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 solver 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: Level: advanced
38: Notes:
39: One must call `KSPSetComputeSingularValues()` before calling `KSPSetUp()`
40: (or use the option -ksp_view_eigenvalues) in order for this routine to work correctly.
42: Many users may just want to use the monitoring routine
43: `KSPMonitorSingularValue()` (which can be set with option -ksp_monitor_singular_value)
44: to print the extreme singular values at each iteration of the linear solve.
46: Estimates of the smallest singular value may be very inaccurate, especially if the Krylov method has not converged.
47: The largest singular value is usually accurate to within a few percent if the method has converged, but is still not
48: intended for eigenanalysis. Consider the excellent package `SLEPc` if accurate values are required.
50: Disable restarts if using KSPGMRES, otherwise this estimate will only be using those iterations after the last
51: restart. See `KSPGMRESSetRestart()` for more details.
53: .seealso: [](ch_ksp), `KSPSetComputeSingularValues()`, `KSPMonitorSingularValue()`, `KSPComputeEigenvalues()`, `KSP`, `KSPComputeRitz()`
54: @*/
55: PetscErrorCode KSPComputeExtremeSingularValues(KSP ksp, PetscReal *emax, PetscReal *emin)
56: {
57: PetscFunctionBegin;
59: PetscAssertPointer(emax, 2);
60: PetscAssertPointer(emin, 3);
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 solver 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 Key:
88: . -ksp_view_eigenvalues - Prints eigenvalues to stdout
90: Level: advanced
92: Notes:
93: The number of eigenvalues estimated depends on the size of the Krylov space
94: generated during the `KSPSolve()` ; for example, with
95: `KSPCG` it corresponds to the number of CG iterations, for `KSPGMRES` it is the number
96: of GMRES iterations SINCE the last restart. Any extra space in `r` and `c`
97: will be ignored.
99: `KSPComputeEigenvalues()` does not usually provide accurate estimates; it is
100: intended only for assistance in understanding the convergence of iterative
101: methods, not for eigenanalysis. For accurate computation of eigenvalues we recommend using
102: the excellent package SLEPc.
104: One must call `KSPSetComputeEigenvalues()` before calling `KSPSetUp()`
105: in order for this routine to work correctly.
107: Many users may just want to use the monitoring routine
108: `KSPMonitorSingularValue()` (which can be set with option -ksp_monitor_singular_value)
109: to print the singular values at each iteration of the linear solve.
111: `KSPComputeRitz()` provides estimates for both the eigenvalues and their corresponding eigenvectors.
113: .seealso: [](ch_ksp), `KSPSetComputeEigenvalues()`, `KSPSetComputeSingularValues()`, `KSPMonitorSingularValue()`, `KSPComputeExtremeSingularValues()`, `KSP`, `KSPComputeRitz()`
114: @*/
115: PetscErrorCode KSPComputeEigenvalues(KSP ksp, PetscInt n, PetscReal r[], PetscReal c[], PetscInt *neig)
116: {
117: PetscFunctionBegin;
119: if (n) PetscAssertPointer(r, 3);
120: if (n) PetscAssertPointer(c, 4);
121: PetscCheck(n >= 0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Requested < 0 Eigenvalues");
122: PetscAssertPointer(neig, 5);
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 solver 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: Level: advanced
149: Notes:
150: This only works with a `KSPType` of `KSPGMRES`.
152: One must call `KSPSetComputeRitz()` before calling `KSPSetUp()` in order for this routine to work correctly.
154: This routine must be called after `KSPSolve()`.
156: In `KSPGMRES`, the (harmonic) Ritz pairs are computed from the Hessenberg matrix obtained during
157: the last complete cycle of the GMRES solve, or during the partial cycle if the solve ended before
158: a restart (that is a complete GMRES cycle was never achieved).
160: The number of actual (harmonic) Ritz pairs computed is less than or equal to the restart
161: parameter for GMRES if a complete cycle has been performed or less or equal to the number of GMRES
162: iterations.
164: `KSPComputeEigenvalues()` provides estimates for only the eigenvalues (Ritz values).
166: For real matrices, the (harmonic) Ritz pairs can be complex-valued. In such a case,
167: the routine selects the complex (harmonic) Ritz value and its conjugate, and two successive entries of the
168: vectors `S` are equal to the real and the imaginary parts of the associated vectors.
169: When PETSc has been built with complex scalars, the real and imaginary parts of the Ritz
170: values are still returned in `tetar` and `tetai`, as is done in `KSPComputeEigenvalues()`, but
171: the Ritz vectors S are complex.
173: The (harmonic) Ritz pairs are given in order of increasing (harmonic) Ritz values in modulus.
175: The Ritz pairs do not necessarily accurately reflect the eigenvalues and eigenvectors of the operator, consider the
176: excellent package `SLEPc` if accurate values are required.
178: .seealso: [](ch_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: }
189: /*@
190: KSPSetUpOnBlocks - Sets up the preconditioner for each block in
191: the block Jacobi `PCJACOBI`, overlapping Schwarz `PCASM`, and fieldsplit `PCFIELDSPLIT` preconditioners
193: Collective
195: Input Parameter:
196: . ksp - the `KSP` context
198: Level: advanced
200: Notes:
201: `KSPSetUpOnBlocks()` is a routine that the user can optionally call for
202: more precise profiling (via `-log_view`) of the setup phase for these
203: block preconditioners. If the user does not call `KSPSetUpOnBlocks()`,
204: it will automatically be called from within `KSPSolve()`.
206: Calling `KSPSetUpOnBlocks()` is the same as calling `PCSetUpOnBlocks()`
207: on the `PC` context within the `KSP` context.
209: .seealso: [](ch_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(PCGetFailedReason(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 VecFlag() 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 for future `KSPSolve()`, do not construct a new preconditioner even if the `Mat` operator
235: in the `KSP` has different values
237: Collective
239: Input Parameters:
240: + ksp - iterative solver obtained from `KSPCreate()`
241: - flag - `PETSC_TRUE` to reuse the current preconditioner, or `PETSC_FALSE` to construct a new preconditioner
243: Options Database Key:
244: . -ksp_reuse_preconditioner <true,false> - reuse the previously computed preconditioner
246: Level: intermediate
248: Note:
249: When using `SNES` one can use `SNESSetLagPreconditioner()` to determine when preconditioners are reused.
251: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSolve()`, `KSPDestroy()`, `KSP`, `KSPGetReusePreconditioner()`,
252: `SNESSetLagPreconditioner()`, `SNES`
253: @*/
254: PetscErrorCode KSPSetReusePreconditioner(KSP ksp, PetscBool flag)
255: {
256: PC pc;
258: PetscFunctionBegin;
260: PetscCall(KSPGetPC(ksp, &pc));
261: PetscCall(PCSetReusePreconditioner(pc, flag));
262: PetscFunctionReturn(PETSC_SUCCESS);
263: }
265: /*@
266: KSPGetReusePreconditioner - Determines if the `KSP` reuses the current preconditioner even if the `Mat` operator in the `KSP` has changed.
268: Collective
270: Input Parameter:
271: . ksp - iterative solver obtained from `KSPCreate()`
273: Output Parameter:
274: . flag - the boolean flag indicating if the current preconditioner should be reused
276: Level: intermediate
278: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSolve()`, `KSPDestroy()`, `KSPSetReusePreconditioner()`, `KSP`
279: @*/
280: PetscErrorCode KSPGetReusePreconditioner(KSP ksp, PetscBool *flag)
281: {
282: PetscFunctionBegin;
284: PetscAssertPointer(flag, 2);
285: *flag = PETSC_FALSE;
286: if (ksp->pc) PetscCall(PCGetReusePreconditioner(ksp->pc, flag));
287: PetscFunctionReturn(PETSC_SUCCESS);
288: }
290: /*@
291: KSPSetSkipPCSetFromOptions - prevents `KSPSetFromOptions()` from calling `PCSetFromOptions()`.
292: This is used if the same `PC` is shared by more than one `KSP` so its options are not reset for each `KSP`
294: Collective
296: Input Parameters:
297: + ksp - iterative solver obtained from `KSPCreate()`
298: - flag - `PETSC_TRUE` to skip calling the `PCSetFromOptions()`
300: Level: intermediate
302: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSolve()`, `KSPDestroy()`, `PCSetReusePreconditioner()`, `KSP`
303: @*/
304: PetscErrorCode KSPSetSkipPCSetFromOptions(KSP ksp, PetscBool flag)
305: {
306: PetscFunctionBegin;
308: ksp->skippcsetfromoptions = flag;
309: PetscFunctionReturn(PETSC_SUCCESS);
310: }
312: /*@
313: KSPSetUp - Sets up the internal data structures for the
314: later use `KSPSolve()` the `KSP` linear iterative solver.
316: Collective
318: Input Parameter:
319: . ksp - iterative solver, `KSP`, obtained from `KSPCreate()`
321: Level: developer
323: Note:
324: This is called automatically by `KSPSolve()` so usually does not need to be called directly.
326: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSolve()`, `KSPDestroy()`, `KSP`, `KSPSetUpOnBlocks()`
327: @*/
328: PetscErrorCode KSPSetUp(KSP ksp)
329: {
330: Mat A, B;
331: Mat mat, pmat;
332: MatNullSpace nullsp;
333: PCFailedReason pcreason;
334: PC pc;
335: PetscBool pcmpi;
337: PetscFunctionBegin;
339: PetscCall(KSPGetPC(ksp, &pc));
340: PetscCall(PetscObjectTypeCompare((PetscObject)pc, PCMPI, &pcmpi));
341: if (pcmpi) {
342: PetscBool ksppreonly;
343: PetscCall(PetscObjectTypeCompare((PetscObject)ksp, KSPPREONLY, &ksppreonly));
344: if (!ksppreonly) PetscCall(KSPSetType(ksp, KSPPREONLY));
345: }
346: level++;
348: /* reset the convergence flag from the previous solves */
349: ksp->reason = KSP_CONVERGED_ITERATING;
351: if (!((PetscObject)ksp)->type_name) PetscCall(KSPSetType(ksp, KSPGMRES));
352: PetscCall(KSPSetUpNorms_Private(ksp, PETSC_TRUE, &ksp->normtype, &ksp->pc_side));
354: if (ksp->dmActive && !ksp->setupstage) {
355: /* first time in so build matrix and vector data structures using DM */
356: if (!ksp->vec_rhs) PetscCall(DMCreateGlobalVector(ksp->dm, &ksp->vec_rhs));
357: if (!ksp->vec_sol) PetscCall(DMCreateGlobalVector(ksp->dm, &ksp->vec_sol));
358: PetscCall(DMCreateMatrix(ksp->dm, &A));
359: PetscCall(KSPSetOperators(ksp, A, A));
360: PetscCall(PetscObjectDereference((PetscObject)A));
361: }
363: if (ksp->dmActive) {
364: DMKSP kdm;
365: PetscCall(DMGetDMKSP(ksp->dm, &kdm));
367: if (kdm->ops->computeinitialguess && ksp->setupstage != KSP_SETUP_NEWRHS) {
368: /* only computes initial guess the first time through */
369: PetscCallBack("KSP callback initial guess", (*kdm->ops->computeinitialguess)(ksp, ksp->vec_sol, kdm->initialguessctx));
370: PetscCall(KSPSetInitialGuessNonzero(ksp, PETSC_TRUE));
371: }
372: if (kdm->ops->computerhs) PetscCallBack("KSP callback rhs", (*kdm->ops->computerhs)(ksp, ksp->vec_rhs, kdm->rhsctx));
374: if (ksp->setupstage != KSP_SETUP_NEWRHS) {
375: PetscCheck(kdm->ops->computeoperators, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONGSTATE, "You called KSPSetDM() but did not use DMKSPSetComputeOperators() or KSPSetDMActive(ksp,PETSC_FALSE);");
376: PetscCall(KSPGetOperators(ksp, &A, &B));
377: PetscCallBack("KSP callback operators", (*kdm->ops->computeoperators)(ksp, A, B, kdm->operatorsctx));
378: }
379: }
381: if (ksp->setupstage == KSP_SETUP_NEWRHS) {
382: level--;
383: PetscFunctionReturn(PETSC_SUCCESS);
384: }
385: PetscCall(PetscLogEventBegin(KSP_SetUp, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
387: switch (ksp->setupstage) {
388: case KSP_SETUP_NEW:
389: PetscUseTypeMethod(ksp, setup);
390: break;
391: case KSP_SETUP_NEWMATRIX: /* This should be replaced with a more general mechanism */
392: if (ksp->setupnewmatrix) PetscUseTypeMethod(ksp, setup);
393: break;
394: default:
395: break;
396: }
398: if (!ksp->pc) PetscCall(KSPGetPC(ksp, &ksp->pc));
399: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
400: /* scale the matrix if requested */
401: if (ksp->dscale) {
402: PetscScalar *xx;
403: PetscInt i, n;
404: PetscBool zeroflag = PETSC_FALSE;
406: if (!ksp->diagonal) { /* allocate vector to hold diagonal */
407: PetscCall(MatCreateVecs(pmat, &ksp->diagonal, NULL));
408: }
409: PetscCall(MatGetDiagonal(pmat, ksp->diagonal));
410: PetscCall(VecGetLocalSize(ksp->diagonal, &n));
411: PetscCall(VecGetArray(ksp->diagonal, &xx));
412: for (i = 0; i < n; i++) {
413: if (xx[i] != 0.0) xx[i] = 1.0 / PetscSqrtReal(PetscAbsScalar(xx[i]));
414: else {
415: xx[i] = 1.0;
416: zeroflag = PETSC_TRUE;
417: }
418: }
419: PetscCall(VecRestoreArray(ksp->diagonal, &xx));
420: if (zeroflag) PetscCall(PetscInfo(ksp, "Zero detected in diagonal of matrix, using 1 at those locations\n"));
421: PetscCall(MatDiagonalScale(pmat, ksp->diagonal, ksp->diagonal));
422: if (mat != pmat) PetscCall(MatDiagonalScale(mat, ksp->diagonal, ksp->diagonal));
423: ksp->dscalefix2 = PETSC_FALSE;
424: }
425: PetscCall(PetscLogEventEnd(KSP_SetUp, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
426: PetscCall(PCSetErrorIfFailure(ksp->pc, ksp->errorifnotconverged));
427: PetscCall(PCSetUp(ksp->pc));
428: PetscCall(PCGetFailedReason(ksp->pc, &pcreason));
429: /* 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 */
430: if (pcreason) ksp->reason = KSP_DIVERGED_PC_FAILED;
432: PetscCall(MatGetNullSpace(mat, &nullsp));
433: if (nullsp) {
434: PetscBool test = PETSC_FALSE;
435: PetscCall(PetscOptionsGetBool(((PetscObject)ksp)->options, ((PetscObject)ksp)->prefix, "-ksp_test_null_space", &test, NULL));
436: if (test) PetscCall(MatNullSpaceTest(nullsp, mat, NULL));
437: }
438: ksp->setupstage = KSP_SETUP_NEWRHS;
439: level--;
440: PetscFunctionReturn(PETSC_SUCCESS);
441: }
443: /*@
444: KSPConvergedReasonView - Displays the reason a `KSP` solve converged or diverged, `KSPConvergedReason` to a `PetscViewer`
446: Collective
448: Input Parameters:
449: + ksp - iterative solver obtained from `KSPCreate()`
450: - viewer - the `PetscViewer` on which to display the reason
452: Options Database Keys:
453: + -ksp_converged_reason - print reason for converged or diverged, also prints number of iterations
454: - -ksp_converged_reason ::failed - only print reason and number of iterations when diverged
456: Level: beginner
458: Note:
459: Use `KSPConvergedReasonViewFromOptions()` to display the reason based on values in the PETSc options database.
461: To change the format of the output call `PetscViewerPushFormat`(`viewer`,`format`) before this call. Use `PETSC_VIEWER_DEFAULT` for the default,
462: use `PETSC_VIEWER_FAILED` to only display a reason if it fails.
