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_singularvalues`) 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 general, be less than this.
81: Output Parameters:
82: + r - real part of computed eigenvalues, provided by user with a dimension of at least `n`
83: . c - complex part of computed eigenvalues, provided by user with a dimension of at least `n`
84: - neig - actual number of eigenvalues computed (will be less than or equal to `n`)
86: Options Database Key:
87: . -ksp_view_eigenvalues - Prints eigenvalues to stdout
89: Level: advanced
91: Notes:
92: The number of eigenvalues estimated depends on the size of the Krylov space
93: generated during the `KSPSolve()` ; for example, with
94: `KSPCG` it corresponds to the number of CG iterations, for `KSPGMRES` it is the number
95: of GMRES iterations SINCE the last restart. Any extra space in `r` and `c`
96: will be ignored.
98: `KSPComputeEigenvalues()` does not usually provide accurate estimates; it is
99: intended only for assistance in understanding the convergence of iterative
100: methods, not for eigenanalysis. For accurate computation of eigenvalues we recommend using
101: the excellent package SLEPc.
103: One must call `KSPSetComputeEigenvalues()` before calling `KSPSetUp()`
104: in order for this routine to work correctly.
106: Many users may just want to use the monitoring routine
107: `KSPMonitorSingularValue()` (which can be set with option `-ksp_monitor_singular_value`)
108: to print the singular values at each iteration of the linear solve.
110: `KSPComputeRitz()` provides estimates for both the eigenvalues and their corresponding eigenvectors.
112: .seealso: [](ch_ksp), `KSPSetComputeEigenvalues()`, `KSPSetComputeSingularValues()`, `KSPMonitorSingularValue()`, `KSPComputeExtremeSingularValues()`, `KSP`, `KSPComputeRitz()`
113: @*/
114: PetscErrorCode KSPComputeEigenvalues(KSP ksp, PetscInt n, PetscReal r[], PetscReal c[], PetscInt *neig)
115: {
116: PetscFunctionBegin;
118: if (n) PetscAssertPointer(r, 3);
119: if (n) PetscAssertPointer(c, 4);
120: PetscCheck(n >= 0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Requested < 0 Eigenvalues");
121: PetscAssertPointer(neig, 5);
122: PetscCheck(ksp->calc_sings, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONGSTATE, "Eigenvalues not requested before KSPSetUp()");
124: if (n && ksp->ops->computeeigenvalues) PetscUseTypeMethod(ksp, computeeigenvalues, n, r, c, neig);
125: else *neig = 0;
126: PetscFunctionReturn(PETSC_SUCCESS);
127: }
129: /*@
130: KSPComputeRitz - Computes the Ritz or harmonic Ritz pairs associated with the
131: smallest or largest in modulus, for the preconditioned operator.
133: Not Collective
135: Input Parameters:
136: + ksp - iterative solver obtained from `KSPCreate()`
137: . ritz - `PETSC_TRUE` or `PETSC_FALSE` for Ritz pairs or harmonic Ritz pairs, respectively
138: - small - `PETSC_TRUE` or `PETSC_FALSE` for smallest or largest (harmonic) Ritz values, respectively
140: Output Parameters:
141: + nrit - On input number of (harmonic) Ritz pairs to compute; on output, actual number of computed (harmonic) Ritz pairs
142: . S - an array of the Ritz vectors, pass in an array of vectors of size `nrit`
143: . tetar - real part of the Ritz values, pass in an array of size `nrit`
144: - tetai - imaginary part of the Ritz values, pass in an array of size `nrit`
146: Level: advanced
148: Notes:
149: This only works with a `KSPType` of `KSPGMRES`.
151: One must call `KSPSetComputeRitz()` before calling `KSPSetUp()` in order for this routine to work correctly.
153: This routine must be called after `KSPSolve()`.
155: In `KSPGMRES`, the (harmonic) Ritz pairs are computed from the Hessenberg matrix obtained during
156: the last complete cycle of the GMRES solve, or during the partial cycle if the solve ended before
157: a restart (that is a complete GMRES cycle was never achieved).
159: The number of actual (harmonic) Ritz pairs computed is less than or equal to the restart
160: parameter for GMRES if a complete cycle has been performed or less or equal to the number of GMRES
161: iterations.
163: `KSPComputeEigenvalues()` provides estimates for only the eigenvalues (Ritz values).
165: For real matrices, the (harmonic) Ritz pairs can be complex-valued. In such a case,
166: the routine selects the complex (harmonic) Ritz value and its conjugate, and two successive entries of the
167: vectors `S` are equal to the real and the imaginary parts of the associated vectors.
168: When PETSc has been built with complex scalars, the real and imaginary parts of the Ritz
169: values are still returned in `tetar` and `tetai`, as is done in `KSPComputeEigenvalues()`, but
170: the Ritz vectors S are complex.
172: The (harmonic) Ritz pairs are given in order of increasing (harmonic) Ritz values in modulus.
174: The Ritz pairs do not necessarily accurately reflect the eigenvalues and eigenvectors of the operator, consider the
175: excellent package SLEPc if accurate values are required.
177: .seealso: [](ch_ksp), `KSPSetComputeRitz()`, `KSP`, `KSPGMRES`, `KSPComputeEigenvalues()`, `KSPSetComputeSingularValues()`, `KSPMonitorSingularValue()`
178: @*/
179: PetscErrorCode KSPComputeRitz(KSP ksp, PetscBool ritz, PetscBool small, PetscInt *nrit, Vec S[], PetscReal tetar[], PetscReal tetai[])
180: {
181: PetscFunctionBegin;
183: PetscCheck(ksp->calc_ritz, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONGSTATE, "Ritz pairs not requested before KSPSetUp()");
184: PetscTryTypeMethod(ksp, computeritz, ritz, small, nrit, S, tetar, tetai);
185: PetscFunctionReturn(PETSC_SUCCESS);
186: }
188: /*@
189: KSPSetUpOnBlocks - Sets up the preconditioner for each block in
190: the block Jacobi `PCJACOBI`, overlapping Schwarz `PCASM`, and fieldsplit `PCFIELDSPLIT` preconditioners
192: Collective
194: Input Parameter:
195: . ksp - the `KSP` context
197: Level: advanced
199: Notes:
200: `KSPSetUpOnBlocks()` is a routine that the user can optionally call for
201: more precise profiling (via `-log_view`) of the setup phase for these
202: block preconditioners. If the user does not call `KSPSetUpOnBlocks()`,
203: it will automatically be called from within `KSPSolve()`.
205: Calling `KSPSetUpOnBlocks()` is the same as calling `PCSetUpOnBlocks()`
206: on the `PC` context within the `KSP` context.
208: .seealso: [](ch_ksp), `PCSetUpOnBlocks()`, `KSPSetUp()`, `PCSetUp()`, `KSP`
209: @*/
210: PetscErrorCode KSPSetUpOnBlocks(KSP ksp)
211: {
212: PC pc;
213: PCFailedReason pcreason;
215: PetscFunctionBegin;
217: level++;
218: PetscCall(KSPGetPC(ksp, &pc));
219: PetscCall(PCSetUpOnBlocks(pc));
220: PetscCall(PCGetFailedReason(pc, &pcreason));
221: level--;
222: /*
223: This is tricky since only a subset of MPI ranks may set this; each KSPSolve_*() is responsible for checking
224: this flag and initializing an appropriate vector with VecFlag() so that the first norm computation can
225: produce a result at KSPCheckNorm() thus communicating the known problem to all MPI ranks so they may
226: terminate the Krylov solve. For many KSP implementations this is handled within KSPInitialResidual()
227: */
228: if (pcreason) ksp->reason = KSP_DIVERGED_PC_FAILED;
229: PetscFunctionReturn(PETSC_SUCCESS);
230: }
232: /*@
233: KSPSetReusePreconditioner - reuse the current preconditioner for future `KSPSolve()`, do not construct a new preconditioner even if the `Mat` operator
234: in the `KSP` has different values
236: Collective
238: Input Parameters:
239: + ksp - iterative solver obtained from `KSPCreate()`
240: - flag - `PETSC_TRUE` to reuse the current preconditioner, or `PETSC_FALSE` to construct a new preconditioner
242: Options Database Key:
243: . -ksp_reuse_preconditioner <true,false> - reuse the previously computed preconditioner
245: Level: intermediate
247: Notes:
248: When using `SNES` one can use `SNESSetLagPreconditioner()` to determine when preconditioners are reused.
250: Reusing the preconditioner reduces the time needed to form new preconditioners but may (significantly) increase the number
251: of iterations needed for future solves depending on how much the matrix entries have changed.
253: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSolve()`, `KSPDestroy()`, `KSP`, `KSPGetReusePreconditioner()`,
254: `SNESSetLagPreconditioner()`, `SNES`
255: @*/
256: PetscErrorCode KSPSetReusePreconditioner(KSP ksp, PetscBool flag)
257: {
258: PC pc;
260: PetscFunctionBegin;
262: PetscCall(KSPGetPC(ksp, &pc));
263: PetscCall(PCSetReusePreconditioner(pc, flag));
264: PetscFunctionReturn(PETSC_SUCCESS);
265: }
267: /*@
268: KSPGetReusePreconditioner - Determines if the `KSP` reuses the current preconditioner even if the `Mat` operator in the `KSP` has changed.
270: Collective
272: Input Parameter:
273: . ksp - iterative solver obtained from `KSPCreate()`
275: Output Parameter:
276: . flag - the boolean flag indicating if the current preconditioner should be reused
278: Level: intermediate
280: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSolve()`, `KSPDestroy()`, `KSPSetReusePreconditioner()`, `KSP`
281: @*/
282: PetscErrorCode KSPGetReusePreconditioner(KSP ksp, PetscBool *flag)
283: {
284: PetscFunctionBegin;
286: PetscAssertPointer(flag, 2);
287: *flag = PETSC_FALSE;
288: if (ksp->pc) PetscCall(PCGetReusePreconditioner(ksp->pc, flag));
289: PetscFunctionReturn(PETSC_SUCCESS);
290: }
292: /*@
293: KSPSetSkipPCSetFromOptions - prevents `KSPSetFromOptions()` from calling `PCSetFromOptions()`.
294: This is used if the same `PC` is shared by more than one `KSP` so its options are not reset for each `KSP`
296: Collective
298: Input Parameters:
299: + ksp - iterative solver obtained from `KSPCreate()`
300: - flag - `PETSC_TRUE` to skip calling the `PCSetFromOptions()`
302: Level: developer
304: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSolve()`, `KSPDestroy()`, `PCSetReusePreconditioner()`, `KSP`
305: @*/
306: PetscErrorCode KSPSetSkipPCSetFromOptions(KSP ksp, PetscBool flag)
307: {
308: PetscFunctionBegin;
310: ksp->skippcsetfromoptions = flag;
311: PetscFunctionReturn(PETSC_SUCCESS);
312: }
314: /*@
315: KSPSetUp - Sets up the internal data structures for the
316: later use `KSPSolve()` the `KSP` linear iterative solver.
318: Collective
320: Input Parameter:
321: . ksp - iterative solver, `KSP`, obtained from `KSPCreate()`
323: Level: developer
325: Note:
326: This is called automatically by `KSPSolve()` so usually does not need to be called directly.
328: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSolve()`, `KSPDestroy()`, `KSP`, `KSPSetUpOnBlocks()`
329: @*/
330: PetscErrorCode KSPSetUp(KSP ksp)
331: {
332: Mat A, B;
333: Mat mat, pmat;
334: MatNullSpace nullsp;
335: PCFailedReason pcreason;
336: PC pc;
337: PetscBool pcmpi;
339: PetscFunctionBegin;
341: PetscCall(KSPGetPC(ksp, &pc));
342: PetscCall(PetscObjectTypeCompare((PetscObject)pc, PCMPI, &pcmpi));
343: if (pcmpi) {
344: PetscBool ksppreonly;
345: PetscCall(PetscObjectTypeCompare((PetscObject)ksp, KSPPREONLY, &ksppreonly));
346: if (!ksppreonly) PetscCall(KSPSetType(ksp, KSPPREONLY));
347: }
348: level++;
350: /* reset the convergence flag from the previous solves */
351: ksp->reason = KSP_CONVERGED_ITERATING;
353: if (!((PetscObject)ksp)->type_name) PetscCall(KSPSetType(ksp, KSPGMRES));
354: PetscCall(KSPSetUpNorms_Private(ksp, PETSC_TRUE, &ksp->normtype, &ksp->pc_side));
356: if (ksp->dmActive && !ksp->setupstage) {
357: /* first time in so build matrix and vector data structures using DM */
358: if (!ksp->vec_rhs) PetscCall(DMCreateGlobalVector(ksp->dm, &ksp->vec_rhs));
359: if (!ksp->vec_sol) PetscCall(DMCreateGlobalVector(ksp->dm, &ksp->vec_sol));
360: PetscCall(DMCreateMatrix(ksp->dm, &A));
361: PetscCall(KSPSetOperators(ksp, A, A));
362: PetscCall(PetscObjectDereference((PetscObject)A));
363: }
365: if (ksp->dmActive) {
366: DMKSP kdm;
367: PetscCall(DMGetDMKSP(ksp->dm, &kdm));
369: if (kdm->ops->computeinitialguess && ksp->setupstage != KSP_SETUP_NEWRHS) {
370: /* only computes initial guess the first time through */
371: PetscCallBack("KSP callback initial guess", (*kdm->ops->computeinitialguess)(ksp, ksp->vec_sol, kdm->initialguessctx));
372: PetscCall(KSPSetInitialGuessNonzero(ksp, PETSC_TRUE));
373: }
374: if (kdm->ops->computerhs) PetscCallBack("KSP callback rhs", (*kdm->ops->computerhs)(ksp, ksp->vec_rhs, kdm->rhsctx));
376: if (ksp->setupstage != KSP_SETUP_NEWRHS) {
377: PetscCheck(kdm->ops->computeoperators, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONGSTATE, "You called KSPSetDM() but did not use DMKSPSetComputeOperators() or KSPSetDMActive(ksp,PETSC_FALSE);");
378: PetscCall(KSPGetOperators(ksp, &A, &B));
379: PetscCallBack("KSP callback operators", (*kdm->ops->computeoperators)(ksp, A, B, kdm->operatorsctx));
380: }
381: }
383: if (ksp->setupstage == KSP_SETUP_NEWRHS) {
384: level--;
385: PetscFunctionReturn(PETSC_SUCCESS);
386: }
387: PetscCall(PetscLogEventBegin(KSP_SetUp, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
389: switch (ksp->setupstage) {
390: case KSP_SETUP_NEW:
391: PetscUseTypeMethod(ksp, setup);
392: break;
393: case KSP_SETUP_NEWMATRIX: /* This should be replaced with a more general mechanism */
394: if (ksp->setupnewmatrix) PetscUseTypeMethod(ksp, setup);
395: break;
396: default:
397: break;
398: }
400: if (!ksp->pc) PetscCall(KSPGetPC(ksp, &ksp->pc));
401: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
402: /* scale the matrix if requested */
403: if (ksp->dscale) {
404: PetscScalar *xx;
405: PetscInt i, n;
406: PetscBool zeroflag = PETSC_FALSE;
408: if (!ksp->diagonal) { /* allocate vector to hold diagonal */
409: PetscCall(MatCreateVecs(pmat, &ksp->diagonal, NULL));
410: }
411: PetscCall(MatGetDiagonal(pmat, ksp->diagonal));
412: PetscCall(VecGetLocalSize(ksp->diagonal, &n));
413: PetscCall(VecGetArray(ksp->diagonal, &xx));
414: for (i = 0; i < n; i++) {
415: if (xx[i] != 0.0) xx[i] = 1.0 / PetscSqrtReal(PetscAbsScalar(xx[i]));
416: else {
417: xx[i] = 1.0;
418: zeroflag = PETSC_TRUE;
419: }
420: }
421: PetscCall(VecRestoreArray(ksp->diagonal, &xx));
422: if (zeroflag) PetscCall(PetscInfo(ksp, "Zero detected in diagonal of matrix, using 1 at those locations\n"));
423: PetscCall(MatDiagonalScale(pmat, ksp->diagonal, ksp->diagonal));
424: if (mat != pmat) PetscCall(MatDiagonalScale(mat, ksp->diagonal, ksp->diagonal));
425: ksp->dscalefix2 = PETSC_FALSE;
426: }
427: PetscCall(PetscLogEventEnd(KSP_SetUp, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
428: PetscCall(PCSetErrorIfFailure(ksp->pc, ksp->errorifnotconverged));
429: PetscCall(PCSetUp(ksp->pc));
430: PetscCall(PCGetFailedReason(ksp->pc, &pcreason));
431: /* 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 */
432: if (pcreason) ksp->reason = KSP_DIVERGED_PC_FAILED;
434: PetscCall(MatGetNullSpace(mat, &nullsp));
435: if (nullsp) {
436: PetscBool test = PETSC_FALSE;
437: PetscCall(PetscOptionsGetBool(((PetscObject)ksp)->options, ((PetscObject)ksp)->prefix, "-ksp_test_null_space", &test, NULL));
438: if (test) PetscCall(MatNullSpaceTest(nullsp, mat, NULL));
439: }
440: ksp->setupstage = KSP_SETUP_NEWRHS;
441: level--;
442: PetscFunctionReturn(PETSC_SUCCESS);
443: }
445: /*@
446: KSPConvergedReasonView - Displays the reason a `KSP` solve converged or diverged, `KSPConvergedReason` to a `PetscViewer`
448: Collective
450: Input Parameters:
451: + ksp - iterative solver obtained from `KSPCreate()`
452: - viewer - the `PetscViewer` on which to display the reason
454: Options Database Keys:
455: + -ksp_converged_reason - print reason for converged or diverged, also prints number of iterations
456: - -ksp_converged_reason ::failed - only print reason and number of iterations when diverged
458: Level: beginner
460: Note:
461: Use `KSPConvergedReasonViewFromOptions()` to display the reason based on values in the PETSc options database.
