Actual source code: itfunc.c

  1: /*
  2:       Interface KSP routines that the user calls.
  3: */

  5: #include <petsc/private/kspimpl.h>
  6: #include <petsc/private/matimpl.h>
  7: #include <petscdm.h>

  9: /* number of nested levels of KSPSetUp/Solve(). This is used to determine if KSP_DIVERGED_ITS should be fatal. */
 10: static PetscInt level = 0;

 12: static inline PetscErrorCode ObjectView(PetscObject obj, PetscViewer viewer, PetscViewerFormat format)
 13: {
 14:   PetscCall(PetscViewerPushFormat(viewer, format));
 15:   PetscCall(PetscObjectView(obj, viewer));
 16:   PetscCall(PetscViewerPopFormat(viewer));
 17:   return PETSC_SUCCESS;
 18: }

 20: /*@
 21:   KSPComputeExtremeSingularValues - Computes the extreme singular values
 22:   for the preconditioned operator. Called after or during `KSPSolve()`.

 24:   Not Collective

 26:   Input Parameter:
 27: . ksp - iterative context obtained from `KSPCreate()`

 29:   Output Parameters:
 30: + emax - maximum estimated singular value
 31: - emin - minimum estimated singular value

 33:   Options Database Key:
 34: . -ksp_view_singularvalues - compute extreme singular values and print when `KSPSolve()` completes.

 36:   Level: advanced

 38:   Notes:
 39:   One must call `KSPSetComputeSingularValues()` before calling `KSPSetUp()`
 40:   (or use the option -ksp_view_eigenvalues) in order for this routine to work correctly.

 42:   Many users may just want to use the monitoring routine
 43:   `KSPMonitorSingularValue()` (which can be set with option -ksp_monitor_singular_value)
 44:   to print the extreme singular values at each iteration of the linear solve.

 46:   Estimates of the smallest singular value may be very inaccurate, especially if the Krylov method has not converged.
 47:   The largest singular value is usually accurate to within a few percent if the method has converged, but is still not
 48:   intended for eigenanalysis. Consider the excellent package `SLEPc` if accurate values are required.

 50:   Disable restarts if using KSPGMRES, otherwise this estimate will only be using those iterations after the last
 51:   restart. See `KSPGMRESSetRestart()` for more details.

 53: .seealso: [](ch_ksp), `KSPSetComputeSingularValues()`, `KSPMonitorSingularValue()`, `KSPComputeEigenvalues()`, `KSP`, `KSPComputeRitz()`
 54: @*/
 55: PetscErrorCode KSPComputeExtremeSingularValues(KSP ksp, PetscReal *emax, PetscReal *emin)
 56: {
 57:   PetscFunctionBegin;
 59:   PetscAssertPointer(emax, 2);
 60:   PetscAssertPointer(emin, 3);
 61:   PetscCheck(ksp->calc_sings, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONGSTATE, "Singular values not requested before KSPSetUp()");

 63:   if (ksp->ops->computeextremesingularvalues) PetscUseTypeMethod(ksp, computeextremesingularvalues, emax, emin);
 64:   else {
 65:     *emin = -1.0;
 66:     *emax = -1.0;
 67:   }
 68:   PetscFunctionReturn(PETSC_SUCCESS);
 69: }

 71: /*@
 72:   KSPComputeEigenvalues - Computes the extreme eigenvalues for the
 73:   preconditioned operator. Called after or during `KSPSolve()`.

 75:   Not Collective

 77:   Input Parameters:
 78: + ksp - iterative context obtained from `KSPCreate()`
 79: - n   - size of arrays `r` and `c`. The number of eigenvalues computed `neig` will, in
 80:        general, be less than this.

 82:   Output Parameters:
 83: + r    - real part of computed eigenvalues, provided by user with a dimension of at least `n`
 84: . c    - complex part of computed eigenvalues, provided by user with a dimension of at least `n`
 85: - neig - actual number of eigenvalues computed (will be less than or equal to `n`)

 87:   Options Database Key:
 88: . -ksp_view_eigenvalues - Prints eigenvalues to stdout

 90:   Level: advanced

 92:   Notes:
 93:   The number of eigenvalues estimated depends on the size of the Krylov space
 94:   generated during the `KSPSolve()` ; for example, with
 95:   `KSPCG` it corresponds to the number of CG iterations, for `KSPGMRES` it is the number
 96:   of GMRES iterations SINCE the last restart. Any extra space in `r` and `c`
 97:   will be ignored.

 99:   `KSPComputeEigenvalues()` does not usually provide accurate estimates; it is
100:   intended only for assistance in understanding the convergence of iterative
101:   methods, not for eigenanalysis. For accurate computation of eigenvalues we recommend using
102:   the excellent package SLEPc.

104:   One must call `KSPSetComputeEigenvalues()` before calling `KSPSetUp()`
105:   in order for this routine to work correctly.

107:   Many users may just want to use the monitoring routine
108:   `KSPMonitorSingularValue()` (which can be set with option -ksp_monitor_singular_value)
109:   to print the singular values at each iteration of the linear solve.

111:   `KSPComputeRitz()` provides estimates for both the eigenvalues and their corresponding eigenvectors.

113: .seealso: [](ch_ksp), `KSPSetComputeEigenvalues()`, `KSPSetComputeSingularValues()`, `KSPMonitorSingularValue()`, `KSPComputeExtremeSingularValues()`, `KSP`, `KSPComputeRitz()`
114: @*/
115: PetscErrorCode KSPComputeEigenvalues(KSP ksp, PetscInt n, PetscReal r[], PetscReal c[], PetscInt *neig)
116: {
117:   PetscFunctionBegin;
119:   if (n) PetscAssertPointer(r, 3);
120:   if (n) PetscAssertPointer(c, 4);
121:   PetscCheck(n >= 0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Requested < 0 Eigenvalues");
122:   PetscAssertPointer(neig, 5);
123:   PetscCheck(ksp->calc_sings, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONGSTATE, "Eigenvalues not requested before KSPSetUp()");

125:   if (n && ksp->ops->computeeigenvalues) PetscUseTypeMethod(ksp, computeeigenvalues, n, r, c, neig);
126:   else *neig = 0;
127:   PetscFunctionReturn(PETSC_SUCCESS);
128: }

130: /*@
131:   KSPComputeRitz - Computes the Ritz or harmonic Ritz pairs associated with the
132:   smallest or largest in modulus, for the preconditioned operator.

134:   Not Collective

136:   Input Parameters:
137: + ksp   - iterative context obtained from `KSPCreate()`
138: . ritz  - `PETSC_TRUE` or `PETSC_FALSE` for Ritz pairs or harmonic Ritz pairs, respectively
139: - small - `PETSC_TRUE` or `PETSC_FALSE` for smallest or largest (harmonic) Ritz values, respectively

141:   Output Parameters:
142: + nrit  - On input number of (harmonic) Ritz pairs to compute; on output, actual number of computed (harmonic) Ritz pairs
143: . S     - an array of the Ritz vectors, pass in an array of vectors of size `nrit`
144: . tetar - real part of the Ritz values, pass in an array of size `nrit`
145: - tetai - imaginary part of the Ritz values, pass in an array of size `nrit`

147:   Level: advanced

149:   Notes:
150:   This only works with a `KSPType` of `KSPGMRES`.

152:   One must call `KSPSetComputeRitz()` before calling `KSPSetUp()` in order for this routine to work correctly.

154:   This routine must be called after `KSPSolve()`.

156:   In `KSPGMRES`, the (harmonic) Ritz pairs are computed from the Hessenberg matrix obtained during
157:   the last complete cycle of the GMRES solve, or during the partial cycle if the solve ended before
158:   a restart (that is a complete GMRES cycle was never achieved).

160:   The number of actual (harmonic) Ritz pairs computed is less than or equal to the restart
161:   parameter for GMRES if a complete cycle has been performed or less or equal to the number of GMRES
162:   iterations.

164:   `KSPComputeEigenvalues()` provides estimates for only the eigenvalues (Ritz values).

166:   For real matrices, the (harmonic) Ritz pairs can be complex-valued. In such a case,
167:   the routine selects the complex (harmonic) Ritz value and its conjugate, and two successive entries of the
168:   vectors `S` are equal to the real and the imaginary parts of the associated vectors.
169:   When PETSc has been built with complex scalars, the real and imaginary parts of the Ritz
170:   values are still returned in `tetar` and `tetai`, as is done in `KSPComputeEigenvalues()`, but
171:   the Ritz vectors S are complex.

173:   The (harmonic) Ritz pairs are given in order of increasing (harmonic) Ritz values in modulus.

175:   The Ritz pairs do not necessarily accurately reflect the eigenvalues and eigenvectors of the operator, consider the
176:   excellent package `SLEPc` if accurate values are required.

178: .seealso: [](ch_ksp), `KSPSetComputeRitz()`, `KSP`, `KSPGMRES`, `KSPComputeEigenvalues()`, `KSPSetComputeSingularValues()`, `KSPMonitorSingularValue()`
179: @*/
180: PetscErrorCode KSPComputeRitz(KSP ksp, PetscBool ritz, PetscBool small, PetscInt *nrit, Vec S[], PetscReal tetar[], PetscReal tetai[])
181: {
182:   PetscFunctionBegin;
184:   PetscCheck(ksp->calc_ritz, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONGSTATE, "Ritz pairs not requested before KSPSetUp()");
185:   PetscTryTypeMethod(ksp, computeritz, ritz, small, nrit, S, tetar, tetai);
186:   PetscFunctionReturn(PETSC_SUCCESS);
187: }
188: /*@
189:   KSPSetUpOnBlocks - Sets up the preconditioner for each block in
190:   the block Jacobi, block Gauss-Seidel, and overlapping Schwarz
191:   methods.

193:   Collective

195:   Input Parameter:
196: . ksp - the `KSP` context

198:   Level: advanced

200:   Notes:
201:   `KSPSetUpOnBlocks()` is a routine that the user can optionally call for
202:   more precise profiling (via -log_view) of the setup phase for these
203:   block preconditioners.  If the user does not call `KSPSetUpOnBlocks()`,
204:   it will automatically be called from within `KSPSolve()`.

206:   Calling `KSPSetUpOnBlocks()` is the same as calling `PCSetUpOnBlocks()`
207:   on the PC context within the `KSP` context.

209: .seealso: [](ch_ksp), `PCSetUpOnBlocks()`, `KSPSetUp()`, `PCSetUp()`, `KSP`
210: @*/
211: PetscErrorCode KSPSetUpOnBlocks(KSP ksp)
212: {
213:   PC             pc;
214:   PCFailedReason pcreason;

216:   PetscFunctionBegin;
218:   level++;
219:   PetscCall(KSPGetPC(ksp, &pc));
220:   PetscCall(PCSetUpOnBlocks(pc));
221:   PetscCall(PCGetFailedReasonRank(pc, &pcreason));
222:   level--;
223:   /*
224:      This is tricky since only a subset of MPI ranks may set this; each KSPSolve_*() is responsible for checking
225:      this flag and initializing an appropriate vector with VecSetInf() so that the first norm computation can
226:      produce a result at KSPCheckNorm() thus communicating the known problem to all MPI ranks so they may
227:      terminate the Krylov solve. For many KSP implementations this is handled within KSPInitialResidual()
228:   */
229:   if (pcreason) ksp->reason = KSP_DIVERGED_PC_FAILED;
230:   PetscFunctionReturn(PETSC_SUCCESS);
231: }

233: /*@
234:   KSPSetReusePreconditioner - reuse the current preconditioner, do not construct a new one even if the operator changes

236:   Collective

238:   Input Parameters:
239: + ksp  - iterative context obtained from `KSPCreate()`
240: - flag - `PETSC_TRUE` to reuse the current preconditioner

242:   Level: intermediate

244: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSolve()`, `KSPDestroy()`, `PCSetReusePreconditioner()`, `KSP`
245: @*/
246: PetscErrorCode KSPSetReusePreconditioner(KSP ksp, PetscBool flag)
247: {
248:   PC pc;

250:   PetscFunctionBegin;
252:   PetscCall(KSPGetPC(ksp, &pc));
253:   PetscCall(PCSetReusePreconditioner(pc, flag));
254:   PetscFunctionReturn(PETSC_SUCCESS);
255: }

257: /*@
258:   KSPGetReusePreconditioner - Determines if the `KSP` reuses the current preconditioner even if the operator in the preconditioner has changed.

260:   Collective

262:   Input Parameter:
263: . ksp - iterative context obtained from `KSPCreate()`

265:   Output Parameter:
266: . flag - the boolean flag

268:   Level: intermediate

270: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSolve()`, `KSPDestroy()`, `KSPSetReusePreconditioner()`, `KSP`
271: @*/
272: PetscErrorCode KSPGetReusePreconditioner(KSP ksp, PetscBool *flag)
273: {
274:   PetscFunctionBegin;
276:   PetscAssertPointer(flag, 2);
277:   *flag = PETSC_FALSE;
278:   if (ksp->pc) PetscCall(PCGetReusePreconditioner(ksp->pc, flag));
279:   PetscFunctionReturn(PETSC_SUCCESS);
280: }

282: /*@
283:   KSPSetSkipPCSetFromOptions - prevents `KSPSetFromOptions()` from calling `PCSetFromOptions()`. This is used if the same `PC` is shared by more than one `KSP` so its options are not resettable for each `KSP`

285:   Collective

287:   Input Parameters:
288: + ksp  - iterative context obtained from `KSPCreate()`
289: - flag - `PETSC_TRUE` to skip calling the `PCSetFromOptions()`

291:   Level: intermediate

293: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSolve()`, `KSPDestroy()`, `PCSetReusePreconditioner()`, `KSP`
294: @*/
295: PetscErrorCode KSPSetSkipPCSetFromOptions(KSP ksp, PetscBool flag)
296: {
297:   PetscFunctionBegin;
299:   ksp->skippcsetfromoptions = flag;
300:   PetscFunctionReturn(PETSC_SUCCESS);
301: }

303: /*@
304:   KSPSetUp - Sets up the internal data structures for the
305:   later use of an iterative solver.

307:   Collective

309:   Input Parameter:
310: . ksp - iterative context obtained from `KSPCreate()`

312:   Level: developer

314: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSolve()`, `KSPDestroy()`, `KSP`
315: @*/
316: PetscErrorCode KSPSetUp(KSP ksp)
317: {
318:   Mat            A, B;
319:   Mat            mat, pmat;
320:   MatNullSpace   nullsp;
321:   PCFailedReason pcreason;
322:   PC             pc;
323:   PetscBool      pcmpi;

325:   PetscFunctionBegin;
327:   PetscCall(KSPGetPC(ksp, &pc));
328:   PetscCall(PetscObjectTypeCompare((PetscObject)pc, PCMPI, &pcmpi));
329:   if (pcmpi) {
330:     PetscBool ksppreonly;
331:     PetscCall(PetscObjectTypeCompare((PetscObject)ksp, KSPPREONLY, &ksppreonly));
332:     if (!ksppreonly) PetscCall(KSPSetType(ksp, KSPPREONLY));
333:   }
334:   level++;

336:   /* reset the convergence flag from the previous solves */
337:   ksp->reason = KSP_CONVERGED_ITERATING;

339:   if (!((PetscObject)ksp)->type_name) PetscCall(KSPSetType(ksp, KSPGMRES));
340:   PetscCall(KSPSetUpNorms_Private(ksp, PETSC_TRUE, &ksp->normtype, &ksp->pc_side));

342:   if (ksp->dmActive && !ksp->setupstage) {
343:     /* first time in so build matrix and vector data structures using DM */
344:     if (!ksp->vec_rhs) PetscCall(DMCreateGlobalVector(ksp->dm, &ksp->vec_rhs));
345:     if (!ksp->vec_sol) PetscCall(DMCreateGlobalVector(ksp->dm, &ksp->vec_sol));
346:     PetscCall(DMCreateMatrix(ksp->dm, &A));
347:     PetscCall(KSPSetOperators(ksp, A, A));
348:     PetscCall(PetscObjectDereference((PetscObject)A));
349:   }

351:   if (ksp->dmActive) {
352:     DMKSP kdm;
353:     PetscCall(DMGetDMKSP(ksp->dm, &kdm));

355:     if (kdm->ops->computeinitialguess && ksp->setupstage != KSP_SETUP_NEWRHS) {
356:       /* only computes initial guess the first time through */
357:       PetscCallBack("KSP callback initial guess", (*kdm->ops->computeinitialguess)(ksp, ksp->vec_sol, kdm->initialguessctx));
358:       PetscCall(KSPSetInitialGuessNonzero(ksp, PETSC_TRUE));
359:     }
360:     if (kdm->ops->computerhs) PetscCallBack("KSP callback rhs", (*kdm->ops->computerhs)(ksp, ksp->vec_rhs, kdm->rhsctx));

362:     if (ksp->setupstage != KSP_SETUP_NEWRHS) {
363:       PetscCheck(kdm->ops->computeoperators, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONGSTATE, "You called KSPSetDM() but did not use DMKSPSetComputeOperators() or KSPSetDMActive(ksp,PETSC_FALSE);");
364:       PetscCall(KSPGetOperators(ksp, &A, &B));
365:       PetscCallBack("KSP callback operators", (*kdm->ops->computeoperators)(ksp, A, B, kdm->operatorsctx));
366:     }
367:   }

369:   if (ksp->setupstage == KSP_SETUP_NEWRHS) {
370:     level--;
371:     PetscFunctionReturn(PETSC_SUCCESS);
372:   }
373:   PetscCall(PetscLogEventBegin(KSP_SetUp, ksp, ksp->vec_rhs, ksp->vec_sol, 0));

