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_DMACTIVE_OPERATOR) && !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 && (ksp->dmActive & KSP_DMACTIVE_INITIAL_GUESS)) {
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 && (ksp->dmActive & KSP_DMACTIVE_RHS)) PetscCallBack("KSP callback rhs", (*kdm->ops->computerhs)(ksp, ksp->vec_rhs, kdm->rhsctx));
375: if ((ksp->setupstage != KSP_SETUP_NEWRHS) && (ksp->dmActive & KSP_DMACTIVE_OPERATOR)) {
376: PetscCheck(kdm->ops->computeoperators, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONGSTATE, "You called KSPSetDM() but did not use DMKSPSetComputeOperators() or KSPSetDMActive(ksp, KSP_DMACTIVE_ALL, PETSC_FALSE);");
377: PetscCall(KSPGetOperators(ksp, &A, &B));
378: PetscCallBack("KSP callback operators", (*kdm->ops->computeoperators)(ksp, A, B, kdm->operatorsctx));
379: }
380: }
382: if (ksp->setupstage == KSP_SETUP_NEWRHS) {
383: level--;
384: PetscFunctionReturn(PETSC_SUCCESS);
385: }
386: PetscCall(PetscLogEventBegin(KSP_SetUp, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
388: switch (ksp->setupstage) {
389: case KSP_SETUP_NEW:
390: PetscUseTypeMethod(ksp, setup);
391: break;
392: case KSP_SETUP_NEWMATRIX: /* This should be replaced with a more general mechanism */
393: if (ksp->setupnewmatrix) PetscUseTypeMethod(ksp, setup);
394: break;
395: default:
396: break;
397: }
399: if (!ksp->pc) PetscCall(KSPGetPC(ksp, &ksp->pc));
400: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
401: /* scale the matrix if requested */
402: if (ksp->dscale) {
403: PetscScalar *xx;
404: PetscInt i, n;
405: PetscBool zeroflag = PETSC_FALSE;
407: if (!ksp->diagonal) { /* allocate vector to hold diagonal */
408: PetscCall(MatCreateVecs(pmat, &ksp->diagonal, NULL));
409: }
410: PetscCall(MatGetDiagonal(pmat, ksp->diagonal));
411: PetscCall(VecGetLocalSize(ksp->diagonal, &n));
412: PetscCall(VecGetArray(ksp->diagonal, &xx));
413: for (i = 0; i < n; i++) {
414: if (xx[i] != 0.0) xx[i] = 1.0 / PetscSqrtReal(PetscAbsScalar(xx[i]));
415: else {
416: xx[i] = 1.0;
417: zeroflag = PETSC_TRUE;
418: }
419: }
420: PetscCall(VecRestoreArray(ksp->diagonal, &xx));
421: if (zeroflag) PetscCall(PetscInfo(ksp, "Zero detected in diagonal of matrix, using 1 at those locations\n"));
422: PetscCall(MatDiagonalScale(pmat, ksp->diagonal, ksp->diagonal));
423: if (mat != pmat) PetscCall(MatDiagonalScale(mat, ksp->diagonal, ksp->diagonal));
424: ksp->dscalefix2 = PETSC_FALSE;
425: }
426: PetscCall(PetscLogEventEnd(KSP_SetUp, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
427: PetscCall(PCSetErrorIfFailure(ksp->pc, ksp->errorifnotconverged));
428: PetscCall(PCSetUp(ksp->pc));
429: PetscCall(PCGetFailedReason(ksp->pc, &pcreason));
430: /* 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 */
431: if (pcreason) ksp->reason = KSP_DIVERGED_PC_FAILED;
433: PetscCall(MatGetNullSpace(mat, &nullsp));
434: if (nullsp) {
435: PetscBool test = PETSC_FALSE;
436: PetscCall(PetscOptionsGetBool(((PetscObject)ksp)->options, ((PetscObject)ksp)->prefix, "-ksp_test_null_space", &test, NULL));
437: if (test) PetscCall(MatNullSpaceTest(nullsp, mat, NULL));
438: }
439: ksp->setupstage = KSP_SETUP_NEWRHS;
440: level--;
441: PetscFunctionReturn(PETSC_SUCCESS);
442: }
444: /*@
445: KSPConvergedReasonView - Displays the reason a `KSP` solve converged or diverged, `KSPConvergedReason` to a `PetscViewer`
447: Collective
449: Input Parameters:
450: + ksp - iterative solver obtained from `KSPCreate()`
451: - viewer - the `PetscViewer` on which to display the reason
453: Options Database Keys:
454: + -ksp_converged_reason - print reason for converged or diverged, also prints number of iterations
455: - -ksp_converged_reason ::failed - only print reason and number of iterations when diverged
457: Level: beginner
459: Note:
460: Use `KSPConvergedReasonViewFromOptions()` to display the reason based on values in the PETSc options database.
462: To change the format of the output call `PetscViewerPushFormat`(`viewer`,`format`) before this call. Use `PETSC_VIEWER_DEFAULT` for the default,
463: use `PETSC_VIEWER_FAILED` to only display a reason if it fails.
465: .seealso: [](ch_ksp), `KSPConvergedReasonViewFromOptions()`, `KSPCreate()`, `KSPSetUp()`, `KSPDestroy()`, `KSPSetTolerances()`, `KSPConvergedDefault()`,
466: `KSPSolveTranspose()`, `KSPGetIterationNumber()`, `KSP`, `KSPGetConvergedReason()`, `PetscViewerPushFormat()`, `PetscViewerPopFormat()`
467: @*/
468: PetscErrorCode KSPConvergedReasonView(KSP ksp, PetscViewer viewer)
469: {
470: PetscBool isAscii;
471: PetscViewerFormat format;
473: PetscFunctionBegin;
474: if (!viewer) viewer = PETSC_VIEWER_STDOUT_(PetscObjectComm((PetscObject)ksp));
475: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isAscii));
476: if (isAscii) {
477: PetscCall(PetscViewerGetFormat(viewer, &format));
478: PetscCall(PetscViewerASCIIAddTab(viewer, ((PetscObject)ksp)->tablevel + 1));
479: if (ksp->reason > 0 && format != PETSC_VIEWER_FAILED) {
480: if (((PetscObject)ksp)->prefix) {
481: PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve converged due to %s iterations %" PetscInt_FMT "\n", ((PetscObject)ksp)->prefix, KSPConvergedReasons[ksp->reason], ksp->its));
482: } else {
483: PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve converged due to %s iterations %" PetscInt_FMT "\n", KSPConvergedReasons[ksp->reason], ksp->its));
484: }
485: } else if (ksp->reason <= 0) {
486: if (((PetscObject)ksp)->prefix) {
487: PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve did not converge due to %s iterations %" PetscInt_FMT "\n", ((PetscObject)ksp)->prefix, KSPConvergedReasons[ksp->reason], ksp->its));
488: } else {
489: PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve did not converge due to %s iterations %" PetscInt_FMT "\n", KSPConvergedReasons[ksp->reason], ksp->its));
490: }
491: if (ksp->reason == KSP_DIVERGED_PC_FAILED) {
492: PCFailedReason reason;
493: PetscCall(PCGetFailedReason(ksp->pc, &reason));
494: PetscCall(PetscViewerASCIIPrintf(viewer, " PC failed due to %s\n", PCFailedReasons[reason]));
495: }
496: }
497: PetscCall(PetscViewerASCIISubtractTab(viewer, ((PetscObject)ksp)->tablevel + 1));
498: }
499: PetscFunctionReturn(PETSC_SUCCESS);
500: }
502: /*@C
503: KSPConvergedReasonViewSet - Sets an ADDITIONAL function that is to be used at the
504: end of the linear solver to display the convergence reason of the linear solver.
506: Logically Collective
508: Input Parameters:
509: + ksp - the `KSP` context
510: . f - the `ksp` converged reason view function, see `KSPConvergedReasonViewFn`
511: . ctx - [optional] context for private data for the `KSPConvergedReason` view routine (use `NULL` if context is not needed)
512: - reasonviewdestroy - [optional] routine that frees `ctx` (may be `NULL`), see `PetscCtxDestroyFn` for the calling sequence
514: Options Database Keys:
515: + -ksp_converged_reason - sets a default `KSPConvergedReasonView()`
516: - -ksp_converged_reason_view_cancel - cancels all converged reason viewers that have been hardwired into a code by
517: calls to `KSPConvergedReasonViewSet()`, but does not cancel those set via the options database.
519: Level: intermediate
521: Note:
522: Several different converged reason view routines may be set by calling
523: `KSPConvergedReasonViewSet()` multiple times; all will be called in the
524: order in which they were set.
526: Developer Note:
527: Should be named KSPConvergedReasonViewAdd().
529: .seealso: [](ch_ksp), `KSPConvergedReasonView()`, `KSPConvergedReasonViewFn`, `KSPConvergedReasonViewCancel()`, `PetscCtxDestroyFn`
530: @*/
531: PetscErrorCode KSPConvergedReasonViewSet(KSP ksp, KSPConvergedReasonViewFn *f, PetscCtx ctx, PetscCtxDestroyFn *reasonviewdestroy)
532: {
533: PetscFunctionBegin;
535: for (PetscInt i = 0; i < ksp->numberreasonviews; i++) {
536: PetscBool identical;
538: PetscCall(PetscMonitorCompare((PetscErrorCode (*)(void))(PetscVoidFn *)f, ctx, reasonviewdestroy, (PetscErrorCode (*)(void))(PetscVoidFn *)ksp->reasonview[i], ksp->reasonviewcontext[i], ksp->reasonviewdestroy[i], &identical));
539: if (identical) PetscFunctionReturn(PETSC_SUCCESS);
540: }
541: PetscCheck(ksp->numberreasonviews < MAXKSPREASONVIEWS, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Too many KSP reasonview set");
542: ksp->reasonview[ksp->numberreasonviews] = f;
543: ksp->reasonviewdestroy[ksp->numberreasonviews] = reasonviewdestroy;
544: ksp->reasonviewcontext[ksp->numberreasonviews++] = ctx;
545: PetscFunctionReturn(PETSC_SUCCESS);
546: }
548: /*@
549: KSPConvergedReasonViewCancel - Clears all the `KSPConvergedReason` view functions for a `KSP` object set with `KSPConvergedReasonViewSet()`
550: as well as the default viewer.
552: Collective
554: Input Parameter:
555: . ksp - iterative solver obtained from `KSPCreate()`
557: Level: intermediate
559: .seealso: [](ch_ksp), `KSPCreate()`, `KSPDestroy()`, `KSPReset()`, `KSPConvergedReasonViewSet()`
560: @*/
561: PetscErrorCode KSPConvergedReasonViewCancel(KSP ksp)
562: {
563: PetscInt i;
565: PetscFunctionBegin;
567: for (i = 0; i < ksp->numberreasonviews; i++) {
568: if (ksp->reasonviewdestroy[i]) PetscCall((*ksp->reasonviewdestroy[i])(&ksp->reasonviewcontext[i]));
569: }
570: ksp->numberreasonviews = 0;
571: PetscCall(PetscViewerDestroy(&ksp->convergedreasonviewer));
572: PetscFunctionReturn(PETSC_SUCCESS);
573: }
575: /*@
576: KSPConvergedReasonViewFromOptions - Processes command line options to determine if/how a `KSPConvergedReason` is to be viewed.
578: Collective
580: Input Parameter:
581: . ksp - the `KSP` object
583: Level: intermediate
585: Note:
586: This is called automatically at the conclusion of `KSPSolve()` so is rarely called directly by user code.
588: .seealso: [](ch_ksp), `KSPConvergedReasonView()`, `KSPConvergedReasonViewSet()`
589: @*/
590: PetscErrorCode KSPConvergedReasonViewFromOptions(KSP ksp)
591: {
592: PetscFunctionBegin;
593: /* Call all user-provided reason review routines */
594: for (PetscInt i = 0; i < ksp->numberreasonviews; i++) PetscCall((*ksp->reasonview[i])(ksp, ksp->reasonviewcontext[i]));
596: /* Call the default PETSc routine */
597: if (ksp->convergedreasonviewer) {
598: PetscCall(PetscViewerPushFormat(ksp->convergedreasonviewer, ksp->convergedreasonformat));
599: PetscCall(KSPConvergedReasonView(ksp, ksp->convergedreasonviewer));
600: PetscCall(PetscViewerPopFormat(ksp->convergedreasonviewer));
601: }
602: PetscFunctionReturn(PETSC_SUCCESS);
603: }
605: /*@
606: KSPConvergedRateView - Displays the convergence rate <https://en.wikipedia.org/wiki/Coefficient_of_determination> of `KSPSolve()` to a viewer
608: Collective
610: Input Parameters:
611: + ksp - iterative solver obtained from `KSPCreate()`
612: - viewer - the `PetscViewer` to display the reason
614: Options Database Key:
615: . -ksp_converged_rate - print reason for convergence or divergence and the convergence rate (or 0.0 for divergence)
617: Level: intermediate
619: Notes:
620: To change the format of the output, call `PetscViewerPushFormat`(`viewer`,`format`) before this call.
