Actual source code: mg.c

  1: /*
  2:     Defines the multigrid preconditioner interface.
  3: */
  4: #include <petsc/private/pcmgimpl.h>
  5: #include <petsc/private/kspimpl.h>
  6: #include <petscdm.h>
  7: PETSC_INTERN PetscErrorCode PCPreSolveChangeRHS(PC, PetscBool *);

  9: /*
 10:    Contains the list of registered coarse space construction routines
 11: */
 12: PetscFunctionList PCMGCoarseList = NULL;

 14: PetscErrorCode PCMGMCycle_Private(PC pc, PC_MG_Levels **mglevelsin, PetscBool transpose, PetscBool matapp, PCRichardsonConvergedReason *reason)
 15: {
 16:   PC_MG        *mg = (PC_MG *)pc->data;
 17:   PC_MG_Levels *mgc, *mglevels = *mglevelsin;
 18:   PetscInt      cycles = (mglevels->level == 1) ? 1 : mglevels->cycles;

 20:   PetscFunctionBegin;
 21:   if (mglevels->eventsmoothsolve) PetscCall(PetscLogEventBegin(mglevels->eventsmoothsolve, 0, 0, 0, 0));
 22:   if (!transpose) {
 23:     if (matapp) {
 24:       PetscCall(KSPMatSolve(mglevels->smoothd, mglevels->B, mglevels->X)); /* pre-smooth */
 25:       PetscCall(KSPCheckSolve(mglevels->smoothd, pc, NULL));
 26:     } else {
 27:       PetscCall(KSPSolve(mglevels->smoothd, mglevels->b, mglevels->x)); /* pre-smooth */
 28:       PetscCall(KSPCheckSolve(mglevels->smoothd, pc, mglevels->x));
 29:     }
 30:   } else {
 31:     PetscCheck(!matapp, PetscObjectComm((PetscObject)pc), PETSC_ERR_SUP, "Not supported");
 32:     PetscCall(KSPSolveTranspose(mglevels->smoothu, mglevels->b, mglevels->x)); /* transpose of post-smooth */
 33:     PetscCall(KSPCheckSolve(mglevels->smoothu, pc, mglevels->x));
 34:   }
 35:   if (mglevels->eventsmoothsolve) PetscCall(PetscLogEventEnd(mglevels->eventsmoothsolve, 0, 0, 0, 0));
 36:   if (mglevels->level) { /* not the coarsest grid */
 37:     if (mglevels->eventresidual) PetscCall(PetscLogEventBegin(mglevels->eventresidual, 0, 0, 0, 0));
 38:     if (matapp && !mglevels->R) PetscCall(MatDuplicate(mglevels->B, MAT_DO_NOT_COPY_VALUES, &mglevels->R));
 39:     if (!transpose) {
 40:       if (matapp) PetscCall((*mglevels->matresidual)(mglevels->A, mglevels->B, mglevels->X, mglevels->R));
 41:       else PetscCall((*mglevels->residual)(mglevels->A, mglevels->b, mglevels->x, mglevels->r));
 42:     } else {
 43:       if (matapp) PetscCall((*mglevels->matresidualtranspose)(mglevels->A, mglevels->B, mglevels->X, mglevels->R));
 44:       else PetscCall((*mglevels->residualtranspose)(mglevels->A, mglevels->b, mglevels->x, mglevels->r));
 45:     }
 46:     if (mglevels->eventresidual) PetscCall(PetscLogEventEnd(mglevels->eventresidual, 0, 0, 0, 0));

 48:     /* if on finest level and have convergence criteria set */
 49:     if (mglevels->level == mglevels->levels - 1 && mg->ttol && reason) {
 50:       PetscReal rnorm;
 51:       PetscCall(VecNorm(mglevels->r, NORM_2, &rnorm));
 52:       if (rnorm <= mg->ttol) {
 53:         if (rnorm < mg->abstol) {
 54:           *reason = PCRICHARDSON_CONVERGED_ATOL;
 55:           PetscCall(PetscInfo(pc, "Linear solver has converged. Residual norm %g is less than absolute tolerance %g\n", (double)rnorm, (double)mg->abstol));
 56:         } else {
 57:           *reason = PCRICHARDSON_CONVERGED_RTOL;
 58:           PetscCall(PetscInfo(pc, "Linear solver has converged. Residual norm %g is less than relative tolerance times initial residual norm %g\n", (double)rnorm, (double)mg->ttol));
 59:         }
 60:         PetscFunctionReturn(PETSC_SUCCESS);
 61:       }
 62:     }

 64:     mgc = *(mglevelsin - 1);
 65:     if (mglevels->eventinterprestrict) PetscCall(PetscLogEventBegin(mglevels->eventinterprestrict, 0, 0, 0, 0));
 66:     if (!transpose) {
 67:       if (matapp) PetscCall(MatMatRestrict(mglevels->restrct, mglevels->R, &mgc->B));
 68:       else PetscCall(MatRestrict(mglevels->restrct, mglevels->r, mgc->b));
 69:     } else {
 70:       if (matapp) PetscCall(MatMatRestrict(mglevels->interpolate, mglevels->R, &mgc->B));
 71:       else PetscCall(MatRestrict(mglevels->interpolate, mglevels->r, mgc->b));
 72:     }
 73:     if (mglevels->eventinterprestrict) PetscCall(PetscLogEventEnd(mglevels->eventinterprestrict, 0, 0, 0, 0));
 74:     if (matapp) {
 75:       if (!mgc->X) {
 76:         PetscCall(MatDuplicate(mgc->B, MAT_DO_NOT_COPY_VALUES, &mgc->X));
 77:       } else {
 78:         PetscCall(MatZeroEntries(mgc->X));
 79:       }
 80:     } else {
 81:       PetscCall(VecZeroEntries(mgc->x));
 82:     }
 83:     while (cycles--) PetscCall(PCMGMCycle_Private(pc, mglevelsin - 1, transpose, matapp, reason));
 84:     if (mglevels->eventinterprestrict) PetscCall(PetscLogEventBegin(mglevels->eventinterprestrict, 0, 0, 0, 0));
 85:     if (!transpose) {
 86:       if (matapp) PetscCall(MatMatInterpolateAdd(mglevels->interpolate, mgc->X, mglevels->X, &mglevels->X));
 87:       else PetscCall(MatInterpolateAdd(mglevels->interpolate, mgc->x, mglevels->x, mglevels->x));
 88:     } else {
 89:       PetscCall(MatInterpolateAdd(mglevels->restrct, mgc->x, mglevels->x, mglevels->x));
 90:     }
 91:     if (mglevels->eventinterprestrict) PetscCall(PetscLogEventEnd(mglevels->eventinterprestrict, 0, 0, 0, 0));
 92:     if (mglevels->eventsmoothsolve) PetscCall(PetscLogEventBegin(mglevels->eventsmoothsolve, 0, 0, 0, 0));
 93:     if (!transpose) {
 94:       if (matapp) {
 95:         PetscCall(KSPMatSolve(mglevels->smoothu, mglevels->B, mglevels->X)); /* post smooth */
 96:         PetscCall(KSPCheckSolve(mglevels->smoothu, pc, NULL));
 97:       } else {
 98:         PetscCall(KSPSolve(mglevels->smoothu, mglevels->b, mglevels->x)); /* post smooth */
 99:         PetscCall(KSPCheckSolve(mglevels->smoothu, pc, mglevels->x));
100:       }
101:     } else {
102:       PetscCheck(!matapp, PetscObjectComm((PetscObject)pc), PETSC_ERR_SUP, "Not supported");
103:       PetscCall(KSPSolveTranspose(mglevels->smoothd, mglevels->b, mglevels->x)); /* post smooth */
104:       PetscCall(KSPCheckSolve(mglevels->smoothd, pc, mglevels->x));
105:     }
106:     if (mglevels->cr) {
107:       PetscCheck(!matapp, PetscObjectComm((PetscObject)pc), PETSC_ERR_SUP, "Not supported");
108:       /* TODO Turn on copy and turn off noisy if we have an exact solution
109:       PetscCall(VecCopy(mglevels->x, mglevels->crx));
110:       PetscCall(VecCopy(mglevels->b, mglevels->crb)); */
111:       PetscCall(KSPSetNoisy_Private(mglevels->crx));
112:       PetscCall(KSPSolve(mglevels->cr, mglevels->crb, mglevels->crx)); /* compatible relaxation */
113:       PetscCall(KSPCheckSolve(mglevels->cr, pc, mglevels->crx));
114:     }
115:     if (mglevels->eventsmoothsolve) PetscCall(PetscLogEventEnd(mglevels->eventsmoothsolve, 0, 0, 0, 0));
116:   }
117:   PetscFunctionReturn(PETSC_SUCCESS);
118: }

