCall(PCGetReusePreconditioner(ksp->pc, flag));
279: PetscFunctionReturn(PETSC_SUCCESS);
280: }
282: /*@
283: KSPSetSkipPCSetFromOptions - prevents `KSPSetFromOptions()` from calling `PCSetFromOptions()`. This is used if the same `PC` is shared by more than one `KSP` so its options are not resettable for each `KSP`
285: Collective
287: Input Parameters:
288: + ksp - iterative context obtained from `KSPCreate()`
289: - flag - `PETSC_TRUE` to skip calling the `PCSetFromOptions()`
291: Level: intermediate
293: .seealso: [](chapter_ksp), `KSPCreate()`, `KSPSolve()`, `KSPDestroy()`, `PCSetReusePreconditioner()`, `KSP`
294: @*/
295: PetscErrorCode KSPSetSkipPCSetFromOptions(KSP ksp, PetscBool flag)
296: {
297: PetscFunctionBegin;
299: ksp->skippcsetfromoptions = flag;
300: PetscFunctionReturn(PETSC_SUCCESS);
301: }
303: /*@
304: KSPSetUp - Sets up the internal data structures for the
305: later use of an iterative solver.
307: Collective
309: Input Parameter:
310: . ksp - iterative context obtained from `KSPCreate()`
312: Level: developer
314: .seealso: [](chapter_ksp), `KSPCreate()`, `KSPSolve()`, `KSPDestroy()`, `KSP`
315: @*/
316: PetscErrorCode KSPSetUp(KSP ksp)
317: {
318: Mat A, B;
319: Mat mat, pmat;
320: MatNullSpace nullsp;
321: PCFailedReason pcreason;
323: PetscFunctionBegin;
325: level++;
327: /* reset the convergence flag from the previous solves */
328: ksp->reason = KSP_CONVERGED_ITERATING;
330: if (!((PetscObject)ksp)->type_name) PetscCall(KSPSetType(ksp, KSPGMRES));
331: PetscCall(KSPSetUpNorms_Private(ksp, PETSC_TRUE, &ksp->normtype, &ksp->pc_side));
333: if (ksp->dmActive && !ksp->setupstage) {
334: /* first time in so build matrix and vector data structures using DM */
335: if (!ksp->vec_rhs) PetscCall(DMCreateGlobalVector(ksp->dm, &ksp->vec_rhs));
336: if (!ksp->vec_sol) PetscCall(DMCreateGlobalVector(ksp->dm, &ksp->vec_sol));
337: PetscCall(DMCreateMatrix(ksp->dm, &A));
338: PetscCall(KSPSetOperators(ksp, A, A));
339: PetscCall(PetscObjectDereference((PetscObject)A));
340: }
342: if (ksp->dmActive) {
343: DMKSP kdm;
344: PetscCall(DMGetDMKSP(ksp->dm, &kdm));
346: if (kdm->ops->computeinitialguess && ksp->setupstage != KSP_SETUP_NEWRHS) {
347: /* only computes initial guess the first time through */
348: PetscCallBack("KSP callback initial guess", (*kdm->ops->computeinitialguess)(ksp, ksp->vec_sol, kdm->initialguessctx));
349: PetscCall(KSPSetInitialGuessNonzero(ksp, PETSC_TRUE));
350: }
351: if (kdm->ops->computerhs) PetscCallBack("KSP callback rhs", (*kdm->ops->computerhs)(ksp, ksp->vec_rhs, kdm->rhsctx));
353: if (ksp->setupstage != KSP_SETUP_NEWRHS) {
354: PetscCheck(kdm->ops->computeoperators, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONGSTATE, "You called KSPSetDM() but did not use DMKSPSetComputeOperators() or KSPSetDMActive(ksp,PETSC_FALSE);");
355: PetscCall(KSPGetOperators(ksp, &A, &B));
356: PetscCallBack("KSP callback operators", (*kdm->ops->computeoperators)(ksp, A, B, kdm->operatorsctx));
357: }
358: }
360: if (ksp->setupstage == KSP_SETUP_NEWRHS) {
361: level--;
362: PetscFunctionReturn(PETSC_SUCCESS);
363: }
364: PetscCall(PetscLogEventBegin(KSP_SetUp, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
366: switch (ksp->setupstage) {
367: case KSP_SETUP_NEW:
368: PetscUseTypeMethod(ksp, setup);
369: break;
370: case KSP_SETUP_NEWMATRIX: { /* This should be replaced with a more general mechanism */
371: if (ksp->setupnewmatrix) PetscUseTypeMethod(ksp, setup);
372: } break;
373: default:
374: break;
375: }
377: if (!ksp->pc) PetscCall(KSPGetPC(ksp, &ksp->pc));
378: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
379: /* scale the matrix if requested */
380: if (ksp->dscale) {
381: PetscScalar *xx;
382: PetscInt i, n;
383: PetscBool zeroflag = PETSC_FALSE;
384: if (!ksp->pc) PetscCall(KSPGetPC(ksp, &ksp->pc));
385: if (!ksp->diagonal) { /* allocate vector to hold diagonal */
386: PetscCall(MatCreateVecs(pmat, &ksp->diagonal, NULL));
387: }
388: PetscCall(MatGetDiagonal(pmat, ksp->diagonal));
389: PetscCall(VecGetLocalSize(ksp->diagonal, &n));
390: PetscCall(VecGetArray(ksp->diagonal, &xx));
391: for (i = 0; i < n; i++) {
392: if (xx[i] != 0.0) xx[i] = 1.0 / PetscSqrtReal(PetscAbsScalar(xx[i]));
393: else {
394: xx[i] = 1.0;
395: zeroflag = PETSC_TRUE;
396: }
397: }
398: PetscCall(VecRestoreArray(ksp->diagonal, &xx));
399: if (zeroflag) PetscCall(PetscInfo(ksp, "Zero detected in diagonal of matrix, using 1 at those locations\n"));
400: PetscCall(MatDiagonalScale(pmat, ksp->diagonal, ksp->diagonal));
401: if (mat != pmat) PetscCall(MatDiagonalScale(mat, ksp->diagonal, ksp->diagonal));
402: ksp->dscalefix2 = PETSC_FALSE;
403: }
404: PetscCall(PetscLogEventEnd(KSP_SetUp, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
405: PetscCall(PCSetErrorIfFailure(ksp->pc, ksp->errorifnotconverged));
406: PetscCall(PCSetUp(ksp->pc));
407: PetscCall(PCGetFailedReasonRank(ksp->pc, &pcreason));
408: /* TODO: this code was wrong and is still wrong, there is no way to propagate the failure to all processes; their is no code to handle a ksp->reason on only some ranks */
409: if (pcreason) ksp->reason = KSP_DIVERGED_PC_FAILED;
411: PetscCall(MatGetNullSpace(mat, &nullsp));
412: if (nullsp) {
413: PetscBool test = PETSC_FALSE;
414: PetscCall(PetscOptionsGetBool(((PetscObject)ksp)->options, ((PetscObject)ksp)->prefix, "-ksp_test_null_space", &test, NULL));
415: if (test) PetscCall(MatNullSpaceTest(nullsp, mat, NULL));
416: }
417: ksp->setupstage = KSP_SETUP_NEWRHS;
418: level--;
419: PetscFunctionReturn(PETSC_SUCCESS);
420: }
422: /*@C
423: KSPConvergedReasonView - Displays the reason a `KSP` solve converged or diverged to a viewer
425: Collective
427: Parameter:
428: + ksp - iterative context obtained from `KSPCreate()`
429: - viewer - the viewer to display the reason
431: Options Database Keys:
432: + -ksp_converged_reason - print reason for converged or diverged, also prints number of iterations
433: - -ksp_converged_reason ::failed - only print reason and number of iterations when diverged
435: Notes:
436: To change the format of the output call PetscViewerPushFormat(viewer,format) before this call. Use PETSC_VIEWER_DEFAULT for the default,
437: use PETSC_VIEWER_FAILED to only display a reason if it fails.
439: Level: beginner
441: .seealso: [](chapter_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPDestroy()`, `KSPSetTolerances()`, `KSPConvergedDefault()`,
442: `KSPSolveTranspose()`, `KSPGetIterationNumber()`, `KSP`, `KSPGetConvergedReason()`, `PetscViewerPushFormat()`, `PetscViewerPopFormat()`
443: @*/
444: PetscErrorCode KSPConvergedReasonView(KSP ksp, PetscViewer viewer)
445: {
446: PetscBool isAscii;
447: PetscViewerFormat format;
449: PetscFunctionBegin;
450: if (!viewer) viewer = PETSC_VIEWER_STDOUT_(PetscObjectComm((PetscObject)ksp));
451: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isAscii));
452: if (isAscii) {
453: PetscCall(PetscViewerGetFormat(viewer, &format));
454: PetscCall(PetscViewerASCIIAddTab(viewer, ((PetscObject)ksp)->tablevel));
455: if (ksp->reason > 0 && format != PETSC_VIEWER_FAILED) {
456: if (((PetscObject)ksp)->prefix) {
457: PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve converged due to %s iterations %" PetscInt_FMT "\n", ((PetscObject)ksp)->prefix, KSPConvergedReasons[ksp->reason], ksp->its));
458: } else {
459: PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve converged due to %s iterations %" PetscInt_FMT "\n", KSPConvergedReasons[ksp->reason], ksp->its));
460: }
461: } else if (ksp->reason <= 0) {
462: if (((PetscObject)ksp)->prefix) {
463: PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve did not converge due to %s iterations %" PetscInt_FMT "\n", ((PetscObject)ksp)->prefix, KSPConvergedReasons[ksp->reason], ksp->its));
464: } else {
465: PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve did not converge due to %s iterations %" PetscInt_FMT "\n", KSPConvergedReasons[ksp->reason], ksp->its));
466: }
467: if (ksp->reason == KSP_DIVERGED_PC_FAILED) {
468: PCFailedReason reason;
469: PetscCall(PCGetFailedReason(ksp->pc, &reason));
470: PetscCall(PetscViewerASCIIPrintf(viewer, " PC failed due to %s \n", PCFailedReasons[reason]));
471: }
472: }
473: PetscCall(PetscViewerASCIISubtractTab(viewer, ((PetscObject)ksp)->tablevel));
474: }
475: PetscFunctionReturn(PETSC_SUCCESS);
476: }
478: /*@C
479: KSPConvergedReasonViewSet - Sets an ADDITIONAL function that is to be used at the
480: end of the linear solver to display the convergence reason of the linear solver.
482: Logically Collective
484: Input Parameters:
485: + ksp - the `KSP` context
486: . f - the ksp converged reason view function
487: . vctx - [optional] user-defined context for private data for the
488: ksp converged reason view routine (use `NULL` if no context is desired)
489: - reasonviewdestroy - [optional] routine that frees reasonview context
490: (may be `NULL`)
492: Options Database Keys:
493: + -ksp_converged_reason - sets a default `KSPConvergedReasonView()`
494: - -ksp_converged_reason_view_cancel - cancels all converged reason viewers that have
495: been hardwired into a code by
496: calls to `KSPConvergedReasonViewSet()`, but
497: does not cancel those set via
498: the options database.
500: Notes:
501: Several different converged reason view routines may be set by calling
502: `KSPConvergedReasonViewSet()` multiple times; all will be called in the
503: order in which they were set.
505: Level: intermediate
507: .seealso: [](chapter_ksp), `KSPConvergedReasonView()`, `KSPConvergedReasonViewCancel()`
508: @*/
509: PetscErrorCode KSPConvergedReasonViewSet(KSP ksp, PetscErrorCode (*f)(KSP, void *), void *vctx, PetscErrorCode (*reasonviewdestroy)(void **))
510: {
511: PetscInt i;
512: PetscBool identical;
514: PetscFunctionBegin;
516: for (i = 0; i < ksp->numberreasonviews; i++) {
517: PetscCall(PetscMonitorCompare((PetscErrorCode(*)(void))f, vctx, reasonviewdestroy, (PetscErrorCode(*)(void))ksp->reasonview[i], ksp->reasonviewcontext[i], ksp->reasonviewdestroy[i], &identical));
518: if (identical) PetscFunctionReturn(PETSC_SUCCESS);
519: }
520: PetscCheck(ksp->numberreasonviews < MAXKSPREASONVIEWS, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Too many KSP reasonview set");
521: ksp->reasonview[ksp->numberreasonviews] = f;
522: ksp->reasonviewdestroy[ksp->numberreasonviews] = reasonviewdestroy;
523: ksp->reasonviewcontext[ksp->numberreasonviews++] = (void *)vctx;
524: PetscFunctionReturn(PETSC_SUCCESS);
525: }
527: /*@
528: KSPConvergedReasonViewCancel - Clears all the reasonview functions for a `KSP` object.
530: Collective
532: Input Parameter:
533: . ksp - iterative context obtained from `KSPCreate()`
535: Level: intermediate
537: .seealso: [](chapter_ksp), `KSPCreate()`, `KSPDestroy()`, `KSPReset()`
538: @*/
539: PetscErrorCode KSPConvergedReasonViewCancel(KSP ksp)
540: {
541: PetscInt i;
543: PetscFunctionBegin;
545: for (i = 0; i < ksp->numberreasonviews; i++) {
546: if (ksp->reasonviewdestroy[i]) PetscCall((*ksp->reasonviewdestroy[i])(&ksp->reasonviewcontext[i]));
547: }
548: ksp->numberreasonviews = 0;
549: PetscFunctionReturn(PETSC_SUCCESS);
550: }
552: /*@
553: KSPConvergedReasonViewFromOptions - Processes command line options to determine if/how a KSPReason is to be viewed.
555: Collective
557: Input Parameter:
558: . ksp - the `KSP` object
560: Level: intermediate
562: .seealso: [](chapter_ksp), `KSPConvergedReasonView()`
563: @*/
564: PetscErrorCode KSPConvergedReasonViewFromOptions(KSP ksp)
565: {
566: PetscViewer viewer;
567: PetscBool flg;
568: PetscViewerFormat format;
569: PetscInt i;
571: PetscFunctionBegin;
573: /* Call all user-provided reason review routines */
574: for (i = 0; i < ksp->numberreasonviews; i++) PetscCall((*ksp->reasonview[i])(ksp, ksp->reasonviewcontext[i]));
576: /* Call the default PETSc routine */
577: PetscCall(PetscOptionsGetViewer(PetscObjectComm((PetscObject)ksp), ((PetscObject)ksp)->options, ((PetscObject)ksp)->prefix, "-ksp_converged_reason", &viewer, &format, &flg));
578: if (flg) {
579: PetscCall(PetscViewerPushFormat(viewer, format));
580: PetscCall(KSPConvergedReasonView(ksp, viewer));
581: PetscCall(PetscViewerPopFormat(viewer));
582: PetscCall(PetscViewerDestroy(&viewer));
583: }
584: PetscFunctionReturn(PETSC_SUCCESS);
585: }
587: /*@C
588: KSPConvergedRateView - Displays the reason a `KSP` solve converged or diverged to a viewer
590: Collective
592: Input Parameters:
593: + ksp - iterative context obtained from `KSPCreate()`
594: - viewer - the viewer to display the reason
596: Options Database Key:
597: . -ksp_converged_rate - print reason for convergence or divergence and the convergence rate (or 0.0 for divergence)
599: Notes:
600: To change the format of the output, call PetscViewerPushFormat(viewer,format) before this call.