622: 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,
623: the slope is $\log c$. The coefficient of determination is given by $1 - \frac{\sum_i (y_i - f(x_i))^2}{\sum_i (y_i - \bar y)}$,
625: .seealso: [](ch_ksp), `KSPConvergedReasonView()`, `KSPGetConvergedRate()`, `KSPSetTolerances()`, `KSPConvergedDefault()`
626: @*/
627: PetscErrorCode KSPConvergedRateView(KSP ksp, PetscViewer viewer)
628: {
629: PetscViewerFormat format;
630: PetscBool isAscii;
631: PetscReal rrate, rRsq, erate = 0.0, eRsq = 0.0;
632: PetscInt its;
633: const char *prefix, *reason = KSPConvergedReasons[ksp->reason];
635: PetscFunctionBegin;
636: PetscCall(KSPGetIterationNumber(ksp, &its));
637: PetscCall(KSPComputeConvergenceRate(ksp, &rrate, &rRsq, &erate, &eRsq));
638: if (!viewer) viewer = PETSC_VIEWER_STDOUT_(PetscObjectComm((PetscObject)ksp));
639: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isAscii));
640: if (isAscii) {
641: PetscCall(KSPGetOptionsPrefix(ksp, &prefix));
642: PetscCall(PetscViewerGetFormat(viewer, &format));
643: PetscCall(PetscViewerASCIIAddTab(viewer, ((PetscObject)ksp)->tablevel));
644: if (ksp->reason > 0) {
645: if (prefix) PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve converged due to %s iterations %" PetscInt_FMT, prefix, reason, its));
646: else PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve converged due to %s iterations %" PetscInt_FMT, reason, its));
647: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
648: if (rRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " res rate %g R^2 %g", (double)rrate, (double)rRsq));
649: if (eRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " error rate %g R^2 %g", (double)erate, (double)eRsq));
650: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
651: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
652: } else if (ksp->reason <= 0) {
653: if (prefix) PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve did not converge due to %s iterations %" PetscInt_FMT, prefix, reason, its));
654: else PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve did not converge due to %s iterations %" PetscInt_FMT, reason, its));
655: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
656: if (rRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " res rate %g R^2 %g", (double)rrate, (double)rRsq));
657: if (eRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " error rate %g R^2 %g", (double)erate, (double)eRsq));
658: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
659: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
660: if (ksp->reason == KSP_DIVERGED_PC_FAILED) {
661: PCFailedReason reason;
662: PetscCall(PCGetFailedReason(ksp->pc, &reason));
663: PetscCall(PetscViewerASCIIPrintf(viewer, " PC failed due to %s\n", PCFailedReasons[reason]));
664: }
665: }
666: PetscCall(PetscViewerASCIISubtractTab(viewer, ((PetscObject)ksp)->tablevel));
667: }
668: PetscFunctionReturn(PETSC_SUCCESS);
669: }
671: #include <petscdraw.h>
673: static PetscErrorCode KSPViewEigenvalues_Internal(KSP ksp, PetscBool isExplicit, PetscViewer viewer, PetscViewerFormat format)
674: {
675: PetscReal *r, *c;
676: PetscInt n, i, neig;
677: PetscBool isascii, isdraw;
678: PetscMPIInt rank;
680: PetscFunctionBegin;
681: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)ksp), &rank));
682: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
683: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
684: if (isExplicit) {
685: PetscCall(VecGetSize(ksp->vec_sol, &n));
686: PetscCall(PetscMalloc2(n, &r, n, &c));
687: PetscCall(KSPComputeEigenvaluesExplicitly(ksp, n, r, c));
688: neig = n;
689: } else {
690: PetscInt nits;
692: PetscCall(KSPGetIterationNumber(ksp, &nits));
693: n = nits + 2;
694: if (!nits) {
695: PetscCall(PetscViewerASCIIPrintf(viewer, "Zero iterations in solver, cannot approximate any eigenvalues\n"));
696: PetscFunctionReturn(PETSC_SUCCESS);
697: }
698: PetscCall(PetscMalloc2(n, &r, n, &c));
699: PetscCall(KSPComputeEigenvalues(ksp, n, r, c, &neig));
700: }
701: if (isascii) {
702: PetscCall(PetscViewerASCIIPrintf(viewer, "%s computed eigenvalues\n", isExplicit ? "Explicitly" : "Iteratively"));
703: for (i = 0; i < neig; ++i) {
704: if (c[i] >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, "%g + %gi\n", (double)r[i], (double)c[i]));
705: else PetscCall(PetscViewerASCIIPrintf(viewer, "%g - %gi\n", (double)r[i], -(double)c[i]));
706: }
707: } else if (isdraw && rank == 0) {
708: PetscDraw draw;
709: PetscDrawSP drawsp;
711: if (format == PETSC_VIEWER_DRAW_CONTOUR) {
712: PetscCall(KSPPlotEigenContours_Private(ksp, neig, r, c));
713: } else {
714: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
715: PetscCall(PetscDrawSPCreate(draw, 1, &drawsp));
716: PetscCall(PetscDrawSPReset(drawsp));
717: for (i = 0; i < neig; ++i) PetscCall(PetscDrawSPAddPoint(drawsp, r + i, c + i));
718: PetscCall(PetscDrawSPDraw(drawsp, PETSC_TRUE));
719: PetscCall(PetscDrawSPSave(drawsp));
720: PetscCall(PetscDrawSPDestroy(&drawsp));
721: }
722: }
723: PetscCall(PetscFree2(r, c));
724: PetscFunctionReturn(PETSC_SUCCESS);
725: }
727: static PetscErrorCode KSPViewSingularvalues_Internal(KSP ksp, PetscViewer viewer, PetscViewerFormat format)
728: {
729: PetscReal smax, smin;
730: PetscInt nits;
731: PetscBool isascii;
733: PetscFunctionBegin;
734: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
735: PetscCall(KSPGetIterationNumber(ksp, &nits));
736: if (!nits) {
737: PetscCall(PetscViewerASCIIPrintf(viewer, "Zero iterations in solver, cannot approximate any singular values\n"));
738: PetscFunctionReturn(PETSC_SUCCESS);
739: }
740: PetscCall(KSPComputeExtremeSingularValues(ksp, &smax, &smin));
741: 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)));
742: PetscFunctionReturn(PETSC_SUCCESS);
743: }
745: static PetscErrorCode KSPViewFinalResidual_Internal(KSP ksp, PetscViewer viewer, PetscViewerFormat format)
746: {
747: PetscBool isascii;
749: PetscFunctionBegin;
750: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
751: 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");
752: if (isascii) {
753: Mat A;
754: Vec t;
755: PetscReal norm;
757: PetscCall(PCGetOperators(ksp->pc, &A, NULL));
758: PetscCall(VecDuplicate(ksp->vec_rhs, &t));
759: PetscCall(KSP_MatMult(ksp, A, ksp->vec_sol, t));
760: PetscCall(VecAYPX(t, -1.0, ksp->vec_rhs));
761: PetscCall(PetscOptionsPushCreateViewerOff(PETSC_FALSE));
762: PetscCall(VecViewFromOptions(t, (PetscObject)ksp, "-ksp_view_final_residual_vec"));
763: PetscCall(PetscOptionsPopCreateViewerOff());
764: PetscCall(VecNorm(t, NORM_2, &norm));
765: PetscCall(VecDestroy(&t));
766: PetscCall(PetscViewerASCIIPrintf(viewer, "KSP final norm of residual %g\n", (double)norm));
767: }
768: PetscFunctionReturn(PETSC_SUCCESS);
769: }
771: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode PetscMonitorPauseFinal_Internal(PetscInt n, PetscCtx ctx[])
772: {
773: PetscFunctionBegin;
774: for (PetscInt i = 0; i < n; ++i) {
775: PetscViewerAndFormat *vf = (PetscViewerAndFormat *)ctx[i];
776: PetscDraw draw;
777: PetscReal lpause;
778: PetscBool isdraw;
780: if (!vf) continue;
781: if (!PetscCheckPointer(vf->viewer, PETSC_OBJECT)) continue;
782: if (((PetscObject)vf->viewer)->classid != PETSC_VIEWER_CLASSID) continue;
783: PetscCall(PetscObjectTypeCompare((PetscObject)vf->viewer, PETSCVIEWERDRAW, &isdraw));
784: if (!isdraw) continue;
786: PetscCall(PetscViewerDrawGetDraw(vf->viewer, 0, &draw));
787: PetscCall(PetscDrawGetPause(draw, &lpause));
788: PetscCall(PetscDrawSetPause(draw, -1.0));
789: PetscCall(PetscDrawPause(draw));
790: PetscCall(PetscDrawSetPause(draw, lpause));
791: }
792: PetscFunctionReturn(PETSC_SUCCESS);
793: }
795: static PetscErrorCode KSPMonitorPauseFinal_Internal(KSP ksp)
796: {
797: PetscFunctionBegin;
798: if (!ksp->pauseFinal) PetscFunctionReturn(PETSC_SUCCESS);
799: PetscCall(PetscMonitorPauseFinal_Internal(ksp->numbermonitors, ksp->monitorcontext));
800: PetscFunctionReturn(PETSC_SUCCESS);
801: }
803: static PetscErrorCode KSPSolve_Private(KSP ksp, Vec b, Vec x)
804: {
805: PetscBool flg = PETSC_FALSE, inXisinB = PETSC_FALSE, guess_zero;
806: Mat mat, pmat;
807: MPI_Comm comm;
808: MatNullSpace nullsp;
809: Vec btmp, vec_rhs = NULL;
811: PetscFunctionBegin;
812: level++;
813: comm = PetscObjectComm((PetscObject)ksp);
814: if (x && x == b) {
815: PetscCheck(ksp->guess_zero, comm, PETSC_ERR_ARG_INCOMP, "Cannot use x == b with nonzero initial guess");
816: PetscCall(VecDuplicate(b, &x));
817: inXisinB = PETSC_TRUE;
818: }
819: if (b) {
820: PetscCall(PetscObjectReference((PetscObject)b));
821: PetscCall(VecDestroy(&ksp->vec_rhs));
822: ksp->vec_rhs = b;
823: }
824: if (x) {
825: PetscCall(PetscObjectReference((PetscObject)x));
826: PetscCall(VecDestroy(&ksp->vec_sol));
827: ksp->vec_sol = x;
828: }
830: if (ksp->viewPre) PetscCall(ObjectView((PetscObject)ksp, ksp->viewerPre, ksp->formatPre));
832: if (ksp->presolve) PetscCall((*ksp->presolve)(ksp, ksp->vec_rhs, ksp->vec_sol, ksp->prectx));
834: /* reset the residual history list if requested */
835: if (ksp->res_hist_reset) ksp->res_hist_len = 0;
836: if (ksp->err_hist_reset) ksp->err_hist_len = 0;
838: /* KSPSetUp() scales the matrix if needed */
839: PetscCall(KSPSetUp(ksp));
840: PetscCall(KSPSetUpOnBlocks(ksp));
842: if (ksp->guess) {
843: PetscObjectState ostate, state;
845: PetscCall(KSPGuessSetUp(ksp->guess));
846: PetscCall(PetscObjectStateGet((PetscObject)ksp->vec_sol, &ostate));
847: PetscCall(KSPGuessFormGuess(ksp->guess, ksp->vec_rhs, ksp->vec_sol));
848: PetscCall(PetscObjectStateGet((PetscObject)ksp->vec_sol, &state));
849: if (state != ostate) {
850: ksp->guess_zero = PETSC_FALSE;
851: } else {
852: PetscCall(PetscInfo(ksp, "Using zero initial guess since the KSPGuess object did not change the vector\n"));
853: ksp->guess_zero = PETSC_TRUE;
854: }
855: }
857: PetscCall(VecSetErrorIfLocked(ksp->vec_sol, 3));
859: PetscCall(PetscLogEventBegin(!ksp->transpose_solve ? KSP_Solve : KSP_SolveTranspose, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
860: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
861: /* diagonal scale RHS if called for */
862: if (ksp->dscale) {
863: PetscCall(VecPointwiseMult(ksp->vec_rhs, ksp->vec_rhs, ksp->diagonal));
864: /* second time in, but matrix was scaled back to original */
865: if (ksp->dscalefix && ksp->dscalefix2) {
866: Mat mat, pmat;
868: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
869: PetscCall(MatDiagonalScale(pmat, ksp->diagonal, ksp->diagonal));
870: if (mat != pmat) PetscCall(MatDiagonalScale(mat, ksp->diagonal, ksp->diagonal));
871: }
873: /* scale initial guess */
874: if (!ksp->guess_zero) {
875: if (!ksp->truediagonal) {
876: PetscCall(VecDuplicate(ksp->diagonal, &ksp->truediagonal));
877: PetscCall(VecCopy(ksp->diagonal, ksp->truediagonal));
878: PetscCall(VecReciprocal(ksp->truediagonal));
879: }
880: PetscCall(VecPointwiseMult(ksp->vec_sol, ksp->vec_sol, ksp->truediagonal));
881: }
882: }
883: PetscCall(PCPreSolve(ksp->pc, ksp));
885: if (ksp->guess_zero && !ksp->guess_not_read) PetscCall(VecSet(ksp->vec_sol, 0.0));
886: if (ksp->guess_knoll) { /* The Knoll trick is independent on the KSPGuess specified */
887: PetscCall(PCApply(ksp->pc, ksp->vec_rhs, ksp->vec_sol));
888: PetscCall(KSP_RemoveNullSpace(ksp, ksp->vec_sol));
889: ksp->guess_zero = PETSC_FALSE;
890: }
892: /* can we mark the initial guess as zero for this solve? */
893: guess_zero = ksp->guess_zero;
894: if (!ksp->guess_zero) {
895: PetscReal norm;
897: PetscCall(VecNormAvailable(ksp->vec_sol, NORM_2, &flg, &norm));
898: if (flg && !norm) ksp->guess_zero = PETSC_TRUE;
899: }
900: if (ksp->transpose_solve) {
901: PetscCall(MatGetNullSpace(mat, &nullsp));
902: } else {
903: PetscCall(MatGetTransposeNullSpace(mat, &nullsp));
904: }
905: if (nullsp) {
906: PetscCall(VecDuplicate(ksp->vec_rhs, &btmp));
907: PetscCall(VecCopy(ksp->vec_rhs, btmp));
908: PetscCall(MatNullSpaceRemove(nullsp, btmp));
909: vec_rhs = ksp->vec_rhs;
910: ksp->vec_rhs = btmp;
911: }
912: PetscCall(VecLockReadPush(ksp->vec_rhs));
913: PetscUseTypeMethod(ksp, solve);
914: PetscCall(KSPMonitorPauseFinal_Internal(ksp));
916: PetscCall(VecLockReadPop(ksp->vec_rhs));
917: if (nullsp) {
918: ksp->vec_rhs = vec_rhs;
919: PetscCall(VecDestroy(&btmp));
920: }
922: ksp->guess_zero = guess_zero;
924: PetscCheck(ksp->reason, comm, PETSC_ERR_PLIB, "Internal error, solver returned without setting converged reason");
925: ksp->totalits += ksp->its;
927: PetscCall(KSPConvergedReasonViewFromOptions(ksp));
929: if (ksp->viewRate) {
930: PetscCall(PetscViewerPushFormat(ksp->viewerRate, ksp->formatRate));
931: PetscCall(KSPConvergedRateView(ksp, ksp->viewerRate));
932: PetscCall(PetscViewerPopFormat(ksp->viewerRate));
933: }
934: PetscCall(PCPostSolve(ksp->pc, ksp));
936: /* diagonal scale solution if called for */
937: if (ksp->dscale) {
938: PetscCall(VecPointwiseMult(ksp->vec_sol, ksp->vec_sol, ksp->diagonal));
939: /* unscale right-hand side and matrix */
940: if (ksp->dscalefix) {
941: Mat mat, pmat;
943: PetscCall(VecReciprocal(ksp->diagonal));
944: PetscCall(VecPointwiseMult(ksp->vec_rhs, ksp->vec_rhs, ksp->diagonal));
945: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
946: PetscCall(MatDiagonalScale(pmat, ksp->diagonal, ksp->diagonal));
947: if (mat != pmat) PetscCall(MatDiagonalScale(mat, ksp->diagonal, ksp->diagonal));
948: PetscCall(VecReciprocal(ksp->diagonal));
949: ksp->dscalefix2 = PETSC_TRUE;
950: }
951: }
952: PetscCall(PetscLogEventEnd(!ksp->transpose_solve ? KSP_Solve : KSP_SolveTranspose, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
953: if (ksp->guess) PetscCall(KSPGuessUpdate(ksp->guess, ksp->vec_rhs, ksp->vec_sol));
954: if (ksp->postsolve) PetscCall((*ksp->postsolve)(ksp, ksp->vec_rhs, ksp->vec_sol, ksp->postctx));
956: PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
957: if (ksp->viewEV) PetscCall(KSPViewEigenvalues_Internal(ksp, PETSC_FALSE, ksp->viewerEV, ksp->formatEV));
958: if (ksp->viewEVExp) PetscCall(KSPViewEigenvalues_Internal(ksp, PETSC_TRUE, ksp->viewerEVExp, ksp->formatEVExp));
959: if (ksp->viewSV) PetscCall(KSPViewSingularvalues_Internal(ksp, ksp->viewerSV, ksp->formatSV));
960: if (ksp->viewFinalRes) PetscCall(KSPViewFinalResidual_Internal(ksp, ksp->viewerFinalRes, ksp->formatFinalRes));
961: if (ksp->viewMat) PetscCall(ObjectView((PetscObject)mat, ksp->viewerMat, ksp->formatMat));
962: if (ksp->viewPMat) PetscCall(ObjectView((PetscObject)pmat, ksp->viewerPMat, ksp->formatPMat));
963: if (ksp->viewRhs) PetscCall(ObjectView((PetscObject)ksp->vec_rhs, ksp->viewerRhs, ksp->formatRhs));
964: if (ksp->viewSol) PetscCall(ObjectView((PetscObject)ksp->vec_sol, ksp->viewerSol, ksp->formatSol));
965: if (ksp->view) PetscCall(ObjectView((PetscObject)ksp, ksp->viewer, ksp->format));
966: if (ksp->viewDScale) PetscCall(ObjectView((PetscObject)ksp->diagonal, ksp->viewerDScale, ksp->formatDScale));
967: if (ksp->viewMatExp) {
968: Mat A, B;
970: PetscCall(PCGetOperators(ksp->pc, &A, NULL));
971: if (ksp->transpose_solve) {
972: Mat AT;
974: PetscCall(MatCreateTranspose(A, &AT));
975: PetscCall(MatComputeOperator(AT, MATAIJ, &B));
976: PetscCall(MatDestroy(&AT));
977: } else {
978: PetscCall(MatComputeOperator(A, MATAIJ, &B));
979: }
980: PetscCall(ObjectView((PetscObject)B, ksp->viewerMatExp, ksp->formatMatExp));
981: PetscCall(MatDestroy(&B));
982: }
983: if (ksp->viewPOpExp) {
984: Mat B;
986: PetscCall(KSPComputeOperator(ksp, MATAIJ, &B));
987: PetscCall(ObjectView((PetscObject)B, ksp->viewerPOpExp, ksp->formatPOpExp));
988: PetscCall(MatDestroy(&B));
989: }
991: if (inXisinB) {
992: PetscCall(VecCopy(x, b));
993: PetscCall(VecDestroy(&x));
994: }
995: PetscCall(PetscObjectSAWsBlock((PetscObject)ksp));
996: if (ksp->errorifnotconverged && ksp->reason < 0 && ((level == 1) || (ksp->reason != KSP_DIVERGED_ITS))) {
997: PCFailedReason reason;
999: 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]);
1000: PetscCall(PCGetFailedReason(ksp->pc, &reason));
1001: 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]);
1002: }
1003: level--;
1004: PetscFunctionReturn(PETSC_SUCCESS);
1005: }
1007: /*@
1008: KSPSolve - Solves a linear system associated with `KSP` object
1010: Collective
1012: Input Parameters:
1013: + ksp - iterative solver obtained from `KSPCreate()`
1014: . b - the right-hand side vector
1015: - x - the solution (this may be the same vector as `b`, then `b` will be overwritten with the answer)
1017: Options Database Keys:
1018: + -ksp_view_eigenvalues - compute preconditioned operators eigenvalues
1019: . -ksp_view_eigenvalues_explicit - compute the eigenvalues by forming the dense operator and using LAPACK
1020: . -ksp_view_mat binary - save matrix to the default binary viewer
1021: . -ksp_view_pmat binary - save matrix used to build preconditioner to the default binary viewer
1022: . -ksp_view_rhs binary - save right-hand side vector to the default binary viewer
1023: . -ksp_view_solution binary - save computed solution vector to the default binary viewer
1024: (can be read later with src/ksp/tutorials/ex10.c for testing solvers)
1025: . -ksp_view_mat_explicit - for matrix-free operators, computes the matrix entries and views them
1026: . -ksp_view_preconditioned_operator_explicit - computes the product of the preconditioner and matrix as an explicit matrix and views it
1027: . -ksp_converged_reason - print reason for converged or diverged, also prints number of iterations
1028: . -ksp_view_final_residual - print 2-norm of true linear system residual at the end of the solution process
1029: . -ksp_view_final_residual_vec - print true linear system residual vector at the end of the solution process;
1030: `-ksp_view_final_residual` must to be called first to enable this option
1031: . -ksp_error_if_not_converged - stop the program as soon as an error is detected in a `KSPSolve()`
1032: . -ksp_view_pre - print the ksp data structure before the system solution
1033: - -ksp_view - print the ksp data structure at the end of the system solution
1035: Level: beginner
1037: Notes:
1038: See `KSPSetFromOptions()` for additional options database keys that affect `KSPSolve()`
1040: If one uses `KSPSetDM()` then `x` or `b` need not be passed. Use `KSPGetSolution()` to access the solution in this case.