463: To change the format of the output call `PetscViewerPushFormat`(`viewer`,`format`) before this call. Use `PETSC_VIEWER_DEFAULT` for the default,
464: use `PETSC_VIEWER_FAILED` to only display a reason if it fails.
466: .seealso: [](ch_ksp), `KSPConvergedReasonViewFromOptions()`, `KSPCreate()`, `KSPSetUp()`, `KSPDestroy()`, `KSPSetTolerances()`, `KSPConvergedDefault()`,
467: `KSPSolveTranspose()`, `KSPGetIterationNumber()`, `KSP`, `KSPGetConvergedReason()`, `PetscViewerPushFormat()`, `PetscViewerPopFormat()`
468: @*/
469: PetscErrorCode KSPConvergedReasonView(KSP ksp, PetscViewer viewer)
470: {
471: PetscBool isAscii;
472: PetscViewerFormat format;
474: PetscFunctionBegin;
475: if (!viewer) viewer = PETSC_VIEWER_STDOUT_(PetscObjectComm((PetscObject)ksp));
476: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isAscii));
477: if (isAscii) {
478: PetscCall(PetscViewerGetFormat(viewer, &format));
479: PetscCall(PetscViewerASCIIAddTab(viewer, ((PetscObject)ksp)->tablevel + 1));
480: if (ksp->reason > 0 && format != PETSC_VIEWER_FAILED) {
481: if (((PetscObject)ksp)->prefix) {
482: PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve converged due to %s iterations %" PetscInt_FMT "\n", ((PetscObject)ksp)->prefix, KSPConvergedReasons[ksp->reason], ksp->its));
483: } else {
484: PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve converged due to %s iterations %" PetscInt_FMT "\n", KSPConvergedReasons[ksp->reason], ksp->its));
485: }
486: } else if (ksp->reason <= 0) {
487: if (((PetscObject)ksp)->prefix) {
488: PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve did not converge due to %s iterations %" PetscInt_FMT "\n", ((PetscObject)ksp)->prefix, KSPConvergedReasons[ksp->reason], ksp->its));
489: } else {
490: PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve did not converge due to %s iterations %" PetscInt_FMT "\n", KSPConvergedReasons[ksp->reason], ksp->its));
491: }
492: if (ksp->reason == KSP_DIVERGED_PC_FAILED) {
493: PCFailedReason reason;
494: PetscCall(PCGetFailedReason(ksp->pc, &reason));
495: PetscCall(PetscViewerASCIIPrintf(viewer, " PC failed due to %s\n", PCFailedReasons[reason]));
496: }
497: }
498: PetscCall(PetscViewerASCIISubtractTab(viewer, ((PetscObject)ksp)->tablevel + 1));
499: }
500: PetscFunctionReturn(PETSC_SUCCESS);
501: }
503: /*@C
504: KSPConvergedReasonViewSet - Sets an ADDITIONAL function that is to be used at the
505: end of the linear solver to display the convergence reason of the linear solver.
507: Logically Collective
509: Input Parameters:
510: + ksp - the `KSP` context
511: . f - the `ksp` converged reason view function, see `KSPConvergedReasonViewFn`
512: . vctx - [optional] user-defined context for private data for the
513: `KSPConvergedReason` view routine (use `NULL` if no context is desired)
514: - reasonviewdestroy - [optional] routine that frees `vctx` (may be `NULL`), see `PetscCtxDestroyFn` for the calling sequence
516: Options Database Keys:
517: + -ksp_converged_reason - sets a default `KSPConvergedReasonView()`
518: - -ksp_converged_reason_view_cancel - cancels all converged reason viewers that have been hardwired into a code by
519: calls to `KSPConvergedReasonViewSet()`, but does not cancel those set via the options database.
521: Level: intermediate
523: Note:
524: Several different converged reason view routines may be set by calling
525: `KSPConvergedReasonViewSet()` multiple times; all will be called in the
526: order in which they were set.
528: Developer Note:
529: Should be named KSPConvergedReasonViewAdd().
531: .seealso: [](ch_ksp), `KSPConvergedReasonView()`, `KSPConvergedReasonViewFn`, `KSPConvergedReasonViewCancel()`, `PetscCtxDestroyFn`
532: @*/
533: PetscErrorCode KSPConvergedReasonViewSet(KSP ksp, KSPConvergedReasonViewFn *f, void *vctx, PetscCtxDestroyFn *reasonviewdestroy)
534: {
535: PetscFunctionBegin;
537: for (PetscInt i = 0; i < ksp->numberreasonviews; i++) {
538: PetscBool identical;
540: PetscCall(PetscMonitorCompare((PetscErrorCode (*)(void))(PetscVoidFn *)f, vctx, reasonviewdestroy, (PetscErrorCode (*)(void))(PetscVoidFn *)ksp->reasonview[i], ksp->reasonviewcontext[i], ksp->reasonviewdestroy[i], &identical));
541: if (identical) PetscFunctionReturn(PETSC_SUCCESS);
542: }
543: PetscCheck(ksp->numberreasonviews < MAXKSPREASONVIEWS, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Too many KSP reasonview set");
544: ksp->reasonview[ksp->numberreasonviews] = f;
545: ksp->reasonviewdestroy[ksp->numberreasonviews] = reasonviewdestroy;
546: ksp->reasonviewcontext[ksp->numberreasonviews++] = vctx;
547: PetscFunctionReturn(PETSC_SUCCESS);
548: }
550: /*@
551: KSPConvergedReasonViewCancel - Clears all the `KSPConvergedReason` view functions for a `KSP` object set with `KSPConvergedReasonViewSet()`
552: as well as the default viewer.
554: Collective
556: Input Parameter:
557: . ksp - iterative solver obtained from `KSPCreate()`
559: Level: intermediate
561: .seealso: [](ch_ksp), `KSPCreate()`, `KSPDestroy()`, `KSPReset()`, `KSPConvergedReasonViewSet()`
562: @*/
563: PetscErrorCode KSPConvergedReasonViewCancel(KSP ksp)
564: {
565: PetscInt i;
567: PetscFunctionBegin;
569: for (i = 0; i < ksp->numberreasonviews; i++) {
570: if (ksp->reasonviewdestroy[i]) PetscCall((*ksp->reasonviewdestroy[i])(&ksp->reasonviewcontext[i]));
571: }
572: ksp->numberreasonviews = 0;
573: PetscCall(PetscViewerDestroy(&ksp->convergedreasonviewer));
574: PetscFunctionReturn(PETSC_SUCCESS);
575: }
577: /*@
578: KSPConvergedReasonViewFromOptions - Processes command line options to determine if/how a `KSPReason` is to be viewed.
580: Collective
582: Input Parameter:
583: . ksp - the `KSP` object
585: Level: intermediate
587: Note:
588: This is called automatically at the conclusion of `KSPSolve()` so is rarely called directly by user code.
590: .seealso: [](ch_ksp), `KSPConvergedReasonView()`, `KSPConvergedReasonViewSet()`
591: @*/
592: PetscErrorCode KSPConvergedReasonViewFromOptions(KSP ksp)
593: {
594: PetscFunctionBegin;
595: /* Call all user-provided reason review routines */
596: for (PetscInt i = 0; i < ksp->numberreasonviews; i++) PetscCall((*ksp->reasonview[i])(ksp, ksp->reasonviewcontext[i]));
598: /* Call the default PETSc routine */
599: if (ksp->convergedreasonviewer) {
600: PetscCall(PetscViewerPushFormat(ksp->convergedreasonviewer, ksp->convergedreasonformat));
601: PetscCall(KSPConvergedReasonView(ksp, ksp->convergedreasonviewer));
602: PetscCall(PetscViewerPopFormat(ksp->convergedreasonviewer));
603: }
604: PetscFunctionReturn(PETSC_SUCCESS);
605: }
607: /*@
608: KSPConvergedRateView - Displays the convergence rate <https://en.wikipedia.org/wiki/Coefficient_of_determination> of `KSPSolve()` to a viewer
610: Collective
612: Input Parameters:
613: + ksp - iterative solver obtained from `KSPCreate()`
614: - viewer - the `PetscViewer` to display the reason
616: Options Database Key:
617: . -ksp_converged_rate - print reason for convergence or divergence and the convergence rate (or 0.0 for divergence)
619: Level: intermediate
621: Notes:
622: To change the format of the output, call `PetscViewerPushFormat`(`viewer`,`format`) before this call.
