Actual source code: pgmres.c
2: /*
3: This file implements PGMRES (a Pipelined Generalized Minimal Residual method)
4: */
6: #include <../src/ksp/ksp/impls/gmres/pgmres/pgmresimpl.h>
7: #define PGMRES_DELTA_DIRECTIONS 10
8: #define PGMRES_DEFAULT_MAXK 30
10: static PetscErrorCode KSPPGMRESUpdateHessenberg(KSP, PetscInt, PetscBool *, PetscReal *);
11: static PetscErrorCode KSPPGMRESBuildSoln(PetscScalar *, Vec, Vec, KSP, PetscInt);
13: /*
15: KSPSetUp_PGMRES - Sets up the workspace needed by pgmres.
17: This is called once, usually automatically by KSPSolve() or KSPSetUp(),
18: but can be called directly by KSPSetUp().
20: */
21: static PetscErrorCode KSPSetUp_PGMRES(KSP ksp)
22: {
23: PetscFunctionBegin;
24: PetscCall(KSPSetUp_GMRES(ksp));
25: PetscFunctionReturn(PETSC_SUCCESS);
26: }
28: /*
30: KSPPGMRESCycle - Run pgmres, possibly with restart. Return residual
31: history if requested.
33: input parameters:
34: . pgmres - structure containing parameters and work areas
36: output parameters:
37: . itcount - number of iterations used. If null, ignored.
38: . converged - 0 if not converged
40: Notes:
41: On entry, the value in vector VEC_VV(0) should be
42: the initial residual.
44: */
45: static PetscErrorCode KSPPGMRESCycle(PetscInt *itcount, KSP ksp)
46: {
47: KSP_PGMRES *pgmres = (KSP_PGMRES *)(ksp->data);
48: PetscReal res_norm, res, newnorm;
49: PetscInt it = 0, j, k;
50: PetscBool hapend = PETSC_FALSE;
52: PetscFunctionBegin;
53: if (itcount) *itcount = 0;
54: PetscCall(VecNormalize(VEC_VV(0), &res_norm));
55: KSPCheckNorm(ksp, res_norm);
56: res = res_norm;
57: *RS(0) = res_norm;
59: /* check for the convergence */
60: PetscCall(PetscObjectSAWsTakeAccess((PetscObject)ksp));
61: if (ksp->normtype != KSP_NORM_NONE) ksp->rnorm = res;
62: else ksp->rnorm = 0;
63: PetscCall(PetscObjectSAWsGrantAccess((PetscObject)ksp));
64: pgmres->it = it - 2;
65: PetscCall(KSPLogResidualHistory(ksp, ksp->rnorm));
66: PetscCall(KSPMonitor(ksp, ksp->its, ksp->rnorm));
67: if (!res) {
68: ksp->reason = KSP_CONVERGED_ATOL;
69: PetscCall(PetscInfo(ksp, "Converged due to zero residual norm on entry\n"));
70: PetscFunctionReturn(PETSC_SUCCESS);
71: }
73: PetscCall((*ksp->converged)(ksp, ksp->its, ksp->rnorm, &ksp->reason, ksp->cnvP));
74: for (; !ksp->reason; it++) {
75: Vec Zcur, Znext;
76: if (pgmres->vv_allocated <= it + VEC_OFFSET + 1) PetscCall(KSPGMRESGetNewVectors(ksp, it + 1));
77: /* VEC_VV(it-1) is orthogonal, it will be normalized once the VecNorm arrives. */
78: Zcur = VEC_VV(it); /* Zcur is not yet orthogonal, but the VecMDot to orthogonalize it has been started. */
79: Znext = VEC_VV(it + 1); /* This iteration will compute Znext, update with a deferred correction once we know how
80: * Zcur relates to the previous vectors, and start the reduction to orthogonalize it. */
82: if (it < pgmres->max_k + 1 && ksp->its + 1 < PetscMax(2, ksp->max_it)) { /* We don't know whether what we have computed is enough, so apply the matrix. */
83: PetscCall(KSP_PCApplyBAorAB(ksp, Zcur, Znext, VEC_TEMP_MATOP));
84: }
86: if (it > 1) { /* Complete the pending reduction */