464: .seealso: [](ch_ksp), `KSPConvergedReasonViewFromOptions()`, `KSPCreate()`, `KSPSetUp()`, `KSPDestroy()`, `KSPSetTolerances()`, `KSPConvergedDefault()`,
465: `KSPSolveTranspose()`, `KSPGetIterationNumber()`, `KSP`, `KSPGetConvergedReason()`, `PetscViewerPushFormat()`, `PetscViewerPopFormat()`
466: @*/
467: PetscErrorCode KSPConvergedReasonView(KSP ksp, PetscViewer viewer)
468: {
469: PetscBool isAscii;
470: PetscViewerFormat format;
472: PetscFunctionBegin;
473: if (!viewer) viewer = PETSC_VIEWER_STDOUT_(PetscObjectComm((PetscObject)ksp));
474: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isAscii));
475: if (isAscii) {
476: PetscCall(PetscViewerGetFormat(viewer, &format));
477: PetscCall(PetscViewerASCIIAddTab(viewer, ((PetscObject)ksp)->tablevel + 1));
478: if (ksp->reason > 0 && format != PETSC_VIEWER_FAILED) {
479: if (((PetscObject)ksp)->prefix) {
480: PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve converged due to %s iterations %" PetscInt_FMT "\n", ((PetscObject)ksp)->prefix, KSPConvergedReasons[ksp->reason], ksp->its));
481: } else {
482: PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve converged due to %s iterations %" PetscInt_FMT "\n", KSPConvergedReasons[ksp->reason], ksp->its));
483: }
484: } else if (ksp->reason <= 0) {
485: if (((PetscObject)ksp)->prefix) {
486: PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve did not converge due to %s iterations %" PetscInt_FMT "\n", ((PetscObject)ksp)->prefix, KSPConvergedReasons[ksp->reason], ksp->its));
487: } else {
488: PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve did not converge due to %s iterations %" PetscInt_FMT "\n", KSPConvergedReasons[ksp->reason], ksp->its));
489: }
490: if (ksp->reason == KSP_DIVERGED_PC_FAILED) {
491: PCFailedReason reason;
492: PetscCall(PCGetFailedReason(ksp->pc, &reason));
493: PetscCall(PetscViewerASCIIPrintf(viewer, " PC failed due to %s \n", PCFailedReasons[reason]));
494: }
495: }
496: PetscCall(PetscViewerASCIISubtractTab(viewer, ((PetscObject)ksp)->tablevel + 1));
497: }
498: PetscFunctionReturn(PETSC_SUCCESS);
499: }
501: /*@C
502: KSPConvergedReasonViewSet - Sets an ADDITIONAL function that is to be used at the
503: end of the linear solver to display the convergence reason of the linear solver.
505: Logically Collective
507: Input Parameters:
508: + ksp - the `KSP` context
509: . f - the ksp converged reason view function
510: . vctx - [optional] user-defined context for private data for the
511: `KSPConvergedReason` view routine (use `NULL` if no context is desired)
512: - reasonviewdestroy - [optional] routine that frees `vctx` (may be `NULL`)
514: Options Database Keys:
515: + -ksp_converged_reason - sets a default `KSPConvergedReasonView()`
516: - -ksp_converged_reason_view_cancel - cancels all converged reason viewers that have been hardwired into a code by
517: calls to `KSPConvergedReasonViewSet()`, but does not cancel those set via the options database.
519: Level: intermediate
521: Note:
522: Several different converged reason view routines may be set by calling
523: `KSPConvergedReasonViewSet()` multiple times; all will be called in the
524: order in which they were set.
526: Developer Note:
527: Should be named KSPConvergedReasonViewAdd().
529: .seealso: [](ch_ksp), `KSPConvergedReasonView()`, `KSPConvergedReasonViewCancel()`
530: @*/
531: PetscErrorCode KSPConvergedReasonViewSet(KSP ksp, PetscErrorCode (*f)(KSP, void *), void *vctx, PetscErrorCode (*reasonviewdestroy)(void **))
532: {
533: PetscInt i;
534: PetscBool identical;
536: PetscFunctionBegin;
538: for (i = 0; i < ksp->numberreasonviews; i++) {
539: PetscCall(PetscMonitorCompare((PetscErrorCode (*)(void))f, vctx, reasonviewdestroy, (PetscErrorCode (*)(void))ksp->reasonview[i], ksp->reasonviewcontext[i], ksp->reasonviewdestroy[i], &identical));
540: if (identical) PetscFunctionReturn(PETSC_SUCCESS);
541: }
542: PetscCheck(ksp->numberreasonviews < MAXKSPREASONVIEWS, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Too many KSP reasonview set");
543: ksp->reasonview[ksp->numberreasonviews] = f;
544: ksp->reasonviewdestroy[ksp->numberreasonviews] = reasonviewdestroy;
545: ksp->reasonviewcontext[ksp->numberreasonviews++] = (void *)vctx;
546: PetscFunctionReturn(PETSC_SUCCESS);
547: }
549: /*@
550: KSPConvergedReasonViewCancel - Clears all the reasonview functions for a `KSP` object set with `KSPConvergedReasonViewSet()`
551: as well as the default viewer.
553: Collective
555: Input Parameter:
556: . ksp - iterative solver obtained from `KSPCreate()`
558: Level: intermediate
560: .seealso: [](ch_ksp), `KSPCreate()`, `KSPDestroy()`, `KSPReset()`, `KSPConvergedReasonViewSet()`
561: @*/
562: PetscErrorCode KSPConvergedReasonViewCancel(KSP ksp)
563: {
564: PetscInt i;
566: PetscFunctionBegin;
568: for (i = 0; i < ksp->numberreasonviews; i++) {
569: if (ksp->reasonviewdestroy[i]) PetscCall((*ksp->reasonviewdestroy[i])(&ksp->reasonviewcontext[i]));
570: }
571: ksp->numberreasonviews = 0;
572: PetscCall(PetscViewerDestroy(&ksp->convergedreasonviewer));
573: PetscFunctionReturn(PETSC_SUCCESS);
574: }
576: /*@
577: KSPConvergedReasonViewFromOptions - Processes command line options to determine if/how a `KSPReason` is to be viewed.
579: Collective
581: Input Parameter:
582: . ksp - the `KSP` object
584: Level: intermediate
586: Note:
587: This is called automatically at the conclusion of `KSPSolve()` so is rarely called directly by user code.
589: .seealso: [](ch_ksp), `KSPConvergedReasonView()`, `KSPConvergedReasonViewSet()`
590: @*/
591: PetscErrorCode KSPConvergedReasonViewFromOptions(KSP ksp)
592: {
593: PetscFunctionBegin;
594: /* Call all user-provided reason review routines */
595: for (PetscInt i = 0; i < ksp->numberreasonviews; i++) PetscCall((*ksp->reasonview[i])(ksp, ksp->reasonviewcontext[i]));
597: /* Call the default PETSc routine */
598: if (ksp->convergedreasonviewer) {
599: PetscCall(PetscViewerPushFormat(ksp->convergedreasonviewer, ksp->convergedreasonformat));
600: PetscCall(KSPConvergedReasonView(ksp, ksp->convergedreasonviewer));
601: PetscCall(PetscViewerPopFormat(ksp->convergedreasonviewer));
602: }
603: PetscFunctionReturn(PETSC_SUCCESS);
604: }
606: /*@
607: KSPConvergedRateView - Displays the convergence rate <https://en.wikipedia.org/wiki/Coefficient_of_determination> of `KSPSolve()` to a viewer
609: Collective
611: Input Parameters:
612: + ksp - iterative solver obtained from `KSPCreate()`
613: - viewer - the `PetscViewer` to display the reason
615: Options Database Key:
616: . -ksp_converged_rate - print reason for convergence or divergence and the convergence rate (or 0.0 for divergence)
618: Level: intermediate
620: Notes:
621: To change the format of the output, call `PetscViewerPushFormat`(`viewer`,`format`) before this call.
623: 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,
624: the slope is $\log c$. The coefficient of determination is given by $1 - \frac{\sum_i (y_i - f(x_i))^2}{\sum_i (y_i - \bar y)}$,
626: .seealso: [](ch_ksp), `KSPConvergedReasonView()`, `KSPGetConvergedRate()`, `KSPSetTolerances()`, `KSPConvergedDefault()`
627: @*/
628: PetscErrorCode KSPConvergedRateView(KSP ksp, PetscViewer viewer)
629: {
630: PetscViewerFormat format;
631: PetscBool isAscii;
632: PetscReal rrate, rRsq, erate = 0.0, eRsq = 0.0;
633: PetscInt its;
634: const char *prefix, *reason = KSPConvergedReasons[ksp->reason];
636: PetscFunctionBegin;
637: PetscCall(KSPGetOptionsPrefix(ksp, &prefix));
638: PetscCall(KSPGetIterationNumber(ksp, &its));
639: PetscCall(KSPComputeConvergenceRate(ksp, &rrate, &rRsq, &erate, &eRsq));
640: if (!viewer) viewer = PETSC_VIEWER_STDOUT_(PetscObjectComm((PetscObject)ksp));
641: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isAscii));
642: if (isAscii) {
643: PetscCall(PetscViewerGetFormat(viewer, &format));
644: PetscCall(PetscViewerASCIIAddTab(viewer, ((PetscObject)ksp)->tablevel));
645: if (ksp->reason > 0) {
646: if (prefix) PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve converged due to %s iterations %" PetscInt_FMT, prefix, reason, its));
647: else PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve converged due to %s iterations %" PetscInt_FMT, reason, its));
648: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
649: if (rRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " res rate %g R^2 %g", (double)rrate, (double)rRsq));
650: if (eRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " error rate %g R^2 %g", (double)erate, (double)eRsq));
651: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
652: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
653: } else if (ksp->reason <= 0) {
654: if (prefix) PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve did not converge due to %s iterations %" PetscInt_FMT, prefix, reason, its));
655: else PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve did not converge due to %s iterations %" PetscInt_FMT, reason, its));
656: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
657: if (rRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " res rate %g R^2 %g", (double)rrate, (double)rRsq));
658: if (eRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " error rate %g R^2 %g", (double)erate, (double)eRsq));
659: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
660: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
661: if (ksp->reason == KSP_DIVERGED_PC_FAILED) {
662: PCFailedReason reason;
663: PetscCall(PCGetFailedReason(ksp->pc, &reason));
664: PetscCall(PetscViewerASCIIPrintf(viewer, " PC failed due to %s \n", PCFailedReasons[reason]));
665: }
666: }
667: PetscCall(PetscViewerASCIISubtractTab(viewer, ((PetscObject)ksp)->tablevel));
668: }
669: PetscFunctionReturn(PETSC_SUCCESS);
670: }
672: #include <petscdraw.h>
674: static PetscErrorCode KSPViewEigenvalues_Internal(KSP ksp, PetscBool isExplicit, PetscViewer viewer, PetscViewerFormat format)
675: {
676: PetscReal *r, *c;
677: PetscInt n, i, neig;
678: PetscBool isascii, isdraw;
679: PetscMPIInt rank;
681: PetscFunctionBegin;
682: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)ksp), &rank));
683: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
684: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
685: if (isExplicit) {
686: PetscCall(VecGetSize(ksp->vec_sol, &n));
687: PetscCall(PetscMalloc2(n, &r, n, &c));
688: PetscCall(KSPComputeEigenvaluesExplicitly(ksp, n, r, c));
689: neig = n;
690: } else {
691: PetscInt nits;
693: PetscCall(KSPGetIterationNumber(ksp, &nits));
694: n = nits + 2;
695: if (!nits) {
696: PetscCall(PetscViewerASCIIPrintf(viewer, "Zero iterations in solver, cannot approximate any eigenvalues\n"));
697: PetscFunctionReturn(PETSC_SUCCESS);
698: }
699: PetscCall(PetscMalloc2(n, &r, n, &c));
700: PetscCall(KSPComputeEigenvalues(ksp, n, r, c, &neig));
701: }
702: if (isascii) {
703: PetscCall(PetscViewerASCIIPrintf(viewer, "%s computed eigenvalues\n", isExplicit ? "Explicitly" : "Iteratively"));
704: for (i = 0; i < neig; ++i) {
705: if (c[i] >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, "%g + %gi\n", (double)r[i], (double)c[i]));
706: else PetscCall(PetscViewerASCIIPrintf(viewer, "%g - %gi\n", (double)r[i], -(double)c[i]));
707: }
708: } else if (isdraw && rank == 0) {
709: PetscDraw draw;
710: PetscDrawSP drawsp;
712: if (format == PETSC_VIEWER_DRAW_CONTOUR) {
713: PetscCall(KSPPlotEigenContours_Private(ksp, neig, r, c));
714: } else {
715: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
716: PetscCall(PetscDrawSPCreate(draw, 1, &drawsp));
717: PetscCall(PetscDrawSPReset(drawsp));
718: for (i = 0; i < neig; ++i) PetscCall(PetscDrawSPAddPoint(drawsp, r + i, c + i));
719: PetscCall(PetscDrawSPDraw(drawsp, PETSC_TRUE));
720: PetscCall(PetscDrawSPSave(drawsp));
721: PetscCall(PetscDrawSPDestroy(&drawsp));
722: }
723: }
724: PetscCall(PetscFree2(r, c));
725: PetscFunctionReturn(PETSC_SUCCESS);
726: }
728: static PetscErrorCode KSPViewSingularvalues_Internal(KSP ksp, PetscViewer viewer, PetscViewerFormat format)
729: {
730: PetscReal smax, smin;
731: PetscInt nits;
732: PetscBool isascii;
734: PetscFunctionBegin;
735: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
736: PetscCall(KSPGetIterationNumber(ksp, &nits));
737: if (!nits) {
738: PetscCall(PetscViewerASCIIPrintf(viewer, "Zero iterations in solver, cannot approximate any singular values\n"));
739: PetscFunctionReturn(PETSC_SUCCESS);
740: }
741: PetscCall(KSPComputeExtremeSingularValues(ksp, &smax, &smin));
742: if (isascii) PetscCall(PetscViewerASCIIPrintf(viewer, "Iteratively computed extreme %svalues: max %g min %g max/min %g\n", smin < 0 ? "eigen" : "singular ", (double)smax, (double)smin, (double)(smax / smin)));
743: PetscFunctionReturn(PETSC_SUCCESS);
744: }
746: static PetscErrorCode KSPViewFinalResidual_Internal(KSP ksp, PetscViewer viewer, PetscViewerFormat format)
747: {
748: PetscBool isascii;
750: PetscFunctionBegin;
751: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
752: 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");
753: if (isascii) {
754: Mat A;
755: Vec t;
756: PetscReal norm;
758: PetscCall(PCGetOperators(ksp->pc, &A, NULL));
759: PetscCall(VecDuplicate(ksp->vec_rhs, &t));
760: PetscCall(KSP_MatMult(ksp, A, ksp->vec_sol, t));
761: PetscCall(VecAYPX(t, -1.0, ksp->vec_rhs));
762: PetscCall(VecViewFromOptions(t, (PetscObject)ksp, "-ksp_view_final_residual_vec"));
763: PetscCall(VecNorm(t, NORM_2, &norm));
764: PetscCall(VecDestroy(&t));
765: PetscCall(PetscViewerASCIIPrintf(viewer, "KSP final norm of residual %g\n", (double)norm));
766: }
767: PetscFunctionReturn(PETSC_SUCCESS);
768: }
770: static PetscErrorCode KSPMonitorPauseFinal_Internal(KSP ksp)
771: {
772: PetscInt i;
774: PetscFunctionBegin;
775: if (!ksp->pauseFinal) PetscFunctionReturn(PETSC_SUCCESS);
776: for (i = 0; i < ksp->numbermonitors; ++i) {
777: PetscViewerAndFormat *vf = (PetscViewerAndFormat *)ksp->monitorcontext[i];
778: PetscDraw draw;
779: PetscReal lpause;
781: if (!vf) continue;
782: if (vf->lg) {
783: if (!PetscCheckPointer(vf->lg, PETSC_OBJECT)) continue;
784: if (((PetscObject)vf->lg)->classid != PETSC_DRAWLG_CLASSID) continue;
785: PetscCall(PetscDrawLGGetDraw(vf->lg, &draw));
786: PetscCall(PetscDrawGetPause(draw, &lpause));
787: PetscCall(PetscDrawSetPause(draw, -1.0));
788: PetscCall(PetscDrawPause(draw));
789: PetscCall(PetscDrawSetPause(draw, lpause));
790: } else {
791: PetscBool isdraw;
793: if (!PetscCheckPointer(vf->viewer, PETSC_OBJECT)) continue;
794: if (((PetscObject)vf->viewer)->classid != PETSC_VIEWER_CLASSID) continue;
795: PetscCall(PetscObjectTypeCompare((PetscObject)vf->viewer, PETSCVIEWERDRAW, &isdraw));