624: 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,
625: the slope is $\log c$. The coefficient of determination is given by $1 - \frac{\sum_i (y_i - f(x_i))^2}{\sum_i (y_i - \bar y)}$,
627: .seealso: [](ch_ksp), `KSPConvergedReasonView()`, `KSPGetConvergedRate()`, `KSPSetTolerances()`, `KSPConvergedDefault()`
628: @*/
629: PetscErrorCode KSPConvergedRateView(KSP ksp, PetscViewer viewer)
630: {
631: PetscViewerFormat format;
632: PetscBool isAscii;
633: PetscReal rrate, rRsq, erate = 0.0, eRsq = 0.0;
634: PetscInt its;
635: const char *prefix, *reason = KSPConvergedReasons[ksp->reason];
637: PetscFunctionBegin;
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(KSPGetOptionsPrefix(ksp, &prefix));
644: PetscCall(PetscViewerGetFormat(viewer, &format));
645: PetscCall(PetscViewerASCIIAddTab(viewer, ((PetscObject)ksp)->tablevel));
646: if (ksp->reason > 0) {
647: if (prefix) PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve converged due to %s iterations %" PetscInt_FMT, prefix, reason, its));
648: else PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve converged due to %s iterations %" PetscInt_FMT, reason, its));
649: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
650: if (rRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " res rate %g R^2 %g", (double)rrate, (double)rRsq));
651: if (eRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " error rate %g R^2 %g", (double)erate, (double)eRsq));
652: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
653: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
654: } else if (ksp->reason <= 0) {
655: if (prefix) PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve did not converge due to %s iterations %" PetscInt_FMT, prefix, reason, its));
656: else PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve did not converge due to %s iterations %" PetscInt_FMT, reason, its));
657: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
658: if (rRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " res rate %g R^2 %g", (double)rrate, (double)rRsq));
659: if (eRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " error rate %g R^2 %g", (double)erate, (double)eRsq));
660: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
661: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
662: if (ksp->reason == KSP_DIVERGED_PC_FAILED) {
663: PCFailedReason reason;
664: PetscCall(PCGetFailedReason(ksp->pc, &reason));
665: PetscCall(PetscViewerASCIIPrintf(viewer, " PC failed due to %s\n", PCFailedReasons[reason]));
666: }
667: }
668: PetscCall(PetscViewerASCIISubtractTab(viewer, ((PetscObject)ksp)->tablevel));
669: }
670: PetscFunctionReturn(PETSC_SUCCESS);
671: }
673: #include <petscdraw.h>
675: static PetscErrorCode KSPViewEigenvalues_Internal(KSP ksp, PetscBool isExplicit, PetscViewer viewer, PetscViewerFormat format)
676: {
677: PetscReal *r, *c;
678: PetscInt n, i, neig;
679: PetscBool isascii, isdraw;
680: PetscMPIInt rank;
682: PetscFunctionBegin;
683: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)ksp), &rank));
684: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
685: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
686: if (isExplicit) {
687: PetscCall(VecGetSize(ksp->vec_sol, &n));
688: PetscCall(PetscMalloc2(n, &r, n, &c));
689: PetscCall(KSPComputeEigenvaluesExplicitly(ksp, n, r, c));
690: neig = n;
691: } else {
692: PetscInt nits;
694: PetscCall(KSPGetIterationNumber(ksp, &nits));
695: n = nits + 2;
696: if (!nits) {
697: PetscCall(PetscViewerASCIIPrintf(viewer, "Zero iterations in solver, cannot approximate any eigenvalues\n"));
698: PetscFunctionReturn(PETSC_SUCCESS);
699: }
700: PetscCall(PetscMalloc2(n, &r, n, &c));
701: PetscCall(KSPComputeEigenvalues(ksp, n, r, c, &neig));
702: }
703: if (isascii) {
704: PetscCall(PetscViewerASCIIPrintf(viewer, "%s computed eigenvalues\n", isExplicit ? "Explicitly" : "Iteratively"));
705: for (i = 0; i < neig; ++i) {
706: if (c[i] >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, "%g + %gi\n", (double)r[i], (double)c[i]));
707: else PetscCall(PetscViewerASCIIPrintf(viewer, "%g - %gi\n", (double)r[i], -(double)c[i]));
708: }
709: } else if (isdraw && rank == 0) {
710: PetscDraw draw;
711: PetscDrawSP drawsp;
713: if (format == PETSC_VIEWER_DRAW_CONTOUR) {
714: PetscCall(KSPPlotEigenContours_Private(ksp, neig, r, c));
715: } else {
716: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
717: PetscCall(PetscDrawSPCreate(draw, 1, &drawsp));
718: PetscCall(PetscDrawSPReset(drawsp));
719: for (i = 0; i < neig; ++i) PetscCall(PetscDrawSPAddPoint(drawsp, r + i, c + i));
720: PetscCall(PetscDrawSPDraw(drawsp, PETSC_TRUE));
721: PetscCall(PetscDrawSPSave(drawsp));
722: PetscCall(PetscDrawSPDestroy(&drawsp));
723: }
724: }
725: PetscCall(PetscFree2(r, c));
726: PetscFunctionReturn(PETSC_SUCCESS);
727: }
729: static PetscErrorCode KSPViewSingularvalues_Internal(KSP ksp, PetscViewer viewer, PetscViewerFormat format)
730: {
731: PetscReal smax, smin;
732: PetscInt nits;
733: PetscBool isascii;
735: PetscFunctionBegin;
736: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
737: PetscCall(KSPGetIterationNumber(ksp, &nits));
738: if (!nits) {
739: PetscCall(PetscViewerASCIIPrintf(viewer, "Zero iterations in solver, cannot approximate any singular values\n"));
740: PetscFunctionReturn(PETSC_SUCCESS);
741: }
742: PetscCall(KSPComputeExtremeSingularValues(ksp, &smax, &smin));
743: 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)));
744: PetscFunctionReturn(PETSC_SUCCESS);
745: }
747: static PetscErrorCode KSPViewFinalResidual_Internal(KSP ksp, PetscViewer viewer, PetscViewerFormat format)
748: {
749: PetscBool isascii;
751: PetscFunctionBegin;
752: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
753: 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");
754: if (isascii) {
755: Mat A;
756: Vec t;
757: PetscReal norm;
759: PetscCall(PCGetOperators(ksp->pc, &A, NULL));
760: PetscCall(VecDuplicate(ksp->vec_rhs, &t));
761: PetscCall(KSP_MatMult(ksp, A, ksp->vec_sol, t));
762: PetscCall(VecAYPX(t, -1.0, ksp->vec_rhs));
763: PetscCall(PetscOptionsPushCreateViewerOff(PETSC_FALSE));
764: PetscCall(VecViewFromOptions(t, (PetscObject)ksp, "-ksp_view_final_residual_vec"));
765: PetscCall(PetscOptionsPopCreateViewerOff());
766: PetscCall(VecNorm(t, NORM_2, &norm));
767: PetscCall(VecDestroy(&t));
768: PetscCall(PetscViewerASCIIPrintf(viewer, "KSP final norm of residual %g\n", (double)norm));
769: }
770: PetscFunctionReturn(PETSC_SUCCESS);
771: }
773: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode PetscMonitorPauseFinal_Internal(PetscInt n, void *ctx[])
774: {
775: PetscFunctionBegin;
776: for (PetscInt i = 0; i < n; ++i) {
777: PetscViewerAndFormat *vf = (PetscViewerAndFormat *)ctx[i];
778: PetscDraw draw;
779: PetscReal lpause;
780: PetscBool isdraw;
782: if (!vf) continue;
783: if (!PetscCheckPointer(vf->viewer, PETSC_OBJECT)) continue;
784: if (((PetscObject)vf->viewer)->classid != PETSC_VIEWER_CLASSID) continue;
785: PetscCall(PetscObjectTypeCompare((PetscObject)vf->viewer, PETSCVIEWERDRAW, &isdraw));
786: if (!isdraw) continue;
788: PetscCall(PetscViewerDrawGetDraw(vf->viewer, 0, &draw));
789: PetscCall(PetscDrawGetPause(draw, &lpause));
790: PetscCall(PetscDrawSetPause(draw, -1.0));
791: PetscCall(PetscDrawPause(draw));
792: PetscCall(PetscDrawSetPause(draw, lpause));
793: }
794: PetscFunctionReturn(PETSC_SUCCESS);
795: }
797: static PetscErrorCode KSPMonitorPauseFinal_Internal(KSP ksp)
798: {
799: PetscFunctionBegin;
800: if (!ksp->pauseFinal) PetscFunctionReturn(PETSC_SUCCESS);
801: PetscCall(PetscMonitorPauseFinal_Internal(ksp->numbermonitors, ksp->monitorcontext));
802: PetscFunctionReturn(PETSC_SUCCESS);
803: }
805: static PetscErrorCode KSPSolve_Private(KSP ksp, Vec b, Vec x)
806: {
807: PetscBool flg = PETSC_FALSE, inXisinB = PETSC_FALSE, guess_zero;
808: Mat mat, pmat;
809: MPI_Comm comm;
810: MatNullSpace nullsp;
811: Vec btmp, vec_rhs = NULL;
813: PetscFunctionBegin;
814: level++;
815: comm = PetscObjectComm((PetscObject)ksp);
816: if (x && x == b) {
817: PetscCheck(ksp->guess_zero, comm, PETSC_ERR_ARG_INCOMP, "Cannot use x == b with nonzero initial guess");
818: PetscCall(VecDuplicate(b, &x));
819: inXisinB = PETSC_TRUE;
820: }
821: if (b) {
822: PetscCall(PetscObjectReference((PetscObject)b));
823: PetscCall(VecDestroy(&ksp->vec_rhs));
824: ksp->vec_rhs = b;
825: }
826: if (x) {
827: PetscCall(PetscObjectReference((PetscObject)x));
828: PetscCall(VecDestroy(&ksp->vec_sol));
829: ksp->vec_sol = x;
830: }
832: if (ksp->viewPre) PetscCall(ObjectView((PetscObject)ksp, ksp->viewerPre, ksp->formatPre));
834: if (ksp->presolve) PetscCall((*ksp->presolve)(ksp, ksp->vec_rhs, ksp->vec_sol, ksp->prectx));
836: /* reset the residual history list if requested */
837: if (ksp->res_hist_reset) ksp->res_hist_len = 0;
838: if (ksp->err_hist_reset) ksp->err_hist_len = 0;
840: /* KSPSetUp() scales the matrix if needed */
841: PetscCall(KSPSetUp(ksp));
842: PetscCall(KSPSetUpOnBlocks(ksp));
844: if (ksp->guess) {
845: PetscObjectState ostate, state;
847: PetscCall(KSPGuessSetUp(ksp->guess));
848: PetscCall(PetscObjectStateGet((PetscObject)ksp->vec_sol, &ostate));
849: PetscCall(KSPGuessFormGuess(ksp->guess, ksp->vec_rhs, ksp->vec_sol));
850: PetscCall(PetscObjectStateGet((PetscObject)ksp->vec_sol, &state));
851: if (state != ostate) {
852: ksp->guess_zero = PETSC_FALSE;
853: } else {
854: PetscCall(PetscInfo(ksp, "Using zero initial guess since the KSPGuess object did not change the vector\n"));
855: ksp->guess_zero = PETSC_TRUE;
856: }
857: }
859: PetscCall(VecSetErrorIfLocked(ksp->vec_sol, 3));
861: PetscCall(PetscLogEventBegin(!ksp->transpose_solve ? KSP_Solve : KSP_SolveTranspose, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
862: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
863: /* diagonal scale RHS if called for */
864: if (ksp->dscale) {
865: PetscCall(VecPointwiseMult(ksp->vec_rhs, ksp->vec_rhs, ksp->diagonal));
866: /* second time in, but matrix was scaled back to original */
867: if (ksp->dscalefix && ksp->dscalefix2) {
868: Mat mat, pmat;
870: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
871: PetscCall(MatDiagonalScale(pmat, ksp->diagonal, ksp->diagonal));
872: if (mat != pmat) PetscCall(MatDiagonalScale(mat, ksp->diagonal, ksp->diagonal));
873: }
875: /* scale initial guess */
876: if (!ksp->guess_zero) {
877: if (!ksp->truediagonal) {
878: PetscCall(VecDuplicate(ksp->diagonal, &ksp->truediagonal));
879: PetscCall(VecCopy(ksp->diagonal, ksp->truediagonal));
880: PetscCall(VecReciprocal(ksp->truediagonal));
881: }
882: PetscCall(VecPointwiseMult(ksp->vec_sol, ksp->vec_sol, ksp->truediagonal));
883: }
884: }
885: PetscCall(PCPreSolve(ksp->pc, ksp));
887: if (ksp->guess_zero && !ksp->guess_not_read) PetscCall(VecSet(ksp->vec_sol, 0.0));
888: if (ksp->guess_knoll) { /* The Knoll trick is independent on the KSPGuess specified */
889: PetscCall(PCApply(ksp->pc, ksp->vec_rhs, ksp->vec_sol));
890: PetscCall(KSP_RemoveNullSpace(ksp, ksp->vec_sol));
891: ksp->guess_zero = PETSC_FALSE;
892: }
894: /* can we mark the initial guess as zero for this solve? */
895: guess_zero = ksp->guess_zero;
896: if (!ksp->guess_zero) {
897: PetscReal norm;
899: PetscCall(VecNormAvailable(ksp->vec_sol, NORM_2, &flg, &norm));
900: if (flg && !norm) ksp->guess_zero = PETSC_TRUE;
901: }
902: if (ksp->transpose_solve) {
903: PetscCall(MatGetNullSpace(mat, &nullsp));
904: } else {
905: PetscCall(MatGetTransposeNullSpace(mat, &nullsp));
906: }
907: if (nullsp) {
908: PetscCall(VecDuplicate(ksp->vec_rhs, &btmp));
909: PetscCall(VecCopy(ksp->vec_rhs, btmp));
910: PetscCall(MatNullSpaceRemove(nullsp, btmp));
911: vec_rhs = ksp->vec_rhs;
912: ksp->vec_rhs = btmp;
913: }
914: PetscCall(VecLockReadPush(ksp->vec_rhs));
915: PetscUseTypeMethod(ksp, solve);
916: PetscCall(KSPMonitorPauseFinal_Internal(ksp));
918: PetscCall(VecLockReadPop(ksp->vec_rhs));
919: if (nullsp) {
920: ksp->vec_rhs = vec_rhs;
921: PetscCall(VecDestroy(&btmp));
922: }
924: ksp->guess_zero = guess_zero;
926: PetscCheck(ksp->reason, comm, PETSC_ERR_PLIB, "Internal error, solver returned without setting converged reason");
927: ksp->totalits += ksp->its;
929: PetscCall(KSPConvergedReasonViewFromOptions(ksp));
931: if (ksp->viewRate) {
932: PetscCall(PetscViewerPushFormat(ksp->viewerRate, ksp->formatRate));
933: PetscCall(KSPConvergedRateView(ksp, ksp->viewerRate));
934: PetscCall(PetscViewerPopFormat(ksp->viewerRate));
935: }
936: PetscCall(PCPostSolve(ksp->pc, ksp));
938: /* diagonal scale solution if called for */
939: if (ksp->dscale) {
940: PetscCall(VecPointwiseMult(ksp->vec_sol, ksp->vec_sol, ksp->diagonal));
941: /* unscale right-hand side and matrix */
942: if (ksp->dscalefix) {
943: Mat mat, pmat;
945: PetscCall(VecReciprocal(ksp->diagonal));
946: PetscCall(VecPointwiseMult(ksp->vec_rhs, ksp->vec_rhs, ksp->diagonal));
947: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
948: PetscCall(MatDiagonalScale(pmat, ksp->diagonal, ksp->diagonal));
949: if (mat != pmat) PetscCall(MatDiagonalScale(mat, ksp->diagonal, ksp->diagonal));
950: PetscCall(VecReciprocal(ksp->diagonal));
951: ksp->dscalefix2 = PETSC_TRUE;
952: }
953: }
954: PetscCall(PetscLogEventEnd(!ksp->transpose_solve ? KSP_Solve : KSP_SolveTranspose, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
955: if (ksp->guess) PetscCall(KSPGuessUpdate(ksp->guess, ksp->vec_rhs, ksp->vec_sol));
956: if (ksp->postsolve) PetscCall((*ksp->postsolve)(ksp, ksp->vec_rhs, ksp->vec_sol, ksp->postctx));
958: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
959: if (ksp->viewEV) PetscCall(KSPViewEigenvalues_Internal(ksp, PETSC_FALSE, ksp->viewerEV, ksp->formatEV));
960: if (ksp->viewEVExp) PetscCall(KSPViewEigenvalues_Internal(ksp, PETSC_TRUE, ksp->viewerEVExp, ksp->formatEVExp));
961: if (ksp->viewSV) PetscCall(KSPViewSingularvalues_Internal(ksp, ksp->viewerSV, ksp->formatSV));
962: if (ksp->viewFinalRes) PetscCall(KSPViewFinalResidual_Internal(ksp, ksp->viewerFinalRes, ksp->formatFinalRes));
963: if (ksp->viewMat) PetscCall(ObjectView((PetscObject)mat, ksp->viewerMat, ksp->formatMat));
964: if (ksp->viewPMat) PetscCall(ObjectView((PetscObject)pmat, ksp->viewerPMat, ksp->formatPMat));
965: if (ksp->viewRhs) PetscCall(ObjectView((PetscObject)ksp->vec_rhs, ksp->viewerRhs, ksp->formatRhs));
966: if (ksp->viewSol) PetscCall(ObjectView((PetscObject)ksp->vec_sol, ksp->viewerSol, ksp->formatSol));
967: if (ksp->view) PetscCall(ObjectView((PetscObject)ksp, ksp->viewer, ksp->format));
968: if (ksp->viewDScale) PetscCall(ObjectView((PetscObject)ksp->diagonal, ksp->viewerDScale, ksp->formatDScale));
969: if (ksp->viewMatExp) {
970: Mat A, B;
972: PetscCall(PCGetOperators(ksp->pc, &A, NULL));
973: if (ksp->transpose_solve) {
974: Mat AT;
976: PetscCall(MatCreateTranspose(A, &AT));
977: PetscCall(MatComputeOperator(AT, MATAIJ, &B));
978: PetscCall(MatDestroy(&AT));
979: } else {
980: PetscCall(MatComputeOperator(A, MATAIJ, &B));
981: }
982: PetscCall(ObjectView((PetscObject)B, ksp->viewerMatExp, ksp->formatMatExp));
983: PetscCall(MatDestroy(&B));
984: }
985: if (ksp->viewPOpExp) {
986: Mat B;
988: PetscCall(KSPComputeOperator(ksp, MATAIJ, &B));
989: PetscCall(ObjectView((PetscObject)B, ksp->viewerPOpExp, ksp->formatPOpExp));
990: PetscCall(MatDestroy(&B));
991: }
993: if (inXisinB) {
994: PetscCall(VecCopy(x, b));
995: PetscCall(VecDestroy(&x));
996: }
997: PetscCall(PetscObjectSAWsBlock((PetscObject)ksp));
998: if (ksp->errorifnotconverged && ksp->reason < 0 && ((level == 1) || (ksp->reason != KSP_DIVERGED_ITS))) {
999: PCFailedReason reason;
1001: 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]);
1002: PetscCall(PCGetFailedReason(ksp->pc, &reason));
1003: 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]);
1004: }
1005: level--;
1006: PetscFunctionReturn(PETSC_SUCCESS);
1007: }
1009: /*@
1010: KSPSolve - Solves a linear system associated with `KSP` object
1012: Collective
1014: Input Parameters:
1015: + ksp - iterative solver obtained from `KSPCreate()`
1016: . b - the right-hand side vector
1017: - x - the solution (this may be the same vector as `b`, then `b` will be overwritten with the answer)
1019: Options Database Keys:
1020: + -ksp_view_eigenvalues - compute preconditioned operators eigenvalues
1021: . -ksp_view_eigenvalues_explicit - compute the eigenvalues by forming the dense operator and using LAPACK
1022: . -ksp_view_mat binary - save matrix to the default binary viewer
1023: . -ksp_view_pmat binary - save matrix used to build preconditioner to the default binary viewer
1024: . -ksp_view_rhs binary - save right-hand side vector to the default binary viewer
1025: . -ksp_view_solution binary - save computed solution vector to the default binary viewer
1026: (can be read later with src/ksp/tutorials/ex10.c for testing solvers)
1027: . -ksp_view_mat_explicit - for matrix-free operators, computes the matrix entries and views them
1028: . -ksp_view_preconditioned_operator_explicit - computes the product of the preconditioner and matrix as an explicit matrix and views it
1029: . -ksp_converged_reason - print reason for converged or diverged, also prints number of iterations
1030: . -ksp_view_final_residual - print 2-norm of true linear system residual at the end of the solution process
1031: . -ksp_view_final_residual_vec - print true linear system residual vector at the end of the solution process;
1032: `-ksp_view_final_residual` must to be called first to enable this option
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^T)$ (note symmetric matrices always satisfy this property) then both left and right preconditioning will work
1064: If $nullspace(A) \neq nullspace(A^T)$ then left preconditioning will work but right preconditioning may not work (or it may).