87: PetscCall(VecNormEnd(VEC_VV(it - 1), NORM_2, &newnorm));
88: *HH(it - 1, it - 2) = newnorm;
89: }
90: if (it > 0) { /* Finish the reduction computing the latest column of H */
91: PetscCall(VecMDotEnd(Zcur, it, &(VEC_VV(0)), HH(0, it - 1)));
92: }
94: if (it > 1) {
95: /* normalize the base vector from two iterations ago, basis is complete up to here */
96: PetscCall(VecScale(VEC_VV(it - 1), 1. / *HH(it - 1, it - 2)));
98: PetscCall(KSPPGMRESUpdateHessenberg(ksp, it - 2, &hapend, &res));
99: pgmres->it = it - 2;
100: ksp->its++;
101: if (ksp->normtype != KSP_NORM_NONE) ksp->rnorm = res;
102: else ksp->rnorm = 0;
104: PetscCall((*ksp->converged)(ksp, ksp->its, ksp->rnorm, &ksp->reason, ksp->cnvP));
105: if (it < pgmres->max_k + 1 || ksp->reason || ksp->its == ksp->max_it) { /* Monitor if we are done or still iterating, but not before a restart. */
106: PetscCall(KSPLogResidualHistory(ksp, ksp->rnorm));
107: PetscCall(KSPMonitor(ksp, ksp->its, ksp->rnorm));
108: }
109: if (ksp->reason) break;
110: /* Catch error in happy breakdown and signal convergence and break from loop */
111: if (hapend) {
112: PetscCheck(!ksp->errorifnotconverged, PetscObjectComm((PetscObject)ksp), PETSC_ERR_NOT_CONVERGED, "You reached the happy break down, but convergence was not indicated. Residual norm = %g", (double)res);
113: ksp->reason = KSP_DIVERGED_BREAKDOWN;
114: break;
115: }
117: if (!(it < pgmres->max_k + 1 && ksp->its < ksp->max_it)) break;
119: /* The it-2 column of H was not scaled when we computed Zcur, apply correction */
120: PetscCall(VecScale(Zcur, 1. / *HH(it - 1, it - 2)));
121: /* And Znext computed in this iteration was computed using the under-scaled Zcur */
122: PetscCall(VecScale(Znext, 1. / *HH(it - 1, it - 2)));
124: /* In the previous iteration, we projected an unnormalized Zcur against the Krylov basis, so we need to fix the column of H resulting from that projection. */
125: for (k = 0; k < it; k++) *HH(k, it - 1) /= *HH(it - 1, it - 2);
126: /* When Zcur was projected against the Krylov basis, VV(it-1) was still not normalized, so fix that too. This
127: * column is complete except for HH(it,it-1) which we won't know until the next iteration. */
128: *HH(it - 1, it - 1) /= *HH(it - 1, it - 2);
129: }
131: if (it > 0) {
132: PetscScalar *work;
133: if (!pgmres->orthogwork) PetscCall(PetscMalloc1(pgmres->max_k + 2, &pgmres->orthogwork));
134: work = pgmres->orthogwork;
135: /* Apply correction computed by the VecMDot in the last iteration to Znext. The original form is
136: *
137: * Znext -= sum_{j=0}^{i-1} Z[j+1] * H[j,i-1]
138: *
139: * where
140: *
141: * Z[j] = sum_{k=0}^j V[k] * H[k,j-1]
142: *
143: * substituting
144: *
145: * Znext -= sum_{j=0}^{i-1} sum_{k=0}^{j+1} V[k] * H[k,j] * H[j,i-1]
146: *
147: * rearranging the iteration space from row-column to column-row
148: *
149: * Znext -= sum_{k=0}^i sum_{j=k-1}^{i-1} V[k] * H[k,j] * H[j,i-1]
150: *
151: * Note that column it-1 of HH is correct. For all previous columns, we must look at HES because HH has already
152: * been transformed to upper triangular form.