120: static PetscErrorCode PCApplyRichardson_MG(PC pc, Vec b, Vec x, Vec w, PetscReal rtol, PetscReal abstol, PetscReal dtol, PetscInt its, PetscBool zeroguess, PetscInt *outits, PCRichardsonConvergedReason *reason)
121: {
122:   PC_MG         *mg       = (PC_MG *)pc->data;
123:   PC_MG_Levels **mglevels = mg->levels;
124:   PC             tpc;
125:   PetscBool      changeu, changed;
126:   PetscInt       levels = mglevels[0]->levels, i;

128:   PetscFunctionBegin;
129:   /* When the DM is supplying the matrix then it will not exist until here */
130:   for (i = 0; i < levels; i++) {
131:     if (!mglevels[i]->A) {
132:       PetscCall(KSPGetOperators(mglevels[i]->smoothu, &mglevels[i]->A, NULL));
133:       PetscCall(PetscObjectReference((PetscObject)mglevels[i]->A));
134:     }
135:   }

137:   PetscCall(KSPGetPC(mglevels[levels - 1]->smoothd, &tpc));
138:   PetscCall(PCPreSolveChangeRHS(tpc, &changed));
139:   PetscCall(KSPGetPC(mglevels[levels - 1]->smoothu, &tpc));
140:   PetscCall(PCPreSolveChangeRHS(tpc, &changeu));
141:   if (!changed && !changeu) {
142:     PetscCall(VecDestroy(&mglevels[levels - 1]->b));
143:     mglevels[levels - 1]->b = b;
144:   } else { /* if the smoother changes the rhs during PreSolve, we cannot use the input vector */
145:     if (!mglevels[levels - 1]->b) {
146:       Vec *vec;

148:       PetscCall(KSPCreateVecs(mglevels[levels - 1]->smoothd, 1, &vec, 0, NULL));
149:       mglevels[levels - 1]->b = *vec;
150:       PetscCall(PetscFree(vec));
151:     }
152:     PetscCall(VecCopy(b, mglevels[levels - 1]->b));
153:   }
154:   mglevels[levels - 1]->x = x;

156:   mg->rtol   = rtol;
157:   mg->abstol = abstol;
158:   mg->dtol   = dtol;
159:   if (rtol) {
160:     /* compute initial residual norm for relative convergence test */
161:     PetscReal rnorm;
162:     if (zeroguess) {
163:       PetscCall(VecNorm(b, NORM_2, &rnorm));
164:     } else {
165:       PetscCall((*mglevels[levels - 1]->residual)(mglevels[levels - 1]->A, b, x, w));
166:       PetscCall(VecNorm(w, NORM_2, &rnorm));
167:     }
168:     mg->ttol = PetscMax(rtol * rnorm, abstol);
169:   } else if (abstol) mg->ttol = abstol;
170:   else mg->ttol = 0.0;

172:   /* since smoother is applied to full system, not just residual we need to make sure that smoothers don't
173:      stop prematurely due to small residual */
174:   for (i = 1; i < levels; i++) {
175:     PetscCall(KSPSetTolerances(mglevels[i]->smoothu, 0, PETSC_CURRENT, PETSC_CURRENT, PETSC_CURRENT));
176:     if (mglevels[i]->smoothu != mglevels[i]->smoothd) {
177:       /* For Richardson the initial guess is nonzero since it is solving in each cycle the original system not just applying as a preconditioner */
178:       PetscCall(KSPSetInitialGuessNonzero(mglevels[i]->smoothd, PETSC_TRUE));
179:       PetscCall(KSPSetTolerances(mglevels[i]->smoothd, 0, PETSC_CURRENT, PETSC_CURRENT, PETSC_CURRENT));
180:     }
181:   }

183:   *reason = PCRICHARDSON_NOT_SET;
184:   for (i = 0; i < its; i++) {
185:     PetscCall(PCMGMCycle_Private(pc, mglevels + levels - 1, PETSC_FALSE, PETSC_FALSE, reason));
186:     if (*reason) break;
187:   }
188:   if (*reason == PCRICHARDSON_NOT_SET) *reason = PCRICHARDSON_CONVERGED_ITS;
189:   *outits = i;
190:   if (!changed && !changeu) mglevels[levels - 1]->b = NULL;
191:   PetscFunctionReturn(PETSC_SUCCESS);
192: }

194: PetscErrorCode PCReset_MG(PC pc)
195: {
196:   PC_MG         *mg       = (PC_MG *)pc->data;
197:   PC_MG_Levels **mglevels = mg->levels;
198:   PetscInt       i, n;