375:   switch (ksp->setupstage) {
376:   case KSP_SETUP_NEW:
377:     PetscUseTypeMethod(ksp, setup);
378:     break;
379:   case KSP_SETUP_NEWMATRIX: /* This should be replaced with a more general mechanism */
380:     if (ksp->setupnewmatrix) PetscUseTypeMethod(ksp, setup);
381:     break;
382:   default:
383:     break;
384:   }

386:   if (!ksp->pc) PetscCall(KSPGetPC(ksp, &ksp->pc));
387:   PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
388:   /* scale the matrix if requested */
389:   if (ksp->dscale) {
390:     PetscScalar *xx;
391:     PetscInt     i, n;
392:     PetscBool    zeroflag = PETSC_FALSE;

394:     if (!ksp->diagonal) { /* allocate vector to hold diagonal */
395:       PetscCall(MatCreateVecs(pmat, &ksp->diagonal, NULL));
396:     }
397:     PetscCall(MatGetDiagonal(pmat, ksp->diagonal));
398:     PetscCall(VecGetLocalSize(ksp->diagonal, &n));
399:     PetscCall(VecGetArray(ksp->diagonal, &xx));
400:     for (i = 0; i < n; i++) {
401:       if (xx[i] != 0.0) xx[i] = 1.0 / PetscSqrtReal(PetscAbsScalar(xx[i]));
402:       else {
403:         xx[i]    = 1.0;
404:         zeroflag = PETSC_TRUE;
405:       }
406:     }
407:     PetscCall(VecRestoreArray(ksp->diagonal, &xx));
408:     if (zeroflag) PetscCall(PetscInfo(ksp, "Zero detected in diagonal of matrix, using 1 at those locations\n"));
409:     PetscCall(MatDiagonalScale(pmat, ksp->diagonal, ksp->diagonal));
410:     if (mat != pmat) PetscCall(MatDiagonalScale(mat, ksp->diagonal, ksp->diagonal));
411:     ksp->dscalefix2 = PETSC_FALSE;
412:   }
413:   PetscCall(PetscLogEventEnd(KSP_SetUp, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
414:   PetscCall(PCSetErrorIfFailure(ksp->pc, ksp->errorifnotconverged));
415:   PetscCall(PCSetUp(ksp->pc));
416:   PetscCall(PCGetFailedReasonRank(ksp->pc, &pcreason));
417:   /* 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 */
418:   if (pcreason) ksp->reason = KSP_DIVERGED_PC_FAILED;

420:   PetscCall(MatGetNullSpace(mat, &nullsp));
421:   if (nullsp) {
422:     PetscBool test = PETSC_FALSE;
423:     PetscCall(PetscOptionsGetBool(((PetscObject)ksp)->options, ((PetscObject)ksp)->prefix, "-ksp_test_null_space", &test, NULL));
424:     if (test) PetscCall(MatNullSpaceTest(nullsp, mat, NULL));
425:   }
426:   ksp->setupstage = KSP_SETUP_NEWRHS;
427:   level--;
428:   PetscFunctionReturn(PETSC_SUCCESS);
429: }

431: /*@C
432:   KSPConvergedReasonView - Displays the reason a `KSP` solve converged or diverged to a viewer

434:   Collective

436:   Input Parameters:
437: + ksp    - iterative context obtained from `KSPCreate()`
438: - viewer - the viewer to display the reason

440:   Options Database Keys:
441: + -ksp_converged_reason          - print reason for converged or diverged, also prints number of iterations
442: - -ksp_converged_reason ::failed - only print reason and number of iterations when diverged

444:   Level: beginner

446:   Note:
447:   To change the format of the output call `PetscViewerPushFormat`(`viewer`,`format`) before this call. Use `PETSC_VIEWER_DEFAULT` for the default,
448:   use `PETSC_VIEWER_FAILED` to only display a reason if it fails.

450: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPDestroy()`, `KSPSetTolerances()`, `KSPConvergedDefault()`,
451:           `KSPSolveTranspose()`, `KSPGetIterationNumber()`, `KSP`, `KSPGetConvergedReason()`, `PetscViewerPushFormat()`, `PetscViewerPopFormat()`
452: @*/
453: PetscErrorCode KSPConvergedReasonView(KSP ksp, PetscViewer viewer)
454: {
455:   PetscBool         isAscii;
456:   PetscViewerFormat format;

458:   PetscFunctionBegin;
459:   if (!viewer) viewer = PETSC_VIEWER_STDOUT_(PetscObjectComm((PetscObject)ksp));
460:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isAscii));
461:   if (isAscii) {
462:     PetscCall(PetscViewerGetFormat(viewer, &format));
463:     PetscCall(PetscViewerASCIIAddTab(viewer, ((PetscObject)ksp)->tablevel));
464:     if (ksp->reason > 0 && format != PETSC_VIEWER_FAILED) {
465:       if (((PetscObject)ksp)->prefix) {
466:         PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve converged due to %s iterations %" PetscInt_FMT "\n", ((PetscObject)ksp)->prefix, KSPConvergedReasons[ksp->reason], ksp->its));
467:       } else {
468:         PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve converged due to %s iterations %" PetscInt_FMT "\n", KSPConvergedReasons[ksp->reason], ksp->its));
469:       }
470:     } else if (ksp->reason <= 0) {
471:       if (((PetscObject)ksp)->prefix) {
472:         PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve did not converge due to %s iterations %" PetscInt_FMT "\n", ((PetscObject)ksp)->prefix, KSPConvergedReasons[ksp->reason], ksp->its));
473:       } else {
474:         PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve did not converge due to %s iterations %" PetscInt_FMT "\n", KSPConvergedReasons[ksp->reason], ksp->its));
475:       }
476:       if (ksp->reason == KSP_DIVERGED_PC_FAILED) {
477:         PCFailedReason reason;
478:         PetscCall(PCGetFailedReason(ksp->pc, &reason));
479:         PetscCall(PetscViewerASCIIPrintf(viewer, "               PC failed due to %s \n", PCFailedReasons[reason]));
480:       }
481:     }
482:     PetscCall(PetscViewerASCIISubtractTab(viewer, ((PetscObject)ksp)->tablevel));
483:   }
484:   PetscFunctionReturn(PETSC_SUCCESS);
485: }

487: /*@C
488:   KSPConvergedReasonViewSet - Sets an ADDITIONAL function that is to be used at the
489:   end of the linear solver to display the convergence reason of the linear solver.

491:   Logically Collective

493:   Input Parameters:
494: + ksp               - the `KSP` context
495: . f                 - the ksp converged reason view function
496: . vctx              - [optional] user-defined context for private data for the
497:                       `KSPConvergedReason` view routine (use `NULL` if no context is desired)
498: - reasonviewdestroy - [optional] routine that frees `vctx` (may be `NULL`)

500:   Options Database Keys:
501: + -ksp_converged_reason             - sets a default `KSPConvergedReasonView()`
502: - -ksp_converged_reason_view_cancel - cancels all converged reason viewers that have been hardwired into a code by
503:                                       calls to `KSPConvergedReasonViewSet()`, but does not cancel those set via the options database.

505:   Level: intermediate

507:   Note:
508:   Several different converged reason view routines may be set by calling
509:   `KSPConvergedReasonViewSet()` multiple times; all will be called in the
510:   order in which they were set.

512:   Developer Note:
513:   Should be named KSPConvergedReasonViewAdd().

515: .seealso: [](ch_ksp), `KSPConvergedReasonView()`, `KSPConvergedReasonViewCancel()`
516: @*/
517: PetscErrorCode KSPConvergedReasonViewSet(KSP ksp, PetscErrorCode (*f)(KSP, void *), void *vctx, PetscErrorCode (*reasonviewdestroy)(void **))
518: {
519:   PetscInt  i;
520:   PetscBool identical;

522:   PetscFunctionBegin;
524:   for (i = 0; i < ksp->numberreasonviews; i++) {
525:     PetscCall(PetscMonitorCompare((PetscErrorCode(*)(void))f, vctx, reasonviewdestroy, (PetscErrorCode(*)(void))ksp->reasonview[i], ksp->reasonviewcontext[i], ksp->reasonviewdestroy[i], &identical));
526:     if (identical) PetscFunctionReturn(PETSC_SUCCESS);
527:   }
528:   PetscCheck(ksp->numberreasonviews < MAXKSPREASONVIEWS, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Too many KSP reasonview set");
529:   ksp->reasonview[ksp->numberreasonviews]          = f;
530:   ksp->reasonviewdestroy[ksp->numberreasonviews]   = reasonviewdestroy;
531:   ksp->reasonviewcontext[ksp->numberreasonviews++] = (void *)vctx;
532:   PetscFunctionReturn(PETSC_SUCCESS);
533: }

535: /*@
536:   KSPConvergedReasonViewCancel - Clears all the reasonview functions for a `KSP` object set with `KSPConvergedReasonViewSet()`.

538:   Collective

540:   Input Parameter:
541: . ksp - iterative context obtained from `KSPCreate()`

543:   Level: intermediate

545: .seealso: [](ch_ksp), `KSPCreate()`, `KSPDestroy()`, `KSPReset()`, `KSPConvergedReasonViewSet()`
546: @*/
547: PetscErrorCode KSPConvergedReasonViewCancel(KSP ksp)
548: {
549:   PetscInt i;

551:   PetscFunctionBegin;
553:   for (i = 0; i < ksp->numberreasonviews; i++) {
554:     if (ksp->reasonviewdestroy[i]) PetscCall((*ksp->reasonviewdestroy[i])(&ksp->reasonviewcontext[i]));
555:   }
556:   ksp->numberreasonviews = 0;
557:   PetscFunctionReturn(PETSC_SUCCESS);
558: }

560: /*@
561:   KSPConvergedReasonViewFromOptions - Processes command line options to determine if/how a `KSPReason` is to be viewed.

563:   Collective

565:   Input Parameter:
566: . ksp - the `KSP` object

568:   Level: intermediate

570: .seealso: [](ch_ksp), `KSPConvergedReasonView()`, `KSPConvergedReasonViewSet()`
571: @*/
572: PetscErrorCode KSPConvergedReasonViewFromOptions(KSP ksp)
573: {
574:   PetscViewer       viewer;
575:   PetscBool         flg;
576:   PetscViewerFormat format;
577:   PetscInt          i;

579:   PetscFunctionBegin;
580:   /* Call all user-provided reason review routines */
581:   for (i = 0; i < ksp->numberreasonviews; i++) PetscCall((*ksp->reasonview[i])(ksp, ksp->reasonviewcontext[i]));

583:   /* Call the default PETSc routine */
584:   PetscCall(PetscOptionsGetViewer(PetscObjectComm((PetscObject)ksp), ((PetscObject)ksp)->options, ((PetscObject)ksp)->prefix, "-ksp_converged_reason", &viewer, &format, &flg));
585:   if (flg) {
586:     PetscCall(PetscViewerPushFormat(viewer, format));
587:     PetscCall(KSPConvergedReasonView(ksp, viewer));
588:     PetscCall(PetscViewerPopFormat(viewer));
589:     PetscCall(PetscOptionsRestoreViewer(&viewer));
590:   }
591:   PetscFunctionReturn(PETSC_SUCCESS);
592: }

594: /*@C
595:   KSPConvergedRateView - Displays the convergence rate <https://en.wikipedia.org/wiki/Coefficient_of_determination> of `KSPSolve()` to a viewer

597:   Collective

599:   Input Parameters:
600: + ksp    - iterative context obtained from `KSPCreate()`
601: - viewer - the viewer to display the reason

603:   Options Database Key:
604: . -ksp_converged_rate - print reason for convergence or divergence and the convergence rate (or 0.0 for divergence)

606:   Level: intermediate

608:   Notes:
609:   To change the format of the output, call `PetscViewerPushFormat`(`viewer`,`format`) before this call.

611:   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,
612:   the slope is $\log c$. The coefficient of determination is given by $1 - \frac{\sum_i (y_i - f(x_i))^2}{\sum_i (y_i - \bar y)}$,

614: .seealso: [](ch_ksp), `KSPConvergedReasonView()`, `KSPGetConvergedRate()`, `KSPSetTolerances()`, `KSPConvergedDefault()`
615: @*/
616: PetscErrorCode KSPConvergedRateView(KSP ksp, PetscViewer viewer)
617: {
618:   PetscViewerFormat format;
619:   PetscBool         isAscii;
620:   PetscReal         rrate, rRsq, erate = 0.0, eRsq = 0.0;
621:   PetscInt          its;
622:   const char       *prefix, *reason = KSPConvergedReasons[ksp->reason];

624:   PetscFunctionBegin;
625:   PetscCall(KSPGetOptionsPrefix(ksp, &prefix));
626:   PetscCall(KSPGetIterationNumber(ksp, &its));
627:   PetscCall(KSPComputeConvergenceRate(ksp, &rrate, &rRsq, &erate, &eRsq));
628:   if (!viewer) viewer = PETSC_VIEWER_STDOUT_(PetscObjectComm((PetscObject)ksp));
629:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isAscii));
630:   if (isAscii) {
631:     PetscCall(PetscViewerGetFormat(viewer, &format));
632:     PetscCall(PetscViewerASCIIAddTab(viewer, ((PetscObject)ksp)->tablevel));
633:     if (ksp->reason > 0) {
634:       if (prefix) PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve converged due to %s iterations %" PetscInt_FMT, prefix, reason, its));
635:       else PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve converged due to %s iterations %" PetscInt_FMT, reason, its));
636:       PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
637:       if (rRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " res rate %g R^2 %g", (double)rrate, (double)rRsq));
638:       if (eRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " error rate %g R^2 %g", (double)erate, (double)eRsq));
639:       PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
640:       PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
641:     } else if (ksp->reason <= 0) {
642:       if (prefix) PetscCall(PetscViewerASCIIPrintf(viewer, "Linear %s solve did not converge due to %s iterations %" PetscInt_FMT, prefix, reason, its));
643:       else PetscCall(PetscViewerASCIIPrintf(viewer, "Linear solve did not converge due to %s iterations %" PetscInt_FMT, reason, its));
644:       PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
645:       if (rRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " res rate %g R^2 %g", (double)rrate, (double)rRsq));
646:       if (eRsq >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " error rate %g R^2 %g", (double)erate, (double)eRsq));
647:       PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
648:       PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
649:       if (ksp->reason == KSP_DIVERGED_PC_FAILED) {
650:         PCFailedReason reason;
651:         PetscCall(PCGetFailedReason(ksp->pc, &reason));
652:         PetscCall(PetscViewerASCIIPrintf(viewer, "               PC failed due to %s \n", PCFailedReasons[reason]));
653:       }
654:     }
655:     PetscCall(PetscViewerASCIISubtractTab(viewer, ((PetscObject)ksp)->tablevel));
656:   }
657:   PetscFunctionReturn(PETSC_SUCCESS);
658: }

660: #include <petscdraw.h>

662: static PetscErrorCode KSPViewEigenvalues_Internal(KSP ksp, PetscBool isExplicit, PetscViewer viewer, PetscViewerFormat format)
663: {
664:   PetscReal  *r, *c;
665:   PetscInt    n, i, neig;
666:   PetscBool   isascii, isdraw;
667:   PetscMPIInt rank;

669:   PetscFunctionBegin;
670:   PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)ksp), &rank));
671:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
672:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
673:   if (isExplicit) {
674:     PetscCall(VecGetSize(ksp->vec_sol, &n));
675:     PetscCall(PetscMalloc2(n, &r, n, &c));
676:     PetscCall(KSPComputeEigenvaluesExplicitly(ksp, n, r, c));
677:     neig = n;
678:   } else {
679:     PetscInt nits;

681:     PetscCall(KSPGetIterationNumber(ksp, &nits));
682:     n = nits + 2;
683:     if (!nits) {
684:       PetscCall(PetscViewerASCIIPrintf(viewer, "Zero iterations in solver, cannot approximate any eigenvalues\n"));
685:       PetscFunctionReturn(PETSC_SUCCESS);
686:     }
687:     PetscCall(PetscMalloc2(n, &r, n, &c));
688:     PetscCall(KSPComputeEigenvalues(ksp, n, r, c, &neig));
689:   }
690:   if (isascii) {
691:     PetscCall(PetscViewerASCIIPrintf(viewer, "%s computed eigenvalues\n", isExplicit ? "Explicitly" : "Iteratively"));
692:     for (i = 0; i < neig; ++i) {
693:       if (c[i] >= 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, "%g + %gi\n", (double)r[i], (double)c[i]));
694:       else PetscCall(PetscViewerASCIIPrintf(viewer, "%g - %gi\n", (double)r[i], -(double)c[i]));
695:     }
696:   } else if (isdraw && rank == 0) {
697:     PetscDraw   draw;
698:     PetscDrawSP drawsp;

700:     if (format == PETSC_VIEWER_DRAW_CONTOUR) {
701:       PetscCall(KSPPlotEigenContours_Private(ksp, neig, r, c));
702:     } else {
703:       PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
704:       PetscCall(PetscDrawSPCreate(draw, 1, &drawsp));
705:       PetscCall(PetscDrawSPReset(drawsp));
706:       for (i = 0; i < neig; ++i) PetscCall(PetscDrawSPAddPoint(drawsp, r + i, c + i));
707:       PetscCall(PetscDrawSPDraw(drawsp, PETSC_TRUE));
708:       PetscCall(PetscDrawSPSave(drawsp));
709:       PetscCall(PetscDrawSPDestroy(&drawsp));
710:     }
711:   }
712:   PetscCall(PetscFree2(r, c));
713:   PetscFunctionReturn(PETSC_SUCCESS);
714: }

716: static PetscErrorCode KSPViewSingularvalues_Internal(KSP ksp, PetscViewer viewer, PetscViewerFormat format)
717: {
718:   PetscReal smax, smin;
719:   PetscInt  nits;
720:   PetscBool isascii;