796: if (!isdraw) continue;
797: PetscCall(PetscViewerDrawGetDraw(vf->viewer, 0, &draw));
798: PetscCall(PetscDrawGetPause(draw, &lpause));
799: PetscCall(PetscDrawSetPause(draw, -1.0));
800: PetscCall(PetscDrawPause(draw));
801: PetscCall(PetscDrawSetPause(draw, lpause));
802: }
803: }
804: PetscFunctionReturn(PETSC_SUCCESS);
805: }
807: static PetscErrorCode KSPSolve_Private(KSP ksp, Vec b, Vec x)
808: {
809: PetscBool flg = PETSC_FALSE, inXisinB = PETSC_FALSE, guess_zero;
810: Mat mat, pmat;
811: MPI_Comm comm;
812: MatNullSpace nullsp;
813: Vec btmp, vec_rhs = NULL;
815: PetscFunctionBegin;
816: level++;
817: comm = PetscObjectComm((PetscObject)ksp);
818: if (x && x == b) {
819: PetscCheck(ksp->guess_zero, comm, PETSC_ERR_ARG_INCOMP, "Cannot use x == b with nonzero initial guess");
820: PetscCall(VecDuplicate(b, &x));
821: inXisinB = PETSC_TRUE;
822: }
823: if (b) {
824: PetscCall(PetscObjectReference((PetscObject)b));
825: PetscCall(VecDestroy(&ksp->vec_rhs));
826: ksp->vec_rhs = b;
827: }
828: if (x) {
829: PetscCall(PetscObjectReference((PetscObject)x));
830: PetscCall(VecDestroy(&ksp->vec_sol));
831: ksp->vec_sol = x;
832: }
834: if (ksp->viewPre) PetscCall(ObjectView((PetscObject)ksp, ksp->viewerPre, ksp->formatPre));
836: if (ksp->presolve) PetscCall((*ksp->presolve)(ksp, ksp->vec_rhs, ksp->vec_sol, ksp->prectx));
838: /* reset the residual history list if requested */
839: if (ksp->res_hist_reset) ksp->res_hist_len = 0;
840: if (ksp->err_hist_reset) ksp->err_hist_len = 0;
842: /* KSPSetUp() scales the matrix if needed */
843: PetscCall(KSPSetUp(ksp));
844: PetscCall(KSPSetUpOnBlocks(ksp));
846: if (ksp->guess) {
847: PetscObjectState ostate, state;
849: PetscCall(KSPGuessSetUp(ksp->guess));
850: PetscCall(PetscObjectStateGet((PetscObject)ksp->vec_sol, &ostate));
851: PetscCall(KSPGuessFormGuess(ksp->guess, ksp->vec_rhs, ksp->vec_sol));
852: PetscCall(PetscObjectStateGet((PetscObject)ksp->vec_sol, &state));
853: if (state != ostate) {
854: ksp->guess_zero = PETSC_FALSE;
855: } else {
856: PetscCall(PetscInfo(ksp, "Using zero initial guess since the KSPGuess object did not change the vector\n"));
857: ksp->guess_zero = PETSC_TRUE;
858: }
859: }
861: PetscCall(VecSetErrorIfLocked(ksp->vec_sol, 3));
863: PetscCall(PetscLogEventBegin(!ksp->transpose_solve ? KSP_Solve : KSP_SolveTranspose, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
864: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
865: /* diagonal scale RHS if called for */
866: if (ksp->dscale) {
867: PetscCall(VecPointwiseMult(ksp->vec_rhs, ksp->vec_rhs, ksp->diagonal));
868: /* second time in, but matrix was scaled back to original */
869: if (ksp->dscalefix && ksp->dscalefix2) {
870: Mat mat, pmat;
872: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
873: PetscCall(MatDiagonalScale(pmat, ksp->diagonal, ksp->diagonal));
874: if (mat != pmat) PetscCall(MatDiagonalScale(mat, ksp->diagonal, ksp->diagonal));
875: }
877: /* scale initial guess */
878: if (!ksp->guess_zero) {
879: if (!ksp->truediagonal) {
880: PetscCall(VecDuplicate(ksp->diagonal, &ksp->truediagonal));
881: PetscCall(VecCopy(ksp->diagonal, ksp->truediagonal));
882: PetscCall(VecReciprocal(ksp->truediagonal));
883: }
884: PetscCall(VecPointwiseMult(ksp->vec_sol, ksp->vec_sol, ksp->truediagonal));
885: }
886: }
887: PetscCall(PCPreSolve(ksp->pc, ksp));
889: if (ksp->guess_zero && !ksp->guess_not_read) PetscCall(VecSet(ksp->vec_sol, 0.0));
890: if (ksp->guess_knoll) { /* The Knoll trick is independent on the KSPGuess specified */
891: PetscCall(PCApply(ksp->pc, ksp->vec_rhs, ksp->vec_sol));
892: PetscCall(KSP_RemoveNullSpace(ksp, ksp->vec_sol));
893: ksp->guess_zero = PETSC_FALSE;
894: }
896: /* can we mark the initial guess as zero for this solve? */
897: guess_zero = ksp->guess_zero;
898: if (!ksp->guess_zero) {
899: PetscReal norm;
901: PetscCall(VecNormAvailable(ksp->vec_sol, NORM_2, &flg, &norm));
902: if (flg && !norm) ksp->guess_zero = PETSC_TRUE;
903: }
904: if (ksp->transpose_solve) {
905: PetscCall(MatGetNullSpace(pmat, &nullsp));
906: } else {
907: PetscCall(MatGetTransposeNullSpace(pmat, &nullsp));
908: }
909: if (nullsp) {
910: PetscCall(VecDuplicate(ksp->vec_rhs, &btmp));
911: PetscCall(VecCopy(ksp->vec_rhs, btmp));
912: PetscCall(MatNullSpaceRemove(nullsp, btmp));
913: vec_rhs = ksp->vec_rhs;
914: ksp->vec_rhs = btmp;
915: }
916: PetscCall(VecLockReadPush(ksp->vec_rhs));
917: PetscUseTypeMethod(ksp, solve);
918: PetscCall(KSPMonitorPauseFinal_Internal(ksp));
920: PetscCall(VecLockReadPop(ksp->vec_rhs));
921: if (nullsp) {
922: ksp->vec_rhs = vec_rhs;
923: PetscCall(VecDestroy(&btmp));
924: }
926: ksp->guess_zero = guess_zero;
928: PetscCheck(ksp->reason, comm, PETSC_ERR_PLIB, "Internal error, solver returned without setting converged reason");
929: ksp->totalits += ksp->its;
931: PetscCall(KSPConvergedReasonViewFromOptions(ksp));
933: if (ksp->viewRate) {
934: PetscCall(PetscViewerPushFormat(ksp->viewerRate, ksp->formatRate));
935: PetscCall(KSPConvergedRateView(ksp, ksp->viewerRate));
936: PetscCall(PetscViewerPopFormat(ksp->viewerRate));
937: }
938: PetscCall(PCPostSolve(ksp->pc, ksp));
940: /* diagonal scale solution if called for */
941: if (ksp->dscale) {
942: PetscCall(VecPointwiseMult(ksp->vec_sol, ksp->vec_sol, ksp->diagonal));
943: /* unscale right-hand side and matrix */
944: if (ksp->dscalefix) {
945: Mat mat, pmat;
947: PetscCall(VecReciprocal(ksp->diagonal));
948: PetscCall(VecPointwiseMult(ksp->vec_rhs, ksp->vec_rhs, ksp->diagonal));
949: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
950: PetscCall(MatDiagonalScale(pmat, ksp->diagonal, ksp->diagonal));
951: if (mat != pmat) PetscCall(MatDiagonalScale(mat, ksp->diagonal, ksp->diagonal));
952: PetscCall(VecReciprocal(ksp->diagonal));
953: ksp->dscalefix2 = PETSC_TRUE;
954: }
955: }
956: PetscCall(PetscLogEventEnd(!ksp->transpose_solve ? KSP_Solve : KSP_SolveTranspose, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
957: if (ksp->guess) PetscCall(KSPGuessUpdate(ksp->guess, ksp->vec_rhs, ksp->vec_sol));
958: if (ksp->postsolve) PetscCall((*ksp->postsolve)(ksp, ksp->vec_rhs, ksp->vec_sol, ksp->postctx));
960: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
961: if (ksp->viewEV) PetscCall(KSPViewEigenvalues_Internal(ksp, PETSC_FALSE, ksp->viewerEV, ksp->formatEV));
962: if (ksp->viewEVExp) PetscCall(KSPViewEigenvalues_Internal(ksp, PETSC_TRUE, ksp->viewerEVExp, ksp->formatEVExp));
963: if (ksp->viewSV) PetscCall(KSPViewSingularvalues_Internal(ksp, ksp->viewerSV, ksp->formatSV));
964: if (ksp->viewFinalRes) PetscCall(KSPViewFinalResidual_Internal(ksp, ksp->viewerFinalRes, ksp->formatFinalRes));
965: if (ksp->viewMat) PetscCall(ObjectView((PetscObject)mat, ksp->viewerMat, ksp->formatMat));
966: if (ksp->viewPMat) PetscCall(ObjectView((PetscObject)pmat, ksp->viewerPMat, ksp->formatPMat));
967: if (ksp->viewRhs) PetscCall(ObjectView((PetscObject)ksp->vec_rhs, ksp->viewerRhs, ksp->formatRhs));
968: if (ksp->viewSol) PetscCall(ObjectView((PetscObject)ksp->vec_sol, ksp->viewerSol, ksp->formatSol));
969: if (ksp->view) PetscCall(ObjectView((PetscObject)ksp, ksp->viewer, ksp->format));
970: if (ksp->viewDScale) PetscCall(ObjectView((PetscObject)ksp->diagonal, ksp->viewerDScale, ksp->formatDScale));
971: if (ksp->viewMatExp) {
972: Mat A, B;
974: PetscCall(PCGetOperators(ksp->pc, &A, NULL));
975: if (ksp->transpose_solve) {
976: Mat AT;
978: PetscCall(MatCreateTranspose(A, &AT));
979: PetscCall(MatComputeOperator(AT, MATAIJ, &B));
980: PetscCall(MatDestroy(&AT));
981: } else {
982: PetscCall(MatComputeOperator(A, MATAIJ, &B));
983: }
984: PetscCall(ObjectView((PetscObject)B, ksp->viewerMatExp, ksp->formatMatExp));
985: PetscCall(MatDestroy(&B));
986: }
987: if (ksp->viewPOpExp) {
988: Mat B;
990: PetscCall(KSPComputeOperator(ksp, MATAIJ, &B));
991: PetscCall(ObjectView((PetscObject)B, ksp->viewerPOpExp, ksp->formatPOpExp));
992: PetscCall(MatDestroy(&B));
993: }
995: if (inXisinB) {
996: PetscCall(VecCopy(x, b));
997: PetscCall(VecDestroy(&x));
998: }
999: PetscCall(PetscObjectSAWsBlock((PetscObject)ksp));
1000: if (ksp->errorifnotconverged && ksp->reason < 0 && ((level == 1) || (ksp->reason != KSP_DIVERGED_ITS))) {
1001: PCFailedReason reason;
1003: PetscCheck(ksp->reason == KSP_DIVERGED_PC_FAILED, comm, PETSC_ERR_NOT_CONVERGED, "KSPSolve%s() has not converged, reason %s", !ksp->transpose_solve ? "" : "Transpose", KSPConvergedReasons[ksp->reason]);
1004: PetscCall(PCGetFailedReason(ksp->pc, &reason));
1005: SETERRQ(comm, PETSC_ERR_NOT_CONVERGED, "KSPSolve%s() has not converged, reason %s PC failed due to %s", !ksp->transpose_solve ? "" : "Transpose", KSPConvergedReasons[ksp->reason], PCFailedReasons[reason]);
1006: }
1007: level--;
1008: PetscFunctionReturn(PETSC_SUCCESS);
1009: }
1011: /*@
1012: KSPSolve - Solves a linear system associated with `KSP` object
1014: Collective
1016: Input Parameters:
1017: + ksp - iterative solver obtained from `KSPCreate()`
1018: . b - the right-hand side vector
1019: - x - the solution (this may be the same vector as `b`, then `b` will be overwritten with the answer)
1021: Options Database Keys:
1022: + -ksp_view_eigenvalues - compute preconditioned operators eigenvalues
1023: . -ksp_view_eigenvalues_explicit - compute the eigenvalues by forming the dense operator and using LAPACK
1024: . -ksp_view_mat binary - save matrix to the default binary viewer
1025: . -ksp_view_pmat binary - save matrix used to build preconditioner to the default binary viewer
1026: . -ksp_view_rhs binary - save right-hand side vector to the default binary viewer
1027: . -ksp_view_solution binary - save computed solution vector to the default binary viewer
1028: (can be read later with src/ksp/tutorials/ex10.c for testing solvers)
1029: . -ksp_view_mat_explicit - for matrix-free operators, computes the matrix entries and views them
1030: . -ksp_view_preconditioned_operator_explicit - computes the product of the preconditioner and matrix as an explicit matrix and views it
1031: . -ksp_converged_reason - print reason for converged or diverged, also prints number of iterations
1032: . -ksp_view_final_residual - print 2-norm of true linear system residual at the end of the solution process
1033: . -ksp_error_if_not_converged - stop the program as soon as an error is detected in a `KSPSolve()`
1034: . -ksp_view_pre - print the ksp data structure before the system solution
1035: - -ksp_view - print the ksp data structure at the end of the system solution
1037: Level: beginner
1039: Notes:
1040: See `KSPSetFromOptions()` for additional options database keys that affect `KSPSolve()`
1042: If one uses `KSPSetDM()` then `x` or `b` need not be passed. Use `KSPGetSolution()` to access the solution in this case.
1044: The operator is specified with `KSPSetOperators()`.
1046: `KSPSolve()` will normally return without generating an error regardless of whether the linear system was solved or if constructing the preconditioner failed.
1047: Call `KSPGetConvergedReason()` to determine if the solver converged or failed and why. The option -ksp_error_if_not_converged or function `KSPSetErrorIfNotConverged()`
1048: will cause `KSPSolve()` to error as soon as an error occurs in the linear solver. In inner `KSPSolve()` `KSP_DIVERGED_ITS` is not treated as an error because when using nested solvers
1049: it may be fine that inner solvers in the preconditioner do not converge during the solution process.
1051: The number of iterations can be obtained from `KSPGetIterationNumber()`.
1053: If you provide a matrix that has a `MatSetNullSpace()` and `MatSetTransposeNullSpace()` this will use that information to solve singular systems
1054: in the least squares sense with a norm minimizing solution.
1056: $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()`).
1058: `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
1059: 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
1060: direction thus the solution which is a linear combination of the search directions has no component in the nullspace(A).
1062: We recommend always using `KSPGMRES` for such singular systems.
1063: If nullspace(A) = nullspace(A') (note symmetric matrices always satisfy this property) then both left and right preconditioning will work
1064: If nullspace(A) != nullspace(A') then left preconditioning will work but right preconditioning may not work (or it may).
1066: Developer Notes:
1067: The reason we cannot always solve nullspace(A) != nullspace(A') systems with right preconditioning is because we need to remove at each iteration
1068: 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
1069: such as diagonal scaling we cannot apply the inverse of the preconditioner to a vector and thus cannot compute the nullspace(AB).
1071: If using a direct method (e.g., via the `KSP` solver
1072: `KSPPREONLY` and a preconditioner such as `PCLU` or `PCCHOLESKY` then usually one iteration of the `KSP` method will be needed for convergence.
1074: To solve a linear system with the transpose of the matrix use `KSPSolveTranspose()`.
1076: Understanding Convergence\:
1077: The manual pages `KSPMonitorSet()`, `KSPComputeEigenvalues()`, and
1078: `KSPComputeEigenvaluesExplicitly()` provide information on additional
1079: options to monitor convergence and print eigenvalue information.
1081: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPDestroy()`, `KSPSetTolerances()`, `KSPConvergedDefault()`,
1082: `KSPSolveTranspose()`, `KSPGetIterationNumber()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatSetTransposeNullSpace()`, `KSP`,
1083: `KSPConvergedReasonView()`, `KSPCheckSolve()`, `KSPSetErrorIfNotConverged()`
1084: @*/
1085: PetscErrorCode KSPSolve(KSP ksp, Vec b, Vec x)
1086: {
1087: PetscBool isPCMPI;
1089: PetscFunctionBegin;
1093: ksp->transpose_solve = PETSC_FALSE;
1094: PetscCall(KSPSolve_Private(ksp, b, x));
1095: PetscCall(PetscObjectTypeCompare((PetscObject)ksp->pc, PCMPI, &isPCMPI));
1096: if (PCMPIServerActive && isPCMPI) {
1097: KSP subksp;
1099: PetscCall(PCMPIGetKSP(ksp->pc, &subksp));
1100: ksp->its = subksp->its;
1101: ksp->reason = subksp->reason;
1102: }
1103: PetscFunctionReturn(PETSC_SUCCESS);
1104: }
1106: /*@
1107: KSPSolveTranspose - Solves a linear system with the transpose of the matrix associated with the `KSP` object, $ A^T x = b$.
1109: Collective
1111: Input Parameters:
1112: + ksp - iterative solver obtained from `KSPCreate()`
1113: . b - right-hand side vector
1114: - x - solution vector
1116: Level: developer
1118: Note:
1119: For complex numbers this solve the non-Hermitian transpose system.
