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
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()`, `KSPConvergedReasonViewCancel()`, `PetscCtxDestroyFn`
532: @*/
533: PetscErrorCode KSPConvergedReasonViewSet(KSP ksp, PetscErrorCode (*f)(KSP, void *), void *vctx, PetscCtxDestroyFn *reasonviewdestroy)
534: {
535: PetscInt i;
536: PetscBool identical;
538: PetscFunctionBegin;
540: for (i = 0; i < ksp->numberreasonviews; i++) {
541: PetscCall(PetscMonitorCompare((PetscErrorCode (*)(void))f, vctx, reasonviewdestroy, (PetscErrorCode (*)(void))ksp->reasonview[i], ksp->reasonviewcontext[i], ksp->reasonviewdestroy[i], &identical));
542: if (identical) PetscFunctionReturn(PETSC_SUCCESS);
543: }
544: PetscCheck(ksp->numberreasonviews < MAXKSPREASONVIEWS, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Too many KSP reasonview set");
545: ksp->reasonview[ksp->numberreasonviews] = f;
546: ksp->reasonviewdestroy[ksp->numberreasonviews] = reasonviewdestroy;
547: ksp->reasonviewcontext[ksp->numberreasonviews++] = vctx;
548: PetscFunctionReturn(PETSC_SUCCESS);
549: }
551: /*@
552: KSPConvergedReasonViewCancel - Clears all the `KSPConvergedReason` view functions for a `KSP` object set with `KSPConvergedReasonViewSet()`
553: as well as the default viewer.
555: Collective
557: Input Parameter:
558: . ksp - iterative solver obtained from `KSPCreate()`
560: Level: intermediate
562: .seealso: [](ch_ksp), `KSPCreate()`, `KSPDestroy()`, `KSPReset()`, `KSPConvergedReasonViewSet()`
563: @*/
564: PetscErrorCode KSPConvergedReasonViewCancel(KSP ksp)
565: {
566: PetscInt i;
568: PetscFunctionBegin;
570: for (i = 0; i < ksp->numberreasonviews; i++) {
571: if (ksp->reasonviewdestroy[i]) PetscCall((*ksp->reasonviewdestroy[i])(&ksp->reasonviewcontext[i]));
572: }
573: ksp->numberreasonviews = 0;
574: PetscCall(PetscViewerDestroy(&ksp->convergedreasonviewer));
575: PetscFunctionReturn(PETSC_SUCCESS);
576: }
578: /*@
579: KSPConvergedReasonViewFromOptions - Processes command line options to determine if/how a `KSPReason` is to be viewed.
581: Collective
583: Input Parameter:
584: . ksp - the `KSP` object
586: Level: intermediate
588: Note:
589: This is called automatically at the conclusion of `KSPSolve()` so is rarely called directly by user code.
591: .seealso: [](ch_ksp), `KSPConvergedReasonView()`, `KSPConvergedReasonViewSet()`
592: @*/
593: PetscErrorCode KSPConvergedReasonViewFromOptions(KSP ksp)
594: {
595: PetscFunctionBegin;
596: /* Call all user-provided reason review routines */
597: for (PetscInt i = 0; i < ksp->numberreasonviews; i++) PetscCall((*ksp->reasonview[i])(ksp, ksp->reasonviewcontext[i]));
599: /* Call the default PETSc routine */
600: if (ksp->convergedreasonviewer) {
601: PetscCall(PetscViewerPushFormat(ksp->convergedreasonviewer, ksp->convergedreasonformat));
602: PetscCall(KSPConvergedReasonView(ksp, ksp->convergedreasonviewer));
603: PetscCall(PetscViewerPopFormat(ksp->convergedreasonviewer));
604: }
605: PetscFunctionReturn(PETSC_SUCCESS);
606: }
608: /*@
609: KSPConvergedRateView - Displays the convergence rate <https://en.wikipedia.org/wiki/Coefficient_of_determination> of `KSPSolve()` to a viewer
611: Collective
613: Input Parameters:
614: + ksp - iterative solver obtained from `KSPCreate()`
615: - viewer - the `PetscViewer` to display the reason
617: Options Database Key:
618: . -ksp_converged_rate - print reason for convergence or divergence and the convergence rate (or 0.0 for divergence)
620: Level: intermediate
622: Notes:
623: To change the format of the output, call `PetscViewerPushFormat`(`viewer`,`format`) before this call.
625: 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,
626: the slope is $\log c$. The coefficient of determination is given by $1 - \frac{\sum_i (y_i - f(x_i))^2}{\sum_i (y_i - \bar y)}$,
628: .seealso: [](ch_ksp), `KSPConvergedReasonView()`, `KSPGetConvergedRate()`, `KSPSetTolerances()`, `KSPConvergedDefault()`
629: @*/
630: PetscErrorCode KSPConvergedRateView(KSP ksp, PetscViewer viewer)
631: {
632: PetscViewerFormat format;
633: PetscBool isAscii;
634: PetscReal rrate, rRsq, erate = 0.0, eRsq = 0.0;
635: PetscInt its;
636: const char *prefix, *reason = KSPConvergedReasons[ksp->reason];
638: PetscFunctionBegin;
639: PetscCall(KSPGetIterationNumber(ksp, &its));
640: PetscCall(KSPComputeConvergenceRate(ksp, &rrate, &rRsq, &erate, &eRsq));
641: if (!viewer) viewer = PETSC_VIEWER_STDOUT_(PetscObjectComm((PetscObject)ksp));
642: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isAscii));
643: if (isAscii) {
644: PetscCall(KSPGetOptionsPrefix(ksp, &prefix));
645: PetscCall(PetscViewerGetFormat(viewer, &format));
646: PetscCall(PetscViewerASCIIAddTab(viewer, ((PetscObject)ksp)->tablevel));
647: if (ksp->reason > 0) {
648: if (prefix) PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve converged due to %s iterations %" PetscInt_FMT, prefix, reason, its));
649: else PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve converged due to %s iterations %" PetscInt_FMT, reason, its));
650: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
651: if (rRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " res rate %g R^2 %g", (double)rrate, (double)rRsq));
652: if (eRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " error rate %g R^2 %g", (double)erate, (double)eRsq));
653: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
654: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
655: } else if (ksp->reason <= 0) {
656: if (prefix) PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve did not converge due to %s iterations %" PetscInt_FMT, prefix, reason, its));
657: else PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve did not converge due to %s iterations %" PetscInt_FMT, reason, its));
658: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
659: if (rRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " res rate %g R^2 %g", (double)rrate, (double)rRsq));
660: if (eRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " error rate %g R^2 %g", (double)erate, (double)eRsq));
661: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
662: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
663: if (ksp->reason == KSP_DIVERGED_PC_FAILED) {
664: PCFailedReason reason;
665: PetscCall(PCGetFailedReason(ksp->pc, &reason));
666: PetscCall(PetscViewerASCIIPrintf(viewer, " PC failed due to %s\n", PCFailedReasons[reason]));
667: }
668: }
669: PetscCall(PetscViewerASCIISubtractTab(viewer, ((PetscObject)ksp)->tablevel));
670: }
671: PetscFunctionReturn(PETSC_SUCCESS);
672: }
674: #include <petscdraw.h>
676: static PetscErrorCode KSPViewEigenvalues_Internal(KSP ksp, PetscBool isExplicit, PetscViewer viewer, PetscViewerFormat format)
677: {
678: PetscReal *r, *c;
679: PetscInt n, i, neig;
680: PetscBool isascii, isdraw;
681: PetscMPIInt rank;
683: PetscFunctionBegin;
684: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)ksp), &rank));
685: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
686: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
687: if (isExplicit) {
688: PetscCall(VecGetSize(ksp->vec_sol, &n));
689: PetscCall(PetscMalloc2(n, &r, n, &c));
690: PetscCall(KSPComputeEigenvaluesExplicitly(ksp, n, r, c));
691: neig = n;
692: } else {
693: PetscInt nits;
695: PetscCall(KSPGetIterationNumber(ksp, &nits));
696: n = nits + 2;
697: if (!nits) {
698: PetscCall(PetscViewerASCIIPrintf(viewer, "Zero iterations in solver, cannot approximate any eigenvalues\n"));
699: PetscFunctionReturn(PETSC_SUCCESS);
700: }
701: PetscCall(PetscMalloc2(n, &r, n, &c));
702: PetscCall(KSPComputeEigenvalues(ksp, n, r, c, &neig));
703: }
704: if (isascii) {
705: PetscCall(PetscViewerASCIIPrintf(viewer, "%s computed eigenvalues\n", isExplicit ? "Explicitly" : "Iteratively"));
706: for (i = 0; i < neig; ++i) {
707: if (c[i] >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, "%g + %gi\n", (double)r[i], (double)c[i]));
708: else PetscCall(PetscViewerASCIIPrintf(viewer, "%g - %gi\n", (double)r[i], -(double)c[i]));
709: }
710: } else if (isdraw && rank == 0) {
711: PetscDraw draw;
712: PetscDrawSP drawsp;
714: if (format == PETSC_VIEWER_DRAW_CONTOUR) {
715: PetscCall(KSPPlotEigenContours_Private(ksp, neig, r, c));
716: } else {
717: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
718: PetscCall(PetscDrawSPCreate(draw, 1, &drawsp));
719: PetscCall(PetscDrawSPReset(drawsp));
720: for (i = 0; i < neig; ++i) PetscCall(PetscDrawSPAddPoint(drawsp, r + i, c + i));
721: PetscCall(PetscDrawSPDraw(drawsp, PETSC_TRUE));
722: PetscCall(PetscDrawSPSave(drawsp));
723: PetscCall(PetscDrawSPDestroy(&drawsp));
724: }
725: }
726: PetscCall(PetscFree2(r, c));
727: PetscFunctionReturn(PETSC_SUCCESS);
728: }
730: static PetscErrorCode KSPViewSingularvalues_Internal(KSP ksp, PetscViewer viewer, PetscViewerFormat format)
731: {
732: PetscReal smax, smin;
733: PetscInt nits;
734: PetscBool isascii;
736: PetscFunctionBegin;
737: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
738: PetscCall(KSPGetIterationNumber(ksp, &nits));
739: if (!nits) {
740: PetscCall(PetscViewerASCIIPrintf(viewer, "Zero iterations in solver, cannot approximate any singular values\n"));
741: PetscFunctionReturn(PETSC_SUCCESS);
742: }
743: PetscCall(KSPComputeExtremeSingularValues(ksp, &smax, &smin));
744: 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)));
745: PetscFunctionReturn(PETSC_SUCCESS);
746: }
748: static PetscErrorCode KSPViewFinalResidual_Internal(KSP ksp, PetscViewer viewer, PetscViewerFormat format)
749: {
750: PetscBool isascii;
752: PetscFunctionBegin;
753: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
754: 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");
755: if (isascii) {
756: Mat A;
757: Vec t;
758: PetscReal norm;
760: PetscCall(PCGetOperators(ksp->pc, &A, NULL));
761: PetscCall(VecDuplicate(ksp->vec_rhs, &t));
762: PetscCall(KSP_MatMult(ksp, A, ksp->vec_sol, t));
763: PetscCall(VecAYPX(t, -1.0, ksp->vec_rhs));
764: PetscCall(VecViewFromOptions(t, (PetscObject)ksp, "-ksp_view_final_residual_vec"));
765: PetscCall(VecNorm(t, NORM_2, &norm));
766: PetscCall(VecDestroy(&t));
767: PetscCall(PetscViewerASCIIPrintf(viewer, "KSP final norm of residual %g\n", (double)norm));
768: }
769: PetscFunctionReturn(PETSC_SUCCESS);
770: }
772: PETSC_EXTERN PetscErrorCode PetscMonitorPauseFinal_Internal(PetscInt n, void *ctx[])
773: {
774: PetscFunctionBegin;
775: for (PetscInt i = 0; i < n; ++i) {
776: PetscViewerAndFormat *vf = (PetscViewerAndFormat *)ctx[i];
777: PetscDraw draw;
778: PetscReal lpause;
779: PetscBool isdraw;
781: if (!vf) continue;
782: if (!PetscCheckPointer(vf->viewer, PETSC_OBJECT)) continue;
783: if (((PetscObject)vf->viewer)->classid != PETSC_VIEWER_CLASSID) continue;
784: PetscCall(PetscObjectTypeCompare((PetscObject)vf->viewer, PETSCVIEWERDRAW, &isdraw));
785: if (!isdraw) continue;
787: PetscCall(PetscViewerDrawGetDraw(vf->viewer, 0, &draw));
788: PetscCall(PetscDrawGetPause(draw, &lpause));
789: PetscCall(PetscDrawSetPause(draw, -1.0));
790: PetscCall(PetscDrawPause(draw));
791: PetscCall(PetscDrawSetPause(draw, lpause));
792: }
793: PetscFunctionReturn(PETSC_SUCCESS);
794: }
796: static PetscErrorCode KSPMonitorPauseFinal_Internal(KSP ksp)
797: {
798: PetscFunctionBegin;
799: if (!ksp->pauseFinal) PetscFunctionReturn(PETSC_SUCCESS);
800: PetscCall(PetscMonitorPauseFinal_Internal(ksp->numbermonitors, ksp->monitorcontext));
801: PetscFunctionReturn(PETSC_SUCCESS);
802: }
804: static PetscErrorCode KSPSolve_Private(KSP ksp, Vec b, Vec x)
805: {
806: PetscBool flg = PETSC_FALSE, inXisinB = PETSC_FALSE, guess_zero;
807: Mat mat, pmat;
808: MPI_Comm comm;
809: MatNullSpace nullsp;
810: Vec btmp, vec_rhs = NULL;
812: PetscFunctionBegin;
813: level++;
814: comm = PetscObjectComm((PetscObject)ksp);
815: if (x && x == b) {
816: PetscCheck(ksp->guess_zero, comm, PETSC_ERR_ARG_INCOMP, "Cannot use x == b with nonzero initial guess");
817: PetscCall(VecDuplicate(b, &x));
818: inXisinB = PETSC_TRUE;
819: }
820: if (b) {
821: PetscCall(PetscObjectReference((PetscObject)b));
822: PetscCall(VecDestroy(&ksp->vec_rhs));
823: ksp->vec_rhs = b;
824: }
825: if (x) {
826: PetscCall(PetscObjectReference((PetscObject)x));
827: PetscCall(VecDestroy(&ksp->vec_sol));
828: ksp->vec_sol = x;
829: }
831: if (ksp->viewPre) PetscCall(ObjectView((PetscObject)ksp, ksp->viewerPre, ksp->formatPre));
833: if (ksp->presolve) PetscCall((*ksp->presolve)(ksp, ksp->vec_rhs, ksp->vec_sol, ksp->prectx));