722:   PetscFunctionBegin;
723:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
724:   PetscCall(KSPGetIterationNumber(ksp, &nits));
725:   if (!nits) {
726:     PetscCall(PetscViewerASCIIPrintf(viewer, "Zero iterations in solver, cannot approximate any singular values\n"));
727:     PetscFunctionReturn(PETSC_SUCCESS);
728:   }
729:   PetscCall(KSPComputeExtremeSingularValues(ksp, &smax, &smin));
730:   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)));
731:   PetscFunctionReturn(PETSC_SUCCESS);
732: }

734: static PetscErrorCode KSPViewFinalResidual_Internal(KSP ksp, PetscViewer viewer, PetscViewerFormat format)
735: {
736:   PetscBool isascii;

738:   PetscFunctionBegin;
739:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
740:   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");
741:   if (isascii) {
742:     Mat       A;
743:     Vec       t;
744:     PetscReal norm;

746:     PetscCall(PCGetOperators(ksp->pc, &A, NULL));
747:     PetscCall(VecDuplicate(ksp->vec_rhs, &t));
748:     PetscCall(KSP_MatMult(ksp, A, ksp->vec_sol, t));
749:     PetscCall(VecAYPX(t, -1.0, ksp->vec_rhs));
750:     PetscCall(VecViewFromOptions(t, (PetscObject)ksp, "-ksp_view_final_residual_vec"));
751:     PetscCall(VecNorm(t, NORM_2, &norm));
752:     PetscCall(VecDestroy(&t));
753:     PetscCall(PetscViewerASCIIPrintf(viewer, "KSP final norm of residual %g\n", (double)norm));
754:   }
755:   PetscFunctionReturn(PETSC_SUCCESS);
756: }

758: static PetscErrorCode KSPMonitorPauseFinal_Internal(KSP ksp)
759: {
760:   PetscInt i;

762:   PetscFunctionBegin;
763:   if (!ksp->pauseFinal) PetscFunctionReturn(PETSC_SUCCESS);
764:   for (i = 0; i < ksp->numbermonitors; ++i) {
765:     PetscViewerAndFormat *vf = (PetscViewerAndFormat *)ksp->monitorcontext[i];
766:     PetscDraw             draw;
767:     PetscReal             lpause;

769:     if (!vf) continue;
770:     if (vf->lg) {
771:       if (!PetscCheckPointer(vf->lg, PETSC_OBJECT)) continue;
772:       if (((PetscObject)vf->lg)->classid != PETSC_DRAWLG_CLASSID) continue;
773:       PetscCall(PetscDrawLGGetDraw(vf->lg, &draw));
774:       PetscCall(PetscDrawGetPause(draw, &lpause));
775:       PetscCall(PetscDrawSetPause(draw, -1.0));
776:       PetscCall(PetscDrawPause(draw));
777:       PetscCall(PetscDrawSetPause(draw, lpause));
778:     } else {
779:       PetscBool isdraw;

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;
785:       PetscCall(PetscViewerDrawGetDraw(vf->viewer, 0, &draw));
786:       PetscCall(PetscDrawGetPause(draw, &lpause));
787:       PetscCall(PetscDrawSetPause(draw, -1.0));
788:       PetscCall(PetscDrawPause(draw));
789:       PetscCall(PetscDrawSetPause(draw, lpause));
790:     }
791:   }
792:   PetscFunctionReturn(PETSC_SUCCESS);
793: }

795: static PetscErrorCode KSPSolve_Private(KSP ksp, Vec b, Vec x)
796: {
797:   PetscBool    flg = PETSC_FALSE, inXisinB = PETSC_FALSE, guess_zero;
798:   Mat          mat, pmat;
799:   MPI_Comm     comm;
800:   MatNullSpace nullsp;
801:   Vec          btmp, vec_rhs = NULL;

803:   PetscFunctionBegin;
804:   level++;
805:   comm = PetscObjectComm((PetscObject)ksp);
806:   if (x && x == b) {
807:     PetscCheck(ksp->guess_zero, comm, PETSC_ERR_ARG_INCOMP, "Cannot use x == b with nonzero initial guess");
808:     PetscCall(VecDuplicate(b, &x));
809:     inXisinB = PETSC_TRUE;
810:   }
811:   if (b) {
812:     PetscCall(PetscObjectReference((PetscObject)b));
813:     PetscCall(VecDestroy(&ksp->vec_rhs));
814:     ksp->vec_rhs = b;
815:   }
816:   if (x) {
817:     PetscCall(PetscObjectReference((PetscObject)x));
818:     PetscCall(VecDestroy(&ksp->vec_sol));
819:     ksp->vec_sol = x;
820:   }

822:   if (ksp->viewPre) PetscCall(ObjectView((PetscObject)ksp, ksp->viewerPre, ksp->formatPre));

824:   if (ksp->presolve) PetscCall((*ksp->presolve)(ksp, ksp->vec_rhs, ksp->vec_sol, ksp->prectx));

826:   /* reset the residual history list if requested */
827:   if (ksp->res_hist_reset) ksp->res_hist_len = 0;
828:   if (ksp->err_hist_reset) ksp->err_hist_len = 0;

830:   /* KSPSetUp() scales the matrix if needed */
831:   PetscCall(KSPSetUp(ksp));
832:   PetscCall(KSPSetUpOnBlocks(ksp));

834:   if (ksp->guess) {
835:     PetscObjectState ostate, state;

837:     PetscCall(KSPGuessSetUp(ksp->guess));
838:     PetscCall(PetscObjectStateGet((PetscObject)ksp->vec_sol, &ostate));
839:     PetscCall(KSPGuessFormGuess(ksp->guess, ksp->vec_rhs, ksp->vec_sol));
840:     PetscCall(PetscObjectStateGet((PetscObject)ksp->vec_sol, &state));
841:     if (state != ostate) {
842:       ksp->guess_zero = PETSC_FALSE;
843:     } else {
844:       PetscCall(PetscInfo(ksp, "Using zero initial guess since the KSPGuess object did not change the vector\n"));
845:       ksp->guess_zero = PETSC_TRUE;
846:     }
847:   }

849:   PetscCall(VecSetErrorIfLocked(ksp->vec_sol, 3));

851:   PetscCall(PetscLogEventBegin(!ksp->transpose_solve ? KSP_Solve : KSP_SolveTranspose, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
852:   PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
853:   /* diagonal scale RHS if called for */
854:   if (ksp->dscale) {
855:     PetscCall(VecPointwiseMult(ksp->vec_rhs, ksp->vec_rhs, ksp->diagonal));
856:     /* second time in, but matrix was scaled back to original */
857:     if (ksp->dscalefix && ksp->dscalefix2) {
858:       Mat mat, pmat;

860:       PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
861:       PetscCall(MatDiagonalScale(pmat, ksp->diagonal, ksp->diagonal));
862:       if (mat != pmat) PetscCall(MatDiagonalScale(mat, ksp->diagonal, ksp->diagonal));
863:     }

865:     /* scale initial guess */
866:     if (!ksp->guess_zero) {
867:       if (!ksp->truediagonal) {
868:         PetscCall(VecDuplicate(ksp->diagonal, &ksp->truediagonal));
869:         PetscCall(VecCopy(ksp->diagonal, ksp->truediagonal));
870:         PetscCall(VecReciprocal(ksp->truediagonal));
871:       }
872:       PetscCall(VecPointwiseMult(ksp->vec_sol, ksp->vec_sol, ksp->truediagonal));
873:     }
874:   }
875:   PetscCall(PCPreSolve(ksp->pc, ksp));

877:   if (ksp->guess_zero && !ksp->guess_not_read) PetscCall(VecSet(ksp->vec_sol, 0.0));
878:   if (ksp->guess_knoll) { /* The Knoll trick is independent on the KSPGuess specified */
879:     PetscCall(PCApply(ksp->pc, ksp->vec_rhs, ksp->vec_sol));
880:     PetscCall(KSP_RemoveNullSpace(ksp, ksp->vec_sol));
881:     ksp->guess_zero = PETSC_FALSE;
882:   }

884:   /* can we mark the initial guess as zero for this solve? */
885:   guess_zero = ksp->guess_zero;
886:   if (!ksp->guess_zero) {
887:     PetscReal norm;

889:     PetscCall(VecNormAvailable(ksp->vec_sol, NORM_2, &flg, &norm));
890:     if (flg && !norm) ksp->guess_zero = PETSC_TRUE;
891:   }
892:   if (ksp->transpose_solve) {
893:     PetscCall(MatGetNullSpace(pmat, &nullsp));
894:   } else {
895:     PetscCall(MatGetTransposeNullSpace(pmat, &nullsp));
896:   }
897:   if (nullsp) {
898:     PetscCall(VecDuplicate(ksp->vec_rhs, &btmp));
899:     PetscCall(VecCopy(ksp->vec_rhs, btmp));
900:     PetscCall(MatNullSpaceRemove(nullsp, btmp));
901:     vec_rhs      = ksp->vec_rhs;
902:     ksp->vec_rhs = btmp;
903:   }
904:   PetscCall(VecLockReadPush(ksp->vec_rhs));
905:   PetscUseTypeMethod(ksp, solve);
906:   PetscCall(KSPMonitorPauseFinal_Internal(ksp));

908:   PetscCall(VecLockReadPop(ksp->vec_rhs));
909:   if (nullsp) {
910:     ksp->vec_rhs = vec_rhs;
911:     PetscCall(VecDestroy(&btmp));
912:   }

914:   ksp->guess_zero = guess_zero;

916:   PetscCheck(ksp->reason, comm, PETSC_ERR_PLIB, "Internal error, solver returned without setting converged reason");
917:   ksp->totalits += ksp->its;

919:   PetscCall(KSPConvergedReasonViewFromOptions(ksp));

921:   if (ksp->viewRate) {
922:     PetscCall(PetscViewerPushFormat(ksp->viewerRate, ksp->formatRate));
923:     PetscCall(KSPConvergedRateView(ksp, ksp->viewerRate));
924:     PetscCall(PetscViewerPopFormat(ksp->viewerRate));
925:   }
926:   PetscCall(PCPostSolve(ksp->pc, ksp));

928:   /* diagonal scale solution if called for */
929:   if (ksp->dscale) {
930:     PetscCall(VecPointwiseMult(ksp->vec_sol, ksp->vec_sol, ksp->diagonal));
931:     /* unscale right hand side and matrix */
932:     if (ksp->dscalefix) {
933:       Mat mat, pmat;

935:       PetscCall(VecReciprocal(ksp->diagonal));
936:       PetscCall(VecPointwiseMult(ksp->vec_rhs, ksp->vec_rhs, ksp->diagonal));
937:       PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
938:       PetscCall(MatDiagonalScale(pmat, ksp->diagonal, ksp->diagonal));
939:       if (mat != pmat) PetscCall(MatDiagonalScale(mat, ksp->diagonal, ksp->diagonal));
940:       PetscCall(VecReciprocal(ksp->diagonal));
941:       ksp->dscalefix2 = PETSC_TRUE;
942:     }
943:   }
944:   PetscCall(PetscLogEventEnd(!ksp->transpose_solve ? KSP_Solve : KSP_SolveTranspose, ksp, ksp->vec_rhs, ksp->vec_sol, 0));
945:   if (ksp->guess) PetscCall(KSPGuessUpdate(ksp->guess, ksp->vec_rhs, ksp->vec_sol));
946:   if (ksp->postsolve) PetscCall((*ksp->postsolve)(ksp, ksp->vec_rhs, ksp->vec_sol, ksp->postctx));

948:   PetscCall(PCGetOperators(ksp->pc, &mat, &pmat));
949:   if (ksp->viewEV) PetscCall(KSPViewEigenvalues_Internal(ksp, PETSC_FALSE, ksp->viewerEV, ksp->formatEV));
950:   if (ksp->viewEVExp) PetscCall(KSPViewEigenvalues_Internal(ksp, PETSC_TRUE, ksp->viewerEVExp, ksp->formatEVExp));
951:   if (ksp->viewSV) PetscCall(KSPViewSingularvalues_Internal(ksp, ksp->viewerSV, ksp->formatSV));
952:   if (ksp->viewFinalRes) PetscCall(KSPViewFinalResidual_Internal(ksp, ksp->viewerFinalRes, ksp->formatFinalRes));
953:   if (ksp->viewMat) PetscCall(ObjectView((PetscObject)mat, ksp->viewerMat, ksp->formatMat));
954:   if (ksp->viewPMat) PetscCall(ObjectView((PetscObject)pmat, ksp->viewerPMat, ksp->formatPMat));
955:   if (ksp->viewRhs) PetscCall(ObjectView((PetscObject)ksp->vec_rhs, ksp->viewerRhs, ksp->formatRhs));
956:   if (ksp->viewSol) PetscCall(ObjectView((PetscObject)ksp->vec_sol, ksp->viewerSol, ksp->formatSol));
957:   if (ksp->view) PetscCall(ObjectView((PetscObject)ksp, ksp->viewer, ksp->format));
958:   if (ksp->viewDScale) PetscCall(ObjectView((PetscObject)ksp->diagonal, ksp->viewerDScale, ksp->formatDScale));
959:   if (ksp->viewMatExp) {
960:     Mat A, B;

962:     PetscCall(PCGetOperators(ksp->pc, &A, NULL));
963:     if (ksp->transpose_solve) {
964:       Mat AT;

966:       PetscCall(MatCreateTranspose(A, &AT));
967:       PetscCall(MatComputeOperator(AT, MATAIJ, &B));
968:       PetscCall(MatDestroy(&AT));
969:     } else {
970:       PetscCall(MatComputeOperator(A, MATAIJ, &B));
971:     }
972:     PetscCall(ObjectView((PetscObject)B, ksp->viewerMatExp, ksp->formatMatExp));
973:     PetscCall(MatDestroy(&B));
974:   }
975:   if (ksp->viewPOpExp) {
976:     Mat B;

978:     PetscCall(KSPComputeOperator(ksp, MATAIJ, &B));
979:     PetscCall(ObjectView((PetscObject)B, ksp->viewerPOpExp, ksp->formatPOpExp));
980:     PetscCall(MatDestroy(&B));
981:   }

983:   if (inXisinB) {
984:     PetscCall(VecCopy(x, b));
985:     PetscCall(VecDestroy(&x));
986:   }
987:   PetscCall(PetscObjectSAWsBlock((PetscObject)ksp));
988:   if (ksp->errorifnotconverged && ksp->reason < 0 && ((level == 1) || (ksp->reason != KSP_DIVERGED_ITS))) {
989:     PCFailedReason reason;

991:     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]);
992:     PetscCall(PCGetFailedReason(ksp->pc, &reason));
993:     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]);
994:   }
995:   level--;
996:   PetscFunctionReturn(PETSC_SUCCESS);
997: }

999: /*@
1000:   KSPSolve - Solves linear system.

1002:   Collective

1004:   Input Parameters:
1005: + ksp - iterative context obtained from `KSPCreate()`
1006: . b   - the right hand side vector
1007: - x   - the solution (this may be the same vector as `b`, then `b` will be overwritten with answer)

1009:   Options Database Keys:
1010: + -ksp_view_eigenvalues                      - compute preconditioned operators eigenvalues
1011: . -ksp_view_eigenvalues_explicit             - compute the eigenvalues by forming the dense operator and using LAPACK
1012: . -ksp_view_mat binary                       - save matrix to the default binary viewer
1013: . -ksp_view_pmat binary                      - save matrix used to build preconditioner to the default binary viewer
1014: . -ksp_view_rhs binary                       - save right hand side vector to the default binary viewer
1015: . -ksp_view_solution binary                  - save computed solution vector to the default binary viewer
1016:                                                (can be read later with src/ksp/tutorials/ex10.c for testing solvers)
1017: . -ksp_view_mat_explicit                     - for matrix-free operators, computes the matrix entries and views them
1018: . -ksp_view_preconditioned_operator_explicit - computes the product of the preconditioner and matrix as an explicit matrix and views it
1019: . -ksp_converged_reason                      - print reason for converged or diverged, also prints number of iterations
1020: . -ksp_view_final_residual                   - print 2-norm of true linear system residual at the end of the solution process
1021: . -ksp_error_if_not_converged                - stop the program as soon as an error is detected in a `KSPSolve()`
1022: . -ksp_view_pre                              - print the ksp data structure before the system solution
1023: - -ksp_view                                  - print the ksp data structure at the end of the system solution

1025:   Level: beginner

1027:   Notes:
1028:   If one uses `KSPSetDM()` then `x` or `b` need not be passed. Use `KSPGetSolution()` to access the solution in this case.

1030:   The operator is specified with `KSPSetOperators()`.

1032:   `KSPSolve()` will normally return without generating an error regardless of whether the linear system was solved or if constructing the preconditioner failed.
1033:   Call `KSPGetConvergedReason()` to determine if the solver converged or failed and why. The option -ksp_error_if_not_converged or function `KSPSetErrorIfNotConverged()`
1034:   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
1035:   it may be fine that inner solvers in the preconditioner do not converge during the solution process.

1037:   The number of iterations can be obtained from `KSPGetIterationNumber()`.

1039:   If you provide a matrix that has a `MatSetNullSpace()` and `MatSetTransposeNullSpace()` this will use that information to solve singular systems
1040:   in the least squares sense with a norm minimizing solution.

1042:   $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()`).

1044:   `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
1045:   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
1046:   direction thus the solution which is a linear combination of the search directions has no component in the nullspace(A).

1048:   We recommend always using `KSPGMRES` for such singular systems.
1049:   If nullspace(A) = nullspace(A') (note symmetric matrices always satisfy this property) then both left and right preconditioning will work
1050:   If nullspace(A) != nullspace(A') then left preconditioning will work but right preconditioning may not work (or it may).