200:   PetscFunctionBegin;
201:   if (mglevels) {
202:     n = mglevels[0]->levels;
203:     for (i = 0; i < n - 1; i++) {
204:       PetscCall(VecDestroy(&mglevels[i + 1]->r));
205:       PetscCall(VecDestroy(&mglevels[i]->b));
206:       PetscCall(VecDestroy(&mglevels[i]->x));
207:       PetscCall(MatDestroy(&mglevels[i + 1]->R));
208:       PetscCall(MatDestroy(&mglevels[i]->B));
209:       PetscCall(MatDestroy(&mglevels[i]->X));
210:       PetscCall(VecDestroy(&mglevels[i]->crx));
211:       PetscCall(VecDestroy(&mglevels[i]->crb));
212:       PetscCall(MatDestroy(&mglevels[i + 1]->restrct));
213:       PetscCall(MatDestroy(&mglevels[i + 1]->interpolate));
214:       PetscCall(MatDestroy(&mglevels[i + 1]->inject));
215:       PetscCall(VecDestroy(&mglevels[i + 1]->rscale));
216:     }
217:     PetscCall(VecDestroy(&mglevels[n - 1]->crx));
218:     PetscCall(VecDestroy(&mglevels[n - 1]->crb));
219:     /* this is not null only if the smoother on the finest level
220:        changes the rhs during PreSolve */
221:     PetscCall(VecDestroy(&mglevels[n - 1]->b));
222:     PetscCall(MatDestroy(&mglevels[n - 1]->B));

224:     for (i = 0; i < n; i++) {
225:       PetscCall(MatDestroy(&mglevels[i]->coarseSpace));
226:       PetscCall(MatDestroy(&mglevels[i]->A));
227:       if (mglevels[i]->smoothd != mglevels[i]->smoothu) PetscCall(KSPReset(mglevels[i]->smoothd));
228:       PetscCall(KSPReset(mglevels[i]->smoothu));
229:       if (mglevels[i]->cr) PetscCall(KSPReset(mglevels[i]->cr));
230:     }
231:     mg->Nc = 0;
232:   }
233:   PetscFunctionReturn(PETSC_SUCCESS);
234: }

236: /* Implementing CR

238: We only want to make corrections that ``do not change'' the coarse solution. What we mean by not changing is that if I prolong my coarse solution to the fine grid and then inject that fine solution back to the coarse grid, I get the same answer. Injection is what Brannick calls R. We want the complementary projector to Inj, which we will call S, after Brannick, so that Inj S = 0. Now the orthogonal projector onto the range of Inj^T is

240:   Inj^T (Inj Inj^T)^{-1} Inj

242: and if Inj is a VecScatter, as it is now in PETSc, we have

244:   Inj^T Inj

246: and

248:   S = I - Inj^T Inj

250: since

252:   Inj S = Inj - (Inj Inj^T) Inj = 0.

254: Brannick suggests

256:   A \to S^T A S  \qquad\mathrm{and}\qquad M \to S^T M S

258: but I do not think his :math:`S^T S = I` is correct. Our S is an orthogonal projector, so :math:`S^T S = S^2 = S`. We will use

260:   M^{-1} A \to S M^{-1} A S

262: In fact, since it is somewhat hard in PETSc to do the symmetric application, we will just apply S on the left.

264:   Check: || Inj P - I ||_F < tol
265:   Check: In general, Inj Inj^T = I
266: */

268: typedef struct {
269:   PC       mg;  /* The PCMG object */
270:   PetscInt l;   /* The multigrid level for this solver */
271:   Mat      Inj; /* The injection matrix */
272:   Mat      S;   /* I - Inj^T Inj */
273: } CRContext;

275: static PetscErrorCode CRSetup_Private(PC pc)
276: {
277:   CRContext *ctx;
278:   Mat        It;

280:   PetscFunctionBeginUser;
281:   PetscCall(PCShellGetContext(pc, &ctx));
282:   PetscCall(PCMGGetInjection(ctx->mg, ctx->l, &It));
283:   PetscCheck(It, PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_WRONGSTATE, "CR requires that injection be defined for this PCMG");
284:   PetscCall(MatCreateTranspose(It, &ctx->Inj));
285:   PetscCall(MatCreateNormal(ctx->Inj, &ctx->S));
286:   PetscCall(MatScale(ctx->S, -1.0));
287:   PetscCall(MatShift(ctx->S, 1.0));
288:   PetscFunctionReturn(PETSC_SUCCESS);
289: }

291: static PetscErrorCode CRApply_Private(PC pc, Vec x, Vec y)
292: {
293:   CRContext *ctx;

295:   PetscFunctionBeginUser;
296:   PetscCall(PCShellGetContext(pc, &ctx));
297:   PetscCall(MatMult(ctx->S, x, y));
298:   PetscFunctionReturn(PETSC_SUCCESS);
299: }

301: static PetscErrorCode CRDestroy_Private(PC pc)
302: {
303:   CRContext *ctx;

305:   PetscFunctionBeginUser;
306:   PetscCall(PCShellGetContext(pc, &ctx));
307:   PetscCall(MatDestroy(&ctx->Inj));
308:   PetscCall(MatDestroy(&ctx->S));
309:   PetscCall(PetscFree(ctx));
310:   PetscCall(PCShellSetContext(pc, NULL));
311:   PetscFunctionReturn(PETSC_SUCCESS);
312: }

314: static PetscErrorCode CreateCR_Private(PC pc, PetscInt l, PC *cr)
315: {
316:   CRContext *ctx;

318:   PetscFunctionBeginUser;
319:   PetscCall(PCCreate(PetscObjectComm((PetscObject)pc), cr));
320:   PetscCall(PetscObjectSetName((PetscObject)*cr, "S (complementary projector to injection)"));
321:   PetscCall(PetscCalloc1(1, &ctx));
322:   ctx->mg = pc;
323:   ctx->l  = l;
324:   PetscCall(PCSetType(*cr, PCSHELL));
325:   PetscCall(PCShellSetContext(*cr, ctx));
326:   PetscCall(PCShellSetApply(*cr, CRApply_Private));
327:   PetscCall(PCShellSetSetUp(*cr, CRSetup_Private));
328:   PetscCall(PCShellSetDestroy(*cr, CRDestroy_Private));
329:   PetscFunctionReturn(PETSC_SUCCESS);
330: }

332: PETSC_EXTERN PetscErrorCode PetscOptionsFindPairPrefix_Private(PetscOptions, const char[], const char[], const char *[], const char *[], PetscBool *);