1121: Developer Note:
1122: We need to implement a `KSPSolveHermitianTranspose()`
1124: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPDestroy()`, `KSPSetTolerances()`, `KSPConvergedDefault()`,
1125: `KSPSolve()`, `KSP`, `KSPSetOperators()`
1126: @*/
1127: PetscErrorCode KSPSolveTranspose(KSP ksp, Vec b, Vec x)
1128: {
1129: PetscFunctionBegin;
1133: if (ksp->transpose.use_explicittranspose) {
1134: Mat J, Jpre;
1135: PetscCall(KSPGetOperators(ksp, &J, &Jpre));
1136: if (!ksp->transpose.reuse_transpose) {
1137: PetscCall(MatTranspose(J, MAT_INITIAL_MATRIX, &ksp->transpose.AT));
1138: if (J != Jpre) PetscCall(MatTranspose(Jpre, MAT_INITIAL_MATRIX, &ksp->transpose.BT));
1139: ksp->transpose.reuse_transpose = PETSC_TRUE;
1140: } else {
1141: PetscCall(MatTranspose(J, MAT_REUSE_MATRIX, &ksp->transpose.AT));
1142: if (J != Jpre) PetscCall(MatTranspose(Jpre, MAT_REUSE_MATRIX, &ksp->transpose.BT));
1143: }
1144: if (J == Jpre && ksp->transpose.BT != ksp->transpose.AT) {
1145: PetscCall(PetscObjectReference((PetscObject)ksp->transpose.AT));
1146: ksp->transpose.BT = ksp->transpose.AT;
1147: }
1148: PetscCall(KSPSetOperators(ksp, ksp->transpose.AT, ksp->transpose.BT));
1149: } else {
1150: ksp->transpose_solve = PETSC_TRUE;
1151: }
1152: PetscCall(KSPSolve_Private(ksp, b, x));
1153: PetscFunctionReturn(PETSC_SUCCESS);
1154: }
1156: static PetscErrorCode KSPViewFinalMatResidual_Internal(KSP ksp, Mat B, Mat X, PetscViewer viewer, PetscViewerFormat format, PetscInt shift)
1157: {
1158: Mat A, R;
1159: PetscReal *norms;
1160: PetscInt i, N;
1161: PetscBool flg;
1163: PetscFunctionBegin;
1164: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &flg));
1165: if (flg) {
1166: PetscCall(PCGetOperators(ksp->pc, &A, NULL));
1167: if (!ksp->transpose_solve) PetscCall(MatMatMult(A, X, MAT_INITIAL_MATRIX, PETSC_DETERMINE, &R));
1168: else PetscCall(MatTransposeMatMult(A, X, MAT_INITIAL_MATRIX, PETSC_DETERMINE, &R));
1169: PetscCall(MatAYPX(R, -1.0, B, SAME_NONZERO_PATTERN));
1170: PetscCall(MatGetSize(R, NULL, &N));
1171: PetscCall(PetscMalloc1(N, &norms));
1172: PetscCall(MatGetColumnNorms(R, NORM_2, norms));
1173: PetscCall(MatDestroy(&R));
1174: 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]));
1175: PetscCall(PetscFree(norms));
1176: }
1177: PetscFunctionReturn(PETSC_SUCCESS);
1178: }
1180: static PetscErrorCode KSPMatSolve_Private(KSP ksp, Mat B, Mat X)
1181: {
1182: Mat A, P, vB, vX;
1183: Vec cb, cx;
1184: PetscInt n1, N1, n2, N2, Bbn = PETSC_DECIDE;
1185: PetscBool match;
1187: PetscFunctionBegin;
1191: PetscCheckSameComm(ksp, 1, B, 2);
1192: PetscCheckSameComm(ksp, 1, X, 3);
1193: PetscCheckSameType(B, 2, X, 3);
1194: PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
1195: MatCheckPreallocated(X, 3);
1196: if (!X->assembled) {
1197: PetscCall(MatSetOption(X, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
1198: PetscCall(MatAssemblyBegin(X, MAT_FINAL_ASSEMBLY));
1199: PetscCall(MatAssemblyEnd(X, MAT_FINAL_ASSEMBLY));
1200: }
1201: PetscCheck(B != X, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_IDN, "B and X must be different matrices");
1202: PetscCheck(!ksp->transpose_solve || !ksp->transpose.use_explicittranspose, PetscObjectComm((PetscObject)ksp), PETSC_ERR_SUP, "KSPMatSolveTranspose() does not support -ksp_use_explicittranspose");
1203: PetscCall(KSPGetOperators(ksp, &A, &P));
1204: PetscCall(MatGetLocalSize(B, NULL, &n2));
1205: PetscCall(MatGetLocalSize(X, NULL, &n1));
1206: PetscCall(MatGetSize(B, NULL, &N2));
1207: PetscCall(MatGetSize(X, NULL, &N1));
1208: 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);
1209: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)B, &match, MATSEQDENSE, MATMPIDENSE, ""));
1210: PetscCheck(match, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Provided block of right-hand sides not stored in a dense Mat");
1211: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)X, &match, MATSEQDENSE, MATMPIDENSE, ""));
1212: PetscCheck(match, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Provided block of solutions not stored in a dense Mat");
1213: PetscCall(KSPSetUp(ksp));
1214: PetscCall(KSPSetUpOnBlocks(ksp));
1215: if (ksp->ops->matsolve) {
1216: level++;
1217: if (ksp->guess_zero) PetscCall(MatZeroEntries(X));
1218: PetscCall(PetscLogEventBegin(!ksp->transpose_solve ? KSP_MatSolve : KSP_MatSolveTranspose, ksp, B, X, 0));
1219: PetscCall(KSPGetMatSolveBatchSize(ksp, &Bbn));
1220: /* by default, do a single solve with all columns */
1221: if (Bbn == PETSC_DECIDE) Bbn = N2;
1222: else PetscCheck(Bbn >= 1, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "KSPMatSolve() batch size %" PetscInt_FMT " must be positive", Bbn);
1223: PetscCall(PetscInfo(ksp, "KSP type %s solving using batches of width at most %" PetscInt_FMT "\n", ((PetscObject)ksp)->type_name, Bbn));
1224: /* if -ksp_matsolve_batch_size is greater than the actual number of columns, do a single solve with all columns */
1225: if (Bbn >= N2) {
1226: PetscUseTypeMethod(ksp, matsolve, B, X);
1227: if (ksp->viewFinalRes) PetscCall(KSPViewFinalMatResidual_Internal(ksp, B, X, ksp->viewerFinalRes, ksp->formatFinalRes, 0));
1229: PetscCall(KSPConvergedReasonViewFromOptions(ksp));
1231: if (ksp->viewRate) {
1232: PetscCall(PetscViewerPushFormat(ksp->viewerRate, PETSC_VIEWER_DEFAULT));
1233: PetscCall(KSPConvergedRateView(ksp, ksp->viewerRate));
1234: PetscCall(PetscViewerPopFormat(ksp->viewerRate));
1235: }
1236: } else {
1237: for (n2 = 0; n2 < N2; n2 += Bbn) {
1238: PetscCall(MatDenseGetSubMatrix(B, PETSC_DECIDE, PETSC_DECIDE, n2, PetscMin(n2 + Bbn, N2), &vB));
1239: PetscCall(MatDenseGetSubMatrix(X, PETSC_DECIDE, PETSC_DECIDE, n2, PetscMin(n2 + Bbn, N2), &vX));
1240: PetscUseTypeMethod(ksp, matsolve, vB, vX);
1241: if (ksp->viewFinalRes) PetscCall(KSPViewFinalMatResidual_Internal(ksp, vB, vX, ksp->viewerFinalRes, ksp->formatFinalRes, n2));
1243: PetscCall(KSPConvergedReasonViewFromOptions(ksp));
1245: if (ksp->viewRate) {
1246: PetscCall(PetscViewerPushFormat(ksp->viewerRate, PETSC_VIEWER_DEFAULT));
1247: PetscCall(KSPConvergedRateView(ksp, ksp->viewerRate));
1248: PetscCall(PetscViewerPopFormat(ksp->viewerRate));
1249: }
1250: PetscCall(MatDenseRestoreSubMatrix(B, &vB));
1251: PetscCall(MatDenseRestoreSubMatrix(X, &vX));
1252: }
1253: }
1254: if (ksp->viewMat) PetscCall(ObjectView((PetscObject)A, ksp->viewerMat, ksp->formatMat));
1255: if (ksp->viewPMat) PetscCall(ObjectView((PetscObject)P, ksp->viewerPMat, ksp->formatPMat));
1256: if (ksp->viewRhs) PetscCall(ObjectView((PetscObject)B, ksp->viewerRhs, ksp->formatRhs));
1257: if (ksp->viewSol) PetscCall(ObjectView((PetscObject)X, ksp->viewerSol, ksp->formatSol));
1258: if (ksp->view) PetscCall(KSPView(ksp, ksp->viewer));
1259: PetscCall(PetscLogEventEnd(!ksp->transpose_solve ? KSP_MatSolve : KSP_MatSolveTranspose, ksp, B, X, 0));
1260: if (ksp->errorifnotconverged && ksp->reason < 0 && (level == 1 || ksp->reason != KSP_DIVERGED_ITS)) {
1261: PCFailedReason reason;
1263: PetscCheck(ksp->reason == KSP_DIVERGED_PC_FAILED, PetscObjectComm((PetscObject)ksp), PETSC_ERR_NOT_CONVERGED, "KSPMatSolve%s() has not converged, reason %s", !ksp->transpose_solve ? "" : "Transpose", KSPConvergedReasons[ksp->reason]);
1264: PetscCall(PCGetFailedReason(ksp->pc, &reason));
1265: SETERRQ(PetscObjectComm((PetscObject)ksp), PETSC_ERR_NOT_CONVERGED, "KSPMatSolve%s() has not converged, reason %s PC failed due to %s", !ksp->transpose_solve ? "" : "Transpose", KSPConvergedReasons[ksp->reason], PCFailedReasons[reason]);
1266: }
1267: level--;
1268: } else {
1269: PetscCall(PetscInfo(ksp, "KSP type %s solving column by column\n", ((PetscObject)ksp)->type_name));
1270: for (n2 = 0; n2 < N2; ++n2) {
1271: PetscCall(MatDenseGetColumnVecRead(B, n2, &cb));
1272: PetscCall(MatDenseGetColumnVecWrite(X, n2, &cx));
1273: PetscCall(KSPSolve_Private(ksp, cb, cx));
1274: PetscCall(MatDenseRestoreColumnVecWrite(X, n2, &cx));
1275: PetscCall(MatDenseRestoreColumnVecRead(B, n2, &cb));
1276: }
1277: }
1278: PetscFunctionReturn(PETSC_SUCCESS);
1279: }
1281: /*@
1282: KSPMatSolve - Solves a linear system with multiple right-hand sides stored as a `MATDENSE`.
1284: Input Parameters:
1285: + ksp - iterative solver
1286: - B - block of right-hand sides
1288: Output Parameter:
1289: . X - block of solutions
1291: Level: intermediate
1293: Notes:
1294: This is a stripped-down version of `KSPSolve()`, which only handles `-ksp_view`, `-ksp_converged_reason`, `-ksp_converged_rate`, and `-ksp_view_final_residual`.
1296: Unlike with `KSPSolve()`, `B` and `X` must be different matrices.
1298: .seealso: [](ch_ksp), `KSPSolve()`, `MatMatSolve()`, `KSPMatSolveTranspose()`, `MATDENSE`, `KSPHPDDM`, `PCBJACOBI`, `PCASM`, `KSPSetMatSolveBatchSize()`
1299: @*/
1300: PetscErrorCode KSPMatSolve(KSP ksp, Mat B, Mat X)
1301: {
1302: PetscFunctionBegin;
1303: ksp->transpose_solve = PETSC_FALSE;
1304: PetscCall(KSPMatSolve_Private(ksp, B, X));
1305: PetscFunctionReturn(PETSC_SUCCESS);
1306: }
1308: /*@
1309: KSPMatSolveTranspose - Solves a linear system with the transposed matrix with multiple right-hand sides stored as a `MATDENSE`.
1311: Input Parameters:
1312: + ksp - iterative solver
1313: - B - block of right-hand sides
1315: Output Parameter:
1316: . X - block of solutions
1318: Level: intermediate
1320: Notes:
1321: This is a stripped-down version of `KSPSolveTranspose()`, which only handles `-ksp_view`, `-ksp_converged_reason`, `-ksp_converged_rate`, and `-ksp_view_final_residual`.
1323: Unlike `KSPSolveTranspose()`,
1324: `B` and `X` must be different matrices and the transposed matrix cannot be assembled explicitly for the user.
1326: .seealso: [](ch_ksp), `KSPSolveTranspose()`, `MatMatTransposeSolve()`, `KSPMatSolve()`, `MATDENSE`, `KSPHPDDM`, `PCBJACOBI`, `PCASM`
1327: @*/
1328: PetscErrorCode KSPMatSolveTranspose(KSP ksp, Mat B, Mat X)
1329: {
1330: PetscFunctionBegin;
1331: ksp->transpose_solve = PETSC_TRUE;
1332: PetscCall(KSPMatSolve_Private(ksp, B, X));
1333: PetscFunctionReturn(PETSC_SUCCESS);
1334: }
1336: /*@
1337: KSPSetMatSolveBatchSize - Sets the maximum number of columns treated simultaneously in `KSPMatSolve()`.
1339: Logically Collective
1341: Input Parameters:
1342: + ksp - the `KSP` iterative solver
1343: - bs - batch size
1345: Level: advanced
1347: .seealso: [](ch_ksp), `KSPMatSolve()`, `KSPGetMatSolveBatchSize()`, `-mat_mumps_icntl_27`, `-matmatmult_Bbn`
1348: @*/
1349: PetscErrorCode KSPSetMatSolveBatchSize(KSP ksp, PetscInt bs)
1350: {
1351: PetscFunctionBegin;
1354: ksp->nmax = bs;
1355: PetscFunctionReturn(PETSC_SUCCESS);
1356: }
1358: /*@
1359: KSPGetMatSolveBatchSize - Gets the maximum number of columns treated simultaneously in `KSPMatSolve()`.
1361: Input Parameter:
1362: . ksp - iterative solver context
1364: Output Parameter:
1365: . bs - batch size
1367: Level: advanced
1369: .seealso: [](ch_ksp), `KSPMatSolve()`, `KSPSetMatSolveBatchSize()`, `-mat_mumps_icntl_27`, `-matmatmult_Bbn`
1370: @*/
1371: PetscErrorCode KSPGetMatSolveBatchSize(KSP ksp, PetscInt *bs)
1372: {
1373: PetscFunctionBegin;
1375: PetscAssertPointer(bs, 2);
1376: *bs = ksp->nmax;
1377: PetscFunctionReturn(PETSC_SUCCESS);
1378: }
1380: /*@
1381: KSPResetViewers - Resets all the viewers set from the options database during `KSPSetFromOptions()`
1383: Collective
1385: Input Parameter:
1386: . ksp - the `KSP` iterative solver context obtained from `KSPCreate()`
1388: Level: beginner
1390: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPSolve()`, `KSPSetFromOptions()`, `KSP`
1391: @*/
1392: PetscErrorCode KSPResetViewers(KSP ksp)
1393: {
1394: PetscFunctionBegin;
1396: if (!ksp) PetscFunctionReturn(PETSC_SUCCESS);
1397: PetscCall(PetscViewerDestroy(&ksp->viewer));
1398: PetscCall(PetscViewerDestroy(&ksp->viewerPre));
1399: PetscCall(PetscViewerDestroy(&ksp->viewerRate));
1400: PetscCall(PetscViewerDestroy(&ksp->viewerMat));
1401: PetscCall(PetscViewerDestroy(&ksp->viewerPMat));
1402: PetscCall(PetscViewerDestroy(&ksp->viewerRhs));
1403: PetscCall(PetscViewerDestroy(&ksp->viewerSol));
1404: PetscCall(PetscViewerDestroy(&ksp->viewerMatExp));
1405: PetscCall(PetscViewerDestroy(&ksp->viewerEV));
1406: PetscCall(PetscViewerDestroy(&ksp->viewerSV));
1407: PetscCall(PetscViewerDestroy(&ksp->viewerEVExp));
1408: PetscCall(PetscViewerDestroy(&ksp->viewerFinalRes));
1409: PetscCall(PetscViewerDestroy(&ksp->viewerPOpExp));
1410: PetscCall(PetscViewerDestroy(&ksp->viewerDScale));
1411: ksp->view = PETSC_FALSE;
1412: ksp->viewPre = PETSC_FALSE;
1413: ksp->viewMat = PETSC_FALSE;
1414: ksp->viewPMat = PETSC_FALSE;
1415: ksp->viewRhs = PETSC_FALSE;
1416: ksp->viewSol = PETSC_FALSE;
1417: ksp->viewMatExp = PETSC_FALSE;
1418: ksp->viewEV = PETSC_FALSE;
1419: ksp->viewSV = PETSC_FALSE;
1420: ksp->viewEVExp = PETSC_FALSE;
1421: ksp->viewFinalRes = PETSC_FALSE;
1422: ksp->viewPOpExp = PETSC_FALSE;
1423: ksp->viewDScale = PETSC_FALSE;
1424: PetscFunctionReturn(PETSC_SUCCESS);
1425: }
1427: /*@
1428: KSPReset - Removes any allocated `Vec` and `Mat` from the `KSP` data structures.
1430: Collective
1432: Input Parameter:
1433: . ksp - iterative solver obtained from `KSPCreate()`
1435: Level: beginner
1437: Notes:
1438: Any options set in the `KSP`, including those set with `KSPSetFromOptions()` remain.
1440: Call `KSPReset()` only before you call `KSPSetOperators()` with a different sized matrix than the previous matrix used with the `KSP`.
1442: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPSolve()`, `KSP`
1443: @*/
1444: PetscErrorCode KSPReset(KSP ksp)
1445: {
1446: PetscFunctionBegin;
1448: if (!ksp) PetscFunctionReturn(PETSC_SUCCESS);
1449: PetscTryTypeMethod(ksp, reset);
1450: if (ksp->pc) PetscCall(PCReset(ksp->pc));
1451: if (ksp->guess) {
1452: KSPGuess guess = ksp->guess;
1453: PetscTryTypeMethod(guess, reset);
1454: }
1455: PetscCall(VecDestroyVecs(ksp->nwork, &ksp->work));
1456: PetscCall(VecDestroy(&ksp->vec_rhs));
1457: PetscCall(VecDestroy(&ksp->vec_sol));
1458: PetscCall(VecDestroy(&ksp->diagonal));
1459: PetscCall(VecDestroy(&ksp->truediagonal));
1461: ksp->setupstage = KSP_SETUP_NEW;
1462: ksp->nmax = PETSC_DECIDE;
1463: PetscFunctionReturn(PETSC_SUCCESS);
1464: }
1466: /*@
1467: KSPDestroy - Destroys a `KSP` context.