1066: Developer Notes:
1067: The reason we cannot always solve $nullspace(A) \neq nullspace(A^T)$ systems with right preconditioning is because we need to remove at each iteration
1068: $ nullspace(AB) $ from the search direction. While we know the $nullspace(A)$, $nullspace(AB)$ equals $B^{-1}$ times $nullspace(A)$ 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 $nullspace(AB)$.
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%s solving using batches of width at most %" PetscInt_FMT "\n", ((PetscObject)ksp)->type_name, ksp->transpose_solve ? " transpose" : "", 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: Note:
1348: Using a larger block size can improve the efficiency of the solver.
1350: .seealso: [](ch_ksp), `KSPMatSolve()`, `KSPGetMatSolveBatchSize()`, `-mat_mumps_icntl_27`, `-matmatmult_Bbn`
1351: @*/
1352: PetscErrorCode KSPSetMatSolveBatchSize(KSP ksp, PetscInt bs)
1353: {
1354: PetscFunctionBegin;
1357: ksp->nmax = bs;
1358: PetscFunctionReturn(PETSC_SUCCESS);
1359: }
1361: /*@
1362: KSPGetMatSolveBatchSize - Gets the maximum number of columns treated simultaneously in `KSPMatSolve()`.
1364: Input Parameter:
1365: . ksp - iterative solver context
1367: Output Parameter:
1368: . bs - batch size
1370: Level: advanced
1372: .seealso: [](ch_ksp), `KSPMatSolve()`, `KSPSetMatSolveBatchSize()`, `-mat_mumps_icntl_27`, `-matmatmult_Bbn`
1373: @*/
1374: PetscErrorCode KSPGetMatSolveBatchSize(KSP ksp, PetscInt *bs)
1375: {
1376: PetscFunctionBegin;
1378: PetscAssertPointer(bs, 2);
1379: *bs = ksp->nmax;
1380: PetscFunctionReturn(PETSC_SUCCESS);
1381: }
1383: /*@
1384: KSPResetViewers - Resets all the viewers set from the options database during `KSPSetFromOptions()`
1386: Collective
1388: Input Parameter:
1389: . ksp - the `KSP` iterative solver context obtained from `KSPCreate()`
1391: Level: beginner
1393: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPSolve()`, `KSPSetFromOptions()`, `KSP`
1394: @*/
1395: PetscErrorCode KSPResetViewers(KSP ksp)
1396: {
1397: PetscFunctionBegin;
1399: if (!ksp) PetscFunctionReturn(PETSC_SUCCESS);
1400: PetscCall(PetscViewerDestroy(&ksp->viewer));
1401: PetscCall(PetscViewerDestroy(&ksp->viewerPre));
1402: PetscCall(PetscViewerDestroy(&ksp->viewerRate));
1403: PetscCall(PetscViewerDestroy(&ksp->viewerMat));
1404: PetscCall(PetscViewerDestroy(&ksp->viewerPMat));
1405: PetscCall(PetscViewerDestroy(&ksp->viewerRhs));
1406: PetscCall(PetscViewerDestroy(&ksp->viewerSol));
1407: PetscCall(PetscViewerDestroy(&ksp->viewerMatExp));
1408: PetscCall(PetscViewerDestroy(&ksp->viewerEV));
1409: PetscCall(PetscViewerDestroy(&ksp->viewerSV));
1410: PetscCall(PetscViewerDestroy(&ksp->viewerEVExp));
1411: PetscCall(PetscViewerDestroy(&ksp->viewerFinalRes));
1412: PetscCall(PetscViewerDestroy(&ksp->viewerPOpExp));
1413: PetscCall(PetscViewerDestroy(&ksp->viewerDScale));
1414: ksp->view = PETSC_FALSE;
1415: ksp->viewPre = PETSC_FALSE;
1416: ksp->viewMat = PETSC_FALSE;
1417: ksp->viewPMat = PETSC_FALSE;
1418: ksp->viewRhs = PETSC_FALSE;
1419: ksp->viewSol = PETSC_FALSE;
1420: ksp->viewMatExp = PETSC_FALSE;
1421: ksp->viewEV = PETSC_FALSE;
1422: ksp->viewSV = PETSC_FALSE;
1423: ksp->viewEVExp = PETSC_FALSE;
1424: ksp->viewFinalRes = PETSC_FALSE;
1425: ksp->viewPOpExp = PETSC_FALSE;
1426: ksp->viewDScale = PETSC_FALSE;
1427: PetscFunctionReturn(PETSC_SUCCESS);
1428: }
1430: /*@
1431: KSPReset - Removes any allocated `Vec` and `Mat` from the `KSP` data structures.
1433: Collective
1435: Input Parameter:
1436: . ksp - iterative solver obtained from `KSPCreate()`
1438: Level: intermediate
1440: Notes:
1441: Any options set in the `KSP`, including those set with `KSPSetFromOptions()` remain.
1443: Call `KSPReset()` only before you call `KSPSetOperators()` with a different sized matrix than the previous matrix used with the `KSP`.
1445: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPSolve()`, `KSP`
1446: @*/
1447: PetscErrorCode KSPReset(KSP ksp)
1448: {
1449: PetscFunctionBegin;
1451: if (!ksp) PetscFunctionReturn(PETSC_SUCCESS);
1452: PetscTryTypeMethod(ksp, reset);
1453: if (ksp->pc) PetscCall(PCReset(ksp->pc));
1454: if (ksp->guess) {
1455: KSPGuess guess = ksp->guess;
1456: PetscTryTypeMethod(guess, reset);
1457: }
1458: PetscCall(VecDestroyVecs(ksp->nwork, &ksp->work));
1459: PetscCall(VecDestroy(&ksp->vec_rhs));
1460: PetscCall(VecDestroy(&ksp->vec_sol));
1461: PetscCall(VecDestroy(&ksp->diagonal));
1462: PetscCall(VecDestroy(&ksp->truediagonal));
1464: ksp->setupstage = KSP_SETUP_NEW;
1465: ksp->nmax = PETSC_DECIDE;
1466: PetscFunctionReturn(PETSC_SUCCESS);
1467: }
1469: /*@
1470: KSPDestroy - Destroys a `KSP` context.
1472: Collective
1474: Input Parameter:
1475: . ksp - iterative solver obtained from `KSPCreate()`
1477: Level: beginner
1479: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPSolve()`, `KSP`
1480: @*/
1481: PetscErrorCode KSPDestroy(KSP *ksp)
1482: {
1483: PC pc;
1485: PetscFunctionBegin;
1486: if (!*ksp) PetscFunctionReturn(PETSC_SUCCESS);
1488: if (--((PetscObject)*ksp)->refct > 0) {
1489: *ksp = NULL;
1490: PetscFunctionReturn(PETSC_SUCCESS);
1491: }
1493: PetscCall(PetscObjectSAWsViewOff((PetscObject)*ksp));
1495: /*
1496: Avoid a cascading call to PCReset(ksp->pc) from the following call:
1497: PCReset() shouldn't be called from KSPDestroy() as it is unprotected by pc's
1498: refcount (and may be shared, e.g., by other ksps).
1499: */
1500: pc = (*ksp)->pc;
1501: (*ksp)->pc = NULL;
1502: PetscCall(KSPReset(*ksp));
1503: PetscCall(KSPResetViewers(*ksp));
1504: (*ksp)->pc = pc;
1505: PetscTryTypeMethod(*ksp, destroy);
1507: if ((*ksp)->transpose.use_explicittranspose) {
1508: PetscCall(MatDestroy(&(*ksp)->transpose.AT));
1509: PetscCall(MatDestroy(&(*ksp)->transpose.BT));
1510: (*ksp)->transpose.reuse_transpose = PETSC_FALSE;
1511: }
1513: PetscCall(KSPGuessDestroy(&(*ksp)->guess));
1514: PetscCall(DMDestroy(&(*ksp)->dm));
1515: PetscCall(PCDestroy(&(*ksp)->pc));
1516: PetscCall(PetscFree((*ksp)->res_hist_alloc));
1517: PetscCall(PetscFree((*ksp)->err_hist_alloc));
1518: if ((*ksp)->convergeddestroy) PetscCall((*(*ksp)->convergeddestroy)(&(*ksp)->cnvP));
1519: PetscCall(KSPMonitorCancel(*ksp));
1520: PetscCall(KSPConvergedReasonViewCancel(*ksp));
1521: PetscCall(PetscHeaderDestroy(ksp));
1522: PetscFunctionReturn(PETSC_SUCCESS);
1523: }
1525: /*@
1526: KSPSetPCSide - Sets the preconditioning side.
1528: Logically Collective
1530: Input Parameter:
1531: . ksp - iterative solver obtained from `KSPCreate()`
1533: Output Parameter:
1534: . side - the preconditioning side, where side is one of
1535: .vb
1536: PC_LEFT - left preconditioning (default)
1537: PC_RIGHT - right preconditioning
1538: PC_SYMMETRIC - symmetric preconditioning
1539: .ve
1541: Options Database Key:
1542: . -ksp_pc_side <right,left,symmetric> - `KSP` preconditioner side
1544: Level: intermediate
1546: Notes:
1547: Left preconditioning is used by default for most Krylov methods except `KSPFGMRES` which only supports right preconditioning.
1549: For methods changing the side of the preconditioner changes the norm type that is used, see `KSPSetNormType()`.
1551: Symmetric preconditioning is currently available only for the `KSPQCG` method. However, note that
1552: symmetric preconditioning can be emulated by using either right or left
1553: preconditioning, modifying the application of the matrix (with a custom `Mat` argument to `KSPSetOperators()`,
1554: and using a pre 'KSPSetPreSolve()` or post processing `KSPSetPostSolve()` step).
1556: Setting the `PCSide` often affects the default norm type. See `KSPSetNormType()` for details.
1558: .seealso: [](ch_ksp), `KSPGetPCSide()`, `KSPSetNormType()`, `KSPGetNormType()`, `KSP`, `KSPSetPreSolve()`, `KSPSetPostSolve()`
1559: @*/
1560: PetscErrorCode KSPSetPCSide(KSP ksp, PCSide side)
1561: {
1562: PetscFunctionBegin;
1565: ksp->pc_side = ksp->pc_side_set = side;
1566: PetscFunctionReturn(PETSC_SUCCESS);
1567: }
1569: /*@
1570: KSPGetPCSide - Gets the preconditioning side.
1572: Not Collective
1574: Input Parameter:
1575: . ksp - iterative solver obtained from `KSPCreate()`
1577: Output Parameter:
1578: . side - the preconditioning side, where side is one of
1579: .vb
1580: PC_LEFT - left preconditioning (default)
1581: PC_RIGHT - right preconditioning
1582: PC_SYMMETRIC - symmetric preconditioning
1583: .ve
1585: Level: intermediate
1587: .seealso: [](ch_ksp), `KSPSetPCSide()`, `KSP`
1588: @*/
1589: PetscErrorCode KSPGetPCSide(KSP ksp, PCSide *side)
1590: {
1591: PetscFunctionBegin;
1593: PetscAssertPointer(side, 2);
1594: PetscCall(KSPSetUpNorms_Private(ksp, PETSC_TRUE, &ksp->normtype, &ksp->pc_side));
1595: *side = ksp->pc_side;
1596: PetscFunctionReturn(PETSC_SUCCESS);
1597: }
1599: /*@
1600: KSPGetTolerances - Gets the relative, absolute, divergence, and maximum
1601: iteration tolerances used by the default `KSP` convergence tests.
1603: Not Collective
1605: Input Parameter:
1606: . ksp - the Krylov subspace context
1608: Output Parameters:
1609: + rtol - the relative convergence tolerance
1610: . abstol - the absolute convergence tolerance
1611: . dtol - the divergence tolerance
1612: - maxits - maximum number of iterations
1614: Level: intermediate
1616: Note:
1617: The user can specify `NULL` for any parameter that is not needed.
1619: .seealso: [](ch_ksp), `KSPSetTolerances()`, `KSP`, `KSPSetMinimumIterations()`, `KSPGetMinimumIterations()`
1620: @*/
1621: PetscErrorCode KSPGetTolerances(KSP ksp, PeOp PetscReal *rtol, PeOp PetscReal *abstol, PeOp PetscReal *dtol, PeOp PetscInt *maxits)
1622: {
1623: PetscFunctionBegin;
1625: if (abstol) *abstol = ksp->abstol;
1626: if (rtol) *rtol = ksp->rtol;
1627: if (dtol) *dtol = ksp->divtol;
1628: if (maxits) *maxits = ksp->max_it;
1629: PetscFunctionReturn(PETSC_SUCCESS);
1630: }
1632: /*@
1633: KSPSetTolerances - Sets the relative, absolute, divergence, and maximum
1634: iteration tolerances used by the default `KSP` convergence testers.