334: PetscErrorCode PCMGSetLevels_MG(PC pc, PetscInt levels, MPI_Comm *comms)
335: {
336:   PC_MG         *mg = (PC_MG *)pc->data;
337:   MPI_Comm       comm;
338:   PC_MG_Levels **mglevels = mg->levels;
339:   PCMGType       mgtype   = mg->am;
340:   PetscInt       mgctype  = (PetscInt)PC_MG_CYCLE_V;
341:   PetscInt       i;
342:   PetscMPIInt    size;
343:   const char    *prefix;
344:   PC             ipc;
345:   PetscInt       n;

347:   PetscFunctionBegin;
350:   if (mg->nlevels == levels) PetscFunctionReturn(PETSC_SUCCESS);
351:   PetscCall(PetscObjectGetComm((PetscObject)pc, &comm));
352:   if (mglevels) {
353:     mgctype = mglevels[0]->cycles;
354:     /* changing the number of levels so free up the previous stuff */
355:     PetscCall(PCReset_MG(pc));
356:     n = mglevels[0]->levels;
357:     for (i = 0; i < n; i++) {
358:       if (mglevels[i]->smoothd != mglevels[i]->smoothu) PetscCall(KSPDestroy(&mglevels[i]->smoothd));
359:       PetscCall(KSPDestroy(&mglevels[i]->smoothu));
360:       PetscCall(KSPDestroy(&mglevels[i]->cr));
361:       PetscCall(PetscFree(mglevels[i]));
362:     }
363:     PetscCall(PetscFree(mg->levels));
364:   }

366:   mg->nlevels = levels;

368:   PetscCall(PetscMalloc1(levels, &mglevels));

370:   PetscCall(PCGetOptionsPrefix(pc, &prefix));

372:   mg->stageApply = 0;
373:   for (i = 0; i < levels; i++) {
374:     PetscCall(PetscNew(&mglevels[i]));

376:     mglevels[i]->level               = i;
377:     mglevels[i]->levels              = levels;
378:     mglevels[i]->cycles              = mgctype;
379:     mg->default_smoothu              = 2;
380:     mg->default_smoothd              = 2;
381:     mglevels[i]->eventsmoothsetup    = 0;
382:     mglevels[i]->eventsmoothsolve    = 0;
383:     mglevels[i]->eventresidual       = 0;
384:     mglevels[i]->eventinterprestrict = 0;

386:     if (comms) comm = comms[i];
387:     if (comm != MPI_COMM_NULL) {
388:       PetscCall(KSPCreate(comm, &mglevels[i]->smoothd));
389:       PetscCall(KSPSetNestLevel(mglevels[i]->smoothd, pc->kspnestlevel));
390:       PetscCall(KSPSetErrorIfNotConverged(mglevels[i]->smoothd, pc->erroriffailure));
391:       PetscCall(PetscObjectIncrementTabLevel((PetscObject)mglevels[i]->smoothd, (PetscObject)pc, levels - i));
392:       PetscCall(KSPSetOptionsPrefix(mglevels[i]->smoothd, prefix));
393:       PetscCall(PetscObjectComposedDataSetInt((PetscObject)mglevels[i]->smoothd, PetscMGLevelId, mglevels[i]->level));
394:       if (i == 0 && levels > 1) { // coarse grid
395:         PetscCall(KSPAppendOptionsPrefix(mglevels[0]->smoothd, "mg_coarse_"));

397:         /* coarse solve is (redundant) LU by default; set shifttype NONZERO to avoid annoying zero-pivot in LU preconditioner */
398:         PetscCall(KSPSetType(mglevels[0]->smoothd, KSPPREONLY));
399:         PetscCall(KSPGetPC(mglevels[0]->smoothd, &ipc));
400:         PetscCallMPI(MPI_Comm_size(comm, &size));
401:         if (size > 1) {
402:           PetscCall(PCSetType(ipc, PCREDUNDANT));
403:         } else {
404:           PetscCall(PCSetType(ipc, PCLU));
405:         }
406:         PetscCall(PCFactorSetShiftType(ipc, MAT_SHIFT_INBLOCKS));
407:       } else {
408:         char tprefix[128];

410:         PetscCall(KSPSetType(mglevels[i]->smoothd, KSPCHEBYSHEV));
411:         PetscCall(KSPSetConvergenceTest(mglevels[i]->smoothd, KSPConvergedSkip, NULL, NULL));
412:         PetscCall(KSPSetNormType(mglevels[i]->smoothd, KSP_NORM_NONE));
413:         PetscCall(KSPGetPC(mglevels[i]->smoothd, &ipc));
414:         PetscCall(PCSetType(ipc, PCSOR));
415:         PetscCall(KSPSetTolerances(mglevels[i]->smoothd, PETSC_CURRENT, PETSC_CURRENT, PETSC_CURRENT, mg->default_smoothd));

417:         if (i == levels - 1 && levels > 1) { // replace 'mg_finegrid_' with 'mg_levels_X_'
418:           PetscBool set;
419:           PetscCall(PetscOptionsFindPairPrefix_Private(((PetscObject)mglevels[i]->smoothd)->options, ((PetscObject)mglevels[i]->smoothd)->prefix, "-mg_fine_", NULL, NULL, &set));
420:           if (set) {
421:             if (prefix) PetscCall(PetscSNPrintf(tprefix, 128, "%smg_fine_", prefix));
422:             else PetscCall(PetscSNPrintf(tprefix, 128, "mg_fine_"));
423:             PetscCall(KSPSetOptionsPrefix(mglevels[i]->smoothd, tprefix));
424:           } else {
425:             PetscCall(PetscSNPrintf(tprefix, 128, "mg_levels_%" PetscInt_FMT "_", i));
426:             PetscCall(KSPAppendOptionsPrefix(mglevels[i]->smoothd, tprefix));
427:           }
428:         } else {
429:           PetscCall(PetscSNPrintf(tprefix, 128, "mg_levels_%" PetscInt_FMT "_", i));
430:           PetscCall(KSPAppendOptionsPrefix(mglevels[i]->smoothd, tprefix));
431:         }
432:       }
433:     }
434:     mglevels[i]->smoothu = mglevels[i]->smoothd;
435:     mg->rtol             = 0.0;
436:     mg->abstol           = 0.0;
437:     mg->dtol             = 0.0;
438:     mg->ttol             = 0.0;
439:     mg->cyclesperpcapply = 1;
440:   }
441:   mg->levels = mglevels;
442:   PetscCall(PCMGSetType(pc, mgtype));
443:   PetscFunctionReturn(PETSC_SUCCESS);
444: }

446: /*@C
447:   PCMGSetLevels - Sets the number of levels to use with `PCMG`.
448:   Must be called before any other `PCMG` routine.