1469: Collective
1471: Input Parameter:
1472: . ksp - iterative solver obtained from `KSPCreate()`
1474: Level: beginner
1476: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPSolve()`, `KSP`
1477: @*/
1478: PetscErrorCode KSPDestroy(KSP *ksp)
1479: {
1480: PC pc;
1482: PetscFunctionBegin;
1483: if (!*ksp) PetscFunctionReturn(PETSC_SUCCESS);
1485: if (--((PetscObject)*ksp)->refct > 0) {
1486: *ksp = NULL;
1487: PetscFunctionReturn(PETSC_SUCCESS);
1488: }
1490: PetscCall(PetscObjectSAWsViewOff((PetscObject)*ksp));
1492: /*
1493: Avoid a cascading call to PCReset(ksp->pc) from the following call:
1494: PCReset() shouldn't be called from KSPDestroy() as it is unprotected by pc's
1495: refcount (and may be shared, e.g., by other ksps).
1496: */
1497: pc = (*ksp)->pc;
1498: (*ksp)->pc = NULL;
1499: PetscCall(KSPReset(*ksp));
1500: PetscCall(KSPResetViewers(*ksp));
1501: (*ksp)->pc = pc;
1502: PetscTryTypeMethod(*ksp, destroy);
1504: if ((*ksp)->transpose.use_explicittranspose) {
1505: PetscCall(MatDestroy(&(*ksp)->transpose.AT));
1506: PetscCall(MatDestroy(&(*ksp)->transpose.BT));
1507: (*ksp)->transpose.reuse_transpose = PETSC_FALSE;
1508: }
1510: PetscCall(KSPGuessDestroy(&(*ksp)->guess));
1511: PetscCall(DMDestroy(&(*ksp)->dm));
1512: PetscCall(PCDestroy(&(*ksp)->pc));
1513: PetscCall(PetscFree((*ksp)->res_hist_alloc));
1514: PetscCall(PetscFree((*ksp)->err_hist_alloc));
1515: if ((*ksp)->convergeddestroy) PetscCall((*(*ksp)->convergeddestroy)((*ksp)->cnvP));
1516: PetscCall(KSPMonitorCancel(*ksp));
1517: PetscCall(KSPConvergedReasonViewCancel(*ksp));
1518: PetscCall(PetscHeaderDestroy(ksp));
1519: PetscFunctionReturn(PETSC_SUCCESS);
1520: }
1522: /*@
1523: KSPSetPCSide - Sets the preconditioning side.
1525: Logically Collective
1527: Input Parameter:
1528: . ksp - iterative solver obtained from `KSPCreate()`
1530: Output Parameter:
1531: . side - the preconditioning side, where side is one of
1532: .vb
1533: PC_LEFT - left preconditioning (default)
1534: PC_RIGHT - right preconditioning
1535: PC_SYMMETRIC - symmetric preconditioning
1536: .ve
1538: Options Database Key:
1539: . -ksp_pc_side <right,left,symmetric> - `KSP` preconditioner side
1541: Level: intermediate
1543: Notes:
1544: Left preconditioning is used by default for most Krylov methods except `KSPFGMRES` which only supports right preconditioning.
1546: For methods changing the side of the preconditioner changes the norm type that is used, see `KSPSetNormType()`.
1548: Symmetric preconditioning is currently available only for the `KSPQCG` method. However, note that
1549: symmetric preconditioning can be emulated by using either right or left
1550: preconditioning, modifying the application of the matrix (with a custom `Mat` argument to `KSPSetOperators()`,
1551: and using a pre 'KSPSetPreSolve()` or post processing `KSPSetPostSolve()` step).
1553: Setting the `PCSide` often affects the default norm type. See `KSPSetNormType()` for details.
1555: .seealso: [](ch_ksp), `KSPGetPCSide()`, `KSPSetNormType()`, `KSPGetNormType()`, `KSP`, `KSPSetPreSolve()`, `KSPSetPostSolve()`
1556: @*/
1557: PetscErrorCode KSPSetPCSide(KSP ksp, PCSide side)
1558: {
1559: PetscFunctionBegin;
1562: ksp->pc_side = ksp->pc_side_set = side;
1563: PetscFunctionReturn(PETSC_SUCCESS);
1564: }
1566: /*@
1567: KSPGetPCSide - Gets the preconditioning side.
1569: Not Collective
1571: Input Parameter:
1572: . ksp - iterative solver obtained from `KSPCreate()`
1574: Output Parameter:
1575: . side - the preconditioning side, where side is one of
1576: .vb
1577: PC_LEFT - left preconditioning (default)
1578: PC_RIGHT - right preconditioning
1579: PC_SYMMETRIC - symmetric preconditioning
1580: .ve
1582: Level: intermediate
1584: .seealso: [](ch_ksp), `KSPSetPCSide()`, `KSP`
1585: @*/
1586: PetscErrorCode KSPGetPCSide(KSP ksp, PCSide *side)
1587: {
1588: PetscFunctionBegin;
1590: PetscAssertPointer(side, 2);
1591: PetscCall(KSPSetUpNorms_Private(ksp, PETSC_TRUE, &ksp->normtype, &ksp->pc_side));
1592: *side = ksp->pc_side;
1593: PetscFunctionReturn(PETSC_SUCCESS);
1594: }
1596: /*@
1597: KSPGetTolerances - Gets the relative, absolute, divergence, and maximum
1598: iteration tolerances used by the default `KSP` convergence tests.
1600: Not Collective
1602: Input Parameter:
1603: . ksp - the Krylov subspace context
1605: Output Parameters:
1606: + rtol - the relative convergence tolerance
1607: . abstol - the absolute convergence tolerance
1608: . dtol - the divergence tolerance
1609: - maxits - maximum number of iterations
1611: Level: intermediate
1613: Note:
1614: The user can specify `NULL` for any parameter that is not needed.
1616: .seealso: [](ch_ksp), `KSPSetTolerances()`, `KSP`, `KSPSetMinimumIterations()`, `KSPGetMinimumIterations()`
1617: @*/
1618: PetscErrorCode KSPGetTolerances(KSP ksp, PetscReal *rtol, PetscReal *abstol, PetscReal *dtol, PetscInt *maxits)
1619: {
1620: PetscFunctionBegin;
1622: if (abstol) *abstol = ksp->abstol;
1623: if (rtol) *rtol = ksp->rtol;
1624: if (dtol) *dtol = ksp->divtol;
1625: if (maxits) *maxits = ksp->max_it;
1626: PetscFunctionReturn(PETSC_SUCCESS);
1627: }
1629: /*@
1630: KSPSetTolerances - Sets the relative, absolute, divergence, and maximum
1631: iteration tolerances used by the default `KSP` convergence testers.
1633: Logically Collective
1635: Input Parameters:
1636: + ksp - the Krylov subspace context
1637: . rtol - the relative convergence tolerance, relative decrease in the (possibly preconditioned) residual norm
1638: . abstol - the absolute convergence tolerance absolute size of the (possibly preconditioned) residual norm
1639: . dtol - the divergence tolerance, amount (possibly preconditioned) residual norm can increase before `KSPConvergedDefault()` concludes that the method is diverging
1640: - maxits - maximum number of iterations to use
1642: Options Database Keys:
1643: + -ksp_atol <abstol> - Sets `abstol`
1644: . -ksp_rtol <rtol> - Sets `rtol`
1645: . -ksp_divtol <dtol> - Sets `dtol`
1646: - -ksp_max_it <maxits> - Sets `maxits`
1648: Level: intermediate
1650: Notes:
1651: The tolerances are with respect to a norm of the residual of the equation $ \| b - A x^n \|$, they do not directly use the error of the equation.
1652: The norm used depends on the `KSPNormType` that has been set with `KSPSetNormType()`, the default depends on the `KSPType` used.
1654: All parameters must be non-negative.
1656: Use `PETSC_CURRENT` to retain the current value of any of the parameters. The deprecated `PETSC_DEFAULT` also retains the current value (though the name is confusing).
1658: Use `PETSC_DETERMINE` to use the default value for the given `KSP`. The default value is the value when the object's type is set.
1660: For `dtol` and `maxits` use `PETSC_UMLIMITED` to indicate there is no upper bound on these values
1662: See `KSPConvergedDefault()` for details how these parameters are used in the default convergence test. See also `KSPSetConvergenceTest()`
1663: for setting user-defined stopping criteria.
1665: Fortran Note:
1666: Use `PETSC_CURRENT_INTEGER`, `PETSC_CURRENT_REAL`, `PETSC_DETERMINE_INTEGER`, or `PETSC_DETERMINE_REAL`
1668: .seealso: [](ch_ksp), `KSPGetTolerances()`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSP`, `KSPSetMinimumIterations()`
1669: @*/
1670: PetscErrorCode KSPSetTolerances(KSP ksp, PetscReal rtol, PetscReal abstol, PetscReal dtol, PetscInt maxits)
1671: {
1672: PetscFunctionBegin;
1679: if (rtol == (PetscReal)PETSC_DETERMINE) {
1680: ksp->rtol = ksp->default_rtol;
1681: } else if (rtol != (PetscReal)PETSC_CURRENT) {
1682: 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);
1683: ksp->rtol = rtol;
1684: }
1685: if (abstol == (PetscReal)PETSC_DETERMINE) {
1686: ksp->abstol = ksp->default_abstol;
1687: } else if (abstol != (PetscReal)PETSC_CURRENT) {
1688: PetscCheck(abstol >= 0.0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Absolute tolerance %g must be non-negative", (double)abstol);
1689: ksp->abstol = abstol;
1690: }
1691: if (dtol == (PetscReal)PETSC_DETERMINE) {
1692: ksp->divtol = ksp->default_divtol;
1693: } else if (dtol == (PetscReal)PETSC_UNLIMITED) {
1694: ksp->divtol = PETSC_MAX_REAL;
1695: } else if (dtol != (PetscReal)PETSC_CURRENT) {
1696: PetscCheck(dtol >= 0.0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Divergence tolerance %g must be larger than 1.0", (double)dtol);
1697: ksp->divtol = dtol;
1698: }
1699: if (maxits == (PetscInt)PETSC_DETERMINE) {
1700: ksp->max_it = ksp->default_max_it;
1701: } else if (maxits == (PetscInt)PETSC_UNLIMITED) {
1702: ksp->max_it = PETSC_INT_MAX;
1703: } else if (maxits != (PetscInt)PETSC_CURRENT) {
1704: PetscCheck(maxits >= 0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Maximum number of iterations %" PetscInt_FMT " must be non-negative", maxits);
1705: ksp->max_it = maxits;
1706: }
1707: PetscFunctionReturn(PETSC_SUCCESS);
1708: }
1710: /*@
1711: KSPSetMinimumIterations - Sets the minimum number of iterations to use, regardless of the tolerances
1713: Logically Collective
1715: Input Parameters:
1716: + ksp - the Krylov subspace context
1717: - minit - minimum number of iterations to use
1719: Options Database Key:
1720: . -ksp_min_it <minits> - Sets `minit`
1722: Level: intermediate
1724: Notes:
1725: Use `KSPSetTolerances()` to set a variety of other tolerances
1727: See `KSPConvergedDefault()` for details on how these parameters are used in the default convergence test. See also `KSPSetConvergenceTest()`
1728: for setting user-defined stopping criteria.
1730: .seealso: [](ch_ksp), `KSPGetTolerances()`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSP`, `KSPSetTolerances()`, `KSPGetMinimumIterations()`
1731: @*/
1732: PetscErrorCode KSPSetMinimumIterations(KSP ksp, PetscInt minit)
1733: {
1734: PetscFunctionBegin;
1738: PetscCheck(minit >= 0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Minimum number of iterations %" PetscInt_FMT " must be non-negative", minit);
1739: ksp->min_it = minit;
1740: PetscFunctionReturn(PETSC_SUCCESS);
1741: }
1743: /*@
1744: KSPGetMinimumIterations - Gets the minimum number of iterations to use, regardless of the tolerances, that was set with `KSPSetMinimumIterations()` or `-ksp_min_it`
1746: Not Collective
1748: Input Parameter:
1749: . ksp - the Krylov subspace context
1751: Output Parameter:
1752: . minit - minimum number of iterations to use
1754: Level: intermediate
1756: .seealso: [](ch_ksp), `KSPGetTolerances()`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSP`, `KSPSetTolerances()`, `KSPSetMinimumIterations()`
1757: @*/
1758: PetscErrorCode KSPGetMinimumIterations(KSP ksp, PetscInt *minit)
1759: {
1760: PetscFunctionBegin;
1762: PetscAssertPointer(minit, 2);
1764: *minit = ksp->min_it;
1765: PetscFunctionReturn(PETSC_SUCCESS);
1766: }
1768: /*@
1769: KSPSetInitialGuessNonzero - Tells the iterative solver that the
1770: initial guess is nonzero; otherwise `KSP` assumes the initial guess
1771: is to be zero (and thus zeros it out before solving).
1773: Logically Collective
1775: Input Parameters:
1776: + ksp - iterative solver obtained from `KSPCreate()`
1777: - flg - ``PETSC_TRUE`` indicates the guess is non-zero, `PETSC_FALSE` indicates the guess is zero
1779: Options Database Key:
1780: . -ksp_initial_guess_nonzero <true,false> - use nonzero initial guess
1782: Level: beginner
1784: Note:
1785: If this is not called the X vector is zeroed in the call to `KSPSolve()`.
1787: .seealso: [](ch_ksp), `KSPGetInitialGuessNonzero()`, `KSPGuessSetType()`, `KSPGuessType`, `KSP`
1788: @*/
1789: PetscErrorCode KSPSetInitialGuessNonzero(KSP ksp, PetscBool flg)
1790: {
1791: PetscFunctionBegin;
1794: ksp->guess_zero = (PetscBool)!flg;
1795: PetscFunctionReturn(PETSC_SUCCESS);
1796: }
1798: /*@
1799: KSPGetInitialGuessNonzero - Determines whether the `KSP` solver is using
1800: a zero initial guess.
1802: Not Collective
1804: Input Parameter:
1805: . ksp - iterative solver obtained from `KSPCreate()`
1807: Output Parameter:
1808: . flag - `PETSC_TRUE` if guess is nonzero, else `PETSC_FALSE`
1810: Level: intermediate
1812: .seealso: [](ch_ksp), `KSPSetInitialGuessNonzero()`, `KSP`
1813: @*/
1814: PetscErrorCode KSPGetInitialGuessNonzero(KSP ksp, PetscBool *flag)
1815: {
1816: PetscFunctionBegin;
1818: PetscAssertPointer(flag, 2);
1819: if (ksp->guess_zero) *flag = PETSC_FALSE;
1820: else *flag = PETSC_TRUE;
1821: PetscFunctionReturn(PETSC_SUCCESS);
1822: }
1824: /*@
1825: KSPSetErrorIfNotConverged - Causes `KSPSolve()` to generate an error if the solver has not converged as soon as the error is detected.
1827: Logically Collective
1829: Input Parameters:
1830: + ksp - iterative solver obtained from `KSPCreate()`
1831: - flg - `PETSC_TRUE` indicates you want the error generated
1833: Options Database Key:
1834: . -ksp_error_if_not_converged <true,false> - generate an error and stop the program
1836: Level: intermediate
1838: Notes:
1839: Normally PETSc continues if a linear solver fails to converge, you can call `KSPGetConvergedReason()` after a `KSPSolve()`
1840: to determine if it has converged.
1842: A `KSP_DIVERGED_ITS` will not generate an error in a `KSPSolve()` inside a nested linear solver
1844: .seealso: [](ch_ksp), `KSPGetErrorIfNotConverged()`, `KSP`
1845: @*/
1846: PetscErrorCode KSPSetErrorIfNotConverged(KSP ksp, PetscBool flg)
1847: {
1848: PetscFunctionBegin;
1851: ksp->errorifnotconverged = flg;
1852: PetscFunctionReturn(PETSC_SUCCESS);
1853: }
1855: /*@
1856: KSPGetErrorIfNotConverged - Will `KSPSolve()` generate an error if the solver does not converge?