602: Suppose that the residual is reduced linearly, $r_k = c^k r_0$, which means $log r_k = log r_0 + k log c$. After linear regression,
603: the slope is $\log c$. The coefficient of determination is given by $1 - \frac{\sum_i (y_i - f(x_i))^2}{\sum_i (y_i - \bar y)}$,
604: see also https://en.wikipedia.org/wiki/Coefficient_of_determination
606: Level: intermediate
608: .seealso: [](chapter_ksp), `KSPConvergedReasonView()`, `KSPGetConvergedRate()`, `KSPSetTolerances()`, `KSPConvergedDefault()`
609: @*/
610: PetscErrorCode KSPConvergedRateView(KSP ksp, PetscViewer viewer)
611: {
612: PetscViewerFormat format;
613: PetscBool isAscii;
614: PetscReal rrate, rRsq, erate = 0.0, eRsq = 0.0;
615: PetscInt its;
616: const char *prefix, *reason = KSPConvergedReasons[ksp->reason];
618: PetscFunctionBegin;
619: PetscCall(KSPGetOptionsPrefix(ksp, &prefix));
620: PetscCall(KSPGetIterationNumber(ksp, &its));
621: PetscCall(KSPComputeConvergenceRate(ksp, &rrate, &rRsq, &erate, &eRsq));
622: if (!viewer) viewer = PETSC_VIEWER_STDOUT_(PetscObjectComm((PetscObject)ksp));
623: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isAscii));
624: if (isAscii) {
625: PetscCall(PetscViewerGetFormat(viewer, &format));
626: PetscCall(PetscViewerASCIIAddTab(viewer, ((PetscObject)ksp)->tablevel));
627: if (ksp->reason > 0) {
628: if (prefix) PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve converged due to %s iterations %" PetscInt_FMT, prefix, reason, its));
629: else PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve converged due to %s iterations %" PetscInt_FMT, reason, its));
630: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
631: if (rRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " res rate %g R^2 %g", (double)rrate, (double)rRsq));
632: if (eRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " error rate %g R^2 %g", (double)erate, (double)eRsq));
633: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
634: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
635: } else if (ksp->reason <= 0) {
636: if (prefix) PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve did not converge due to %s iterations %" PetscInt_FMT, prefix, reason, its));
637: else PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve did not converge due to %s iterations %" PetscInt_FMT, reason, its));
638: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
639: if (rRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " res rate %g R^2 %g", (double)rrate, (double)rRsq));
640: if (eRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " error rate %g R^2 %g", (double)erate, (double)eRsq));
641: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
642: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
643: if (ksp->reason == KSP_DIVERGED_PC_FAILED) {
644: PCFailedReason reason;
645: PetscCall(PCGetFailedReason(ksp->pc, &reason));
646: PetscCall(PetscViewerASCIIPrintf(viewer, " PC failed due to %s \n", PCFailedReasons[reason]));
647: }
648: }
649: PetscCall(PetscViewerASCIISubtractTab(viewer, ((PetscObject)ksp)->tablevel));
650: }
651: PetscFunctionReturn(PETSC_SUCCESS);
652: }
654: #include <petscdraw.h>
656: static PetscErrorCode KSPViewEigenvalues_Internal(KSP ksp, PetscBool isExplicit, PetscViewer viewer, PetscViewerFormat format)
657: {
658: PetscReal *r, *c;
659: PetscInt n, i, neig;
660: PetscBool isascii, isdraw;
661: PetscMPIInt rank;
663: PetscFunctionBegin;
664: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)ksp), &rank));
665: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
666: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
667: if (isExplicit) {
668: PetscCall(VecGetSize(ksp->vec_sol, &n));
669: PetscCall(PetscMalloc2(n, &r, n, &c));
670: PetscCall(KSPComputeEigenvaluesExplicitly(ksp, n, r, c));
671: neig = n;
672: } else {
673: PetscInt nits;
675: PetscCall(KSPGetIterationNumber(ksp, &nits));
676: n = nits + 2;
677: if (!nits) {
678: PetscCall(PetscViewerASCIIPrintf(viewer, "Zero iterations in solver, cannot approximate any eigenvalues\n"));
679: PetscFunctionReturn(PETSC_SUCCESS);
680: }
681: PetscCall(PetscMalloc2(n, &r, n, &c));
682: PetscCall(KSPComputeEigenvalues(ksp, n, r, c, &neig));
683: }
684: if (isascii) {
685: PetscCall(PetscViewerASCIIPrintf(viewer, "%s computed eigenvalues\n", isExplicit ? "Explicitly" : "Iteratively"));
686: for (i = 0; i < neig; ++i) {
687: if (c[i] >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, "%g + %gi\n", (double)r[i], (double)c[i]));
688: else PetscCall(PetscViewerASCIIPrintf(viewer, "%g - %gi\n", (double)r[i], -(double)c[i]));
689: }
690: } else if (isdraw && rank == 0) {
691: PetscDraw draw;
692: PetscDrawSP drawsp;
694: if (format == PETSC_VIEWER_DRAW_CONTOUR) {
695: PetscCall(KSPPlotEigenContours_Private(ksp, neig, r, c));
696: } else {
697: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
698: PetscCall(PetscDrawSPCreate(draw, 1, &drawsp));
699: PetscCall(PetscDrawSPReset(drawsp));
700: for (i = 0; i < neig; ++i) PetscCall(PetscDrawSPAddPoint(drawsp, r + i, c + i));
701: PetscCall(PetscDrawSPDraw(drawsp, PETSC_TRUE));
702: PetscCall(PetscDrawSPSave(drawsp));
703: PetscCall(PetscDrawSPDestroy(&drawsp));
704: }
705: }
706: PetscCall(PetscFree2(r, c));
707: PetscFunctionReturn(PETSC_SUCCESS);
708: }
710: static PetscErrorCode KSPViewSingularvalues_Internal(KSP ksp, PetscViewer viewer, PetscViewerFormat format)
711: {
712: PetscReal smax, smin;
713: PetscInt nits;
714: PetscBool isascii;
716: PetscFunctionBegin;
717: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
718: PetscCall(KSPGetIterationNumber(ksp, &nits));
719: if (!nits) {
720: PetscCall(PetscViewerASCIIPrintf(viewer, "Zero iterations in solver, cannot approximate any singular values\n"));
721: PetscFunctionReturn(PETSC_SUCCESS);
722: }
723: PetscCall(KSPComputeExtremeSingularValues(ksp, &smax, &smin));
724: if (isascii) PetscCall(PetscViewerASCIIPrintf(viewer, "Iteratively computed extreme singular values: max %g min %g max/min %g\n", (double)smax, (double)smin, (double)(smax / smin)));
725: PetscFunctionReturn(PETSC_SUCCESS);
726: }
728: static PetscErrorCode KSPViewFinalResidual_Internal(KSP ksp, PetscViewer viewer, PetscViewerFormat format)
729: {
730: PetscBool isascii;
732: PetscFunctionBegin;
733: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
734: PetscCheck(!ksp->dscale || ksp->dscalefix, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONGSTATE, "Cannot compute final scale with -ksp_diagonal_scale except also with -ksp_diagonal_scale_fix");
735: if (isascii) {
736: Mat A;
737: Vec t;
738: PetscReal norm;
740: PetscCall(PCGetOperators(ksp->pc, &A, NULL));
741: PetscCall(VecDuplicate(ksp->vec_rhs, &t));
742: PetscCall(KSP_MatMult(ksp, A, ksp->vec_sol, t));
743: PetscCall(VecAYPX(t, -1.0, ksp->vec_rhs));
744: PetscCall(VecNorm(t, NORM_2, &norm));
745: PetscCall(VecDestroy(&t));
746: PetscCall(PetscViewerASCIIPrintf(viewer, "KSP final norm of residual %g\n", (double)norm));
747: }
748: PetscFunctionReturn(PETSC_SUCCESS);
749: }
751: static PetscErrorCode KSPMonitorPauseFinal_Internal(KSP ksp)
752: {
753: PetscInt i;
755: PetscFunctionBegin;
756: if (!ksp->pauseFinal) PetscFunctionReturn(PETSC_SUCCESS);
757: for (i = 0; i < ksp->numbermonitors; ++i) {
758: PetscViewerAndFormat *vf = (PetscViewerAndFormat *)ksp->monitorcontext[i];
759: PetscDraw draw;
760: PetscReal lpause;
762: if (!vf) continue;
763: if (vf->lg) {
764: if (!PetscCheckPointer(vf->lg, PETSC_OBJECT)) continue;
765: if (((PetscObject)vf->lg)->classid != PETSC_DRAWLG_CLASSID) continue;
766: PetscCall(PetscDrawLGGetDraw(vf->lg, &draw));
767: PetscCall(PetscDrawGetPause(draw, &lpause));
768: PetscCall(PetscDrawSetPause(draw, -1.0));
769: PetscCall(PetscDrawPause(draw));
770: PetscCall(PetscDrawSetPause(draw, lpause));
771: } else {
772: PetscBool isdraw;
774: if (!PetscCheckPointer(vf->viewer, PETSC_OBJECT)) continue;
775: if (((PetscObject)vf->viewer)->classid != PETSC_VIEWER_CLASSID) continue;
776: PetscCall(PetscObjectTypeCompare((PetscObject)vf->viewer, PETSCVIEWERDRAW, &isdraw));
777: if (!isdraw) continue;
778: PetscCall(PetscViewerDrawGetDraw(vf->viewer, 0, &draw));
779: PetscCall(PetscDrawGetPause(draw, &lpause));
780: PetscCall(PetscDrawSetPause(draw, -1.0));
781: PetscCall(PetscDrawPause(draw));
782: PetscCall(PetscDrawSetPause(draw, lpause));
783: }
784: }
785: PetscFunctionReturn(PETSC_SUCCESS);
786: }
788: static PetscErrorCode KSPSolve_Private(KSP ksp, Vec b, Vec x)
789: {
790: PetscBool flg = PETSC_FALSE, inXisinB = PETSC_FALSE, guess_zero;
791: Mat mat, pmat;
792: MPI_Comm comm;
793: MatNullSpace nullsp;
794: Vec btmp, vec_rhs = NULL;
796: PetscFunctionBegin;
797: level++;
798: comm = PetscObjectComm((PetscObject)ksp);
799: if (x && x == b) {
800: PetscCheck(ksp->guess_zero, comm, PETSC_ERR_ARG_INCOMP, "Cannot use x == b with nonzero initial guess");
801: PetscCall(VecDuplicate(b, &x));
802: inXisinB = PETSC_TRUE;
803: }
804: if (b) {
805: PetscCall(PetscObjectReference((PetscObject)b));
806: PetscCall(VecDestroy(&ksp->vec_rhs));
807: ksp->vec_rhs = b;
808: }
809: if (x) {
810: PetscCall(PetscObjectReference((PetscObject)x));
811: PetscCall(VecDestroy(&ksp->vec_sol));
812: ksp->vec_sol = x;
813: }
815: if (ksp->viewPre) PetscCall(ObjectView((PetscObject)ksp, ksp->viewerPre, ksp->formatPre));
817: if (ksp->presolve) PetscCall((*ksp->presolve)(ksp, ksp->vec_rhs, ksp->vec_sol, ksp->prectx));
819: /* reset the residual history list if requested */
820: if (ksp->res_hist_reset) ksp->res_hist_len = 0;
821: if (ksp->err_hist_reset) ksp->err_hist_len = 0;
823: /* KSPSetUp() scales the matrix if needed */
824: PetscCall(KSPSetUp(ksp));
825: PetscCall(KSPSetUpOnBlocks(ksp));
827: if (ksp->guess) {
828: PetscObjectState ostate, state;
830: PetscCall(KSPGuessSetUp(ksp->guess));
831: PetscCall(PetscObjectStateGet((PetscObject)ksp->vec_sol, &ostate));
832: PetscCall(KSPGuessFormGuess(ksp->guess, ksp->vec_rhs, ksp->vec_sol));
833: PetscCall(PetscObjectStateGet((PetscObject)ksp->vec_sol, &state));
834: if (state != ostate) {
835: ksp->guess_zero = PETSC_FALSE;
836: } else {
837: PetscCall(PetscInfo(ksp, "Using zero initial guess since the KSPGuess object did not change the vector\n"));
838: ksp->guess_zero = PETSC_TRUE;
839: }
840: }
842: PetscCall(VecSetErrorIfLocked(ksp->vec_sol, 3));
844: PetscCall(PetscLogEventBegin(!ksp->transpose_solve ? KSP_Solve : KSP_SolveTranspose, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
845: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
846: /* diagonal scale RHS if called for */
847: if (ksp->dscale) {
848: PetscCall(VecPointwiseMult(ksp->vec_rhs, ksp->vec_rhs, ksp->diagonal));
849: /* second time in, but matrix was scaled back to original */
850: if (ksp->dscalefix && ksp->dscalefix2) {
851: Mat mat, pmat;
853: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
854: PetscCall(MatDiagonalScale(pmat, ksp->diagonal, ksp->diagonal));
855: if (mat != pmat) PetscCall(MatDiagonalScale(mat, ksp->diagonal, ksp->diagonal));
856: }
858: /* scale initial guess */
859: if (!ksp->guess_zero) {
860: if (!ksp->truediagonal) {
861: PetscCall(VecDuplicate(ksp->diagonal, &ksp->truediagonal));