1052:   Developer Notes:
1053:   The reason we cannot always solve  nullspace(A) != nullspace(A') systems with right preconditioning is because we need to remove at each iteration
1054:   the nullspace(AB) from the search direction. While we know the nullspace(A) the nullspace(AB) equals B^-1 times the nullspace(A) but except for trivial preconditioners
1055:   such as diagonal scaling we cannot apply the inverse of the preconditioner to a vector and thus cannot compute the nullspace(AB).

1057:   If using a direct method (e.g., via the `KSP` solver
1058:   `KSPPREONLY` and a preconditioner such as `PCLU` or `PCILU`,
1059:   then its=1.  See `KSPSetTolerances()` and `KSPConvergedDefault()`
1060:   for more details.

1062:   Understanding Convergence\:
1063:   The routines `KSPMonitorSet()`, `KSPComputeEigenvalues()`, and
1064:   `KSPComputeEigenvaluesExplicitly()` provide information on additional
1065:   options to monitor convergence and print eigenvalue information.

1067: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPDestroy()`, `KSPSetTolerances()`, `KSPConvergedDefault()`,
1068:           `KSPSolveTranspose()`, `KSPGetIterationNumber()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatSetTransposeNullSpace()`, `KSP`,
1069:           `KSPConvergedReasonView()`, `KSPCheckSolve()`, `KSPSetErrorIfNotConverged()`
1070: @*/
1071: PetscErrorCode KSPSolve(KSP ksp, Vec b, Vec x)
1072: {
1073:   PetscFunctionBegin;
1077:   ksp->transpose_solve = PETSC_FALSE;
1078:   PetscCall(KSPSolve_Private(ksp, b, x));
1079:   PetscFunctionReturn(PETSC_SUCCESS);
1080: }

1082: /*@
1083:   KSPSolveTranspose - Solves a linear system with the transpose of the matrix, $ A^T x = b$.

1085:   Collective

1087:   Input Parameters:
1088: + ksp - iterative context obtained from `KSPCreate()`
1089: . b   - right hand side vector
1090: - x   - solution vector

1092:   Level: developer

1094:   Note:
1095:   For complex numbers this solve the non-Hermitian transpose system.

1097:   Developer Note:
1098:   We need to implement a `KSPSolveHermitianTranspose()`

1100: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPDestroy()`, `KSPSetTolerances()`, `KSPConvergedDefault()`,
1101:           `KSPSolve()`, `KSP`
1102: @*/
1103: PetscErrorCode KSPSolveTranspose(KSP ksp, Vec b, Vec x)
1104: {
1105:   PetscFunctionBegin;
1109:   if (ksp->transpose.use_explicittranspose) {
1110:     Mat J, Jpre;
1111:     PetscCall(KSPGetOperators(ksp, &J, &Jpre));
1112:     if (!ksp->transpose.reuse_transpose) {
1113:       PetscCall(MatTranspose(J, MAT_INITIAL_MATRIX, &ksp->transpose.AT));
1114:       if (J != Jpre) PetscCall(MatTranspose(Jpre, MAT_INITIAL_MATRIX, &ksp->transpose.BT));
1115:       ksp->transpose.reuse_transpose = PETSC_TRUE;
1116:     } else {
1117:       PetscCall(MatTranspose(J, MAT_REUSE_MATRIX, &ksp->transpose.AT));
1118:       if (J != Jpre) PetscCall(MatTranspose(Jpre, MAT_REUSE_MATRIX, &ksp->transpose.BT));
1119:     }
1120:     if (J == Jpre && ksp->transpose.BT != ksp->transpose.AT) {
1121:       PetscCall(PetscObjectReference((PetscObject)ksp->transpose.AT));
1122:       ksp->transpose.BT = ksp->transpose.AT;
1123:     }
1124:     PetscCall(KSPSetOperators(ksp, ksp->transpose.AT, ksp->transpose.BT));
1125:   } else {
1126:     ksp->transpose_solve = PETSC_TRUE;
1127:   }
1128:   PetscCall(KSPSolve_Private(ksp, b, x));
1129:   PetscFunctionReturn(PETSC_SUCCESS);
1130: }

1132: static PetscErrorCode KSPViewFinalMatResidual_Internal(KSP ksp, Mat B, Mat X, PetscViewer viewer, PetscViewerFormat format, PetscInt shift)
1133: {
1134:   Mat        A, R;
1135:   PetscReal *norms;
1136:   PetscInt   i, N;
1137:   PetscBool  flg;

1139:   PetscFunctionBegin;
1140:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &flg));
1141:   if (flg) {
1142:     PetscCall(PCGetOperators(ksp->pc, &A, NULL));
1143:     if (!ksp->transpose_solve) PetscCall(MatMatMult(A, X, MAT_INITIAL_MATRIX, PETSC_DEFAULT, &R));
1144:     else PetscCall(MatTransposeMatMult(A, X, MAT_INITIAL_MATRIX, PETSC_DEFAULT, &R));
1145:     PetscCall(MatAYPX(R, -1.0, B, SAME_NONZERO_PATTERN));
1146:     PetscCall(MatGetSize(R, NULL, &N));
1147:     PetscCall(PetscMalloc1(N, &norms));
1148:     PetscCall(MatGetColumnNorms(R, NORM_2, norms));
1149:     PetscCall(MatDestroy(&R));
1150:     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]));
1151:     PetscCall(PetscFree(norms));
1152:   }
1153:   PetscFunctionReturn(PETSC_SUCCESS);
1154: }

1156: static PetscErrorCode KSPMatSolve_Private(KSP ksp, Mat B, Mat X)
1157: {
1158:   Mat       A, P, vB, vX;
1159:   Vec       cb, cx;
1160:   PetscInt  n1, N1, n2, N2, Bbn = PETSC_DECIDE;
1161:   PetscBool match;

1163:   PetscFunctionBegin;
1167:   PetscCheckSameComm(ksp, 1, B, 2);
1168:   PetscCheckSameComm(ksp, 1, X, 3);
1169:   PetscCheckSameType(B, 2, X, 3);
1170:   PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
1171:   MatCheckPreallocated(X, 3);
1172:   if (!X->assembled) {
1173:     PetscCall(MatSetOption(X, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
1174:     PetscCall(MatAssemblyBegin(X, MAT_FINAL_ASSEMBLY));
1175:     PetscCall(MatAssemblyEnd(X, MAT_FINAL_ASSEMBLY));
1176:   }
1177:   PetscCheck(B != X, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_IDN, "B and X must be different matrices");
1178:   PetscCheck(!ksp->transpose_solve || !ksp->transpose.use_explicittranspose, PetscObjectComm((PetscObject)ksp), PETSC_ERR_SUP, "KSPMatSolveTranspose() does not support -ksp_use_explicittranspose");
1179:   PetscCall(KSPGetOperators(ksp, &A, &P));
1180:   PetscCall(MatGetLocalSize(B, NULL, &n2));
1181:   PetscCall(MatGetLocalSize(X, NULL, &n1));
1182:   PetscCall(MatGetSize(B, NULL, &N2));
1183:   PetscCall(MatGetSize(X, NULL, &N1));
1184:   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);
1185:   PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)B, &match, MATSEQDENSE, MATMPIDENSE, ""));
1186:   PetscCheck(match, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Provided block of right-hand sides not stored in a dense Mat");
1187:   PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)X, &match, MATSEQDENSE, MATMPIDENSE, ""));
1188:   PetscCheck(match, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Provided block of solutions not stored in a dense Mat");
1189:   PetscCall(KSPSetUp(ksp));
1190:   PetscCall(KSPSetUpOnBlocks(ksp));
1191:   if (ksp->ops->matsolve) {
1192:     level++;
1193:     if (ksp->guess_zero) PetscCall(MatZeroEntries(X));
1194:     PetscCall(PetscLogEventBegin(!ksp->transpose_solve ? KSP_MatSolve : KSP_MatSolveTranspose, ksp, B, X, 0));
1195:     PetscCall(KSPGetMatSolveBatchSize(ksp, &Bbn));
1196:     /* by default, do a single solve with all columns */
1197:     if (Bbn == PETSC_DECIDE) Bbn = N2;
1198:     else PetscCheck(Bbn >= 1, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "KSPMatSolve() batch size %" PetscInt_FMT " must be positive", Bbn);
1199:     PetscCall(PetscInfo(ksp, "KSP type %s solving using batches of width at most %" PetscInt_FMT "\n", ((PetscObject)ksp)->type_name, Bbn));
1200:     /* if -ksp_matsolve_batch_size is greater than the actual number of columns, do a single solve with all columns */
1201:     if (Bbn >= N2) {
1202:       PetscUseTypeMethod(ksp, matsolve, B, X);
1203:       if (ksp->viewFinalRes) PetscCall(KSPViewFinalMatResidual_Internal(ksp, B, X, ksp->viewerFinalRes, ksp->formatFinalRes, 0));

1205:       PetscCall(KSPConvergedReasonViewFromOptions(ksp));

1207:       if (ksp->viewRate) {
1208:         PetscCall(PetscViewerPushFormat(ksp->viewerRate, PETSC_VIEWER_DEFAULT));
1209:         PetscCall(KSPConvergedRateView(ksp, ksp->viewerRate));
1210:         PetscCall(PetscViewerPopFormat(ksp->viewerRate));
1211:       }
1212:     } else {
1213:       for (n2 = 0; n2 < N2; n2 += Bbn) {
1214:         PetscCall(MatDenseGetSubMatrix(B, PETSC_DECIDE, PETSC_DECIDE, n2, PetscMin(n2 + Bbn, N2), &vB));
1215:         PetscCall(MatDenseGetSubMatrix(X, PETSC_DECIDE, PETSC_DECIDE, n2, PetscMin(n2 + Bbn, N2), &vX));
1216:         PetscUseTypeMethod(ksp, matsolve, vB, vX);
1217:         if (ksp->viewFinalRes) PetscCall(KSPViewFinalMatResidual_Internal(ksp, vB, vX, ksp->viewerFinalRes, ksp->formatFinalRes, n2));

1219:         PetscCall(KSPConvergedReasonViewFromOptions(ksp));

1221:         if (ksp->viewRate) {
1222:           PetscCall(PetscViewerPushFormat(ksp->viewerRate, PETSC_VIEWER_DEFAULT));
1223:           PetscCall(KSPConvergedRateView(ksp, ksp->viewerRate));
1224:           PetscCall(PetscViewerPopFormat(ksp->viewerRate));
1225:         }
1226:         PetscCall(MatDenseRestoreSubMatrix(B, &vB));
1227:         PetscCall(MatDenseRestoreSubMatrix(X, &vX));
1228:       }
1229:     }
1230:     if (ksp->viewMat) PetscCall(ObjectView((PetscObject)A, ksp->viewerMat, ksp->formatMat));
1231:     if (ksp->viewPMat) PetscCall(ObjectView((PetscObject)P, ksp->viewerPMat, ksp->formatPMat));
1232:     if (ksp->viewRhs) PetscCall(ObjectView((PetscObject)B, ksp->viewerRhs, ksp->formatRhs));
1233:     if (ksp->viewSol) PetscCall(ObjectView((PetscObject)X, ksp->viewerSol, ksp->formatSol));
1234:     if (ksp->view) PetscCall(KSPView(ksp, ksp->viewer));
1235:     PetscCall(PetscLogEventEnd(!ksp->transpose_solve ? KSP_MatSolve : KSP_MatSolveTranspose, ksp, B, X, 0));
1236:     if (ksp->errorifnotconverged && ksp->reason < 0 && (level == 1 || ksp->reason != KSP_DIVERGED_ITS)) {
1237:       PCFailedReason reason;

1239:       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]);
1240:       PetscCall(PCGetFailedReason(ksp->pc, &reason));
1241:       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]);
1242:     }
1243:     level--;
1244:   } else {
1245:     PetscCall(PetscInfo(ksp, "KSP type %s solving column by column\n", ((PetscObject)ksp)->type_name));
1246:     for (n2 = 0; n2 < N2; ++n2) {
1247:       PetscCall(MatDenseGetColumnVecRead(B, n2, &cb));
1248:       PetscCall(MatDenseGetColumnVecWrite(X, n2, &cx));
1249:       PetscCall(KSPSolve_Private(ksp, cb, cx));
1250:       PetscCall(MatDenseRestoreColumnVecWrite(X, n2, &cx));
1251:       PetscCall(MatDenseRestoreColumnVecRead(B, n2, &cb));
1252:     }
1253:   }
1254:   PetscFunctionReturn(PETSC_SUCCESS);
1255: }

1257: /*@
1258:   KSPMatSolve - Solves a linear system with multiple right-hand sides stored as a `MATDENSE`. Unlike `KSPSolve()`, `B` and `X` must be different matrices.

1260:   Input Parameters:
1261: + ksp - iterative context
1262: - B   - block of right-hand sides

1264:   Output Parameter:
1265: . X - block of solutions

1267:   Level: intermediate

1269:   Note:
1270:   This is a stripped-down version of `KSPSolve()`, which only handles `-ksp_view`, `-ksp_converged_reason`, `-ksp_converged_rate`, and `-ksp_view_final_residual`.

1272: .seealso: [](ch_ksp), `KSPSolve()`, `MatMatSolve()`, `KSPMatSolveTranspose()`, `MATDENSE`, `KSPHPDDM`, `PCBJACOBI`, `PCASM`
1273: @*/
1274: PetscErrorCode KSPMatSolve(KSP ksp, Mat B, Mat X)
1275: {
1276:   PetscFunctionBegin;
1277:   ksp->transpose_solve = PETSC_FALSE;
1278:   PetscCall(KSPMatSolve_Private(ksp, B, X));
1279:   PetscFunctionReturn(PETSC_SUCCESS);
1280: }

1282: /*@
1283:   KSPMatSolveTranspose - Solves a linear system with the transposed matrix with multiple right-hand sides stored as a `MATDENSE`. Unlike `KSPSolveTranspose()`,
1284:   `B` and `X` must be different matrices and the transposed matrix cannot be assembled explicitly for the user.

1286:   Input Parameters:
1287: + ksp - iterative context
1288: - B   - block of right-hand sides

1290:   Output Parameter:
1291: . X - block of solutions

1293:   Level: intermediate

1295:   Note:
1296:   This is a stripped-down version of `KSPSolveTranspose()`, which only handles `-ksp_view`, `-ksp_converged_reason`, `-ksp_converged_rate`, and `-ksp_view_final_residual`.

1298: .seealso: [](ch_ksp), `KSPSolveTranspose()`, `MatMatTransposeSolve()`, `KSPMatSolve()`, `MATDENSE`, `KSPHPDDM`, `PCBJACOBI`, `PCASM`
1299: @*/
1300: PetscErrorCode KSPMatSolveTranspose(KSP ksp, Mat B, Mat X)
1301: {
1302:   PetscFunctionBegin;
1303:   ksp->transpose_solve = PETSC_TRUE;
1304:   PetscCall(KSPMatSolve_Private(ksp, B, X));
1305:   PetscFunctionReturn(PETSC_SUCCESS);
1306: }

1308: /*@
1309:   KSPSetMatSolveBatchSize - Sets the maximum number of columns treated simultaneously in `KSPMatSolve()`.

1311:   Logically Collective

1313:   Input Parameters:
1314: + ksp - iterative context
1315: - bs  - batch size

1317:   Level: advanced

1319: .seealso: [](ch_ksp), `KSPMatSolve()`, `KSPGetMatSolveBatchSize()`, `-mat_mumps_icntl_27`, `-matmatmult_Bbn`
1320: @*/
1321: PetscErrorCode KSPSetMatSolveBatchSize(KSP ksp, PetscInt bs)
1322: {
1323:   PetscFunctionBegin;
1326:   ksp->nmax = bs;
1327:   PetscFunctionReturn(PETSC_SUCCESS);
1328: }

1330: /*@
1331:   KSPGetMatSolveBatchSize - Gets the maximum number of columns treated simultaneously in `KSPMatSolve()`.