1042: The operator is specified with `KSPSetOperators()`.
1044: `KSPSolve()` will normally return without generating an error regardless of whether the linear system was solved or if constructing the preconditioner failed.
1045: Call `KSPGetConvergedReason()` to determine if the solver converged or failed and why. The option -ksp_error_if_not_converged or function `KSPSetErrorIfNotConverged()`
1046: 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
1047: it may be fine that inner solvers in the preconditioner do not converge during the solution process.
1049: The number of iterations can be obtained from `KSPGetIterationNumber()`.
1051: If you provide a matrix that has a `MatSetNullSpace()` and `MatSetTransposeNullSpace()` this will use that information to solve singular systems
1052: in the least squares sense with a norm minimizing solution.
1054: $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()`).
1056: `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
1057: 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
1058: direction thus the solution which is a linear combination of the search directions has no component in the nullspace(A).
1060: We recommend always using `KSPGMRES` for such singular systems.
1061: If $ nullspace(A) = nullspace(A^T)$ (note symmetric matrices always satisfy this property) then both left and right preconditioning will work
1062: If $nullspace(A) \neq nullspace(A^T)$ then left preconditioning will work but right preconditioning may not work (or it may).
1064: Developer Notes:
1065: 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
1066: $ 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
1067: such as diagonal scaling we cannot apply the inverse of the preconditioner to a vector and thus cannot compute $nullspace(AB)$.
1069: If using a direct method (e.g., via the `KSP` solver
1070: `KSPPREONLY` and a preconditioner such as `PCLU` or `PCCHOLESKY` then usually one iteration of the `KSP` method will be needed for convergence.
1072: To solve a linear system with the transpose of the matrix use `KSPSolveTranspose()`.
1074: Understanding Convergence\:
1075: The manual pages `KSPMonitorSet()`, `KSPComputeEigenvalues()`, and
1076: `KSPComputeEigenvaluesExplicitly()` provide information on additional
1077: options to monitor convergence and print eigenvalue information.
1079: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPDestroy()`, `KSPSetTolerances()`, `KSPConvergedDefault()`,
1080: `KSPSolveTranspose()`, `KSPGetIterationNumber()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatSetTransposeNullSpace()`, `KSP`,
1081: `KSPConvergedReasonView()`, `KSPCheckSolve()`, `KSPSetErrorIfNotConverged()`
1082: @*/
1083: PetscErrorCode KSPSolve(KSP ksp, Vec b, Vec x)
1084: {
1085: PetscBool isPCMPI;
1087: PetscFunctionBegin;
1091: ksp->transpose_solve = PETSC_FALSE;
1092: PetscCall(KSPSolve_Private(ksp, b, x));
1093: PetscCall(PetscObjectTypeCompare((PetscObject)ksp->pc, PCMPI, &isPCMPI));
1094: if (PCMPIServerActive && isPCMPI) {
1095: KSP subksp;
1097: PetscCall(PCMPIGetKSP(ksp->pc, &subksp));
1098: ksp->its = subksp->its;
1099: ksp->reason = subksp->reason;
1100: }
1101: PetscFunctionReturn(PETSC_SUCCESS);
1102: }
1104: static PetscErrorCode KSPUseExplicitTranspose_Private(KSP ksp)
1105: {
1106: Mat J, Jpre;
1108: PetscFunctionBegin;
1109: PetscCall(KSPGetOperators(ksp, &J, &Jpre));
1110: if (!ksp->transpose.reuse_transpose) {
1111: PetscCall(MatTranspose(J, MAT_INITIAL_MATRIX, &ksp->transpose.AT));
1112: if (J != Jpre) PetscCall(MatTranspose(Jpre, MAT_INITIAL_MATRIX, &ksp->transpose.BT));
1113: ksp->transpose.reuse_transpose = PETSC_TRUE;
1114: } else {
1115: PetscCall(MatTranspose(J, MAT_REUSE_MATRIX, &ksp->transpose.AT));
1116: if (J != Jpre) PetscCall(MatTranspose(Jpre, MAT_REUSE_MATRIX, &ksp->transpose.BT));
1117: }
1118: if (J == Jpre && ksp->transpose.BT != ksp->transpose.AT) {
1119: PetscCall(PetscObjectReference((PetscObject)ksp->transpose.AT));
1120: ksp->transpose.BT = ksp->transpose.AT;
1121: }
1122: PetscCall(KSPSetOperators(ksp, ksp->transpose.AT, ksp->transpose.BT));
1123: PetscFunctionReturn(PETSC_SUCCESS);
1124: }
1126: /*@
1127: KSPSolveTranspose - Solves a linear system with the transpose of the matrix associated with the `KSP` object, $A^T x = b$.
1129: Collective
1131: Input Parameters:
1132: + ksp - iterative solver obtained from `KSPCreate()`
1133: . b - right-hand side vector
1134: - x - solution vector
1136: Level: developer
1138: Note:
1139: For complex numbers, this solve the non-Hermitian transpose system.
1141: Developer Note:
1142: We need to implement a `KSPSolveHermitianTranspose()`
1144: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPDestroy()`, `KSPSetTolerances()`, `KSPConvergedDefault()`,
1145: `KSPSolve()`, `KSP`, `KSPSetOperators()`
1146: @*/
1147: PetscErrorCode KSPSolveTranspose(KSP ksp, Vec b, Vec x)
1148: {
1149: PetscFunctionBegin;
1153: if (ksp->transpose.use_explicittranspose) {
1154: Mat J, Jpre;
1155: PetscCall(KSPGetOperators(ksp, &J, &Jpre));
1156: if (!ksp->transpose.reuse_transpose) {
1157: PetscCall(MatTranspose(J, MAT_INITIAL_MATRIX, &ksp->transpose.AT));
1158: if (J != Jpre) PetscCall(MatTranspose(Jpre, MAT_INITIAL_MATRIX, &ksp->transpose.BT));
1159: ksp->transpose.reuse_transpose = PETSC_TRUE;
1160: } else {
1161: PetscCall(MatTranspose(J, MAT_REUSE_MATRIX, &ksp->transpose.AT));
1162: if (J != Jpre) PetscCall(MatTranspose(Jpre, MAT_REUSE_MATRIX, &ksp->transpose.BT));
1163: }
1164: if (J == Jpre && ksp->transpose.BT != ksp->transpose.AT) {
1165: PetscCall(PetscObjectReference((PetscObject)ksp->transpose.AT));
1166: ksp->transpose.BT = ksp->transpose.AT;
1167: }
1168: PetscCall(KSPSetOperators(ksp, ksp->transpose.AT, ksp->transpose.BT));
1169: } else {
1170: ksp->transpose_solve = PETSC_TRUE;
1171: }
1172: PetscCall(KSPSolve_Private(ksp, b, x));
1173: PetscFunctionReturn(PETSC_SUCCESS);
1174: }
1176: static PetscErrorCode KSPViewFinalMatResidual_Internal(KSP ksp, Mat B, Mat X, PetscViewer viewer, PetscViewerFormat format, PetscInt shift)
1177: {
1178: Mat A, R;
1179: PetscReal *norms;
1180: PetscInt i, N;
1181: PetscBool flg;
1183: PetscFunctionBegin;
1184: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &flg));
1185: if (flg) {
1186: PetscCall(PCGetOperators(ksp->pc, &A, NULL));
1187: if (!ksp->transpose_solve) PetscCall(MatMatMult(A, X, MAT_INITIAL_MATRIX, PETSC_DETERMINE, &R));
1188: else PetscCall(MatTransposeMatMult(A, X, MAT_INITIAL_MATRIX, PETSC_DETERMINE, &R));
1189: PetscCall(MatAYPX(R, -1.0, B, SAME_NONZERO_PATTERN));
1190: PetscCall(MatGetSize(R, NULL, &N));
1191: PetscCall(PetscMalloc1(N, &norms));
1192: PetscCall(MatGetColumnNorms(R, NORM_2, norms));
1193: PetscCall(MatDestroy(&R));
1194: 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]));
1195: PetscCall(PetscFree(norms));
1196: }
1197: PetscFunctionReturn(PETSC_SUCCESS);
1198: }
1200: static PetscErrorCode KSPMatSolve_Private(KSP ksp, Mat B, Mat X)
1201: {
1202: Mat A, P, vB, vX;
1203: Vec cb, cx;
1204: PetscInt n1, N1, n2, N2, Bbn = PETSC_DECIDE;
1205: PetscBool match;
1207: PetscFunctionBegin;
1211: PetscCheckSameComm(ksp, 1, B, 2);
1212: PetscCheckSameComm(ksp, 1, X, 3);
1213: PetscCheckSameType(B, 2, X, 3);
1214: PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
1215: MatCheckPreallocated(X, 3);
1216: if (!X->assembled) {
1217: PetscCall(MatSetOption(X, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
1218: PetscCall(MatAssemblyBegin(X, MAT_FINAL_ASSEMBLY));
1219: PetscCall(MatAssemblyEnd(X, MAT_FINAL_ASSEMBLY));
1220: }
1221: PetscCheck(B != X, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_IDN, "B and X must be different matrices");
1222: PetscCall(KSPGetOperators(ksp, &A, &P));
1223: PetscCall(MatGetLocalSize(B, NULL, &n2));
1224: PetscCall(MatGetLocalSize(X, NULL, &n1));
1225: PetscCall(MatGetSize(B, NULL, &N2));
1226: PetscCall(MatGetSize(X, NULL, &N1));
1227: 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);
1228: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)B, &match, MATSEQDENSE, MATMPIDENSE, ""));
1229: PetscCheck(match, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Provided block of right-hand sides not stored in a dense Mat");
1230: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)X, &match, MATSEQDENSE, MATMPIDENSE, ""));
1231: PetscCheck(match, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Provided block of solutions not stored in a dense Mat");
1232: PetscCall(KSPSetUp(ksp));
1233: PetscCall(KSPSetUpOnBlocks(ksp));
1234: if (ksp->ops->matsolve) {
1235: level++;
1236: if (ksp->guess_zero) PetscCall(MatZeroEntries(X));
1237: PetscCall(PetscLogEventBegin(!ksp->transpose_solve ? KSP_MatSolve : KSP_MatSolveTranspose, ksp, B, X, 0));
1238: PetscCall(KSPGetMatSolveBatchSize(ksp, &Bbn));
1239: /* by default, do a single solve with all columns */
1240: if (Bbn == PETSC_DECIDE) Bbn = N2;
1241: else PetscCheck(Bbn >= 1, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "KSPMatSolve() batch size %" PetscInt_FMT " must be positive", Bbn);
1242: PetscCall(PetscInfo(ksp, "KSP type %s%s solving using batches of width at most %" PetscInt_FMT "\n", ((PetscObject)ksp)->type_name, ksp->transpose_solve ? " transpose" : "", Bbn));
1243: /* if -ksp_matsolve_batch_size is greater than the actual number of columns, do a single solve with all columns */
1244: if (Bbn >= N2) {
1245: PetscUseTypeMethod(ksp, matsolve, B, X);
1246: if (ksp->viewFinalRes) PetscCall(KSPViewFinalMatResidual_Internal(ksp, B, X, ksp->viewerFinalRes, ksp->formatFinalRes, 0));
1248: PetscCall(KSPConvergedReasonViewFromOptions(ksp));
1250: if (ksp->viewRate) {
1251: PetscCall(PetscViewerPushFormat(ksp->viewerRate, PETSC_VIEWER_DEFAULT));
1252: PetscCall(KSPConvergedRateView(ksp, ksp->viewerRate));
1253: PetscCall(PetscViewerPopFormat(ksp->viewerRate));
1254: }
1255: } else {
1256: for (n2 = 0; n2 < N2; n2 += Bbn) {
1257: PetscCall(MatDenseGetSubMatrix(B, PETSC_DECIDE, PETSC_DECIDE, n2, PetscMin(n2 + Bbn, N2), &vB));
1258: PetscCall(MatDenseGetSubMatrix(X, PETSC_DECIDE, PETSC_DECIDE, n2, PetscMin(n2 + Bbn, N2), &vX));
1259: PetscUseTypeMethod(ksp, matsolve, vB, vX);
1260: if (ksp->viewFinalRes) PetscCall(KSPViewFinalMatResidual_Internal(ksp, vB, vX, ksp->viewerFinalRes, ksp->formatFinalRes, n2));
1262: PetscCall(KSPConvergedReasonViewFromOptions(ksp));
1264: if (ksp->viewRate) {
1265: PetscCall(PetscViewerPushFormat(ksp->viewerRate, PETSC_VIEWER_DEFAULT));
1266: PetscCall(KSPConvergedRateView(ksp, ksp->viewerRate));
1267: PetscCall(PetscViewerPopFormat(ksp->viewerRate));
1268: }
1269: PetscCall(MatDenseRestoreSubMatrix(B, &vB));
1270: PetscCall(MatDenseRestoreSubMatrix(X, &vX));
1271: }
1272: }
1273: if (ksp->viewMat) PetscCall(ObjectView((PetscObject)A, ksp->viewerMat, ksp->formatMat));
1274: if (ksp->viewPMat) PetscCall(ObjectView((PetscObject)P, ksp->viewerPMat, ksp->formatPMat));
1275: if (ksp->viewRhs) PetscCall(ObjectView((PetscObject)B, ksp->viewerRhs, ksp->formatRhs));
1276: if (ksp->viewSol) PetscCall(ObjectView((PetscObject)X, ksp->viewerSol, ksp->formatSol));
1277: if (ksp->view) PetscCall(KSPView(ksp, ksp->viewer));
1278: PetscCall(PetscLogEventEnd(!ksp->transpose_solve ? KSP_MatSolve : KSP_MatSolveTranspose, ksp, B, X, 0));
1279: if (ksp->errorifnotconverged && ksp->reason < 0 && (level == 1 || ksp->reason != KSP_DIVERGED_ITS)) {
1280: PCFailedReason reason;
1282: 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]);
1283: PetscCall(PCGetFailedReason(ksp->pc, &reason));
1284: 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]);
1285: }
1286: level--;
1287: } else {
1288: PetscCall(PetscInfo(ksp, "KSP type %s solving column by column\n", ((PetscObject)ksp)->type_name));
1289: for (n2 = 0; n2 < N2; ++n2) {
1290: PetscCall(MatDenseGetColumnVecRead(B, n2, &cb));
1291: PetscCall(MatDenseGetColumnVecWrite(X, n2, &cx));
1292: PetscCall(KSPSolve_Private(ksp, cb, cx));
1293: PetscCall(MatDenseRestoreColumnVecWrite(X, n2, &cx));
1294: PetscCall(MatDenseRestoreColumnVecRead(B, n2, &cb));
1295: }
1296: }
1297: PetscFunctionReturn(PETSC_SUCCESS);
1298: }
1300: /*@
1301: KSPMatSolve - Solves a linear system with multiple right-hand sides stored as a `MATDENSE`.