862: PetscCall(VecCopy(ksp->diagonal, ksp->truediagonal));
863: PetscCall(VecReciprocal(ksp->truediagonal));
864: }
865: PetscCall(VecPointwiseMult(ksp->vec_sol, ksp->vec_sol, ksp->truediagonal));
866: }
867: }
868: PetscCall(PCPreSolve(ksp->pc, ksp));
870: if (ksp->guess_zero) PetscCall(VecSet(ksp->vec_sol, 0.0));
871: if (ksp->guess_knoll) { /* The Knoll trick is independent on the KSPGuess specified */
872: PetscCall(PCApply(ksp->pc, ksp->vec_rhs, ksp->vec_sol));
873: PetscCall(KSP_RemoveNullSpace(ksp, ksp->vec_sol));
874: ksp->guess_zero = PETSC_FALSE;
875: }
877: /* can we mark the initial guess as zero for this solve? */
878: guess_zero = ksp->guess_zero;
879: if (!ksp->guess_zero) {
880: PetscReal norm;
882: PetscCall(VecNormAvailable(ksp->vec_sol, NORM_2, &flg, &norm));
883: if (flg && !norm) ksp->guess_zero = PETSC_TRUE;
884: }
885: if (ksp->transpose_solve) {
886: PetscCall(MatGetNullSpace(pmat, &nullsp));
887: } else {
888: PetscCall(MatGetTransposeNullSpace(pmat, &nullsp));
889: }
890: if (nullsp) {
891: PetscCall(VecDuplicate(ksp->vec_rhs, &btmp));
892: PetscCall(VecCopy(ksp->vec_rhs, btmp));
893: PetscCall(MatNullSpaceRemove(nullsp, btmp));
894: vec_rhs = ksp->vec_rhs;
895: ksp->vec_rhs = btmp;
896: }
897: PetscCall(VecLockReadPush(ksp->vec_rhs));
898: PetscUseTypeMethod(ksp, solve);
899: PetscCall(KSPMonitorPauseFinal_Internal(ksp));
901: PetscCall(VecLockReadPop(ksp->vec_rhs));
902: if (nullsp) {
903: ksp->vec_rhs = vec_rhs;
904: PetscCall(VecDestroy(&btmp));
905: }
907: ksp->guess_zero = guess_zero;
909: PetscCheck(ksp->reason, comm, PETSC_ERR_PLIB, "Internal error, solver returned without setting converged reason");
910: ksp->totalits += ksp->its;
912: PetscCall(KSPConvergedReasonViewFromOptions(ksp));
914: if (ksp->viewRate) {
915: PetscCall(PetscViewerPushFormat(ksp->viewerRate, ksp->formatRate));
916: PetscCall(KSPConvergedRateView(ksp, ksp->viewerRate));
917: PetscCall(PetscViewerPopFormat(ksp->viewerRate));
918: }
919: PetscCall(PCPostSolve(ksp->pc, ksp));
921: /* diagonal scale solution if called for */
922: if (ksp->dscale) {
923: PetscCall(VecPointwiseMult(ksp->vec_sol, ksp->vec_sol, ksp->diagonal));
924: /* unscale right hand side and matrix */
925: if (ksp->dscalefix) {
926: Mat mat, pmat;
928: PetscCall(VecReciprocal(ksp->diagonal));
929: PetscCall(VecPointwiseMult(ksp->vec_rhs, ksp->vec_rhs, ksp->diagonal));
930: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
931: PetscCall(MatDiagonalScale(pmat, ksp->diagonal, ksp->diagonal));
932: if (mat != pmat) PetscCall(MatDiagonalScale(mat, ksp->diagonal, ksp->diagonal));
933: PetscCall(VecReciprocal(ksp->diagonal));
934: ksp->dscalefix2 = PETSC_TRUE;
935: }
936: }
937: PetscCall(PetscLogEventEnd(!ksp->transpose_solve ? KSP_Solve : KSP_SolveTranspose, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
938: if (ksp->guess) PetscCall(KSPGuessUpdate(ksp->guess, ksp->vec_rhs, ksp->vec_sol));
939: if (ksp->postsolve) PetscCall((*ksp->postsolve)(ksp, ksp->vec_rhs, ksp->vec_sol, ksp->postctx));
941: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
942: if (ksp->viewEV) PetscCall(KSPViewEigenvalues_Internal(ksp, PETSC_FALSE, ksp->viewerEV, ksp->formatEV));
943: if (ksp->viewEVExp) PetscCall(KSPViewEigenvalues_Internal(ksp, PETSC_TRUE, ksp->viewerEVExp, ksp->formatEVExp));
944: if (ksp->viewSV) PetscCall(KSPViewSingularvalues_Internal(ksp, ksp->viewerSV, ksp->formatSV));
945: if (ksp->viewFinalRes) PetscCall(KSPViewFinalResidual_Internal(ksp, ksp->viewerFinalRes, ksp->formatFinalRes));
946: if (ksp->viewMat) PetscCall(ObjectView((PetscObject)mat, ksp->viewerMat, ksp->formatMat));
947: if (ksp->viewPMat) PetscCall(ObjectView((PetscObject)pmat, ksp->viewerPMat, ksp->formatPMat));
948: if (ksp->viewRhs) PetscCall(ObjectView((PetscObject)ksp->vec_rhs, ksp->viewerRhs, ksp->formatRhs));
949: if (ksp->viewSol) PetscCall(ObjectView((PetscObject)ksp->vec_sol, ksp->viewerSol, ksp->formatSol));
950: if (ksp->view) PetscCall(ObjectView((PetscObject)ksp, ksp->viewer, ksp->format));
951: if (ksp->viewDScale) PetscCall(ObjectView((PetscObject)ksp->diagonal, ksp->viewerDScale, ksp->formatDScale));
952: if (ksp->viewMatExp) {
953: Mat A, B;
955: PetscCall(PCGetOperators(ksp->pc, &A, NULL));
956: if (ksp->transpose_solve) {
957: Mat AT;
959: PetscCall(MatCreateTranspose(A, &AT));
960: PetscCall(MatComputeOperator(AT, MATAIJ, &B));
961: PetscCall(MatDestroy(&AT));
962: } else {
963: PetscCall(MatComputeOperator(A, MATAIJ, &B));
964: }
965: PetscCall(ObjectView((PetscObject)B, ksp->viewerMatExp, ksp->formatMatExp));
966: PetscCall(MatDestroy(&B));
967: }
968: if (ksp->viewPOpExp) {
969: Mat B;
971: PetscCall(KSPComputeOperator(ksp, MATAIJ, &B));
972: PetscCall(ObjectView((PetscObject)B, ksp->viewerPOpExp, ksp->formatPOpExp));
973: PetscCall(MatDestroy(&B));
974: }
976: if (inXisinB) {
977: PetscCall(VecCopy(x, b));
978: PetscCall(VecDestroy(&x));
979: }
980: PetscCall(PetscObjectSAWsBlock((PetscObject)ksp));
981: if (ksp->errorifnotconverged && ksp->reason < 0 && ((level == 1) || (ksp->reason != KSP_DIVERGED_ITS))) {
982: PCFailedReason reason;
984: PetscCheck(ksp->reason == KSP_DIVERGED_PC_FAILED, comm, PETSC_ERR_NOT_CONVERGED, "KSPSolve has not converged, reason %s", KSPConvergedReasons[ksp->reason]);
985: PetscCall(PCGetFailedReason(ksp->pc, &reason));
986: SETERRQ(comm, PETSC_ERR_NOT_CONVERGED, "KSPSolve has not converged, reason %s PC failed due to %s", KSPConvergedReasons[ksp->reason], PCFailedReasons[reason]);
987: }
988: level--;
989: PetscFunctionReturn(PETSC_SUCCESS);
990: }
992: /*@
993: KSPSolve - Solves linear system.
995: Collective
997: Parameters:
998: + ksp - iterative context obtained from `KSPCreate()`
999: . b - the right hand side vector
1000: - x - the solution (this may be the same vector as b, then b will be overwritten with answer)
1002: Options Database Keys:
1003: + -ksp_view_eigenvalues - compute preconditioned operators eigenvalues
1004: . -ksp_view_eigenvalues_explicit - compute the eigenvalues by forming the dense operator and using LAPACK
1005: . -ksp_view_mat binary - save matrix to the default binary viewer
1006: . -ksp_view_pmat binary - save matrix used to build preconditioner to the default binary viewer
1007: . -ksp_view_rhs binary - save right hand side vector to the default binary viewer
1008: . -ksp_view_solution binary - save computed solution vector to the default binary viewer
1009: (can be read later with src/ksp/tutorials/ex10.c for testing solvers)
1010: . -ksp_view_mat_explicit - for matrix-free operators, computes the matrix entries and views them
1011: . -ksp_view_preconditioned_operator_explicit - computes the product of the preconditioner and matrix as an explicit matrix and views it
1012: . -ksp_converged_reason - print reason for converged or diverged, also prints number of iterations
1013: . -ksp_view_final_residual - print 2-norm of true linear system residual at the end of the solution process
1014: . -ksp_error_if_not_converged - stop the program as soon as an error is detected in a `KSPSolve()`
1015: - -ksp_view - print the ksp data structure at the end of the system solution
1017: Notes:
1019: If one uses `KSPSetDM()` then x or b need not be passed. Use `KSPGetSolution()` to access the solution in this case.
1021: The operator is specified with `KSPSetOperators()`.
1023: `KSPSolve()` will normally return without generating an error regardless of whether the linear system was solved or if constructing the preconditioner failed.
1024: Call `KSPGetConvergedReason()` to determine if the solver converged or failed and why. The option -ksp_error_if_not_converged or function `KSPSetErrorIfNotConverged()`
1025: will cause `KSPSolve()` to error as soon as an error occurs in the linear solver. In inner KSPSolves() KSP_DIVERGED_ITS is not treated as an error because when using nested solvers
1026: it may be fine that inner solvers in the preconditioner do not converge during the solution process.
1028: The number of iterations can be obtained from `KSPGetIterationNumber()`.
1030: If you provide a matrix that has a `MatSetNullSpace()` and `MatSetTransposeNullSpace()` this will use that information to solve singular systems
1031: in the least squares sense with a norm minimizing solution.
1033: A x = b where b = b_p + b_t where b_t is not in the range of A (and hence by the fundamental theorem of linear algebra is in the nullspace(A') see `MatSetNullSpace()`
1035: `KSP` first removes b_t producing the linear system A x = b_p (which has multiple solutions) and solves this to find the ||x|| minimizing solution (and hence
1036: it finds the solution x orthogonal to the nullspace(A). The algorithm is simply in each iteration of the Krylov method we remove the nullspace(A) from the search
1037: direction thus the solution which is a linear combination of the search directions has no component in the nullspace(A).
1039: We recommend always using `KSPGMRES` for such singular systems.
1040: If nullspace(A) = nullspace(A') (note symmetric matrices always satisfy this property) then both left and right preconditioning will work
1041: If nullspace(A) != nullspace(A') then left preconditioning will work but right preconditioning may not work (or it may).
1043: Developer Note: The reason we cannot always solve nullspace(A) != nullspace(A') systems with right preconditioning is because we need to remove at each iteration
1044: the nullspace(AB) from the search direction. While we know the nullspace(A) the nullspace(AB) equals B^-1 times the nullspace(A) but except for trivial preconditioners
1045: such as diagonal scaling we cannot apply the inverse of the preconditioner to a vector and thus cannot compute the nullspace(AB).
1047: If using a direct method (e.g., via the `KSP` solver
1048: `KSPPREONLY` and a preconditioner such as `PCLU` or `PCILU`,
1049: then its=1. See `KSPSetTolerances()` and `KSPConvergedDefault()`
1050: for more details.
1052: Understanding Convergence:
1053: The routines `KSPMonitorSet()`, `KSPComputeEigenvalues()`, and
1054: `KSPComputeEigenvaluesExplicitly()` provide information on additional
1055: options to monitor convergence and print eigenvalue information.
1057: Level: beginner
1059: .seealso: [](chapter_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPDestroy()`, `KSPSetTolerances()`, `KSPConvergedDefault()`,
1060: `KSPSolveTranspose()`, `KSPGetIterationNumber()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatSetTransposeNullSpace()`, `KSP`,
1061: `KSPConvergedReasonView()`, `KSPCheckSolve()`, `KSPSetErrorIfNotConverged()`
1062: @*/
1063: PetscErrorCode KSPSolve(KSP ksp, Vec b, Vec x)
1064: {
1065: PetscFunctionBegin;
1069: ksp->transpose_solve = PETSC_FALSE;
1070: PetscCall(KSPSolve_Private(ksp, b, x));
1071: PetscFunctionReturn(PETSC_SUCCESS);
1072: }
1074: /*@
1075: KSPSolveTranspose - Solves a linear system with the transposed matrix.
1077: Collective
1079: Input Parameters:
1080: + ksp - iterative context obtained from `KSPCreate()`
1081: . b - right hand side vector
1082: - x - solution vector
1084: Notes:
1085: For complex numbers this solve the non-Hermitian transpose system.