450:   Logically Collective

452:   Input Parameters:
453: + pc     - the preconditioner context
454: . levels - the number of levels
455: - comms  - optional communicators for each level; this is to allow solving the coarser problems
456:            on smaller sets of processes. For processes that are not included in the computation
457:            you must pass `MPI_COMM_NULL`. Use comms = `NULL` to specify that all processes
458:            should participate in each level of problem.

460:   Level: intermediate

462:   Notes:
463:   If the number of levels is one then the multigrid uses the `-mg_levels` prefix
464:   for setting the level options rather than the `-mg_coarse` or `-mg_fine` prefix.

466:   You can free the information in comms after this routine is called.

468:   The array of MPI communicators must contain `MPI_COMM_NULL` for those ranks that at each level
469:   are not participating in the coarser solve. For example, with 2 levels and 1 and 2 ranks on
470:   the two levels, rank 0 in the original communicator will pass in an array of 2 communicators
471:   of size 2 and 1, while rank 1 in the original communicator will pass in array of 2 communicators
472:   the first of size 2 and the second of value `MPI_COMM_NULL` since the rank 1 does not participate
473:   in the coarse grid solve.

475:   Since each coarser level may have a new `MPI_Comm` with fewer ranks than the previous, one
476:   must take special care in providing the restriction and interpolation operation. We recommend
477:   providing these as two step operations; first perform a standard restriction or interpolation on
478:   the full number of ranks for that level and then use an MPI call to copy the resulting vector
479:   array entries (after calls to VecGetArray()) to the smaller or larger number of ranks, note in both
480:   cases the MPI calls must be made on the larger of the two communicators. Traditional MPI send and
481:   receives or `MPI_AlltoAllv()` could be used to do the reshuffling of the vector entries.

483:   Fortran Notes:
484:   Use comms = `PETSC_NULL_MPI_COMM` as the equivalent of `NULL` in the C interface. Note `PETSC_NULL_MPI_COMM`
485:   is not `MPI_COMM_NULL`. It is more like `PETSC_NULL_INTEGER`, `PETSC_NULL_REAL` etc.

487: .seealso: [](ch_ksp), `PCMGSetType()`, `PCMGGetLevels()`
488: @*/
489: PetscErrorCode PCMGSetLevels(PC pc, PetscInt levels, MPI_Comm *comms)
490: {
491:   PetscFunctionBegin;
493:   if (comms) PetscAssertPointer(comms, 3);
494:   PetscTryMethod(pc, "PCMGSetLevels_C", (PC, PetscInt, MPI_Comm *), (pc, levels, comms));
495:   PetscFunctionReturn(PETSC_SUCCESS);
496: }

498: PetscErrorCode PCDestroy_MG(PC pc)
499: {
500:   PC_MG         *mg       = (PC_MG *)pc->data;
501:   PC_MG_Levels **mglevels = mg->levels;
502:   PetscInt       i, n;

504:   PetscFunctionBegin;
505:   PetscCall(PCReset_MG(pc));
506:   if (mglevels) {
507:     n = mglevels[0]->levels;
508:     for (i = 0; i < n; i++) {
509:       if (mglevels[i]->smoothd != mglevels[i]->smoothu) PetscCall(KSPDestroy(&mglevels[i]->smoothd));
510:       PetscCall(KSPDestroy(&mglevels[i]->smoothu));
511:       PetscCall(KSPDestroy(&mglevels[i]->cr));
512:       PetscCall(PetscFree(mglevels[i]));
513:     }
514:     PetscCall(PetscFree(mg->levels));
515:   }
516:   PetscCall(PetscFree(pc->data));
517:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGetInterpolations_C", NULL));
518:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGetCoarseOperators_C", NULL));
519:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetGalerkin_C", NULL));
520:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetLevels_C", NULL));
521:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetLevels_C", NULL));
522:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGetInterpolations_C", NULL));
523:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGetCoarseOperators_C", NULL));
524:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetAdaptInterpolation_C", NULL));
525:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetAdaptInterpolation_C", NULL));
526:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetAdaptCR_C", NULL));
527:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetAdaptCR_C", NULL));
528:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetAdaptCoarseSpaceType_C", NULL));
529:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetAdaptCoarseSpaceType_C", NULL));
530:   PetscFunctionReturn(PETSC_SUCCESS);
531: }

533: /*
534:    PCApply_MG - Runs either an additive, multiplicative, Kaskadic
535:              or full cycle of multigrid.

537:   Note:
538:   A simple wrapper which calls PCMGMCycle(),PCMGACycle(), or PCMGFCycle().
539: */
540: static PetscErrorCode PCApply_MG_Internal(PC pc, Vec b, Vec x, Mat B, Mat X, PetscBool transpose)
541: {
542:   PC_MG         *mg       = (PC_MG *)pc->data;
543:   PC_MG_Levels **mglevels = mg->levels;
544:   PC             tpc;
545:   PetscInt       levels = mglevels[0]->levels, i;
546:   PetscBool      changeu, changed, matapp;

548:   PetscFunctionBegin;
549:   matapp = (PetscBool)(B && X);
550:   if (mg->stageApply) PetscCall(PetscLogStagePush(mg->stageApply));
551:   /* When the DM is supplying the matrix then it will not exist until here */
552:   for (i = 0; i < levels; i++) {
553:     if (!mglevels[i]->A) {
554:       PetscCall(KSPGetOperators(mglevels[i]->smoothu, &mglevels[i]->A, NULL));
555:       PetscCall(PetscObjectReference((PetscObject)mglevels[i]->A));
556:     }
557:   }

559:   PetscCall(KSPGetPC(mglevels[levels - 1]->smoothd, &tpc));
560:   PetscCall(PCPreSolveChangeRHS(tpc, &changed));
561:   PetscCall(KSPGetPC(mglevels[levels - 1]->smoothu, &tpc));
562:   PetscCall(PCPreSolveChangeRHS(tpc, &changeu));
563:   if (!changeu && !changed) {
564:     if (matapp) {
565:       PetscCall(MatDestroy(&mglevels[levels - 1]->B));
566:       mglevels[levels - 1]->B = B;
567:     } else {
568:       PetscCall(VecDestroy(&mglevels[levels - 1]->b));
569:       mglevels[levels - 1]->b = b;
570:     }
571:   } else { /* if the smoother changes the rhs during PreSolve, we cannot use the input vector */
572:     if (matapp) {
573:       if (mglevels[levels - 1]->B) {
574:         PetscInt  N1, N2;
575:         PetscBool flg;