835: /* reset the residual history list if requested */
836: if (ksp->res_hist_reset) ksp->res_hist_len = 0;
837: if (ksp->err_hist_reset) ksp->err_hist_len = 0;
839: /* KSPSetUp() scales the matrix if needed */
840: PetscCall(KSPSetUp(ksp));
841: PetscCall(KSPSetUpOnBlocks(ksp));
843: if (ksp->guess) {
844: PetscObjectState ostate, state;
846: PetscCall(KSPGuessSetUp(ksp->guess));
847: PetscCall(PetscObjectStateGet((PetscObject)ksp->vec_sol, &ostate));
848: PetscCall(KSPGuessFormGuess(ksp->guess, ksp->vec_rhs, ksp->vec_sol));
849: PetscCall(PetscObjectStateGet((PetscObject)ksp->vec_sol, &state));
850: if (state != ostate) {
851: ksp->guess_zero = PETSC_FALSE;
852: } else {
853: PetscCall(PetscInfo(ksp, "Using zero initial guess since the KSPGuess object did not change the vector\n"));
854: ksp->guess_zero = PETSC_TRUE;
855: }
856: }
858: PetscCall(VecSetErrorIfLocked(ksp->vec_sol, 3));
860: PetscCall(PetscLogEventBegin(!ksp->transpose_solve ? KSP_Solve : KSP_SolveTranspose, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
861: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
862: /* diagonal scale RHS if called for */
863: if (ksp->dscale) {
864: PetscCall(VecPointwiseMult(ksp->vec_rhs, ksp->vec_rhs, ksp->diagonal));
865: /* second time in, but matrix was scaled back to original */
866: if (ksp->dscalefix && ksp->dscalefix2) {
867: Mat mat, pmat;
869: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
870: PetscCall(MatDiagonalScale(pmat, ksp->diagonal, ksp->diagonal));
871: if (mat != pmat) PetscCall(MatDiagonalScale(mat, ksp->diagonal, ksp->diagonal));
872: }
874: /* scale initial guess */
875: if (!ksp->guess_zero) {
876: if (!ksp->truediagonal) {
877: PetscCall(VecDuplicate(ksp->diagonal, &ksp->truediagonal));
878: PetscCall(VecCopy(ksp->diagonal, ksp->truediagonal));
879: PetscCall(VecReciprocal(ksp->truediagonal));
880: }
881: PetscCall(VecPointwiseMult(ksp->vec_sol, ksp->vec_sol, ksp->truediagonal));
882: }
883: }
884: PetscCall(PCPreSolve(ksp->pc, ksp));
886: if (ksp->guess_zero && !ksp->guess_not_read) PetscCall(VecSet(ksp->vec_sol, 0.0));
887: if (ksp->guess_knoll) { /* The Knoll trick is independent on the KSPGuess specified */
888: PetscCall(PCApply(ksp->pc, ksp->vec_rhs, ksp->vec_sol));
889: PetscCall(KSP_RemoveNullSpace(ksp, ksp->vec_sol));
890: ksp->guess_zero = PETSC_FALSE;
891: }
893: /* can we mark the initial guess as zero for this solve? */
894: guess_zero = ksp->guess_zero;
895: if (!ksp->guess_zero) {
896: PetscReal norm;
898: PetscCall(VecNormAvailable(ksp->vec_sol, NORM_2, &flg, &norm));
899: if (flg && !norm) ksp->guess_zero = PETSC_TRUE;
900: }
901: if (ksp->transpose_solve) {
902: PetscCall(MatGetNullSpace(mat, &nullsp));
903: } else {
904: PetscCall(MatGetTransposeNullSpace(mat, &nullsp));
905: }
906: if (nullsp) {
907: PetscCall(VecDuplicate(ksp->vec_rhs, &btmp));
908: PetscCall(VecCopy(ksp->vec_rhs, btmp));
909: PetscCall(MatNullSpaceRemove(nullsp, btmp));
910: vec_rhs = ksp->vec_rhs;
911: ksp->vec_rhs = btmp;
912: }
913: PetscCall(VecLockReadPush(ksp->vec_rhs));
914: PetscUseTypeMethod(ksp, solve);
915: PetscCall(KSPMonitorPauseFinal_Internal(ksp));
917: PetscCall(VecLockReadPop(ksp->vec_rhs));
918: if (nullsp) {
919: ksp->vec_rhs = vec_rhs;
920: PetscCall(VecDestroy(&btmp));
921: }
923: ksp->guess_zero = guess_zero;
925: PetscCheck(ksp->reason, comm, PETSC_ERR_PLIB, "Internal error, solver returned without setting converged reason");
926: ksp->totalits += ksp->its;
928: PetscCall(KSPConvergedReasonViewFromOptions(ksp));
930: if (ksp->viewRate) {
931: PetscCall(PetscViewerPushFormat(ksp->viewerRate, ksp->formatRate));
932: PetscCall(KSPConvergedRateView(ksp, ksp->viewerRate));
933: PetscCall(PetscViewerPopFormat(ksp->viewerRate));
934: }
935: PetscCall(PCPostSolve(ksp->pc, ksp));
937: /* diagonal scale solution if called for */
938: if (ksp->dscale) {
939: PetscCall(VecPointwiseMult(ksp->vec_sol, ksp->vec_sol, ksp->diagonal));
940: /* unscale right-hand side and matrix */
941: if (ksp->dscalefix) {
942: Mat mat, pmat;
944: PetscCall(VecReciprocal(ksp->diagonal));
945: PetscCall(VecPointwiseMult(ksp->vec_rhs, ksp->vec_rhs, ksp->diagonal));
946: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
947: PetscCall(MatDiagonalScale(pmat, ksp->diagonal, ksp->diagonal));
948: if (mat != pmat) PetscCall(MatDiagonalScale(mat, ksp->diagonal, ksp->diagonal));
949: PetscCall(VecReciprocal(ksp->diagonal));
950: ksp->dscalefix2 = PETSC_TRUE;
951: }
952: }
953: PetscCall(PetscLogEventEnd(!ksp->transpose_solve ? KSP_Solve : KSP_SolveTranspose, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
954: if (ksp->guess) PetscCall(KSPGuessUpdate(ksp->guess, ksp->vec_rhs, ksp->vec_sol));
955: if (ksp->postsolve) PetscCall((*ksp->postsolve)(ksp, ksp->vec_rhs, ksp->vec_sol, ksp->postctx));
957: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
958: if (ksp->viewEV) PetscCall(KSPViewEigenvalues_Internal(ksp, PETSC_FALSE, ksp->viewerEV, ksp->formatEV));
959: if (ksp->viewEVExp) PetscCall(KSPViewEigenvalues_Internal(ksp, PETSC_TRUE, ksp->viewerEVExp, ksp->formatEVExp));
960: if (ksp->viewSV) PetscCall(KSPViewSingularvalues_Internal(ksp, ksp->viewerSV, ksp->formatSV));
961: if (ksp->viewFinalRes) PetscCall(KSPViewFinalResidual_Internal(ksp, ksp->viewerFinalRes, ksp->formatFinalRes));
962: if (ksp->viewMat) PetscCall(ObjectView((PetscObject)mat, ksp->viewerMat, ksp->formatMat));
963: if (ksp->viewPMat) PetscCall(ObjectView((PetscObject)pmat, ksp->viewerPMat, ksp->formatPMat));
964: if (ksp->viewRhs) PetscCall(ObjectView((PetscObject)ksp->vec_rhs, ksp->viewerRhs, ksp->formatRhs));
965: if (ksp->viewSol) PetscCall(ObjectView((PetscObject)ksp->vec_sol, ksp->viewerSol, ksp->formatSol));
966: if (ksp->view) PetscCall(ObjectView((PetscObject)ksp, ksp->viewer, ksp->format));
967: if (ksp->viewDScale) PetscCall(ObjectView((PetscObject)ksp->diagonal, ksp->viewerDScale, ksp->formatDScale));
968: if (ksp->viewMatExp) {
969: Mat A, B;
971: PetscCall(PCGetOperators(ksp->pc, &A, NULL));
972: if (ksp->transpose_solve) {
973: Mat AT;
975: PetscCall(MatCreateTranspose(A, &AT));
976: PetscCall(MatComputeOperator(AT, MATAIJ, &B));
977: PetscCall(MatDestroy(&AT));
978: } else {
979: PetscCall(MatComputeOperator(A, MATAIJ, &B));
980: }
981: PetscCall(ObjectView((PetscObject)B, ksp->viewerMatExp, ksp->formatMatExp));
982: PetscCall(MatDestroy(&B));
983: }
984: if (ksp->viewPOpExp) {
985: Mat B;
987: PetscCall(KSPComputeOperator(ksp, MATAIJ, &B));
988: PetscCall(ObjectView((PetscObject)B, ksp->viewerPOpExp, ksp->formatPOpExp));
989: PetscCall(MatDestroy(&B));
990: }
992: if (inXisinB) {
993: PetscCall(VecCopy(x, b));
994: PetscCall(VecDestroy(&x));
995: }
996: PetscCall(PetscObjectSAWsBlock((PetscObject)ksp));
997: if (ksp->errorifnotconverged && ksp->reason < 0 && ((level == 1) || (ksp->reason != KSP_DIVERGED_ITS))) {
998: PCFailedReason reason;
1000: 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]);
1001: PetscCall(PCGetFailedReason(ksp->pc, &reason));
1002: 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]);
1003: }
1004: level--;
1005: PetscFunctionReturn(PETSC_SUCCESS);
1006: }
1008: /*@
1009: KSPSolve - Solves a linear system associated with `KSP` object
1011: Collective
1013: Input Parameters:
1014: + ksp - iterative solver obtained from `KSPCreate()`
1015: . b - the right-hand side vector
1016: - x - the solution (this may be the same vector as `b`, then `b` will be overwritten with the answer)
1018: Options Database Keys:
1019: + -ksp_view_eigenvalues - compute preconditioned operators eigenvalues
1020: . -ksp_view_eigenvalues_explicit - compute the eigenvalues by forming the dense operator and using LAPACK
1021: . -ksp_view_mat binary - save matrix to the default binary viewer
1022: . -ksp_view_pmat binary - save matrix used to build preconditioner to the default binary viewer
1023: . -ksp_view_rhs binary - save right-hand side vector to the default binary viewer
1024: . -ksp_view_solution binary - save computed solution vector to the default binary viewer
1025: (can be read later with src/ksp/tutorials/ex10.c for testing solvers)
1026: . -ksp_view_mat_explicit - for matrix-free operators, computes the matrix entries and views them
1027: . -ksp_view_preconditioned_operator_explicit - computes the product of the preconditioner and matrix as an explicit matrix and views it
1028: . -ksp_converged_reason - print reason for converged or diverged, also prints number of iterations
1029: . -ksp_view_final_residual - print 2-norm of true linear system residual at the end of the solution process
1030: . -ksp_error_if_not_converged - stop the program as soon as an error is detected in a `KSPSolve()`
1031: . -ksp_view_pre - print the ksp data structure before the system solution
1032: - -ksp_view - print the ksp data structure at the end of the system solution
1034: Level: beginner
1036: Notes:
1037: See `KSPSetFromOptions()` for additional options database keys that affect `KSPSolve()`
1039: If one uses `KSPSetDM()` then `x` or `b` need not be passed. Use `KSPGetSolution()` to access the solution in this case.
1041: The operator is specified with `KSPSetOperators()`.
1043: `KSPSolve()` will normally return without generating an error regardless of whether the linear system was solved or if constructing the preconditioner failed.
1044: Call `KSPGetConvergedReason()` to determine if the solver converged or failed and why. The option -ksp_error_if_not_converged or function `KSPSetErrorIfNotConverged()`
1045: 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
1046: it may be fine that inner solvers in the preconditioner do not converge during the solution process.
1048: The number of iterations can be obtained from `KSPGetIterationNumber()`.
1050: If you provide a matrix that has a `MatSetNullSpace()` and `MatSetTransposeNullSpace()` this will use that information to solve singular systems
1051: in the least squares sense with a norm minimizing solution.
1053: $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()`).
1055: `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
1056: 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
1057: direction thus the solution which is a linear combination of the search directions has no component in the nullspace(A).
1059: We recommend always using `KSPGMRES` for such singular systems.
1060: If $ nullspace(A) = nullspace(A^T)$ (note symmetric matrices always satisfy this property) then both left and right preconditioning will work
1061: If $nullspace(A) \neq nullspace(A^T)$ then left preconditioning will work but right preconditioning may not work (or it may).
1063: Developer Notes:
1064: 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
1065: $ 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
1066: such as diagonal scaling we cannot apply the inverse of the preconditioner to a vector and thus cannot compute $nullspace(AB)$.
1068: If using a direct method (e.g., via the `KSP` solver
1069: `KSPPREONLY` and a preconditioner such as `PCLU` or `PCCHOLESKY` then usually one iteration of the `KSP` method will be needed for convergence.
1071: To solve a linear system with the transpose of the matrix use `KSPSolveTranspose()`.
1073: Understanding Convergence\:
1074: The manual pages `KSPMonitorSet()`, `KSPComputeEigenvalues()`, and
1075: `KSPComputeEigenvaluesExplicitly()` provide information on additional
1076: options to monitor convergence and print eigenvalue information.
1078: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPDestroy()`, `KSPSetTolerances()`, `KSPConvergedDefault()`,
1079: `KSPSolveTranspose()`, `KSPGetIterationNumber()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatSetTransposeNullSpace()`, `KSP`,
1080: `KSPConvergedReasonView()`, `KSPCheckSolve()`, `KSPSetErrorIfNotConverged()`
1081: @*/
1082: PetscErrorCode KSPSolve(KSP ksp, Vec b, Vec x)
1083: {
1084: PetscBool isPCMPI;
1086: PetscFunctionBegin;
1090: ksp->transpose_solve = PETSC_FALSE;
1091: PetscCall(KSPSolve_Private(ksp, b, x));
1092: PetscCall(PetscObjectTypeCompare((PetscObject)ksp->pc, PCMPI, &isPCMPI));
1093: if (PCMPIServerActive && isPCMPI) {
1094: KSP subksp;
1096: PetscCall(PCMPIGetKSP(ksp->pc, &subksp));
1097: ksp->its = subksp->its;
1098: ksp->reason = subksp->reason;
1099: }
1100: PetscFunctionReturn(PETSC_SUCCESS);
1101: }
1103: /*@
1104: KSPSolveTranspose - Solves a linear system with the transpose of the matrix associated with the `KSP` object, $ A^T x = b$.