1087: Level: developer
1089: Developer Notes:
1090: We need to implement a `KSPSolveHermitianTranspose()`
1092: .seealso: [](chapter_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPDestroy()`, `KSPSetTolerances()`, `KSPConvergedDefault()`,
1093: `KSPSolve()`, `KSP`
1094: @*/
1095: PetscErrorCode KSPSolveTranspose(KSP ksp, Vec b, Vec x)
1096: {
1097: PetscFunctionBegin;
1101: if (ksp->transpose.use_explicittranspose) {
1102: Mat J, Jpre;
1103: PetscCall(KSPGetOperators(ksp, &J, &Jpre));
1104: if (!ksp->transpose.reuse_transpose) {
1105: PetscCall(MatTranspose(J, MAT_INITIAL_MATRIX, &ksp->transpose.AT));
1106: if (J != Jpre) PetscCall(MatTranspose(Jpre, MAT_INITIAL_MATRIX, &ksp->transpose.BT));
1107: ksp->transpose.reuse_transpose = PETSC_TRUE;
1108: } else {
1109: PetscCall(MatTranspose(J, MAT_REUSE_MATRIX, &ksp->transpose.AT));
1110: if (J != Jpre) PetscCall(MatTranspose(Jpre, MAT_REUSE_MATRIX, &ksp->transpose.BT));
1111: }
1112: if (J == Jpre && ksp->transpose.BT != ksp->transpose.AT) {
1113: PetscCall(PetscObjectReference((PetscObject)ksp->transpose.AT));
1114: ksp->transpose.BT = ksp->transpose.AT;
1115: }
1116: PetscCall(KSPSetOperators(ksp, ksp->transpose.AT, ksp->transpose.BT));
1117: } else {
1118: ksp->transpose_solve = PETSC_TRUE;
1119: }
1120: PetscCall(KSPSolve_Private(ksp, b, x));
1121: PetscFunctionReturn(PETSC_SUCCESS);
1122: }
1124: static PetscErrorCode KSPViewFinalMatResidual_Internal(KSP ksp, Mat B, Mat X, PetscViewer viewer, PetscViewerFormat format, PetscInt shift)
1125: {
1126: Mat A, R;
1127: PetscReal *norms;
1128: PetscInt i, N;
1129: PetscBool flg;
1131: PetscFunctionBegin;
1132: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &flg));
1133: if (flg) {
1134: PetscCall(PCGetOperators(ksp->pc, &A, NULL));
1135: if (!ksp->transpose_solve) PetscCall(MatMatMult(A, X, MAT_INITIAL_MATRIX, PETSC_DEFAULT, &R));
1136: else PetscCall(MatTransposeMatMult(A, X, MAT_INITIAL_MATRIX, PETSC_DEFAULT, &R));
1137: PetscCall(MatAYPX(R, -1.0, B, SAME_NONZERO_PATTERN));
1138: PetscCall(MatGetSize(R, NULL, &N));
1139: PetscCall(PetscMalloc1(N, &norms));
1140: PetscCall(MatGetColumnNorms(R, NORM_2, norms));
1141: PetscCall(MatDestroy(&R));
1142: for (i = 0; i < N; ++i) PetscCall(PetscViewerASCIIPrintf(viewer, "%s #%" PetscInt_FMT " %g\n", i == 0 ? "KSP final norm of residual" : " ", shift + i, (double)norms[i]));
1143: PetscCall(PetscFree(norms));
1144: }
1145: PetscFunctionReturn(PETSC_SUCCESS);
1146: }
1148: PetscErrorCode KSPMatSolve_Private(KSP ksp, Mat B, Mat X)
1149: {
1150: Mat A, P, vB, vX;
1151: Vec cb, cx;
1152: PetscInt n1, N1, n2, N2, Bbn = PETSC_DECIDE;
1153: PetscBool match;
1155: PetscFunctionBegin;
1159: PetscCheckSameComm(ksp, 1, B, 2);
1160: PetscCheckSameComm(ksp, 1, X, 3);
1161: PetscCheckSameType(B, 2, X, 3);
1162: PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
1163: MatCheckPreallocated(X, 3);
1164: if (!X->assembled) {
1165: PetscCall(MatSetOption(X, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
1166: PetscCall(MatAssemblyBegin(X, MAT_FINAL_ASSEMBLY));
1167: PetscCall(MatAssemblyEnd(X, MAT_FINAL_ASSEMBLY));
1168: }
1169: PetscCheck(B != X, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_IDN, "B and X must be different matrices");
1170: PetscCheck(!ksp->transpose_solve || !ksp->transpose.use_explicittranspose, PetscObjectComm((PetscObject)ksp), PETSC_ERR_SUP, "KSPMatSolveTranspose() does not support -ksp_use_explicittranspose");
1171: PetscCall(KSPGetOperators(ksp, &A, &P));
1172: PetscCall(MatGetLocalSize(B, NULL, &n2));
1173: PetscCall(MatGetLocalSize(X, NULL, &n1));
1174: PetscCall(MatGetSize(B, NULL, &N2));
1175: PetscCall(MatGetSize(X, NULL, &N1));
1176: PetscCheck(n1 == n2 && N1 == N2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible number of columns between block of right-hand sides (n,N) = (%" PetscInt_FMT ",%" PetscInt_FMT ") and block of solutions (n,N) = (%" PetscInt_FMT ",%" PetscInt_FMT ")", n2, N2, n1, N1);
1177: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)B, &match, MATSEQDENSE, MATMPIDENSE, ""));
1178: PetscCheck(match, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Provided block of right-hand sides not stored in a dense Mat");
1179: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)X, &match, MATSEQDENSE, MATMPIDENSE, ""));
1180: PetscCheck(match, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Provided block of solutions not stored in a dense Mat");
1181: PetscCall(KSPSetUp(ksp));
1182: PetscCall(KSPSetUpOnBlocks(ksp));
1183: if (ksp->ops->matsolve) {
1184: if (ksp->guess_zero) PetscCall(MatZeroEntries(X));
1185: PetscCall(PetscLogEventBegin(!ksp->transpose_solve ? KSP_MatSolve : KSP_MatSolveTranspose, ksp, B, X, 0));
1186: PetscCall(KSPGetMatSolveBatchSize(ksp, &Bbn));
1187: /* by default, do a single solve with all columns */
1188: if (Bbn == PETSC_DECIDE) Bbn = N2;
1189: else PetscCheck(Bbn >= 1, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "KSPMatSolve() batch size %" PetscInt_FMT " must be positive", Bbn);
1190: PetscCall(PetscInfo(ksp, "KSP type %s solving using batches of width at most %" PetscInt_FMT "\n", ((PetscObject)ksp)->type_name, Bbn));
1191: /* if -ksp_matsolve_batch_size is greater than the actual number of columns, do a single solve with all columns */
1192: if (Bbn >= N2) {
1193: PetscUseTypeMethod(ksp, matsolve, B, X);
1194: if (ksp->viewFinalRes) PetscCall(KSPViewFinalMatResidual_Internal(ksp, B, X, ksp->viewerFinalRes, ksp->formatFinalRes, 0));
1196: PetscCall(KSPConvergedReasonViewFromOptions(ksp));
1198: if (ksp->viewRate) {
1199: PetscCall(PetscViewerPushFormat(ksp->viewerRate, PETSC_VIEWER_DEFAULT));
1200: PetscCall(KSPConvergedRateView(ksp, ksp->viewerRate));
1201: PetscCall(PetscViewerPopFormat(ksp->viewerRate));
1202: }
1203: } else {
1204: for (n2 = 0; n2 < N2; n2 += Bbn) {
1205: PetscCall(MatDenseGetSubMatrix(B, PETSC_DECIDE, PETSC_DECIDE, n2, PetscMin(n2 + Bbn, N2), &vB));
1206: PetscCall(MatDenseGetSubMatrix(X, PETSC_DECIDE, PETSC_DECIDE, n2, PetscMin(n2 + Bbn, N2), &vX));
1207: PetscUseTypeMethod(ksp, matsolve, vB, vX);
1208: if (ksp->viewFinalRes) PetscCall(KSPViewFinalMatResidual_Internal(ksp, vB, vX, ksp->viewerFinalRes, ksp->formatFinalRes, n2));
1210: PetscCall(KSPConvergedReasonViewFromOptions(ksp));
1212: if (ksp->viewRate) {
1213: PetscCall(PetscViewerPushFormat(ksp->viewerRate, PETSC_VIEWER_DEFAULT));
1214: PetscCall(KSPConvergedRateView(ksp, ksp->viewerRate));
1215: PetscCall(PetscViewerPopFormat(ksp->viewerRate));
1216: }
1217: PetscCall(MatDenseRestoreSubMatrix(B, &vB));
1218: PetscCall(MatDenseRestoreSubMatrix(X, &vX));
1219: }
1220: }
1221: if (ksp->viewMat) PetscCall(ObjectView((PetscObject)A, ksp->viewerMat, ksp->formatMat));
1222: if (ksp->viewPMat) PetscCall(ObjectView((PetscObject)P, ksp->viewerPMat, ksp->formatPMat));
1223: if (ksp->viewRhs) PetscCall(ObjectView((PetscObject)B, ksp->viewerRhs, ksp->formatRhs));
1224: if (ksp->viewSol) PetscCall(ObjectView((PetscObject)X, ksp->viewerSol, ksp->formatSol));
1225: if (ksp->view) PetscCall(KSPView(ksp, ksp->viewer));
1226: PetscCall(PetscLogEventEnd(!ksp->transpose_solve ? KSP_MatSolve : KSP_MatSolveTranspose, ksp, B, X, 0));
1227: } else {
1228: PetscCall(PetscInfo(ksp, "KSP type %s solving column by column\n", ((PetscObject)ksp)->type_name));
1229: for (n2 = 0; n2 < N2; ++n2) {
1230: PetscCall(MatDenseGetColumnVecRead(B, n2, &cb));
1231: PetscCall(MatDenseGetColumnVecWrite(X, n2, &cx));
1232: PetscCall(KSPSolve_Private(ksp, cb, cx));
1233: PetscCall(MatDenseRestoreColumnVecWrite(X, n2, &cx));
1234: PetscCall(MatDenseRestoreColumnVecRead(B, n2, &cb));
1235: }
1236: }
1237: PetscFunctionReturn(PETSC_SUCCESS);
1238: }
1240: /*@
1241: KSPMatSolve - Solves a linear system with multiple right-hand sides stored as a `MATDENSE`. Unlike `KSPSolve()`, `B` and `X` must be different matrices.
1243: Input Parameters:
1244: + ksp - iterative context
1245: - B - block of right-hand sides
1247: Output Parameter:
1248: . X - block of solutions
1250: Notes:
1251: This is a stripped-down version of `KSPSolve()`, which only handles `-ksp_view`, `-ksp_converged_reason`, `-ksp_converged_rate`, and `-ksp_view_final_residual`.
1253: Level: intermediate
1255: .seealso: [](chapter_ksp), `KSPSolve()`, `MatMatSolve()`, `KSPMatSolveTranspose()`, `MATDENSE`, `KSPHPDDM`, `PCBJACOBI`, `PCASM`
1256: @*/
1257: PetscErrorCode KSPMatSolve(KSP ksp, Mat B, Mat X)
1258: {
1259: PetscFunctionBegin;
1260: ksp->transpose_solve = PETSC_FALSE;
1261: PetscCall(KSPMatSolve_Private(ksp, B, X));
1262: PetscFunctionReturn(PETSC_SUCCESS);
1263: }
1265: /*@
1266: KSPMatSolveTranspose - Solves a linear system with the transposed matrix with multiple right-hand sides stored as a `MATDENSE`. Unlike `KSPSolveTranspose()`, `B` and `X` must be different matrices and the transposed matrix cannot be assembled explicitly for the user.
1268: Input Parameters:
1269: + ksp - iterative context
1270: - B - block of right-hand sides
1272: Output Parameter:
1273: . X - block of solutions
1275: Notes:
1276: This is a stripped-down version of `KSPSolveTranspose()`, which only handles `-ksp_view`, `-ksp_converged_reason`, `-ksp_converged_rate`, and `-ksp_view_final_residual`.
1278: Level: intermediate
1280: .seealso: [](chapter_ksp), `KSPSolveTranspose()`, `MatMatTransposeSolve()`, `KSPMatSolve()`, `MATDENSE`, `KSPHPDDM`, `PCBJACOBI`, `PCASM`
1281: @*/
1282: PetscErrorCode KSPMatSolveTranspose(KSP ksp, Mat B, Mat X)
1283: {
1284: PetscFunctionBegin;
1285: ksp->transpose_solve = PETSC_TRUE;
1286: PetscCall(KSPMatSolve_Private(ksp, B, X));
1287: PetscFunctionReturn(PETSC_SUCCESS);
1288: }
1290: /*@
1291: KSPSetMatSolveBatchSize - Sets the maximum number of columns treated simultaneously in `KSPMatSolve()`.
1293: Logically Collective
1295: Input Parameters:
1296: + ksp - iterative context
1297: - bs - batch size
1299: Level: advanced
1301: .seealso: [](chapter_ksp), `KSPMatSolve()`, `KSPGetMatSolveBatchSize()`, `-mat_mumps_icntl_27`, `-matmatmult_Bbn`
1302: @*/
1303: PetscErrorCode KSPSetMatSolveBatchSize(KSP ksp, PetscInt bs)
1304: {
1305: PetscFunctionBegin;
1308: ksp->nmax = bs;
1309: PetscFunctionReturn(PETSC_SUCCESS);
1310: }
1312: /*@
1313: KSPGetMatSolveBatchSize - Gets the maximum number of columns treated simultaneously in `KSPMatSolve()`.
1315: Input Parameter:
1316: . ksp - iterative context
1318: Output Parameter:
1319: . bs - batch size
1321: Level: advanced
1323: .seealso: [](chapter_ksp), `KSPMatSolve()`, `KSPSetMatSolveBatchSize()`, `-mat_mumps_icntl_27`, `-matmatmult_Bbn`
1324: @*/
1325: PetscErrorCode KSPGetMatSolveBatchSize(KSP ksp, PetscInt *bs)
1326: {
1327: PetscFunctionBegin;
1330: *bs = ksp->nmax;
1331: PetscFunctionReturn(PETSC_SUCCESS);
1332: }
1334: /*@
1335: KSPResetViewers - Resets all the viewers set from the options database during `KSPSetFromOptions()`
1337: Collective
1339: Input Parameter:
1340: . ksp - iterative context obtained from `KSPCreate()`
1342: Level: beginner
1344: .seealso: [](chapter_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPSolve()`, `KSPSetFromOptions()`, `KSP`
1345: @*/
1346: PetscErrorCode KSPResetViewers(KSP ksp)
1347: {
1348: PetscFunctionBegin;
1350: if (!ksp) PetscFunctionReturn(PETSC_SUCCESS);
1351: PetscCall(PetscViewerDestroy(&ksp->viewer));
1352: PetscCall(PetscViewerDestroy(&ksp->viewerPre));
1353: PetscCall(PetscViewerDestroy(&ksp->viewerRate));
1354: PetscCall(PetscViewerDestroy(&ksp->viewerMat));
1355: PetscCall(PetscViewerDestroy(&ksp->viewerPMat));
1356: PetscCall(PetscViewerDestroy(&ksp->viewerRhs));
1357: PetscCall(PetscViewerDestroy(&ksp->viewerSol));
1358: PetscCall(PetscViewerDestroy(&ksp->viewerMatExp));
1359: PetscCall(PetscViewerDestroy(&ksp->viewerEV));
1360: PetscCall(PetscViewerDestroy(&ksp->viewerSV));
1361: PetscCall(PetscViewerDestroy(&ksp->viewerEVExp));
1362: PetscCall(PetscViewerDestroy(&ksp->viewerFinalRes));
1363: PetscCall(PetscViewerDestroy(&ksp->viewerPOpExp));
1364: PetscCall(PetscViewerDestroy(&ksp->viewerDScale));
1365: ksp->view = PETSC_FALSE;
1366: ksp->viewPre = PETSC_FALSE;
1367: ksp->viewMat = PETSC_FALSE;
1368: ksp->viewPMat = PETSC_FALSE;
1369: ksp->viewRhs = PETSC_FALSE;
1370: ksp->viewSol = PETSC_FALSE;
1371: ksp->viewMatExp = PETSC_FALSE;
1372: ksp->viewEV = PETSC_FALSE;
1373: ksp->viewSV = PETSC_FALSE;
1374: ksp->viewEVExp = PETSC_FALSE;
1375: ksp->viewFinalRes = PETSC_FALSE;
1376: ksp->viewPOpExp = PETSC_FALSE;
1377: ksp->viewDScale = PETSC_FALSE;
1378: PetscFunctionReturn(PETSC_SUCCESS);
1379: }
1381: /*@
1382: KSPReset - Resets a `KSP` context to the kspsetupcalled = 0 state and removes any allocated Vecs and Mats
1384: Collective
1386: Input Parameter:
1387: . ksp - iterative context obtained from `KSPCreate()`
1389: Level: beginner
1391: .seealso: [](chapter_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPSolve()`, `KSP`
1392: @*/
1393: PetscErrorCode KSPReset(KSP ksp)
1394: {
1395: PetscFunctionBegin;
1397: if (!ksp) PetscFunctionReturn(PETSC_SUCCESS);
1398: PetscTryTypeMethod(ksp, reset);
1399: if (ksp->pc) PetscCall(PCReset(ksp->pc));
1400: if (ksp->guess) {
1401: KSPGuess guess = ksp->guess;
1402: PetscTryTypeMethod(guess, reset);
1403: }
1404: PetscCall(VecDestroyVecs(ksp->nwork, &ksp->work));
1405: PetscCall(VecDestroy(&ksp->vec_rhs));
1406: PetscCall(VecDestroy(&ksp->vec_sol));
1407: PetscCall(VecDestroy(&ksp->diagonal));
1408: PetscCall(VecDestroy(&ksp->truediagonal));
1410: PetscCall(KSPResetViewers(ksp));
1412: ksp->setupstage = KSP_SETUP_NEW;
1413: ksp->nmax = PETSC_DECIDE;
1414: PetscFunctionReturn(PETSC_SUCCESS);
1415: }
1417: /*@C
1418: KSPDestroy - Destroys `KSP` context.
1420: Collective
1422: Input Parameter:
1423: . ksp - iterative context obtained from `KSPCreate()`
1425: Level: beginner
1427: .seealso: [](chapter_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPSolve()`, `KSP`
1428: @*/
1429: PetscErrorCode KSPDestroy(KSP *ksp)
1430: {
1431: PC pc;
1433: PetscFunctionBegin;
1434: if (!*ksp) PetscFunctionReturn(PETSC_SUCCESS);
1436: if (--((PetscObject)(*ksp))->refct > 0) {
1437: *ksp = NULL;
1438: PetscFunctionReturn(PETSC_SUCCESS);
1439: }
1441: PetscCall(PetscObjectSAWsViewOff((PetscObject)*ksp));
1443: /*
1444: Avoid a cascading call to PCReset(ksp->pc) from the following call:
1445: PCReset() shouldn't be called from KSPDestroy() as it is unprotected by pc's
1446: refcount (and may be shared, e.g., by other ksps).