1636: Logically Collective
1638: Input Parameters:
1639: + ksp - the Krylov subspace context
1640: . rtol - the relative convergence tolerance, relative decrease in the (possibly preconditioned) residual norm
1641: . abstol - the absolute convergence tolerance absolute size of the (possibly preconditioned) residual norm
1642: . dtol - the divergence tolerance, amount (possibly preconditioned) residual norm can increase before `KSPConvergedDefault()` concludes that the method is diverging
1643: - maxits - maximum number of iterations to use
1645: Options Database Keys:
1646: + -ksp_atol <abstol> - Sets `abstol`
1647: . -ksp_rtol <rtol> - Sets `rtol`
1648: . -ksp_divtol <dtol> - Sets `dtol`
1649: - -ksp_max_it <maxits> - Sets `maxits`
1651: Level: intermediate
1653: Notes:
1654: 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.
1655: The norm used depends on the `KSPNormType` that has been set with `KSPSetNormType()`, the default depends on the `KSPType` used.
1657: All parameters must be non-negative.
1659: 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).
1661: 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.
1663: For `dtol` and `maxits` use `PETSC_UMLIMITED` to indicate there is no upper bound on these values
1665: See `KSPConvergedDefault()` for details how these parameters are used in the default convergence test. See also `KSPSetConvergenceTest()`
1666: for setting user-defined stopping criteria.
1668: Fortran Note:
1669: Use `PETSC_CURRENT_INTEGER`, `PETSC_CURRENT_REAL`, `PETSC_DETERMINE_INTEGER`, or `PETSC_DETERMINE_REAL`
1671: .seealso: [](ch_ksp), `KSPGetTolerances()`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSP`, `KSPSetMinimumIterations()`
1672: @*/
1673: PetscErrorCode KSPSetTolerances(KSP ksp, PetscReal rtol, PetscReal abstol, PetscReal dtol, PetscInt maxits)
1674: {
1675: PetscFunctionBegin;
1682: if (rtol == (PetscReal)PETSC_DETERMINE) {
1683: ksp->rtol = ksp->default_rtol;
1684: } else if (rtol != (PetscReal)PETSC_CURRENT) {
1685: 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);
1686: ksp->rtol = rtol;
1687: }
1688: if (abstol == (PetscReal)PETSC_DETERMINE) {
1689: ksp->abstol = ksp->default_abstol;
1690: } else if (abstol != (PetscReal)PETSC_CURRENT) {
1691: PetscCheck(abstol >= 0.0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Absolute tolerance %g must be non-negative", (double)abstol);
1692: ksp->abstol = abstol;
1693: }
1694: if (dtol == (PetscReal)PETSC_DETERMINE) {
1695: ksp->divtol = ksp->default_divtol;
1696: } else if (dtol == (PetscReal)PETSC_UNLIMITED) {
1697: ksp->divtol = PETSC_MAX_REAL;
1698: } else if (dtol != (PetscReal)PETSC_CURRENT) {
1699: PetscCheck(dtol >= 0.0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Divergence tolerance %g must be larger than 1.0", (double)dtol);
1700: ksp->divtol = dtol;
1701: }
1702: if (maxits == PETSC_DETERMINE) {
1703: ksp->max_it = ksp->default_max_it;
1704: } else if (maxits == PETSC_UNLIMITED) {
1705: ksp->max_it = PETSC_INT_MAX;
1706: } else if (maxits != PETSC_CURRENT) {
1707: PetscCheck(maxits >= 0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Maximum number of iterations %" PetscInt_FMT " must be non-negative", maxits);
1708: ksp->max_it = maxits;
1709: }
1710: PetscFunctionReturn(PETSC_SUCCESS);
1711: }
1713: /*@
1714: KSPSetMinimumIterations - Sets the minimum number of iterations to use, regardless of the tolerances
1716: Logically Collective
1718: Input Parameters:
1719: + ksp - the Krylov subspace context
1720: - minit - minimum number of iterations to use
1722: Options Database Key:
1723: . -ksp_min_it <minits> - Sets `minit`
1725: Level: intermediate
1727: Notes:
1728: Use `KSPSetTolerances()` to set a variety of other tolerances
1730: See `KSPConvergedDefault()` for details on how these parameters are used in the default convergence test. See also `KSPSetConvergenceTest()`
1731: for setting user-defined stopping criteria.
1733: If the initial residual norm is small enough solvers may return immediately without computing any improvement to the solution. Using this routine
1734: prevents that which usually ensures the solution is changed (often minimally) from the previous solution. This option may be used with ODE integrators
1735: to ensure the integrator does not fall into a false steady-state solution of the ODE.
1737: .seealso: [](ch_ksp), `KSPGetTolerances()`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSP`, `KSPSetTolerances()`, `KSPGetMinimumIterations()`
1738: @*/
1739: PetscErrorCode KSPSetMinimumIterations(KSP ksp, PetscInt minit)
1740: {
1741: PetscFunctionBegin;
1745: PetscCheck(minit >= 0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Minimum number of iterations %" PetscInt_FMT " must be non-negative", minit);
1746: ksp->min_it = minit;
1747: PetscFunctionReturn(PETSC_SUCCESS);
1748: }
1750: /*@
1751: KSPGetMinimumIterations - Gets the minimum number of iterations to use, regardless of the tolerances, that was set with `KSPSetMinimumIterations()` or `-ksp_min_it`
1753: Not Collective
1755: Input Parameter:
1756: . ksp - the Krylov subspace context
1758: Output Parameter:
1759: . minit - minimum number of iterations to use
1761: Level: intermediate
1763: .seealso: [](ch_ksp), `KSPGetTolerances()`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSP`, `KSPSetTolerances()`, `KSPSetMinimumIterations()`
1764: @*/
1765: PetscErrorCode KSPGetMinimumIterations(KSP ksp, PetscInt *minit)
1766: {
1767: PetscFunctionBegin;
1769: PetscAssertPointer(minit, 2);
1771: *minit = ksp->min_it;
1772: PetscFunctionReturn(PETSC_SUCCESS);
1773: }
1775: /*@
1776: KSPSetInitialGuessNonzero - Tells the iterative solver that the
1777: initial guess is nonzero; otherwise `KSP` assumes the initial guess
1778: is to be zero (and thus zeros it out before solving).
1780: Logically Collective
1782: Input Parameters:
1783: + ksp - iterative solver obtained from `KSPCreate()`
1784: - flg - ``PETSC_TRUE`` indicates the guess is non-zero, `PETSC_FALSE` indicates the guess is zero
1786: Options Database Key:
1787: . -ksp_initial_guess_nonzero <true,false> - use nonzero initial guess
1789: Level: beginner
1791: .seealso: [](ch_ksp), `KSPGetInitialGuessNonzero()`, `KSPGuessSetType()`, `KSPGuessType`, `KSP`
1792: @*/
1793: PetscErrorCode KSPSetInitialGuessNonzero(KSP ksp, PetscBool flg)
1794: {
1795: PetscFunctionBegin;
1798: ksp->guess_zero = (PetscBool)!flg;
1799: PetscFunctionReturn(PETSC_SUCCESS);
1800: }
1802: /*@
1803: KSPGetInitialGuessNonzero - Determines whether the `KSP` solver is using
1804: a zero initial guess.
1806: Not Collective
1808: Input Parameter:
1809: . ksp - iterative solver obtained from `KSPCreate()`
1811: Output Parameter:
1812: . flag - `PETSC_TRUE` if guess is nonzero, else `PETSC_FALSE`
1814: Level: intermediate
1816: .seealso: [](ch_ksp), `KSPSetInitialGuessNonzero()`, `KSP`
1817: @*/
1818: PetscErrorCode KSPGetInitialGuessNonzero(KSP ksp, PetscBool *flag)
1819: {
1820: PetscFunctionBegin;
1822: PetscAssertPointer(flag, 2);
1823: if (ksp->guess_zero) *flag = PETSC_FALSE;
1824: else *flag = PETSC_TRUE;
1825: PetscFunctionReturn(PETSC_SUCCESS);
1826: }
1828: /*@
1829: KSPSetErrorIfNotConverged - Causes `KSPSolve()` to generate an error if the solver has not converged as soon as the error is detected.
1831: Logically Collective
1833: Input Parameters:
1834: + ksp - iterative solver obtained from `KSPCreate()`
1835: - flg - `PETSC_TRUE` indicates you want the error generated
1837: Options Database Key:
1838: . -ksp_error_if_not_converged <true,false> - generate an error and stop the program
1840: Level: intermediate
1842: Notes:
1843: Normally PETSc continues if a linear solver fails to converge, you can call `KSPGetConvergedReason()` after a `KSPSolve()`
1844: to determine if it has converged. This functionality is mostly helpful while running in a debugger (`-start_in_debugger`) to determine exactly where
1845: the failure occurs and why.
1847: A `KSP_DIVERGED_ITS` will not generate an error in a `KSPSolve()` inside a nested linear solver
1849: .seealso: [](ch_ksp), `KSPGetErrorIfNotConverged()`, `KSP`
1850: @*/
1851: PetscErrorCode KSPSetErrorIfNotConverged(KSP ksp, PetscBool flg)
1852: {
1853: PetscFunctionBegin;
1856: ksp->errorifnotconverged = flg;
1857: PetscFunctionReturn(PETSC_SUCCESS);
1858: }
1860: /*@
1861: KSPGetErrorIfNotConverged - Will `KSPSolve()` generate an error if the solver does not converge?
1863: Not Collective
1865: Input Parameter:
1866: . ksp - iterative solver obtained from KSPCreate()
1868: Output Parameter:
1869: . flag - `PETSC_TRUE` if it will generate an error, else `PETSC_FALSE`
1871: Level: intermediate
1873: .seealso: [](ch_ksp), `KSPSetErrorIfNotConverged()`, `KSP`
1874: @*/
1875: PetscErrorCode KSPGetErrorIfNotConverged(KSP ksp, PetscBool *flag)
1876: {
1877: PetscFunctionBegin;
1879: PetscAssertPointer(flag, 2);
1880: *flag = ksp->errorifnotconverged;
1881: PetscFunctionReturn(PETSC_SUCCESS);
1882: }
1884: /*@
1885: KSPSetInitialGuessKnoll - Tells the iterative solver to use `PCApply()` on the right hand side vector to compute the initial guess (The Knoll trick)
1887: Logically Collective
1889: Input Parameters:
1890: + ksp - iterative solver obtained from `KSPCreate()`
1891: - flg - `PETSC_TRUE` or `PETSC_FALSE`
1893: Level: advanced
1895: Developer Note:
1896: The Knoll trick is not currently implemented using the `KSPGuess` class which provides a variety of ways of computing
1897: an initial guess based on previous solves.
1899: .seealso: [](ch_ksp), `KSPGetInitialGuessKnoll()`, `KSPGuess`, `KSPSetInitialGuessNonzero()`, `KSPGetInitialGuessNonzero()`, `KSP`
1900: @*/
1901: PetscErrorCode KSPSetInitialGuessKnoll(KSP ksp, PetscBool flg)
1902: {
1903: PetscFunctionBegin;
1906: ksp->guess_knoll = flg;
1907: PetscFunctionReturn(PETSC_SUCCESS);
1908: }
1910: /*@
1911: KSPGetInitialGuessKnoll - Determines whether the `KSP` solver is using the Knoll trick (using PCApply(pc,b,...) to compute
1912: the initial guess
1914: Not Collective
1916: Input Parameter:
1917: . ksp - iterative solver obtained from `KSPCreate()`
1919: Output Parameter:
1920: . flag - `PETSC_TRUE` if using Knoll trick, else `PETSC_FALSE`
1922: Level: advanced
1924: .seealso: [](ch_ksp), `KSPSetInitialGuessKnoll()`, `KSPSetInitialGuessNonzero()`, `KSPGetInitialGuessNonzero()`, `KSP`
1925: @*/
1926: PetscErrorCode KSPGetInitialGuessKnoll(KSP ksp, PetscBool *flag)
1927: {
1928: PetscFunctionBegin;
1930: PetscAssertPointer(flag, 2);
1931: *flag = ksp->guess_knoll;
1932: PetscFunctionReturn(PETSC_SUCCESS);
1933: }
1935: /*@
1936: KSPGetComputeSingularValues - Gets the flag indicating whether the extreme singular
1937: values will be calculated via a Lanczos or Arnoldi process as the linear
1938: system is solved.
1940: Not Collective
1942: Input Parameter:
1943: . ksp - iterative solver obtained from `KSPCreate()`
1945: Output Parameter:
1946: . flg - `PETSC_TRUE` or `PETSC_FALSE`
1948: Options Database Key:
1949: . -ksp_monitor_singular_value - Activates `KSPSetComputeSingularValues()`
1951: Level: advanced
1953: Notes:
1954: This option is not valid for `KSPType`.
1956: Many users may just want to use the monitoring routine
1957: `KSPMonitorSingularValue()` (which can be set with option `-ksp_monitor_singular_value`)
1958: to print the singular values at each iteration of the linear solve.
1960: .seealso: [](ch_ksp), `KSPComputeExtremeSingularValues()`, `KSPMonitorSingularValue()`, `KSP`
1961: @*/
1962: PetscErrorCode KSPGetComputeSingularValues(KSP ksp, PetscBool *flg)
1963: {
1964: PetscFunctionBegin;
1966: PetscAssertPointer(flg, 2);
1967: *flg = ksp->calc_sings;
1968: PetscFunctionReturn(PETSC_SUCCESS);
1969: }
1971: /*@
1972: KSPSetComputeSingularValues - Sets a flag so that the extreme singular
1973: values will be calculated via a Lanczos or Arnoldi process as the linear
1974: system is solved.
1976: Logically Collective
1978: Input Parameters:
1979: + ksp - iterative solver obtained from `KSPCreate()`
1980: - flg - `PETSC_TRUE` or `PETSC_FALSE`
1982: Options Database Key:
1983: . -ksp_monitor_singular_value - Activates `KSPSetComputeSingularValues()`
1985: Level: advanced
1987: Notes:
1988: This option is not valid for all iterative methods.
1990: Many users may just want to use the monitoring routine
1991: `KSPMonitorSingularValue()` (which can be set with option `-ksp_monitor_singular_value`)
1992: to print the singular values at each iteration of the linear solve.
1994: Consider using the excellent package SLEPc for accurate efficient computations of singular or eigenvalues.
1996: .seealso: [](ch_ksp), `KSPComputeExtremeSingularValues()`, `KSPMonitorSingularValue()`, `KSP`, `KSPSetComputeRitz()`
1997: @*/
1998: PetscErrorCode KSPSetComputeSingularValues(KSP ksp, PetscBool flg)
1999: {
2000: PetscFunctionBegin;
2003: ksp->calc_sings = flg;
2004: PetscFunctionReturn(PETSC_SUCCESS);
2005: }
2007: /*@
2008: KSPGetComputeEigenvalues - Gets the flag indicating that the extreme eigenvalues
2009: values will be calculated via a Lanczos or Arnoldi process as the linear
2010: system is solved.