1106: Collective
1108: Input Parameters:
1109: + ksp - iterative solver obtained from `KSPCreate()`
1110: . b - right-hand side vector
1111: - x - solution vector
1113: Level: developer
1115: Note:
1116: For complex numbers this solve the non-Hermitian transpose system.
1118: Developer Note:
1119: We need to implement a `KSPSolveHermitianTranspose()`
1121: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPDestroy()`, `KSPSetTolerances()`, `KSPConvergedDefault()`,
1122: `KSPSolve()`, `KSP`, `KSPSetOperators()`
1123: @*/
1124: PetscErrorCode KSPSolveTranspose(KSP ksp, Vec b, Vec x)
1125: {
1126: PetscFunctionBegin;
1130: if (ksp->transpose.use_explicittranspose) {
1131: Mat J, Jpre;
1132: PetscCall(KSPGetOperators(ksp, &J, &Jpre));
1133: if (!ksp->transpose.reuse_transpose) {
1134: PetscCall(MatTranspose(J, MAT_INITIAL_MATRIX, &ksp->transpose.AT));
1135: if (J != Jpre) PetscCall(MatTranspose(Jpre, MAT_INITIAL_MATRIX, &ksp->transpose.BT));
1136: ksp->transpose.reuse_transpose = PETSC_TRUE;
1137: } else {
1138: PetscCall(MatTranspose(J, MAT_REUSE_MATRIX, &ksp->transpose.AT));
1139: if (J != Jpre) PetscCall(MatTranspose(Jpre, MAT_REUSE_MATRIX, &ksp->transpose.BT));
1140: }
1141: if (J == Jpre && ksp->transpose.BT != ksp->transpose.AT) {
1142: PetscCall(PetscObjectReference((PetscObject)ksp->transpose.AT));
1143: ksp->transpose.BT = ksp->transpose.AT;
1144: }
1145: PetscCall(KSPSetOperators(ksp, ksp->transpose.AT, ksp->transpose.BT));
1146: } else {
1147: ksp->transpose_solve = PETSC_TRUE;
1148: }
1149: PetscCall(KSPSolve_Private(ksp, b, x));
1150: PetscFunctionReturn(PETSC_SUCCESS);
1151: }
1153: static PetscErrorCode KSPViewFinalMatResidual_Internal(KSP ksp, Mat B, Mat X, PetscViewer viewer, PetscViewerFormat format, PetscInt shift)
1154: {
1155: Mat A, R;
1156: PetscReal *norms;
1157: PetscInt i, N;
1158: PetscBool flg;
1160: PetscFunctionBegin;
1161: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &flg));
1162: if (flg) {
1163: PetscCall(PCGetOperators(ksp->pc, &A, NULL));
1164: if (!ksp->transpose_solve) PetscCall(MatMatMult(A, X, MAT_INITIAL_MATRIX, PETSC_DETERMINE, &R));
1165: else PetscCall(MatTransposeMatMult(A, X, MAT_INITIAL_MATRIX, PETSC_DETERMINE, &R));
1166: PetscCall(MatAYPX(R, -1.0, B, SAME_NONZERO_PATTERN));
1167: PetscCall(MatGetSize(R, NULL, &N));
1168: PetscCall(PetscMalloc1(N, &norms));
1169: PetscCall(MatGetColumnNorms(R, NORM_2, norms));
1170: PetscCall(MatDestroy(&R));
1171: 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]));
1172: PetscCall(PetscFree(norms));
1173: }
1174: PetscFunctionReturn(PETSC_SUCCESS);
1175: }
1177: static PetscErrorCode KSPMatSolve_Private(KSP ksp, Mat B, Mat X)
1178: {
1179: Mat A, P, vB, vX;
1180: Vec cb, cx;
1181: PetscInt n1, N1, n2, N2, Bbn = PETSC_DECIDE;
1182: PetscBool match;
1184: PetscFunctionBegin;
1188: PetscCheckSameComm(ksp, 1, B, 2);
1189: PetscCheckSameComm(ksp, 1, X, 3);
1190: PetscCheckSameType(B, 2, X, 3);
1191: PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
1192: MatCheckPreallocated(X, 3);
1193: if (!X->assembled) {
1194: PetscCall(MatSetOption(X, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
1195: PetscCall(MatAssemblyBegin(X, MAT_FINAL_ASSEMBLY));
1196: PetscCall(MatAssemblyEnd(X, MAT_FINAL_ASSEMBLY));
1197: }
1198: PetscCheck(B != X, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_IDN, "B and X must be different matrices");
1199: PetscCheck(!ksp->transpose_solve || !ksp->transpose.use_explicittranspose, PetscObjectComm((PetscObject)ksp), PETSC_ERR_SUP, "KSPMatSolveTranspose() does not support -ksp_use_explicittranspose");
1200: PetscCall(KSPGetOperators(ksp, &A, &P));
1201: PetscCall(MatGetLocalSize(B, NULL, &n2));
1202: PetscCall(MatGetLocalSize(X, NULL, &n1));
1203: PetscCall(MatGetSize(B, NULL, &N2));
1204: PetscCall(MatGetSize(X, NULL, &N1));
1205: 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);
1206: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)B, &match, MATSEQDENSE, MATMPIDENSE, ""));
1207: PetscCheck(match, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Provided block of right-hand sides not stored in a dense Mat");
1208: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)X, &match, MATSEQDENSE, MATMPIDENSE, ""));
1209: PetscCheck(match, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Provided block of solutions not stored in a dense Mat");
1210: PetscCall(KSPSetUp(ksp));
1211: PetscCall(KSPSetUpOnBlocks(ksp));
1212: if (ksp->ops->matsolve) {
1213: level++;
1214: if (ksp->guess_zero) PetscCall(MatZeroEntries(X));
1215: PetscCall(PetscLogEventBegin(!ksp->transpose_solve ? KSP_MatSolve : KSP_MatSolveTranspose, ksp, B, X, 0));
1216: PetscCall(KSPGetMatSolveBatchSize(ksp, &Bbn));
1217: /* by default, do a single solve with all columns */
1218: if (Bbn == PETSC_DECIDE) Bbn = N2;
1219: else PetscCheck(Bbn >= 1, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "KSPMatSolve() batch size %" PetscInt_FMT " must be positive", Bbn);
1220: PetscCall(PetscInfo(ksp, "KSP type %s solving using batches of width at most %" PetscInt_FMT "\n", ((PetscObject)ksp)->type_name, Bbn));
1221: /* if -ksp_matsolve_batch_size is greater than the actual number of columns, do a single solve with all columns */
1222: if (Bbn >= N2) {
1223: PetscUseTypeMethod(ksp, matsolve, B, X);
1224: if (ksp->viewFinalRes) PetscCall(KSPViewFinalMatResidual_Internal(ksp, B, X, ksp->viewerFinalRes, ksp->formatFinalRes, 0));
1226: PetscCall(KSPConvergedReasonViewFromOptions(ksp));
1228: if (ksp->viewRate) {
1229: PetscCall(PetscViewerPushFormat(ksp->viewerRate, PETSC_VIEWER_DEFAULT));
1230: PetscCall(KSPConvergedRateView(ksp, ksp->viewerRate));
1231: PetscCall(PetscViewerPopFormat(ksp->viewerRate));
1232: }
1233: } else {
1234: for (n2 = 0; n2 < N2; n2 += Bbn) {
1235: PetscCall(MatDenseGetSubMatrix(B, PETSC_DECIDE, PETSC_DECIDE, n2, PetscMin(n2 + Bbn, N2), &vB));
1236: PetscCall(MatDenseGetSubMatrix(X, PETSC_DECIDE, PETSC_DECIDE, n2, PetscMin(n2 + Bbn, N2), &vX));
1237: PetscUseTypeMethod(ksp, matsolve, vB, vX);
1238: if (ksp->viewFinalRes) PetscCall(KSPViewFinalMatResidual_Internal(ksp, vB, vX, ksp->viewerFinalRes, ksp->formatFinalRes, n2));
1240: PetscCall(KSPConvergedReasonViewFromOptions(ksp));
1242: if (ksp->viewRate) {
1243: PetscCall(PetscViewerPushFormat(ksp->viewerRate, PETSC_VIEWER_DEFAULT));
1244: PetscCall(KSPConvergedRateView(ksp, ksp->viewerRate));
1245: PetscCall(PetscViewerPopFormat(ksp->viewerRate));
1246: }
1247: PetscCall(MatDenseRestoreSubMatrix(B, &vB));
1248: PetscCall(MatDenseRestoreSubMatrix(X, &vX));
1249: }
1250: }
1251: if (ksp->viewMat) PetscCall(ObjectView((PetscObject)A, ksp->viewerMat, ksp->formatMat));
1252: if (ksp->viewPMat) PetscCall(ObjectView((PetscObject)P, ksp->viewerPMat, ksp->formatPMat));
1253: if (ksp->viewRhs) PetscCall(ObjectView((PetscObject)B, ksp->viewerRhs, ksp->formatRhs));
1254: if (ksp->viewSol) PetscCall(ObjectView((PetscObject)X, ksp->viewerSol, ksp->formatSol));
1255: if (ksp->view) PetscCall(KSPView(ksp, ksp->viewer));
1256: PetscCall(PetscLogEventEnd(!ksp->transpose_solve ? KSP_MatSolve : KSP_MatSolveTranspose, ksp, B, X, 0));
1257: if (ksp->errorifnotconverged && ksp->reason < 0 && (level == 1 || ksp->reason != KSP_DIVERGED_ITS)) {
1258: PCFailedReason reason;
1260: 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]);
1261: PetscCall(PCGetFailedReason(ksp->pc, &reason));
1262: 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]);
1263: }
1264: level--;
1265: } else {
1266: PetscCall(PetscInfo(ksp, "KSP type %s solving column by column\n", ((PetscObject)ksp)->type_name));
1267: for (n2 = 0; n2 < N2; ++n2) {
1268: PetscCall(MatDenseGetColumnVecRead(B, n2, &cb));
1269: PetscCall(MatDenseGetColumnVecWrite(X, n2, &cx));
1270: PetscCall(KSPSolve_Private(ksp, cb, cx));
1271: PetscCall(MatDenseRestoreColumnVecWrite(X, n2, &cx));
1272: PetscCall(MatDenseRestoreColumnVecRead(B, n2, &cb));
1273: }
1274: }
1275: PetscFunctionReturn(PETSC_SUCCESS);
1276: }
1278: /*@
1279: KSPMatSolve - Solves a linear system with multiple right-hand sides stored as a `MATDENSE`.
1281: Input Parameters:
1282: + ksp - iterative solver
1283: - B - block of right-hand sides
1285: Output Parameter:
1286: . X - block of solutions
1288: Level: intermediate
1290: Notes:
1291: This is a stripped-down version of `KSPSolve()`, which only handles `-ksp_view`, `-ksp_converged_reason`, `-ksp_converged_rate`, and `-ksp_view_final_residual`.
1293: Unlike with `KSPSolve()`, `B` and `X` must be different matrices.
1295: .seealso: [](ch_ksp), `KSPSolve()`, `MatMatSolve()`, `KSPMatSolveTranspose()`, `MATDENSE`, `KSPHPDDM`, `PCBJACOBI`, `PCASM`, `KSPSetMatSolveBatchSize()`
1296: @*/
1297: PetscErrorCode KSPMatSolve(KSP ksp, Mat B, Mat X)
1298: {
1299: PetscFunctionBegin;
1300: ksp->transpose_solve = PETSC_FALSE;
1301: PetscCall(KSPMatSolve_Private(ksp, B, X));
1302: PetscFunctionReturn(PETSC_SUCCESS);
1303: }
1305: /*@
1306: KSPMatSolveTranspose - Solves a linear system with the transposed matrix with multiple right-hand sides stored as a `MATDENSE`.
1308: Input Parameters:
1309: + ksp - iterative solver
1310: - B - block of right-hand sides
1312: Output Parameter:
1313: . X - block of solutions
1315: Level: intermediate
1317: Notes:
1318: This is a stripped-down version of `KSPSolveTranspose()`, which only handles `-ksp_view`, `-ksp_converged_reason`, `-ksp_converged_rate`, and `-ksp_view_final_residual`.
1320: Unlike `KSPSolveTranspose()`,
1321: `B` and `X` must be different matrices and the transposed matrix cannot be assembled explicitly for the user.
1323: .seealso: [](ch_ksp), `KSPSolveTranspose()`, `MatMatTransposeSolve()`, `KSPMatSolve()`, `MATDENSE`, `KSPHPDDM`, `PCBJACOBI`, `PCASM`
1324: @*/
1325: PetscErrorCode KSPMatSolveTranspose(KSP ksp, Mat B, Mat X)
1326: {
1327: PetscFunctionBegin;
1328: ksp->transpose_solve = PETSC_TRUE;
1329: PetscCall(KSPMatSolve_Private(ksp, B, X));
1330: PetscFunctionReturn(PETSC_SUCCESS);
1331: }
1333: /*@
1334: KSPSetMatSolveBatchSize - Sets the maximum number of columns treated simultaneously in `KSPMatSolve()`.
1336: Logically Collective
1338: Input Parameters:
1339: + ksp - the `KSP` iterative solver
1340: - bs - batch size
1342: Level: advanced
1344: Note:
1345: Using a larger block size can improve the efficiency of the solver.
1347: .seealso: [](ch_ksp), `KSPMatSolve()`, `KSPGetMatSolveBatchSize()`, `-mat_mumps_icntl_27`, `-matmatmult_Bbn`
1348: @*/
1349: PetscErrorCode KSPSetMatSolveBatchSize(KSP ksp, PetscInt bs)
1350: {
1351: PetscFunctionBegin;
1354: ksp->nmax = bs;
1355: PetscFunctionReturn(PETSC_SUCCESS);
1356: }
1358: /*@
1359: KSPGetMatSolveBatchSize - Gets the maximum number of columns treated simultaneously in `KSPMatSolve()`.