1303: Input Parameters:
1304: + ksp - iterative solver
1305: - B - block of right-hand sides
1307: Output Parameter:
1308: . X - block of solutions
1310: Level: intermediate
1312: Notes:
1313: This is a stripped-down version of `KSPSolve()`, which only handles `-ksp_view`, `-ksp_converged_reason`, `-ksp_converged_rate`, and `-ksp_view_final_residual`.
1315: Unlike with `KSPSolve()`, `B` and `X` must be different matrices.
1317: .seealso: [](ch_ksp), `KSPSolve()`, `MatMatSolve()`, `KSPMatSolveTranspose()`, `MATDENSE`, `KSPHPDDM`, `PCBJACOBI`, `PCASM`, `KSPSetMatSolveBatchSize()`
1318: @*/
1319: PetscErrorCode KSPMatSolve(KSP ksp, Mat B, Mat X)
1320: {
1321: PetscFunctionBegin;
1322: ksp->transpose_solve = PETSC_FALSE;
1323: PetscCall(KSPMatSolve_Private(ksp, B, X));
1324: PetscFunctionReturn(PETSC_SUCCESS);
1325: }
1327: /*@
1328: KSPMatSolveTranspose - Solves a linear system with the transposed matrix with multiple right-hand sides stored as a `MATDENSE`.
1330: Input Parameters:
1331: + ksp - iterative solver
1332: - B - block of right-hand sides
1334: Output Parameter:
1335: . X - block of solutions
1337: Level: intermediate
1339: Notes:
1340: This is a stripped-down version of `KSPSolveTranspose()`, which only handles `-ksp_view`, `-ksp_converged_reason`, `-ksp_converged_rate`, and `-ksp_view_final_residual`.
1342: Unlike `KSPSolveTranspose()`,
1343: `B` and `X` must be different matrices and the transposed matrix cannot be assembled explicitly for the user.
1345: .seealso: [](ch_ksp), `KSPSolveTranspose()`, `MatMatTransposeSolve()`, `KSPMatSolve()`, `MATDENSE`, `KSPHPDDM`, `PCBJACOBI`, `PCASM`
1346: @*/
1347: PetscErrorCode KSPMatSolveTranspose(KSP ksp, Mat B, Mat X)
1348: {
1349: PetscFunctionBegin;
1350: if (ksp->transpose.use_explicittranspose) PetscCall(KSPUseExplicitTranspose_Private(ksp));
1351: else ksp->transpose_solve = PETSC_TRUE;
1352: PetscCall(KSPMatSolve_Private(ksp, B, X));
1353: PetscFunctionReturn(PETSC_SUCCESS);
1354: }
1356: /*@
1357: KSPSetMatSolveBatchSize - Sets the maximum number of columns treated simultaneously in `KSPMatSolve()`.
1359: Logically Collective
1361: Input Parameters:
1362: + ksp - the `KSP` iterative solver
1363: - bs - batch size
1365: Level: advanced
1367: Note:
1368: Using a larger block size can improve the efficiency of the solver.
1370: .seealso: [](ch_ksp), `KSPMatSolve()`, `KSPGetMatSolveBatchSize()`, `-mat_mumps_icntl_27`, `-matproduct_batch_size`
1371: @*/
1372: PetscErrorCode KSPSetMatSolveBatchSize(KSP ksp, PetscInt bs)
1373: {
1374: PetscFunctionBegin;
1377: ksp->nmax = bs;
1378: PetscFunctionReturn(PETSC_SUCCESS);
1379: }
1381: /*@
1382: KSPGetMatSolveBatchSize - Gets the maximum number of columns treated simultaneously in `KSPMatSolve()`.
1384: Input Parameter:
1385: . ksp - iterative solver context
1387: Output Parameter:
1388: . bs - batch size
1390: Level: advanced
1392: .seealso: [](ch_ksp), `KSPMatSolve()`, `KSPSetMatSolveBatchSize()`, `-mat_mumps_icntl_27`, `-matproduct_batch_size`
1393: @*/
1394: PetscErrorCode KSPGetMatSolveBatchSize(KSP ksp, PetscInt *bs)
1395: {
1396: PetscFunctionBegin;
1398: PetscAssertPointer(bs, 2);
1399: *bs = ksp->nmax;
1400: PetscFunctionReturn(PETSC_SUCCESS);
1401: }
1403: /*@
1404: KSPResetViewers - Resets all the viewers set from the options database during `KSPSetFromOptions()`
1406: Collective
1408: Input Parameter:
1409: . ksp - the `KSP` iterative solver context obtained from `KSPCreate()`
1411: Level: beginner
1413: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPSolve()`, `KSPSetFromOptions()`, `KSP`
1414: @*/
1415: PetscErrorCode KSPResetViewers(KSP ksp)
1416: {
1417: PetscFunctionBegin;
1419: if (!ksp) PetscFunctionReturn(PETSC_SUCCESS);
1420: PetscCall(PetscViewerDestroy(&ksp->viewer));
1421: PetscCall(PetscViewerDestroy(&ksp->viewerPre));
1422: PetscCall(PetscViewerDestroy(&ksp->viewerRate));
1423: PetscCall(PetscViewerDestroy(&ksp->viewerMat));
1424: PetscCall(PetscViewerDestroy(&ksp->viewerPMat));
1425: PetscCall(PetscViewerDestroy(&ksp->viewerRhs));
1426: PetscCall(PetscViewerDestroy(&ksp->viewerSol));
1427: PetscCall(PetscViewerDestroy(&ksp->viewerMatExp));
1428: PetscCall(PetscViewerDestroy(&ksp->viewerEV));
1429: PetscCall(PetscViewerDestroy(&ksp->viewerSV));
1430: PetscCall(PetscViewerDestroy(&ksp->viewerEVExp));
1431: PetscCall(PetscViewerDestroy(&ksp->viewerFinalRes));
1432: PetscCall(PetscViewerDestroy(&ksp->viewerPOpExp));
1433: PetscCall(PetscViewerDestroy(&ksp->viewerDScale));
1434: ksp->view = PETSC_FALSE;
1435: ksp->viewPre = PETSC_FALSE;
1436: ksp->viewMat = PETSC_FALSE;
1437: ksp->viewPMat = PETSC_FALSE;
1438: ksp->viewRhs = PETSC_FALSE;
1439: ksp->viewSol = PETSC_FALSE;
1440: ksp->viewMatExp = PETSC_FALSE;
1441: ksp->viewEV = PETSC_FALSE;
1442: ksp->viewSV = PETSC_FALSE;
1443: ksp->viewEVExp = PETSC_FALSE;
1444: ksp->viewFinalRes = PETSC_FALSE;
1445: ksp->viewPOpExp = PETSC_FALSE;
1446: ksp->viewDScale = PETSC_FALSE;
1447: PetscFunctionReturn(PETSC_SUCCESS);
1448: }
1450: /*@
1451: KSPReset - Removes any allocated `Vec` and `Mat` from the `KSP` data structures.
1453: Collective
1455: Input Parameter:
1456: . ksp - iterative solver obtained from `KSPCreate()`
1458: Level: intermediate
1460: Notes:
1461: Any options set in the `KSP`, including those set with `KSPSetFromOptions()` remain.
1463: Call `KSPReset()` only before you call `KSPSetOperators()` with a different sized matrix than the previous matrix used with the `KSP`.
1465: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPSolve()`, `KSP`
1466: @*/
1467: PetscErrorCode KSPReset(KSP ksp)
1468: {
1469: PetscFunctionBegin;
1471: if (!ksp) PetscFunctionReturn(PETSC_SUCCESS);
1472: PetscTryTypeMethod(ksp, reset);
1473: if (ksp->pc) PetscCall(PCReset(ksp->pc));
1474: if (ksp->guess) {
1475: KSPGuess guess = ksp->guess;
1476: PetscTryTypeMethod(guess, reset);
1477: }
1478: PetscCall(VecDestroyVecs(ksp->nwork, &ksp->work));
1479: PetscCall(VecDestroy(&ksp->vec_rhs));
1480: PetscCall(VecDestroy(&ksp->vec_sol));
1481: PetscCall(VecDestroy(&ksp->diagonal));
1482: PetscCall(VecDestroy(&ksp->truediagonal));
1484: ksp->setupstage = KSP_SETUP_NEW;
1485: ksp->nmax = PETSC_DECIDE;
1486: PetscFunctionReturn(PETSC_SUCCESS);
1487: }
1489: /*@
1490: KSPDestroy - Destroys a `KSP` context.
1492: Collective
1494: Input Parameter:
1495: . ksp - iterative solver obtained from `KSPCreate()`
1497: Level: beginner
1499: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPSolve()`, `KSP`
1500: @*/
1501: PetscErrorCode KSPDestroy(KSP *ksp)
1502: {
1503: PC pc;
1505: PetscFunctionBegin;
1506: if (!*ksp) PetscFunctionReturn(PETSC_SUCCESS);
1508: if (--((PetscObject)*ksp)->refct > 0) {
1509: *ksp = NULL;
1510: PetscFunctionReturn(PETSC_SUCCESS);
1511: }
1513: PetscCall(PetscObjectSAWsViewOff((PetscObject)*ksp));
1515: /*
1516: Avoid a cascading call to PCReset(ksp->pc) from the following call:
1517: PCReset() shouldn't be called from KSPDestroy() as it is unprotected by pc's
1518: refcount (and may be shared, e.g., by other ksps).
1519: */
1520: pc = (*ksp)->pc;
1521: (*ksp)->pc = NULL;
1522: PetscCall(KSPReset(*ksp));
1523: PetscCall(KSPResetViewers(*ksp));
1524: (*ksp)->pc = pc;
1525: PetscTryTypeMethod(*ksp, destroy);
1527: if ((*ksp)->transpose.use_explicittranspose) {
1528: PetscCall(MatDestroy(&(*ksp)->transpose.AT));
1529: PetscCall(MatDestroy(&(*ksp)->transpose.BT));
1530: (*ksp)->transpose.reuse_transpose = PETSC_FALSE;
1531: }
1533: PetscCall(KSPGuessDestroy(&(*ksp)->guess));
1534: PetscCall(DMDestroy(&(*ksp)->dm));
1535: PetscCall(PCDestroy(&(*ksp)->pc));
1536: PetscCall(PetscFree((*ksp)->res_hist_alloc));
1537: PetscCall(PetscFree((*ksp)->err_hist_alloc));
1538: if ((*ksp)->convergeddestroy) PetscCall((*(*ksp)->convergeddestroy)(&(*ksp)->cnvP));
1539: PetscCall(KSPMonitorCancel(*ksp));
1540: PetscCall(KSPConvergedReasonViewCancel(*ksp));
1541: PetscCall(PetscHeaderDestroy(ksp));
1542: PetscFunctionReturn(PETSC_SUCCESS);
1543: }
1545: /*@
1546: KSPSetPCSide - Sets the preconditioning side.
1548: Logically Collective
1550: Input Parameter:
1551: . ksp - iterative solver obtained from `KSPCreate()`
1553: Output Parameter:
1554: . side - the preconditioning side, where side is one of
1555: .vb
1556: PC_LEFT - left preconditioning (default)
1557: PC_RIGHT - right preconditioning
1558: PC_SYMMETRIC - symmetric preconditioning
1559: .ve
1561: Options Database Key:
1562: . -ksp_pc_side (right|left|symmetric) - `KSP` preconditioner side
1564: Level: intermediate
1566: Notes:
1567: Left preconditioning is used by default for most Krylov methods except `KSPFGMRES` which only supports right preconditioning.
1569: For methods changing the side of the preconditioner changes the norm type that is used, see `KSPSetNormType()`.
1571: Symmetric preconditioning is currently available only for the `KSPQCG` method. However, note that
1572: symmetric preconditioning can be emulated by using either right or left
1573: preconditioning, modifying the application of the matrix (with a custom `Mat` argument to `KSPSetOperators()`,
1574: and using a pre 'KSPSetPreSolve()` or post processing `KSPSetPostSolve()` step).
1576: Setting the `PCSide` often affects the default norm type. See `KSPSetNormType()` for details.
1578: .seealso: [](ch_ksp), `KSPGetPCSide()`, `KSPSetNormType()`, `KSPGetNormType()`, `KSP`, `KSPSetPreSolve()`, `KSPSetPostSolve()`
1579: @*/
1580: PetscErrorCode KSPSetPCSide(KSP ksp, PCSide side)
1581: {
1582: PetscFunctionBegin;
1585: ksp->pc_side = ksp->pc_side_set = side;
1586: PetscFunctionReturn(PETSC_SUCCESS);
1587: }
1589: /*@
1590: KSPGetPCSide - Gets the preconditioning side.