1333:   Input Parameter:
1334: . ksp - iterative context

1336:   Output Parameter:
1337: . bs - batch size

1339:   Level: advanced

1341: .seealso: [](ch_ksp), `KSPMatSolve()`, `KSPSetMatSolveBatchSize()`, `-mat_mumps_icntl_27`, `-matmatmult_Bbn`
1342: @*/
1343: PetscErrorCode KSPGetMatSolveBatchSize(KSP ksp, PetscInt *bs)
1344: {
1345:   PetscFunctionBegin;
1347:   PetscAssertPointer(bs, 2);
1348:   *bs = ksp->nmax;
1349:   PetscFunctionReturn(PETSC_SUCCESS);
1350: }

1352: /*@
1353:   KSPResetViewers - Resets all the viewers set from the options database during `KSPSetFromOptions()`

1355:   Collective

1357:   Input Parameter:
1358: . ksp - iterative context obtained from `KSPCreate()`

1360:   Level: beginner

1362: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPSolve()`, `KSPSetFromOptions()`, `KSP`
1363: @*/
1364: PetscErrorCode KSPResetViewers(KSP ksp)
1365: {
1366:   PetscFunctionBegin;
1368:   if (!ksp) PetscFunctionReturn(PETSC_SUCCESS);
1369:   PetscCall(PetscOptionsRestoreViewer(&ksp->viewer));
1370:   PetscCall(PetscOptionsRestoreViewer(&ksp->viewerPre));
1371:   PetscCall(PetscOptionsRestoreViewer(&ksp->viewerRate));
1372:   PetscCall(PetscOptionsRestoreViewer(&ksp->viewerMat));
1373:   PetscCall(PetscOptionsRestoreViewer(&ksp->viewerPMat));
1374:   PetscCall(PetscOptionsRestoreViewer(&ksp->viewerRhs));
1375:   PetscCall(PetscOptionsRestoreViewer(&ksp->viewerSol));
1376:   PetscCall(PetscOptionsRestoreViewer(&ksp->viewerMatExp));
1377:   PetscCall(PetscOptionsRestoreViewer(&ksp->viewerEV));
1378:   PetscCall(PetscOptionsRestoreViewer(&ksp->viewerSV));
1379:   PetscCall(PetscOptionsRestoreViewer(&ksp->viewerEVExp));
1380:   PetscCall(PetscOptionsRestoreViewer(&ksp->viewerFinalRes));
1381:   PetscCall(PetscOptionsRestoreViewer(&ksp->viewerPOpExp));
1382:   PetscCall(PetscOptionsRestoreViewer(&ksp->viewerDScale));
1383:   ksp->view         = PETSC_FALSE;
1384:   ksp->viewPre      = PETSC_FALSE;
1385:   ksp->viewMat      = PETSC_FALSE;
1386:   ksp->viewPMat     = PETSC_FALSE;
1387:   ksp->viewRhs      = PETSC_FALSE;
1388:   ksp->viewSol      = PETSC_FALSE;
1389:   ksp->viewMatExp   = PETSC_FALSE;
1390:   ksp->viewEV       = PETSC_FALSE;
1391:   ksp->viewSV       = PETSC_FALSE;
1392:   ksp->viewEVExp    = PETSC_FALSE;
1393:   ksp->viewFinalRes = PETSC_FALSE;
1394:   ksp->viewPOpExp   = PETSC_FALSE;
1395:   ksp->viewDScale   = PETSC_FALSE;
1396:   PetscFunctionReturn(PETSC_SUCCESS);
1397: }

1399: /*@
1400:   KSPReset - Resets a `KSP` context to the kspsetupcalled = 0 state and removes any allocated Vecs and Mats

1402:   Collective

1404:   Input Parameter:
1405: . ksp - iterative context obtained from `KSPCreate()`

1407:   Level: beginner

1409: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPSolve()`, `KSP`
1410: @*/
1411: PetscErrorCode KSPReset(KSP ksp)
1412: {
1413:   PetscFunctionBegin;
1415:   if (!ksp) PetscFunctionReturn(PETSC_SUCCESS);
1416:   PetscTryTypeMethod(ksp, reset);
1417:   if (ksp->pc) PetscCall(PCReset(ksp->pc));
1418:   if (ksp->guess) {
1419:     KSPGuess guess = ksp->guess;
1420:     PetscTryTypeMethod(guess, reset);
1421:   }
1422:   PetscCall(VecDestroyVecs(ksp->nwork, &ksp->work));
1423:   PetscCall(VecDestroy(&ksp->vec_rhs));
1424:   PetscCall(VecDestroy(&ksp->vec_sol));
1425:   PetscCall(VecDestroy(&ksp->diagonal));
1426:   PetscCall(VecDestroy(&ksp->truediagonal));

1428:   ksp->setupstage = KSP_SETUP_NEW;
1429:   ksp->nmax       = PETSC_DECIDE;
1430:   PetscFunctionReturn(PETSC_SUCCESS);
1431: }

1433: /*@C
1434:   KSPDestroy - Destroys a `KSP` context.

1436:   Collective

1438:   Input Parameter:
1439: . ksp - iterative context obtained from `KSPCreate()`

1441:   Level: beginner

1443: .seealso: [](ch_ksp), `KSPCreate()`, `KSPSetUp()`, `KSPSolve()`, `KSP`
1444: @*/
1445: PetscErrorCode KSPDestroy(KSP *ksp)
1446: {
1447:   PC pc;

1449:   PetscFunctionBegin;
1450:   if (!*ksp) PetscFunctionReturn(PETSC_SUCCESS);
1452:   if (--((PetscObject)*ksp)->refct > 0) {
1453:     *ksp = NULL;
1454:     PetscFunctionReturn(PETSC_SUCCESS);
1455:   }

1457:   PetscCall(PetscObjectSAWsViewOff((PetscObject)*ksp));

1459:   /*
1460:    Avoid a cascading call to PCReset(ksp->pc) from the following call:
1461:    PCReset() shouldn't be called from KSPDestroy() as it is unprotected by pc's
1462:    refcount (and may be shared, e.g., by other ksps).
1463:    */
1464:   pc         = (*ksp)->pc;
1465:   (*ksp)->pc = NULL;
1466:   PetscCall(KSPReset(*ksp));
1467:   PetscCall(KSPResetViewers(*ksp));
1468:   (*ksp)->pc = pc;
1469:   PetscTryTypeMethod(*ksp, destroy);

1471:   if ((*ksp)->transpose.use_explicittranspose) {
1472:     PetscCall(MatDestroy(&(*ksp)->transpose.AT));
1473:     PetscCall(MatDestroy(&(*ksp)->transpose.BT));
1474:     (*ksp)->transpose.reuse_transpose = PETSC_FALSE;
1475:   }

1477:   PetscCall(KSPGuessDestroy(&(*ksp)->guess));
1478:   PetscCall(DMDestroy(&(*ksp)->dm));
1479:   PetscCall(PCDestroy(&(*ksp)->pc));
1480:   PetscCall(PetscFree((*ksp)->res_hist_alloc));
1481:   PetscCall(PetscFree((*ksp)->err_hist_alloc));
1482:   if ((*ksp)->convergeddestroy) PetscCall((*(*ksp)->convergeddestroy)((*ksp)->cnvP));
1483:   PetscCall(KSPMonitorCancel(*ksp));
1484:   PetscCall(KSPConvergedReasonViewCancel(*ksp));
1485:   PetscCall(PetscHeaderDestroy(ksp));
1486:   PetscFunctionReturn(PETSC_SUCCESS);
1487: }

1489: /*@
1490:   KSPSetPCSide - Sets the preconditioning side.

1492:   Logically Collective

1494:   Input Parameter:
1495: . ksp - iterative context obtained from `KSPCreate()`

1497:   Output Parameter:
1498: . side - the preconditioning side, where side is one of
1499: .vb
1500:       PC_LEFT      - left preconditioning (default)
1501:       PC_RIGHT     - right preconditioning
1502:       PC_SYMMETRIC - symmetric preconditioning
1503: .ve

1505:   Options Database Key:
1506: . -ksp_pc_side <right,left,symmetric> - `KSP` preconditioner side

1508:   Level: intermediate

1510:   Notes:
1511:   Left preconditioning is used by default for most Krylov methods except `KSPFGMRES` which only supports right preconditioning.

1513:   For methods changing the side of the preconditioner changes the norm type that is used, see `KSPSetNormType()`.

1515:   Symmetric preconditioning is currently available only for the `KSPQCG` method. However, note that
1516:   symmetric preconditioning can be emulated by using either right or left
1517:   preconditioning and a pre or post processing step.

1519:   Setting the `PCSide` often affects the default norm type.  See `KSPSetNormType()` for details.

1521: .seealso: [](ch_ksp), `KSPGetPCSide()`, `KSPSetNormType()`, `KSPGetNormType()`, `KSP`
1522: @*/
1523: PetscErrorCode KSPSetPCSide(KSP ksp, PCSide side)
1524: {
1525:   PetscFunctionBegin;
1528:   ksp->pc_side = ksp->pc_side_set = side;
1529:   PetscFunctionReturn(PETSC_SUCCESS);
1530: }

1532: /*@
1533:   KSPGetPCSide - Gets the preconditioning side.

1535:   Not Collective

1537:   Input Parameter:
1538: . ksp - iterative context obtained from `KSPCreate()`

1540:   Output Parameter:
1541: . side - the preconditioning side, where side is one of
1542: .vb
1543:       PC_LEFT      - left preconditioning (default)
1544:       PC_RIGHT     - right preconditioning
1545:       PC_SYMMETRIC - symmetric preconditioning
1546: .ve

1548:   Level: intermediate

1550: .seealso: [](ch_ksp), `KSPSetPCSide()`, `KSP`
1551: @*/
1552: PetscErrorCode KSPGetPCSide(KSP ksp, PCSide *side)
1553: {
1554:   PetscFunctionBegin;
1556:   PetscAssertPointer(side, 2);
1557:   PetscCall(KSPSetUpNorms_Private(ksp, PETSC_TRUE, &ksp->normtype, &ksp->pc_side));
1558:   *side = ksp->pc_side;
1559:   PetscFunctionReturn(PETSC_SUCCESS);
1560: }

1562: /*@
1563:   KSPGetTolerances - Gets the relative, absolute, divergence, and maximum
1564:   iteration tolerances used by the default `KSP` convergence tests.

1566:   Not Collective

1568:   Input Parameter:
1569: . ksp - the Krylov subspace context

1571:   Output Parameters:
1572: + rtol   - the relative convergence tolerance
1573: . abstol - the absolute convergence tolerance
1574: . dtol   - the divergence tolerance
1575: - maxits - maximum number of iterations

1577:   Level: intermediate

1579:   Note:
1580:   The user can specify `NULL` for any parameter that is not needed.

1582: .seealso: [](ch_ksp), `KSPSetTolerances()`, `KSP`, `KSPSetMinimumIterations()`, `KSPGetMinimumIterations()`
1583: @*/
1584: PetscErrorCode KSPGetTolerances(KSP ksp, PetscReal *rtol, PetscReal *abstol, PetscReal *dtol, PetscInt *maxits)
1585: {
1586:   PetscFunctionBegin;
1588:   if (abstol) *abstol = ksp->abstol;
1589:   if (rtol) *rtol = ksp->rtol;
1590:   if (dtol) *dtol = ksp->divtol;
1591:   if (maxits) *maxits = ksp->max_it;
1592:   PetscFunctionReturn(PETSC_SUCCESS);
1593: }

1595: /*@
1596:   KSPSetTolerances - Sets the relative, absolute, divergence, and maximum
1597:   iteration tolerances used by the default `KSP` convergence testers.

1599:   Logically Collective

1601:   Input Parameters:
1602: + ksp    - the Krylov subspace context
1603: . rtol   - the relative convergence tolerance, relative decrease in the (possibly preconditioned) residual norm
1604: . abstol - the absolute convergence tolerance   absolute size of the (possibly preconditioned) residual norm
1605: . dtol   - the divergence tolerance,   amount (possibly preconditioned) residual norm can increase before `KSPConvergedDefault()` concludes that the method is diverging
1606: - maxits - maximum number of iterations to use

1608:   Options Database Keys:
1609: + -ksp_atol <abstol>   - Sets `abstol`
1610: . -ksp_rtol <rtol>     - Sets `rtol`
1611: . -ksp_divtol <dtol>   - Sets `dtol`
1612: - -ksp_max_it <maxits> - Sets `maxits`

1614:   Level: intermediate

1616:   Notes:
1617:   Use `PETSC_DEFAULT` to retain the default value of any of the tolerances.

1619:   See `KSPConvergedDefault()` for details how these parameters are used in the default convergence test.  See also `KSPSetConvergenceTest()`
1620:   for setting user-defined stopping criteria.

1622: .seealso: [](ch_ksp), `KSPGetTolerances()`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSP`, `KSPSetMinimumIterations()`
1623: @*/
1624: PetscErrorCode KSPSetTolerances(KSP ksp, PetscReal rtol, PetscReal abstol, PetscReal dtol, PetscInt maxits)
1625: {
1626:   PetscFunctionBegin;

1633:   if (rtol != (PetscReal)PETSC_DEFAULT) {
1634:     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);
1635:     ksp->rtol = rtol;
1636:   }
1637:   if (abstol != (PetscReal)PETSC_DEFAULT) {
1638:     PetscCheck(abstol >= 0.0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Absolute tolerance %g must be non-negative", (double)abstol);
1639:     ksp->abstol = abstol;
1640:   }
1641:   if (dtol != (PetscReal)PETSC_DEFAULT) {
1642:     PetscCheck(dtol >= 0.0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Divergence tolerance %g must be larger than 1.0", (double)dtol);
1643:     ksp->divtol = dtol;
1644:   }
1645:   if (maxits != PETSC_DEFAULT) {
1646:     PetscCheck(maxits >= 0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Maximum number of iterations %" PetscInt_FMT " must be non-negative", maxits);
1647:     ksp->max_it = maxits;
1648:   }
1649:   PetscFunctionReturn(PETSC_SUCCESS);
1650: }

1652: /*@
1653:   KSPSetMinimumIterations - Sets the minimum number of iterations to use, regardless of the tolerances

1655:   Logically Collective

1657:   Input Parameters:
1658: + ksp   - the Krylov subspace context
1659: - minit - minimum number of iterations to use

1661:   Options Database Key:
1662: . -ksp_min_it <minits> - Sets `minit`

1664:   Level: intermediate

1666:   Notes:
1667:   Use `KSPSetTolerances()` to set a variety of other tolerances

1669:   See `KSPConvergedDefault()` for details on how these parameters are used in the default convergence test. See also `KSPSetConvergenceTest()`
1670:   for setting user-defined stopping criteria.

1672: .seealso: [](ch_ksp), `KSPGetTolerances()`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSP`, `KSPSetTolerances()`, `KSPGetMinimumIterations()`
1673: @*/
1674: PetscErrorCode KSPSetMinimumIterations(KSP ksp, PetscInt minit)
1675: {
1676:   PetscFunctionBegin;

1680:   PetscCheck(minit >= 0, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Minimum number of iterations %" PetscInt_FMT " must be non-negative", minit);
1681:   ksp->min_it = minit;
1682:   PetscFunctionReturn(PETSC_SUCCESS);
1683: }

1685: /*@
1686:   KSPGetMinimumIterations - Gets the minimum number of iterations to use, regardless of the tolerances, that was set with `KSPSetMinimumIterations()` or `-ksp_min_it`

1688:   Not Collective

1690:   Input Parameter:
1691: . ksp - the Krylov subspace context

1693:   Output Parameter:
1694: . minit - minimum number of iterations to use

1696:   Level: intermediate

1698: .seealso: [](ch_ksp), `KSPGetTolerances()`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSP`, `KSPSetTolerances()`, `KSPSetMinimumIterations()`
1699: @*/
1700: PetscErrorCode KSPGetMinimumIterations(KSP ksp, PetscInt *minit)
1701: {
1702:   PetscFunctionBegin;
1704:   PetscAssertPointer(minit, 2);

1706:   *minit = ksp->min_it;
1707:   PetscFunctionReturn(PETSC_SUCCESS);
1708: }

1710: /*@
1711:   KSPSetInitialGuessNonzero - Tells the iterative solver that the
1712:   initial guess is nonzero; otherwise `KSP` assumes the initial guess
1713:   is to be zero (and thus zeros it out before solving).

1715:   Logically Collective

1717:   Input Parameters:
1718: + ksp - iterative context obtained from `KSPCreate()`
1719: - flg - ``PETSC_TRUE`` indicates the guess is non-zero, `PETSC_FALSE` indicates the guess is zero

1721:   Options Database Key:
1722: . -ksp_initial_guess_nonzero <true,false> - use nonzero initial guess

1724:   Level: beginner

1726:   Note:
1727:   If this is not called the X vector is zeroed in the call to `KSPSolve()`.

1729: .seealso: [](ch_ksp), `KSPGetInitialGuessNonzero()`, `KSPGuessSetType()`, `KSPGuessType`, `KSP`
1730: @*/
1731: PetscErrorCode KSPSetInitialGuessNonzero(KSP ksp, PetscBool flg)
1732: {
1733:   PetscFunctionBegin;
1736:   ksp->guess_zero = (PetscBool) !(int)flg;
1737:   PetscFunctionReturn(PETSC_SUCCESS);
1738: }

1740: /*@
1741:   KSPGetInitialGuessNonzero - Determines whether the `KSP` solver is using
1742:   a zero initial guess.

1744:   Not Collective

1746:   Input Parameter:
1747: . ksp - iterative context obtained from `KSPCreate()`

1749:   Output Parameter:
1750: . flag - `PETSC_TRUE` if guess is nonzero, else `PETSC_FALSE`

1752:   Level: intermediate

1754: .seealso: [](ch_ksp), `KSPSetInitialGuessNonzero()`, `KSP`
1755: @*/
1756: PetscErrorCode KSPGetInitialGuessNonzero(KSP ksp, PetscBool *flag)
1757: {
1758:   PetscFunctionBegin;
1760:   PetscAssertPointer(flag, 2);
1761:   if (ksp->guess_zero) *flag = PETSC_FALSE;
1762:   else *flag = PETSC_TRUE;
1763:   PetscFunctionReturn(PETSC_SUCCESS);
1764: }

1766: /*@
1767:   KSPSetErrorIfNotConverged - Causes `KSPSolve()` to generate an error if the solver has not converged as soon as the error is detected.

1769:   Logically Collective

1771:   Input Parameters:
1772: + ksp - iterative context obtained from `KSPCreate()`
1773: - flg - `PETSC_TRUE` indicates you want the error generated

1775:   Options Database Key:
1776: . -ksp_error_if_not_converged <true,false> - generate an error and stop the program

1778:   Level: intermediate

1780:   Notes:
1781:   Normally PETSc continues if a linear solver fails to converge, you can call `KSPGetConvergedReason()` after a `KSPSolve()`
1782:   to determine if it has converged.

1784:   A `KSP_DIVERGED_ITS` will not generate an error in a `KSPSolve()` inside a nested linear solver

1786: .seealso: [](ch_ksp), `KSPGetErrorIfNotConverged()`, `KSP`
1787: @*/
1788: PetscErrorCode KSPSetErrorIfNotConverged(KSP ksp, PetscBool flg)
1789: {
1790:   PetscFunctionBegin;
1793:   ksp->errorifnotconverged = flg;
1794:   PetscFunctionReturn(PETSC_SUCCESS);
1795: }

1797: /*@
1798:   KSPGetErrorIfNotConverged - Will `KSPSolve()` generate an error if the solver does not converge?