1361: Input Parameter:
1362: . ksp - iterative solver context
1364: Output Parameter:
1365: . bs - batch size
1367: Level: advanced
1369: .seealso: [](ch_ksp), `KSPMatSolve()`, `KSPSetMatSolveBatchSize()`, `-mat_mumps_icntl_27`, `-matmatmult_Bbn`
1370: @*/
1371: PetscErrorCode KSPGetMatSolveBatchSize(KSP ksp, PetscInt *bs)
1372: {
1373: PetscFunctionBegin;
1375: PetscAssertPointer(bs, 2);
1376: *bs = ksp->nmax;
1377: PetscFunctionReturn(PETSC_SUCCESS);
1378: }
1380: /*@
1381: KSPResetViewers - Resets all the viewers set from the options database during `KSPSetFromOptions()`
1383: Collective
1385: Input Parameter:
1386: . ksp - the `KSP` iterative solver context obtained from `KSPCreate()`
1388: Level: beginner
1390: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPSolve()`, `KSPSetFromOptions()`, `KSP`
1391: @*/
1392: PetscErrorCode KSPResetViewers(KSP ksp)
1393: {
1394: PetscFunctionBegin;
1396: if (!ksp) PetscFunctionReturn(PETSC_SUCCESS);
1397: PetscCall(PetscViewerDestroy(&ksp->viewer));
1398: PetscCall(PetscViewerDestroy(&ksp->viewerPre));
1399: PetscCall(PetscViewerDestroy(&ksp->viewerRate));
1400: PetscCall(PetscViewerDestroy(&ksp->viewerMat));
1401: PetscCall(PetscViewerDestroy(&ksp->viewerPMat));
1402: PetscCall(PetscViewerDestroy(&ksp->viewerRhs));
1403: PetscCall(PetscViewerDestroy(&ksp->viewerSol));
1404: PetscCall(PetscViewerDestroy(&ksp->viewerMatExp));
1405: PetscCall(PetscViewerDestroy(&ksp->viewerEV));
1406: PetscCall(PetscViewerDestroy(&ksp->viewerSV));
1407: PetscCall(PetscViewerDestroy(&ksp->viewerEVExp));
1408: PetscCall(PetscViewerDestroy(&ksp->viewerFinalRes));
1409: PetscCall(PetscViewerDestroy(&ksp->viewerPOpExp));
1410: PetscCall(PetscViewerDestroy(&ksp->viewerDScale));
1411: ksp->view = PETSC_FALSE;
1412: ksp->viewPre = PETSC_FALSE;
1413: ksp->viewMat = PETSC_FALSE;
1414: ksp->viewPMat = PETSC_FALSE;
1415: ksp->viewRhs = PETSC_FALSE;
1416: ksp->viewSol = PETSC_FALSE;
1417: ksp->viewMatExp = PETSC_FALSE;
1418: ksp->viewEV = PETSC_FALSE;
1419: ksp->viewSV = PETSC_FALSE;
1420: ksp->viewEVExp = PETSC_FALSE;
1421: ksp->viewFinalRes = PETSC_FALSE;
1422: ksp->viewPOpExp = PETSC_FALSE;
1423: ksp->viewDScale = PETSC_FALSE;
1424: PetscFunctionReturn(PETSC_SUCCESS);
1425: }
1427: /*@
1428: KSPReset - Removes any allocated `Vec` and `Mat` from the `KSP` data structures.
1430: Collective
1432: Input Parameter:
1433: . ksp - iterative solver obtained from `KSPCreate()`
1435: Level: intermediate
1437: Notes:
1438: Any options set in the `KSP`, including those set with `KSPSetFromOptions()` remain.
1440: Call `KSPReset()` only before you call `KSPSetOperators()` with a different sized matrix than the previous matrix used with the `KSP`.
1442: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPSolve()`, `KSP`
1443: @*/
1444: PetscErrorCode KSPReset(KSP ksp)
1445: {
1446: PetscFunctionBegin;
1448: if (!ksp) PetscFunctionReturn(PETSC_SUCCESS);
1449: PetscTryTypeMethod(ksp, reset);
1450: if (ksp->pc) PetscCall(PCReset(ksp->pc));
1451: if (ksp->guess) {
1452: KSPGuess guess = ksp->guess;
1453: PetscTryTypeMethod(guess, reset);
1454: }
1455: PetscCall(VecDestroyVecs(ksp->nwork, &ksp->work));
1456: PetscCall(VecDestroy(&ksp->vec_rhs));
1457: PetscCall(VecDestroy(&ksp->vec_sol));
1458: PetscCall(VecDestroy(&ksp->diagonal));
1459: PetscCall(VecDestroy(&ksp->truediagonal));
1461: ksp->setupstage = KSP_SETUP_NEW;
1462: ksp->nmax = PETSC_DECIDE;
1463: PetscFunctionReturn(PETSC_SUCCESS);
1464: }
1466: /*@
1467: KSPDestroy - Destroys a `KSP` context.
1469: Collective
1471: Input Parameter:
1472: . ksp - iterative solver obtained from `KSPCreate()`
1474: Level: beginner
1476: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPSolve()`, `KSP`
1477: @*/
1478: PetscErrorCode KSPDestroy(KSP *ksp)
1479: {
1480: PC pc;
1482: PetscFunctionBegin;
1483: if (!*ksp) PetscFunctionReturn(PETSC_SUCCESS);
1485: if (--((PetscObject)*ksp)->refct > 0) {
1486: *ksp = NULL;
1487: PetscFunctionReturn(PETSC_SUCCESS);
1488: }
1490: PetscCall(PetscObjectSAWsViewOff((PetscObject)*ksp));
1492: /*
1493: Avoid a cascading call to PCReset(ksp->pc) from the following call:
1494: PCReset() shouldn't be called from KSPDestroy() as it is unprotected by pc's
1495: refcount (and may be shared, e.g., by other ksps).
1496: */
1497: pc = (*ksp)->pc;
1498: (*ksp)->pc = NULL;
1499: PetscCall(KSPReset(*ksp));
1500: PetscCall(KSPResetViewers(*ksp));
1501: (*ksp)->pc = pc;
1502: PetscTryTypeMethod(*ksp, destroy);
1504: if ((*ksp)->transpose.use_explicittranspose) {
1505: PetscCall(MatDestroy(&(*ksp)->transpose.AT));
1506: PetscCall(MatDestroy(&(*ksp)->transpose.BT));
1507: (*ksp)->transpose.reuse_transpose = PETSC_FALSE;
1508: }
1510: PetscCall(KSPGuessDestroy(&(*ksp)->guess));
1511: PetscCall(DMDestroy(&(*ksp)->dm));
1512: PetscCall(PCDestroy(&(*ksp)->pc));
1513: PetscCall(PetscFree((*ksp)->res_hist_alloc));
1514: PetscCall(PetscFree((*ksp)->err_hist_alloc));
1515: if ((*ksp)->convergeddestroy) PetscCall((*(*ksp)->convergeddestroy)((*ksp)->cnvP));
1516: PetscCall(KSPMonitorCancel(*ksp));
1517: PetscCall(KSPConvergedReasonViewCancel(*ksp));
1518: PetscCall(PetscHeaderDestroy(ksp));
1519: PetscFunctionReturn(PETSC_SUCCESS);
1520: }
1522: /*@
1523: KSPSetPCSide - Sets the preconditioning side.
1525: Logically Collective
1527: Input Parameter:
1528: . ksp - iterative solver obtained from `KSPCreate()`
1530: Output Parameter:
1531: . side - the preconditioning side, where side is one of
1532: .vb
1533: PC_LEFT - left preconditioning (default)
1534: PC_RIGHT - right preconditioning
1535: PC_SYMMETRIC - symmetric preconditioning
1536: .ve
1538: Options Database Key:
1539: . -ksp_pc_side <right,left,symmetric> - `KSP` preconditioner side
1541: Level: intermediate
1543: Notes:
1544: Left preconditioning is used by default for most Krylov methods except `KSPFGMRES` which only supports right preconditioning.
1546: For methods changing the side of the preconditioner changes the norm type that is used, see `KSPSetNormType()`.
1548: Symmetric preconditioning is currently available only for the `KSPQCG` method. However, note that
1549: symmetric preconditioning can be emulated by using either right or left
1550: preconditioning, modifying the application of the matrix (with a custom `Mat` argument to `KSPSetOperators()`,
1551: and using a pre 'KSPSetPreSolve()` or post processing `KSPSetPostSolve()` step).
1553: Setting the `PCSide` often affects the default norm type. See `KSPSetNormType()` for details.
1555: .seealso: [](ch_ksp), `KSPGetPCSide()`, `KSPSetNormType()`, `KSPGetNormType()`, `KSP`, `KSPSetPreSolve()`, `KSPSetPostSolve()`
1556: @*/
1557: PetscErrorCode KSPSetPCSide(KSP ksp, PCSide side)
1558: {
1559: PetscFunctionBegin;
1562: ksp->pc_side = ksp->pc_side_set = side;
1563: PetscFunctionReturn(PETSC_SUCCESS);
1564: }
1566: /*@
1567: KSPGetPCSide - Gets the preconditioning side.
1569: Not Collective
1571: Input Parameter:
1572: . ksp - iterative solver obtained from `KSPCreate()`
1574: Output Parameter:
1575: . side - the preconditioning side, where side is one of
1576: .vb
1577: PC_LEFT - left preconditioning (default)
1578: PC_RIGHT - right preconditioning
1579: PC_SYMMETRIC - symmetric preconditioning
1580: .ve
1582: Level: intermediate
1584: .seealso: [](ch_ksp), `KSPSetPCSide()`, `KSP`
1585: @*/
1586: PetscErrorCode KSPGetPCSide(KSP ksp, PCSide *side)
1587: {
1588: PetscFunctionBegin;
1590: PetscAssertPointer(side, 2);
1591: PetscCall(KSPSetUpNorms_Private(ksp, PETSC_TRUE, &ksp->normtype, &ksp->pc_side));
1592: *side = ksp->pc_side;
1593: PetscFunctionReturn(PETSC_SUCCESS);
1594: }
1596: /*@
1597: KSPGetTolerances - Gets the relative, absolute, divergence, and maximum
1598: iteration tolerances used by the default `KSP` convergence tests.
1600: Not Collective
1602: Input Parameter:
1603: . ksp - the Krylov subspace context
1605: Output Parameters:
1606: + rtol - the relative convergence tolerance
1607: . abstol - the absolute convergence tolerance
1608: . dtol - the divergence tolerance
1609: - maxits - maximum number of iterations
1611: Level: intermediate
1613: Note:
1614: The user can specify `NULL` for any parameter that is not needed.
1616: .seealso: [](ch_ksp), `KSPSetTolerances()`, `KSP`, `KSPSetMinimumIterations()`, `KSPGetMinimumIterations()`
1617: @*/
1618: PetscErrorCode KSPGetTolerances(KSP ksp, PeOp PetscReal *rtol, PeOp PetscReal *abstol, PeOp PetscReal *dtol, PeOp PetscInt *maxits)
1619: {
1620: PetscFunctionBegin;
1622: if (abstol) *abstol = ksp->abstol;
1623: if (rtol) *rtol = ksp->rtol;
1624: if (dtol) *dtol = ksp->divtol;
1625: if (maxits) *maxits = ksp->max_it;
1626: PetscFunctionReturn(PETSC_SUCCESS);
1627: }
1629: /*@
1630: KSPSetTolerances - Sets the relative, absolute, divergence, and maximum
1631: iteration tolerances used by the default `KSP` convergence testers.
1633: Logically Collective
1635: Input Parameters:
1636: + ksp - the Krylov subspace context
1637: . rtol - the relative convergence tolerance, relative decrease in the (possibly preconditioned) residual norm
1638: . abstol - the absolute convergence tolerance absolute size of the (possibly preconditioned) residual norm
1639: . dtol - the divergence tolerance, amount (possibly preconditioned) residual norm can increase before `KSPConvergedDefault()` concludes that the method is diverging
1640: - maxits - maximum number of iterations to use
1642: Options Database Keys:
1643: + -ksp_atol <abstol> - Sets `abstol`
1644: . -ksp_rtol <rtol> - Sets `rtol`
1645: . -ksp_divtol <dtol> - Sets `dtol`
1646: - -ksp_max_it <maxits> - Sets `maxits`
1648: Level: intermediate
1650: Notes:
1651: The tolerances are with respect to a norm of the residual of the equation $ \| b - A x^n \|$, they do not directly use the error of the equation.
1652: The norm used depends on the `KSPNormType` that has been set with `KSPSetNormType()`, the default depends on the `KSPType` used.
1654: All parameters must be non-negative.
1656: Use `PETSC_CURRENT` to retain the current value of any of the parameters. The deprecated `PETSC_DEFAULT` also retains the current value (though the name is confusing).
1658: Use `PETSC_DETERMINE` to use the default value for the given `KSP`. The default value is the value when the object's type is set.
1660: For `dtol` and `maxits` use `PETSC_UMLIMITED` to indicate there is no upper bound on these values
1662: See `KSPConvergedDefault()` for details how these parameters are used in the default convergence test. See also `KSPSetConvergenceTest()`
1663: for setting user-defined stopping criteria.
1665: Fortran Note:
1666: Use `PETSC_CURRENT_INTEGER`, `PETSC_CURRENT_REAL`, `PETSC_DETERMINE_INTEGER`, or `PETSC_DETERMINE_REAL`
1668: .seealso: [](ch_ksp), `KSPGetTolerances()`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSP`, `KSPSetMinimumIterations()`
1669: @*/
1670: PetscErrorCode KSPSetTolerances(KSP ksp, PetscReal rtol, PetscReal abstol, PetscReal dtol, PetscInt maxits)
1671: {
1672: PetscFunctionBegin;
1679: if (rtol == (PetscReal)PETSC_DETERMINE) {
1680: ksp->rtol = ksp->default_rtol;
1681: } else if (rtol != (PetscReal)PETSC_CURRENT) {
1682: PetscCheck(rtol >= 0.0 && rtol < 1.0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Relative tolerance %g must be non-negative and less than 1.0", (double)rtol);
1683: ksp->rtol = rtol;
1684: }
1685: if (abstol == (PetscReal)PETSC_DETERMINE) {
1686: ksp->abstol = ksp->default_abstol;
1687: } else if (abstol != (PetscReal)PETSC_CURRENT) {
1688: PetscCheck(abstol >= 0.0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Absolute tolerance %g must be non-negative", (double)abstol);
1689: ksp->abstol = abstol;
1690: }
1691: if (dtol == (PetscReal)PETSC_DETERMINE) {
1692: ksp->divtol = ksp->default_divtol;
1693: } else if (dtol == (PetscReal)PETSC_UNLIMITED) {
1694: ksp->divtol = PETSC_MAX_REAL;
1695: } else if (dtol != (PetscReal)PETSC_CURRENT) {
1696: PetscCheck(dtol >= 0.0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Divergence tolerance %g must be larger than 1.0", (double)dtol);
1697: ksp->divtol = dtol;
1698: }
1699: if (maxits == PETSC_DETERMINE) {
1700: ksp->max_it = ksp->default_max_it;
1701: } else if (maxits == PETSC_UNLIMITED) {
1702: ksp->max_it = PETSC_INT_MAX;
1703: } else if (maxits != PETSC_CURRENT) {
1704: PetscCheck(maxits >= 0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Maximum number of iterations %" PetscInt_FMT " must be non-negative", maxits);
1705: ksp->max_it = maxits;
1706: }
1707: PetscFunctionReturn(PETSC_SUCCESS);
1708: }
1710: /*@
1711: KSPSetMinimumIterations - Sets the minimum number of iterations to use, regardless of the tolerances
1713: Logically Collective
1715: Input Parameters:
1716: + ksp - the Krylov subspace context
1717: - minit - minimum number of iterations to use
1719: Options Database Key:
1720: . -ksp_min_it <minits> - Sets `minit`
1722: Level: intermediate
1724: Notes:
1725: Use `KSPSetTolerances()` to set a variety of other tolerances
1727: See `KSPConvergedDefault()` for details on how these parameters are used in the default convergence test. See also `KSPSetConvergenceTest()`
1728: for setting user-defined stopping criteria.