153: */
154: for (k = 0; k < it + 1; k++) {
155: work[k] = 0;
156: for (j = PetscMax(0, k - 1); j < it - 1; j++) work[k] -= *HES(k, j) * *HH(j, it - 1);
157: }
158: PetscCall(VecMAXPY(Znext, it + 1, work, &VEC_VV(0)));
159: PetscCall(VecAXPY(Znext, -*HH(it - 1, it - 1), Zcur));
161: /* Orthogonalize Zcur against existing basis vectors. */
162: for (k = 0; k < it; k++) work[k] = -*HH(k, it - 1);
163: PetscCall(VecMAXPY(Zcur, it, work, &VEC_VV(0)));
164: /* Zcur is now orthogonal, and will be referred to as VEC_VV(it) again, though it is still not normalized. */
165: /* Begin computing the norm of the new vector, will be normalized after the MatMult in the next iteration. */
166: PetscCall(VecNormBegin(VEC_VV(it), NORM_2, &newnorm));
167: }
169: /* Compute column of H (to the diagonal, but not the subdiagonal) to be able to orthogonalize the newest vector. */
170: PetscCall(VecMDotBegin(Znext, it + 1, &VEC_VV(0), HH(0, it)));
172: /* Start an asynchronous split-mode reduction, the result of the MDot and Norm will be collected on the next iteration. */
173: PetscCall(PetscCommSplitReductionBegin(PetscObjectComm((PetscObject)Znext)));
174: }
176: if (itcount) *itcount = it - 1; /* Number of iterations actually completed. */
178: /*
179: Down here we have to solve for the "best" coefficients of the Krylov
180: columns, add the solution values together, and possibly unwind the
181: preconditioning from the solution
182: */
183: /* Form the solution (or the solution so far) */
184: PetscCall(KSPPGMRESBuildSoln(RS(0), ksp->vec_sol, ksp->vec_sol, ksp, it - 2));
185: PetscFunctionReturn(PETSC_SUCCESS);
186: }
188: /*
189: KSPSolve_PGMRES - This routine applies the PGMRES method.
191: Input Parameter:
192: . ksp - the Krylov space object that was set to use pgmres
194: Output Parameter:
195: . outits - number of iterations used
197: */
198: static PetscErrorCode KSPSolve_PGMRES(KSP ksp)
199: {
200: PetscInt its, itcount;
201: KSP_PGMRES *pgmres = (KSP_PGMRES *)ksp->data;
202: PetscBool guess_zero = ksp->guess_zero;
204: PetscFunctionBegin;
205: PetscCheck(!ksp->calc_sings || pgmres->Rsvd, PetscObjectComm((PetscObject)ksp), PETSC_ERR_ORDER, "Must call KSPSetComputeSingularValues() before KSPSetUp() is called");
206: PetscCall(PetscObjectSAWsTakeAccess((PetscObject)ksp));
207: ksp->its = 0;
208: PetscCall(PetscObjectSAWsGrantAccess((PetscObject)ksp));
210: itcount = 0;
211: ksp->reason = KSP_CONVERGED_ITERATING;
212: while (!ksp->reason) {
213: PetscCall(KSPInitialResidual(ksp, ksp->vec_sol, VEC_TEMP, VEC_TEMP_MATOP, VEC_VV(0), ksp->vec_rhs));
214: PetscCall(KSPPGMRESCycle(&its, ksp));
215: itcount += its;
216: if (itcount >= ksp->max_it) {
217: if (!ksp->reason) ksp->reason = KSP_DIVERGED_ITS;
218: break;
219: }
220: ksp->guess_zero = PETSC_FALSE; /* every future call to KSPInitialResidual() will have nonzero guess */
221: }
222: ksp->guess_zero = guess_zero; /* restore if user provided nonzero initial guess */
223: PetscFunctionReturn(PETSC_SUCCESS);
224: }
226: static PetscErrorCode KSPDestroy_PGMRES(KSP ksp)
227: {
228: PetscFunctionBegin;
229: PetscCall(KSPDestroy_GMRES(ksp));
230: PetscFunctionReturn(PETSC_SUCCESS);
231: }
233: /*
234: KSPPGMRESBuildSoln - create the solution from the starting vector and the
235: current iterates.
237: Input parameters:
238: nrs - work area of size it + 1.
239: vguess - index of initial guess
240: vdest - index of result. Note that vguess may == vdest (replace
241: guess with the solution).
242: it - HH upper triangular part is a block of size (it+1) x (it+1)
244: This is an internal routine that knows about the PGMRES internals.