1592: Not Collective
1594: Input Parameter:
1595: . ksp - iterative solver obtained from `KSPCreate()`
1597: Output Parameter:
1598: . side - the preconditioning side, where side is one of
1599: .vb
1600: PC_LEFT - left preconditioning (default)
1601: PC_RIGHT - right preconditioning
1602: PC_SYMMETRIC - symmetric preconditioning
1603: .ve
1605: Level: intermediate
1607: .seealso: [](ch_ksp), `KSPSetPCSide()`, `KSP`
1608: @*/
1609: PetscErrorCode KSPGetPCSide(KSP ksp, PCSide *side)
1610: {
1611: PetscFunctionBegin;
1613: PetscAssertPointer(side, 2);
1614: PetscCall(KSPSetUpNorms_Private(ksp, PETSC_TRUE, &ksp->normtype, &ksp->pc_side));
1615: *side = ksp->pc_side;
1616: PetscFunctionReturn(PETSC_SUCCESS);
1617: }
1619: /*@
1620: KSPGetTolerances - Gets the relative, absolute, divergence, and maximum
1621: iteration tolerances used by the default `KSP` convergence tests.
1623: Not Collective
1625: Input Parameter:
1626: . ksp - the Krylov subspace context
1628: Output Parameters:
1629: + rtol - the relative convergence tolerance
1630: . abstol - the absolute convergence tolerance
1631: . dtol - the divergence tolerance
1632: - maxits - maximum number of iterations
1634: Level: intermediate
1636: Note:
1637: The user can specify `NULL` for any parameter that is not needed.
1639: .seealso: [](ch_ksp), `KSPSetTolerances()`, `KSP`, `KSPSetMinimumIterations()`, `KSPGetMinimumIterations()`
1640: @*/
1641: PetscErrorCode KSPGetTolerances(KSP ksp, PeOp PetscReal *rtol, PeOp PetscReal *abstol, PeOp PetscReal *dtol, PeOp PetscInt *maxits)
1642: {
1643: PetscFunctionBegin;
1645: if (abstol) *abstol = ksp->abstol;
1646: if (rtol) *rtol = ksp->rtol;
1647: if (dtol) *dtol = ksp->divtol;
1648: if (maxits) *maxits = ksp->max_it;
1649: PetscFunctionReturn(PETSC_SUCCESS);
1650: }
1652: /*@
1653: KSPSetTolerances - Sets the relative, absolute, divergence, and maximum
1654: iteration tolerances used by the default `KSP` convergence testers.
1656: Logically Collective
1658: Input Parameters:
1659: + ksp - the Krylov subspace context
1660: . rtol - the relative convergence tolerance, relative decrease in the (possibly preconditioned) residual norm
1661: . abstol - the absolute convergence tolerance absolute size of the (possibly preconditioned) residual norm
1662: . dtol - the divergence tolerance, amount (possibly preconditioned) residual norm can increase before `KSPConvergedDefault()` concludes that the method is diverging
1663: - maxits - maximum number of iterations to use
1665: Options Database Keys:
1666: + -ksp_atol abstol - Sets `abstol`
1667: . -ksp_rtol rtol - Sets `rtol`
1668: . -ksp_divtol dtol - Sets `dtol`
1669: - -ksp_max_it maxits - Sets `maxits`
1671: Level: intermediate
1673: Notes:
1674: 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.
1675: The norm used depends on the `KSPNormType` that has been set with `KSPSetNormType()`, the default depends on the `KSPType` used.
1677: All parameters must be non-negative.
1679: 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).
1681: 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.
1683: For `dtol` and `maxits` use `PETSC_UNLIMITED` to indicate there is no upper bound on these values
1685: See `KSPConvergedDefault()` for details how these parameters are used in the default convergence test. See also `KSPSetConvergenceTest()`
1686: for setting user-defined stopping criteria.
1688: Fortran Note:
1689: Use `PETSC_CURRENT_INTEGER`, `PETSC_CURRENT_REAL`, `PETSC_DETERMINE_INTEGER`, or `PETSC_DETERMINE_REAL`
1691: .seealso: [](ch_ksp), `KSPGetTolerances()`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSP`, `KSPSetMinimumIterations()`
1692: @*/
1693: PetscErrorCode KSPSetTolerances(KSP ksp, PetscReal rtol, PetscReal abstol, PetscReal dtol, PetscInt maxits)
1694: {
1695: PetscFunctionBegin;
1702: if (rtol == (PetscReal)PETSC_DETERMINE) {
1703: ksp->rtol = ksp->default_rtol;
1704: } else if (rtol != (PetscReal)PETSC_CURRENT) {
1705: 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);
1706: ksp->rtol = rtol;
1707: }
1708: if (abstol == (PetscReal)PETSC_DETERMINE) {
1709: ksp->abstol = ksp->default_abstol;
1710: } else if (abstol != (PetscReal)PETSC_CURRENT) {
1711: PetscCheck(abstol >= 0.0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Absolute tolerance %g must be non-negative", (double)abstol);
1712: ksp->abstol = abstol;
1713: }
1714: if (dtol == (PetscReal)PETSC_DETERMINE) {
1715: ksp->divtol = ksp->default_divtol;
1716: } else if (dtol == (PetscReal)PETSC_UNLIMITED) {
1717: ksp->divtol = PETSC_MAX_REAL;
1718: } else if (dtol != (PetscReal)PETSC_CURRENT) {
1719: PetscCheck(dtol >= 0.0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Divergence tolerance %g must be larger than 1.0", (double)dtol);
1720: ksp->divtol = dtol;
1721: }
1722: if (maxits == PETSC_DETERMINE) {
1723: ksp->max_it = ksp->default_max_it;
1724: } else if (maxits == PETSC_UNLIMITED) {
1725: ksp->max_it = PETSC_INT_MAX;
1726: } else if (maxits != PETSC_CURRENT) {
1727: PetscCheck(maxits >= 0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Maximum number of iterations %" PetscInt_FMT " must be non-negative", maxits);
1728: ksp->max_it = maxits;
1729: }
1730: PetscFunctionReturn(PETSC_SUCCESS);
1731: }
1733: /*@
1734: KSPSetMinimumIterations - Sets the minimum number of iterations to use, regardless of the tolerances
1736: Logically Collective
1738: Input Parameters:
1739: + ksp - the Krylov subspace context
1740: - minit - minimum number of iterations to use
1742: Options Database Key:
1743: . -ksp_min_it minit - Sets `minit`
1745: Level: intermediate
1747: Notes:
1748: Use `KSPSetTolerances()` to set a variety of other tolerances
1750: See `KSPConvergedDefault()` for details on how these parameters are used in the default convergence test. See also `KSPSetConvergenceTest()`
1751: for setting user-defined stopping criteria.
1753: If the initial residual norm is small enough solvers may return immediately without computing any improvement to the solution. Using this routine
1754: prevents that which usually ensures the solution is changed (often minimally) from the previous solution. This option may be used with ODE integrators
1755: to ensure the integrator does not fall into a false steady-state solution of the ODE.
1757: .seealso: [](ch_ksp), `KSPGetTolerances()`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSP`, `KSPSetTolerances()`, `KSPGetMinimumIterations()`
1758: @*/
1759: PetscErrorCode KSPSetMinimumIterations(KSP ksp, PetscInt minit)
1760: {
1761: PetscFunctionBegin;
1765: PetscCheck(minit >= 0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Minimum number of iterations %" PetscInt_FMT " must be non-negative", minit);
1766: ksp->min_it = minit;
1767: PetscFunctionReturn(PETSC_SUCCESS);
1768: }
1770: /*@
1771: KSPGetMinimumIterations - Gets the minimum number of iterations to use, regardless of the tolerances, that was set with `KSPSetMinimumIterations()` or `-ksp_min_it`
1773: Not Collective
1775: Input Parameter:
1776: . ksp - the Krylov subspace context
1778: Output Parameter:
1779: . minit - minimum number of iterations to use
1781: Level: intermediate
1783: .seealso: [](ch_ksp), `KSPGetTolerances()`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSP`, `KSPSetTolerances()`, `KSPSetMinimumIterations()`
1784: @*/
1785: PetscErrorCode KSPGetMinimumIterations(KSP ksp, PetscInt *minit)
1786: {
1787: PetscFunctionBegin;
1789: PetscAssertPointer(minit, 2);
1791: *minit = ksp->min_it;
1792: PetscFunctionReturn(PETSC_SUCCESS);
1793: }
1795: /*@
1796: KSPSetInitialGuessNonzero - Tells the iterative solver that the
1797: initial guess is nonzero; otherwise `KSP` assumes the initial guess
1798: is to be zero (and thus zeros it out before solving).
1800: Logically Collective
1802: Input Parameters:
1803: + ksp - iterative solver obtained from `KSPCreate()`
1804: - flg - ``PETSC_TRUE`` indicates the guess is non-zero, `PETSC_FALSE` indicates the guess is zero
1806: Options Database Key:
1807: . -ksp_initial_guess_nonzero (true|false) - use nonzero initial guess
1809: Level: beginner
1811: .seealso: [](ch_ksp), `KSPGetInitialGuessNonzero()`, `KSPGuessSetType()`, `KSPGuessType`, `KSP`
1812: @*/
1813: PetscErrorCode KSPSetInitialGuessNonzero(KSP ksp, PetscBool flg)
1814: {
1815: PetscFunctionBegin;
1818: ksp->guess_zero = (PetscBool)!flg;
1819: PetscFunctionReturn(PETSC_SUCCESS);
1820: }
1822: /*@
1823: KSPGetInitialGuessNonzero - Determines whether the `KSP` solver is using
1824: a zero initial guess.
1826: Not Collective
1828: Input Parameter:
1829: . ksp - iterative solver obtained from `KSPCreate()`
1831: Output Parameter:
1832: . flag - `PETSC_TRUE` if guess is nonzero, else `PETSC_FALSE`
1834: Level: intermediate
1836: .seealso: [](ch_ksp), `KSPSetInitialGuessNonzero()`, `KSP`
1837: @*/
1838: PetscErrorCode KSPGetInitialGuessNonzero(KSP ksp, PetscBool *flag)
1839: {
1840: PetscFunctionBegin;
1842: PetscAssertPointer(flag, 2);
1843: if (ksp->guess_zero) *flag = PETSC_FALSE;
1844: else *flag = PETSC_TRUE;
1845: PetscFunctionReturn(PETSC_SUCCESS);
1846: }
1848: /*@
1849: KSPSetErrorIfNotConverged - Causes `KSPSolve()` to generate an error if the solver has not converged as soon as the error is detected.
1851: Logically Collective
1853: Input Parameters:
1854: + ksp - iterative solver obtained from `KSPCreate()`
1855: - flg - `PETSC_TRUE` indicates you want the error generated
1857: Options Database Key:
1858: . -ksp_error_if_not_converged (true|false) - generate an error and stop the program
1860: Level: intermediate
1862: Notes:
1863: Normally PETSc continues if a linear solver fails to converge, you can call `KSPGetConvergedReason()` after a `KSPSolve()`
1864: to determine if it has converged. This functionality is mostly helpful while running in a debugger (`-start_in_debugger`) to determine exactly where
1865: the failure occurs and why.
1867: A `KSP_DIVERGED_ITS` will not generate an error in a `KSPSolve()` inside a nested linear solver
1869: .seealso: [](ch_ksp), `KSPGetErrorIfNotConverged()`, `KSP`
1870: @*/
1871: PetscErrorCode KSPSetErrorIfNotConverged(KSP ksp, PetscBool flg)
1872: {
1873: PC pc;
1875: PetscFunctionBegin;
1878: ksp->errorifnotconverged = flg;
1879: PetscCall(KSPGetPC(ksp, &pc));
1880: PetscCall(PCSetErrorIfFailure(pc, flg));
1881: PetscFunctionReturn(PETSC_SUCCESS);
1882: }
1884: /*@
1885: KSPGetErrorIfNotConverged - Will `KSPSolve()` generate an error if the solver does not converge?
1887: Not Collective
1889: Input Parameter:
1890: . ksp - iterative solver obtained from KSPCreate()
1892: Output Parameter:
1893: . flag - `PETSC_TRUE` if it will generate an error, else `PETSC_FALSE`
1895: Level: intermediate
1897: .seealso: [](ch_ksp), `KSPSetErrorIfNotConverged()`, `KSP`
1898: @*/
1899: PetscErrorCode KSPGetErrorIfNotConverged(KSP ksp, PetscBool *flag)
1900: {
1901: PetscFunctionBegin;
1903: PetscAssertPointer(flag, 2);
1904: *flag = ksp->errorifnotconverged;
1905: PetscFunctionReturn(PETSC_SUCCESS);
1906: }
1908: /*@
1909: KSPSetInitialGuessKnoll - Tells the iterative solver to use `PCApply()` on the right hand side vector to compute the initial guess (The Knoll trick)
1911: Logically Collective
1913: Input Parameters:
1914: + ksp - iterative solver obtained from `KSPCreate()`
1915: - flg - `PETSC_TRUE` or `PETSC_FALSE`
1917: Level: advanced
1919: Developer Note:
1920: The Knoll trick is not currently implemented using the `KSPGuess` class which provides a variety of ways of computing
1921: an initial guess based on previous solves.
1923: .seealso: [](ch_ksp), `KSPGetInitialGuessKnoll()`, `KSPGuess`, `KSPSetInitialGuessNonzero()`, `KSPGetInitialGuessNonzero()`, `KSP`
1924: @*/
1925: PetscErrorCode KSPSetInitialGuessKnoll(KSP ksp, PetscBool flg)
1926: {
1927: PetscFunctionBegin;
1930: ksp->guess_knoll = flg;
1931: PetscFunctionReturn(PETSC_SUCCESS);
1932: }
1934: /*@
1935: KSPGetInitialGuessKnoll - Determines whether the `KSP` solver is using the Knoll trick (using PCApply(pc,b,...) to compute
1936: the initial guess
1938: Not Collective
1940: Input Parameter:
1941: . ksp - iterative solver obtained from `KSPCreate()`
1943: Output Parameter:
1944: . flag - `PETSC_TRUE` if using Knoll trick, else `PETSC_FALSE`
1946: Level: advanced
1948: .seealso: [](ch_ksp), `KSPSetInitialGuessKnoll()`, `KSPSetInitialGuessNonzero()`, `KSPGetInitialGuessNonzero()`, `KSP`
1949: @*/
1950: PetscErrorCode KSPGetInitialGuessKnoll(KSP ksp, PetscBool *flag)
1951: {
1952: PetscFunctionBegin;
1954: PetscAssertPointer(flag, 2);
1955: *flag = ksp->guess_knoll;
1956: PetscFunctionReturn(PETSC_SUCCESS);
1957: }
1959: /*@
1960: KSPGetComputeSingularValues - Gets the flag indicating whether the extreme singular
1961: values will be calculated via a Lanczos or Arnoldi process as the linear
1962: system is solved.
1964: Not Collective
1966: Input Parameter:
1967: . ksp - iterative solver obtained from `KSPCreate()`
1969: Output Parameter:
1970: . flg - `PETSC_TRUE` or `PETSC_FALSE`
1972: Options Database Key:
1973: . -ksp_monitor_singular_value - Activates `KSPSetComputeSingularValues()`
1975: Level: advanced
1977: Notes:
1978: This option is not valid for `KSPType`.
1980: Many users may just want to use the monitoring routine
1981: `KSPMonitorSingularValue()` (which can be set with option `-ksp_monitor_singular_value`)
1982: to print the singular values at each iteration of the linear solve.
1984: .seealso: [](ch_ksp), `KSPComputeExtremeSingularValues()`, `KSPMonitorSingularValue()`, `KSP`
1985: @*/
1986: PetscErrorCode KSPGetComputeSingularValues(KSP ksp, PetscBool *flg)
1987: {
1988: PetscFunctionBegin;
1990: PetscAssertPointer(flg, 2);
1991: *flg = ksp->calc_sings;
1992: PetscFunctionReturn(PETSC_SUCCESS);
1993: }
1995: /*@
1996: KSPSetComputeSingularValues - Sets a flag so that the extreme singular
1997: values will be calculated via a Lanczos or Arnoldi process as the linear
1998: system is solved.