1730: If the initial residual norm is small enough solvers may return immediately without computing any improvement to the solution. Using this routine
1731: prevents that which usually ensures the solution is changed (often minimally) from the previous solution. This option may be used with ODE integrators
1732: to ensure the integrator does not fall into a false steady-state solution of the ODE.
1734: .seealso: [](ch_ksp), `KSPGetTolerances()`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSP`, `KSPSetTolerances()`, `KSPGetMinimumIterations()`
1735: @*/
1736: PetscErrorCode KSPSetMinimumIterations(KSP ksp, PetscInt minit)
1737: {
1738: PetscFunctionBegin;
1742: PetscCheck(minit >= 0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Minimum number of iterations %" PetscInt_FMT " must be non-negative", minit);
1743: ksp->min_it = minit;
1744: PetscFunctionReturn(PETSC_SUCCESS);
1745: }
1747: /*@
1748: KSPGetMinimumIterations - Gets the minimum number of iterations to use, regardless of the tolerances, that was set with `KSPSetMinimumIterations()` or `-ksp_min_it`
1750: Not Collective
1752: Input Parameter:
1753: . ksp - the Krylov subspace context
1755: Output Parameter:
1756: . minit - minimum number of iterations to use
1758: Level: intermediate
1760: .seealso: [](ch_ksp), `KSPGetTolerances()`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSP`, `KSPSetTolerances()`, `KSPSetMinimumIterations()`
1761: @*/
1762: PetscErrorCode KSPGetMinimumIterations(KSP ksp, PetscInt *minit)
1763: {
1764: PetscFunctionBegin;
1766: PetscAssertPointer(minit, 2);
1768: *minit = ksp->min_it;
1769: PetscFunctionReturn(PETSC_SUCCESS);
1770: }
1772: /*@
1773: KSPSetInitialGuessNonzero - Tells the iterative solver that the
1774: initial guess is nonzero; otherwise `KSP` assumes the initial guess
1775: is to be zero (and thus zeros it out before solving).
1777: Logically Collective
1779: Input Parameters:
1780: + ksp - iterative solver obtained from `KSPCreate()`
1781: - flg - ``PETSC_TRUE`` indicates the guess is non-zero, `PETSC_FALSE` indicates the guess is zero
1783: Options Database Key:
1784: . -ksp_initial_guess_nonzero <true,false> - use nonzero initial guess
1786: Level: beginner
1788: .seealso: [](ch_ksp), `KSPGetInitialGuessNonzero()`, `KSPGuessSetType()`, `KSPGuessType`, `KSP`
1789: @*/
1790: PetscErrorCode KSPSetInitialGuessNonzero(KSP ksp, PetscBool flg)
1791: {
1792: PetscFunctionBegin;
1795: ksp->guess_zero = (PetscBool)!flg;
1796: PetscFunctionReturn(PETSC_SUCCESS);
1797: }
1799: /*@
1800: KSPGetInitialGuessNonzero - Determines whether the `KSP` solver is using
1801: a zero initial guess.
1803: Not Collective
1805: Input Parameter:
1806: . ksp - iterative solver obtained from `KSPCreate()`
1808: Output Parameter:
1809: . flag - `PETSC_TRUE` if guess is nonzero, else `PETSC_FALSE`
1811: Level: intermediate
1813: .seealso: [](ch_ksp), `KSPSetInitialGuessNonzero()`, `KSP`
1814: @*/
1815: PetscErrorCode KSPGetInitialGuessNonzero(KSP ksp, PetscBool *flag)
1816: {
1817: PetscFunctionBegin;
1819: PetscAssertPointer(flag, 2);
1820: if (ksp->guess_zero) *flag = PETSC_FALSE;
1821: else *flag = PETSC_TRUE;
1822: PetscFunctionReturn(PETSC_SUCCESS);
1823: }
1825: /*@
1826: KSPSetErrorIfNotConverged - Causes `KSPSolve()` to generate an error if the solver has not converged as soon as the error is detected.
1828: Logically Collective
1830: Input Parameters:
1831: + ksp - iterative solver obtained from `KSPCreate()`
1832: - flg - `PETSC_TRUE` indicates you want the error generated
1834: Options Database Key:
1835: . -ksp_error_if_not_converged <true,false> - generate an error and stop the program
1837: Level: intermediate
1839: Notes:
1840: Normally PETSc continues if a linear solver fails to converge, you can call `KSPGetConvergedReason()` after a `KSPSolve()`
1841: to determine if it has converged. This functionality is mostly helpful while running in a debugger (`-start_in_debugger`) to determine exactly where
1842: the failure occurs and why.
1844: A `KSP_DIVERGED_ITS` will not generate an error in a `KSPSolve()` inside a nested linear solver
1846: .seealso: [](ch_ksp), `KSPGetErrorIfNotConverged()`, `KSP`
1847: @*/
1848: PetscErrorCode KSPSetErrorIfNotConverged(KSP ksp, PetscBool flg)
1849: {
1850: PetscFunctionBegin;
1853: ksp->errorifnotconverged = flg;
1854: PetscFunctionReturn(PETSC_SUCCESS);
1855: }
1857: /*@
1858: KSPGetErrorIfNotConverged - Will `KSPSolve()` generate an error if the solver does not converge?
1860: Not Collective
1862: Input Parameter:
1863: . ksp - iterative solver obtained from KSPCreate()
1865: Output Parameter:
1866: . flag - `PETSC_TRUE` if it will generate an error, else `PETSC_FALSE`
1868: Level: intermediate
1870: .seealso: [](ch_ksp), `KSPSetErrorIfNotConverged()`, `KSP`
1871: @*/
1872: PetscErrorCode KSPGetErrorIfNotConverged(KSP ksp, PetscBool *flag)
1873: {
1874: PetscFunctionBegin;
1876: PetscAssertPointer(flag, 2);
1877: *flag = ksp->errorifnotconverged;
1878: PetscFunctionReturn(PETSC_SUCCESS);
1879: }
1881: /*@
1882: KSPSetInitialGuessKnoll - Tells the iterative solver to use `PCApply()` on the right hand side vector to compute the initial guess (The Knoll trick)
1884: Logically Collective
1886: Input Parameters:
1887: + ksp - iterative solver obtained from `KSPCreate()`
1888: - flg - `PETSC_TRUE` or `PETSC_FALSE`
1890: Level: advanced
1892: Developer Note:
1893: The Knoll trick is not currently implemented using the `KSPGuess` class which provides a variety of ways of computing
1894: an initial guess based on previous solves.
1896: .seealso: [](ch_ksp), `KSPGetInitialGuessKnoll()`, `KSPGuess`, `KSPSetInitialGuessNonzero()`, `KSPGetInitialGuessNonzero()`, `KSP`
1897: @*/
1898: PetscErrorCode KSPSetInitialGuessKnoll(KSP ksp, PetscBool flg)
1899: {
1900: PetscFunctionBegin;
1903: ksp->guess_knoll = flg;
1904: PetscFunctionReturn(PETSC_SUCCESS);
1905: }
1907: /*@
1908: KSPGetInitialGuessKnoll - Determines whether the `KSP` solver is using the Knoll trick (using PCApply(pc,b,...) to compute
1909: the initial guess
1911: Not Collective
1913: Input Parameter:
1914: . ksp - iterative solver obtained from `KSPCreate()`
1916: Output Parameter:
1917: . flag - `PETSC_TRUE` if using Knoll trick, else `PETSC_FALSE`
1919: Level: advanced
1921: .seealso: [](ch_ksp), `KSPSetInitialGuessKnoll()`, `KSPSetInitialGuessNonzero()`, `KSPGetInitialGuessNonzero()`, `KSP`
1922: @*/
1923: PetscErrorCode KSPGetInitialGuessKnoll(KSP ksp, PetscBool *flag)
1924: {
1925: PetscFunctionBegin;
1927: PetscAssertPointer(flag, 2);
1928: *flag = ksp->guess_knoll;
1929: PetscFunctionReturn(PETSC_SUCCESS);
1930: }
1932: /*@
1933: KSPGetComputeSingularValues - Gets the flag indicating whether the extreme singular
1934: values will be calculated via a Lanczos or Arnoldi process as the linear
1935: system is solved.
1937: Not Collective
1939: Input Parameter:
1940: . ksp - iterative solver obtained from `KSPCreate()`
1942: Output Parameter:
1943: . flg - `PETSC_TRUE` or `PETSC_FALSE`
1945: Options Database Key:
1946: . -ksp_monitor_singular_value - Activates `KSPSetComputeSingularValues()`
1948: Level: advanced
1950: Notes:
1951: This option is not valid for `KSPType`.
1953: Many users may just want to use the monitoring routine
1954: `KSPMonitorSingularValue()` (which can be set with option `-ksp_monitor_singular_value`)
1955: to print the singular values at each iteration of the linear solve.
1957: .seealso: [](ch_ksp), `KSPComputeExtremeSingularValues()`, `KSPMonitorSingularValue()`, `KSP`
1958: @*/
1959: PetscErrorCode KSPGetComputeSingularValues(KSP ksp, PetscBool *flg)
1960: {
1961: PetscFunctionBegin;
1963: PetscAssertPointer(flg, 2);
1964: *flg = ksp->calc_sings;
1965: PetscFunctionReturn(PETSC_SUCCESS);
1966: }
1968: /*@
1969: KSPSetComputeSingularValues - Sets a flag so that the extreme singular
1970: values will be calculated via a Lanczos or Arnoldi process as the linear
1971: system is solved.
1973: Logically Collective
1975: Input Parameters:
1976: + ksp - iterative solver obtained from `KSPCreate()`
1977: - flg - `PETSC_TRUE` or `PETSC_FALSE`
1979: Options Database Key:
1980: . -ksp_monitor_singular_value - Activates `KSPSetComputeSingularValues()`
1982: Level: advanced
1984: Notes:
1985: This option is not valid for all iterative methods.
1987: Many users may just want to use the monitoring routine
1988: `KSPMonitorSingularValue()` (which can be set with option `-ksp_monitor_singular_value`)
1989: to print the singular values at each iteration of the linear solve.
1991: Consider using the excellant package SLEPc for accurate efficient computations of singular or eigenvalues.
1993: .seealso: [](ch_ksp), `KSPComputeExtremeSingularValues()`, `KSPMonitorSingularValue()`, `KSP`, `KSPSetComputeRitz()`
1994: @*/
1995: PetscErrorCode KSPSetComputeSingularValues(KSP ksp, PetscBool flg)
1996: {
1997: PetscFunctionBegin;
2000: ksp->calc_sings = flg;
2001: PetscFunctionReturn(PETSC_SUCCESS);
2002: }
2004: /*@
2005: KSPGetComputeEigenvalues - Gets the flag indicating that the extreme eigenvalues
2006: values will be calculated via a Lanczos or Arnoldi process as the linear
2007: system is solved.
2009: Not Collective
2011: Input Parameter:
2012: . ksp - iterative solver obtained from `KSPCreate()`
2014: Output Parameter:
2015: . flg - `PETSC_TRUE` or `PETSC_FALSE`
2017: Level: advanced
2019: Note:
2020: Currently this option is not valid for all iterative methods.
2022: .seealso: [](ch_ksp), `KSPComputeEigenvalues()`, `KSPComputeEigenvaluesExplicitly()`, `KSP`, `KSPSetComputeRitz()`
2023: @*/
2024: PetscErrorCode KSPGetComputeEigenvalues(KSP ksp, PetscBool *flg)
2025: {
2026: PetscFunctionBegin;
2028: PetscAssertPointer(flg, 2);
2029: *flg = ksp->calc_sings;
2030: PetscFunctionReturn(PETSC_SUCCESS);
2031: }
2033: /*@
2034: KSPSetComputeEigenvalues - Sets a flag so that the extreme eigenvalues
2035: values will be calculated via a Lanczos or Arnoldi process as the linear
2036: system is solved.
2038: Logically Collective
2040: Input Parameters:
2041: + ksp - iterative solver obtained from `KSPCreate()`
2042: - flg - `PETSC_TRUE` or `PETSC_FALSE`
2044: Level: advanced
2046: Note:
2047: Currently this option is not valid for all iterative methods.
2049: Consider using the excellant package SLEPc for accurate efficient computations of singular or eigenvalues.
2051: .seealso: [](ch_ksp), `KSPComputeEigenvalues()`, `KSPComputeEigenvaluesExplicitly()`, `KSP`, `KSPSetComputeRitz()`
2052: @*/
2053: PetscErrorCode KSPSetComputeEigenvalues(KSP ksp, PetscBool flg)
2054: {
2055: PetscFunctionBegin;
2058: ksp->calc_sings = flg;
2059: PetscFunctionReturn(PETSC_SUCCESS);
2060: }
2062: /*@
2063: KSPSetComputeRitz - Sets a flag so that the Ritz or harmonic Ritz pairs
2064: will be calculated via a Lanczos or Arnoldi process as the linear
2065: system is solved.
2067: Logically Collective
2069: Input Parameters:
2070: + ksp - iterative solver obtained from `KSPCreate()`
2071: - flg - `PETSC_TRUE` or `PETSC_FALSE`
2073: Level: advanced
2075: Note:
2076: Currently this option is only valid for the `KSPGMRES` method.
2078: .seealso: [](ch_ksp), `KSPComputeRitz()`, `KSP`, `KSPComputeEigenvalues()`, `KSPComputeExtremeSingularValues()`
2079: @*/
2080: PetscErrorCode KSPSetComputeRitz(KSP ksp, PetscBool flg)
2081: {
2082: PetscFunctionBegin;
2085: ksp->calc_ritz = flg;
2086: PetscFunctionReturn(PETSC_SUCCESS);
2087: }
2089: /*@
2090: KSPGetRhs - Gets the right-hand-side vector for the linear system to
2091: be solved.