1858: Not Collective
1860: Input Parameter:
1861: . ksp - iterative solver obtained from KSPCreate()
1863: Output Parameter:
1864: . flag - `PETSC_TRUE` if it will generate an error, else `PETSC_FALSE`
1866: Level: intermediate
1868: .seealso: [](ch_ksp), `KSPSetErrorIfNotConverged()`, `KSP`
1869: @*/
1870: PetscErrorCode KSPGetErrorIfNotConverged(KSP ksp, PetscBool *flag)
1871: {
1872: PetscFunctionBegin;
1874: PetscAssertPointer(flag, 2);
1875: *flag = ksp->errorifnotconverged;
1876: PetscFunctionReturn(PETSC_SUCCESS);
1877: }
1879: /*@
1880: KSPSetInitialGuessKnoll - Tells the iterative solver to use `PCApply()` on the right hand side vector to compute the initial guess (The Knoll trick)
1882: Logically Collective
1884: Input Parameters:
1885: + ksp - iterative solver obtained from `KSPCreate()`
1886: - flg - `PETSC_TRUE` or `PETSC_FALSE`
1888: Level: advanced
1890: Developer Note:
1891: The Knoll trick is not currently implemented using the `KSPGuess` class
1893: .seealso: [](ch_ksp), `KSPGetInitialGuessKnoll()`, `KSPSetInitialGuessNonzero()`, `KSPGetInitialGuessNonzero()`, `KSP`
1894: @*/
1895: PetscErrorCode KSPSetInitialGuessKnoll(KSP ksp, PetscBool flg)
1896: {
1897: PetscFunctionBegin;
1900: ksp->guess_knoll = flg;
1901: PetscFunctionReturn(PETSC_SUCCESS);
1902: }
1904: /*@
1905: KSPGetInitialGuessKnoll - Determines whether the `KSP` solver is using the Knoll trick (using PCApply(pc,b,...) to compute
1906: the initial guess
1908: Not Collective
1910: Input Parameter:
1911: . ksp - iterative solver obtained from `KSPCreate()`
1913: Output Parameter:
1914: . flag - `PETSC_TRUE` if using Knoll trick, else `PETSC_FALSE`
1916: Level: advanced
1918: .seealso: [](ch_ksp), `KSPSetInitialGuessKnoll()`, `KSPSetInitialGuessNonzero()`, `KSPGetInitialGuessNonzero()`, `KSP`
1919: @*/
1920: PetscErrorCode KSPGetInitialGuessKnoll(KSP ksp, PetscBool *flag)
1921: {
1922: PetscFunctionBegin;
1924: PetscAssertPointer(flag, 2);
1925: *flag = ksp->guess_knoll;
1926: PetscFunctionReturn(PETSC_SUCCESS);
1927: }
1929: /*@
1930: KSPGetComputeSingularValues - Gets the flag indicating whether the extreme singular
1931: values will be calculated via a Lanczos or Arnoldi process as the linear
1932: system is solved.
1934: Not Collective
1936: Input Parameter:
1937: . ksp - iterative solver obtained from `KSPCreate()`
1939: Output Parameter:
1940: . flg - `PETSC_TRUE` or `PETSC_FALSE`
1942: Options Database Key:
1943: . -ksp_monitor_singular_value - Activates `KSPSetComputeSingularValues()`
1945: Level: advanced
1947: Notes:
1948: This option is not valid for `KSPType`.
1950: Many users may just want to use the monitoring routine
1951: `KSPMonitorSingularValue()` (which can be set with option `-ksp_monitor_singular_value`)
1952: to print the singular values at each iteration of the linear solve.
1954: .seealso: [](ch_ksp), `KSPComputeExtremeSingularValues()`, `KSPMonitorSingularValue()`, `KSP`
1955: @*/
1956: PetscErrorCode KSPGetComputeSingularValues(KSP ksp, PetscBool *flg)
1957: {
1958: PetscFunctionBegin;
1960: PetscAssertPointer(flg, 2);
1961: *flg = ksp->calc_sings;
1962: PetscFunctionReturn(PETSC_SUCCESS);
1963: }
1965: /*@
1966: KSPSetComputeSingularValues - Sets a flag so that the extreme singular
1967: values will be calculated via a Lanczos or Arnoldi process as the linear
1968: system is solved.
1970: Logically Collective
1972: Input Parameters:
1973: + ksp - iterative solver obtained from `KSPCreate()`
1974: - flg - `PETSC_TRUE` or `PETSC_FALSE`
1976: Options Database Key:
1977: . -ksp_monitor_singular_value - Activates `KSPSetComputeSingularValues()`
1979: Level: advanced
1981: Notes:
1982: This option is not valid for all iterative methods.
1984: Many users may just want to use the monitoring routine
1985: `KSPMonitorSingularValue()` (which can be set with option `-ksp_monitor_singular_value`)
1986: to print the singular values at each iteration of the linear solve.
1988: .seealso: [](ch_ksp), `KSPComputeExtremeSingularValues()`, `KSPMonitorSingularValue()`, `KSP`, `KSPSetComputeRitz()`
1989: @*/
1990: PetscErrorCode KSPSetComputeSingularValues(KSP ksp, PetscBool flg)
1991: {
1992: PetscFunctionBegin;
1995: ksp->calc_sings = flg;
1996: PetscFunctionReturn(PETSC_SUCCESS);
1997: }
1999: /*@
2000: KSPGetComputeEigenvalues - Gets the flag indicating that the extreme eigenvalues
2001: values will be calculated via a Lanczos or Arnoldi process as the linear
2002: system is solved.
2004: Not Collective
2006: Input Parameter:
2007: . ksp - iterative solver obtained from `KSPCreate()`
2009: Output Parameter:
2010: . flg - `PETSC_TRUE` or `PETSC_FALSE`
2012: Level: advanced
2014: Note:
2015: Currently this option is not valid for all iterative methods.
2017: .seealso: [](ch_ksp), `KSPComputeEigenvalues()`, `KSPComputeEigenvaluesExplicitly()`, `KSP`, `KSPSetComputeRitz()`
2018: @*/
2019: PetscErrorCode KSPGetComputeEigenvalues(KSP ksp, PetscBool *flg)
2020: {
2021: PetscFunctionBegin;
2023: PetscAssertPointer(flg, 2);
2024: *flg = ksp->calc_sings;
2025: PetscFunctionReturn(PETSC_SUCCESS);
2026: }
2028: /*@
2029: KSPSetComputeEigenvalues - Sets a flag so that the extreme eigenvalues
2030: values will be calculated via a Lanczos or Arnoldi process as the linear
2031: system is solved.
2033: Logically Collective
2035: Input Parameters:
2036: + ksp - iterative solver obtained from `KSPCreate()`
2037: - flg - `PETSC_TRUE` or `PETSC_FALSE`
2039: Level: advanced
2041: Note:
2042: Currently this option is not valid for all iterative methods.
2044: .seealso: [](ch_ksp), `KSPComputeEigenvalues()`, `KSPComputeEigenvaluesExplicitly()`, `KSP`, `KSPSetComputeRitz()`
2045: @*/
2046: PetscErrorCode KSPSetComputeEigenvalues(KSP ksp, PetscBool flg)
2047: {
2048: PetscFunctionBegin;
2051: ksp->calc_sings = flg;
2052: PetscFunctionReturn(PETSC_SUCCESS);
2053: }
2055: /*@
2056: KSPSetComputeRitz - Sets a flag so that the Ritz or harmonic Ritz pairs
2057: will be calculated via a Lanczos or Arnoldi process as the linear
2058: system is solved.
2060: Logically Collective
2062: Input Parameters:
2063: + ksp - iterative solver obtained from `KSPCreate()`
2064: - flg - `PETSC_TRUE` or `PETSC_FALSE`
2066: Level: advanced
2068: Note:
2069: Currently this option is only valid for the `KSPGMRES` method.
2071: .seealso: [](ch_ksp), `KSPComputeRitz()`, `KSP`, `KSPComputeEigenvalues()`, `KSPComputeExtremeSingularValues()`
2072: @*/
2073: PetscErrorCode KSPSetComputeRitz(KSP ksp, PetscBool flg)
2074: {
2075: PetscFunctionBegin;
2078: ksp->calc_ritz = flg;
2079: PetscFunctionReturn(PETSC_SUCCESS);
2080: }
2082: /*@
2083: KSPGetRhs - Gets the right-hand-side vector for the linear system to
2084: be solved.
2086: Not Collective
2088: Input Parameter:
2089: . ksp - iterative solver obtained from `KSPCreate()`
2091: Output Parameter:
2092: . r - right-hand-side vector
2094: Level: developer
2096: .seealso: [](ch_ksp), `KSPGetSolution()`, `KSPSolve()`, `KSP`
2097: @*/
2098: PetscErrorCode KSPGetRhs(KSP ksp, Vec *r)
2099: {
2100: PetscFunctionBegin;
2102: PetscAssertPointer(r, 2);
2103: *r = ksp->vec_rhs;
2104: PetscFunctionReturn(PETSC_SUCCESS);
2105: }
2107: /*@
2108: KSPGetSolution - Gets the location of the solution for the
2109: linear system to be solved.
2111: Not Collective
2113: Input Parameter:
2114: . ksp - iterative solver obtained from `KSPCreate()`
2116: Output Parameter:
2117: . v - solution vector
2119: Level: developer
2121: Note:
2122: If this is called during a `KSPSolve()` the vector's values may not represent the solution
2123: to the linear system.
2125: .seealso: [](ch_ksp), `KSPGetRhs()`, `KSPBuildSolution()`, `KSPSolve()`, `KSP`
2126: @*/
2127: PetscErrorCode KSPGetSolution(KSP ksp, Vec *v)
2128: {
2129: PetscFunctionBegin;
2131: PetscAssertPointer(v, 2);
2132: *v = ksp->vec_sol;
2133: PetscFunctionReturn(PETSC_SUCCESS);
2134: }
2136: /*@
2137: KSPSetPC - Sets the preconditioner to be used to calculate the
2138: application of the preconditioner on a vector into a `KSP`.
2140: Collective
2142: Input Parameters:
2143: + ksp - the `KSP` iterative solver obtained from `KSPCreate()`
2144: - pc - the preconditioner object (if `NULL` it returns the `PC` currently held by the `KSP`)
2146: Level: developer
2148: Note:
2149: This routine is almost never used since `KSP` creates its own `PC` when needed.
2150: Use `KSPGetPC()` to retrieve the preconditioner context instead of creating a new one.
2152: .seealso: [](ch_ksp), `KSPGetPC()`, `KSP`
2153: @*/
2154: PetscErrorCode KSPSetPC(KSP ksp, PC pc)
2155: {
2156: PetscFunctionBegin;
2158: if (pc) {
2160: PetscCheckSameComm(ksp, 1, pc, 2);
2161: }
2162: if (ksp->pc != pc && ksp->setupstage) ksp->setupstage = KSP_SETUP_NEWMATRIX;
2163: PetscCall(PetscObjectReference((PetscObject)pc));
2164: PetscCall(PCDestroy(&ksp->pc));
2165: ksp->pc = pc;
2166: PetscFunctionReturn(PETSC_SUCCESS);
2167: }
2169: PETSC_INTERN PetscErrorCode PCCreate_MPI(PC);
2171: // PetscClangLinter pragma disable: -fdoc-internal-linkage
2172: /*@C
2173: KSPCheckPCMPI - Checks if `-mpi_linear_solver_server` is active and the `PC` should be changed to `PCMPI`
2175: Collective, No Fortran Support
2177: Input Parameter:
2178: . ksp - iterative solver obtained from `KSPCreate()`
2180: Level: developer
2182: .seealso: [](ch_ksp), `KSPSetPC()`, `KSP`, `PCMPIServerBegin()`, `PCMPIServerEnd()`
2183: @*/
2184: PETSC_INTERN PetscErrorCode KSPCheckPCMPI(KSP ksp)
2185: {
2186: PetscBool isPCMPI;
2188: PetscFunctionBegin;
2190: PetscCall(PetscObjectTypeCompare((PetscObject)ksp->pc, PCMPI, &isPCMPI));
2191: if (PCMPIServerActive && ksp->nestlevel == 0 && !isPCMPI) {
2192: const char *prefix;
2193: char *found = NULL;
2195: PetscCall(KSPGetOptionsPrefix(ksp, &prefix));
2196: if (prefix) PetscCall(PetscStrstr(prefix, "mpi_linear_solver_server_", &found));
2197: if (!found) PetscCall(KSPAppendOptionsPrefix(ksp, "mpi_linear_solver_server_"));
2198: PetscCall(PetscInfo(NULL, "In MPI Linear Solver Server and detected (root) PC that must be changed to PCMPI\n"));
2199: PetscCall(PCSetType(ksp->pc, PCMPI));
2200: }
2201: PetscFunctionReturn(PETSC_SUCCESS);
2202: }
2204: /*@
2205: KSPGetPC - Returns a pointer to the preconditioner context with the `KSP`
2207: Not Collective
2209: Input Parameter:
2210: . ksp - iterative solver obtained from `KSPCreate()`
2212: Output Parameter:
2213: . pc - preconditioner context
2215: Level: beginner
2217: Note:
2218: The `PC` is created if it does not already exist.
2220: Developer Note:
2221: Calls `KSPCheckPCMPI()` to check if the `KSP` is effected by `-mpi_linear_solver_server`
2223: .seealso: [](ch_ksp), `KSPSetPC()`, `KSP`, `PC`
2224: @*/
2225: PetscErrorCode KSPGetPC(KSP ksp, PC *pc)
2226: {
2227: PetscFunctionBegin;
2229: PetscAssertPointer(pc, 2);
2230: if (!ksp->pc) {
2231: PetscCall(PCCreate(PetscObjectComm((PetscObject)ksp), &ksp->pc));
2232: PetscCall(PetscObjectIncrementTabLevel((PetscObject)ksp->pc, (PetscObject)ksp, 0));
2233: PetscCall(PetscObjectSetOptions((PetscObject)ksp->pc, ((PetscObject)ksp)->options));
2234: PetscCall(PCSetKSPNestLevel(ksp->pc, ksp->nestlevel));
2235: }
2236: PetscCall(KSPCheckPCMPI(ksp));
2237: *pc = ksp->pc;
2238: PetscFunctionReturn(PETSC_SUCCESS);
2239: }
2241: /*@
2242: KSPMonitor - runs the user provided monitor routines, if they exist
2244: Collective
2246: Input Parameters:
2247: + ksp - iterative solver obtained from `KSPCreate()`
2248: . it - iteration number
2249: - rnorm - relative norm of the residual
2251: Level: developer
2253: Notes:
2254: This routine is called by the `KSP` implementations.
2255: It does not typically need to be called by the user.
2257: For Krylov methods that do not keep a running value of the current solution (such as `KSPGMRES`) this
2258: cannot be called after the `KSPConvergedReason` has been set but before the final solution has been computed.
2260: .seealso: [](ch_ksp), `KSPMonitorSet()`
2261: @*/
2262: PetscErrorCode KSPMonitor(KSP ksp, PetscInt it, PetscReal rnorm)
2263: {
2264: PetscInt i, n = ksp->numbermonitors;
2266: PetscFunctionBegin;
2267: for (i = 0; i < n; i++) PetscCall((*ksp->monitor[i])(ksp, it, rnorm, ksp->monitorcontext[i]));
2268: PetscFunctionReturn(PETSC_SUCCESS);
2269: }
2271: /*@C
2272: KSPMonitorSet - Sets an ADDITIONAL function to be called at every iteration to monitor, i.e. display in some way, perhaps by printing in the terminal,
2273: the residual norm computed in a `KSPSolve()`
2275: Logically Collective
2277: Input Parameters:
2278: + ksp - iterative solver obtained from `KSPCreate()`
2279: . monitor - pointer to function (if this is `NULL`, it turns off monitoring
2280: . ctx - [optional] context for private data for the monitor routine (use `NULL` if no context is needed)
2281: - monitordestroy - [optional] routine that frees monitor context (may be `NULL`)
2283: Calling sequence of `monitor`:
2284: + ksp - iterative solver obtained from `KSPCreate()`
2285: . it - iteration number
2286: . rnorm - (estimated) 2-norm of (preconditioned) residual
2287: - ctx - optional monitoring context, as set by `KSPMonitorSet()`
2289: Calling sequence of `monitordestroy`:
2290: . ctx - optional monitoring context, as set by `KSPMonitorSet()`
2292: Options Database Keys:
2293: + -ksp_monitor - sets `KSPMonitorResidual()`
2294: . -ksp_monitor draw - sets `KSPMonitorResidualDraw()` and plots residual
2295: . -ksp_monitor draw::draw_lg - sets `KSPMonitorResidualDrawLG()` and plots residual
2296: . -ksp_monitor_pause_final - Pauses any graphics when the solve finishes (only works for internal monitors)
2297: . -ksp_monitor_true_residual - sets `KSPMonitorTrueResidual()`
2298: . -ksp_monitor_true_residual draw::draw_lg - sets `KSPMonitorTrueResidualDrawLG()` and plots residual
2299: . -ksp_monitor_max - sets `KSPMonitorTrueResidualMax()`
2300: . -ksp_monitor_singular_value - sets `KSPMonitorSingularValue()`
2301: - -ksp_monitor_cancel - cancels all monitors that have been hardwired into a code by calls to `KSPMonitorSet()`, but
2302: does not cancel those set via the options database.
2304: Level: beginner
2306: Notes:
2307: The options database option `-ksp_monitor` and related options are the easiest way to turn on `KSP` iteration monitoring
2309: The default is to do no monitoring. To print the residual, or preconditioned
2310: residual if `KSPSetNormType`(ksp,`KSP_NORM_PRECONDITIONED`) was called, use
2311: `KSPMonitorResidual()` as the monitoring routine, with a `PETSCVIEWERASCII` as the
2312: context.
2314: Several different monitoring routines may be set by calling
2315: `KSPMonitorSet()` multiple times; all will be called in the
2316: order in which they were set.