2000: Logically Collective
2002: Input Parameters:
2003: + ksp - iterative solver obtained from `KSPCreate()`
2004: - flg - `PETSC_TRUE` or `PETSC_FALSE`
2006: Options Database Key:
2007: . -ksp_monitor_singular_value - Activates `KSPSetComputeSingularValues()`
2009: Level: advanced
2011: Notes:
2012: This option is not valid for all iterative methods.
2014: Many users may just want to use the monitoring routine
2015: `KSPMonitorSingularValue()` (which can be set with option `-ksp_monitor_singular_value`)
2016: to print the singular values at each iteration of the linear solve.
2018: Consider using the excellent package SLEPc for accurate efficient computations of singular or eigenvalues.
2020: .seealso: [](ch_ksp), `KSPComputeExtremeSingularValues()`, `KSPMonitorSingularValue()`, `KSP`, `KSPSetComputeRitz()`
2021: @*/
2022: PetscErrorCode KSPSetComputeSingularValues(KSP ksp, PetscBool flg)
2023: {
2024: PetscFunctionBegin;
2027: ksp->calc_sings = flg;
2028: PetscFunctionReturn(PETSC_SUCCESS);
2029: }
2031: /*@
2032: KSPGetComputeEigenvalues - Gets the flag indicating that the extreme eigenvalues
2033: values will be calculated via a Lanczos or Arnoldi process as the linear
2034: system is solved.
2036: Not Collective
2038: Input Parameter:
2039: . ksp - iterative solver obtained from `KSPCreate()`
2041: Output Parameter:
2042: . flg - `PETSC_TRUE` or `PETSC_FALSE`
2044: Level: advanced
2046: Note:
2047: Currently this option is not valid for all iterative methods.
2049: .seealso: [](ch_ksp), `KSPComputeEigenvalues()`, `KSPComputeEigenvaluesExplicitly()`, `KSP`, `KSPSetComputeRitz()`
2050: @*/
2051: PetscErrorCode KSPGetComputeEigenvalues(KSP ksp, PetscBool *flg)
2052: {
2053: PetscFunctionBegin;
2055: PetscAssertPointer(flg, 2);
2056: *flg = ksp->calc_sings;
2057: PetscFunctionReturn(PETSC_SUCCESS);
2058: }
2060: /*@
2061: KSPSetComputeEigenvalues - Sets a flag so that the extreme eigenvalues
2062: values will be calculated via a Lanczos or Arnoldi process as the linear
2063: system is solved.
2065: Logically Collective
2067: Input Parameters:
2068: + ksp - iterative solver obtained from `KSPCreate()`
2069: - flg - `PETSC_TRUE` or `PETSC_FALSE`
2071: Level: advanced
2073: Note:
2074: Currently this option is not valid for all iterative methods.
2076: Consider using the excellent package SLEPc for accurate efficient computations of singular or eigenvalues.
2078: .seealso: [](ch_ksp), `KSPComputeEigenvalues()`, `KSPComputeEigenvaluesExplicitly()`, `KSP`, `KSPSetComputeRitz()`
2079: @*/
2080: PetscErrorCode KSPSetComputeEigenvalues(KSP ksp, PetscBool flg)
2081: {
2082: PetscFunctionBegin;
2085: ksp->calc_sings = flg;
2086: PetscFunctionReturn(PETSC_SUCCESS);
2087: }
2089: /*@
2090: KSPSetComputeRitz - Sets a flag so that the Ritz or harmonic Ritz pairs
2091: will be calculated via a Lanczos or Arnoldi process as the linear
2092: system is solved.
2094: Logically Collective
2096: Input Parameters:
2097: + ksp - iterative solver obtained from `KSPCreate()`
2098: - flg - `PETSC_TRUE` or `PETSC_FALSE`
2100: Level: advanced
2102: Note:
2103: Currently this option is only valid for the `KSPGMRES` method.
2105: .seealso: [](ch_ksp), `KSPComputeRitz()`, `KSP`, `KSPComputeEigenvalues()`, `KSPComputeExtremeSingularValues()`
2106: @*/
2107: PetscErrorCode KSPSetComputeRitz(KSP ksp, PetscBool flg)
2108: {
2109: PetscFunctionBegin;
2112: ksp->calc_ritz = flg;
2113: PetscFunctionReturn(PETSC_SUCCESS);
2114: }
2116: /*@
2117: KSPGetRhs - Gets the right-hand-side vector for the linear system to
2118: be solved.
2120: Not Collective
2122: Input Parameter:
2123: . ksp - iterative solver obtained from `KSPCreate()`
2125: Output Parameter:
2126: . r - right-hand-side vector
2128: Level: developer
2130: .seealso: [](ch_ksp), `KSPGetSolution()`, `KSPSolve()`, `KSP`
2131: @*/
2132: PetscErrorCode KSPGetRhs(KSP ksp, Vec *r)
2133: {
2134: PetscFunctionBegin;
2136: PetscAssertPointer(r, 2);
2137: *r = ksp->vec_rhs;
2138: PetscFunctionReturn(PETSC_SUCCESS);
2139: }
2141: /*@
2142: KSPGetSolution - Gets the location of the solution for the
2143: linear system to be solved.
2145: Not Collective
2147: Input Parameter:
2148: . ksp - iterative solver obtained from `KSPCreate()`
2150: Output Parameter:
2151: . v - solution vector
2153: Level: developer
2155: Note:
2156: If this is called during a `KSPSolve()` the vector's values may not represent the solution
2157: to the linear system.
2159: .seealso: [](ch_ksp), `KSPGetRhs()`, `KSPBuildSolution()`, `KSPSolve()`, `KSP`
2160: @*/
2161: PetscErrorCode KSPGetSolution(KSP ksp, Vec *v)
2162: {
2163: PetscFunctionBegin;
2165: PetscAssertPointer(v, 2);
2166: *v = ksp->vec_sol;
2167: PetscFunctionReturn(PETSC_SUCCESS);
2168: }
2170: /*@
2171: KSPSetPC - Sets the preconditioner to be used to calculate the
2172: application of the preconditioner on a vector into a `KSP`.
2174: Collective
2176: Input Parameters:
2177: + ksp - the `KSP` iterative solver obtained from `KSPCreate()`
2178: - pc - the preconditioner object (if `NULL` it returns the `PC` currently held by the `KSP`)
2180: Level: developer
2182: Note:
2183: This routine is almost never used since `KSP` creates its own `PC` when needed.
2184: Use `KSPGetPC()` to retrieve the preconditioner context instead of creating a new one.
2186: .seealso: [](ch_ksp), `KSPGetPC()`, `KSP`
2187: @*/
2188: PetscErrorCode KSPSetPC(KSP ksp, PC pc)
2189: {
2190: PetscFunctionBegin;
2192: if (pc) {
2194: PetscCheckSameComm(ksp, 1, pc, 2);
2195: }
2196: if (ksp->pc != pc && ksp->setupstage) ksp->setupstage = KSP_SETUP_NEWMATRIX;
2197: PetscCall(PetscObjectReference((PetscObject)pc));
2198: PetscCall(PCDestroy(&ksp->pc));
2199: ksp->pc = pc;
2200: PetscFunctionReturn(PETSC_SUCCESS);
2201: }
2203: PETSC_INTERN PetscErrorCode PCCreate_MPI(PC);
2205: // PetscClangLinter pragma disable: -fdoc-internal-linkage
2206: /*@C
2207: KSPCheckPCMPI - Checks if `-mpi_linear_solver_server` is active and the `PC` should be changed to `PCMPI`
2209: Collective, No Fortran Support
2211: Input Parameter:
2212: . ksp - iterative solver obtained from `KSPCreate()`
2214: Level: developer
2216: .seealso: [](ch_ksp), `KSPSetPC()`, `KSP`, `PCMPIServerBegin()`, `PCMPIServerEnd()`
2217: @*/
2218: PETSC_INTERN PetscErrorCode KSPCheckPCMPI(KSP ksp)
2219: {
2220: PetscBool isPCMPI;
2222: PetscFunctionBegin;
2224: PetscCall(PetscObjectTypeCompare((PetscObject)ksp->pc, PCMPI, &isPCMPI));
2225: if (PCMPIServerActive && ksp->nestlevel == 0 && !isPCMPI) {
2226: const char *prefix;
2227: char *found = NULL;
2229: PetscCall(KSPGetOptionsPrefix(ksp, &prefix));
2230: if (prefix) PetscCall(PetscStrstr(prefix, "mpi_linear_solver_server_", &found));
2231: if (!found) PetscCall(KSPAppendOptionsPrefix(ksp, "mpi_linear_solver_server_"));
2232: PetscCall(PetscInfo(NULL, "In MPI Linear Solver Server and detected (root) PC that must be changed to PCMPI\n"));
2233: PetscCall(PCSetType(ksp->pc, PCMPI));
2234: }
2235: PetscFunctionReturn(PETSC_SUCCESS);
2236: }
2238: /*@
2239: KSPGetPC - Returns a pointer to the preconditioner context with the `KSP`
2241: Not Collective
2243: Input Parameter:
2244: . ksp - iterative solver obtained from `KSPCreate()`
2246: Output Parameter:
2247: . pc - preconditioner context
2249: Level: beginner
2251: Note:
2252: The `PC` is created if it does not already exist.
2254: Developer Note:
2255: Calls `KSPCheckPCMPI()` to check if the `KSP` is effected by `-mpi_linear_solver_server`
2257: .seealso: [](ch_ksp), `KSPSetPC()`, `KSP`, `PC`
2258: @*/
2259: PetscErrorCode KSPGetPC(KSP ksp, PC *pc)
2260: {
2261: PetscFunctionBegin;
2263: PetscAssertPointer(pc, 2);
2264: if (!ksp->pc) {
2265: PetscCall(PCCreate(PetscObjectComm((PetscObject)ksp), &ksp->pc));
2266: PetscCall(PetscObjectIncrementTabLevel((PetscObject)ksp->pc, (PetscObject)ksp, 0));
2267: PetscCall(PetscObjectSetOptions((PetscObject)ksp->pc, ((PetscObject)ksp)->options));
2268: PetscCall(PCSetKSPNestLevel(ksp->pc, ksp->nestlevel));
2269: PetscCall(PCSetErrorIfFailure(ksp->pc, ksp->errorifnotconverged));
2270: if (ksp->dm) PetscCall(PCSetDM(ksp->pc, ksp->dm));
2271: }
2272: PetscCall(KSPCheckPCMPI(ksp));
2273: *pc = ksp->pc;
2274: PetscFunctionReturn(PETSC_SUCCESS);
2275: }
2277: /*@
2278: KSPMonitor - runs the user provided monitor routines, if they exist
2280: Collective
2282: Input Parameters:
2283: + ksp - iterative solver obtained from `KSPCreate()`
2284: . it - iteration number
2285: - rnorm - relative norm of the residual
2287: Level: developer
2289: Notes:
2290: This routine is called by the `KSP` implementations.
2291: It does not typically need to be called by the user.
2293: For Krylov methods that do not keep a running value of the current solution (such as `KSPGMRES`) this
2294: cannot be called after the `KSPConvergedReason` has been set but before the final solution has been computed.
2296: .seealso: [](ch_ksp), `KSPMonitorSet()`
2297: @*/
2298: PetscErrorCode KSPMonitor(KSP ksp, PetscInt it, PetscReal rnorm)
2299: {
2300: PetscInt i, n = ksp->numbermonitors;
2302: PetscFunctionBegin;
2303: for (i = 0; i < n; i++) PetscCall((*ksp->monitor[i])(ksp, it, rnorm, ksp->monitorcontext[i]));
2304: PetscFunctionReturn(PETSC_SUCCESS);
2305: }
2307: /*@C
2308: 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,
2309: the residual norm computed in a `KSPSolve()`
2311: Logically Collective
2313: Input Parameters:
2314: + ksp - iterative solver obtained from `KSPCreate()`
2315: . monitor - pointer to function (if this is `NULL`, it turns off monitoring, see `KSPMonitorFn`
2316: . ctx - [optional] context for private data for the monitor routine (use `NULL` if no context is needed)
2317: - monitordestroy - [optional] routine that frees monitor context (may be `NULL`), see `PetscCtxDestroyFn` for the calling sequence
2319: Options Database Keys:
2320: + -ksp_monitor - sets `KSPMonitorResidual()`
2321: . -ksp_monitor hdf5:filename - sets `KSPMonitorResidualView()` and saves residual
2322: . -ksp_monitor draw - sets `KSPMonitorResidualView()` and plots residual
2323: . -ksp_monitor draw::draw_lg - sets `KSPMonitorResidualDrawLG()` and plots residual
2324: . -ksp_monitor_pause_final - Pauses any graphics when the solve finishes (only works for internal monitors)
2325: . -ksp_monitor_true_residual - sets `KSPMonitorTrueResidual()`
2326: . -ksp_monitor_true_residual draw::draw_lg - sets `KSPMonitorTrueResidualDrawLG()` and plots residual
2327: . -ksp_monitor_max - sets `KSPMonitorTrueResidualMax()`
2328: . -ksp_monitor_singular_value - sets `KSPMonitorSingularValue()`
2329: - -ksp_monitor_cancel - cancels all monitors that have been hardwired into a code by calls to `KSPMonitorSet()`, but
2330: does not cancel those set via the options database.
2332: Level: beginner
2334: Notes:
2335: The options database option `-ksp_monitor` and related options are the easiest way to turn on `KSP` iteration monitoring
2337: `KSPMonitorRegister()` provides a way to associate an options database key with `KSP` monitor function.
2339: The default is to do no monitoring. To print the residual, or preconditioned
2340: residual if `KSPSetNormType`(ksp,`KSP_NORM_PRECONDITIONED`) was called, use
2341: `KSPMonitorResidual()` as the monitoring routine, with a `PETSCVIEWERASCII` as the
2342: context.
2344: Several different monitoring routines may be set by calling
2345: `KSPMonitorSet()` multiple times; they will be called in the
2346: order in which they were set.
2348: Fortran Note:
2349: Only a single monitor function can be set for each `KSP` object
2351: .seealso: [](ch_ksp), `KSPMonitorResidual()`, `KSPMonitorRegister()`, `KSPMonitorCancel()`, `KSP`, `PetscCtxDestroyFn`
2352: @*/
2353: PetscErrorCode KSPMonitorSet(KSP ksp, KSPMonitorFn *monitor, PetscCtx ctx, PetscCtxDestroyFn *monitordestroy)
2354: {
2355: PetscFunctionBegin;
2357: for (PetscInt i = 0; i < ksp->numbermonitors; i++) {
2358: PetscBool identical;
2360: PetscCall(PetscMonitorCompare((PetscErrorCode (*)(void))(PetscVoidFn *)monitor, ctx, monitordestroy, (PetscErrorCode (*)(void))(PetscVoidFn *)ksp->monitor[i], ksp->monitorcontext[i], ksp->monitordestroy[i], &identical));
2361: if (identical) PetscFunctionReturn(PETSC_SUCCESS);
2362: }
2363: PetscCheck(ksp->numbermonitors < MAXKSPMONITORS, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Too many KSP monitors set");
2364: ksp->monitor[ksp->numbermonitors] = monitor;
2365: ksp->monitordestroy[ksp->numbermonitors] = monitordestroy;
2366: ksp->monitorcontext[ksp->numbermonitors++] = ctx;
2367: PetscFunctionReturn(PETSC_SUCCESS);
2368: }
2370: /*@
2371: KSPMonitorCancel - Clears all monitors for a `KSP` object.