2093: Not Collective
2095: Input Parameter:
2096: . ksp - iterative solver obtained from `KSPCreate()`
2098: Output Parameter:
2099: . r - right-hand-side vector
2101: Level: developer
2103: .seealso: [](ch_ksp), `KSPGetSolution()`, `KSPSolve()`, `KSP`
2104: @*/
2105: PetscErrorCode KSPGetRhs(KSP ksp, Vec *r)
2106: {
2107: PetscFunctionBegin;
2109: PetscAssertPointer(r, 2);
2110: *r = ksp->vec_rhs;
2111: PetscFunctionReturn(PETSC_SUCCESS);
2112: }
2114: /*@
2115: KSPGetSolution - Gets the location of the solution for the
2116: linear system to be solved.
2118: Not Collective
2120: Input Parameter:
2121: . ksp - iterative solver obtained from `KSPCreate()`
2123: Output Parameter:
2124: . v - solution vector
2126: Level: developer
2128: Note:
2129: If this is called during a `KSPSolve()` the vector's values may not represent the solution
2130: to the linear system.
2132: .seealso: [](ch_ksp), `KSPGetRhs()`, `KSPBuildSolution()`, `KSPSolve()`, `KSP`
2133: @*/
2134: PetscErrorCode KSPGetSolution(KSP ksp, Vec *v)
2135: {
2136: PetscFunctionBegin;
2138: PetscAssertPointer(v, 2);
2139: *v = ksp->vec_sol;
2140: PetscFunctionReturn(PETSC_SUCCESS);
2141: }
2143: /*@
2144: KSPSetPC - Sets the preconditioner to be used to calculate the
2145: application of the preconditioner on a vector into a `KSP`.
2147: Collective
2149: Input Parameters:
2150: + ksp - the `KSP` iterative solver obtained from `KSPCreate()`
2151: - pc - the preconditioner object (if `NULL` it returns the `PC` currently held by the `KSP`)
2153: Level: developer
2155: Note:
2156: This routine is almost never used since `KSP` creates its own `PC` when needed.
2157: Use `KSPGetPC()` to retrieve the preconditioner context instead of creating a new one.
2159: .seealso: [](ch_ksp), `KSPGetPC()`, `KSP`
2160: @*/
2161: PetscErrorCode KSPSetPC(KSP ksp, PC pc)
2162: {
2163: PetscFunctionBegin;
2165: if (pc) {
2167: PetscCheckSameComm(ksp, 1, pc, 2);
2168: }
2169: if (ksp->pc != pc && ksp->setupstage) ksp->setupstage = KSP_SETUP_NEWMATRIX;
2170: PetscCall(PetscObjectReference((PetscObject)pc));
2171: PetscCall(PCDestroy(&ksp->pc));
2172: ksp->pc = pc;
2173: PetscFunctionReturn(PETSC_SUCCESS);
2174: }
2176: PETSC_INTERN PetscErrorCode PCCreate_MPI(PC);
2178: // PetscClangLinter pragma disable: -fdoc-internal-linkage
2179: /*@C
2180: KSPCheckPCMPI - Checks if `-mpi_linear_solver_server` is active and the `PC` should be changed to `PCMPI`
2182: Collective, No Fortran Support
2184: Input Parameter:
2185: . ksp - iterative solver obtained from `KSPCreate()`
2187: Level: developer
2189: .seealso: [](ch_ksp), `KSPSetPC()`, `KSP`, `PCMPIServerBegin()`, `PCMPIServerEnd()`
2190: @*/
2191: PETSC_INTERN PetscErrorCode KSPCheckPCMPI(KSP ksp)
2192: {
2193: PetscBool isPCMPI;
2195: PetscFunctionBegin;
2197: PetscCall(PetscObjectTypeCompare((PetscObject)ksp->pc, PCMPI, &isPCMPI));
2198: if (PCMPIServerActive && ksp->nestlevel == 0 && !isPCMPI) {
2199: const char *prefix;
2200: char *found = NULL;
2202: PetscCall(KSPGetOptionsPrefix(ksp, &prefix));
2203: if (prefix) PetscCall(PetscStrstr(prefix, "mpi_linear_solver_server_", &found));
2204: if (!found) PetscCall(KSPAppendOptionsPrefix(ksp, "mpi_linear_solver_server_"));
2205: PetscCall(PetscInfo(NULL, "In MPI Linear Solver Server and detected (root) PC that must be changed to PCMPI\n"));
2206: PetscCall(PCSetType(ksp->pc, PCMPI));
2207: }
2208: PetscFunctionReturn(PETSC_SUCCESS);
2209: }
2211: /*@
2212: KSPGetPC - Returns a pointer to the preconditioner context with the `KSP`
2214: Not Collective
2216: Input Parameter:
2217: . ksp - iterative solver obtained from `KSPCreate()`
2219: Output Parameter:
2220: . pc - preconditioner context
2222: Level: beginner
2224: Note:
2225: The `PC` is created if it does not already exist.
2227: Developer Note:
2228: Calls `KSPCheckPCMPI()` to check if the `KSP` is effected by `-mpi_linear_solver_server`
2230: .seealso: [](ch_ksp), `KSPSetPC()`, `KSP`, `PC`
2231: @*/
2232: PetscErrorCode KSPGetPC(KSP ksp, PC *pc)
2233: {
2234: PetscFunctionBegin;
2236: PetscAssertPointer(pc, 2);
2237: if (!ksp->pc) {
2238: PetscCall(PCCreate(PetscObjectComm((PetscObject)ksp), &ksp->pc));
2239: PetscCall(PetscObjectIncrementTabLevel((PetscObject)ksp->pc, (PetscObject)ksp, 0));
2240: PetscCall(PetscObjectSetOptions((PetscObject)ksp->pc, ((PetscObject)ksp)->options));
2241: PetscCall(PCSetKSPNestLevel(ksp->pc, ksp->nestlevel));
2242: }
2243: PetscCall(KSPCheckPCMPI(ksp));
2244: *pc = ksp->pc;
2245: PetscFunctionReturn(PETSC_SUCCESS);
2246: }
2248: /*@
2249: KSPMonitor - runs the user provided monitor routines, if they exist
2251: Collective
2253: Input Parameters:
2254: + ksp - iterative solver obtained from `KSPCreate()`
2255: . it - iteration number
2256: - rnorm - relative norm of the residual
2258: Level: developer
2260: Notes:
2261: This routine is called by the `KSP` implementations.
2262: It does not typically need to be called by the user.
2264: For Krylov methods that do not keep a running value of the current solution (such as `KSPGMRES`) this
2265: cannot be called after the `KSPConvergedReason` has been set but before the final solution has been computed.
2267: .seealso: [](ch_ksp), `KSPMonitorSet()`
2268: @*/
2269: PetscErrorCode KSPMonitor(KSP ksp, PetscInt it, PetscReal rnorm)
2270: {
2271: PetscInt i, n = ksp->numbermonitors;
2273: PetscFunctionBegin;
2274: for (i = 0; i < n; i++) PetscCall((*ksp->monitor[i])(ksp, it, rnorm, ksp->monitorcontext[i]));
2275: PetscFunctionReturn(PETSC_SUCCESS);
2276: }
2278: /*@C
2279: 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,
2280: the residual norm computed in a `KSPSolve()`
2282: Logically Collective
2284: Input Parameters:
2285: + ksp - iterative solver obtained from `KSPCreate()`
2286: . monitor - pointer to function (if this is `NULL`, it turns off monitoring
2287: . ctx - [optional] context for private data for the monitor routine (use `NULL` if no context is needed)
2288: - monitordestroy - [optional] routine that frees monitor context (may be `NULL`), see `PetscCtxDestroyFn` for the calling sequence
2290: Calling sequence of `monitor`:
2291: + ksp - iterative solver obtained from `KSPCreate()`
2292: . it - iteration number
2293: . rnorm - (estimated) 2-norm of (preconditioned) residual
2294: - ctx - optional monitoring context, as set by `KSPMonitorSet()`
2296: Options Database Keys:
2297: + -ksp_monitor - sets `KSPMonitorResidual()`
2298: . -ksp_monitor draw - sets `KSPMonitorResidualDraw()` and plots residual
2299: . -ksp_monitor draw::draw_lg - sets `KSPMonitorResidualDrawLG()` and plots residual
2300: . -ksp_monitor_pause_final - Pauses any graphics when the solve finishes (only works for internal monitors)
2301: . -ksp_monitor_true_residual - sets `KSPMonitorTrueResidual()`
2302: . -ksp_monitor_true_residual draw::draw_lg - sets `KSPMonitorTrueResidualDrawLG()` and plots residual
2303: . -ksp_monitor_max - sets `KSPMonitorTrueResidualMax()`
2304: . -ksp_monitor_singular_value - sets `KSPMonitorSingularValue()`
2305: - -ksp_monitor_cancel - cancels all monitors that have been hardwired into a code by calls to `KSPMonitorSet()`, but
2306: does not cancel those set via the options database.
2308: Level: beginner
2310: Notes:
2311: The options database option `-ksp_monitor` and related options are the easiest way to turn on `KSP` iteration monitoring
2313: `KSPMonitorRegister()` provides a way to associate an options database key with `KSP` monitor function.
2315: The default is to do no monitoring. To print the residual, or preconditioned
2316: residual if `KSPSetNormType`(ksp,`KSP_NORM_PRECONDITIONED`) was called, use
2317: `KSPMonitorResidual()` as the monitoring routine, with a `PETSCVIEWERASCII` as the
2318: context.
2320: Several different monitoring routines may be set by calling
2321: `KSPMonitorSet()` multiple times; all will be called in the
2322: order in which they were set.
2324: Fortran Note:
2325: Only a single monitor function can be set for each `KSP` object
2327: .seealso: [](ch_ksp), `KSPMonitorResidual()`, `KSPMonitorRegister()`, `KSPMonitorCancel()`, `KSP`, `PetscCtxDestroyFn`
2328: @*/
2329: PetscErrorCode KSPMonitorSet(KSP ksp, PetscErrorCode (*monitor)(KSP ksp, PetscInt it, PetscReal rnorm, void *ctx), void *ctx, PetscCtxDestroyFn *monitordestroy)
2330: {
2331: PetscInt i;
2332: PetscBool identical;
2334: PetscFunctionBegin;
2336: for (i = 0; i < ksp->numbermonitors; i++) {
2337: PetscCall(PetscMonitorCompare((PetscErrorCode (*)(void))monitor, ctx, monitordestroy, (PetscErrorCode (*)(void))ksp->monitor[i], ksp->monitorcontext[i], ksp->monitordestroy[i], &identical));
2338: if (identical) PetscFunctionReturn(PETSC_SUCCESS);
2339: }
2340: PetscCheck(ksp->numbermonitors < MAXKSPMONITORS, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Too many KSP monitors set");
2341: ksp->monitor[ksp->numbermonitors] = monitor;
2342: ksp->monitordestroy[ksp->numbermonitors] = monitordestroy;
2343: ksp->monitorcontext[ksp->numbermonitors++] = ctx;
2344: PetscFunctionReturn(PETSC_SUCCESS);
2345: }
2347: /*@
2348: KSPMonitorCancel - Clears all monitors for a `KSP` object.
2350: Logically Collective
2352: Input Parameter:
2353: . ksp - iterative solver obtained from `KSPCreate()`
2355: Options Database Key:
2356: . -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.
2358: Level: intermediate
2360: .seealso: [](ch_ksp), `KSPMonitorResidual()`, `KSPMonitorSet()`, `KSP`
2361: @*/
2362: PetscErrorCode KSPMonitorCancel(KSP ksp)
2363: {
2364: PetscInt i;
2366: PetscFunctionBegin;
2368: for (i = 0; i < ksp->numbermonitors; i++) {
2369: if (ksp->monitordestroy[i]) PetscCall((*ksp->monitordestroy[i])(&ksp->monitorcontext[i]));
2370: }
2371: ksp->numbermonitors = 0;
2372: PetscFunctionReturn(PETSC_SUCCESS);
2373: }
2375: /*@C
2376: KSPGetMonitorContext - Gets the monitoring context, as set by `KSPMonitorSet()` for the FIRST monitor only.
2378: Not Collective
2380: Input Parameter:
2381: . ksp - iterative solver obtained from `KSPCreate()`
2383: Output Parameter:
2384: . ctx - monitoring context
2386: Level: intermediate
2388: .seealso: [](ch_ksp), `KSPMonitorResidual()`, `KSP`
2389: @*/
2390: PetscErrorCode KSPGetMonitorContext(KSP ksp, void *ctx)
2391: {
2392: PetscFunctionBegin;
2394: *(void **)ctx = ksp->monitorcontext[0];
2395: PetscFunctionReturn(PETSC_SUCCESS);
2396: }
2398: /*@
2399: KSPSetResidualHistory - Sets the array used to hold the residual history.
2400: If set, this array will contain the residual norms computed at each
2401: iteration of the solver.
2403: Not Collective
2405: Input Parameters:
2406: + ksp - iterative solver obtained from `KSPCreate()`
2407: . a - array to hold history
2408: . na - size of `a`
2409: - reset - `PETSC_TRUE` indicates the history counter is reset to zero
2410: for each new linear solve
2412: Level: advanced
2414: Notes:
2415: 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.
2416: If 'a' is `NULL` then space is allocated for the history. If 'na' `PETSC_DECIDE` or (deprecated) `PETSC_DEFAULT` then a
2417: default array of length 10,000 is allocated.
2419: If the array is not long enough then once the iterations is longer than the array length `KSPSolve()` stops recording the history
2421: .seealso: [](ch_ksp), `KSPGetResidualHistory()`, `KSP`
2422: @*/
2423: PetscErrorCode KSPSetResidualHistory(KSP ksp, PetscReal a[], PetscCount na, PetscBool reset)
2424: {
2425: PetscFunctionBegin;
2428: PetscCall(PetscFree(ksp->res_hist_alloc));
2429: if (na != PETSC_DECIDE && na != PETSC_DEFAULT && a) {
2430: ksp->res_hist = a;
2431: ksp->res_hist_max = na;
2432: } else {
2433: if (na != PETSC_DECIDE && na != PETSC_DEFAULT) ksp->res_hist_max = (size_t)na;
2434: else ksp->res_hist_max = 10000; /* like default ksp->max_it */
2435: PetscCall(PetscCalloc1(ksp->res_hist_max, &ksp->res_hist_alloc));
2437: ksp->res_hist = ksp->res_hist_alloc;
2438: }
2439: ksp->res_hist_len = 0;
2440: ksp->res_hist_reset = reset;
2441: PetscFunctionReturn(PETSC_SUCCESS);
2442: }
2444: /*@C
2445: KSPGetResidualHistory - Gets the array used to hold the residual history and the number of residuals it contains.