1800:   Not Collective

1802:   Input Parameter:
1803: . ksp - iterative context obtained from KSPCreate()

1805:   Output Parameter:
1806: . flag - `PETSC_TRUE` if it will generate an error, else `PETSC_FALSE`

1808:   Level: intermediate

1810: .seealso: [](ch_ksp), `KSPSetErrorIfNotConverged()`, `KSP`
1811: @*/
1812: PetscErrorCode KSPGetErrorIfNotConverged(KSP ksp, PetscBool *flag)
1813: {
1814:   PetscFunctionBegin;
1816:   PetscAssertPointer(flag, 2);
1817:   *flag = ksp->errorifnotconverged;
1818:   PetscFunctionReturn(PETSC_SUCCESS);
1819: }

1821: /*@
1822:   KSPSetInitialGuessKnoll - Tells the iterative solver to use `PCApply()` to compute the initial guess (The Knoll trick)

1824:   Logically Collective

1826:   Input Parameters:
1827: + ksp - iterative context obtained from `KSPCreate()`
1828: - flg - `PETSC_TRUE` or `PETSC_FALSE`

1830:   Level: advanced

1832:   Developer Note:
1833:   The Knoll trick is not currently implemented using the `KSPGuess` class

1835: .seealso: [](ch_ksp), `KSPGetInitialGuessKnoll()`, `KSPSetInitialGuessNonzero()`, `KSPGetInitialGuessNonzero()`, `KSP`
1836: @*/
1837: PetscErrorCode KSPSetInitialGuessKnoll(KSP ksp, PetscBool flg)
1838: {
1839:   PetscFunctionBegin;
1842:   ksp->guess_knoll = flg;
1843:   PetscFunctionReturn(PETSC_SUCCESS);
1844: }

1846: /*@
1847:   KSPGetInitialGuessKnoll - Determines whether the `KSP` solver is using the Knoll trick (using PCApply(pc,b,...) to compute
1848:   the initial guess

1850:   Not Collective

1852:   Input Parameter:
1853: . ksp - iterative context obtained from `KSPCreate()`

1855:   Output Parameter:
1856: . flag - `PETSC_TRUE` if using Knoll trick, else `PETSC_FALSE`

1858:   Level: advanced

1860: .seealso: [](ch_ksp), `KSPSetInitialGuessKnoll()`, `KSPSetInitialGuessNonzero()`, `KSPGetInitialGuessNonzero()`, `KSP`
1861: @*/
1862: PetscErrorCode KSPGetInitialGuessKnoll(KSP ksp, PetscBool *flag)
1863: {
1864:   PetscFunctionBegin;
1866:   PetscAssertPointer(flag, 2);
1867:   *flag = ksp->guess_knoll;
1868:   PetscFunctionReturn(PETSC_SUCCESS);
1869: }

1871: /*@
1872:   KSPGetComputeSingularValues - Gets the flag indicating whether the extreme singular
1873:   values will be calculated via a Lanczos or Arnoldi process as the linear
1874:   system is solved.

1876:   Not Collective

1878:   Input Parameter:
1879: . ksp - iterative context obtained from `KSPCreate()`

1881:   Output Parameter:
1882: . flg - `PETSC_TRUE` or `PETSC_FALSE`

1884:   Options Database Key:
1885: . -ksp_monitor_singular_value - Activates `KSPSetComputeSingularValues()`

1887:   Level: advanced

1889:   Notes:
1890:   Currently this option is not valid for all iterative methods.

1892:   Many users may just want to use the monitoring routine
1893:   `KSPMonitorSingularValue()` (which can be set with option -ksp_monitor_singular_value)
1894:   to print the singular values at each iteration of the linear solve.

1896: .seealso: [](ch_ksp), `KSPComputeExtremeSingularValues()`, `KSPMonitorSingularValue()`, `KSP`
1897: @*/
1898: PetscErrorCode KSPGetComputeSingularValues(KSP ksp, PetscBool *flg)
1899: {
1900:   PetscFunctionBegin;
1902:   PetscAssertPointer(flg, 2);
1903:   *flg = ksp->calc_sings;
1904:   PetscFunctionReturn(PETSC_SUCCESS);
1905: }

1907: /*@
1908:   KSPSetComputeSingularValues - Sets a flag so that the extreme singular
1909:   values will be calculated via a Lanczos or Arnoldi process as the linear
1910:   system is solved.

1912:   Logically Collective

1914:   Input Parameters:
1915: + ksp - iterative context obtained from `KSPCreate()`
1916: - flg - `PETSC_TRUE` or `PETSC_FALSE`

1918:   Options Database Key:
1919: . -ksp_monitor_singular_value - Activates `KSPSetComputeSingularValues()`

1921:   Level: advanced

1923:   Notes:
1924:   Currently this option is not valid for all iterative methods.

1926:   Many users may just want to use the monitoring routine
1927:   `KSPMonitorSingularValue()` (which can be set with option -ksp_monitor_singular_value)
1928:   to print the singular values at each iteration of the linear solve.

1930: .seealso: [](ch_ksp), `KSPComputeExtremeSingularValues()`, `KSPMonitorSingularValue()`, `KSP`, `KSPSetComputeRitz()`
1931: @*/
1932: PetscErrorCode KSPSetComputeSingularValues(KSP ksp, PetscBool flg)
1933: {
1934:   PetscFunctionBegin;
1937:   ksp->calc_sings = flg;
1938:   PetscFunctionReturn(PETSC_SUCCESS);
1939: }

1941: /*@
1942:   KSPGetComputeEigenvalues - Gets the flag indicating that the extreme eigenvalues
1943:   values will be calculated via a Lanczos or Arnoldi process as the linear
1944:   system is solved.

1946:   Not Collective

1948:   Input Parameter:
1949: . ksp - iterative context obtained from `KSPCreate()`

1951:   Output Parameter:
1952: . flg - `PETSC_TRUE` or `PETSC_FALSE`

1954:   Level: advanced

1956:   Note:
1957:   Currently this option is not valid for all iterative methods.

1959: .seealso: [](ch_ksp), `KSPComputeEigenvalues()`, `KSPComputeEigenvaluesExplicitly()`, `KSP`, `KSPSetComputeRitz()`
1960: @*/
1961: PetscErrorCode KSPGetComputeEigenvalues(KSP ksp, PetscBool *flg)
1962: {
1963:   PetscFunctionBegin;
1965:   PetscAssertPointer(flg, 2);
1966:   *flg = ksp->calc_sings;
1967:   PetscFunctionReturn(PETSC_SUCCESS);
1968: }

1970: /*@
1971:   KSPSetComputeEigenvalues - Sets a flag so that the extreme eigenvalues
1972:   values will be calculated via a Lanczos or Arnoldi process as the linear
1973:   system is solved.

1975:   Logically Collective

1977:   Input Parameters:
1978: + ksp - iterative context obtained from `KSPCreate()`
1979: - flg - `PETSC_TRUE` or `PETSC_FALSE`

1981:   Level: advanced

1983:   Note:
1984:   Currently this option is not valid for all iterative methods.

1986: .seealso: [](ch_ksp), `KSPComputeEigenvalues()`, `KSPComputeEigenvaluesExplicitly()`, `KSP`, `KSPSetComputeRitz()`
1987: @*/
1988: PetscErrorCode KSPSetComputeEigenvalues(KSP ksp, PetscBool flg)
1989: {
1990:   PetscFunctionBegin;
1993:   ksp->calc_sings = flg;
1994:   PetscFunctionReturn(PETSC_SUCCESS);
1995: }

1997: /*@
1998:   KSPSetComputeRitz - Sets a flag so that the Ritz or harmonic Ritz pairs
1999:   will be calculated via a Lanczos or Arnoldi process as the linear
2000:   system is solved.

2002:   Logically Collective

2004:   Input Parameters:
2005: + ksp - iterative context obtained from `KSPCreate()`
2006: - flg - `PETSC_TRUE` or `PETSC_FALSE`

2008:   Level: advanced

2010:   Note:
2011:   Currently this option is only valid for the `KSPGMRES` method.

2013: .seealso: [](ch_ksp), `KSPComputeRitz()`, `KSP`
2014: @*/
2015: PetscErrorCode KSPSetComputeRitz(KSP ksp, PetscBool flg)
2016: {
2017:   PetscFunctionBegin;
2020:   ksp->calc_ritz = flg;
2021:   PetscFunctionReturn(PETSC_SUCCESS);
2022: }

2024: /*@
2025:   KSPGetRhs - Gets the right-hand-side vector for the linear system to
2026:   be solved.

2028:   Not Collective

2030:   Input Parameter:
2031: . ksp - iterative context obtained from `KSPCreate()`

2033:   Output Parameter:
2034: . r - right-hand-side vector

2036:   Level: developer

2038: .seealso: [](ch_ksp), `KSPGetSolution()`, `KSPSolve()`, `KSP`
2039: @*/
2040: PetscErrorCode KSPGetRhs(KSP ksp, Vec *r)
2041: {
2042:   PetscFunctionBegin;
2044:   PetscAssertPointer(r, 2);
2045:   *r = ksp->vec_rhs;
2046:   PetscFunctionReturn(PETSC_SUCCESS);
2047: }

2049: /*@
2050:   KSPGetSolution - Gets the location of the solution for the
2051:   linear system to be solved.  Note that this may not be where the solution
2052:   is stored during the iterative process; see `KSPBuildSolution()`.

2054:   Not Collective

2056:   Input Parameter:
2057: . ksp - iterative context obtained from `KSPCreate()`

2059:   Output Parameter:
2060: . v - solution vector

2062:   Level: developer

2064: .seealso: [](ch_ksp), `KSPGetRhs()`, `KSPBuildSolution()`, `KSPSolve()`, `KSP`
2065: @*/
2066: PetscErrorCode KSPGetSolution(KSP ksp, Vec *v)
2067: {
2068:   PetscFunctionBegin;
2070:   PetscAssertPointer(v, 2);
2071:   *v = ksp->vec_sol;
2072:   PetscFunctionReturn(PETSC_SUCCESS);
2073: }

2075: /*@
2076:   KSPSetPC - Sets the preconditioner to be used to calculate the
2077:   application of the preconditioner on a vector.

2079:   Collective

2081:   Input Parameters:
2082: + ksp - iterative context obtained from `KSPCreate()`
2083: - pc  - the preconditioner object (can be `NULL`)

2085:   Level: developer

2087:   Note:
2088:   Use `KSPGetPC()` to retrieve the preconditioner context.

2090: .seealso: [](ch_ksp), `KSPGetPC()`, `KSP`
2091: @*/
2092: PetscErrorCode KSPSetPC(KSP ksp, PC pc)
2093: {
2094:   PetscFunctionBegin;
2096:   if (pc) {
2098:     PetscCheckSameComm(ksp, 1, pc, 2);
2099:   }
2100:   if (ksp->pc != pc && ksp->setupstage) ksp->setupstage = KSP_SETUP_NEWMATRIX;
2101:   PetscCall(PetscObjectReference((PetscObject)pc));
2102:   PetscCall(PCDestroy(&ksp->pc));
2103:   ksp->pc = pc;
2104:   PetscFunctionReturn(PETSC_SUCCESS);
2105: }

2107: PETSC_INTERN PetscErrorCode PCCreate_MPI(PC);

2109: // PetscClangLinter pragma disable: -fdoc-internal-linkage
2110: /*@C
2111:    KSPCheckPCMPI - Checks if `-mpi_linear_solver_server` is active and the `PC` should be changed to `PCMPI`

2113:    Collective

2115:    Input Parameter:
2116: .  ksp - iterative context obtained from `KSPCreate()`

2118:    Level: developer

2120: .seealso: [](ch_ksp), `KSPSetPC()`, `KSP`, `PCMPIServerBegin()`, `PCMPIServerEnd()`
2121: @*/
2122: PETSC_INTERN PetscErrorCode KSPCheckPCMPI(KSP ksp)
2123: {
2124:   PetscBool isPCMPI;

2126:   PetscFunctionBegin;
2128:   PetscCall(PetscObjectTypeCompare((PetscObject)ksp->pc, PCMPI, &isPCMPI));
2129:   if (PCMPIServerActive && ksp->nestlevel == 0 && !isPCMPI) {
2130:     const char *prefix;
2131:     char       *found = NULL;

2133:     PetscCall(KSPGetOptionsPrefix(ksp, &prefix));
2134:     if (prefix) PetscCall(PetscStrstr(prefix, "mpi_linear_solver_server_", &found));
2135:     if (!found) PetscCall(KSPAppendOptionsPrefix(ksp, "mpi_linear_solver_server_"));
2136:     PetscCall(PCSetType(ksp->pc, PCMPI));
2137:   }
2138:   PetscFunctionReturn(PETSC_SUCCESS);
2139: }

2141: /*@
2142:   KSPGetPC - Returns a pointer to the preconditioner context
2143:   set with `KSPSetPC()`.

2145:   Not Collective

2147:   Input Parameter:
2148: . ksp - iterative context obtained from `KSPCreate()`

2150:   Output Parameter:
2151: . pc - preconditioner context

2153:   Level: developer

2155: .seealso: [](ch_ksp), `KSPSetPC()`, `KSP`, `PC`
2156: @*/
2157: PetscErrorCode KSPGetPC(KSP ksp, PC *pc)
2158: {
2159:   PetscFunctionBegin;
2161:   PetscAssertPointer(pc, 2);
2162:   if (!ksp->pc) {
2163:     PetscCall(PCCreate(PetscObjectComm((PetscObject)ksp), &ksp->pc));
2164:     PetscCall(PetscObjectIncrementTabLevel((PetscObject)ksp->pc, (PetscObject)ksp, 0));
2165:     PetscCall(PetscObjectSetOptions((PetscObject)ksp->pc, ((PetscObject)ksp)->options));
2166:     PetscCall(PCSetKSPNestLevel(ksp->pc, ksp->nestlevel));
2167:   }
2168:   PetscCall(KSPCheckPCMPI(ksp));
2169:   *pc = ksp->pc;
2170:   PetscFunctionReturn(PETSC_SUCCESS);
2171: }

2173: /*@
2174:   KSPMonitor - runs the user provided monitor routines, if they exist

2176:   Collective

2178:   Input Parameters:
2179: + ksp   - iterative context obtained from `KSPCreate()`
2180: . it    - iteration number
2181: - rnorm - relative norm of the residual

2183:   Level: developer

2185:   Note:
2186:   This routine is called by the `KSP` implementations.
2187:   It does not typically need to be called by the user.

2189: .seealso: [](ch_ksp), `KSPMonitorSet()`
2190: @*/
2191: PetscErrorCode KSPMonitor(KSP ksp, PetscInt it, PetscReal rnorm)
2192: {
2193:   PetscInt i, n = ksp->numbermonitors;

2195:   PetscFunctionBegin;
2196:   for (i = 0; i < n; i++) PetscCall((*ksp->monitor[i])(ksp, it, rnorm, ksp->monitorcontext[i]));
2197:   PetscFunctionReturn(PETSC_SUCCESS);
2198: }

2200: /*@C
2201:   KSPMonitorSet - Sets an ADDITIONAL function to be called at every iteration to monitor
2202:   the residual/error etc.

2204:   Logically Collective

2206:   Input Parameters:
2207: + ksp            - iterative context obtained from `KSPCreate()`
2208: . monitor        - pointer to function (if this is `NULL`, it turns off monitoring
2209: . ctx            - [optional] context for private data for the monitor routine (use `NULL` if no context is needed)
2210: - monitordestroy - [optional] routine that frees monitor context (may be `NULL`)

2212:   Calling sequence of `monitor`:
2213: + ksp   - iterative context obtained from `KSPCreate()`
2214: . it    - iteration number
2215: . rnorm - (estimated) 2-norm of (preconditioned) residual
2216: - ctx   - optional monitoring context, as set by `KSPMonitorSet()`

2218:   Calling sequence of `monitordestroy`:
2219: . ctx - optional monitoring context, as set by `KSPMonitorSet()`

2221:   Options Database Keys:
2222: + -ksp_monitor                             - sets `KSPMonitorResidual()`
2223: . -ksp_monitor draw                        - sets `KSPMonitorResidualDraw()` and plots residual
2224: . -ksp_monitor draw::draw_lg               - sets `KSPMonitorResidualDrawLG()` and plots residual
2225: . -ksp_monitor_pause_final                 - Pauses any graphics when the solve finishes (only works for internal monitors)
2226: . -ksp_monitor_true_residual               - sets `KSPMonitorTrueResidual()`
2227: . -ksp_monitor_true_residual draw::draw_lg - sets `KSPMonitorTrueResidualDrawLG()` and plots residual
2228: . -ksp_monitor_max                         - sets `KSPMonitorTrueResidualMax()`
2229: . -ksp_monitor_singular_value              - sets `KSPMonitorSingularValue()`
2230: - -ksp_monitor_cancel                      - cancels all monitors that have been hardwired into a code by calls to `KSPMonitorSet()`, but
2231:                                              does not cancel those set via the options database.

2233:   Level: beginner

2235:   Notes:
2236:   The default is to do nothing.  To print the residual, or preconditioned
2237:   residual if `KSPSetNormType`(ksp,`KSP_NORM_PRECONDITIONED`) was called, use
2238:   `KSPMonitorResidual()` as the monitoring routine, with a `PETSCVIEWERASCII` as the
2239:   context.