577:         PetscCall(MatGetSize(mglevels[levels - 1]->B, NULL, &N1));
578:         PetscCall(MatGetSize(B, NULL, &N2));
579:         PetscCall(PetscObjectTypeCompare((PetscObject)mglevels[levels - 1]->B, ((PetscObject)B)->type_name, &flg));
580:         if (N1 != N2 || !flg) PetscCall(MatDestroy(&mglevels[levels - 1]->B));
581:       }
582:       if (!mglevels[levels - 1]->B) {
583:         PetscCall(MatDuplicate(B, MAT_COPY_VALUES, &mglevels[levels - 1]->B));
584:       } else {
585:         PetscCall(MatCopy(B, mglevels[levels - 1]->B, SAME_NONZERO_PATTERN));
586:       }
587:     } else {
588:       if (!mglevels[levels - 1]->b) {
589:         Vec *vec;

591:         PetscCall(KSPCreateVecs(mglevels[levels - 1]->smoothd, 1, &vec, 0, NULL));
592:         mglevels[levels - 1]->b = *vec;
593:         PetscCall(PetscFree(vec));
594:       }
595:       PetscCall(VecCopy(b, mglevels[levels - 1]->b));
596:     }
597:   }
598:   if (matapp) {
599:     mglevels[levels - 1]->X = X;
600:   } else {
601:     mglevels[levels - 1]->x = x;
602:   }

604:   /* If coarser Xs are present, it means we have already block applied the PC at least once
605:      Reset operators if sizes/type do no match */
606:   if (matapp && levels > 1 && mglevels[levels - 2]->X) {
607:     PetscInt  Xc, Bc;
608:     PetscBool flg;

610:     PetscCall(MatGetSize(mglevels[levels - 2]->X, NULL, &Xc));
611:     PetscCall(MatGetSize(mglevels[levels - 1]->B, NULL, &Bc));
612:     PetscCall(PetscObjectTypeCompare((PetscObject)mglevels[levels - 2]->X, ((PetscObject)mglevels[levels - 1]->X)->type_name, &flg));
613:     if (Xc != Bc || !flg) {
614:       PetscCall(MatDestroy(&mglevels[levels - 1]->R));
615:       for (i = 0; i < levels - 1; i++) {
616:         PetscCall(MatDestroy(&mglevels[i]->R));
617:         PetscCall(MatDestroy(&mglevels[i]->B));
618:         PetscCall(MatDestroy(&mglevels[i]->X));
619:       }
620:     }
621:   }

623:   if (mg->am == PC_MG_MULTIPLICATIVE) {
624:     if (matapp) PetscCall(MatZeroEntries(X));
625:     else PetscCall(VecZeroEntries(x));
626:     for (i = 0; i < mg->cyclesperpcapply; i++) PetscCall(PCMGMCycle_Private(pc, mglevels + levels - 1, transpose, matapp, NULL));
627:   } else if (mg->am == PC_MG_ADDITIVE) {
628:     PetscCall(PCMGACycle_Private(pc, mglevels, transpose, matapp));
629:   } else if (mg->am == PC_MG_KASKADE) {
630:     PetscCall(PCMGKCycle_Private(pc, mglevels, transpose, matapp));
631:   } else {
632:     PetscCall(PCMGFCycle_Private(pc, mglevels, transpose, matapp));
633:   }
634:   if (mg->stageApply) PetscCall(PetscLogStagePop());
635:   if (!changeu && !changed) {
636:     if (matapp) {
637:       mglevels[levels - 1]->B = NULL;
638:     } else {
639:       mglevels[levels - 1]->b = NULL;
640:     }
641:   }
642:   PetscFunctionReturn(PETSC_SUCCESS);
643: }

645: static PetscErrorCode PCApply_MG(PC pc, Vec b, Vec x)
646: {
647:   PetscFunctionBegin;
648:   PetscCall(PCApply_MG_Internal(pc, b, x, NULL, NULL, PETSC_FALSE));
649:   PetscFunctionReturn(PETSC_SUCCESS);
650: }

652: static PetscErrorCode PCApplyTranspose_MG(PC pc, Vec b, Vec x)
653: {
654:   PetscFunctionBegin;
655:   PetscCall(PCApply_MG_Internal(pc, b, x, NULL, NULL, PETSC_TRUE));
656:   PetscFunctionReturn(PETSC_SUCCESS);
657: }

659: static PetscErrorCode PCMatApply_MG(PC pc, Mat b, Mat x)
660: {
661:   PetscFunctionBegin;
662:   PetscCall(PCApply_MG_Internal(pc, NULL, NULL, b, x, PETSC_FALSE));
663:   PetscFunctionReturn(PETSC_SUCCESS);
664: }

666: PetscErrorCode PCSetFromOptions_MG(PC pc, PetscOptionItems *PetscOptionsObject)
667: {
668:   PetscInt            levels, cycles;
669:   PetscBool           flg, flg2;
670:   PC_MG              *mg = (PC_MG *)pc->data;
671:   PC_MG_Levels      **mglevels;
672:   PCMGType            mgtype;
673:   PCMGCycleType       mgctype;
674:   PCMGGalerkinType    gtype;
675:   PCMGCoarseSpaceType coarseSpaceType;

677:   PetscFunctionBegin;
678:   levels = PetscMax(mg->nlevels, 1);
679:   PetscOptionsHeadBegin(PetscOptionsObject, "Multigrid options");
680:   PetscCall(PetscOptionsInt("-pc_mg_levels", "Number of Levels", "PCMGSetLevels", levels, &levels, &flg));
681:   if (!flg && !mg->levels && pc->dm) {
682:     PetscCall(DMGetRefineLevel(pc->dm, &levels));
683:     levels++;
684:     mg->usedmfornumberoflevels = PETSC_TRUE;
685:   }
686:   PetscCall(PCMGSetLevels(pc, levels, NULL));
687:   mglevels = mg->levels;