1447: */
1448: pc = (*ksp)->pc;
1449: (*ksp)->pc = NULL;
1450: PetscCall(KSPReset((*ksp)));
1451: (*ksp)->pc = pc;
1452: PetscTryTypeMethod((*ksp), destroy);
1454: if ((*ksp)->transpose.use_explicittranspose) {
1455: PetscCall(MatDestroy(&(*ksp)->transpose.AT));
1456: PetscCall(MatDestroy(&(*ksp)->transpose.BT));
1457: (*ksp)->transpose.reuse_transpose = PETSC_FALSE;
1458: }
1460: PetscCall(KSPGuessDestroy(&(*ksp)->guess));
1461: PetscCall(DMDestroy(&(*ksp)->dm));
1462: PetscCall(PCDestroy(&(*ksp)->pc));
1463: PetscCall(PetscFree((*ksp)->res_hist_alloc));
1464: PetscCall(PetscFree((*ksp)->err_hist_alloc));
1465: if ((*ksp)->convergeddestroy) PetscCall((*(*ksp)->convergeddestroy)((*ksp)->cnvP));
1466: PetscCall(KSPMonitorCancel((*ksp)));
1467: PetscCall(KSPConvergedReasonViewCancel((*ksp)));
1468: PetscCall(PetscHeaderDestroy(ksp));
1469: PetscFunctionReturn(PETSC_SUCCESS);
1470: }
1472: /*@
1473: KSPSetPCSide - Sets the preconditioning side.
1475: Logically Collective
1477: Input Parameter:
1478: . ksp - iterative context obtained from `KSPCreate()`
1480: Output Parameter:
1481: . side - the preconditioning side, where side is one of
1482: .vb
1483: PC_LEFT - left preconditioning (default)
1484: PC_RIGHT - right preconditioning
1485: PC_SYMMETRIC - symmetric preconditioning
1486: .ve
1488: Options Database Key:
1489: . -ksp_pc_side <right,left,symmetric> - `KSP` preconditioner side
1491: Notes:
1492: Left preconditioning is used by default for most Krylov methods except KSPFGMRES which only supports right preconditioning.
1494: For methods changing the side of the preconditioner changes the norm type that is used, see `KSPSetNormType()`.
1496: Symmetric preconditioning is currently available only for the KSPQCG method. Note, however, that
1497: symmetric preconditioning can be emulated by using either right or left
1498: preconditioning and a pre or post processing step.
1500: Setting the PC side often affects the default norm type. See `KSPSetNormType()` for details.
1502: Level: intermediate
1504: .seealso: [](chapter_ksp), `KSPGetPCSide()`, `KSPSetNormType()`, `KSPGetNormType()`, `KSP`
1505: @*/
1506: PetscErrorCode KSPSetPCSide(KSP ksp, PCSide side)
1507: {
1508: PetscFunctionBegin;
1511: ksp->pc_side = ksp->pc_side_set = side;
1512: PetscFunctionReturn(PETSC_SUCCESS);
1513: }
1515: /*@
1516: KSPGetPCSide - Gets the preconditioning side.
1518: Not Collective
1520: Input Parameter:
1521: . ksp - iterative context obtained from `KSPCreate()`
1523: Output Parameter:
1524: . side - the preconditioning side, where side is one of
1525: .vb
1526: PC_LEFT - left preconditioning (default)
1527: PC_RIGHT - right preconditioning
1528: PC_SYMMETRIC - symmetric preconditioning
1529: .ve
1531: Level: intermediate
1533: .seealso: [](chapter_ksp), `KSPSetPCSide()`, `KSP`
1534: @*/
1535: PetscErrorCode KSPGetPCSide(KSP ksp, PCSide *side)
1536: {
1537: PetscFunctionBegin;
1540: PetscCall(KSPSetUpNorms_Private(ksp, PETSC_TRUE, &ksp->normtype, &ksp->pc_side));
1541: *side = ksp->pc_side;
1542: PetscFunctionReturn(PETSC_SUCCESS);
1543: }
1545: /*@
1546: KSPGetTolerances - Gets the relative, absolute, divergence, and maximum
1547: iteration tolerances used by the default `KSP` convergence tests.
1549: Not Collective
1551: Input Parameter:
1552: . ksp - the Krylov subspace context
1554: Output Parameters:
1555: + rtol - the relative convergence tolerance
1556: . abstol - the absolute convergence tolerance
1557: . dtol - the divergence tolerance
1558: - maxits - maximum number of iterations
1560: Notes:
1561: The user can specify `NULL` for any parameter that is not needed.
1563: Level: intermediate
1565: maximum, iterations
1567: .seealso: [](chapter_ksp), `KSPSetTolerances()`, `KSP`
1568: @*/
1569: PetscErrorCode KSPGetTolerances(KSP ksp, PetscReal *rtol, PetscReal *abstol, PetscReal *dtol, PetscInt *maxits)
1570: {
1571: PetscFunctionBegin;
1573: if (abstol) *abstol = ksp->abstol;
1574: if (rtol) *rtol = ksp->rtol;
1575: if (dtol) *dtol = ksp->divtol;
1576: if (maxits) *maxits = ksp->max_it;
1577: PetscFunctionReturn(PETSC_SUCCESS);
1578: }
1580: /*@
1581: KSPSetTolerances - Sets the relative, absolute, divergence, and maximum
1582: iteration tolerances used by the default `KSP` convergence testers.
1584: Logically Collective
1586: Input Parameters:
1587: + ksp - the Krylov subspace context
1588: . rtol - the relative convergence tolerance, relative decrease in the (possibly preconditioned) residual norm
1589: . abstol - the absolute convergence tolerance absolute size of the (possibly preconditioned) residual norm
1590: . dtol - the divergence tolerance, amount (possibly preconditioned) residual norm can increase before `KSPConvergedDefault()` concludes that the method is diverging
1591: - maxits - maximum number of iterations to use
1593: Options Database Keys:
1594: + -ksp_atol <abstol> - Sets abstol
1595: . -ksp_rtol <rtol> - Sets rtol
1596: . -ksp_divtol <dtol> - Sets dtol
1597: - -ksp_max_it <maxits> - Sets maxits
1599: Level: intermediate
1601: Notes:
1602: Use `PETSC_DEFAULT` to retain the default value of any of the tolerances.
1604: See `KSPConvergedDefault()` for details how these parameters are used in the default convergence test. See also `KSPSetConvergenceTest()`
1605: for setting user-defined stopping criteria.
1607: .seealso: [](chapter_ksp), `KSPGetTolerances()`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSP`
1608: @*/
1609: PetscErrorCode KSPSetTolerances(KSP ksp, PetscReal rtol, PetscReal abstol, PetscReal dtol, PetscInt maxits)
1610: {
1611: PetscFunctionBegin;
1618: if (rtol != (PetscReal)PETSC_DEFAULT) {
1619: PetscCheck(rtol >= 0.0 && rtol < 1.0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Relative tolerance %g must be non-negative and less than 1.0", (double)rtol);
1620: ksp->rtol = rtol;
1621: }
1622: if (abstol != (PetscReal)PETSC_DEFAULT) {
1623: PetscCheck(abstol >= 0.0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Absolute tolerance %g must be non-negative", (double)abstol);
1624: ksp->abstol = abstol;
1625: }
1626: if (dtol != (PetscReal)PETSC_DEFAULT) {
1627: PetscCheck(dtol >= 0.0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Divergence tolerance %g must be larger than 1.0", (double)dtol);
1628: ksp->divtol = dtol;
1629: }
1630: if (maxits != PETSC_DEFAULT) {
1631: PetscCheck(maxits >= 0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Maximum number of iterations %" PetscInt_FMT " must be non-negative", maxits);
1632: ksp->max_it = maxits;
1633: }
1634: PetscFunctionReturn(PETSC_SUCCESS);
1635: }
1637: /*@
1638: KSPSetInitialGuessNonzero - Tells the iterative solver that the
1639: initial guess is nonzero; otherwise `KSP` assumes the initial guess
1640: is to be zero (and thus zeros it out before solving).
1642: Logically Collective
1644: Input Parameters:
1645: + ksp - iterative context obtained from `KSPCreate()`
1646: - flg - ``PETSC_TRUE`` indicates the guess is non-zero, `PETSC_FALSE` indicates the guess is zero
1648: Options Database Key:
1649: . -ksp_initial_guess_nonzero <true,false> - use nonzero initial guess
1651: Level: beginner
1653: Notes:
1654: If this is not called the X vector is zeroed in the call to `KSPSolve()`.
1656: .seealso: [](chapter_ksp), `KSPGetInitialGuessNonzero()`, `KSPSetGuessType()`, `KSPGuessType`, `KSP`
1657: @*/
1658: PetscErrorCode KSPSetInitialGuessNonzero(KSP ksp, PetscBool flg)
1659: {
1660: PetscFunctionBegin;
1663: ksp->guess_zero = (PetscBool) !(int)flg;
1664: PetscFunctionReturn(PETSC_SUCCESS);
1665: }
1667: /*@
1668: KSPGetInitialGuessNonzero - Determines whether the `KSP` solver is using
1669: a zero initial guess.
1671: Not Collective
1673: Input Parameter:
1674: . ksp - iterative context obtained from `KSPCreate()`
1676: Output Parameter:
1677: . flag - `PETSC_TRUE` if guess is nonzero, else `PETSC_FALSE`
1679: Level: intermediate
1681: .seealso: [](chapter_ksp), `KSPSetInitialGuessNonzero()`, `KSP`
1682: @*/
1683: PetscErrorCode KSPGetInitialGuessNonzero(KSP ksp, PetscBool *flag)
1684: {
1685: PetscFunctionBegin;
1688: if (ksp->guess_zero) *flag = PETSC_FALSE;
1689: else *flag = PETSC_TRUE;
1690: PetscFunctionReturn(PETSC_SUCCESS);
1691: }
1693: /*@
1694: KSPSetErrorIfNotConverged - Causes `KSPSolve()` to generate an error if the solver has not converged as soon as the error is detected.
1696: Logically Collective
1698: Input Parameters:
1699: + ksp - iterative context obtained from `KSPCreate()`
1700: - flg - `PETSC_TRUE` indicates you want the error generated
1702: Options Database Key:
1703: . -ksp_error_if_not_converged <true,false> - generate an error and stop the program
1705: Level: intermediate
1707: Notes:
1708: Normally PETSc continues if a linear solver fails to converge, you can call `KSPGetConvergedReason()` after a `KSPSolve()`
1709: to determine if it has converged.
1711: A `KSP_DIVERGED_ITS` will not generate an error in a `KSPSolve()` inside a nested linear solver
1713: .seealso: [](chapter_ksp), `KSPGetErrorIfNotConverged()`, `KSP`
1714: @*/
1715: PetscErrorCode KSPSetErrorIfNotConverged(KSP ksp, PetscBool flg)
1716: {
1717: PetscFunctionBegin;
1720: ksp->errorifnotconverged = flg;
1721: PetscFunctionReturn(PETSC_SUCCESS);
1722: }
1724: /*@
1725: KSPGetErrorIfNotConverged - Will `KSPSolve()` generate an error if the solver does not converge?
1727: Not Collective
1729: Input Parameter:
1730: . ksp - iterative context obtained from KSPCreate()
1732: Output Parameter:
1733: . flag - `PETSC_TRUE` if it will generate an error, else `PETSC_FALSE`
1735: Level: intermediate
1737: .seealso: [](chapter_ksp), `KSPSetErrorIfNotConverged()`, `KSP`
1738: @*/
1739: PetscErrorCode KSPGetErrorIfNotConverged(KSP ksp, PetscBool *flag)
1740: {
1741: PetscFunctionBegin;
1744: *flag = ksp->errorifnotconverged;
1745: PetscFunctionReturn(PETSC_SUCCESS);
1746: }
1748: /*@
1749: KSPSetInitialGuessKnoll - Tells the iterative solver to use `PCApply()` to compute the initial guess (The Knoll trick)
1751: Logically Collective
1753: Input Parameters:
1754: + ksp - iterative context obtained from `KSPCreate()`
1755: - flg - `PETSC_TRUE` or `PETSC_FALSE`
1757: Level: advanced
1759: Developer Note:
1760: The Knoll trick is not currently implemented using the `KSPGuess` class
1762: .seealso: [](chapter_ksp), `KSPGetInitialGuessKnoll()`, `KSPSetInitialGuessNonzero()`, `KSPGetInitialGuessNonzero()`, `KSP`
1763: @*/
1764: PetscErrorCode KSPSetInitialGuessKnoll(KSP ksp, PetscBool flg)
1765: {
1766: PetscFunctionBegin;
1769: ksp->guess_knoll = flg;
1770: PetscFunctionReturn(PETSC_SUCCESS);
1771: }
1773: /*@
1774: KSPGetInitialGuessKnoll - Determines whether the `KSP` solver is using the Knoll trick (using PCApply(pc,b,...) to compute
1775: the initial guess
1777: Not Collective
1779: Input Parameter:
1780: . ksp - iterative context obtained from `KSPCreate()`
1782: Output Parameter:
1783: . flag - `PETSC_TRUE` if using Knoll trick, else `PETSC_FALSE`
1785: Level: advanced
1787: .seealso: [](chapter_ksp), `KSPSetInitialGuessKnoll()`, `KSPSetInitialGuessNonzero()`, `KSPGetInitialGuessNonzero()`, `KSP`
1788: @*/
1789: PetscErrorCode KSPGetInitialGuessKnoll(KSP ksp, PetscBool *flag)
1790: {
1791: PetscFunctionBegin;
1794: *flag = ksp->guess_knoll;
1795: PetscFunctionReturn(PETSC_SUCCESS);
1796: }
1798: /*@
1799: KSPGetComputeSingularValues - Gets the flag indicating whether the extreme singular
1800: values will be calculated via a Lanczos or Arnoldi process as the linear
1801: system is solved.
1803: Not Collective
1805: Input Parameter:
1806: . ksp - iterative context obtained from `KSPCreate()`
1808: Output Parameter:
1809: . flg - `PETSC_TRUE` or `PETSC_FALSE`
1811: Options Database Key:
1812: . -ksp_monitor_singular_value - Activates `KSPSetComputeSingularValues()`
1814: Notes:
1815: Currently this option is not valid for all iterative methods.
1817: Many users may just want to use the monitoring routine
1818: `KSPMonitorSingularValue()` (which can be set with option -ksp_monitor_singular_value)
1819: to print the singular values at each iteration of the linear solve.
1821: Level: advanced
1823: .seealso: [](chapter_ksp), `KSPComputeExtremeSingularValues()`, `KSPMonitorSingularValue()`, `KSP`
1824: @*/
1825: PetscErrorCode KSPGetComputeSingularValues(KSP ksp, PetscBool *flg)
1826: {
1827: PetscFunctionBegin;
1830: *flg = ksp->calc_sings;
1831: PetscFunctionReturn(PETSC_SUCCESS);
1832: }
1834: /*@
1835: KSPSetComputeSingularValues - Sets a flag so that the extreme singular
1836: values will be calculated via a Lanczos or Arnoldi process as the linear
1837: system is solved.