2241:   Several different monitoring routines may be set by calling
2242:   `KSPMonitorSet()` multiple times; all will be called in the
2243:   order in which they were set.

2245:   Fortran Note:
2246:   Only a single monitor function can be set for each `KSP` object

2248: .seealso: [](ch_ksp), `KSPMonitorResidual()`, `KSPMonitorCancel()`, `KSP`
2249: @*/
2250: PetscErrorCode KSPMonitorSet(KSP ksp, PetscErrorCode (*monitor)(KSP ksp, PetscInt it, PetscReal rnorm, void *ctx), void *ctx, PetscErrorCode (*monitordestroy)(void **ctx))
2251: {
2252:   PetscInt  i;
2253:   PetscBool identical;

2255:   PetscFunctionBegin;
2257:   for (i = 0; i < ksp->numbermonitors; i++) {
2258:     PetscCall(PetscMonitorCompare((PetscErrorCode(*)(void))monitor, ctx, monitordestroy, (PetscErrorCode(*)(void))ksp->monitor[i], ksp->monitorcontext[i], ksp->monitordestroy[i], &identical));
2259:     if (identical) PetscFunctionReturn(PETSC_SUCCESS);
2260:   }
2261:   PetscCheck(ksp->numbermonitors < MAXKSPMONITORS, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_OUTOFRANGE, "Too many KSP monitors set");
2262:   ksp->monitor[ksp->numbermonitors]          = monitor;
2263:   ksp->monitordestroy[ksp->numbermonitors]   = monitordestroy;
2264:   ksp->monitorcontext[ksp->numbermonitors++] = (void *)ctx;
2265:   PetscFunctionReturn(PETSC_SUCCESS);
2266: }

2268: /*@
2269:   KSPMonitorCancel - Clears all monitors for a `KSP` object.

2271:   Logically Collective

2273:   Input Parameter:
2274: . ksp - iterative context obtained from `KSPCreate()`

2276:   Options Database Key:
2277: . -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.

2279:   Level: intermediate

2281: .seealso: [](ch_ksp), `KSPMonitorResidual()`, `KSPMonitorSet()`, `KSP`
2282: @*/
2283: PetscErrorCode KSPMonitorCancel(KSP ksp)
2284: {
2285:   PetscInt i;

2287:   PetscFunctionBegin;
2289:   for (i = 0; i < ksp->numbermonitors; i++) {
2290:     if (ksp->monitordestroy[i]) PetscCall((*ksp->monitordestroy[i])(&ksp->monitorcontext[i]));
2291:   }
2292:   ksp->numbermonitors = 0;
2293:   PetscFunctionReturn(PETSC_SUCCESS);
2294: }

2296: /*@C
2297:   KSPGetMonitorContext - Gets the monitoring context, as set by `KSPMonitorSet()` for the FIRST monitor only.

2299:   Not Collective

2301:   Input Parameter:
2302: . ksp - iterative context obtained from `KSPCreate()`

2304:   Output Parameter:
2305: . ctx - monitoring context

2307:   Level: intermediate

2309: .seealso: [](ch_ksp), `KSPMonitorResidual()`, `KSP`
2310: @*/
2311: PetscErrorCode KSPGetMonitorContext(KSP ksp, void *ctx)
2312: {
2313:   PetscFunctionBegin;
2315:   *(void **)ctx = ksp->monitorcontext[0];
2316:   PetscFunctionReturn(PETSC_SUCCESS);
2317: }

2319: /*@
2320:   KSPSetResidualHistory - Sets the array used to hold the residual history.
2321:   If set, this array will contain the residual norms computed at each
2322:   iteration of the solver.

2324:   Not Collective

2326:   Input Parameters:
2327: + ksp   - iterative context obtained from `KSPCreate()`
2328: . a     - array to hold history
2329: . na    - size of `a`
2330: - reset - `PETSC_TRUE` indicates the history counter is reset to zero
2331:            for each new linear solve

2333:   Level: advanced

2335:   Notes:
2336:   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.
2337:   If 'a' is `NULL` then space is allocated for the history. If 'na' `PETSC_DECIDE` or `PETSC_DEFAULT` then a
2338:   default array of length 10000 is allocated.

2340:   If the array is not long enough then once the iterations is longer than the array length `KSPSolve()` stops recording the history

2342: .seealso: [](ch_ksp), `KSPGetResidualHistory()`, `KSP`
2343: @*/
2344: PetscErrorCode KSPSetResidualHistory(KSP ksp, PetscReal a[], PetscInt na, PetscBool reset)
2345: {
2346:   PetscFunctionBegin;

2349:   PetscCall(PetscFree(ksp->res_hist_alloc));
2350:   if (na != PETSC_DECIDE && na != PETSC_DEFAULT && a) {
2351:     ksp->res_hist     = a;
2352:     ksp->res_hist_max = (size_t)na;
2353:   } else {
2354:     if (na != PETSC_DECIDE && na != PETSC_DEFAULT) ksp->res_hist_max = (size_t)na;
2355:     else ksp->res_hist_max = 10000; /* like default ksp->max_it */
2356:     PetscCall(PetscCalloc1(ksp->res_hist_max, &ksp->res_hist_alloc));

2358:     ksp->res_hist = ksp->res_hist_alloc;
2359:   }
2360:   ksp->res_hist_len   = 0;
2361:   ksp->res_hist_reset = reset;
2362:   PetscFunctionReturn(PETSC_SUCCESS);
2363: }

2365: /*@C
2366:   KSPGetResidualHistory - Gets the array used to hold the residual history and the number of residuals it contains.

2368:   Not Collective

2370:   Input Parameter:
2371: . ksp - iterative context obtained from `KSPCreate()`

2373:   Output Parameters:
2374: + a  - pointer to array to hold history (or `NULL`)
2375: - na - number of used entries in a (or `NULL`). Note this has different meanings depending on the `reset` argument to `KSPSetResidualHistory()`

2377:   Level: advanced

2379:   Note:
2380:   This array is borrowed and should not be freed by the caller.

2382:   Can only be called after a `KSPSetResidualHistory()` otherwise `a` and `na` are set to `NULL` and zero

2384:   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
2385:   returned with `KSPGetIterationNumber()`.

2387:   Some Krylov methods may not compute the final residual norm when convergence is declared because the maximum number of iterations allowed has been reached.
2388:   In this situation, when `reset` was `PETSC_TRUE`, `na` will then equal the number of iterations reported with `KSPGetIterationNumber()`

2390:   Some Krylov methods (such as `KSPSTCG`), under certain circumstances, do not compute the final residual norm. In this situation, when `reset` was `PETSC_TRUE`,
2391:   `na` will then equal the number of iterations reported with `KSPGetIterationNumber()`

2393:   `KSPBCGSL` does not record the residual norms for the "subiterations" hence the results from `KSPGetResidualHistory()` and `KSPGetIterationNumber()` will be different

2395:   Fortran Note:
2396:   The Fortran version of this routine has a calling sequence
2397: .vb
2398:   call KSPGetResidualHistory(KSP ksp, integer na, integer ierr)
2399: .ve
2400:   note that you have passed a Fortran array into `KSPSetResidualHistory()` and you need
2401:   to access the residual values from this Fortran array you provided. Only the `na` (number of
2402:   residual norms currently held) is set.

2404: .seealso: [](ch_ksp), `KSPSetResidualHistory()`, `KSP`, `KSPGetIterationNumber()`, `KSPSTCG`, `KSPBCGSL`
2405: @*/
2406: PetscErrorCode KSPGetResidualHistory(KSP ksp, const PetscReal *a[], PetscInt *na)
2407: {
2408:   PetscFunctionBegin;
2410:   if (a) *a = ksp->res_hist;
2411:   if (na) *na = (PetscInt)ksp->res_hist_len;
2412:   PetscFunctionReturn(PETSC_SUCCESS);
2413: }

2415: /*@
2416:   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.

2418:   Not Collective

2420:   Input Parameters:
2421: + ksp   - iterative context obtained from `KSPCreate()`
2422: . a     - array to hold history
2423: . na    - size of `a`
2424: - reset - `PETSC_TRUE` indicates the history counter is reset to zero for each new linear solve

2426:   Level: advanced

2428:   Notes:
2429:   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.
2430:   If 'a' is `NULL` then space is allocated for the history. If 'na' is `PETSC_DECIDE` or `PETSC_DEFAULT` then a default array of length 10000 is allocated.

2432:   If the array is not long enough then once the iterations is longer than the array length `KSPSolve()` stops recording the history

2434: .seealso: [](ch_ksp), `KSPGetErrorHistory()`, `KSPSetResidualHistory()`, `KSP`
2435: @*/
2436: PetscErrorCode KSPSetErrorHistory(KSP ksp, PetscReal a[], PetscInt na, PetscBool reset)
2437: {
2438:   PetscFunctionBegin;

2441:   PetscCall(PetscFree(ksp->err_hist_alloc));
2442:   if (na != PETSC_DECIDE && na != PETSC_DEFAULT && a) {
2443:     ksp->err_hist     = a;
2444:     ksp->err_hist_max = (size_t)na;
2445:   } else {
2446:     if (na != PETSC_DECIDE && na != PETSC_DEFAULT) ksp->err_hist_max = (size_t)na;
2447:     else ksp->err_hist_max = 10000; /* like default ksp->max_it */
2448:     PetscCall(PetscCalloc1(ksp->err_hist_max, &ksp->err_hist_alloc));

2450:     ksp->err_hist = ksp->err_hist_alloc;
2451:   }
2452:   ksp->err_hist_len   = 0;
2453:   ksp->err_hist_reset = reset;
2454:   PetscFunctionReturn(PETSC_SUCCESS);
2455: }

2457: /*@C
2458:   KSPGetErrorHistory - Gets the array used to hold the error history and the number of residuals it contains.

2460:   Not Collective

2462:   Input Parameter:
2463: . ksp - iterative context obtained from `KSPCreate()`

2465:   Output Parameters:
2466: + a  - pointer to array to hold history (or `NULL`)
2467: - na - number of used entries in a (or `NULL`)

2469:   Level: advanced

2471:   Note:
2472:   This array is borrowed and should not be freed by the caller.
2473:   Can only be called after a `KSPSetErrorHistory()` otherwise `a` and `na` are set to `NULL` and zero

2475:   Fortran Note:
2476:   The Fortran version of this routine has a calling sequence
2477: .vb
2478:   call KSPGetErrorHistory(KSP ksp, integer na, integer ierr)
2479: .ve
2480:   note that you have passed a Fortran array into `KSPSetErrorHistory()` and you need
2481:   to access the residual values from this Fortran array you provided. Only the `na` (number of
2482:   residual norms currently held) is set.

2484: .seealso: [](ch_ksp), `KSPSetErrorHistory()`, `KSPGetResidualHistory()`, `KSP`
2485: @*/
2486: PetscErrorCode KSPGetErrorHistory(KSP ksp, const PetscReal *a[], PetscInt *na)
2487: {
2488:   PetscFunctionBegin;
2490:   if (a) *a = ksp->err_hist;
2491:   if (na) *na = (PetscInt)ksp->err_hist_len;
2492:   PetscFunctionReturn(PETSC_SUCCESS);
2493: }

2495: /*@
2496:   KSPComputeConvergenceRate - Compute the convergence rate for the iteration <https:/en.wikipedia.org/wiki/Coefficient_of_determination>

2498:   Not Collective

2500:   Input Parameter:
2501: . ksp - The `KSP`

2503:   Output Parameters:
2504: + cr   - The residual contraction rate
2505: . rRsq - The coefficient of determination, $R^2$, indicating the linearity of the data
2506: . ce   - The error contraction rate
2507: - eRsq - The coefficient of determination, $R^2$, indicating the linearity of the data

2509:   Level: advanced

2511:   Note:
2512:   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,
2513:   the slope is $\log c$. The coefficient of determination is given by $1 - \frac{\sum_i (y_i - f(x_i))^2}{\sum_i (y_i - \bar y)}$,

2515: .seealso: [](ch_ksp), `KSP`, `KSPConvergedRateView()`
2516: @*/
2517: PetscErrorCode KSPComputeConvergenceRate(KSP ksp, PetscReal *cr, PetscReal *rRsq, PetscReal *ce, PetscReal *eRsq)
2518: {
2519:   PetscReal const *hist;
2520:   PetscReal       *x, *y, slope, intercept, mean = 0.0, var = 0.0, res = 0.0;
2521:   PetscInt         n, k;

2523:   PetscFunctionBegin;
2524:   if (cr || rRsq) {
2525:     PetscCall(KSPGetResidualHistory(ksp, &hist, &n));
2526:     if (!n) {
2527:       if (cr) *cr = 0.0;
2528:       if (rRsq) *rRsq = -1.0;
2529:     } else {
2530:       PetscCall(PetscMalloc2(n, &x, n, &y));
2531:       for (k = 0; k < n; ++k) {
2532:         x[k] = k;
2533:         y[k] = PetscLogReal(hist[k]);
2534:         mean += y[k];
2535:       }
2536:       mean /= n;
2537:       PetscCall(PetscLinearRegression(n, x, y, &slope, &intercept));
2538:       for (k = 0; k < n; ++k) {
2539:         res += PetscSqr(y[k] - (slope * x[k] + intercept));
2540:         var += PetscSqr(y[k] - mean);
2541:       }
2542:       PetscCall(PetscFree2(x, y));
2543:       if (cr) *cr = PetscExpReal(slope);
2544:       if (rRsq) *rRsq = var < PETSC_MACHINE_EPSILON ? 0.0 : 1.0 - (res / var);
2545:     }
2546:   }
2547:   if (ce || eRsq) {
2548:     PetscCall(KSPGetErrorHistory(ksp, &hist, &n));
2549:     if (!n) {
2550:       if (ce) *ce = 0.0;
2551:       if (eRsq) *eRsq = -1.0;
2552:     } else {
2553:       PetscCall(PetscMalloc2(n, &x, n, &y));
2554:       for (k = 0; k < n; ++k) {
2555:         x[k] = k;
2556:         y[k] = PetscLogReal(hist[k]);
2557:         mean += y[k];
2558:       }
2559:       mean /= n;
2560:       PetscCall(PetscLinearRegression(n, x, y, &slope, &intercept));
2561:       for (k = 0; k < n; ++k) {
2562:         res += PetscSqr(y[k] - (slope * x[k] + intercept));
2563:         var += PetscSqr(y[k] - mean);
2564:       }
2565:       PetscCall(PetscFree2(x, y));
2566:       if (ce) *ce = PetscExpReal(slope);
2567:       if (eRsq) *eRsq = var < PETSC_MACHINE_EPSILON ? 0.0 : 1.0 - (res / var);
2568:     }
2569:   }
2570:   PetscFunctionReturn(PETSC_SUCCESS);
2571: }

2573: /*@C
2574:   KSPSetConvergenceTest - Sets the function to be used to determine convergence.

2576:   Logically Collective

2578:   Input Parameters:
2579: + ksp      - iterative context obtained from `KSPCreate()`
2580: . converge - pointer to the function
2581: . ctx      - context for private data for the convergence routine (may be `NULL`)
2582: - destroy  - a routine for destroying the context (may be `NULL`)

2584:   Calling sequence of `converge`:
2585: + ksp    - iterative context obtained from `KSPCreate()`
2586: . it     - iteration number
2587: . rnorm  - (estimated) 2-norm of (preconditioned) residual
2588: . reason - the reason why it has converged or diverged
2589: - ctx    - optional convergence context, as set by `KSPSetConvergenceTest()`

2591:   Calling sequence of `destroy`:
2592: . ctx - the context

2594:   Level: advanced

2596:   Notes:
2597:   Must be called after the `KSP` type has been set so put this after
2598:   a call to `KSPSetType()`, or `KSPSetFromOptions()`.

2600:   The default convergence test, `KSPConvergedDefault()`, aborts if the
2601:   residual grows to more than 10000 times the initial residual.

2603:   The default is a combination of relative and absolute tolerances.
2604:   The residual value that is tested may be an approximation; routines
2605:   that need exact values should compute them.

2607:   In the default PETSc convergence test, the precise values of reason
2608:   are macros such as `KSP_CONVERGED_RTOL`, which are defined in petscksp.h.

2610: .seealso: [](ch_ksp), `KSP`, `KSPConvergedDefault()`, `KSPGetConvergenceContext()`, `KSPSetTolerances()`, `KSPGetConvergenceTest()`, `KSPGetAndClearConvergenceTest()`
2611: @*/
2612: PetscErrorCode KSPSetConvergenceTest(KSP ksp, PetscErrorCode (*converge)(KSP ksp, PetscInt it, PetscReal rnorm, KSPConvergedReason *reason, void *ctx), void *ctx, PetscErrorCode (*destroy)(void *ctx))
2613: {
2614:   PetscFunctionBegin;
2616:   if (ksp->convergeddestroy) PetscCall((*ksp->convergeddestroy)(ksp->cnvP));
2617:   ksp->converged        = converge;
2618:   ksp->convergeddestroy = destroy;
2619:   ksp->cnvP             = (void *)ctx;
2620:   PetscFunctionReturn(PETSC_SUCCESS);
2621: }

2623: /*@C
2624:   KSPGetConvergenceTest - Gets the function to be used to determine convergence.