689:   mgctype = (PCMGCycleType)mglevels[0]->cycles;
690:   PetscCall(PetscOptionsEnum("-pc_mg_cycle_type", "V cycle or for W-cycle", "PCMGSetCycleType", PCMGCycleTypes, (PetscEnum)mgctype, (PetscEnum *)&mgctype, &flg));
691:   if (flg) PetscCall(PCMGSetCycleType(pc, mgctype));
692:   gtype = mg->galerkin;
693:   PetscCall(PetscOptionsEnum("-pc_mg_galerkin", "Use Galerkin process to compute coarser operators", "PCMGSetGalerkin", PCMGGalerkinTypes, (PetscEnum)gtype, (PetscEnum *)&gtype, &flg));
694:   if (flg) PetscCall(PCMGSetGalerkin(pc, gtype));
695:   coarseSpaceType = mg->coarseSpaceType;
696:   PetscCall(PetscOptionsEnum("-pc_mg_adapt_interp_coarse_space", "Type of adaptive coarse space: none, polynomial, harmonic, eigenvector, generalized_eigenvector, gdsw", "PCMGSetAdaptCoarseSpaceType", PCMGCoarseSpaceTypes, (PetscEnum)coarseSpaceType, (PetscEnum *)&coarseSpaceType, &flg));
697:   if (flg) PetscCall(PCMGSetAdaptCoarseSpaceType(pc, coarseSpaceType));
698:   PetscCall(PetscOptionsInt("-pc_mg_adapt_interp_n", "Size of the coarse space for adaptive interpolation", "PCMGSetCoarseSpace", mg->Nc, &mg->Nc, &flg));
699:   PetscCall(PetscOptionsBool("-pc_mg_mesp_monitor", "Monitor the multilevel eigensolver", "PCMGSetAdaptInterpolation", PETSC_FALSE, &mg->mespMonitor, &flg));
700:   flg2 = PETSC_FALSE;
701:   PetscCall(PetscOptionsBool("-pc_mg_adapt_cr", "Monitor coarse space quality using Compatible Relaxation (CR)", "PCMGSetAdaptCR", PETSC_FALSE, &flg2, &flg));
702:   if (flg) PetscCall(PCMGSetAdaptCR(pc, flg2));
703:   flg = PETSC_FALSE;
704:   PetscCall(PetscOptionsBool("-pc_mg_distinct_smoothup", "Create separate smoothup KSP and append the prefix _up", "PCMGSetDistinctSmoothUp", PETSC_FALSE, &flg, NULL));
705:   if (flg) PetscCall(PCMGSetDistinctSmoothUp(pc));
706:   mgtype = mg->am;
707:   PetscCall(PetscOptionsEnum("-pc_mg_type", "Multigrid type", "PCMGSetType", PCMGTypes, (PetscEnum)mgtype, (PetscEnum *)&mgtype, &flg));
708:   if (flg) PetscCall(PCMGSetType(pc, mgtype));
709:   if (mg->am == PC_MG_MULTIPLICATIVE) {
710:     PetscCall(PetscOptionsInt("-pc_mg_multiplicative_cycles", "Number of cycles for each preconditioner step", "PCMGMultiplicativeSetCycles", mg->cyclesperpcapply, &cycles, &flg));
711:     if (flg) PetscCall(PCMGMultiplicativeSetCycles(pc, cycles));
712:   }
713:   flg = PETSC_FALSE;
714:   PetscCall(PetscOptionsBool("-pc_mg_log", "Log times for each multigrid level", "None", flg, &flg, NULL));
715:   if (flg) {
716:     PetscInt i;
717:     char     eventname[128];

719:     levels = mglevels[0]->levels;
720:     for (i = 0; i < levels; i++) {
721:       PetscCall(PetscSNPrintf(eventname, PETSC_STATIC_ARRAY_LENGTH(eventname), "MGSetup Level %" PetscInt_FMT, i));
722:       PetscCall(PetscLogEventRegister(eventname, ((PetscObject)pc)->classid, &mglevels[i]->eventsmoothsetup));
723:       PetscCall(PetscSNPrintf(eventname, PETSC_STATIC_ARRAY_LENGTH(eventname), "MGSmooth Level %" PetscInt_FMT, i));
724:       PetscCall(PetscLogEventRegister(eventname, ((PetscObject)pc)->classid, &mglevels[i]->eventsmoothsolve));
725:       if (i) {
726:         PetscCall(PetscSNPrintf(eventname, PETSC_STATIC_ARRAY_LENGTH(eventname), "MGResid Level %" PetscInt_FMT, i));
727:         PetscCall(PetscLogEventRegister(eventname, ((PetscObject)pc)->classid, &mglevels[i]->eventresidual));
728:         PetscCall(PetscSNPrintf(eventname, PETSC_STATIC_ARRAY_LENGTH(eventname), "MGInterp Level %" PetscInt_FMT, i));
729:         PetscCall(PetscLogEventRegister(eventname, ((PetscObject)pc)->classid, &mglevels[i]->eventinterprestrict));
730:       }
731:     }

733:     if (PetscDefined(USE_LOG)) {
734:       const char sname[] = "MG Apply";

736:       PetscCall(PetscLogStageGetId(sname, &mg->stageApply));
737:       if (mg->stageApply < 0) PetscCall(PetscLogStageRegister(sname, &mg->stageApply));
738:     }
739:   }
740:   PetscOptionsHeadEnd();
741:   /* Check option consistency */
742:   PetscCall(PCMGGetGalerkin(pc, &gtype));
743:   PetscCall(PCMGGetAdaptInterpolation(pc, &flg));
744:   PetscCheck(!flg || !(gtype >= PC_MG_GALERKIN_NONE), PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_INCOMP, "Must use Galerkin coarse operators when adapting the interpolator");
745:   PetscFunctionReturn(PETSC_SUCCESS);
746: }

748: const char *const PCMGTypes[]            = {"MULTIPLICATIVE", "ADDITIVE", "FULL", "KASKADE", "PCMGType", "PC_MG", NULL};
749: const char *const PCMGCycleTypes[]       = {"invalid", "v", "w", "PCMGCycleType", "PC_MG_CYCLE", NULL};
750: const char *const PCMGGalerkinTypes[]    = {"both", "pmat", "mat", "none", "external", "PCMGGalerkinType", "PC_MG_GALERKIN", NULL};
751: const char *const PCMGCoarseSpaceTypes[] = {"none", "polynomial", "harmonic", "eigenvector", "generalized_eigenvector", "gdsw", "PCMGCoarseSpaceType", "PCMG_ADAPT_NONE", NULL};

753: #include <petscdraw.h>
754: PetscErrorCode PCView_MG(PC pc, PetscViewer viewer)
755: {
756:   PC_MG         *mg       = (PC_MG *)pc->data;
757:   PC_MG_Levels **mglevels = mg->levels;
758:   PetscInt       levels   = mglevels ? mglevels[0]->levels : 0, i;
759:   PetscBool      iascii, isbinary, isdraw;