2012: Not Collective
2014: Input Parameter:
2015: . ksp - iterative solver obtained from `KSPCreate()`
2017: Output Parameter:
2018: . flg - `PETSC_TRUE` or `PETSC_FALSE`
2020: Level: advanced
2022: Note:
2023: Currently this option is not valid for all iterative methods.
2025: .seealso: [](ch_ksp), `KSPComputeEigenvalues()`, `KSPComputeEigenvaluesExplicitly()`, `KSP`, `KSPSetComputeRitz()`
2026: @*/
2027: PetscErrorCode KSPGetComputeEigenvalues(KSP ksp, PetscBool *flg)
2028: {
2029: PetscFunctionBegin;
2031: PetscAssertPointer(flg, 2);
2032: *flg = ksp->calc_sings;
2033: PetscFunctionReturn(PETSC_SUCCESS);
2034: }
2036: /*@
2037: KSPSetComputeEigenvalues - Sets a flag so that the extreme eigenvalues
2038: values will be calculated via a Lanczos or Arnoldi process as the linear
2039: system is solved.
2041: Logically Collective
2043: Input Parameters:
2044: + ksp - iterative solver obtained from `KSPCreate()`
2045: - flg - `PETSC_TRUE` or `PETSC_FALSE`
2047: Level: advanced
2049: Note:
2050: Currently this option is not valid for all iterative methods.
2052: Consider using the excellent package SLEPc for accurate efficient computations of singular or eigenvalues.
2054: .seealso: [](ch_ksp), `KSPComputeEigenvalues()`, `KSPComputeEigenvaluesExplicitly()`, `KSP`, `KSPSetComputeRitz()`
2055: @*/
2056: PetscErrorCode KSPSetComputeEigenvalues(KSP ksp, PetscBool flg)
2057: {
2058: PetscFunctionBegin;
2061: ksp->calc_sings = flg;
2062: PetscFunctionReturn(PETSC_SUCCESS);
2063: }
2065: /*@
2066: KSPSetComputeRitz - Sets a flag so that the Ritz or harmonic Ritz pairs
2067: will be calculated via a Lanczos or Arnoldi process as the linear
2068: system is solved.
2070: Logically Collective
2072: Input Parameters:
2073: + ksp - iterative solver obtained from `KSPCreate()`
2074: - flg - `PETSC_TRUE` or `PETSC_FALSE`
2076: Level: advanced
2078: Note:
2079: Currently this option is only valid for the `KSPGMRES` method.
2081: .seealso: [](ch_ksp), `KSPComputeRitz()`, `KSP`, `KSPComputeEigenvalues()`, `KSPComputeExtremeSingularValues()`
2082: @*/
2083: PetscErrorCode KSPSetComputeRitz(KSP ksp, PetscBool flg)
2084: {
2085: PetscFunctionBegin;
2088: ksp->calc_ritz = flg;
2089: PetscFunctionReturn(PETSC_SUCCESS);
2090: }
2092: /*@
2093: KSPGetRhs - Gets the right-hand-side vector for the linear system to
2094: be solved.
2096: Not Collective
2098: Input Parameter:
2099: . ksp - iterative solver obtained from `KSPCreate()`
2101: Output Parameter:
2102: . r - right-hand-side vector
2104: Level: developer
2106: .seealso: [](ch_ksp), `KSPGetSolution()`, `KSPSolve()`, `KSP`
2107: @*/
2108: PetscErrorCode KSPGetRhs(KSP ksp, Vec *r)
2109: {
2110: PetscFunctionBegin;
2112: PetscAssertPointer(r, 2);
2113: *r = ksp->vec_rhs;
2114: PetscFunctionReturn(PETSC_SUCCESS);
2115: }
2117: /*@
2118: KSPGetSolution - Gets the location of the solution for the
2119: linear system to be solved.
2121: Not Collective
2123: Input Parameter:
2124: . ksp - iterative solver obtained from `KSPCreate()`
2126: Output Parameter:
2127: . v - solution vector
2129: Level: developer
2131: Note:
2132: If this is called during a `KSPSolve()` the vector's values may not represent the solution
2133: to the linear system.
2135: .seealso: [](ch_ksp), `KSPGetRhs()`, `KSPBuildSolution()`, `KSPSolve()`, `KSP`
2136: @*/
2137: PetscErrorCode KSPGetSolution(KSP ksp, Vec *v)
2138: {
2139: PetscFunctionBegin;
2141: PetscAssertPointer(v, 2);
2142: *v = ksp->vec_sol;
2143: PetscFunctionReturn(PETSC_SUCCESS);
2144: }
2146: /*@
2147: KSPSetPC - Sets the preconditioner to be used to calculate the
2148: application of the preconditioner on a vector into a `KSP`.
2150: Collective
2152: Input Parameters:
2153: + ksp - the `KSP` iterative solver obtained from `KSPCreate()`
2154: - pc - the preconditioner object (if `NULL` it returns the `PC` currently held by the `KSP`)
2156: Level: developer
2158: Note:
2159: This routine is almost never used since `KSP` creates its own `PC` when needed.
2160: Use `KSPGetPC()` to retrieve the preconditioner context instead of creating a new one.
2162: .seealso: [](ch_ksp), `KSPGetPC()`, `KSP`
2163: @*/
2164: PetscErrorCode KSPSetPC(KSP ksp, PC pc)
2165: {
2166: PetscFunctionBegin;
2168: if (pc) {
2170: PetscCheckSameComm(ksp, 1, pc, 2);
2171: }
2172: if (ksp->pc != pc && ksp->setupstage) ksp->setupstage = KSP_SETUP_NEWMATRIX;
2173: PetscCall(PetscObjectReference((PetscObject)pc));
2174: PetscCall(PCDestroy(&ksp->pc));
2175: ksp->pc = pc;
2176: PetscFunctionReturn(PETSC_SUCCESS);
2177: }
2179: PETSC_INTERN PetscErrorCode PCCreate_MPI(PC);
2181: // PetscClangLinter pragma disable: -fdoc-internal-linkage
2182: /*@C
2183: KSPCheckPCMPI - Checks if `-mpi_linear_solver_server` is active and the `PC` should be changed to `PCMPI`
2185: Collective, No Fortran Support
2187: Input Parameter:
2188: . ksp - iterative solver obtained from `KSPCreate()`
2190: Level: developer
2192: .seealso: [](ch_ksp), `KSPSetPC()`, `KSP`, `PCMPIServerBegin()`, `PCMPIServerEnd()`
2193: @*/
2194: PETSC_INTERN PetscErrorCode KSPCheckPCMPI(KSP ksp)
2195: {
2196: PetscBool isPCMPI;
2198: PetscFunctionBegin;
2200: PetscCall(PetscObjectTypeCompare((PetscObject)ksp->pc, PCMPI, &isPCMPI));
2201: if (PCMPIServerActive && ksp->nestlevel == 0 && !isPCMPI) {
2202: const char *prefix;
2203: char *found = NULL;
2205: PetscCall(KSPGetOptionsPrefix(ksp, &prefix));
2206: if (prefix) PetscCall(PetscStrstr(prefix, "mpi_linear_solver_server_", &found));
2207: if (!found) PetscCall(KSPAppendOptionsPrefix(ksp, "mpi_linear_solver_server_"));
2208: PetscCall(PetscInfo(NULL, "In MPI Linear Solver Server and detected (root) PC that must be changed to PCMPI\n"));
2209: PetscCall(PCSetType(ksp->pc, PCMPI));
2210: }
2211: PetscFunctionReturn(PETSC_SUCCESS);
2212: }
2214: /*@
2215: KSPGetPC - Returns a pointer to the preconditioner context with the `KSP`
2217: Not Collective
2219: Input Parameter:
2220: . ksp - iterative solver obtained from `KSPCreate()`
2222: Output Parameter:
2223: . pc - preconditioner context
2225: Level: beginner
2227: Note:
2228: The `PC` is created if it does not already exist.
2230: Developer Note:
2231: Calls `KSPCheckPCMPI()` to check if the `KSP` is effected by `-mpi_linear_solver_server`
2233: .seealso: [](ch_ksp), `KSPSetPC()`, `KSP`, `PC`
2234: @*/
2235: PetscErrorCode KSPGetPC(KSP ksp, PC *pc)
2236: {
2237: PetscFunctionBegin;
2239: PetscAssertPointer(pc, 2);
2240: if (!ksp->pc) {
2241: PetscCall(PCCreate(PetscObjectComm((PetscObject)ksp), &ksp->pc));
2242: PetscCall(PetscObjectIncrementTabLevel((PetscObject)ksp->pc, (PetscObject)ksp, 0));
2243: PetscCall(PetscObjectSetOptions((PetscObject)ksp->pc, ((PetscObject)ksp)->options));
2244: PetscCall(PCSetKSPNestLevel(ksp->pc, ksp->nestlevel));
2245: }
2246: PetscCall(KSPCheckPCMPI(ksp));
2247: *pc = ksp->pc;
2248: PetscFunctionReturn(PETSC_SUCCESS);
2249: }
2251: /*@
2252: KSPMonitor - runs the user provided monitor routines, if they exist
2254: Collective
2256: Input Parameters:
2257: + ksp - iterative solver obtained from `KSPCreate()`
2258: . it - iteration number
2259: - rnorm - relative norm of the residual
2261: Level: developer
2263: Notes:
2264: This routine is called by the `KSP` implementations.
2265: It does not typically need to be called by the user.
2267: For Krylov methods that do not keep a running value of the current solution (such as `KSPGMRES`) this
2268: cannot be called after the `KSPConvergedReason` has been set but before the final solution has been computed.
2270: .seealso: [](ch_ksp), `KSPMonitorSet()`
2271: @*/
2272: PetscErrorCode KSPMonitor(KSP ksp, PetscInt it, PetscReal rnorm)
2273: {
2274: PetscInt i, n = ksp->numbermonitors;
2276: PetscFunctionBegin;
2277: for (i = 0; i < n; i++) PetscCall((*ksp->monitor[i])(ksp, it, rnorm, ksp->monitorcontext[i]));
2278: PetscFunctionReturn(PETSC_SUCCESS);
2279: }
2281: /*@C
2282: 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,
2283: the residual norm computed in a `KSPSolve()`
2285: Logically Collective
2287: Input Parameters:
2288: + ksp - iterative solver obtained from `KSPCreate()`
2289: . monitor - pointer to function (if this is `NULL`, it turns off monitoring, see `KSPMonitorFn`
2290: . ctx - [optional] context for private data for the monitor routine (use `NULL` if no context is needed)
2291: - monitordestroy - [optional] routine that frees monitor context (may be `NULL`), see `PetscCtxDestroyFn` for the calling sequence
2293: Options Database Keys:
2294: + -ksp_monitor - sets `KSPMonitorResidual()`
2295: . -ksp_monitor hdf5:filename - sets `KSPMonitorResidualView()` and saves residual
2296: . -ksp_monitor draw - sets `KSPMonitorResidualView()` and plots residual
2297: . -ksp_monitor draw::draw_lg - sets `KSPMonitorResidualDrawLG()` and plots residual
2298: . -ksp_monitor_pause_final - Pauses any graphics when the solve finishes (only works for internal monitors)
2299: . -ksp_monitor_true_residual - sets `KSPMonitorTrueResidual()`
2300: . -ksp_monitor_true_residual draw::draw_lg - sets `KSPMonitorTrueResidualDrawLG()` and plots residual
2301: . -ksp_monitor_max - sets `KSPMonitorTrueResidualMax()`
2302: . -ksp_monitor_singular_value - sets `KSPMonitorSingularValue()`
2303: - -ksp_monitor_cancel - cancels all monitors that have been hardwired into a code by calls to `KSPMonitorSet()`, but
2304: does not cancel those set via the options database.
2306: Level: beginner
2308: Notes:
2309: The options database option `-ksp_monitor` and related options are the easiest way to turn on `KSP` iteration monitoring
2311: `KSPMonitorRegister()` provides a way to associate an options database key with `KSP` monitor function.
2313: The default is to do no monitoring. To print the residual, or preconditioned
2314: residual if `KSPSetNormType`(ksp,`KSP_NORM_PRECONDITIONED`) was called, use
2315: `KSPMonitorResidual()` as the monitoring routine, with a `PETSCVIEWERASCII` as the
2316: context.
2318: Several different monitoring routines may be set by calling
2319: `KSPMonitorSet()` multiple times; they will be called in the
2320: order in which they were set.
2322: Fortran Note:
2323: Only a single monitor function can be set for each `KSP` object
2325: .seealso: [](ch_ksp), `KSPMonitorResidual()`, `KSPMonitorRegister()`, `KSPMonitorCancel()`, `KSP`, `PetscCtxDestroyFn`
2326: @*/
2327: PetscErrorCode KSPMonitorSet(KSP ksp, KSPMonitorFn *monitor, void *ctx, PetscCtxDestroyFn *monitordestroy)
2328: {
2329: PetscFunctionBegin;
2331: for (PetscInt i = 0; i < ksp->numbermonitors; i++) {
2332: PetscBool identical;
2334: PetscCall(PetscMonitorCompare((PetscErrorCode (*)(void))(PetscVoidFn *)monitor, ctx, monitordestroy, (PetscErrorCode (*)(void))(PetscVoidFn *)ksp->monitor[i], ksp->monitorcontext[i], ksp->monitordestroy[i], &identical));
2335: if (identical) PetscFunctionReturn(PETSC_SUCCESS);
2336: }
2337: PetscCheck(ksp->numbermonitors < MAXKSPMONITORS, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Too many KSP monitors set");
2338: ksp->monitor[ksp->numbermonitors] = monitor;
2339: ksp->monitordestroy[ksp->numbermonitors] = monitordestroy;
2340: ksp->monitorcontext[ksp->numbermonitors++] = ctx;
2341: PetscFunctionReturn(PETSC_SUCCESS);
2342: }
2344: /*@
2345: KSPMonitorCancel - Clears all monitors for a `KSP` object.
2347: Logically Collective
2349: Input Parameter:
2350: . ksp - iterative solver obtained from `KSPCreate()`
2352: Options Database Key:
2353: . -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.
2355: Level: intermediate
2357: .seealso: [](ch_ksp), `KSPMonitorResidual()`, `KSPMonitorSet()`, `KSP`
2358: @*/
2359: PetscErrorCode KSPMonitorCancel(KSP ksp)
2360: {
2361: PetscInt i;
2363: PetscFunctionBegin;
2365: for (i = 0; i < ksp->numbermonitors; i++) {
2366: if (ksp->monitordestroy[i]) PetscCall((*ksp->monitordestroy[i])(&ksp->monitorcontext[i]));
2367: }
2368: ksp->numbermonitors = 0;
2369: PetscFunctionReturn(PETSC_SUCCESS);
2370: }
2372: /*@C
2373: KSPGetMonitorContext - Gets the monitoring context, as set by `KSPMonitorSet()` for the FIRST monitor only.