1839: Logically Collective
1841: Input Parameters:
1842: + ksp - iterative context obtained from `KSPCreate()`
1843: - flg - `PETSC_TRUE` or `PETSC_FALSE`
1845: Options Database Key:
1846: . -ksp_monitor_singular_value - Activates `KSPSetComputeSingularValues()`
1848: Level: advanced
1850: Notes:
1851: Currently this option is not valid for all iterative methods.
1853: Many users may just want to use the monitoring routine
1854: `KSPMonitorSingularValue()` (which can be set with option -ksp_monitor_singular_value)
1855: to print the singular values at each iteration of the linear solve.
1857: .seealso: [](chapter_ksp), `KSPComputeExtremeSingularValues()`, `KSPMonitorSingularValue()`, `KSP`
1858: @*/
1859: PetscErrorCode KSPSetComputeSingularValues(KSP ksp, PetscBool flg)
1860: {
1861: PetscFunctionBegin;
1864: ksp->calc_sings = flg;
1865: PetscFunctionReturn(PETSC_SUCCESS);
1866: }
1868: /*@
1869: KSPGetComputeEigenvalues - Gets the flag indicating that the extreme eigenvalues
1870: values will be calculated via a Lanczos or Arnoldi process as the linear
1871: system is solved.
1873: Not Collective
1875: Input Parameter:
1876: . ksp - iterative context obtained from `KSPCreate()`
1878: Output Parameter:
1879: . flg - `PETSC_TRUE` or `PETSC_FALSE`
1881: Level: advanced
1883: Note:
1884: Currently this option is not valid for all iterative methods.
1886: .seealso: [](chapter_ksp), `KSPComputeEigenvalues()`, `KSPComputeEigenvaluesExplicitly()`, `KSP`
1887: @*/
1888: PetscErrorCode KSPGetComputeEigenvalues(KSP ksp, PetscBool *flg)
1889: {
1890: PetscFunctionBegin;
1893: *flg = ksp->calc_sings;
1894: PetscFunctionReturn(PETSC_SUCCESS);
1895: }
1897: /*@
1898: KSPSetComputeEigenvalues - Sets a flag so that the extreme eigenvalues
1899: values will be calculated via a Lanczos or Arnoldi process as the linear
1900: system is solved.
1902: Logically Collective
1904: Input Parameters:
1905: + ksp - iterative context obtained from `KSPCreate()`
1906: - flg - `PETSC_TRUE` or `PETSC_FALSE`
1908: Level: advanced
1910: Note:
1911: Currently this option is not valid for all iterative methods.
1913: .seealso: [](chapter_ksp), `KSPComputeEigenvalues()`, `KSPComputeEigenvaluesExplicitly()`, `KSP`
1914: @*/
1915: PetscErrorCode KSPSetComputeEigenvalues(KSP ksp, PetscBool flg)
1916: {
1917: PetscFunctionBegin;
1920: ksp->calc_sings = flg;
1921: PetscFunctionReturn(PETSC_SUCCESS);
1922: }
1924: /*@
1925: KSPSetComputeRitz - Sets a flag so that the Ritz or harmonic Ritz pairs
1926: will be calculated via a Lanczos or Arnoldi process as the linear
1927: system is solved.
1929: Logically Collective
1931: Input Parameters:
1932: + ksp - iterative context obtained from `KSPCreate()`
1933: - flg - `PETSC_TRUE` or `PETSC_FALSE`
1935: Level: advanced
1937: Note:
1938: Currently this option is only valid for the GMRES method.
1940: .seealso: [](chapter_ksp), `KSPComputeRitz()`, `KSP`
1941: @*/
1942: PetscErrorCode KSPSetComputeRitz(KSP ksp, PetscBool flg)
1943: {
1944: PetscFunctionBegin;
1947: ksp->calc_ritz = flg;
1948: PetscFunctionReturn(PETSC_SUCCESS);
1949: }
1951: /*@
1952: KSPGetRhs - Gets the right-hand-side vector for the linear system to
1953: be solved.
1955: Not Collective
1957: Input Parameter:
1958: . ksp - iterative context obtained from `KSPCreate()`
1960: Output Parameter:
1961: . r - right-hand-side vector
1963: Level: developer
1965: .seealso: [](chapter_ksp), `KSPGetSolution()`, `KSPSolve()`, `KSP`
1966: @*/
1967: PetscErrorCode KSPGetRhs(KSP ksp, Vec *r)
1968: {
1969: PetscFunctionBegin;
1972: *r = ksp->vec_rhs;
1973: PetscFunctionReturn(PETSC_SUCCESS);
1974: }
1976: /*@
1977: KSPGetSolution - Gets the location of the solution for the
1978: linear system to be solved. Note that this may not be where the solution
1979: is stored during the iterative process; see `KSPBuildSolution()`.
1981: Not Collective
1983: Input Parameter:
1984: . ksp - iterative context obtained from `KSPCreate()`
1986: Output Parameter:
1987: . v - solution vector
1989: Level: developer
1991: .seealso: [](chapter_ksp), `KSPGetRhs()`, `KSPBuildSolution()`, `KSPSolve()`, `KSP`
1992: @*/
1993: PetscErrorCode KSPGetSolution(KSP ksp, Vec *v)
1994: {
1995: PetscFunctionBegin;
1998: *v = ksp->vec_sol;
1999: PetscFunctionReturn(PETSC_SUCCESS);
2000: }
2002: /*@
2003: KSPSetPC - Sets the preconditioner to be used to calculate the
2004: application of the preconditioner on a vector.
2006: Collective
2008: Input Parameters:
2009: + ksp - iterative context obtained from `KSPCreate()`
2010: - pc - the preconditioner object (can be `NULL`)
2012: Level: developer
2014: Note:
2015: Use `KSPGetPC()` to retrieve the preconditioner context.
2017: .seealso: [](chapter_ksp), `KSPGetPC()`, `KSP`
2018: @*/
2019: PetscErrorCode KSPSetPC(KSP ksp, PC pc)
2020: {
2021: PetscFunctionBegin;
2023: if (pc) {
2025: PetscCheckSameComm(ksp, 1, pc, 2);
2026: }
2027: PetscCall(PetscObjectReference((PetscObject)pc));
2028: PetscCall(PCDestroy(&ksp->pc));
2029: ksp->pc = pc;
2030: PetscFunctionReturn(PETSC_SUCCESS);
2031: }
2033: /*@
2034: KSPGetPC - Returns a pointer to the preconditioner context
2035: set with `KSPSetPC()`.
2037: Not Collective
2039: Input Parameter:
2040: . ksp - iterative context obtained from `KSPCreate()`
2042: Output Parameter:
2043: . pc - preconditioner context
2045: Level: developer
2047: .seealso: [](chapter_ksp), `KSPSetPC()`, `KSP`
2048: @*/
2049: PetscErrorCode KSPGetPC(KSP ksp, PC *pc)
2050: {
2051: PetscFunctionBegin;
2054: if (!ksp->pc) {
2055: PetscCall(PCCreate(PetscObjectComm((PetscObject)ksp), &ksp->pc));
2056: PetscCall(PetscObjectIncrementTabLevel((PetscObject)ksp->pc, (PetscObject)ksp, 0));
2057: PetscCall(PetscObjectSetOptions((PetscObject)ksp->pc, ((PetscObject)ksp)->options));
2058: }
2059: *pc = ksp->pc;
2060: PetscFunctionReturn(PETSC_SUCCESS);
2061: }
2063: /*@
2064: KSPMonitor - runs the user provided monitor routines, if they exist
2066: Collective
2068: Input Parameters:
2069: + ksp - iterative context obtained from `KSPCreate()`
2070: . it - iteration number
2071: - rnorm - relative norm of the residual
2073: Level: developer
2075: Notes:
2076: This routine is called by the `KSP` implementations.
2077: It does not typically need to be called by the user.
2079: .seealso: [](chapter_ksp), `KSPMonitorSet()`
2080: @*/
2081: PetscErrorCode KSPMonitor(KSP ksp, PetscInt it, PetscReal rnorm)
2082: {
2083: PetscInt i, n = ksp->numbermonitors;
2085: PetscFunctionBegin;
2086: for (i = 0; i < n; i++) PetscCall((*ksp->monitor[i])(ksp, it, rnorm, ksp->monitorcontext[i]));
2087: PetscFunctionReturn(PETSC_SUCCESS);
2088: }
2090: /*@C
2091: KSPMonitorSet - Sets an ADDITIONAL function to be called at every iteration to monitor
2092: the residual/error etc.
2094: Logically Collective
2096: Input Parameters:
2097: + ksp - iterative context obtained from `KSPCreate()`
2098: . monitor - pointer to function (if this is NULL, it turns off monitoring
2099: . mctx - [optional] context for private data for the
2100: monitor routine (use `NULL` if no context is desired)
2101: - monitordestroy - [optional] routine that frees monitor context
2102: (may be `NULL`)
2104: Calling Sequence of `monitor`:
2105: $ PetscErrorCode monitor(KSP ksp, PetscInt it, PetscReal rnorm, void *mctx)
2106: + ksp - iterative context obtained from `KSPCreate()`
2107: . it - iteration number
2108: . rnorm - (estimated) 2-norm of (preconditioned) residual
2109: - mctx - optional monitoring context, as set by `KSPMonitorSet()`
2111: Calling Sequence of `monitordestroy`:
2112: $ PetscErrorCode destroy(void *mctx)
2114: Options Database Keys:
2115: + -ksp_monitor - sets `KSPMonitorResidual()`
2116: . -ksp_monitor draw - sets `KSPMonitorResidualDraw()` and plots residual
2117: . -ksp_monitor draw::draw_lg - sets `KSPMonitorResidualDrawLG()` and plots residual
2118: . -ksp_monitor_pause_final - Pauses any graphics when the solve finishes (only works for internal monitors)
2119: . -ksp_monitor_true_residual - sets `KSPMonitorTrueResidual()`
2120: . -ksp_monitor_true_residual draw::draw_lg - sets `KSPMonitorTrueResidualDrawLG()` and plots residual
2121: . -ksp_monitor_max - sets `KSPMonitorTrueResidualMax()`
2122: . -ksp_monitor_singular_value - sets `KSPMonitorSingularValue()`
2123: - -ksp_monitor_cancel - cancels all monitors that have
2124: been hardwired into a code by
2125: calls to `KSPMonitorSet()`, but
2126: does not cancel those set via
2127: the options database.
2129: Level: beginner
2131: Notes:
2132: The default is to do nothing. To print the residual, or preconditioned
2133: residual if `KSPSetNormType`(ksp,`KSP_NORM_PRECONDITIONED`) was called, use
2134: `KSPMonitorResidual()` as the monitoring routine, with a `PETSCVIEWERASCII` as the
2135: context.
2137: Several different monitoring routines may be set by calling
2138: `KSPMonitorSet()` multiple times; all will be called in the
2139: order in which they were set.
2141: Fortran Notes:
2142: Only a single monitor function can be set for each `KSP` object
2144: .seealso: [](chapter_ksp), `KSPMonitorResidual()`, `KSPMonitorCancel()`, `KSP`
2145: @*/
2146: PetscErrorCode KSPMonitorSet(KSP ksp, PetscErrorCode (*monitor)(KSP, PetscInt, PetscReal, void *), void *mctx, PetscErrorCode (*monitordestroy)(void **))
2147: {
2148: PetscInt i;
2149: PetscBool identical;
2151: PetscFunctionBegin;
2153: for (i = 0; i < ksp->numbermonitors; i++) {
2154: PetscCall(PetscMonitorCompare((PetscErrorCode(*)(void))monitor, mctx, monitordestroy, (PetscErrorCode(*)(void))ksp->monitor[i], ksp->monitorcontext[i], ksp->monitordestroy[i], &identical));
2155: if (identical) PetscFunctionReturn(PETSC_SUCCESS);
2156: }
2157: PetscCheck(ksp->numbermonitors < MAXKSPMONITORS, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Too many KSP monitors set");
2158: ksp->monitor[ksp->numbermonitors] = monitor;
2159: ksp->monitordestroy[ksp->numbermonitors] = monitordestroy;
2160: ksp->monitorcontext[ksp->numbermonitors++] = (void *)mctx;
2161: PetscFunctionReturn(PETSC_SUCCESS);
2162: }
2164: /*@
2165: KSPMonitorCancel - Clears all monitors for a `KSP` object.
2167: Logically Collective
2169: Input Parameter:
2170: . ksp - iterative context obtained from `KSPCreate()`
2172: Options Database Key:
2173: . -ksp_monitor_cancel - Cancels all monitors that have been hardwired into a code by calls to `KSPMonitorSet()`, but does not cancel those set via the options database.
2175: Level: intermediate
2177: .seealso: [](chapter_ksp), `KSPMonitorResidual()`, `KSPMonitorSet()`, `KSP`
2178: @*/
2179: PetscErrorCode KSPMonitorCancel(KSP ksp)
2180: {
2181: PetscInt i;
2183: PetscFunctionBegin;
2185: for (i = 0; i < ksp->numbermonitors; i++) {
2186: if (ksp->monitordestroy[i]) PetscCall((*ksp->monitordestroy[i])(&ksp->monitorcontext[i]));
2187: }
2188: ksp->numbermonitors = 0;
2189: PetscFunctionReturn(PETSC_SUCCESS);
2190: }
2192: /*@C
2193: KSPGetMonitorContext - Gets the monitoring context, as set by `KSPMonitorSet()` for the FIRST monitor only.
2195: Not Collective
2197: Input Parameter:
2198: . ksp - iterative context obtained from `KSPCreate()`
2200: Output Parameter:
2201: . ctx - monitoring context
2203: Level: intermediate
2205: .seealso: [](chapter_ksp), `KSPMonitorResidual()`, `KSP`
2206: @*/
2207: PetscErrorCode KSPGetMonitorContext(KSP ksp, void *ctx)
2208: {
2209: PetscFunctionBegin;
2211: *(void **)ctx = ksp->monitorcontext[0];
2212: PetscFunctionReturn(PETSC_SUCCESS);
2213: }
2215: /*@
2216: KSPSetResidualHistory - Sets the array used to hold the residual history.
2217: If set, this array will contain the residual norms computed at each
2218: iteration of the solver.
2220: Not Collective
2222: Input Parameters:
2223: + ksp - iterative context obtained from `KSPCreate()`
2224: . a - array to hold history
2225: . na - size of a
2226: - reset - `PETSC_TRUE` indicates the history counter is reset to zero
2227: for each new linear solve
2229: Level: advanced
2231: Notes:
2232: If provided, he array is NOT freed by PETSc so the user needs to keep track of it and destroy once the `KSP` object is destroyed.
2233: If 'a' is `NULL` then space is allocated for the history. If 'na' `PETSC_DECIDE` or `PETSC_DEFAULT` then a
2234: default array of length 10000 is allocated.