2373: Logically Collective
2375: Input Parameter:
2376: . ksp - iterative solver obtained from `KSPCreate()`
2378: Options Database Key:
2379: . -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.
2381: Level: intermediate
2383: .seealso: [](ch_ksp), `KSPMonitorResidual()`, `KSPMonitorSet()`, `KSP`
2384: @*/
2385: PetscErrorCode KSPMonitorCancel(KSP ksp)
2386: {
2387: PetscInt i;
2389: PetscFunctionBegin;
2391: for (i = 0; i < ksp->numbermonitors; i++) {
2392: if (ksp->monitordestroy[i]) PetscCall((*ksp->monitordestroy[i])(&ksp->monitorcontext[i]));
2393: }
2394: ksp->numbermonitors = 0;
2395: PetscFunctionReturn(PETSC_SUCCESS);
2396: }
2398: /*@C
2399: KSPGetMonitorContext - Gets the monitoring context, as set by `KSPMonitorSet()` for the FIRST monitor only.
2401: Not Collective
2403: Input Parameter:
2404: . ksp - iterative solver obtained from `KSPCreate()`
2406: Output Parameter:
2407: . ctx - monitoring context
2409: Level: intermediate
2411: Fortran Notes:
2412: This only works when the context is a Fortran derived type or a `PetscObject`. Declare `ctx` with
2413: .vb
2414: type(tUsertype), pointer :: ctx
2415: .ve
2417: .seealso: [](ch_ksp), `KSPMonitorResidual()`, `KSP`
2418: @*/
2419: PetscErrorCode KSPGetMonitorContext(KSP ksp, PetscCtxRt ctx)
2420: {
2421: PetscFunctionBegin;
2423: *(void **)ctx = ksp->monitorcontext[0];
2424: PetscFunctionReturn(PETSC_SUCCESS);
2425: }
2427: /*@
2428: KSPSetResidualHistory - Sets the array used to hold the residual history.
2429: If set, this array will contain the residual norms computed at each
2430: iteration of the solver.
2432: Not Collective
2434: Input Parameters:
2435: + ksp - iterative solver obtained from `KSPCreate()`
2436: . a - array to hold history
2437: . na - size of `a`
2438: - reset - `PETSC_TRUE` indicates the history counter is reset to zero
2439: for each new linear solve
2441: Level: advanced
2443: Notes:
2444: 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.
2445: If 'a' is `NULL` then space is allocated for the history. If 'na' `PETSC_DECIDE` or (deprecated) `PETSC_DEFAULT` then a
2446: default array of length 10,000 is allocated.
2448: If the array is not long enough then once the iterations is longer than the array length `KSPSolve()` stops recording the history
2450: .seealso: [](ch_ksp), `KSPGetResidualHistory()`, `KSP`
2451: @*/
2452: PetscErrorCode KSPSetResidualHistory(KSP ksp, PetscReal a[], PetscCount na, PetscBool reset)
2453: {
2454: PetscFunctionBegin;
2457: PetscCall(PetscFree(ksp->res_hist_alloc));
2458: if (na != PETSC_DECIDE && na != PETSC_DEFAULT && a) {
2459: ksp->res_hist = a;
2460: ksp->res_hist_max = na;
2461: } else {
2462: if (na != PETSC_DECIDE && na != PETSC_DEFAULT) ksp->res_hist_max = (size_t)na;
2463: else ksp->res_hist_max = 10000; /* like default ksp->max_it */
2464: PetscCall(PetscCalloc1(ksp->res_hist_max, &ksp->res_hist_alloc));
2466: ksp->res_hist = ksp->res_hist_alloc;
2467: }
2468: ksp->res_hist_len = 0;
2469: ksp->res_hist_reset = reset;
2470: PetscFunctionReturn(PETSC_SUCCESS);
2471: }
2473: /*@C
2474: KSPGetResidualHistory - Gets the array used to hold the residual history and the number of residuals it contains.
2476: Not Collective
2478: Input Parameter:
2479: . ksp - iterative solver obtained from `KSPCreate()`
2481: Output Parameters:
2482: + a - pointer to array to hold history (or `NULL`)
2483: - na - number of used entries in a (or `NULL`). Note this has different meanings depending on the `reset` argument to `KSPSetResidualHistory()`
2485: Level: advanced
2487: Note:
2488: This array is borrowed and should not be freed by the caller.
2490: Can only be called after a `KSPSetResidualHistory()` otherwise `a` and `na` are set to `NULL` and zero
2492: 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
2493: returned with `KSPGetIterationNumber()`.
2495: Some Krylov methods may not compute the final residual norm when convergence is declared because the maximum number of iterations allowed has been reached.
2496: In this situation, when `reset` was `PETSC_TRUE`, `na` will then equal the number of iterations reported with `KSPGetIterationNumber()`
2498: Some Krylov methods (such as `KSPSTCG`), under certain circumstances, do not compute the final residual norm. In this situation, when `reset` was `PETSC_TRUE`,
2499: `na` will then equal the number of iterations reported with `KSPGetIterationNumber()`
2501: `KSPBCGSL` does not record the residual norms for the "subiterations" hence the results from `KSPGetResidualHistory()` and `KSPGetIterationNumber()` will be different
2503: Fortran Note:
2504: Call `KSPRestoreResidualHistory()` when access to the history is no longer needed.
2506: .seealso: [](ch_ksp), `KSPSetResidualHistory()`, `KSP`, `KSPGetIterationNumber()`, `KSPSTCG`, `KSPBCGSL`
2507: @*/
2508: PetscErrorCode KSPGetResidualHistory(KSP ksp, const PetscReal *a[], PetscInt *na)
2509: {
2510: PetscFunctionBegin;
2512: if (a) *a = ksp->res_hist;
2513: if (na) PetscCall(PetscIntCast(ksp->res_hist_len, na));
2514: PetscFunctionReturn(PETSC_SUCCESS);
2515: }
2517: /*@
2518: 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.
2520: Not Collective
2522: Input Parameters:
2523: + ksp - iterative solver obtained from `KSPCreate()`
2524: . a - array to hold history
2525: . na - size of `a`
2526: - reset - `PETSC_TRUE` indicates the history counter is reset to zero for each new linear solve
2528: Level: advanced
2530: Notes:
2531: 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.
2532: 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.
2534: If the array is not long enough then once the iterations is longer than the array length `KSPSolve()` stops recording the history
2536: .seealso: [](ch_ksp), `KSPGetErrorHistory()`, `KSPSetResidualHistory()`, `KSP`
2537: @*/
2538: PetscErrorCode KSPSetErrorHistory(KSP ksp, PetscReal a[], PetscCount na, PetscBool reset)
2539: {
2540: PetscFunctionBegin;
2543: PetscCall(PetscFree(ksp->err_hist_alloc));
2544: if (na != PETSC_DECIDE && na != PETSC_DEFAULT && a) {
2545: ksp->err_hist = a;
2546: ksp->err_hist_max = na;
2547: } else {
2548: if (na != PETSC_DECIDE && na != PETSC_DEFAULT) ksp->err_hist_max = (size_t)na;
2549: else ksp->err_hist_max = 10000; /* like default ksp->max_it */
2550: PetscCall(PetscCalloc1(ksp->err_hist_max, &ksp->err_hist_alloc));
2551: ksp->err_hist = ksp->err_hist_alloc;
2552: }
2553: ksp->err_hist_len = 0;
2554: ksp->err_hist_reset = reset;
2555: PetscFunctionReturn(PETSC_SUCCESS);
2556: }
2558: /*@C
2559: KSPGetErrorHistory - Gets the array used to hold the error history and the number of residuals it contains.
2561: Not Collective
2563: Input Parameter:
2564: . ksp - iterative solver obtained from `KSPCreate()`
2566: Output Parameters:
2567: + a - pointer to array to hold history (or `NULL`)
2568: - na - number of used entries in a (or `NULL`)
2570: Level: advanced
2572: Note:
2573: This array is borrowed and should not be freed by the caller.
2574: Can only be called after a `KSPSetErrorHistory()` otherwise `a` and `na` are set to `NULL` and zero
2576: Fortran Note:
2577: .vb
2578: PetscReal, pointer :: a(:)
2579: .ve
2581: .seealso: [](ch_ksp), `KSPSetErrorHistory()`, `KSPGetResidualHistory()`, `KSP`
2582: @*/
2583: PetscErrorCode KSPGetErrorHistory(KSP ksp, const PetscReal *a[], PetscInt *na)
2584: {
2585: PetscFunctionBegin;
2587: if (a) *a = ksp->err_hist;
2588: if (na) PetscCall(PetscIntCast(ksp->err_hist_len, na));
2589: PetscFunctionReturn(PETSC_SUCCESS);
2590: }
2592: /*@
2593: KSPComputeConvergenceRate - Compute the convergence rate for the iteration <https:/en.wikipedia.org/wiki/Coefficient_of_determination>
2595: Not Collective
2597: Input Parameter:
2598: . ksp - The `KSP`
2600: Output Parameters:
2601: + cr - The residual contraction rate
2602: . rRsq - The coefficient of determination, $R^2$, indicating the linearity of the data
2603: . ce - The error contraction rate
2604: - eRsq - The coefficient of determination, $R^2$, indicating the linearity of the data
2606: Level: advanced
2608: Note:
2609: 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,
2610: the slope is $\log c$. The coefficient of determination is given by $1 - \frac{\sum_i (y_i - f(x_i))^2}{\sum_i (y_i - \bar y)}$,
2612: .seealso: [](ch_ksp), `KSP`, `KSPConvergedRateView()`
2613: @*/
2614: PetscErrorCode KSPComputeConvergenceRate(KSP ksp, PetscReal *cr, PetscReal *rRsq, PetscReal *ce, PetscReal *eRsq)
2615: {
2616: PetscReal const *hist;
2617: PetscReal *x, *y, slope, intercept, mean = 0.0, var = 0.0, res = 0.0;
2618: PetscInt n, k;
2620: PetscFunctionBegin;
2621: if (cr || rRsq) {
2622: PetscCall(KSPGetResidualHistory(ksp, &hist, &n));
2623: if (!n) {
2624: if (cr) *cr = 0.0;
2625: if (rRsq) *rRsq = -1.0;
2626: } else {
2627: PetscCall(PetscMalloc2(n, &x, n, &y));
2628: for (k = 0; k < n; ++k) {
2629: x[k] = k;
2630: y[k] = PetscLogReal(hist[k]);
2631: mean += y[k];
2632: }
2633: mean /= n;
2634: PetscCall(PetscLinearRegression(n, x, y, &slope, &intercept));
2635: for (k = 0; k < n; ++k) {
2636: res += PetscSqr(y[k] - (slope * x[k] + intercept));
2637: var += PetscSqr(y[k] - mean);
2638: }
2639: PetscCall(PetscFree2(x, y));
2640: if (cr) *cr = PetscExpReal(slope);
2641: if (rRsq) *rRsq = var < PETSC_MACHINE_EPSILON ? 0.0 : 1.0 - (res / var);
2642: }
2643: }
2644: if (ce || eRsq) {
2645: PetscCall(KSPGetErrorHistory(ksp, &hist, &n));
2646: if (!n) {
2647: if (ce) *ce = 0.0;
2648: if (eRsq) *eRsq = -1.0;
2649: } else {
2650: PetscCall(PetscMalloc2(n, &x, n, &y));
2651: for (k = 0; k < n; ++k) {
2652: x[k] = k;
2653: y[k] = PetscLogReal(hist[k]);
2654: mean += y[k];
2655: }
2656: mean /= n;
2657: PetscCall(PetscLinearRegression(n, x, y, &slope, &intercept));
2658: for (k = 0; k < n; ++k) {
2659: res += PetscSqr(y[k] - (slope * x[k] + intercept));
2660: var += PetscSqr(y[k] - mean);
2661: }
2662: PetscCall(PetscFree2(x, y));
2663: if (ce) *ce = PetscExpReal(slope);
2664: if (eRsq) *eRsq = var < PETSC_MACHINE_EPSILON ? 0.0 : 1.0 - (res / var);
2665: }
2666: }
2667: PetscFunctionReturn(PETSC_SUCCESS);
2668: }
2670: /*@C
2671: KSPSetConvergenceTest - Sets the function to be used to determine convergence of `KSPSolve()`
2673: Logically Collective
2675: Input Parameters:
2676: + ksp - iterative solver obtained from `KSPCreate()`
2677: . converge - pointer to the function, see `KSPConvergenceTestFn`
2678: . ctx - context for private data for the convergence routine (may be `NULL`)
2679: - destroy - a routine for destroying the context (may be `NULL`)
2681: Level: advanced
2683: Notes:
2684: Must be called after the `KSP` type has been set so put this after
2685: a call to `KSPSetType()`, or `KSPSetFromOptions()`.
2687: The default convergence test, `KSPConvergedDefault()`, aborts if the
2688: residual grows to more than 10000 times the initial residual.
2690: The default is a combination of relative and absolute tolerances.
2691: The residual value that is tested may be an approximation; routines
2692: that need exact values should compute them.
2694: In the default PETSc convergence test, the precise values of reason
2695: are macros such as `KSP_CONVERGED_RTOL`, which are defined in petscksp.h.
2697: .seealso: [](ch_ksp), `KSP`, `KSPConvergenceTestFn`, `KSPConvergedDefault()`, `KSPGetConvergenceContext()`, `KSPSetTolerances()`, `KSPGetConvergenceTest()`, `KSPGetAndClearConvergenceTest()`
2698: @*/
2699: PetscErrorCode KSPSetConvergenceTest(KSP ksp, KSPConvergenceTestFn *converge, PetscCtx ctx, PetscCtxDestroyFn *destroy)
2700: {
2701: PetscFunctionBegin;
2703: if (ksp->convergeddestroy) PetscCall((*ksp->convergeddestroy)(&ksp->cnvP));
2704: ksp->converged = converge;
2705: ksp->convergeddestroy = destroy;
2706: ksp->cnvP = ctx;
2707: PetscFunctionReturn(PETSC_SUCCESS);
2708: }
2710: /*@C
2711: KSPGetConvergenceTest - Gets the function to be used to determine convergence.
2713: Logically Collective
2715: Input Parameter:
2716: . ksp - iterative solver obtained from `KSPCreate()`
2718: Output Parameters:
2719: + converge - pointer to convergence test function, see `KSPConvergenceTestFn`
2720: . ctx - context for private data for the convergence routine (may be `NULL`)
2721: - destroy - a routine for destroying the context (may be `NULL`)
2723: Level: advanced
2725: .seealso: [](ch_ksp), `KSP`, `KSPConvergedDefault()`, `KSPGetConvergenceContext()`, `KSPSetTolerances()`, `KSPSetConvergenceTest()`, `KSPGetAndClearConvergenceTest()`
2726: @*/
2727: PetscErrorCode KSPGetConvergenceTest(KSP ksp, KSPConvergenceTestFn **converge, PetscCtxRt ctx, PetscCtxDestroyFn **destroy)
2728: {
2729: PetscFunctionBegin;
2731: if (converge) *converge = ksp->converged;
2732: if (destroy) *destroy = ksp->convergeddestroy;
2733: if (ctx) *(void **)ctx = ksp->cnvP;
2734: PetscFunctionReturn(PETSC_SUCCESS);
2735: }
2737: /*@C
2738: KSPGetAndClearConvergenceTest - Gets the function to be used to determine convergence. Removes the current test without calling destroy on the test context
2740: Logically Collective
2742: Input Parameter:
2743: . ksp - iterative solver obtained from `KSPCreate()`
2745: Output Parameters:
2746: + converge - pointer to convergence test function, see `KSPConvergenceTestFn`
2747: . ctx - context for private data for the convergence routine
2748: - destroy - a routine for destroying the context
2750: Level: advanced
2752: Note:
2753: This is intended to be used to allow transferring the convergence test (and its context) to another testing object (for example another `KSP`)
2754: and then calling `KSPSetConvergenceTest()` on this original `KSP`. If you just called `KSPGetConvergenceTest()` followed
2755: by `KSPSetConvergenceTest()` the original context information
2756: would be destroyed and hence the transferred context would be invalid and trigger a crash on use
2758: .seealso: [](ch_ksp), `KSP`, `KSPConvergedDefault()`, `KSPGetConvergenceContext()`, `KSPSetTolerances()`, `KSPSetConvergenceTest()`, `KSPGetConvergenceTest()`
2759: @*/
2760: PetscErrorCode KSPGetAndClearConvergenceTest(KSP ksp, KSPConvergenceTestFn **converge, PetscCtxRt ctx, PetscCtxDestroyFn **destroy)
2761: {
2762: PetscFunctionBegin;
2764: *converge = ksp->converged;
2765: *destroy = ksp->convergeddestroy;
2766: *(void **)ctx = ksp->cnvP;
2767: ksp->converged = NULL;
2768: ksp->cnvP = NULL;
2769: ksp->convergeddestroy = NULL;
2770: PetscFunctionReturn(PETSC_SUCCESS);
2771: }
2773: /*@C
2774: KSPGetConvergenceContext - Gets the convergence context set with `KSPSetConvergenceTest()`.