2375: Not Collective
2377: Input Parameter:
2378: . ksp - iterative solver obtained from `KSPCreate()`
2380: Output Parameter:
2381: . ctx - monitoring context
2383: Level: intermediate
2385: .seealso: [](ch_ksp), `KSPMonitorResidual()`, `KSP`
2386: @*/
2387: PetscErrorCode KSPGetMonitorContext(KSP ksp, void *ctx)
2388: {
2389: PetscFunctionBegin;
2391: *(void **)ctx = ksp->monitorcontext[0];
2392: PetscFunctionReturn(PETSC_SUCCESS);
2393: }
2395: /*@
2396: KSPSetResidualHistory - Sets the array used to hold the residual history.
2397: If set, this array will contain the residual norms computed at each
2398: iteration of the solver.
2400: Not Collective
2402: Input Parameters:
2403: + ksp - iterative solver obtained from `KSPCreate()`
2404: . a - array to hold history
2405: . na - size of `a`
2406: - reset - `PETSC_TRUE` indicates the history counter is reset to zero
2407: for each new linear solve
2409: Level: advanced
2411: Notes:
2412: 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.
2413: If 'a' is `NULL` then space is allocated for the history. If 'na' `PETSC_DECIDE` or (deprecated) `PETSC_DEFAULT` then a
2414: default array of length 10,000 is allocated.
2416: If the array is not long enough then once the iterations is longer than the array length `KSPSolve()` stops recording the history
2418: .seealso: [](ch_ksp), `KSPGetResidualHistory()`, `KSP`
2419: @*/
2420: PetscErrorCode KSPSetResidualHistory(KSP ksp, PetscReal a[], PetscCount na, PetscBool reset)
2421: {
2422: PetscFunctionBegin;
2425: PetscCall(PetscFree(ksp->res_hist_alloc));
2426: if (na != PETSC_DECIDE && na != PETSC_DEFAULT && a) {
2427: ksp->res_hist = a;
2428: ksp->res_hist_max = na;
2429: } else {
2430: if (na != PETSC_DECIDE && na != PETSC_DEFAULT) ksp->res_hist_max = (size_t)na;
2431: else ksp->res_hist_max = 10000; /* like default ksp->max_it */
2432: PetscCall(PetscCalloc1(ksp->res_hist_max, &ksp->res_hist_alloc));
2434: ksp->res_hist = ksp->res_hist_alloc;
2435: }
2436: ksp->res_hist_len = 0;
2437: ksp->res_hist_reset = reset;
2438: PetscFunctionReturn(PETSC_SUCCESS);
2439: }
2441: /*@C
2442: KSPGetResidualHistory - Gets the array used to hold the residual history and the number of residuals it contains.
2444: Not Collective
2446: Input Parameter:
2447: . ksp - iterative solver obtained from `KSPCreate()`
2449: Output Parameters:
2450: + a - pointer to array to hold history (or `NULL`)
2451: - na - number of used entries in a (or `NULL`). Note this has different meanings depending on the `reset` argument to `KSPSetResidualHistory()`
2453: Level: advanced
2455: Note:
2456: This array is borrowed and should not be freed by the caller.
2458: Can only be called after a `KSPSetResidualHistory()` otherwise `a` and `na` are set to `NULL` and zero
2460: 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
2461: returned with `KSPGetIterationNumber()`.
2463: Some Krylov methods may not compute the final residual norm when convergence is declared because the maximum number of iterations allowed has been reached.
2464: In this situation, when `reset` was `PETSC_TRUE`, `na` will then equal the number of iterations reported with `KSPGetIterationNumber()`
2466: Some Krylov methods (such as `KSPSTCG`), under certain circumstances, do not compute the final residual norm. In this situation, when `reset` was `PETSC_TRUE`,
2467: `na` will then equal the number of iterations reported with `KSPGetIterationNumber()`
2469: `KSPBCGSL` does not record the residual norms for the "subiterations" hence the results from `KSPGetResidualHistory()` and `KSPGetIterationNumber()` will be different
2471: Fortran Note:
2472: Call `KSPRestoreResidualHistory()` when access to the history is no longer needed.
2474: .seealso: [](ch_ksp), `KSPSetResidualHistory()`, `KSP`, `KSPGetIterationNumber()`, `KSPSTCG`, `KSPBCGSL`
2475: @*/
2476: PetscErrorCode KSPGetResidualHistory(KSP ksp, const PetscReal *a[], PetscInt *na)
2477: {
2478: PetscFunctionBegin;
2480: if (a) *a = ksp->res_hist;
2481: if (na) PetscCall(PetscIntCast(ksp->res_hist_len, na));
2482: PetscFunctionReturn(PETSC_SUCCESS);
2483: }
2485: /*@
2486: 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.
2488: Not Collective
2490: Input Parameters:
2491: + ksp - iterative solver obtained from `KSPCreate()`
2492: . a - array to hold history
2493: . na - size of `a`
2494: - reset - `PETSC_TRUE` indicates the history counter is reset to zero for each new linear solve
2496: Level: advanced
2498: Notes:
2499: 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.
2500: 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.
2502: If the array is not long enough then once the iterations is longer than the array length `KSPSolve()` stops recording the history
2504: .seealso: [](ch_ksp), `KSPGetErrorHistory()`, `KSPSetResidualHistory()`, `KSP`
2505: @*/
2506: PetscErrorCode KSPSetErrorHistory(KSP ksp, PetscReal a[], PetscCount na, PetscBool reset)
2507: {
2508: PetscFunctionBegin;
2511: PetscCall(PetscFree(ksp->err_hist_alloc));
2512: if (na != PETSC_DECIDE && na != PETSC_DEFAULT && a) {
2513: ksp->err_hist = a;
2514: ksp->err_hist_max = na;
2515: } else {
2516: if (na != PETSC_DECIDE && na != PETSC_DEFAULT) ksp->err_hist_max = (size_t)na;
2517: else ksp->err_hist_max = 10000; /* like default ksp->max_it */
2518: PetscCall(PetscCalloc1(ksp->err_hist_max, &ksp->err_hist_alloc));
2519: ksp->err_hist = ksp->err_hist_alloc;
2520: }
2521: ksp->err_hist_len = 0;
2522: ksp->err_hist_reset = reset;
2523: PetscFunctionReturn(PETSC_SUCCESS);
2524: }
2526: /*@C
2527: KSPGetErrorHistory - Gets the array used to hold the error history and the number of residuals it contains.
2529: Not Collective
2531: Input Parameter:
2532: . ksp - iterative solver obtained from `KSPCreate()`
2534: Output Parameters:
2535: + a - pointer to array to hold history (or `NULL`)
2536: - na - number of used entries in a (or `NULL`)
2538: Level: advanced
2540: Note:
2541: This array is borrowed and should not be freed by the caller.
2542: Can only be called after a `KSPSetErrorHistory()` otherwise `a` and `na` are set to `NULL` and zero
2544: Fortran Note:
2545: .vb
2546: PetscReal, pointer :: a(:)
2547: .ve
2549: .seealso: [](ch_ksp), `KSPSetErrorHistory()`, `KSPGetResidualHistory()`, `KSP`
2550: @*/
2551: PetscErrorCode KSPGetErrorHistory(KSP ksp, const PetscReal *a[], PetscInt *na)
2552: {
2553: PetscFunctionBegin;
2555: if (a) *a = ksp->err_hist;
2556: if (na) PetscCall(PetscIntCast(ksp->err_hist_len, na));
2557: PetscFunctionReturn(PETSC_SUCCESS);
2558: }
2560: /*@
2561: KSPComputeConvergenceRate - Compute the convergence rate for the iteration <https:/en.wikipedia.org/wiki/Coefficient_of_determination>
2563: Not Collective
2565: Input Parameter:
2566: . ksp - The `KSP`
2568: Output Parameters:
2569: + cr - The residual contraction rate
2570: . rRsq - The coefficient of determination, $R^2$, indicating the linearity of the data
2571: . ce - The error contraction rate
2572: - eRsq - The coefficient of determination, $R^2$, indicating the linearity of the data
2574: Level: advanced
2576: Note:
2577: 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,
2578: the slope is $\log c$. The coefficient of determination is given by $1 - \frac{\sum_i (y_i - f(x_i))^2}{\sum_i (y_i - \bar y)}$,
2580: .seealso: [](ch_ksp), `KSP`, `KSPConvergedRateView()`
2581: @*/
2582: PetscErrorCode KSPComputeConvergenceRate(KSP ksp, PetscReal *cr, PetscReal *rRsq, PetscReal *ce, PetscReal *eRsq)
2583: {
2584: PetscReal const *hist;
2585: PetscReal *x, *y, slope, intercept, mean = 0.0, var = 0.0, res = 0.0;
2586: PetscInt n, k;
2588: PetscFunctionBegin;
2589: if (cr || rRsq) {
2590: PetscCall(KSPGetResidualHistory(ksp, &hist, &n));
2591: if (!n) {
2592: if (cr) *cr = 0.0;
2593: if (rRsq) *rRsq = -1.0;
2594: } else {
2595: PetscCall(PetscMalloc2(n, &x, n, &y));
2596: for (k = 0; k < n; ++k) {
2597: x[k] = k;
2598: y[k] = PetscLogReal(hist[k]);
2599: mean += y[k];
2600: }
2601: mean /= n;
2602: PetscCall(PetscLinearRegression(n, x, y, &slope, &intercept));
2603: for (k = 0; k < n; ++k) {
2604: res += PetscSqr(y[k] - (slope * x[k] + intercept));
2605: var += PetscSqr(y[k] - mean);
2606: }
2607: PetscCall(PetscFree2(x, y));
2608: if (cr) *cr = PetscExpReal(slope);
2609: if (rRsq) *rRsq = var < PETSC_MACHINE_EPSILON ? 0.0 : 1.0 - (res / var);
2610: }
2611: }
2612: if (ce || eRsq) {
2613: PetscCall(KSPGetErrorHistory(ksp, &hist, &n));
2614: if (!n) {
2615: if (ce) *ce = 0.0;
2616: if (eRsq) *eRsq = -1.0;
2617: } else {
2618: PetscCall(PetscMalloc2(n, &x, n, &y));
2619: for (k = 0; k < n; ++k) {
2620: x[k] = k;
2621: y[k] = PetscLogReal(hist[k]);
2622: mean += y[k];
2623: }
2624: mean /= n;
2625: PetscCall(PetscLinearRegression(n, x, y, &slope, &intercept));
2626: for (k = 0; k < n; ++k) {
2627: res += PetscSqr(y[k] - (slope * x[k] + intercept));
2628: var += PetscSqr(y[k] - mean);
2629: }
2630: PetscCall(PetscFree2(x, y));
2631: if (ce) *ce = PetscExpReal(slope);
2632: if (eRsq) *eRsq = var < PETSC_MACHINE_EPSILON ? 0.0 : 1.0 - (res / var);
2633: }
2634: }
2635: PetscFunctionReturn(PETSC_SUCCESS);
2636: }
2638: /*@C
2639: KSPSetConvergenceTest - Sets the function to be used to determine convergence of `KSPSolve()`
2641: Logically Collective
2643: Input Parameters:
2644: + ksp - iterative solver obtained from `KSPCreate()`
2645: . converge - pointer to the function, see `KSPConvergenceTestFn`
2646: . ctx - context for private data for the convergence routine (may be `NULL`)
2647: - destroy - a routine for destroying the context (may be `NULL`)
2649: Level: advanced
2651: Notes:
2652: Must be called after the `KSP` type has been set so put this after
2653: a call to `KSPSetType()`, or `KSPSetFromOptions()`.
2655: The default convergence test, `KSPConvergedDefault()`, aborts if the
2656: residual grows to more than 10000 times the initial residual.
2658: The default is a combination of relative and absolute tolerances.
2659: The residual value that is tested may be an approximation; routines
2660: that need exact values should compute them.
2662: In the default PETSc convergence test, the precise values of reason
2663: are macros such as `KSP_CONVERGED_RTOL`, which are defined in petscksp.h.
2665: .seealso: [](ch_ksp), `KSP`, `KSPConvergenceTestFn`, `KSPConvergedDefault()`, `KSPGetConvergenceContext()`, `KSPSetTolerances()`, `KSPGetConvergenceTest()`, `KSPGetAndClearConvergenceTest()`
2666: @*/
2667: PetscErrorCode KSPSetConvergenceTest(KSP ksp, KSPConvergenceTestFn *converge, void *ctx, PetscCtxDestroyFn *destroy)
2668: {
2669: PetscFunctionBegin;
2671: if (ksp->convergeddestroy) PetscCall((*ksp->convergeddestroy)(&ksp->cnvP));
2672: ksp->converged = converge;
2673: ksp->convergeddestroy = destroy;
2674: ksp->cnvP = ctx;
2675: PetscFunctionReturn(PETSC_SUCCESS);
2676: }
2678: /*@C
2679: KSPGetConvergenceTest - Gets the function to be used to determine convergence.
2681: Logically Collective
2683: Input Parameter:
2684: . ksp - iterative solver obtained from `KSPCreate()`
2686: Output Parameters:
2687: + converge - pointer to convergence test function, see `KSPConvergenceTestFn`
2688: . ctx - context for private data for the convergence routine (may be `NULL`)
2689: - destroy - a routine for destroying the context (may be `NULL`)
2691: Level: advanced
2693: .seealso: [](ch_ksp), `KSP`, `KSPConvergedDefault()`, `KSPGetConvergenceContext()`, `KSPSetTolerances()`, `KSPSetConvergenceTest()`, `KSPGetAndClearConvergenceTest()`
2694: @*/
2695: PetscErrorCode KSPGetConvergenceTest(KSP ksp, KSPConvergenceTestFn **converge, void **ctx, PetscCtxDestroyFn **destroy)
2696: {
2697: PetscFunctionBegin;
2699: if (converge) *converge = ksp->converged;
2700: if (destroy) *destroy = ksp->convergeddestroy;
2701: if (ctx) *ctx = ksp->cnvP;
2702: PetscFunctionReturn(PETSC_SUCCESS);
2703: }
2705: /*@C
2706: KSPGetAndClearConvergenceTest - Gets the function to be used to determine convergence. Removes the current test without calling destroy on the test context
2708: Logically Collective
2710: Input Parameter:
2711: . ksp - iterative solver obtained from `KSPCreate()`
2713: Output Parameters:
2714: + converge - pointer to convergence test function, see `KSPConvergenceTestFn`
2715: . ctx - context for private data for the convergence routine
2716: - destroy - a routine for destroying the context
2718: Level: advanced
2720: Note:
2721: This is intended to be used to allow transferring the convergence test (and its context) to another testing object (for example another `KSP`)
2722: and then calling `KSPSetConvergenceTest()` on this original `KSP`. If you just called `KSPGetConvergenceTest()` followed
2723: by `KSPSetConvergenceTest()` the original context information
2724: would be destroyed and hence the transferred context would be invalid and trigger a crash on use
2726: .seealso: [](ch_ksp), `KSP`, `KSPConvergedDefault()`, `KSPGetConvergenceContext()`, `KSPSetTolerances()`, `KSPSetConvergenceTest()`, `KSPGetConvergenceTest()`
2727: @*/
2728: PetscErrorCode KSPGetAndClearConvergenceTest(KSP ksp, KSPConvergenceTestFn **converge, void **ctx, PetscCtxDestroyFn **destroy)
2729: {
2730: PetscFunctionBegin;
2732: *converge = ksp->converged;
2733: *destroy = ksp->convergeddestroy;
2734: *ctx = ksp->cnvP;
2735: ksp->converged = NULL;
2736: ksp->cnvP = NULL;
2737: ksp->convergeddestroy = NULL;
2738: PetscFunctionReturn(PETSC_SUCCESS);
2739: }
2741: /*@C
2742: KSPGetConvergenceContext - Gets the convergence context set with `KSPSetConvergenceTest()`.