2318: Fortran Note:
2319: Only a single monitor function can be set for each `KSP` object
2321: .seealso: [](ch_ksp), `KSPMonitorResidual()`, `KSPMonitorCancel()`, `KSP`
2322: @*/
2323: PetscErrorCode KSPMonitorSet(KSP ksp, PetscErrorCode (*monitor)(KSP ksp, PetscInt it, PetscReal rnorm, void *ctx), void *ctx, PetscErrorCode (*monitordestroy)(void **ctx))
2324: {
2325: PetscInt i;
2326: PetscBool identical;
2328: PetscFunctionBegin;
2330: for (i = 0; i < ksp->numbermonitors; i++) {
2331: PetscCall(PetscMonitorCompare((PetscErrorCode (*)(void))monitor, ctx, monitordestroy, (PetscErrorCode (*)(void))ksp->monitor[i], ksp->monitorcontext[i], ksp->monitordestroy[i], &identical));
2332: if (identical) PetscFunctionReturn(PETSC_SUCCESS);
2333: }
2334: PetscCheck(ksp->numbermonitors < MAXKSPMONITORS, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Too many KSP monitors set");
2335: ksp->monitor[ksp->numbermonitors] = monitor;
2336: ksp->monitordestroy[ksp->numbermonitors] = monitordestroy;
2337: ksp->monitorcontext[ksp->numbermonitors++] = (void *)ctx;
2338: PetscFunctionReturn(PETSC_SUCCESS);
2339: }
2341: /*@
2342: KSPMonitorCancel - Clears all monitors for a `KSP` object.
2344: Logically Collective
2346: Input Parameter:
2347: . ksp - iterative solver obtained from `KSPCreate()`
2349: Options Database Key:
2350: . -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.
2352: Level: intermediate
2354: .seealso: [](ch_ksp), `KSPMonitorResidual()`, `KSPMonitorSet()`, `KSP`
2355: @*/
2356: PetscErrorCode KSPMonitorCancel(KSP ksp)
2357: {
2358: PetscInt i;
2360: PetscFunctionBegin;
2362: for (i = 0; i < ksp->numbermonitors; i++) {
2363: if (ksp->monitordestroy[i]) PetscCall((*ksp->monitordestroy[i])(&ksp->monitorcontext[i]));
2364: }
2365: ksp->numbermonitors = 0;
2366: PetscFunctionReturn(PETSC_SUCCESS);
2367: }
2369: /*@C
2370: KSPGetMonitorContext - Gets the monitoring context, as set by `KSPMonitorSet()` for the FIRST monitor only.
2372: Not Collective
2374: Input Parameter:
2375: . ksp - iterative solver obtained from `KSPCreate()`
2377: Output Parameter:
2378: . ctx - monitoring context
2380: Level: intermediate
2382: .seealso: [](ch_ksp), `KSPMonitorResidual()`, `KSP`
2383: @*/
2384: PetscErrorCode KSPGetMonitorContext(KSP ksp, void *ctx)
2385: {
2386: PetscFunctionBegin;
2388: *(void **)ctx = ksp->monitorcontext[0];
2389: PetscFunctionReturn(PETSC_SUCCESS);
2390: }
2392: /*@
2393: KSPSetResidualHistory - Sets the array used to hold the residual history.
2394: If set, this array will contain the residual norms computed at each
2395: iteration of the solver.
2397: Not Collective
2399: Input Parameters:
2400: + ksp - iterative solver obtained from `KSPCreate()`
2401: . a - array to hold history
2402: . na - size of `a`
2403: - reset - `PETSC_TRUE` indicates the history counter is reset to zero
2404: for each new linear solve
2406: Level: advanced
2408: Notes:
2409: If provided, `a` is NOT freed by PETSc so the user needs to keep track of it and destroy once the `KSP` object is destroyed.
2410: If 'a' is `NULL` then space is allocated for the history. If 'na' `PETSC_DECIDE` or (deprecated) `PETSC_DEFAULT` then a
2411: default array of length 10,000 is allocated.
2413: If the array is not long enough then once the iterations is longer than the array length `KSPSolve()` stops recording the history
2415: .seealso: [](ch_ksp), `KSPGetResidualHistory()`, `KSP`
2416: @*/
2417: PetscErrorCode KSPSetResidualHistory(KSP ksp, PetscReal a[], PetscCount na, PetscBool reset)
2418: {
2419: PetscFunctionBegin;
2422: PetscCall(PetscFree(ksp->res_hist_alloc));
2423: if (na != PETSC_DECIDE && na != PETSC_DEFAULT && a) {
2424: ksp->res_hist = a;
2425: ksp->res_hist_max = na;
2426: } else {
2427: if (na != PETSC_DECIDE && na != PETSC_DEFAULT) ksp->res_hist_max = (size_t)na;
2428: else ksp->res_hist_max = 10000; /* like default ksp->max_it */
2429: PetscCall(PetscCalloc1(ksp->res_hist_max, &ksp->res_hist_alloc));
2431: ksp->res_hist = ksp->res_hist_alloc;
2432: }
2433: ksp->res_hist_len = 0;
2434: ksp->res_hist_reset = reset;
2435: PetscFunctionReturn(PETSC_SUCCESS);
2436: }
2438: /*@C
2439: KSPGetResidualHistory - Gets the array used to hold the residual history and the number of residuals it contains.
2441: Not Collective
2443: Input Parameter:
2444: . ksp - iterative solver obtained from `KSPCreate()`
2446: Output Parameters:
2447: + a - pointer to array to hold history (or `NULL`)
2448: - na - number of used entries in a (or `NULL`). Note this has different meanings depending on the `reset` argument to `KSPSetResidualHistory()`
2450: Level: advanced
2452: Note:
2453: This array is borrowed and should not be freed by the caller.
2455: Can only be called after a `KSPSetResidualHistory()` otherwise `a` and `na` are set to `NULL` and zero
2457: When `reset` was `PETSC_TRUE` since a residual is computed before the first iteration, the value of `na` is generally one more than the value
2458: returned with `KSPGetIterationNumber()`.
2460: Some Krylov methods may not compute the final residual norm when convergence is declared because the maximum number of iterations allowed has been reached.
2461: In this situation, when `reset` was `PETSC_TRUE`, `na` will then equal the number of iterations reported with `KSPGetIterationNumber()`
2463: Some Krylov methods (such as `KSPSTCG`), under certain circumstances, do not compute the final residual norm. In this situation, when `reset` was `PETSC_TRUE`,
2464: `na` will then equal the number of iterations reported with `KSPGetIterationNumber()`
2466: `KSPBCGSL` does not record the residual norms for the "subiterations" hence the results from `KSPGetResidualHistory()` and `KSPGetIterationNumber()` will be different
2468: Fortran Note:
2469: The Fortran version of this routine has a calling sequence
2470: .vb
2471: call KSPGetResidualHistory(KSP ksp, integer na, integer ierr)
2472: .ve
2473: note that you have passed a Fortran array into `KSPSetResidualHistory()` and you need
2474: to access the residual values from this Fortran array you provided. Only the `na` (number of
2475: residual norms currently held) is set.
2477: .seealso: [](ch_ksp), `KSPSetResidualHistory()`, `KSP`, `KSPGetIterationNumber()`, `KSPSTCG`, `KSPBCGSL`
2478: @*/
2479: PetscErrorCode KSPGetResidualHistory(KSP ksp, const PetscReal *a[], PetscInt *na)
2480: {
2481: PetscFunctionBegin;
2483: if (a) *a = ksp->res_hist;
2484: if (na) PetscCall(PetscIntCast(ksp->res_hist_len, na));
2485: PetscFunctionReturn(PETSC_SUCCESS);
2486: }
2488: /*@
2489: 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.
2491: Not Collective
2493: Input Parameters:
2494: + ksp - iterative solver obtained from `KSPCreate()`
2495: . a - array to hold history
2496: . na - size of `a`
2497: - reset - `PETSC_TRUE` indicates the history counter is reset to zero for each new linear solve
2499: Level: advanced
2501: Notes:
2502: If provided, `a` is NOT freed by PETSc so the user needs to keep track of it and destroy once the `KSP` object is destroyed.
2503: If 'a' is `NULL` then space is allocated for the history. If 'na' is `PETSC_DECIDE` or (deprecated) `PETSC_DEFAULT` then a default array of length 1,0000 is allocated.
2505: If the array is not long enough then once the iterations is longer than the array length `KSPSolve()` stops recording the history
2507: .seealso: [](ch_ksp), `KSPGetErrorHistory()`, `KSPSetResidualHistory()`, `KSP`
2508: @*/
2509: PetscErrorCode KSPSetErrorHistory(KSP ksp, PetscReal a[], PetscCount na, PetscBool reset)
2510: {
2511: PetscFunctionBegin;
2514: PetscCall(PetscFree(ksp->err_hist_alloc));
2515: if (na != PETSC_DECIDE && na != PETSC_DEFAULT && a) {
2516: ksp->err_hist = a;
2517: ksp->err_hist_max = na;
2518: } else {
2519: if (na != PETSC_DECIDE && na != PETSC_DEFAULT) ksp->err_hist_max = (size_t)na;
2520: else ksp->err_hist_max = 10000; /* like default ksp->max_it */
2521: PetscCall(PetscCalloc1(ksp->err_hist_max, &ksp->err_hist_alloc));
2522: ksp->err_hist = ksp->err_hist_alloc;
2523: }
2524: ksp->err_hist_len = 0;
2525: ksp->err_hist_reset = reset;
2526: PetscFunctionReturn(PETSC_SUCCESS);
2527: }
2529: /*@C
2530: KSPGetErrorHistory - Gets the array used to hold the error history and the number of residuals it contains.
2532: Not Collective
2534: Input Parameter:
2535: . ksp - iterative solver obtained from `KSPCreate()`
2537: Output Parameters:
2538: + a - pointer to array to hold history (or `NULL`)
2539: - na - number of used entries in a (or `NULL`)
2541: Level: advanced
2543: Note:
2544: This array is borrowed and should not be freed by the caller.
2545: Can only be called after a `KSPSetErrorHistory()` otherwise `a` and `na` are set to `NULL` and zero
2547: Fortran Note:
2548: The Fortran version of this routine has a calling sequence
2549: .vb
2550: call KSPGetErrorHistory(KSP ksp, integer na, integer ierr)
2551: .ve
2552: note that you have passed a Fortran array into `KSPSetErrorHistory()` and you need
2553: to access the residual values from this Fortran array you provided. Only the `na` (number of
2554: residual norms currently held) is set.
2556: .seealso: [](ch_ksp), `KSPSetErrorHistory()`, `KSPGetResidualHistory()`, `KSP`
2557: @*/
2558: PetscErrorCode KSPGetErrorHistory(KSP ksp, const PetscReal *a[], PetscInt *na)
2559: {
2560: PetscFunctionBegin;
2562: if (a) *a = ksp->err_hist;
2563: if (na) PetscCall(PetscIntCast(ksp->err_hist_len, na));
2564: PetscFunctionReturn(PETSC_SUCCESS);
2565: }
2567: /*@
2568: KSPComputeConvergenceRate - Compute the convergence rate for the iteration <https:/en.wikipedia.org/wiki/Coefficient_of_determination>
2570: Not Collective
2572: Input Parameter:
2573: . ksp - The `KSP`
2575: Output Parameters:
2576: + cr - The residual contraction rate
2577: . rRsq - The coefficient of determination, $R^2$, indicating the linearity of the data
2578: . ce - The error contraction rate
2579: - eRsq - The coefficient of determination, $R^2$, indicating the linearity of the data
2581: Level: advanced
2583: Note:
2584: 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,
2585: the slope is $\log c$. The coefficient of determination is given by $1 - \frac{\sum_i (y_i - f(x_i))^2}{\sum_i (y_i - \bar y)}$,
2587: .seealso: [](ch_ksp), `KSP`, `KSPConvergedRateView()`
2588: @*/
2589: PetscErrorCode KSPComputeConvergenceRate(KSP ksp, PetscReal *cr, PetscReal *rRsq, PetscReal *ce, PetscReal *eRsq)
2590: {
2591: PetscReal const *hist;
2592: PetscReal *x, *y, slope, intercept, mean = 0.0, var = 0.0, res = 0.0;
2593: PetscInt n, k;
2595: PetscFunctionBegin;
2596: if (cr || rRsq) {
2597: PetscCall(KSPGetResidualHistory(ksp, &hist, &n));
2598: if (!n) {
2599: if (cr) *cr = 0.0;
2600: if (rRsq) *rRsq = -1.0;
2601: } else {
2602: PetscCall(PetscMalloc2(n, &x, n, &y));
2603: for (k = 0; k < n; ++k) {
2604: x[k] = k;
2605: y[k] = PetscLogReal(hist[k]);
2606: mean += y[k];
2607: }
2608: mean /= n;
2609: PetscCall(PetscLinearRegression(n, x, y, &slope, &intercept));
2610: for (k = 0; k < n; ++k) {
2611: res += PetscSqr(y[k] - (slope * x[k] + intercept));
2612: var += PetscSqr(y[k] - mean);
2613: }
2614: PetscCall(PetscFree2(x, y));
2615: if (cr) *cr = PetscExpReal(slope);
2616: if (rRsq) *rRsq = var < PETSC_MACHINE_EPSILON ? 0.0 : 1.0 - (res / var);
2617: }
2618: }
2619: if (ce || eRsq) {
2620: PetscCall(KSPGetErrorHistory(ksp, &hist, &n));
2621: if (!n) {
2622: if (ce) *ce = 0.0;
2623: if (eRsq) *eRsq = -1.0;
2624: } else {
2625: PetscCall(PetscMalloc2(n, &x, n, &y));
2626: for (k = 0; k < n; ++k) {
2627: x[k] = k;
2628: y[k] = PetscLogReal(hist[k]);
2629: mean += y[k];
2630: }
2631: mean /= n;
2632: PetscCall(PetscLinearRegression(n, x, y, &slope, &intercept));
2633: for (k = 0; k < n; ++k) {
2634: res += PetscSqr(y[k] - (slope * x[k] + intercept));
2635: var += PetscSqr(y[k] - mean);
2636: }
2637: PetscCall(PetscFree2(x, y));
2638: if (ce) *ce = PetscExpReal(slope);
2639: if (eRsq) *eRsq = var < PETSC_MACHINE_EPSILON ? 0.0 : 1.0 - (res / var);
2640: }
2641: }
2642: PetscFunctionReturn(PETSC_SUCCESS);
2643: }
2645: /*@C
2646: KSPSetConvergenceTest - Sets the function to be used to determine convergence of `KSPSolve()`
2648: Logically Collective
2650: Input Parameters:
2651: + ksp - iterative solver obtained from `KSPCreate()`
2652: . converge - pointer to the function
2653: . ctx - context for private data for the convergence routine (may be `NULL`)
2654: - destroy - a routine for destroying the context (may be `NULL`)
2656: Calling sequence of `converge`:
2657: + ksp - iterative solver obtained from `KSPCreate()`
2658: . it - iteration number
2659: . rnorm - (estimated) 2-norm of (preconditioned) residual
2660: . reason - the reason why it has converged or diverged
2661: - ctx - optional convergence context, as set by `KSPSetConvergenceTest()`
2663: Calling sequence of `destroy`:
2664: . ctx - the context
2666: Level: advanced
2668: Notes:
2669: Must be called after the `KSP` type has been set so put this after
2670: a call to `KSPSetType()`, or `KSPSetFromOptions()`.
2672: The default convergence test, `KSPConvergedDefault()`, aborts if the
2673: residual grows to more than 10000 times the initial residual.
2675: The default is a combination of relative and absolute tolerances.
2676: The residual value that is tested may be an approximation; routines
2677: that need exact values should compute them.
2679: In the default PETSc convergence test, the precise values of reason
2680: are macros such as `KSP_CONVERGED_RTOL`, which are defined in petscksp.h.
2682: .seealso: [](ch_ksp), `KSP`, `KSPConvergedDefault()`, `KSPGetConvergenceContext()`, `KSPSetTolerances()`, `KSPGetConvergenceTest()`, `KSPGetAndClearConvergenceTest()`
2683: @*/
2684: PetscErrorCode KSPSetConvergenceTest(KSP ksp, PetscErrorCode (*converge)(KSP ksp, PetscInt it, PetscReal rnorm, KSPConvergedReason *reason, void *ctx), void *ctx, PetscErrorCode (*destroy)(void *ctx))
2685: {
2686: PetscFunctionBegin;
2688: if (ksp->convergeddestroy) PetscCall((*ksp->convergeddestroy)(ksp->cnvP));
2689: ksp->converged = converge;
2690: ksp->convergeddestroy = destroy;
2691: ksp->cnvP = (void *)ctx;
2692: PetscFunctionReturn(PETSC_SUCCESS);
2693: }
2695: /*@C
2696: KSPGetConvergenceTest - Gets the function to be used to determine convergence.