2447: Not Collective
2449: Input Parameter:
2450: . ksp - iterative solver obtained from `KSPCreate()`
2452: Output Parameters:
2453: + a - pointer to array to hold history (or `NULL`)
2454: - na - number of used entries in a (or `NULL`). Note this has different meanings depending on the `reset` argument to `KSPSetResidualHistory()`
2456: Level: advanced
2458: Note:
2459: This array is borrowed and should not be freed by the caller.
2461: Can only be called after a `KSPSetResidualHistory()` otherwise `a` and `na` are set to `NULL` and zero
2463: 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
2464: returned with `KSPGetIterationNumber()`.
2466: Some Krylov methods may not compute the final residual norm when convergence is declared because the maximum number of iterations allowed has been reached.
2467: In this situation, when `reset` was `PETSC_TRUE`, `na` will then equal the number of iterations reported with `KSPGetIterationNumber()`
2469: Some Krylov methods (such as `KSPSTCG`), under certain circumstances, do not compute the final residual norm. In this situation, when `reset` was `PETSC_TRUE`,
2470: `na` will then equal the number of iterations reported with `KSPGetIterationNumber()`
2472: `KSPBCGSL` does not record the residual norms for the "subiterations" hence the results from `KSPGetResidualHistory()` and `KSPGetIterationNumber()` will be different
2474: Fortran Note:
2475: Call `KSPRestoreResidualHistory()` when access to the history is no longer needed.
2477: .seealso: [](ch_ksp), `KSPSetResidualHistory()`, `KSP`, `KSPGetIterationNumber()`, `KSPSTCG`, `KSPBCGSL`
2478: @*/
2479: PetscErrorCode KSPGetResidualHistory(KSP ksp, const PetscReal *a[], PetscInt *na)
2480: {
2481: PetscFunctionBegin;
2483: if (a) *a = ksp->res_hist;
2484: if (na) PetscCall(PetscIntCast(ksp->res_hist_len, na));
2485: PetscFunctionReturn(PETSC_SUCCESS);
2486: }
2488: /*@
2489: KSPSetErrorHistory - Sets the array used to hold the error history. If set, this array will contain the error norms computed at each iteration of the solver.
2491: Not Collective
2493: Input Parameters:
2494: + ksp - iterative solver obtained from `KSPCreate()`
2495: . a - array to hold history
2496: . na - size of `a`
2497: - reset - `PETSC_TRUE` indicates the history counter is reset to zero for each new linear solve
2499: Level: advanced
2501: Notes:
2502: If provided, `a` is NOT freed by PETSc so the user needs to keep track of it and destroy once the `KSP` object is destroyed.
2503: If 'a' is `NULL` then space is allocated for the history. If 'na' is `PETSC_DECIDE` or (deprecated) `PETSC_DEFAULT` then a default array of length 1,0000 is allocated.
2505: If the array is not long enough then once the iterations is longer than the array length `KSPSolve()` stops recording the history
2507: .seealso: [](ch_ksp), `KSPGetErrorHistory()`, `KSPSetResidualHistory()`, `KSP`
2508: @*/
2509: PetscErrorCode KSPSetErrorHistory(KSP ksp, PetscReal a[], PetscCount na, PetscBool reset)
2510: {
2511: PetscFunctionBegin;
2514: PetscCall(PetscFree(ksp->err_hist_alloc));
2515: if (na != PETSC_DECIDE && na != PETSC_DEFAULT && a) {
2516: ksp->err_hist = a;
2517: ksp->err_hist_max = na;
2518: } else {
2519: if (na != PETSC_DECIDE && na != PETSC_DEFAULT) ksp->err_hist_max = (size_t)na;
2520: else ksp->err_hist_max = 10000; /* like default ksp->max_it */
2521: PetscCall(PetscCalloc1(ksp->err_hist_max, &ksp->err_hist_alloc));
2522: ksp->err_hist = ksp->err_hist_alloc;
2523: }
2524: ksp->err_hist_len = 0;
2525: ksp->err_hist_reset = reset;
2526: PetscFunctionReturn(PETSC_SUCCESS);
2527: }
2529: /*@C
2530: KSPGetErrorHistory - Gets the array used to hold the error history and the number of residuals it contains.
2532: Not Collective
2534: Input Parameter:
2535: . ksp - iterative solver obtained from `KSPCreate()`
2537: Output Parameters:
2538: + a - pointer to array to hold history (or `NULL`)
2539: - na - number of used entries in a (or `NULL`)
2541: Level: advanced
2543: Note:
2544: This array is borrowed and should not be freed by the caller.
2545: Can only be called after a `KSPSetErrorHistory()` otherwise `a` and `na` are set to `NULL` and zero
2547: Fortran Note:
2548: .vb
2549: PetscReal, pointer :: a(:)
2550: .ve
2552: .seealso: [](ch_ksp), `KSPSetErrorHistory()`, `KSPGetResidualHistory()`, `KSP`
2553: @*/
2554: PetscErrorCode KSPGetErrorHistory(KSP ksp, const PetscReal *a[], PetscInt *na)
2555: {
2556: PetscFunctionBegin;
2558: if (a) *a = ksp->err_hist;
2559: if (na) PetscCall(PetscIntCast(ksp->err_hist_len, na));
2560: PetscFunctionReturn(PETSC_SUCCESS);
2561: }
2563: /*@
2564: KSPComputeConvergenceRate - Compute the convergence rate for the iteration <https:/en.wikipedia.org/wiki/Coefficient_of_determination>
2566: Not Collective
2568: Input Parameter:
2569: . ksp - The `KSP`
2571: Output Parameters:
2572: + cr - The residual contraction rate
2573: . rRsq - The coefficient of determination, $R^2$, indicating the linearity of the data
2574: . ce - The error contraction rate
2575: - eRsq - The coefficient of determination, $R^2$, indicating the linearity of the data
2577: Level: advanced
2579: Note:
2580: 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,
2581: the slope is $\log c$. The coefficient of determination is given by $1 - \frac{\sum_i (y_i - f(x_i))^2}{\sum_i (y_i - \bar y)}$,
2583: .seealso: [](ch_ksp), `KSP`, `KSPConvergedRateView()`
2584: @*/
2585: PetscErrorCode KSPComputeConvergenceRate(KSP ksp, PetscReal *cr, PetscReal *rRsq, PetscReal *ce, PetscReal *eRsq)
2586: {
2587: PetscReal const *hist;
2588: PetscReal *x, *y, slope, intercept, mean = 0.0, var = 0.0, res = 0.0;
2589: PetscInt n, k;
2591: PetscFunctionBegin;
2592: if (cr || rRsq) {
2593: PetscCall(KSPGetResidualHistory(ksp, &hist, &n));
2594: if (!n) {
2595: if (cr) *cr = 0.0;
2596: if (rRsq) *rRsq = -1.0;
2597: } else {
2598: PetscCall(PetscMalloc2(n, &x, n, &y));
2599: for (k = 0; k < n; ++k) {
2600: x[k] = k;
2601: y[k] = PetscLogReal(hist[k]);
2602: mean += y[k];
2603: }
2604: mean /= n;
2605: PetscCall(PetscLinearRegression(n, x, y, &slope, &intercept));
2606: for (k = 0; k < n; ++k) {
2607: res += PetscSqr(y[k] - (slope * x[k] + intercept));
2608: var += PetscSqr(y[k] - mean);
2609: }
2610: PetscCall(PetscFree2(x, y));
2611: if (cr) *cr = PetscExpReal(slope);
2612: if (rRsq) *rRsq = var < PETSC_MACHINE_EPSILON ? 0.0 : 1.0 - (res / var);
2613: }
2614: }
2615: if (ce || eRsq) {
2616: PetscCall(KSPGetErrorHistory(ksp, &hist, &n));
2617: if (!n) {
2618: if (ce) *ce = 0.0;
2619: if (eRsq) *eRsq = -1.0;
2620: } else {
2621: PetscCall(PetscMalloc2(n, &x, n, &y));
2622: for (k = 0; k < n; ++k) {
2623: x[k] = k;
2624: y[k] = PetscLogReal(hist[k]);
2625: mean += y[k];
2626: }
2627: mean /= n;
2628: PetscCall(PetscLinearRegression(n, x, y, &slope, &intercept));
2629: for (k = 0; k < n; ++k) {
2630: res += PetscSqr(y[k] - (slope * x[k] + intercept));
2631: var += PetscSqr(y[k] - mean);
2632: }
2633: PetscCall(PetscFree2(x, y));
2634: if (ce) *ce = PetscExpReal(slope);
2635: if (eRsq) *eRsq = var < PETSC_MACHINE_EPSILON ? 0.0 : 1.0 - (res / var);
2636: }
2637: }
2638: PetscFunctionReturn(PETSC_SUCCESS);
2639: }
2641: /*@C
2642: KSPSetConvergenceTest - Sets the function to be used to determine convergence of `KSPSolve()`
2644: Logically Collective
2646: Input Parameters:
2647: + ksp - iterative solver obtained from `KSPCreate()`
2648: . converge - pointer to the function
2649: . ctx - context for private data for the convergence routine (may be `NULL`)
2650: - destroy - a routine for destroying the context (may be `NULL`)
2652: Calling sequence of `converge`:
2653: + ksp - iterative solver obtained from `KSPCreate()`
2654: . it - iteration number
2655: . rnorm - (estimated) 2-norm of (preconditioned) residual
2656: . reason - the reason why it has converged or diverged
2657: - ctx - optional convergence context, as set by `KSPSetConvergenceTest()`
2659: Calling sequence of `destroy`:
2660: . ctx - the context
2662: Level: advanced
2664: Notes:
2665: Must be called after the `KSP` type has been set so put this after
2666: a call to `KSPSetType()`, or `KSPSetFromOptions()`.
2668: The default convergence test, `KSPConvergedDefault()`, aborts if the
2669: residual grows to more than 10000 times the initial residual.
2671: The default is a combination of relative and absolute tolerances.
2672: The residual value that is tested may be an approximation; routines
2673: that need exact values should compute them.
2675: In the default PETSc convergence test, the precise values of reason
2676: are macros such as `KSP_CONVERGED_RTOL`, which are defined in petscksp.h.
2678: .seealso: [](ch_ksp), `KSP`, `KSPConvergedDefault()`, `KSPGetConvergenceContext()`, `KSPSetTolerances()`, `KSPGetConvergenceTest()`, `KSPGetAndClearConvergenceTest()`
2679: @*/
2680: PetscErrorCode KSPSetConvergenceTest(KSP ksp, PetscErrorCode (*converge)(KSP ksp, PetscInt it, PetscReal rnorm, KSPConvergedReason *reason, void *ctx), void *ctx, PetscErrorCode (*destroy)(void *ctx))
2681: {
2682: PetscFunctionBegin;
2684: if (ksp->convergeddestroy) PetscCall((*ksp->convergeddestroy)(ksp->cnvP));
2685: ksp->converged = converge;
2686: ksp->convergeddestroy = destroy;
2687: ksp->cnvP = ctx;
2688: PetscFunctionReturn(PETSC_SUCCESS);
2689: }
2691: /*@C
2692: KSPGetConvergenceTest - Gets the function to be used to determine convergence.
2694: Logically Collective
2696: Input Parameter:
2697: . ksp - iterative solver obtained from `KSPCreate()`
2699: Output Parameters:
2700: + converge - pointer to convergence test function
2701: . ctx - context for private data for the convergence routine (may be `NULL`)
2702: - destroy - a routine for destroying the context (may be `NULL`)
2704: Calling sequence of `converge`:
2705: + ksp - iterative solver obtained from `KSPCreate()`
2706: . it - iteration number
2707: . rnorm - (estimated) 2-norm of (preconditioned) residual
2708: . reason - the reason why it has converged or diverged
2709: - ctx - optional convergence context, as set by `KSPSetConvergenceTest()`
2711: Calling sequence of `destroy`:
2712: . ctx - the convergence test context
2714: Level: advanced
2716: .seealso: [](ch_ksp), `KSP`, `KSPConvergedDefault()`, `KSPGetConvergenceContext()`, `KSPSetTolerances()`, `KSPSetConvergenceTest()`, `KSPGetAndClearConvergenceTest()`
2717: @*/
2718: PetscErrorCode KSPGetConvergenceTest(KSP ksp, PetscErrorCode (**converge)(KSP ksp, PetscInt it, PetscReal rnorm, KSPConvergedReason *reason, void *ctx), void **ctx, PetscErrorCode (**destroy)(void *ctx))
2719: {
2720: PetscFunctionBegin;
2722: if (converge) *converge = ksp->converged;
2723: if (destroy) *destroy = ksp->convergeddestroy;
2724: if (ctx) *ctx = ksp->cnvP;
2725: PetscFunctionReturn(PETSC_SUCCESS);
2726: }
2728: /*@C
2729: KSPGetAndClearConvergenceTest - Gets the function to be used to determine convergence. Removes the current test without calling destroy on the test context
2731: Logically Collective
2733: Input Parameter:
2734: . ksp - iterative solver obtained from `KSPCreate()`
2736: Output Parameters:
2737: + converge - pointer to convergence test function
2738: . ctx - context for private data for the convergence routine
2739: - destroy - a routine for destroying the context
2741: Calling sequence of `converge`:
2742: + ksp - iterative solver obtained from `KSPCreate()`
2743: . it - iteration number
2744: . rnorm - (estimated) 2-norm of (preconditioned) residual
2745: . reason - the reason why it has converged or diverged
2746: - ctx - optional convergence context, as set by `KSPSetConvergenceTest()`
2748: Calling sequence of `destroy`:
2749: . ctx - the convergence test context
2751: Level: advanced
2753: Note:
2754: This is intended to be used to allow transferring the convergence test (and its context) to another testing object (for example another `KSP`)
2755: and then calling `KSPSetConvergenceTest()` on this original `KSP`. If you just called `KSPGetConvergenceTest()` followed
2756: by `KSPSetConvergenceTest()` the original context information
2757: would be destroyed and hence the transferred context would be invalid and trigger a crash on use
2759: .seealso: [](ch_ksp), `KSP`, `KSPConvergedDefault()`, `KSPGetConvergenceContext()`, `KSPSetTolerances()`, `KSPSetConvergenceTest()`, `KSPGetConvergenceTest()`
2760: @*/
2761: PetscErrorCode KSPGetAndClearConvergenceTest(KSP ksp, PetscErrorCode (**converge)(KSP ksp, PetscInt it, PetscReal rnorm, KSPConvergedReason *reason, void *ctx), void **ctx, PetscErrorCode (**destroy)(void *ctx))
2762: {
2763: PetscFunctionBegin;
2765: *converge = ksp->converged;
2766: *destroy = ksp->convergeddestroy;
2767: *ctx = ksp->cnvP;
2768: ksp->converged = NULL;
2769: ksp->cnvP = NULL;
2770: ksp->convergeddestroy = NULL;
2771: PetscFunctionReturn(PETSC_SUCCESS);
2772: }
2774: /*@C
2775: KSPGetConvergenceContext - Gets the convergence context set with `KSPSetConvergenceTest()`.