245: */
246: static PetscErrorCode KSPPGMRESBuildSoln(PetscScalar *nrs, Vec vguess, Vec vdest, KSP ksp, PetscInt it)
247: {
248: PetscScalar tt;
249: PetscInt k, j;
250: KSP_PGMRES *pgmres = (KSP_PGMRES *)(ksp->data);
252: PetscFunctionBegin;
253: /* Solve for solution vector that minimizes the residual */
255: if (it < 0) { /* no pgmres steps have been performed */
256: PetscCall(VecCopy(vguess, vdest)); /* VecCopy() is smart, exits immediately if vguess == vdest */
257: PetscFunctionReturn(PETSC_SUCCESS);
258: }
260: /* solve the upper triangular system - RS is the right side and HH is
261: the upper triangular matrix - put soln in nrs */
262: if (*HH(it, it) != 0.0) nrs[it] = *RS(it) / *HH(it, it);
263: else nrs[it] = 0.0;
265: for (k = it - 1; k >= 0; k--) {
266: tt = *RS(k);
267: for (j = k + 1; j <= it; j++) tt -= *HH(k, j) * nrs[j];
268: nrs[k] = tt / *HH(k, k);
269: }
271: /* Accumulate the correction to the solution of the preconditioned problem in TEMP */
272: PetscCall(VecZeroEntries(VEC_TEMP));
273: PetscCall(VecMAXPY(VEC_TEMP, it + 1, nrs, &VEC_VV(0)));
274: PetscCall(KSPUnwindPreconditioner(ksp, VEC_TEMP, VEC_TEMP_MATOP));
275: /* add solution to previous solution */
276: if (vdest == vguess) {
277: PetscCall(VecAXPY(vdest, 1.0, VEC_TEMP));
278: } else {
279: PetscCall(VecWAXPY(vdest, 1.0, VEC_TEMP, vguess));
280: }
281: PetscFunctionReturn(PETSC_SUCCESS);
282: }
284: /*
286: KSPPGMRESUpdateHessenberg - Do the scalar work for the orthogonalization.
287: Return new residual.
289: input parameters:
291: . ksp - Krylov space object
292: . it - plane rotations are applied to the (it+1)th column of the
293: modified hessenberg (i.e. HH(:,it))
294: . hapend - PETSC_FALSE not happy breakdown ending.
296: output parameters:
297: . res - the new residual
299: */
300: /*
301: . it - column of the Hessenberg that is complete, PGMRES is actually computing two columns ahead of this
302: */
303: static PetscErrorCode KSPPGMRESUpdateHessenberg(KSP ksp, PetscInt it, PetscBool *hapend, PetscReal *res)
304: {
305: PetscScalar *hh, *cc, *ss, *rs;
306: PetscInt j;
307: PetscReal hapbnd;
308: KSP_PGMRES *pgmres = (KSP_PGMRES *)(ksp->data);
310: PetscFunctionBegin;
311: hh = HH(0, it); /* pointer to beginning of column to update */
312: cc = CC(0); /* beginning of cosine rotations */
313: ss = SS(0); /* beginning of sine rotations */
314: rs = RS(0); /* right hand side of least squares system */
316: /* The Hessenberg matrix is now correct through column it, save that form for possible spectral analysis */
317: for (j = 0; j <= it + 1; j++) *HES(j, it) = hh[j];
319: /* check for the happy breakdown */
320: hapbnd = PetscMin(PetscAbsScalar(hh[it + 1] / rs[it]), pgmres->haptol);
321: if (PetscAbsScalar(hh[it + 1]) < hapbnd) {
322: PetscCall(PetscInfo(ksp, "Detected happy breakdown, current hapbnd = %14.12e H(%" PetscInt_FMT ",%" PetscInt_FMT ") = %14.12e\n", (double)hapbnd, it + 1, it, (double)PetscAbsScalar(*HH(it + 1, it))));
323: *hapend = PETSC_TRUE;
324: }
326: /* Apply all the previously computed plane rotations to the new column
327: of the Hessenberg matrix */
328: /* Note: this uses the rotation [conj(c) s ; -s c], c= cos(theta), s= sin(theta),
329: and some refs have [c s ; -conj(s) c] (don't be confused!) */
331: for (j = 0; j < it; j++) {
332: PetscScalar hhj = hh[j];
333: hh[j] = PetscConj(cc[j]) * hhj + ss[j] * hh[j + 1];
334: hh[j + 1] = -ss[j] * hhj + cc[j] * hh[j + 1];
335: }
337: /*
338: compute the new plane rotation, and apply it to:
339: 1) the right-hand-side of the Hessenberg system (RS)
340: note: it affects RS(it) and RS(it+1)
341: 2) the new column of the Hessenberg matrix
342: note: it affects HH(it,it) which is currently pointed to
343: by hh and HH(it+1, it) (*(hh+1))
344: thus obtaining the updated value of the residual...