2236: If the array is not long enough then once the iterations is longer than the array length `KSPSolve()` stops recording the history
2238: .seealso: [](chapter_ksp), `KSPGetResidualHistory()`, `KSP`
2239: @*/
2240: PetscErrorCode KSPSetResidualHistory(KSP ksp, PetscReal a[], PetscInt na, PetscBool reset)
2241: {
2242: PetscFunctionBegin;
2245: PetscCall(PetscFree(ksp->res_hist_alloc));
2246: if (na != PETSC_DECIDE && na != PETSC_DEFAULT && a) {
2247: ksp->res_hist = a;
2248: ksp->res_hist_max = (size_t)na;
2249: } else {
2250: if (na != PETSC_DECIDE && na != PETSC_DEFAULT) ksp->res_hist_max = (size_t)na;
2251: else ksp->res_hist_max = 10000; /* like default ksp->max_it */
2252: PetscCall(PetscCalloc1(ksp->res_hist_max, &ksp->res_hist_alloc));
2254: ksp->res_hist = ksp->res_hist_alloc;
2255: }
2256: ksp->res_hist_len = 0;
2257: ksp->res_hist_reset = reset;
2258: PetscFunctionReturn(PETSC_SUCCESS);
2259: }
2261: /*@C
2262: KSPGetResidualHistory - Gets the array used to hold the residual history and the number of residuals it contains.
2264: Not Collective
2266: Input Parameter:
2267: . ksp - iterative context obtained from `KSPCreate()`
2269: Output Parameters:
2270: + a - pointer to array to hold history (or `NULL`)
2271: - na - number of used entries in a (or `NULL`)
2273: Level: advanced
2275: Note:
2276: This array is borrowed and should not be freed by the caller.
2278: Can only be called after a `KSPSetResidualHistory()` otherwise a and na are set to zero
2280: Fortran Note:
2281: The Fortran version of this routine has a calling sequence
2282: $ call KSPGetResidualHistory(KSP ksp, integer na, integer ierr)
2283: note that you have passed a Fortran array into `KSPSetResidualHistory()` and you need
2284: to access the residual values from this Fortran array you provided. Only the `na` (number of
2285: residual norms currently held) is set.
2287: .seealso: [](chapter_ksp), `KSPSetResidualHistory()`, `KSP`
2288: @*/
2289: PetscErrorCode KSPGetResidualHistory(KSP ksp, const PetscReal *a[], PetscInt *na)
2290: {
2291: PetscFunctionBegin;
2293: if (a) *a = ksp->res_hist;
2294: if (na) *na = (PetscInt)ksp->res_hist_len;
2295: PetscFunctionReturn(PETSC_SUCCESS);
2296: }
2298: /*@
2299: KSPSetErrorHistory - Sets the array used to hold the error history. If set, this array will contain the error norms computed at each iteration of the solver.
2301: Not Collective
2303: Input Parameters:
2304: + ksp - iterative context obtained from `KSPCreate()`
2305: . a - array to hold history
2306: . na - size of `a`
2307: - reset - `PETSC_TRUE` indicates the history counter is reset to zero for each new linear solve
2309: Level: advanced
2311: Notes:
2312: If provided, the array is NOT freed by PETSc so the user needs to keep track of it and destroy once the `KSP` object is destroyed.
2313: If 'a' is `NULL` then space is allocated for the history. If 'na' is `PETSC_DECIDE` or `PETSC_DEFAULT` then a default array of length 10000 is allocated.
2315: If the array is not long enough then once the iterations is longer than the array length `KSPSolve()` stops recording the history
2317: .seealso: [](chapter_ksp), `KSPGetErrorHistory()`, `KSPSetResidualHistory()`, `KSP`
2318: @*/
2319: PetscErrorCode KSPSetErrorHistory(KSP ksp, PetscReal a[], PetscInt na, PetscBool reset)
2320: {
2321: PetscFunctionBegin;
2324: PetscCall(PetscFree(ksp->err_hist_alloc));
2325: if (na != PETSC_DECIDE && na != PETSC_DEFAULT && a) {
2326: ksp->err_hist = a;
2327: ksp->err_hist_max = (size_t)na;
2328: } else {
2329: if (na != PETSC_DECIDE && na != PETSC_DEFAULT) ksp->err_hist_max = (size_t)na;
2330: else ksp->err_hist_max = 10000; /* like default ksp->max_it */
2331: PetscCall(PetscCalloc1(ksp->err_hist_max, &ksp->err_hist_alloc));
2333: ksp->err_hist = ksp->err_hist_alloc;
2334: }
2335: ksp->err_hist_len = 0;
2336: ksp->err_hist_reset = reset;
2337: PetscFunctionReturn(PETSC_SUCCESS);
2338: }
2340: /*@C
2341: KSPGetErrorHistory - Gets the array used to hold the error history and the number of residuals it contains.
2343: Not Collective
2345: Input Parameter:
2346: . ksp - iterative context obtained from `KSPCreate()`
2348: Output Parameters:
2349: + a - pointer to array to hold history (or `NULL`)
2350: - na - number of used entries in a (or `NULL`)
2352: Level: advanced
2354: Notes:
2355: This array is borrowed and should not be freed by the caller.
2356: Can only be called after a `KSPSetErrorHistory()` otherwise a and na are set to zero
2358: Fortran Note:
2359: The Fortran version of this routine has a calling sequence
2360: $ call KSPGetErrorHistory(KSP ksp, integer na, integer ierr)
2361: note that you have passed a Fortran array into `KSPSetErrorHistory()` and you need
2362: to access the residual values from this Fortran array you provided. Only the `na` (number of
2363: residual norms currently held) is set.
2365: .seealso: [](chapter_ksp), `KSPSetErrorHistory()`, `KSPGetResidualHistory()`, `KSP`
2366: @*/
2367: PetscErrorCode KSPGetErrorHistory(KSP ksp, const PetscReal *a[], PetscInt *na)
2368: {
2369: PetscFunctionBegin;
2371: if (a) *a = ksp->err_hist;
2372: if (na) *na = (PetscInt)ksp->err_hist_len;
2373: PetscFunctionReturn(PETSC_SUCCESS);
2374: }
2376: /*
2377: KSPComputeConvergenceRate - Compute the convergence rate for the iteration
2379: Not collective
2381: Input Parameter:
2382: . ksp - The `KSP`
2384: Output Parameters:
2385: + cr - The residual contraction rate
2386: . rRsq - The coefficient of determination, R^2, indicating the linearity of the data
2387: . ce - The error contraction rate
2388: - eRsq - The coefficient of determination, R^2, indicating the linearity of the data
2390: Level: advanced
2392: Note:
2393: Suppose that the residual is reduced linearly, $r_k = c^k r_0$, which means $log r_k = log r_0 + k log c$. After linear regression,
2394: the slope is $\log c$. The coefficient of determination is given by $1 - \frac{\sum_i (y_i - f(x_i))^2}{\sum_i (y_i - \bar y)}$,
2395: see also https://en.wikipedia.org/wiki/Coefficient_of_determination
2397: .seealso: [](chapter_ksp), `KSP`, `KSPConvergedRateView()`
2398: */
2399: PetscErrorCode KSPComputeConvergenceRate(KSP ksp, PetscReal *cr, PetscReal *rRsq, PetscReal *ce, PetscReal *eRsq)
2400: {
2401: PetscReal const *hist;
2402: PetscReal *x, *y, slope, intercept, mean = 0.0, var = 0.0, res = 0.0;
2403: PetscInt n, k;
2405: PetscFunctionBegin;
2406: if (cr || rRsq) {
2407: PetscCall(KSPGetResidualHistory(ksp, &hist, &n));
2408: if (!n) {
2409: if (cr) *cr = 0.0;
2410: if (rRsq) *rRsq = -1.0;
2411: } else {
2412: PetscCall(PetscMalloc2(n, &x, n, &y));
2413: for (k = 0; k < n; ++k) {
2414: x[k] = k;
2415: y[k] = PetscLogReal(hist[k]);
2416: mean += y[k];
2417: }
2418: mean /= n;
2419: PetscCall(PetscLinearRegression(n, x, y, &slope, &intercept));
2420: for (k = 0; k < n; ++k) {
2421: res += PetscSqr(y[k] - (slope * x[k] + intercept));
2422: var += PetscSqr(y[k] - mean);
2423: }
2424: PetscCall(PetscFree2(x, y));
2425: if (cr) *cr = PetscExpReal(slope);
2426: if (rRsq) *rRsq = var < PETSC_MACHINE_EPSILON ? 0.0 : 1.0 - (res / var);
2427: }
2428: }
2429: if (ce || eRsq) {
2430: PetscCall(KSPGetErrorHistory(ksp, &hist, &n));
2431: if (!n) {
2432: if (ce) *ce = 0.0;
2433: if (eRsq) *eRsq = -1.0;
2434: } else {
2435: PetscCall(PetscMalloc2(n, &x, n, &y));
2436: for (k = 0; k < n; ++k) {
2437: x[k] = k;
2438: y[k] = PetscLogReal(hist[k]);
2439: mean += y[k];
2440: }
2441: mean /= n;
2442: PetscCall(PetscLinearRegression(n, x, y, &slope, &intercept));
2443: for (k = 0; k < n; ++k) {
2444: res += PetscSqr(y[k] - (slope * x[k] + intercept));
2445: var += PetscSqr(y[k] - mean);
2446: }
2447: PetscCall(PetscFree2(x, y));
2448: if (ce) *ce = PetscExpReal(slope);
2449: if (eRsq) *eRsq = var < PETSC_MACHINE_EPSILON ? 0.0 : 1.0 - (res / var);
2450: }
2451: }
2452: PetscFunctionReturn(PETSC_SUCCESS);
2453: }
2455: /*@C
2456: KSPSetConvergenceTest - Sets the function to be used to determine convergence.
2458: Logically Collective
2460: Input Parameters:
2461: + ksp - iterative context obtained from `KSPCreate()`
2462: . converge - pointer to the function
2463: . cctx - context for private data for the convergence routine (may be null)
2464: - destroy - a routine for destroying the context (may be null)
2466: Calling sequence of `converge`:
2467: $ PetscErrorCode converge(KSP ksp, PetscInt it, PetscReal rnorm, KSPConvergedReason *reason, void *mctx)
2468: + ksp - iterative context obtained from `KSPCreate()`
2469: . it - iteration number
2470: . rnorm - (estimated) 2-norm of (preconditioned) residual
2471: . reason - the reason why it has converged or diverged
2472: - cctx - optional convergence context, as set by `KSPSetConvergenceTest()`
2474: Calling Sequence of `destroy`:
2475: $ PetscErrorCode destroy(void *cctx)
2477: Level: advanced
2479: Notes:
2480: Must be called after the `KSP` type has been set so put this after
2481: a call to `KSPSetType()`, or `KSPSetFromOptions()`.
2483: The default convergence test, `KSPConvergedDefault()`, aborts if the
2484: residual grows to more than 10000 times the initial residual.
2486: The default is a combination of relative and absolute tolerances.
2487: The residual value that is tested may be an approximation; routines
2488: that need exact values should compute them.
2490: In the default PETSc convergence test, the precise values of reason
2491: are macros such as `KSP_CONVERGED_RTOL`, which are defined in petscksp.h.
2493: .seealso: [](chapter_ksp), `KSP`, `KSPConvergedDefault()`, `KSPGetConvergenceContext()`, `KSPSetTolerances()`, `KSP`, `KSPGetConvergenceTest()`, `KSPGetAndClearConvergenceTest()`
2494: @*/
2495: PetscErrorCode KSPSetConvergenceTest(KSP ksp, PetscErrorCode (*converge)(KSP, PetscInt, PetscReal, KSPConvergedReason *, void *), void *cctx, PetscErrorCode (*destroy)(void *))
2496: {
2497: PetscFunctionBegin;
2499: if (ksp->convergeddestroy) PetscCall((*ksp->convergeddestroy)(ksp->cnvP));
2500: ksp->converged = converge;
2501: ksp->convergeddestroy = destroy;
2502: ksp->cnvP = (void *)cctx;
2503: PetscFunctionReturn(PETSC_SUCCESS);
2504: }
2506: /*@C
2507: KSPGetConvergenceTest - Gets the function to be used to determine convergence.
2509: Logically Collective
2511: Input Parameter:
2512: . ksp - iterative context obtained from `KSPCreate()`
2514: Output Parameters:
2515: + converge - pointer to convergence test function
2516: . cctx - context for private data for the convergence routine (may be null)
2517: - destroy - a routine for destroying the context (may be null)
2519: Calling sequence of `converge`:
2520: $ PetscErrorCode converge(KSP ksp, PetscInt it, PetscReal rnorm, KSPConvergedReason *reason, void *mctx)
2521: + ksp - iterative context obtained from `KSPCreate()`
2522: . it - iteration number
2523: . rnorm - (estimated) 2-norm of (preconditioned) residual
2524: . reason - the reason why it has converged or diverged
2525: - cctx - optional convergence context, as set by `KSPSetConvergenceTest()`
2527: Calling Sequence of `destroy`:
2528: $ PetscErrorCode destroy(void *cctx)
2530: Level: advanced
2532: .seealso: [](chapter_ksp), `KSP`, `KSPConvergedDefault()`, `KSPGetConvergenceContext()`, `KSPSetTolerances()`, `KSP`, `KSPSetConvergenceTest()`, `KSPGetAndClearConvergenceTest()`
2533: @*/
2534: PetscErrorCode KSPGetConvergenceTest(KSP ksp, PetscErrorCode (**converge)(KSP, PetscInt, PetscReal, KSPConvergedReason *, void *), void **cctx, PetscErrorCode (**destroy)(void *))
2535: {
2536: PetscFunctionBegin;
2538: if (converge) *converge = ksp->converged;
2539: if (destroy) *destroy = ksp->convergeddestroy;
2540: if (cctx) *cctx = ksp->cnvP;
2541: PetscFunctionReturn(PETSC_SUCCESS);
2542: }
2544: /*@C
2545: KSPGetAndClearConvergenceTest - Gets the function to be used to determine convergence. Removes the current test without calling destroy on the test context
2547: Logically Collective
2549: Input Parameter:
2550: . ksp - iterative context obtained from `KSPCreate()`
2552: Output Parameters:
2553: + converge - pointer to convergence test function
2554: . cctx - context for private data for the convergence routine
2555: - destroy - a routine for destroying the context
2557: Calling sequence of `converge`:
2558: $ PetscErrorCode converge(KSP ksp, PetscInt it, PetscReal rnorm, KSPConvergedReason *reason, void *mctx)
2559: + ksp - iterative context obtained from `KSPCreate()`
2560: . it - iteration number
2561: . rnorm - (estimated) 2-norm of (preconditioned) residual
2562: . reason - the reason why it has converged or diverged
2563: - cctx - optional convergence context, as set by `KSPSetConvergenceTest()`
2565: Calling Sequence of `destroy`:
2566: $ PetscErrorCode destroy(void *cctx)
2568: Level: advanced
2570: Note:
2571: This is intended to be used to allow transferring the convergence test (and its context) to another testing object (for example another `KSP`) and then calling
2572: `KSPSetConvergenceTest()` on this original `KSP`. If you just called `KSPGetConvergenceTest()` followed by `KSPSetConvergenceTest()` the original context information
2573: would be destroyed and hence the transferred context would be invalid and trigger a crash on use
2575: .seealso: [](chapter_ksp), `KSP`, `KSPConvergedDefault()`, `KSPGetConvergenceContext()`, `KSPSetTolerances()`, `KSP`, `KSPSetConvergenceTest()`, `KSPGetConvergenceTest()`
2576: @*/
2577: PetscErrorCode KSPGetAndClearConvergenceTest(KSP ksp, PetscErrorCode (**converge)(KSP, PetscInt, PetscReal, KSPConvergedReason *, void *), void **cctx, PetscErrorCode (**destroy)(void *))
2578: {
2579: PetscFunctionBegin;
2581: *converge = ksp->converged;
2582: *destroy = ksp->convergeddestroy;
2583: *cctx = ksp->cnvP;
2584: ksp->converged = NULL;
2585: ksp->cnvP = NULL;
2586: ksp->convergeddestroy = NULL;
2587: PetscFunctionReturn(PETSC_SUCCESS);
2588: }
2590: /*@C
2591: KSPGetConvergenceContext - Gets the convergence context set with `KSPSetConvergenceTest()`.