2626:   Logically Collective

2628:   Input Parameter:
2629: . ksp - iterative context obtained from `KSPCreate()`

2631:   Output Parameters:
2632: + converge - pointer to convergence test function
2633: . ctx      - context for private data for the convergence routine (may be `NULL`)
2634: - destroy  - a routine for destroying the context (may be `NULL`)

2636:   Calling sequence of `converge`:
2637: + ksp    - iterative context obtained from `KSPCreate()`
2638: . it     - iteration number
2639: . rnorm  - (estimated) 2-norm of (preconditioned) residual
2640: . reason - the reason why it has converged or diverged
2641: - ctx    - optional convergence context, as set by `KSPSetConvergenceTest()`

2643:   Calling sequence of `destroy`:
2644: . ctx - the convergence test context

2646:   Level: advanced

2648: .seealso: [](ch_ksp), `KSP`, `KSPConvergedDefault()`, `KSPGetConvergenceContext()`, `KSPSetTolerances()`, `KSPSetConvergenceTest()`, `KSPGetAndClearConvergenceTest()`
2649: @*/
2650: PetscErrorCode KSPGetConvergenceTest(KSP ksp, PetscErrorCode (**converge)(KSP ksp, PetscInt it, PetscReal rnorm, KSPConvergedReason *reason, void *ctx), void **ctx, PetscErrorCode (**destroy)(void *ctx))
2651: {
2652:   PetscFunctionBegin;
2654:   if (converge) *converge = ksp->converged;
2655:   if (destroy) *destroy = ksp->convergeddestroy;
2656:   if (ctx) *ctx = ksp->cnvP;
2657:   PetscFunctionReturn(PETSC_SUCCESS);
2658: }

2660: /*@C
2661:   KSPGetAndClearConvergenceTest - Gets the function to be used to determine convergence. Removes the current test without calling destroy on the test context

2663:   Logically Collective

2665:   Input Parameter:
2666: . ksp - iterative context obtained from `KSPCreate()`

2668:   Output Parameters:
2669: + converge - pointer to convergence test function
2670: . ctx      - context for private data for the convergence routine
2671: - destroy  - a routine for destroying the context

2673:   Calling sequence of `converge`:
2674: + ksp    - iterative context obtained from `KSPCreate()`
2675: . it     - iteration number
2676: . rnorm  - (estimated) 2-norm of (preconditioned) residual
2677: . reason - the reason why it has converged or diverged
2678: - ctx    - optional convergence context, as set by `KSPSetConvergenceTest()`

2680:   Calling sequence of `destroy`:
2681: . ctx - the convergence test context

2683:   Level: advanced

2685:   Note:
2686:   This is intended to be used to allow transferring the convergence test (and its context) to another testing object (for example another `KSP`)
2687:   and then calling `KSPSetConvergenceTest()` on this original `KSP`. If you just called `KSPGetConvergenceTest()` followed
2688:   by `KSPSetConvergenceTest()` the original context information
2689:   would be destroyed and hence the transferred context would be invalid and trigger a crash on use

2691: .seealso: [](ch_ksp), `KSP`, `KSPConvergedDefault()`, `KSPGetConvergenceContext()`, `KSPSetTolerances()`, `KSPSetConvergenceTest()`, `KSPGetConvergenceTest()`
2692: @*/
2693: PetscErrorCode KSPGetAndClearConvergenceTest(KSP ksp, PetscErrorCode (**converge)(KSP ksp, PetscInt it, PetscReal rnorm, KSPConvergedReason *reason, void *ctx), void **ctx, PetscErrorCode (**destroy)(void *ctx))
2694: {
2695:   PetscFunctionBegin;
2697:   *converge             = ksp->converged;
2698:   *destroy              = ksp->convergeddestroy;
2699:   *ctx                  = ksp->cnvP;
2700:   ksp->converged        = NULL;
2701:   ksp->cnvP             = NULL;
2702:   ksp->convergeddestroy = NULL;
2703:   PetscFunctionReturn(PETSC_SUCCESS);
2704: }

2706: /*@C
2707:   KSPGetConvergenceContext - Gets the convergence context set with `KSPSetConvergenceTest()`.

2709:   Not Collective

2711:   Input Parameter:
2712: . ksp - iterative context obtained from `KSPCreate()`

2714:   Output Parameter:
2715: . ctx - monitoring context

2717:   Level: advanced

2719: .seealso: [](ch_ksp), `KSP`, `KSPConvergedDefault()`, `KSPSetConvergenceTest()`, `KSPGetConvergenceTest()`
2720: @*/
2721: PetscErrorCode KSPGetConvergenceContext(KSP ksp, void *ctx)
2722: {
2723:   PetscFunctionBegin;
2725:   *(void **)ctx = ksp->cnvP;
2726:   PetscFunctionReturn(PETSC_SUCCESS);
2727: }

2729: /*@C
2730:   KSPBuildSolution - Builds the approximate solution in a vector provided.

2732:   Collective

2734:   Input Parameter:
2735: . ksp - iterative context obtained from `KSPCreate()`

2737:   Output Parameter:
2738:    Provide exactly one of
2739: + v - location to stash solution.
2740: - V - the solution is returned in this location. This vector is created internally. This vector should NOT be destroyed by the user with `VecDestroy()`.

2742:   Level: developer

2744:   Notes:
2745:   This routine can be used in one of two ways
2746: .vb
2747:       KSPBuildSolution(ksp,NULL,&V);
2748:    or
2749:       KSPBuildSolution(ksp,v,NULL); or KSPBuildSolution(ksp,v,&v);
2750: .ve
2751:   In the first case an internal vector is allocated to store the solution
2752:   (the user cannot destroy this vector). In the second case the solution
2753:   is generated in the vector that the user provides. Note that for certain
2754:   methods, such as `KSPCG`, the second case requires a copy of the solution,
2755:   while in the first case the call is essentially free since it simply
2756:   returns the vector where the solution already is stored. For some methods
2757:   like `KSPGMRES` this is a reasonably expensive operation and should only be
2758:   used in truly needed.

2760: .seealso: [](ch_ksp), `KSPGetSolution()`, `KSPBuildResidual()`, `KSP`
2761: @*/
2762: PetscErrorCode KSPBuildSolution(KSP ksp, Vec v, Vec *V)
2763: {
2764:   PetscFunctionBegin;
2766:   PetscCheck(V || v, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ARG_WRONG, "Must provide either v or V");
2767:   if (!V) V = &v;
2768:   PetscUseTypeMethod(ksp, buildsolution, v, V);
2769:   PetscFunctionReturn(PETSC_SUCCESS);
2770: }

2772: /*@C
2773:   KSPBuildResidual - Builds the residual in a vector provided.

2775:   Collective

2777:   Input Parameter:
2778: . ksp - iterative context obtained from `KSPCreate()`

2780:   Output Parameters:
2781: + v - optional location to stash residual.  If `v` is not provided,
2782:       then a location is generated.
2783: . t - work vector.  If not provided then one is generated.
2784: - V - the residual

2786:   Level: advanced

2788:   Note:
2789:   Regardless of whether or not `v` is provided, the residual is
2790:   returned in `V`.

2792: .seealso: [](ch_ksp), `KSP`, `KSPBuildSolution()`
2793: @*/
2794: PetscErrorCode KSPBuildResidual(KSP ksp, Vec t, Vec v, Vec *V)
2795: {
2796:   PetscBool flag = PETSC_FALSE;
2797:   Vec       w = v, tt = t;

2799:   PetscFunctionBegin;
2801:   if (!w) PetscCall(VecDuplicate(ksp->vec_rhs, &w));
2802:   if (!tt) {
2803:     PetscCall(VecDuplicate(ksp->vec_sol, &tt));
2804:     flag = PETSC_TRUE;
2805:   }
2806:   PetscUseTypeMethod(ksp, buildresidual, tt, w, V);
2807:   if (flag) PetscCall(VecDestroy(&tt));
2808:   PetscFunctionReturn(PETSC_SUCCESS);
2809: }

2811: /*@
2812:   KSPSetDiagonalScale - Tells `KSP` to symmetrically diagonally scale the system
2813:   before solving. This actually CHANGES the matrix (and right hand side).

2815:   Logically Collective

2817:   Input Parameters:
2818: + ksp   - the `KSP` context
2819: - scale - `PETSC_TRUE` or `PETSC_FALSE`

2821:   Options Database Keys:
2822: + -ksp_diagonal_scale     - perform a diagonal scaling before the solve
2823: - -ksp_diagonal_scale_fix - scale the matrix back AFTER the solve

2825:   Level: advanced

2827:   Notes:
2828:   Scales the matrix by  $D^{-1/2}  A  D^{-1/2}  [D^{1/2} x ] = D^{-1/2} b $
2829:   where $D_{ii}$ is $1/abs(A_{ii}) $ unless $A_{ii}$ is zero and then it is 1.

2831:   BE CAREFUL with this routine: it actually scales the matrix and right
2832:   hand side that define the system. After the system is solved the matrix
2833:   and right hand side remain scaled unless you use `KSPSetDiagonalScaleFix()`

2835:   This should NOT be used within the `SNES` solves if you are using a line
2836:   search.

2838:   If you use this with the `PCType` `PCEISENSTAT` preconditioner than you can
2839:   use the `PCEisenstatSetNoDiagonalScaling()` option, or `-pc_eisenstat_no_diagonal_scaling`
2840:   to save some unneeded, redundant flops.

2842: .seealso: [](ch_ksp), `KSPGetDiagonalScale()`, `KSPSetDiagonalScaleFix()`, `KSP`
2843: @*/
2844: PetscErrorCode KSPSetDiagonalScale(KSP ksp, PetscBool scale)
2845: {
2846:   PetscFunctionBegin;
2849:   ksp->dscale = scale;
2850:   PetscFunctionReturn(PETSC_SUCCESS);
2851: }

2853: /*@
2854:   KSPGetDiagonalScale - Checks if `KSP` solver scales the matrix and right hand side, that is if `KSPSetDiagonalScale()` has been called

2856:   Not Collective

2858:   Input Parameter:
2859: . ksp - the `KSP` context

2861:   Output Parameter:
2862: . scale - `PETSC_TRUE` or `PETSC_FALSE`

2864:   Level: intermediate

2866: .seealso: [](ch_ksp), `KSP`, `KSPSetDiagonalScale()`, `KSPSetDiagonalScaleFix()`
2867: @*/
2868: PetscErrorCode KSPGetDiagonalScale(KSP ksp, PetscBool *scale)
2869: {
2870:   PetscFunctionBegin;
2872:   PetscAssertPointer(scale, 2);
2873:   *scale = ksp->dscale;
2874:   PetscFunctionReturn(PETSC_SUCCESS);
2875: }

2877: /*@
2878:   KSPSetDiagonalScaleFix - Tells `KSP` to diagonally scale the system back after solving.

2880:   Logically Collective

2882:   Input Parameters:
2883: + ksp - the `KSP` context
2884: - fix - `PETSC_TRUE` to scale back after the system solve, `PETSC_FALSE` to not
2885:          rescale (default)

2887:   Level: intermediate

2889:   Notes:
2890:   Must be called after `KSPSetDiagonalScale()`

2892:   Using this will slow things down, because it rescales the matrix before and
2893:   after each linear solve. This is intended mainly for testing to allow one
2894:   to easily get back the original system to make sure the solution computed is
2895:   accurate enough.

2897: .seealso: [](ch_ksp), `KSPGetDiagonalScale()`, `KSPSetDiagonalScale()`, `KSPGetDiagonalScaleFix()`, `KSP`
2898: @*/
2899: PetscErrorCode KSPSetDiagonalScaleFix(KSP ksp, PetscBool fix)
2900: {
2901:   PetscFunctionBegin;
2904:   ksp->dscalefix = fix;
2905:   PetscFunctionReturn(PETSC_SUCCESS);
2906: }

2908: /*@
2909:   KSPGetDiagonalScaleFix - Determines if `KSP` diagonally scales the system back after solving. That is `KSPSetDiagonalScaleFix()` has been called

2911:   Not Collective

2913:   Input Parameter:
2914: . ksp - the `KSP` context

2916:   Output Parameter:
2917: . fix - `PETSC_TRUE` to scale back after the system solve, `PETSC_FALSE` to not
2918:          rescale (default)

2920:   Level: intermediate

2922: .seealso: [](ch_ksp), `KSPGetDiagonalScale()`, `KSPSetDiagonalScale()`, `KSPSetDiagonalScaleFix()`, `KSP`
2923: @*/
2924: PetscErrorCode KSPGetDiagonalScaleFix(KSP ksp, PetscBool *fix)
2925: {
2926:   PetscFunctionBegin;
2928:   PetscAssertPointer(fix, 2);
2929:   *fix = ksp->dscalefix;
2930:   PetscFunctionReturn(PETSC_SUCCESS);
2931: }

2933: /*@C
2934:   KSPSetComputeOperators - set routine to compute the linear operators

2936:   Logically Collective

2938:   Input Parameters:
2939: + ksp  - the `KSP` context
2940: . func - function to compute the operators, see `KSPComputeOperatorsFn` for the calling sequence
2941: - ctx  - optional context

2943:   Level: beginner

2945:   Notes:
2946:   `func()` will be called automatically at the very next call to `KSPSolve()`. It will NOT be called at future `KSPSolve()` calls
2947:   unless either `KSPSetComputeOperators()` or `KSPSetOperators()` is called before that `KSPSolve()` is called. This allows the same system to be solved several times
2948:   with different right hand side functions but is a confusing API since one might expect it to be called for each `KSPSolve()`

2950:   To reuse the same preconditioner for the next `KSPSolve()` and not compute a new one based on the most recently computed matrix call `KSPSetReusePreconditioner()`

2952:   Developer Note:
2953:   Perhaps this routine and `KSPSetComputeRHS()` could be combined into a new API that makes clear when new matrices are computing without requiring call this
2954:   routine to indicate when the new matrix should be computed.

2956: .seealso: [](ch_ksp), `KSP`, `KSPSetOperators()`, `KSPSetComputeRHS()`, `DMKSPSetComputeOperators()`, `KSPSetComputeInitialGuess()`, `KSPComputeOperatorsFn`
2957: @*/
2958: PetscErrorCode KSPSetComputeOperators(KSP ksp, KSPComputeOperatorsFn *func, void *ctx)
2959: {
2960:   DM dm;

2962:   PetscFunctionBegin;
2964:   PetscCall(KSPGetDM(ksp, &dm));
2965:   PetscCall(DMKSPSetComputeOperators(dm, func, ctx));
2966:   if (ksp->setupstage == KSP_SETUP_NEWRHS) ksp->setupstage = KSP_SETUP_NEWMATRIX;
2967:   PetscFunctionReturn(PETSC_SUCCESS);
2968: }

2970: /*@C
2971:   KSPSetComputeRHS - set routine to compute the right hand side of the linear system

2973:   Logically Collective

2975:   Input Parameters:
2976: + ksp  - the `KSP` context
2977: . func - function to compute the right hand side, see `KSPComputeRHSFn` for the calling squence
2978: - ctx  - optional context

2980:   Level: beginner

2982:   Note:
2983:   The routine you provide will be called EACH you call `KSPSolve()` to prepare the new right hand side for that solve

2985: .seealso: [](ch_ksp), `KSP`, `KSPSolve()`, `DMKSPSetComputeRHS()`, `KSPSetComputeOperators()`, `KSPSetOperators()`, `KSPComputeRHSFn`
2986: @*/
2987: PetscErrorCode KSPSetComputeRHS(KSP ksp, KSPComputeRHSFn *func, void *ctx)
2988: {
2989:   DM dm;

2991:   PetscFunctionBegin;
2993:   PetscCall(KSPGetDM(ksp, &dm));
2994:   PetscCall(DMKSPSetComputeRHS(dm, func, ctx));
2995:   PetscFunctionReturn(PETSC_SUCCESS);
2996: }

2998: /*@C
2999:   KSPSetComputeInitialGuess - set routine to compute the initial guess of the linear system

3001:   Logically Collective

3003:   Input Parameters:
3004: + ksp  - the `KSP` context
3005: . func - function to compute the initial guess, see `KSPComputeInitialGuessFn` for calling sequence
3006: - ctx  - optional context

3008:   Level: beginner

3010:   Note:
3011:   This should only be used in conjunction with `KSPSetComputeRHS()` and `KSPSetComputeOperators()`, otherwise
3012:   call `KSPSetInitialGuessNonzero()` and set the initial guess values in the solution vector passed to `KSPSolve()` before calling the solver

3014: .seealso: [](ch_ksp), `KSP`, `KSPSolve()`, `KSPSetComputeRHS()`, `KSPSetComputeOperators()`, `DMKSPSetComputeInitialGuess()`, `KSPSetInitialGuessNonzero()`,
3015:           `KSPComputeInitialGuessFn`
3016: @*/
3017: PetscErrorCode KSPSetComputeInitialGuess(KSP ksp, KSPComputeInitialGuessFn *func, void *ctx)
3018: {
3019:   DM dm;

3021:   PetscFunctionBegin;
3023:   PetscCall(KSPGetDM(ksp, &dm));
3024:   PetscCall(DMKSPSetComputeInitialGuess(dm, func, ctx));
3025:   PetscFunctionReturn(PETSC_SUCCESS);
3026: }

3028: /*@
3029:   KSPSetUseExplicitTranspose - Determines the explicit transpose of the operator is formed in `KSPSolveTranspose()`. In some configurations (like GPUs) it may
3030:   be explicitly formed since the solve is much more efficient.

3032:   Logically Collective

3034:   Input Parameter:
3035: . ksp - the `KSP` context

3037:   Output Parameter:
3038: . flg - `PETSC_TRUE` to transpose the system in `KSPSolveTranspose()`, `PETSC_FALSE` to not transpose (default)

3040:   Level: advanced

3042: .seealso: [](ch_ksp), `KSPSolveTranspose()`, `KSP`
3043: @*/
3044: PetscErrorCode KSPSetUseExplicitTranspose(KSP ksp, PetscBool flg)
3045: {
3046:   PetscFunctionBegin;
3049:   ksp->transpose.use_explicittranspose = flg;
3050:   PetscFunctionReturn(PETSC_SUCCESS);
3051: }