345: */
347: /* compute new plane rotation */
349: if (!*hapend) {
350: PetscReal delta = PetscSqrtReal(PetscSqr(PetscAbsScalar(hh[it])) + PetscSqr(PetscAbsScalar(hh[it + 1])));
351: if (delta == 0.0) {
352: ksp->reason = KSP_DIVERGED_NULL;
353: PetscFunctionReturn(PETSC_SUCCESS);
354: }
356: cc[it] = hh[it] / delta; /* new cosine value */
357: ss[it] = hh[it + 1] / delta; /* new sine value */
359: hh[it] = PetscConj(cc[it]) * hh[it] + ss[it] * hh[it + 1];
360: rs[it + 1] = -ss[it] * rs[it];
361: rs[it] = PetscConj(cc[it]) * rs[it];
362: *res = PetscAbsScalar(rs[it + 1]);
363: } else { /* happy breakdown: HH(it+1, it) = 0, therefore we don't need to apply
364: another rotation matrix (so RH doesn't change). The new residual is
365: always the new sine term times the residual from last time (RS(it)),
366: but now the new sine rotation would be zero...so the residual should
367: be zero...so we will multiply "zero" by the last residual. This might
368: not be exactly what we want to do here -could just return "zero". */
370: *res = 0.0;
371: }
372: PetscFunctionReturn(PETSC_SUCCESS);
373: }
375: /*
376: KSPBuildSolution_PGMRES
378: Input Parameter:
379: . ksp - the Krylov space object
380: . ptr-
382: Output Parameter:
383: . result - the solution
385: Note: this calls KSPPGMRESBuildSoln - the same function that KSPPGMRESCycle
386: calls directly.
388: */
389: PetscErrorCode KSPBuildSolution_PGMRES(KSP ksp, Vec ptr, Vec *result)
390: {
391: KSP_PGMRES *pgmres = (KSP_PGMRES *)ksp->data;
393: PetscFunctionBegin;
394: if (!ptr) {
395: if (!pgmres->sol_temp) PetscCall(VecDuplicate(ksp->vec_sol, &pgmres->sol_temp));
396: ptr = pgmres->sol_temp;
397: }
398: if (!pgmres->nrs) {
399: /* allocate the work area */
400: PetscCall(PetscMalloc1(pgmres->max_k, &pgmres->nrs));
401: }
403: PetscCall(KSPPGMRESBuildSoln(pgmres->nrs, ksp->vec_sol, ptr, ksp, pgmres->it));
404: if (result) *result = ptr;
405: PetscFunctionReturn(PETSC_SUCCESS);
406: }
408: PetscErrorCode KSPSetFromOptions_PGMRES(KSP ksp, PetscOptionItems *PetscOptionsObject)
409: {
410: PetscFunctionBegin;
411: PetscCall(KSPSetFromOptions_GMRES(ksp, PetscOptionsObject));
412: PetscOptionsHeadBegin(PetscOptionsObject, "KSP pipelined GMRES Options");
413: PetscOptionsHeadEnd();
414: PetscFunctionReturn(PETSC_SUCCESS);
415: }
417: PetscErrorCode KSPReset_PGMRES(KSP ksp)
418: {
419: PetscFunctionBegin;
420: PetscCall(KSPReset_GMRES(ksp));
421: PetscFunctionReturn(PETSC_SUCCESS);
422: }
424: /*MC
425: KSPPGMRES - Implements the Pipelined Generalized Minimal Residual method. [](sec_pipelineksp)
427: Options Database Keys:
428: + -ksp_gmres_restart <restart> - the number of Krylov directions to orthogonalize against
429: . -ksp_gmres_haptol <tol> - sets the tolerance for "happy ending" (exact convergence)
430: . -ksp_gmres_preallocate - preallocate all the Krylov search directions initially (otherwise groups of
431: vectors are allocated as needed)
432: . -ksp_gmres_classicalgramschmidt - use classical (unmodified) Gram-Schmidt to orthogonalize against the Krylov space (fast) (the default)
433: . -ksp_gmres_modifiedgramschmidt - use modified Gram-Schmidt in the orthogonalization (more stable, but slower)
434: . -ksp_gmres_cgs_refinement_type <refine_never,refine_ifneeded,refine_always> - determine if iterative refinement is used to increase the
435: stability of the classical Gram-Schmidt orthogonalization.
436: - -ksp_gmres_krylov_monitor - plot the Krylov space generated
438: Level: beginner
440: Note:
441: MPI configuration may be necessary for reductions to make asynchronous progress, which is important for performance of pipelined methods.
442: See [](doc_faq_pipelined)
444: Reference:
445: Ghysels, Ashby, Meerbergen, Vanroose, Hiding global communication latencies in the GMRES algorithm on massively parallel machines, 2012.