2777: Not Collective
2779: Input Parameter:
2780: . ksp - iterative solver obtained from `KSPCreate()`
2782: Output Parameter:
2783: . ctx - monitoring context
2785: Level: advanced
2787: .seealso: [](ch_ksp), `KSP`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSPGetConvergenceTest()`
2788: @*/
2789: PetscErrorCode KSPGetConvergenceContext(KSP ksp, void *ctx)
2790: {
2791: PetscFunctionBegin;
2793: *(void **)ctx = ksp->cnvP;
2794: PetscFunctionReturn(PETSC_SUCCESS);
2795: }
2797: /*@
2798: KSPBuildSolution - Builds the approximate solution in a vector provided.
2800: Collective
2802: Input Parameter:
2803: . ksp - iterative solver obtained from `KSPCreate()`
2805: Output Parameter:
2806: Provide exactly one of
2807: + v - location to stash solution, optional, otherwise pass `NULL`
2808: - V - the solution is returned in this location. This vector is created internally. This vector should NOT be destroyed by the user with `VecDestroy()`.
2810: Level: developer
2812: Notes:
2813: This routine can be used in one of two ways
2814: .vb
2815: KSPBuildSolution(ksp,NULL,&V);
2816: or
2817: KSPBuildSolution(ksp,v,NULL); or KSPBuildSolution(ksp,v,&v);
2818: .ve
2819: In the first case an internal vector is allocated to store the solution
2820: (the user cannot destroy this vector). In the second case the solution
2821: is generated in the vector that the user provides. Note that for certain
2822: methods, such as `KSPCG`, the second case requires a copy of the solution,
2823: while in the first case the call is essentially free since it simply
2824: returns the vector where the solution already is stored. For some methods
2825: like `KSPGMRES` during the solve this is a reasonably expensive operation and should only be
2826: used if truly needed.
2828: .seealso: [](ch_ksp), `KSPGetSolution()`, `KSPBuildResidual()`, `KSP`
2829: @*/
2830: PetscErrorCode KSPBuildSolution(KSP ksp, Vec v, Vec *V)
2831: {
2832: PetscFunctionBegin;
2834: PetscCheck(V || v, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONG, "Must provide either v or V");
2835: if (!V) V = &v;
2836: if (ksp->reason != KSP_CONVERGED_ITERATING) {
2837: if (!v) PetscCall(KSPGetSolution(ksp, V));
2838: else PetscCall(VecCopy(ksp->vec_sol, v));
2839: } else {
2840: PetscUseTypeMethod(ksp, buildsolution, v, V);
2841: }
2842: PetscFunctionReturn(PETSC_SUCCESS);
2843: }
2845: /*@
2846: KSPBuildResidual - Builds the residual in a vector provided.
2848: Collective
2850: Input Parameter:
2851: . ksp - iterative solver obtained from `KSPCreate()`
2853: Output Parameters:
2854: + t - work vector. If not provided then one is generated.
2855: . v - optional location to stash residual. If `v` is not provided, then a location is generated.
2856: - V - the residual
2858: Level: advanced
2860: Note:
2861: Regardless of whether or not `v` is provided, the residual is
2862: returned in `V`.
2864: .seealso: [](ch_ksp), `KSP`, `KSPBuildSolution()`
2865: @*/
2866: PetscErrorCode KSPBuildResidual(KSP ksp, Vec t, Vec v, Vec *V)
2867: {
2868: PetscBool flag = PETSC_FALSE;
2869: Vec w = v, tt = t;
2871: PetscFunctionBegin;
2873: if (!w) PetscCall(VecDuplicate(ksp->vec_rhs, &w));
2874: if (!tt) {
2875: PetscCall(VecDuplicate(ksp->vec_sol, &tt));
2876: flag = PETSC_TRUE;
2877: }
2878: PetscUseTypeMethod(ksp, buildresidual, tt, w, V);
2879: if (flag) PetscCall(VecDestroy(&tt));
2880: PetscFunctionReturn(PETSC_SUCCESS);
2881: }
2883: /*@
2884: KSPSetDiagonalScale - Tells `KSP` to symmetrically diagonally scale the system
2885: before solving. This actually CHANGES the matrix (and right-hand side).
2887: Logically Collective
2889: Input Parameters:
2890: + ksp - the `KSP` context
2891: - scale - `PETSC_TRUE` or `PETSC_FALSE`
2893: Options Database Keys:
2894: + -ksp_diagonal_scale - perform a diagonal scaling before the solve
2895: - -ksp_diagonal_scale_fix - scale the matrix back AFTER the solve
2897: Level: advanced
2899: Notes:
2900: Scales the matrix by $D^{-1/2} A D^{-1/2} [D^{1/2} x ] = D^{-1/2} b $
2901: where $D_{ii}$ is $1/abs(A_{ii}) $ unless $A_{ii}$ is zero and then it is 1.
2903: BE CAREFUL with this routine: it actually scales the matrix and right
2904: hand side that define the system. After the system is solved the matrix
2905: and right-hand side remain scaled unless you use `KSPSetDiagonalScaleFix()`
2907: This should NOT be used within the `SNES` solves if you are using a line
2908: search.
2910: If you use this with the `PCType` `PCEISENSTAT` preconditioner than you can
2911: use the `PCEisenstatSetNoDiagonalScaling()` option, or `-pc_eisenstat_no_diagonal_scaling`
2912: to save some unneeded, redundant flops.
2914: .seealso: [](ch_ksp), `KSPGetDiagonalScale()`, `KSPSetDiagonalScaleFix()`, `KSP`
2915: @*/
2916: PetscErrorCode KSPSetDiagonalScale(KSP ksp, PetscBool scale)
2917: {
2918: PetscFunctionBegin;
2921: ksp->dscale = scale;
2922: PetscFunctionReturn(PETSC_SUCCESS);
2923: }
2925: /*@
2926: KSPGetDiagonalScale - Checks if `KSP` solver scales the matrix and right-hand side, that is if `KSPSetDiagonalScale()` has been called
2928: Not Collective
2930: Input Parameter:
2931: . ksp - the `KSP` context
2933: Output Parameter:
2934: . scale - `PETSC_TRUE` or `PETSC_FALSE`
2936: Level: intermediate
2938: .seealso: [](ch_ksp), `KSP`, `KSPSetDiagonalScale()`, `KSPSetDiagonalScaleFix()`
2939: @*/
2940: PetscErrorCode KSPGetDiagonalScale(KSP ksp, PetscBool *scale)
2941: {
2942: PetscFunctionBegin;
2944: PetscAssertPointer(scale, 2);
2945: *scale = ksp->dscale;
2946: PetscFunctionReturn(PETSC_SUCCESS);
2947: }
2949: /*@
2950: KSPSetDiagonalScaleFix - Tells `KSP` to diagonally scale the system back after solving.
2952: Logically Collective
2954: Input Parameters:
2955: + ksp - the `KSP` context
2956: - fix - `PETSC_TRUE` to scale back after the system solve, `PETSC_FALSE` to not
2957: rescale (default)
2959: Level: intermediate
2961: Notes:
2962: Must be called after `KSPSetDiagonalScale()`
2964: Using this will slow things down, because it rescales the matrix before and
2965: after each linear solve. This is intended mainly for testing to allow one
2966: to easily get back the original system to make sure the solution computed is
2967: accurate enough.
2969: .seealso: [](ch_ksp), `KSPGetDiagonalScale()`, `KSPSetDiagonalScale()`, `KSPGetDiagonalScaleFix()`, `KSP`
2970: @*/
2971: PetscErrorCode KSPSetDiagonalScaleFix(KSP ksp, PetscBool fix)
2972: {
2973: PetscFunctionBegin;
2976: ksp->dscalefix = fix;
2977: PetscFunctionReturn(PETSC_SUCCESS);
2978: }
2980: /*@
2981: KSPGetDiagonalScaleFix - Determines if `KSP` diagonally scales the system back after solving. That is `KSPSetDiagonalScaleFix()` has been called
2983: Not Collective
2985: Input Parameter:
2986: . ksp - the `KSP` context
2988: Output Parameter:
2989: . fix - `PETSC_TRUE` to scale back after the system solve, `PETSC_FALSE` to not
2990: rescale (default)
2992: Level: intermediate
2994: .seealso: [](ch_ksp), `KSPGetDiagonalScale()`, `KSPSetDiagonalScale()`, `KSPSetDiagonalScaleFix()`, `KSP`
2995: @*/
2996: PetscErrorCode KSPGetDiagonalScaleFix(KSP ksp, PetscBool *fix)
2997: {
2998: PetscFunctionBegin;
3000: PetscAssertPointer(fix, 2);
3001: *fix = ksp->dscalefix;
3002: PetscFunctionReturn(PETSC_SUCCESS);
3003: }
3005: /*@C
3006: KSPSetComputeOperators - set routine to compute the linear operators
3008: Logically Collective
3010: Input Parameters:
3011: + ksp - the `KSP` context
3012: . func - function to compute the operators, see `KSPComputeOperatorsFn` for the calling sequence
3013: - ctx - optional context
3015: Level: beginner
3017: Notes:
3018: `func()` will be called automatically at the very next call to `KSPSolve()`. It will NOT be called at future `KSPSolve()` calls
3019: unless either `KSPSetComputeOperators()` or `KSPSetOperators()` is called before that `KSPSolve()` is called. This allows the same system to be solved several times
3020: with different right-hand side functions but is a confusing API since one might expect it to be called for each `KSPSolve()`
3022: To reuse the same preconditioner for the next `KSPSolve()` and not compute a new one based on the most recently computed matrix call `KSPSetReusePreconditioner()`
3024: Developer Note:
3025: Perhaps this routine and `KSPSetComputeRHS()` could be combined into a new API that makes clear when new matrices are computing without requiring call this
3026: routine to indicate when the new matrix should be computed.
3028: .seealso: [](ch_ksp), `KSP`, `KSPSetOperators()`, `KSPSetComputeRHS()`, `DMKSPSetComputeOperators()`, `KSPSetComputeInitialGuess()`, `KSPComputeOperatorsFn`
3029: @*/
3030: PetscErrorCode KSPSetComputeOperators(KSP ksp, KSPComputeOperatorsFn *func, void *ctx)
3031: {
3032: DM dm;
3034: PetscFunctionBegin;
3036: PetscCall(KSPGetDM(ksp, &dm));
3037: PetscCall(DMKSPSetComputeOperators(dm, func, ctx));
3038: if (ksp->setupstage == KSP_SETUP_NEWRHS) ksp->setupstage = KSP_SETUP_NEWMATRIX;
3039: PetscFunctionReturn(PETSC_SUCCESS);
3040: }
3042: /*@C
3043: KSPSetComputeRHS - set routine to compute the right-hand side of the linear system
3045: Logically Collective
3047: Input Parameters:
3048: + ksp - the `KSP` context
3049: . func - function to compute the right-hand side, see `KSPComputeRHSFn` for the calling sequence
3050: - ctx - optional context
3052: Level: beginner
3054: Note:
3055: The routine you provide will be called EACH you call `KSPSolve()` to prepare the new right-hand side for that solve
3057: .seealso: [](ch_ksp), `KSP`, `KSPSolve()`, `DMKSPSetComputeRHS()`, `KSPSetComputeOperators()`, `KSPSetOperators()`, `KSPComputeRHSFn`
3058: @*/
3059: PetscErrorCode KSPSetComputeRHS(KSP ksp, KSPComputeRHSFn *func, void *ctx)
3060: {
3061: DM dm;
3063: PetscFunctionBegin;
3065: PetscCall(KSPGetDM(ksp, &dm));
3066: PetscCall(DMKSPSetComputeRHS(dm, func, ctx));
3067: PetscFunctionReturn(PETSC_SUCCESS);
3068: }
3070: /*@C
3071: KSPSetComputeInitialGuess - set routine to compute the initial guess of the linear system
3073: Logically Collective
3075: Input Parameters:
3076: + ksp - the `KSP` context
3077: . func - function to compute the initial guess, see `KSPComputeInitialGuessFn` for calling sequence
3078: - ctx - optional context
3080: Level: beginner
3082: Note:
3083: This should only be used in conjunction with `KSPSetComputeRHS()` and `KSPSetComputeOperators()`, otherwise
3084: call `KSPSetInitialGuessNonzero()` and set the initial guess values in the solution vector passed to `KSPSolve()` before calling the solver
3086: .seealso: [](ch_ksp), `KSP`, `KSPSolve()`, `KSPSetComputeRHS()`, `KSPSetComputeOperators()`, `DMKSPSetComputeInitialGuess()`, `KSPSetInitialGuessNonzero()`,
3087: `KSPComputeInitialGuessFn`
3088: @*/
3089: PetscErrorCode KSPSetComputeInitialGuess(KSP ksp, KSPComputeInitialGuessFn *func, void *ctx)
3090: {
3091: DM dm;
3093: PetscFunctionBegin;
3095: PetscCall(KSPGetDM(ksp, &dm));
3096: PetscCall(DMKSPSetComputeInitialGuess(dm, func, ctx));
3097: PetscFunctionReturn(PETSC_SUCCESS);
3098: }
3100: /*@
3101: KSPSetUseExplicitTranspose - Determines the explicit transpose of the operator is formed in `KSPSolveTranspose()`. In some configurations (like GPUs) it may
3102: be explicitly formed since the solve is much more efficient.
3104: Logically Collective
3106: Input Parameter:
3107: . ksp - the `KSP` context
3109: Output Parameter:
3110: . flg - `PETSC_TRUE` to transpose the system in `KSPSolveTranspose()`, `PETSC_FALSE` to not transpose (default)
3112: Level: advanced
3114: .seealso: [](ch_ksp), `KSPSolveTranspose()`, `KSP`
3115: @*/
3116: PetscErrorCode KSPSetUseExplicitTranspose(KSP ksp, PetscBool flg)
3117: {
3118: PetscFunctionBegin;
3121: ksp->transpose.use_explicittranspose = flg;
3122: PetscFunctionReturn(PETSC_SUCCESS);
3123: }