2776: Not Collective
2778: Input Parameter:
2779: . ksp - iterative solver obtained from `KSPCreate()`
2781: Output Parameter:
2782: . ctx - monitoring context
2784: Level: advanced
2786: Fortran Note:
2787: This only works when the context is a Fortran derived type or a `PetscObject`. Declare `ctx` with
2788: .vb
2789: type(tUsertype), pointer :: ctx
2790: .ve
2792: .seealso: [](ch_ksp), `KSP`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSPGetConvergenceTest()`
2793: @*/
2794: PetscErrorCode KSPGetConvergenceContext(KSP ksp, PetscCtxRt ctx)
2795: {
2796: PetscFunctionBegin;
2798: *(void **)ctx = ksp->cnvP;
2799: PetscFunctionReturn(PETSC_SUCCESS);
2800: }
2802: /*@
2803: KSPBuildSolution - Builds the approximate solution in a vector provided.
2805: Collective
2807: Input Parameter:
2808: . ksp - iterative solver obtained from `KSPCreate()`
2810: Output Parameter:
2811: Provide exactly one of
2812: + v - location to stash solution, optional, otherwise pass `NULL`
2813: - V - the solution is returned in this location. This vector is created internally. This vector should NOT be destroyed by the user with `VecDestroy()`.
2815: Level: developer
2817: Notes:
2818: This routine can be used in one of two ways
2819: .vb
2820: KSPBuildSolution(ksp,NULL,&V);
2821: or
2822: KSPBuildSolution(ksp,v,NULL); or KSPBuildSolution(ksp,v,&v);
2823: .ve
2824: In the first case an internal vector is allocated to store the solution
2825: (the user cannot destroy this vector). In the second case the solution
2826: is generated in the vector that the user provides. Note that for certain
2827: methods, such as `KSPCG`, the second case requires a copy of the solution,
2828: while in the first case the call is essentially free since it simply
2829: returns the vector where the solution already is stored. For some methods
2830: like `KSPGMRES` during the solve this is a reasonably expensive operation and should only be
2831: used if truly needed.
2833: .seealso: [](ch_ksp), `KSPGetSolution()`, `KSPBuildResidual()`, `KSP`
2834: @*/
2835: PetscErrorCode KSPBuildSolution(KSP ksp, Vec v, Vec *V)
2836: {
2837: PetscFunctionBegin;
2839: PetscCheck(V || v, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONG, "Must provide either v or V");
2840: if (!V) V = &v;
2841: if (ksp->reason != KSP_CONVERGED_ITERATING) {
2842: if (!v) PetscCall(KSPGetSolution(ksp, V));
2843: else PetscCall(VecCopy(ksp->vec_sol, v));
2844: } else {
2845: PetscUseTypeMethod(ksp, buildsolution, v, V);
2846: }
2847: PetscFunctionReturn(PETSC_SUCCESS);
2848: }
2850: /*@
2851: KSPBuildResidual - Builds the residual in a vector provided.
2853: Collective
2855: Input Parameter:
2856: . ksp - iterative solver obtained from `KSPCreate()`
2858: Output Parameters:
2859: + t - work vector. If not provided then one is generated.
2860: . v - optional location to stash residual. If `v` is not provided, then a location is generated.
2861: - V - the residual
2863: Level: advanced
2865: Note:
2866: Regardless of whether or not `v` is provided, the residual is
2867: returned in `V`.
2869: .seealso: [](ch_ksp), `KSP`, `KSPBuildSolution()`
2870: @*/
2871: PetscErrorCode KSPBuildResidual(KSP ksp, Vec t, Vec v, Vec *V)
2872: {
2873: PetscBool flag = PETSC_FALSE;
2874: Vec w = v, tt = t;
2876: PetscFunctionBegin;
2878: if (!w) PetscCall(VecDuplicate(ksp->vec_rhs, &w));
2879: if (!tt) {
2880: PetscCall(VecDuplicate(ksp->vec_sol, &tt));
2881: flag = PETSC_TRUE;
2882: }
2883: PetscUseTypeMethod(ksp, buildresidual, tt, w, V);
2884: if (flag) PetscCall(VecDestroy(&tt));
2885: PetscFunctionReturn(PETSC_SUCCESS);
2886: }
2888: /*@
2889: KSPSetDiagonalScale - Tells `KSP` to symmetrically diagonally scale the system
2890: before solving. This actually CHANGES the matrix (and right-hand side).
2892: Logically Collective
2894: Input Parameters:
2895: + ksp - the `KSP` context
2896: - scale - `PETSC_TRUE` or `PETSC_FALSE`
2898: Options Database Keys:
2899: + -ksp_diagonal_scale - perform a diagonal scaling before the solve
2900: - -ksp_diagonal_scale_fix - scale the matrix back AFTER the solve
2902: Level: advanced
2904: Notes:
2905: Scales the matrix by $D^{-1/2} A D^{-1/2} [D^{1/2} x ] = D^{-1/2} b $
2906: where $D_{ii}$ is $1/abs(A_{ii}) $ unless $A_{ii}$ is zero and then it is 1.
2908: BE CAREFUL with this routine: it actually scales the matrix and right
2909: hand side that define the system. After the system is solved the matrix
2910: and right-hand side remain scaled unless you use `KSPSetDiagonalScaleFix()`
2912: This should NOT be used within the `SNES` solves if you are using a line
2913: search.
2915: If you use this with the `PCType` `PCEISENSTAT` preconditioner than you can
2916: use the `PCEisenstatSetNoDiagonalScaling()` option, or `-pc_eisenstat_no_diagonal_scaling`
2917: to save some unneeded, redundant flops.
2919: .seealso: [](ch_ksp), `KSPGetDiagonalScale()`, `KSPSetDiagonalScaleFix()`, `KSP`
2920: @*/
2921: PetscErrorCode KSPSetDiagonalScale(KSP ksp, PetscBool scale)
2922: {
2923: PetscFunctionBegin;
2926: ksp->dscale = scale;
2927: PetscFunctionReturn(PETSC_SUCCESS);
2928: }
2930: /*@
2931: KSPGetDiagonalScale - Checks if `KSP` solver scales the matrix and right-hand side, that is if `KSPSetDiagonalScale()` has been called
2933: Not Collective
2935: Input Parameter:
2936: . ksp - the `KSP` context
2938: Output Parameter:
2939: . scale - `PETSC_TRUE` or `PETSC_FALSE`
2941: Level: intermediate
2943: .seealso: [](ch_ksp), `KSP`, `KSPSetDiagonalScale()`, `KSPSetDiagonalScaleFix()`
2944: @*/
2945: PetscErrorCode KSPGetDiagonalScale(KSP ksp, PetscBool *scale)
2946: {
2947: PetscFunctionBegin;
2949: PetscAssertPointer(scale, 2);
2950: *scale = ksp->dscale;
2951: PetscFunctionReturn(PETSC_SUCCESS);
2952: }
2954: /*@
2955: KSPSetDiagonalScaleFix - Tells `KSP` to diagonally scale the system back after solving.
2957: Logically Collective
2959: Input Parameters:
2960: + ksp - the `KSP` context
2961: - fix - `PETSC_TRUE` to scale back after the system solve, `PETSC_FALSE` to not
2962: rescale (default)
2964: Level: intermediate
2966: Notes:
2967: Must be called after `KSPSetDiagonalScale()`
2969: Using this will slow things down, because it rescales the matrix before and
2970: after each linear solve. This is intended mainly for testing to allow one
2971: to easily get back the original system to make sure the solution computed is
2972: accurate enough.
2974: .seealso: [](ch_ksp), `KSPGetDiagonalScale()`, `KSPSetDiagonalScale()`, `KSPGetDiagonalScaleFix()`, `KSP`
2975: @*/
2976: PetscErrorCode KSPSetDiagonalScaleFix(KSP ksp, PetscBool fix)
2977: {
2978: PetscFunctionBegin;
2981: ksp->dscalefix = fix;
2982: PetscFunctionReturn(PETSC_SUCCESS);
2983: }
2985: /*@
2986: KSPGetDiagonalScaleFix - Determines if `KSP` diagonally scales the system back after solving. That is `KSPSetDiagonalScaleFix()` has been called
2988: Not Collective
2990: Input Parameter:
2991: . ksp - the `KSP` context
2993: Output Parameter:
2994: . fix - `PETSC_TRUE` to scale back after the system solve, `PETSC_FALSE` to not
2995: rescale (default)
2997: Level: intermediate
2999: .seealso: [](ch_ksp), `KSPGetDiagonalScale()`, `KSPSetDiagonalScale()`, `KSPSetDiagonalScaleFix()`, `KSP`
3000: @*/
3001: PetscErrorCode KSPGetDiagonalScaleFix(KSP ksp, PetscBool *fix)
3002: {
3003: PetscFunctionBegin;
3005: PetscAssertPointer(fix, 2);
3006: *fix = ksp->dscalefix;
3007: PetscFunctionReturn(PETSC_SUCCESS);
3008: }
3010: /*@C
3011: KSPSetComputeOperators - set routine to compute the linear operators
3013: Logically Collective
3015: Input Parameters:
3016: + ksp - the `KSP` context
3017: . func - function to compute the operators, see `KSPComputeOperatorsFn` for the calling sequence
3018: - ctx - optional context
3020: Level: beginner
3022: Notes:
3023: `func()` will be called automatically at the very next call to `KSPSolve()`. It will NOT be called at future `KSPSolve()` calls
3024: unless either `KSPSetComputeOperators()` or `KSPSetOperators()` is called before that `KSPSolve()` is called. This allows the same system to be solved several times
3025: with different right-hand side functions but is a confusing API since one might expect it to be called for each `KSPSolve()`
3027: To reuse the same preconditioner for the next `KSPSolve()` and not compute a new one based on the most recently computed matrix call `KSPSetReusePreconditioner()`
3029: Developer Note:
3030: Perhaps this routine and `KSPSetComputeRHS()` could be combined into a new API that makes clear when new matrices are computing without requiring call this
3031: routine to indicate when the new matrix should be computed.
3033: .seealso: [](ch_ksp), `KSP`, `KSPSetOperators()`, `KSPSetComputeRHS()`, `DMKSPSetComputeOperators()`, `KSPSetComputeInitialGuess()`, `KSPComputeOperatorsFn`
3034: @*/
3035: PetscErrorCode KSPSetComputeOperators(KSP ksp, KSPComputeOperatorsFn *func, PetscCtx ctx)
3036: {
3037: DM dm;
3039: PetscFunctionBegin;
3041: PetscCall(KSPGetDM(ksp, &dm));
3042: PetscCall(DMKSPSetComputeOperators(dm, func, ctx));
3043: if (ksp->setupstage == KSP_SETUP_NEWRHS) ksp->setupstage = KSP_SETUP_NEWMATRIX;
3044: PetscFunctionReturn(PETSC_SUCCESS);
3045: }
3047: /*@C
3048: KSPSetComputeRHS - set routine to compute the right-hand side of the linear system
3050: Logically Collective
3052: Input Parameters:
3053: + ksp - the `KSP` context
3054: . func - function to compute the right-hand side, see `KSPComputeRHSFn` for the calling sequence
3055: - ctx - optional context
3057: Level: beginner
3059: Note:
3060: The routine you provide will be called EACH you call `KSPSolve()` to prepare the new right-hand side for that solve
3062: .seealso: [](ch_ksp), `KSP`, `KSPSolve()`, `DMKSPSetComputeRHS()`, `KSPSetComputeOperators()`, `KSPSetOperators()`, `KSPComputeRHSFn`
3063: @*/
3064: PetscErrorCode KSPSetComputeRHS(KSP ksp, KSPComputeRHSFn *func, PetscCtx ctx)
3065: {
3066: DM dm;
3068: PetscFunctionBegin;
3070: PetscCall(KSPGetDM(ksp, &dm));
3071: PetscCall(DMKSPSetComputeRHS(dm, func, ctx));
3072: PetscFunctionReturn(PETSC_SUCCESS);
3073: }
3075: /*@C
3076: KSPSetComputeInitialGuess - set routine to compute the initial guess of the linear system
3078: Logically Collective
3080: Input Parameters:
3081: + ksp - the `KSP` context
3082: . func - function to compute the initial guess, see `KSPComputeInitialGuessFn` for calling sequence
3083: - ctx - optional context
3085: Level: beginner
3087: Note:
3088: This should only be used in conjunction with `KSPSetComputeRHS()` and `KSPSetComputeOperators()`, otherwise
3089: call `KSPSetInitialGuessNonzero()` and set the initial guess values in the solution vector passed to `KSPSolve()` before calling the solver
3091: .seealso: [](ch_ksp), `KSP`, `KSPSolve()`, `KSPSetComputeRHS()`, `KSPSetComputeOperators()`, `DMKSPSetComputeInitialGuess()`, `KSPSetInitialGuessNonzero()`,
3092: `KSPComputeInitialGuessFn`
3093: @*/
3094: PetscErrorCode KSPSetComputeInitialGuess(KSP ksp, KSPComputeInitialGuessFn *func, PetscCtx ctx)
3095: {
3096: DM dm;
3098: PetscFunctionBegin;
3100: PetscCall(KSPGetDM(ksp, &dm));
3101: PetscCall(DMKSPSetComputeInitialGuess(dm, func, ctx));
3102: PetscFunctionReturn(PETSC_SUCCESS);
3103: }
3105: /*@
3106: KSPSetUseExplicitTranspose - Determines the explicit transpose of the operator is formed in `KSPSolveTranspose()`. In some configurations (like GPUs) it may
3107: be explicitly formed since the solve is much more efficient.
3109: Logically Collective
3111: Input Parameter:
3112: . ksp - the `KSP` context
3114: Output Parameter:
3115: . flg - `PETSC_TRUE` to transpose the system in `KSPSolveTranspose()`, `PETSC_FALSE` to not transpose (default)
3117: Level: advanced
3119: .seealso: [](ch_ksp), `KSPSolveTranspose()`, `KSP`
3120: @*/
3121: PetscErrorCode KSPSetUseExplicitTranspose(KSP ksp, PetscBool flg)
3122: {
3123: PetscFunctionBegin;
3126: ksp->transpose.use_explicittranspose = flg;
3127: PetscFunctionReturn(PETSC_SUCCESS);
3128: }