2698: Logically Collective
2700: Input Parameter:
2701: . ksp - iterative solver obtained from `KSPCreate()`
2703: Output Parameters:
2704: + converge - pointer to convergence test function
2705: . ctx - context for private data for the convergence routine (may be `NULL`)
2706: - destroy - a routine for destroying the context (may be `NULL`)
2708: Calling sequence of `converge`:
2709: + ksp - iterative solver obtained from `KSPCreate()`
2710: . it - iteration number
2711: . rnorm - (estimated) 2-norm of (preconditioned) residual
2712: . reason - the reason why it has converged or diverged
2713: - ctx - optional convergence context, as set by `KSPSetConvergenceTest()`
2715: Calling sequence of `destroy`:
2716: . ctx - the convergence test context
2718: Level: advanced
2720: .seealso: [](ch_ksp), `KSP`, `KSPConvergedDefault()`, `KSPGetConvergenceContext()`, `KSPSetTolerances()`, `KSPSetConvergenceTest()`, `KSPGetAndClearConvergenceTest()`
2721: @*/
2722: PetscErrorCode KSPGetConvergenceTest(KSP ksp, PetscErrorCode (**converge)(KSP ksp, PetscInt it, PetscReal rnorm, KSPConvergedReason *reason, void *ctx), void **ctx, PetscErrorCode (**destroy)(void *ctx))
2723: {
2724: PetscFunctionBegin;
2726: if (converge) *converge = ksp->converged;
2727: if (destroy) *destroy = ksp->convergeddestroy;
2728: if (ctx) *ctx = ksp->cnvP;
2729: PetscFunctionReturn(PETSC_SUCCESS);
2730: }
2732: /*@C
2733: KSPGetAndClearConvergenceTest - Gets the function to be used to determine convergence. Removes the current test without calling destroy on the test context
2735: Logically Collective
2737: Input Parameter:
2738: . ksp - iterative solver obtained from `KSPCreate()`
2740: Output Parameters:
2741: + converge - pointer to convergence test function
2742: . ctx - context for private data for the convergence routine
2743: - destroy - a routine for destroying the context
2745: Calling sequence of `converge`:
2746: + ksp - iterative solver obtained from `KSPCreate()`
2747: . it - iteration number
2748: . rnorm - (estimated) 2-norm of (preconditioned) residual
2749: . reason - the reason why it has converged or diverged
2750: - ctx - optional convergence context, as set by `KSPSetConvergenceTest()`
2752: Calling sequence of `destroy`:
2753: . ctx - the convergence test context
2755: Level: advanced
2757: Note:
2758: This is intended to be used to allow transferring the convergence test (and its context) to another testing object (for example another `KSP`)
2759: and then calling `KSPSetConvergenceTest()` on this original `KSP`. If you just called `KSPGetConvergenceTest()` followed
2760: by `KSPSetConvergenceTest()` the original context information
2761: would be destroyed and hence the transferred context would be invalid and trigger a crash on use
2763: .seealso: [](ch_ksp), `KSP`, `KSPConvergedDefault()`, `KSPGetConvergenceContext()`, `KSPSetTolerances()`, `KSPSetConvergenceTest()`, `KSPGetConvergenceTest()`
2764: @*/
2765: PetscErrorCode KSPGetAndClearConvergenceTest(KSP ksp, PetscErrorCode (**converge)(KSP ksp, PetscInt it, PetscReal rnorm, KSPConvergedReason *reason, void *ctx), void **ctx, PetscErrorCode (**destroy)(void *ctx))
2766: {
2767: PetscFunctionBegin;
2769: *converge = ksp->converged;
2770: *destroy = ksp->convergeddestroy;
2771: *ctx = ksp->cnvP;
2772: ksp->converged = NULL;
2773: ksp->cnvP = NULL;
2774: ksp->convergeddestroy = NULL;
2775: PetscFunctionReturn(PETSC_SUCCESS);
2776: }
2778: /*@C
2779: KSPGetConvergenceContext - Gets the convergence context set with `KSPSetConvergenceTest()`.
2781: Not Collective
2783: Input Parameter:
2784: . ksp - iterative solver obtained from `KSPCreate()`
2786: Output Parameter:
2787: . ctx - monitoring context
2789: Level: advanced
2791: .seealso: [](ch_ksp), `KSP`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSPGetConvergenceTest()`
2792: @*/
2793: PetscErrorCode KSPGetConvergenceContext(KSP ksp, void *ctx)
2794: {
2795: PetscFunctionBegin;
2797: *(void **)ctx = ksp->cnvP;
2798: PetscFunctionReturn(PETSC_SUCCESS);
2799: }
2801: /*@
2802: KSPBuildSolution - Builds the approximate solution in a vector provided.
2804: Collective
2806: Input Parameter:
2807: . ksp - iterative solver obtained from `KSPCreate()`
2809: Output Parameter:
2810: Provide exactly one of
2811: + v - location to stash solution, optional, otherwise pass `NULL`
2812: - V - the solution is returned in this location. This vector is created internally. This vector should NOT be destroyed by the user with `VecDestroy()`.
2814: Level: developer
2816: Notes:
2817: This routine can be used in one of two ways
2818: .vb
2819: KSPBuildSolution(ksp,NULL,&V);
2820: or
2821: KSPBuildSolution(ksp,v,NULL); or KSPBuildSolution(ksp,v,&v);
2822: .ve
2823: In the first case an internal vector is allocated to store the solution
2824: (the user cannot destroy this vector). In the second case the solution
2825: is generated in the vector that the user provides. Note that for certain
2826: methods, such as `KSPCG`, the second case requires a copy of the solution,
2827: while in the first case the call is essentially free since it simply
2828: returns the vector where the solution already is stored. For some methods
2829: like `KSPGMRES` during the solve this is a reasonably expensive operation and should only be
2830: used if truly needed.
2832: .seealso: [](ch_ksp), `KSPGetSolution()`, `KSPBuildResidual()`, `KSP`
2833: @*/
2834: PetscErrorCode KSPBuildSolution(KSP ksp, Vec v, Vec *V)
2835: {
2836: PetscFunctionBegin;
2838: PetscCheck(V || v, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONG, "Must provide either v or V");
2839: if (!V) V = &v;
2840: if (ksp->reason != KSP_CONVERGED_ITERATING) {
2841: if (!v) PetscCall(KSPGetSolution(ksp, V));
2842: else PetscCall(VecCopy(ksp->vec_sol, v));
2843: } else {
2844: PetscUseTypeMethod(ksp, buildsolution, v, V);
2845: }
2846: PetscFunctionReturn(PETSC_SUCCESS);
2847: }
2849: /*@
2850: KSPBuildResidual - Builds the residual in a vector provided.
2852: Collective
2854: Input Parameter:
2855: . ksp - iterative solver obtained from `KSPCreate()`
2857: Output Parameters:
2858: + t - work vector. If not provided then one is generated.
2859: . v - optional location to stash residual. If `v` is not provided, then a location is generated.
2860: - V - the residual
2862: Level: advanced
2864: Note:
2865: Regardless of whether or not `v` is provided, the residual is
2866: returned in `V`.
2868: .seealso: [](ch_ksp), `KSP`, `KSPBuildSolution()`
2869: @*/
2870: PetscErrorCode KSPBuildResidual(KSP ksp, Vec t, Vec v, Vec *V)
2871: {
2872: PetscBool flag = PETSC_FALSE;
2873: Vec w = v, tt = t;
2875: PetscFunctionBegin;
2877: if (!w) PetscCall(VecDuplicate(ksp->vec_rhs, &w));
2878: if (!tt) {
2879: PetscCall(VecDuplicate(ksp->vec_sol, &tt));
2880: flag = PETSC_TRUE;
2881: }
2882: PetscUseTypeMethod(ksp, buildresidual, tt, w, V);
2883: if (flag) PetscCall(VecDestroy(&tt));
2884: PetscFunctionReturn(PETSC_SUCCESS);
2885: }
2887: /*@
2888: KSPSetDiagonalScale - Tells `KSP` to symmetrically diagonally scale the system
2889: before solving. This actually CHANGES the matrix (and right-hand side).
2891: Logically Collective
2893: Input Parameters:
2894: + ksp - the `KSP` context
2895: - scale - `PETSC_TRUE` or `PETSC_FALSE`
2897: Options Database Keys:
2898: + -ksp_diagonal_scale - perform a diagonal scaling before the solve
2899: - -ksp_diagonal_scale_fix - scale the matrix back AFTER the solve
2901: Level: advanced
2903: Notes:
2904: Scales the matrix by $D^{-1/2} A D^{-1/2} [D^{1/2} x ] = D^{-1/2} b $
2905: where $D_{ii}$ is $1/abs(A_{ii}) $ unless $A_{ii}$ is zero and then it is 1.
2907: BE CAREFUL with this routine: it actually scales the matrix and right
2908: hand side that define the system. After the system is solved the matrix
2909: and right-hand side remain scaled unless you use `KSPSetDiagonalScaleFix()`
2911: This should NOT be used within the `SNES` solves if you are using a line
2912: search.
2914: If you use this with the `PCType` `PCEISENSTAT` preconditioner than you can
2915: use the `PCEisenstatSetNoDiagonalScaling()` option, or `-pc_eisenstat_no_diagonal_scaling`
2916: to save some unneeded, redundant flops.
2918: .seealso: [](ch_ksp), `KSPGetDiagonalScale()`, `KSPSetDiagonalScaleFix()`, `KSP`
2919: @*/
2920: PetscErrorCode KSPSetDiagonalScale(KSP ksp, PetscBool scale)
2921: {
2922: PetscFunctionBegin;
2925: ksp->dscale = scale;
2926: PetscFunctionReturn(PETSC_SUCCESS);
2927: }
2929: /*@
2930: KSPGetDiagonalScale - Checks if `KSP` solver scales the matrix and right-hand side, that is if `KSPSetDiagonalScale()` has been called
2932: Not Collective
2934: Input Parameter:
2935: . ksp - the `KSP` context
2937: Output Parameter:
2938: . scale - `PETSC_TRUE` or `PETSC_FALSE`
2940: Level: intermediate
2942: .seealso: [](ch_ksp), `KSP`, `KSPSetDiagonalScale()`, `KSPSetDiagonalScaleFix()`
2943: @*/
2944: PetscErrorCode KSPGetDiagonalScale(KSP ksp, PetscBool *scale)
2945: {
2946: PetscFunctionBegin;
2948: PetscAssertPointer(scale, 2);
2949: *scale = ksp->dscale;
2950: PetscFunctionReturn(PETSC_SUCCESS);
2951: }
2953: /*@
2954: KSPSetDiagonalScaleFix - Tells `KSP` to diagonally scale the system back after solving.
2956: Logically Collective
2958: Input Parameters:
2959: + ksp - the `KSP` context
2960: - fix - `PETSC_TRUE` to scale back after the system solve, `PETSC_FALSE` to not
2961: rescale (default)
2963: Level: intermediate
2965: Notes:
2966: Must be called after `KSPSetDiagonalScale()`
2968: Using this will slow things down, because it rescales the matrix before and
2969: after each linear solve. This is intended mainly for testing to allow one
2970: to easily get back the original system to make sure the solution computed is
2971: accurate enough.
2973: .seealso: [](ch_ksp), `KSPGetDiagonalScale()`, `KSPSetDiagonalScale()`, `KSPGetDiagonalScaleFix()`, `KSP`
2974: @*/
2975: PetscErrorCode KSPSetDiagonalScaleFix(KSP ksp, PetscBool fix)
2976: {
2977: PetscFunctionBegin;
2980: ksp->dscalefix = fix;
2981: PetscFunctionReturn(PETSC_SUCCESS);
2982: }
2984: /*@
2985: KSPGetDiagonalScaleFix - Determines if `KSP` diagonally scales the system back after solving. That is `KSPSetDiagonalScaleFix()` has been called
2987: Not Collective
2989: Input Parameter:
2990: . ksp - the `KSP` context
2992: Output Parameter:
2993: . fix - `PETSC_TRUE` to scale back after the system solve, `PETSC_FALSE` to not
2994: rescale (default)
2996: Level: intermediate
2998: .seealso: [](ch_ksp), `KSPGetDiagonalScale()`, `KSPSetDiagonalScale()`, `KSPSetDiagonalScaleFix()`, `KSP`
2999: @*/
3000: PetscErrorCode KSPGetDiagonalScaleFix(KSP ksp, PetscBool *fix)
3001: {
3002: PetscFunctionBegin;
3004: PetscAssertPointer(fix, 2);
3005: *fix = ksp->dscalefix;
3006: PetscFunctionReturn(PETSC_SUCCESS);
3007: }
3009: /*@C
3010: KSPSetComputeOperators - set routine to compute the linear operators
3012: Logically Collective
3014: Input Parameters:
3015: + ksp - the `KSP` context
3016: . func - function to compute the operators, see `KSPComputeOperatorsFn` for the calling sequence
3017: - ctx - optional context
3019: Level: beginner
3021: Notes:
3022: `func()` will be called automatically at the very next call to `KSPSolve()`. It will NOT be called at future `KSPSolve()` calls
3023: unless either `KSPSetComputeOperators()` or `KSPSetOperators()` is called before that `KSPSolve()` is called. This allows the same system to be solved several times
3024: with different right-hand side functions but is a confusing API since one might expect it to be called for each `KSPSolve()`
3026: To reuse the same preconditioner for the next `KSPSolve()` and not compute a new one based on the most recently computed matrix call `KSPSetReusePreconditioner()`
3028: Developer Note:
3029: Perhaps this routine and `KSPSetComputeRHS()` could be combined into a new API that makes clear when new matrices are computing without requiring call this
3030: routine to indicate when the new matrix should be computed.
3032: .seealso: [](ch_ksp), `KSP`, `KSPSetOperators()`, `KSPSetComputeRHS()`, `DMKSPSetComputeOperators()`, `KSPSetComputeInitialGuess()`, `KSPComputeOperatorsFn`
3033: @*/
3034: PetscErrorCode KSPSetComputeOperators(KSP ksp, KSPComputeOperatorsFn *func, void *ctx)
3035: {
3036: DM dm;
3038: PetscFunctionBegin;
3040: PetscCall(KSPGetDM(ksp, &dm));
3041: PetscCall(DMKSPSetComputeOperators(dm, func, ctx));
3042: if (ksp->setupstage == KSP_SETUP_NEWRHS) ksp->setupstage = KSP_SETUP_NEWMATRIX;
3043: PetscFunctionReturn(PETSC_SUCCESS);
3044: }
3046: /*@C
3047: KSPSetComputeRHS - set routine to compute the right-hand side of the linear system
3049: Logically Collective
3051: Input Parameters:
3052: + ksp - the `KSP` context
3053: . func - function to compute the right-hand side, see `KSPComputeRHSFn` for the calling sequence
3054: - ctx - optional context
3056: Level: beginner
3058: Note:
3059: The routine you provide will be called EACH you call `KSPSolve()` to prepare the new right-hand side for that solve
3061: .seealso: [](ch_ksp), `KSP`, `KSPSolve()`, `DMKSPSetComputeRHS()`, `KSPSetComputeOperators()`, `KSPSetOperators()`, `KSPComputeRHSFn`
3062: @*/
3063: PetscErrorCode KSPSetComputeRHS(KSP ksp, KSPComputeRHSFn *func, void *ctx)
3064: {
3065: DM dm;
3067: PetscFunctionBegin;
3069: PetscCall(KSPGetDM(ksp, &dm));
3070: PetscCall(DMKSPSetComputeRHS(dm, func, ctx));
3071: PetscFunctionReturn(PETSC_SUCCESS);
3072: }
3074: /*@C
3075: KSPSetComputeInitialGuess - set routine to compute the initial guess of the linear system
3077: Logically Collective
3079: Input Parameters:
3080: + ksp - the `KSP` context
3081: . func - function to compute the initial guess, see `KSPComputeInitialGuessFn` for calling sequence
3082: - ctx - optional context
3084: Level: beginner
3086: Note:
3087: This should only be used in conjunction with `KSPSetComputeRHS()` and `KSPSetComputeOperators()`, otherwise
3088: call `KSPSetInitialGuessNonzero()` and set the initial guess values in the solution vector passed to `KSPSolve()` before calling the solver
3090: .seealso: [](ch_ksp), `KSP`, `KSPSolve()`, `KSPSetComputeRHS()`, `KSPSetComputeOperators()`, `DMKSPSetComputeInitialGuess()`, `KSPSetInitialGuessNonzero()`,
3091: `KSPComputeInitialGuessFn`
3092: @*/
3093: PetscErrorCode KSPSetComputeInitialGuess(KSP ksp, KSPComputeInitialGuessFn *func, void *ctx)
3094: {
3095: DM dm;
3097: PetscFunctionBegin;
3099: PetscCall(KSPGetDM(ksp, &dm));
3100: PetscCall(DMKSPSetComputeInitialGuess(dm, func, ctx));
3101: PetscFunctionReturn(PETSC_SUCCESS);
3102: }
3104: /*@
3105: KSPSetUseExplicitTranspose - Determines the explicit transpose of the operator is formed in `KSPSolveTranspose()`. In some configurations (like GPUs) it may
3106: be explicitly formed since the solve is much more efficient.
3108: Logically Collective
3110: Input Parameter:
3111: . ksp - the `KSP` context
3113: Output Parameter:
3114: . flg - `PETSC_TRUE` to transpose the system in `KSPSolveTranspose()`, `PETSC_FALSE` to not transpose (default)
3116: Level: advanced
3118: .seealso: [](ch_ksp), `KSPSolveTranspose()`, `KSP`
3119: @*/
3120: PetscErrorCode KSPSetUseExplicitTranspose(KSP ksp, PetscBool flg)
3121: {
3122: PetscFunctionBegin;
3125: ksp->transpose.use_explicittranspose = flg;
3126: PetscFunctionReturn(PETSC_SUCCESS);
3127: }