2744: Not Collective
2746: Input Parameter:
2747: . ksp - iterative solver obtained from `KSPCreate()`
2749: Output Parameter:
2750: . ctx - monitoring context
2752: Level: advanced
2754: .seealso: [](ch_ksp), `KSP`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSPGetConvergenceTest()`
2755: @*/
2756: PetscErrorCode KSPGetConvergenceContext(KSP ksp, void *ctx)
2757: {
2758: PetscFunctionBegin;
2760: *(void **)ctx = ksp->cnvP;
2761: PetscFunctionReturn(PETSC_SUCCESS);
2762: }
2764: /*@
2765: KSPBuildSolution - Builds the approximate solution in a vector provided.
2767: Collective
2769: Input Parameter:
2770: . ksp - iterative solver obtained from `KSPCreate()`
2772: Output Parameter:
2773: Provide exactly one of
2774: + v - location to stash solution, optional, otherwise pass `NULL`
2775: - V - the solution is returned in this location. This vector is created internally. This vector should NOT be destroyed by the user with `VecDestroy()`.
2777: Level: developer
2779: Notes:
2780: This routine can be used in one of two ways
2781: .vb
2782: KSPBuildSolution(ksp,NULL,&V);
2783: or
2784: KSPBuildSolution(ksp,v,NULL); or KSPBuildSolution(ksp,v,&v);
2785: .ve
2786: In the first case an internal vector is allocated to store the solution
2787: (the user cannot destroy this vector). In the second case the solution
2788: is generated in the vector that the user provides. Note that for certain
2789: methods, such as `KSPCG`, the second case requires a copy of the solution,
2790: while in the first case the call is essentially free since it simply
2791: returns the vector where the solution already is stored. For some methods
2792: like `KSPGMRES` during the solve this is a reasonably expensive operation and should only be
2793: used if truly needed.
2795: .seealso: [](ch_ksp), `KSPGetSolution()`, `KSPBuildResidual()`, `KSP`
2796: @*/
2797: PetscErrorCode KSPBuildSolution(KSP ksp, Vec v, Vec *V)
2798: {
2799: PetscFunctionBegin;
2801: PetscCheck(V || v, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONG, "Must provide either v or V");
2802: if (!V) V = &v;
2803: if (ksp->reason != KSP_CONVERGED_ITERATING) {
2804: if (!v) PetscCall(KSPGetSolution(ksp, V));
2805: else PetscCall(VecCopy(ksp->vec_sol, v));
2806: } else {
2807: PetscUseTypeMethod(ksp, buildsolution, v, V);
2808: }
2809: PetscFunctionReturn(PETSC_SUCCESS);
2810: }
2812: /*@
2813: KSPBuildResidual - Builds the residual in a vector provided.
2815: Collective
2817: Input Parameter:
2818: . ksp - iterative solver obtained from `KSPCreate()`
2820: Output Parameters:
2821: + t - work vector. If not provided then one is generated.
2822: . v - optional location to stash residual. If `v` is not provided, then a location is generated.
2823: - V - the residual
2825: Level: advanced
2827: Note:
2828: Regardless of whether or not `v` is provided, the residual is
2829: returned in `V`.
2831: .seealso: [](ch_ksp), `KSP`, `KSPBuildSolution()`
2832: @*/
2833: PetscErrorCode KSPBuildResidual(KSP ksp, Vec t, Vec v, Vec *V)
2834: {
2835: PetscBool flag = PETSC_FALSE;
2836: Vec w = v, tt = t;
2838: PetscFunctionBegin;
2840: if (!w) PetscCall(VecDuplicate(ksp->vec_rhs, &w));
2841: if (!tt) {
2842: PetscCall(VecDuplicate(ksp->vec_sol, &tt));
2843: flag = PETSC_TRUE;
2844: }
2845: PetscUseTypeMethod(ksp, buildresidual, tt, w, V);
2846: if (flag) PetscCall(VecDestroy(&tt));
2847: PetscFunctionReturn(PETSC_SUCCESS);
2848: }
2850: /*@
2851: KSPSetDiagonalScale - Tells `KSP` to symmetrically diagonally scale the system
2852: before solving. This actually CHANGES the matrix (and right-hand side).
2854: Logically Collective
2856: Input Parameters:
2857: + ksp - the `KSP` context
2858: - scale - `PETSC_TRUE` or `PETSC_FALSE`
2860: Options Database Keys:
2861: + -ksp_diagonal_scale - perform a diagonal scaling before the solve
2862: - -ksp_diagonal_scale_fix - scale the matrix back AFTER the solve
2864: Level: advanced
2866: Notes:
2867: Scales the matrix by $D^{-1/2} A D^{-1/2} [D^{1/2} x ] = D^{-1/2} b $
2868: where $D_{ii}$ is $1/abs(A_{ii}) $ unless $A_{ii}$ is zero and then it is 1.
2870: BE CAREFUL with this routine: it actually scales the matrix and right
2871: hand side that define the system. After the system is solved the matrix
2872: and right-hand side remain scaled unless you use `KSPSetDiagonalScaleFix()`
2874: This should NOT be used within the `SNES` solves if you are using a line
2875: search.
2877: If you use this with the `PCType` `PCEISENSTAT` preconditioner than you can
2878: use the `PCEisenstatSetNoDiagonalScaling()` option, or `-pc_eisenstat_no_diagonal_scaling`
2879: to save some unneeded, redundant flops.
2881: .seealso: [](ch_ksp), `KSPGetDiagonalScale()`, `KSPSetDiagonalScaleFix()`, `KSP`
2882: @*/
2883: PetscErrorCode KSPSetDiagonalScale(KSP ksp, PetscBool scale)
2884: {
2885: PetscFunctionBegin;
2888: ksp->dscale = scale;
2889: PetscFunctionReturn(PETSC_SUCCESS);
2890: }
2892: /*@
2893: KSPGetDiagonalScale - Checks if `KSP` solver scales the matrix and right-hand side, that is if `KSPSetDiagonalScale()` has been called
2895: Not Collective
2897: Input Parameter:
2898: . ksp - the `KSP` context
2900: Output Parameter:
2901: . scale - `PETSC_TRUE` or `PETSC_FALSE`
2903: Level: intermediate
2905: .seealso: [](ch_ksp), `KSP`, `KSPSetDiagonalScale()`, `KSPSetDiagonalScaleFix()`
2906: @*/
2907: PetscErrorCode KSPGetDiagonalScale(KSP ksp, PetscBool *scale)
2908: {
2909: PetscFunctionBegin;
2911: PetscAssertPointer(scale, 2);
2912: *scale = ksp->dscale;
2913: PetscFunctionReturn(PETSC_SUCCESS);
2914: }
2916: /*@
2917: KSPSetDiagonalScaleFix - Tells `KSP` to diagonally scale the system back after solving.
2919: Logically Collective
2921: Input Parameters:
2922: + ksp - the `KSP` context
2923: - fix - `PETSC_TRUE` to scale back after the system solve, `PETSC_FALSE` to not
2924: rescale (default)
2926: Level: intermediate
2928: Notes:
2929: Must be called after `KSPSetDiagonalScale()`
2931: Using this will slow things down, because it rescales the matrix before and
2932: after each linear solve. This is intended mainly for testing to allow one
2933: to easily get back the original system to make sure the solution computed is
2934: accurate enough.
2936: .seealso: [](ch_ksp), `KSPGetDiagonalScale()`, `KSPSetDiagonalScale()`, `KSPGetDiagonalScaleFix()`, `KSP`
2937: @*/
2938: PetscErrorCode KSPSetDiagonalScaleFix(KSP ksp, PetscBool fix)
2939: {
2940: PetscFunctionBegin;
2943: ksp->dscalefix = fix;
2944: PetscFunctionReturn(PETSC_SUCCESS);
2945: }
2947: /*@
2948: KSPGetDiagonalScaleFix - Determines if `KSP` diagonally scales the system back after solving. That is `KSPSetDiagonalScaleFix()` has been called
2950: Not Collective
2952: Input Parameter:
2953: . ksp - the `KSP` context
2955: Output Parameter:
2956: . fix - `PETSC_TRUE` to scale back after the system solve, `PETSC_FALSE` to not
2957: rescale (default)
2959: Level: intermediate
2961: .seealso: [](ch_ksp), `KSPGetDiagonalScale()`, `KSPSetDiagonalScale()`, `KSPSetDiagonalScaleFix()`, `KSP`
2962: @*/
2963: PetscErrorCode KSPGetDiagonalScaleFix(KSP ksp, PetscBool *fix)
2964: {
2965: PetscFunctionBegin;
2967: PetscAssertPointer(fix, 2);
2968: *fix = ksp->dscalefix;
2969: PetscFunctionReturn(PETSC_SUCCESS);
2970: }
2972: /*@C
2973: KSPSetComputeOperators - set routine to compute the linear operators
2975: Logically Collective
2977: Input Parameters:
2978: + ksp - the `KSP` context
2979: . func - function to compute the operators, see `KSPComputeOperatorsFn` for the calling sequence
2980: - ctx - optional context
2982: Level: beginner
2984: Notes:
2985: `func()` will be called automatically at the very next call to `KSPSolve()`. It will NOT be called at future `KSPSolve()` calls
2986: unless either `KSPSetComputeOperators()` or `KSPSetOperators()` is called before that `KSPSolve()` is called. This allows the same system to be solved several times
2987: with different right-hand side functions but is a confusing API since one might expect it to be called for each `KSPSolve()`
2989: To reuse the same preconditioner for the next `KSPSolve()` and not compute a new one based on the most recently computed matrix call `KSPSetReusePreconditioner()`
2991: Developer Note:
2992: Perhaps this routine and `KSPSetComputeRHS()` could be combined into a new API that makes clear when new matrices are computing without requiring call this
2993: routine to indicate when the new matrix should be computed.
2995: .seealso: [](ch_ksp), `KSP`, `KSPSetOperators()`, `KSPSetComputeRHS()`, `DMKSPSetComputeOperators()`, `KSPSetComputeInitialGuess()`, `KSPComputeOperatorsFn`
2996: @*/
2997: PetscErrorCode KSPSetComputeOperators(KSP ksp, KSPComputeOperatorsFn *func, void *ctx)
2998: {
2999: DM dm;
3001: PetscFunctionBegin;
3003: PetscCall(KSPGetDM(ksp, &dm));
3004: PetscCall(DMKSPSetComputeOperators(dm, func, ctx));
3005: if (ksp->setupstage == KSP_SETUP_NEWRHS) ksp->setupstage = KSP_SETUP_NEWMATRIX;
3006: PetscFunctionReturn(PETSC_SUCCESS);
3007: }
3009: /*@C
3010: KSPSetComputeRHS - set routine to compute the right-hand side of the linear system
3012: Logically Collective
3014: Input Parameters:
3015: + ksp - the `KSP` context
3016: . func - function to compute the right-hand side, see `KSPComputeRHSFn` for the calling sequence
3017: - ctx - optional context
3019: Level: beginner
3021: Note:
3022: The routine you provide will be called EACH you call `KSPSolve()` to prepare the new right-hand side for that solve
3024: .seealso: [](ch_ksp), `KSP`, `KSPSolve()`, `DMKSPSetComputeRHS()`, `KSPSetComputeOperators()`, `KSPSetOperators()`, `KSPComputeRHSFn`
3025: @*/
3026: PetscErrorCode KSPSetComputeRHS(KSP ksp, KSPComputeRHSFn *func, void *ctx)
3027: {
3028: DM dm;
3030: PetscFunctionBegin;
3032: PetscCall(KSPGetDM(ksp, &dm));
3033: PetscCall(DMKSPSetComputeRHS(dm, func, ctx));
3034: PetscFunctionReturn(PETSC_SUCCESS);
3035: }
3037: /*@C
3038: KSPSetComputeInitialGuess - set routine to compute the initial guess of the linear system
3040: Logically Collective
3042: Input Parameters:
3043: + ksp - the `KSP` context
3044: . func - function to compute the initial guess, see `KSPComputeInitialGuessFn` for calling sequence
3045: - ctx - optional context
3047: Level: beginner
3049: Note:
3050: This should only be used in conjunction with `KSPSetComputeRHS()` and `KSPSetComputeOperators()`, otherwise
3051: call `KSPSetInitialGuessNonzero()` and set the initial guess values in the solution vector passed to `KSPSolve()` before calling the solver
3053: .seealso: [](ch_ksp), `KSP`, `KSPSolve()`, `KSPSetComputeRHS()`, `KSPSetComputeOperators()`, `DMKSPSetComputeInitialGuess()`, `KSPSetInitialGuessNonzero()`,
3054: `KSPComputeInitialGuessFn`
3055: @*/
3056: PetscErrorCode KSPSetComputeInitialGuess(KSP ksp, KSPComputeInitialGuessFn *func, void *ctx)
3057: {
3058: DM dm;
3060: PetscFunctionBegin;
3062: PetscCall(KSPGetDM(ksp, &dm));
3063: PetscCall(DMKSPSetComputeInitialGuess(dm, func, ctx));
3064: PetscFunctionReturn(PETSC_SUCCESS);
3065: }
3067: /*@
3068: KSPSetUseExplicitTranspose - Determines the explicit transpose of the operator is formed in `KSPSolveTranspose()`. In some configurations (like GPUs) it may
3069: be explicitly formed since the solve is much more efficient.
3071: Logically Collective
3073: Input Parameter:
3074: . ksp - the `KSP` context
3076: Output Parameter:
3077: . flg - `PETSC_TRUE` to transpose the system in `KSPSolveTranspose()`, `PETSC_FALSE` to not transpose (default)
3079: Level: advanced
3081: .seealso: [](ch_ksp), `KSPSolveTranspose()`, `KSP`
3082: @*/
3083: PetscErrorCode KSPSetUseExplicitTranspose(KSP ksp, PetscBool flg)
3084: {
3085: PetscFunctionBegin;
3088: ksp->transpose.use_explicittranspose = flg;
3089: PetscFunctionReturn(PETSC_SUCCESS);
3090: }