447: Developer Note:
448: This object is subclassed off of `KSPGMRES`
450: .seealso: [](ch_ksp), [](sec_pipelineksp), [](doc_faq_pipelined), `KSPCreate()`, `KSPSetType()`, `KSPType`, `KSP`, `KSPGMRES`, `KSPLGMRES`, `KSPPIPECG`, `KSPPIPECR`,
451: `KSPGMRESSetRestart()`, `KSPGMRESSetHapTol()`, `KSPGMRESSetPreAllocateVectors()`, `KSPGMRESSetOrthogonalization()`, `KSPGMRESGetOrthogonalization()`,
452: `KSPGMRESClassicalGramSchmidtOrthogonalization()`, `KSPGMRESModifiedGramSchmidtOrthogonalization()`,
453: `KSPGMRESCGSRefinementType`, `KSPGMRESSetCGSRefinementType()`, `KSPGMRESGetCGSRefinementType()`, `KSPGMRESMonitorKrylov()`
454: M*/
456: PETSC_EXTERN PetscErrorCode KSPCreate_PGMRES(KSP ksp)
457: {
458: KSP_PGMRES *pgmres;
460: PetscFunctionBegin;
461: PetscCall(PetscNew(&pgmres));
463: ksp->data = (void *)pgmres;
464: ksp->ops->buildsolution = KSPBuildSolution_PGMRES;
465: ksp->ops->setup = KSPSetUp_PGMRES;
466: ksp->ops->solve = KSPSolve_PGMRES;
467: ksp->ops->reset = KSPReset_PGMRES;
468: ksp->ops->destroy = KSPDestroy_PGMRES;
469: ksp->ops->view = KSPView_GMRES;
470: ksp->ops->setfromoptions = KSPSetFromOptions_PGMRES;
471: ksp->ops->computeextremesingularvalues = KSPComputeExtremeSingularValues_GMRES;
472: ksp->ops->computeeigenvalues = KSPComputeEigenvalues_GMRES;
474: PetscCall(KSPSetSupportedNorm(ksp, KSP_NORM_PRECONDITIONED, PC_LEFT, 3));
475: PetscCall(KSPSetSupportedNorm(ksp, KSP_NORM_UNPRECONDITIONED, PC_RIGHT, 2));
476: PetscCall(KSPSetSupportedNorm(ksp, KSP_NORM_NONE, PC_RIGHT, 1));
478: PetscCall(PetscObjectComposeFunction((PetscObject)ksp, "KSPGMRESSetPreAllocateVectors_C", KSPGMRESSetPreAllocateVectors_GMRES));
479: PetscCall(PetscObjectComposeFunction((PetscObject)ksp, "KSPGMRESSetOrthogonalization_C", KSPGMRESSetOrthogonalization_GMRES));
480: PetscCall(PetscObjectComposeFunction((PetscObject)ksp, "KSPGMRESGetOrthogonalization_C", KSPGMRESGetOrthogonalization_GMRES));
481: PetscCall(PetscObjectComposeFunction((PetscObject)ksp, "KSPGMRESSetRestart_C", KSPGMRESSetRestart_GMRES));
482: PetscCall(PetscObjectComposeFunction((PetscObject)ksp, "KSPGMRESGetRestart_C", KSPGMRESGetRestart_GMRES));
483: PetscCall(PetscObjectComposeFunction((PetscObject)ksp, "KSPGMRESSetCGSRefinementType_C", KSPGMRESSetCGSRefinementType_GMRES));
484: PetscCall(PetscObjectComposeFunction((PetscObject)ksp, "KSPGMRESGetCGSRefinementType_C", KSPGMRESGetCGSRefinementType_GMRES));
486: pgmres->nextra_vecs = 1;
487: pgmres->haptol = 1.0e-30;
488: pgmres->q_preallocate = 0;
489: pgmres->delta_allocate = PGMRES_DELTA_DIRECTIONS;
490: pgmres->orthog = KSPGMRESClassicalGramSchmidtOrthogonalization;
491: pgmres->nrs = NULL;
492: pgmres->sol_temp = NULL;
493: pgmres->max_k = PGMRES_DEFAULT_MAXK;
494: pgmres->Rsvd = NULL;
495: pgmres->orthogwork = NULL;
496: pgmres->cgstype = KSP_GMRES_CGS_REFINE_NEVER;
497: PetscFunctionReturn(PETSC_SUCCESS);
498: }