2593: Not Collective
2595: Input Parameter:
2596: . ksp - iterative context obtained from `KSPCreate()`
2598: Output Parameter:
2599: . ctx - monitoring context
2601: Level: advanced
2603: .seealso: [](chapter_ksp), `KSP`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSPGetConvergenceTest()`
2604: @*/
2605: PetscErrorCode KSPGetConvergenceContext(KSP ksp, void *ctx)
2606: {
2607: PetscFunctionBegin;
2609: *(void **)ctx = ksp->cnvP;
2610: PetscFunctionReturn(PETSC_SUCCESS);
2611: }
2613: /*@C
2614: KSPBuildSolution - Builds the approximate solution in a vector provided.
2616: Collective
2618: Input Parameter:
2619: . ctx - iterative context obtained from `KSPCreate()`
2621: Output Parameter:
2622: Provide exactly one of
2623: + v - location to stash solution.
2624: - V - the solution is returned in this location. This vector is created
2625: internally. This vector should NOT be destroyed by the user with
2626: `VecDestroy()`.
2628: Level: developer
2630: Notes:
2631: This routine can be used in one of two ways
2632: .vb
2633: KSPBuildSolution(ksp,NULL,&V);
2634: or
2635: KSPBuildSolution(ksp,v,NULL); or KSPBuildSolution(ksp,v,&v);
2636: .ve
2637: In the first case an internal vector is allocated to store the solution
2638: (the user cannot destroy this vector). In the second case the solution
2639: is generated in the vector that the user provides. Note that for certain
2640: methods, such as `KSPCG`, the second case requires a copy of the solution,
2641: while in the first case the call is essentially free since it simply
2642: returns the vector where the solution already is stored. For some methods
2643: like `KSPGMRES` this is a reasonably expensive operation and should only be
2644: used in truly needed.
2646: .seealso: [](chapter_ksp), `KSPGetSolution()`, `KSPBuildResidual()`, `KSP`
2647: @*/
2648: PetscErrorCode KSPBuildSolution(KSP ksp, Vec v, Vec *V)
2649: {
2650: PetscFunctionBegin;
2652: PetscCheck(V || v, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONG, "Must provide either v or V");
2653: if (!V) V = &v;
2654: PetscUseTypeMethod(ksp, buildsolution, v, V);
2655: PetscFunctionReturn(PETSC_SUCCESS);
2656: }
2658: /*@C
2659: KSPBuildResidual - Builds the residual in a vector provided.
2661: Collective
2663: Input Parameter:
2664: . ksp - iterative context obtained from `KSPCreate()`
2666: Output Parameters:
2667: + v - optional location to stash residual. If `v` is not provided,
2668: then a location is generated.
2669: . t - work vector. If not provided then one is generated.
2670: - V - the residual
2672: Level: advanced
2674: Note:
2675: Regardless of whether or not `v` is provided, the residual is
2676: returned in `V`.
2678: .seealso: [](chapter_ksp), `KSP`, `KSPBuildSolution()`
2679: @*/
2680: PetscErrorCode KSPBuildResidual(KSP ksp, Vec t, Vec v, Vec *V)
2681: {
2682: PetscBool flag = PETSC_FALSE;
2683: Vec w = v, tt = t;
2685: PetscFunctionBegin;
2687: if (!w) PetscCall(VecDuplicate(ksp->vec_rhs, &w));
2688: if (!tt) {
2689: PetscCall(VecDuplicate(ksp->vec_sol, &tt));
2690: flag = PETSC_TRUE;
2691: }
2692: PetscUseTypeMethod(ksp, buildresidual, tt, w, V);
2693: if (flag) PetscCall(VecDestroy(&tt));
2694: PetscFunctionReturn(PETSC_SUCCESS);
2695: }
2697: /*@
2698: KSPSetDiagonalScale - Tells `KSP` to symmetrically diagonally scale the system
2699: before solving. This actually CHANGES the matrix (and right hand side).
2701: Logically Collective
2703: Input Parameters:
2704: + ksp - the `KSP` context
2705: - scale - `PETSC_TRUE` or `PETSC_FALSE`
2707: Options Database Keys:
2708: + -ksp_diagonal_scale - perform a diagonal scaling before the solve
2709: - -ksp_diagonal_scale_fix - scale the matrix back AFTER the solve
2711: Level: advanced
2713: Notes:
2714: Scales the matrix by D^(-1/2) A D^(-1/2) [D^(1/2) x ] = D^(-1/2) b
2715: where D_{ii} is 1/abs(A_{ii}) unless A_{ii} is zero and then it is 1.
2717: BE CAREFUL with this routine: it actually scales the matrix and right
2718: hand side that define the system. After the system is solved the matrix
2719: and right hand side remain scaled unless you use `KSPSetDiagonalScaleFix()`
2721: This should NOT be used within the `SNES` solves if you are using a line
2722: search.
2724: If you use this with the `PCType` `PCEISENSTAT` preconditioner than you can
2725: use the `PCEisenstatSetNoDiagonalScaling()` option, or -pc_eisenstat_no_diagonal_scaling
2726: to save some unneeded, redundant flops.
2728: .seealso: [](chapter_ksp), `KSPGetDiagonalScale()`, `KSPSetDiagonalScaleFix()`, `KSP`
2729: @*/
2730: PetscErrorCode KSPSetDiagonalScale(KSP ksp, PetscBool scale)
2731: {
2732: PetscFunctionBegin;
2735: ksp->dscale = scale;
2736: PetscFunctionReturn(PETSC_SUCCESS);
2737: }
2739: /*@
2740: KSPGetDiagonalScale - Checks if `KSP` solver scales the matrix and right hand side, that is if `KSPSetDiagonalScale()` has been called
2742: Not Collective
2744: Input Parameter:
2745: . ksp - the `KSP` context
2747: Output Parameter:
2748: . scale - `PETSC_TRUE` or `PETSC_FALSE`
2750: Level: intermediate
2752: .seealso: [](chapter_ksp), `KSP`, `KSPSetDiagonalScale()`, `KSPSetDiagonalScaleFix()`, `KSP`
2753: @*/
2754: PetscErrorCode KSPGetDiagonalScale(KSP ksp, PetscBool *scale)
2755: {
2756: PetscFunctionBegin;
2759: *scale = ksp->dscale;
2760: PetscFunctionReturn(PETSC_SUCCESS);
2761: }
2763: /*@
2764: KSPSetDiagonalScaleFix - Tells `KSP` to diagonally scale the system back after solving.
2766: Logically Collective
2768: Input Parameters:
2769: + ksp - the `KSP` context
2770: - fix - `PETSC_TRUE` to scale back after the system solve, `PETSC_FALSE` to not
2771: rescale (default)
2773: Level: intermediate
2775: Notes:
2776: Must be called after `KSPSetDiagonalScale()`
2778: Using this will slow things down, because it rescales the matrix before and
2779: after each linear solve. This is intended mainly for testing to allow one
2780: to easily get back the original system to make sure the solution computed is
2781: accurate enough.
2783: .seealso: [](chapter_ksp), `KSPGetDiagonalScale()`, `KSPSetDiagonalScale()`, `KSPGetDiagonalScaleFix()`, `KSP`
2784: @*/
2785: PetscErrorCode KSPSetDiagonalScaleFix(KSP ksp, PetscBool fix)
2786: {
2787: PetscFunctionBegin;
2790: ksp->dscalefix = fix;
2791: PetscFunctionReturn(PETSC_SUCCESS);
2792: }
2794: /*@
2795: KSPGetDiagonalScaleFix - Determines if `KSP` diagonally scales the system back after solving. That is `KSPSetDiagonalScaleFix()` has been called
2797: Not Collective
2799: Input Parameter:
2800: . ksp - the `KSP` context
2802: Output Parameter:
2803: . fix - `PETSC_TRUE` to scale back after the system solve, `PETSC_FALSE` to not
2804: rescale (default)
2806: Level: intermediate
2808: .seealso: [](chapter_ksp), `KSPGetDiagonalScale()`, `KSPSetDiagonalScale()`, `KSPSetDiagonalScaleFix()`, `KSP`
2809: @*/
2810: PetscErrorCode KSPGetDiagonalScaleFix(KSP ksp, PetscBool *fix)
2811: {
2812: PetscFunctionBegin;
2815: *fix = ksp->dscalefix;
2816: PetscFunctionReturn(PETSC_SUCCESS);
2817: }
2819: /*@C
2820: KSPSetComputeOperators - set routine to compute the linear operators
2822: Logically Collective
2824: Input Parameters:
2825: + ksp - the `KSP` context
2826: . func - function to compute the operators
2827: - ctx - optional context
2829: Calling sequence of `func`:
2830: $ PetscErrorCode func(KSP ksp, Mat A, Mat B, void *ctx)
2831: + ksp - the `KSP` context
2832: . A - the linear operator
2833: . B - preconditioning matrix
2834: - ctx - optional user-provided context
2836: Level: beginner
2838: Notes:
2839: The user provided func() will be called automatically at the very next call to `KSPSolve()`. It will NOT be called at future `KSPSolve()` calls
2840: unless either `KSPSetComputeOperators()` or `KSPSetOperators()` is called before that `KSPSolve()` is called. This allows the same system to be solved several times
2841: with different right hand side functions but is a confusing API since one might expect it to be called for each `KSPSolve()`
2843: To reuse the same preconditioner for the next `KSPSolve()` and not compute a new one based on the most recently computed matrix call `KSPSetReusePreconditioner()`
2845: Developer Note:
2846: Perhaps this routine and `KSPSetComputeRHS()` could be combined into a new API that makes clear when new matrices are computing without requiring call this
2847: routine to indicate when the new matrix should be computed.
2849: .seealso: [](chapter_ksp), `KSP`, `KSPSetOperators()`, `KSPSetComputeRHS()`, `DMKSPSetComputeOperators()`, `KSPSetComputeInitialGuess()`
2850: @*/
2851: PetscErrorCode KSPSetComputeOperators(KSP ksp, PetscErrorCode (*func)(KSP, Mat, Mat, void *), void *ctx)
2852: {
2853: DM dm;
2855: PetscFunctionBegin;
2857: PetscCall(KSPGetDM(ksp, &dm));
2858: PetscCall(DMKSPSetComputeOperators(dm, func, ctx));
2859: if (ksp->setupstage == KSP_SETUP_NEWRHS) ksp->setupstage = KSP_SETUP_NEWMATRIX;
2860: PetscFunctionReturn(PETSC_SUCCESS);
2861: }
2863: /*@C
2864: KSPSetComputeRHS - set routine to compute the right hand side of the linear system
2866: Logically Collective
2868: Input Parameters:
2869: + ksp - the `KSP` context
2870: . func - function to compute the right hand side
2871: - ctx - optional context
2873: Calling sequence of `func`:
2874: $ PetscErrorCode func(KSP ksp, Vec b, void *ctx)
2875: + ksp - the `KSP` context
2876: . b - right hand side of linear system
2877: - ctx - optional user-provided context
2879: Level: beginner
2881: Notes:
2882: The routine you provide will be called EACH you call `KSPSolve()` to prepare the new right hand side for that solve
2884: .seealso: [](chapter_ksp), `KSP`, `KSPSolve()`, `DMKSPSetComputeRHS()`, `KSPSetComputeOperators()`, `KSPSetOperators()`
2885: @*/
2886: PetscErrorCode KSPSetComputeRHS(KSP ksp, PetscErrorCode (*func)(KSP, Vec, void *), void *ctx)
2887: {
2888: DM dm;
2890: PetscFunctionBegin;
2892: PetscCall(KSPGetDM(ksp, &dm));
2893: PetscCall(DMKSPSetComputeRHS(dm, func, ctx));
2894: PetscFunctionReturn(PETSC_SUCCESS);
2895: }
2897: /*@C
2898: KSPSetComputeInitialGuess - set routine to compute the initial guess of the linear system
2900: Logically Collective
2902: Input Parameters:
2903: + ksp - the `KSP` context
2904: . func - function to compute the initial guess
2905: - ctx - optional context
2907: Calling sequence of `func`:
2908: $ PetscErrorCode func(KSP ksp, Vec x, void *ctx)
2909: + ksp - the `KSP` context
2910: . x - solution vector
2911: - ctx - optional user-provided context
2913: Level: beginner
2915: Notes:
2916: This should only be used in conjunction with `KSPSetComputeRHS()` and `KSPSetComputeOperators()`, otherwise
2917: call `KSPSetInitialGuessNonzero()` and set the initial guess values in the solution vector passed to `KSPSolve()` before calling the solver
2919: .seealso: [](chapter_ksp), `KSP`, `KSPSolve()`, `KSPSetComputeRHS()`, `KSPSetComputeOperators()`, `DMKSPSetComputeInitialGuess()`, `KSPSetInitialGuessNonzero()`
2920: @*/
2921: PetscErrorCode KSPSetComputeInitialGuess(KSP ksp, PetscErrorCode (*func)(KSP, Vec, void *), void *ctx)
2922: {
2923: DM dm;
2925: PetscFunctionBegin;
2927: PetscCall(KSPGetDM(ksp, &dm));
2928: PetscCall(DMKSPSetComputeInitialGuess(dm, func, ctx));
2929: PetscFunctionReturn(PETSC_SUCCESS);
2930: }
2932: /*@
2933: KSPSetUseExplicitTranspose - Determines the explicit transpose of the operator is formed in `KSPSolveTranspose()`. In some configurations (like GPUs) it may
2934: be explicitly formed when possible since the solve is much more efficient.
2936: Logically Collective
2938: Input Parameter:
2939: . ksp - the `KSP` context
2941: Output Parameter:
2942: . flg - `PETSC_TRUE` to transpose the system in `KSPSolveTranspose()`, `PETSC_FALSE` to not transpose (default)
2944: Level: advanced
2946: .seealso: [](chapter_ksp), `KSPSolveTranspose()`, `KSP`
2947: @*/
2948: PetscErrorCode KSPSetUseExplicitTranspose(KSP ksp, PetscBool flg)
2949: {
2950: PetscFunctionBegin;
2953: ksp->transpose.use_explicittranspose = flg;
2954: PetscFunctionReturn(PETSC_SUCCESS);
2955: }