761:   PetscFunctionBegin;
762:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
763:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
764:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
765:   if (iascii) {
766:     const char *cyclename = levels ? (mglevels[0]->cycles == PC_MG_CYCLE_V ? "v" : "w") : "unknown";
767:     PetscCall(PetscViewerASCIIPrintf(viewer, "  type is %s, levels=%" PetscInt_FMT " cycles=%s\n", PCMGTypes[mg->am], levels, cyclename));
768:     if (mg->am == PC_MG_MULTIPLICATIVE) PetscCall(PetscViewerASCIIPrintf(viewer, "    Cycles per PCApply=%" PetscInt_FMT "\n", mg->cyclesperpcapply));
769:     if (mg->galerkin == PC_MG_GALERKIN_BOTH) {
770:       PetscCall(PetscViewerASCIIPrintf(viewer, "    Using Galerkin computed coarse grid matrices\n"));
771:     } else if (mg->galerkin == PC_MG_GALERKIN_PMAT) {
772:       PetscCall(PetscViewerASCIIPrintf(viewer, "    Using Galerkin computed coarse grid matrices for pmat\n"));
773:     } else if (mg->galerkin == PC_MG_GALERKIN_MAT) {
774:       PetscCall(PetscViewerASCIIPrintf(viewer, "    Using Galerkin computed coarse grid matrices for mat\n"));
775:     } else if (mg->galerkin == PC_MG_GALERKIN_EXTERNAL) {
776:       PetscCall(PetscViewerASCIIPrintf(viewer, "    Using externally compute Galerkin coarse grid matrices\n"));
777:     } else {
778:       PetscCall(PetscViewerASCIIPrintf(viewer, "    Not using Galerkin computed coarse grid matrices\n"));
779:     }
780:     if (mg->view) PetscCall((*mg->view)(pc, viewer));
781:     for (i = 0; i < levels; i++) {
782:       if (i) {
783:         PetscCall(PetscViewerASCIIPrintf(viewer, "Down solver (pre-smoother) on level %" PetscInt_FMT " -------------------------------\n", i));
784:       } else {
785:         PetscCall(PetscViewerASCIIPrintf(viewer, "Coarse grid solver -- level %" PetscInt_FMT " -------------------------------\n", i));
786:       }
787:       PetscCall(PetscViewerASCIIPushTab(viewer));
788:       PetscCall(KSPView(mglevels[i]->smoothd, viewer));
789:       PetscCall(PetscViewerASCIIPopTab(viewer));
790:       if (i && mglevels[i]->smoothd == mglevels[i]->smoothu) {
791:         PetscCall(PetscViewerASCIIPrintf(viewer, "Up solver (post-smoother) same as down solver (pre-smoother)\n"));
792:       } else if (i) {
793:         PetscCall(PetscViewerASCIIPrintf(viewer, "Up solver (post-smoother) on level %" PetscInt_FMT " -------------------------------\n", i));
794:         PetscCall(PetscViewerASCIIPushTab(viewer));
795:         PetscCall(KSPView(mglevels[i]->smoothu, viewer));
796:         PetscCall(PetscViewerASCIIPopTab(viewer));
797:       }
798:       if (i && mglevels[i]->cr) {
799:         PetscCall(PetscViewerASCIIPrintf(viewer, "CR solver on level %" PetscInt_FMT " -------------------------------\n", i));
800:         PetscCall(PetscViewerASCIIPushTab(viewer));
801:         PetscCall(KSPView(mglevels[i]->cr, viewer));
802:         PetscCall(PetscViewerASCIIPopTab(viewer));
803:       }
804:     }
805:   } else if (isbinary) {
806:     for (i = levels - 1; i >= 0; i--) {
807:       PetscCall(KSPView(mglevels[i]->smoothd, viewer));
808:       if (i && mglevels[i]->smoothd != mglevels[i]->smoothu) PetscCall(KSPView(mglevels[i]->smoothu, viewer));
809:     }
810:   } else if (isdraw) {
811:     PetscDraw draw;
812:     PetscReal x, w, y, bottom, th;
813:     PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
814:     PetscCall(PetscDrawGetCurrentPoint(draw, &x, &y));
815:     PetscCall(PetscDrawStringGetSize(draw, NULL, &th));
816:     bottom = y - th;
817:     for (i = levels - 1; i >= 0; i--) {
818:       if (!mglevels[i]->smoothu || (mglevels[i]->smoothu == mglevels[i]->smoothd)) {
819:         PetscCall(PetscDrawPushCurrentPoint(draw, x, bottom));
820:         PetscCall(KSPView(mglevels[i]->smoothd, viewer));
821:         PetscCall(PetscDrawPopCurrentPoint(draw));
822:       } else {
823:         w = 0.5 * PetscMin(1.0 - x, x);
824:         PetscCall(PetscDrawPushCurrentPoint(draw, x + w, bottom));
825:         PetscCall(KSPView(mglevels[i]->smoothd, viewer));
826:         PetscCall(PetscDrawPopCurrentPoint(draw));
827:         PetscCall(PetscDrawPushCurrentPoint(draw, x - w, bottom));
828:         PetscCall(KSPView(mglevels[i]->smoothu, viewer));
829:         PetscCall(PetscDrawPopCurrentPoint(draw));
830:       }
831:       PetscCall(PetscDrawGetBoundingBox(draw, NULL, &bottom, NULL, NULL));
832:       bottom -= th;
833:     }
834:   }
835:   PetscFunctionReturn(PETSC_SUCCESS);
836: }

838: #include <petsc/private/kspimpl.h>

840: /*
841:     Calls setup for the KSP on each level
842: */
843: PetscErrorCode PCSetUp_MG(PC pc)
844: {
845:   PC_MG         *mg       = (PC_MG *)pc->data;
846:   PC_MG_Levels **mglevels = mg->levels;
847:   PetscInt       i, n;
848:   PC             cpc;
849:   PetscBool      dump = PETSC_FALSE, opsset, use_amat, missinginterpolate = PETSC_FALSE;
850:   Mat            dA, dB;
851:   Vec            tvec;
852:   DM            *dms;
853:   PetscViewer    viewer = NULL;
854:   PetscBool      dAeqdB = PETSC_FALSE, needRestricts = PETSC_FALSE, doCR = PETSC_FALSE;
855:   PetscBool      adaptInterpolation = mg->adaptInterpolation;

857:   PetscFunctionBegin;
858:   PetscCheck(mglevels, PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_WRONGSTATE, "Must set MG levels with PCMGSetLevels() before setting up");
859:   n = mglevels[0]->levels;
860:   /* FIX: Move this to PCSetFromOptions_MG? */
861:   if (mg->usedmfornumberoflevels) {
862:     PetscInt levels;
863:     PetscCall(DMGetRefineLevel(pc->dm, &levels));
864:     levels++;
865:     if (levels > n) { /* the problem is now being solved on a finer grid */
866:       PetscCall(PCMGSetLevels(pc, levels, NULL));
867:       n = levels;
868:       PetscCall(PCSetFromOptions(pc)); /* it is bad to call this here, but otherwise will never be called for the new hierarchy */
869:       mglevels = mg->levels;
870:     }
871:   }
872:   PetscCall(KSPGetPC(mglevels[0]->smoothd, &cpc));

874:   /* If user did not provide fine grid operators OR operator was not updated since last global KSPSetOperators() */
875:   /* so use those from global PC */
876:   /* Is this what we always want? What if user wants to keep old one? */
877:   PetscCall(KSPGetOperatorsSet(mglevels[n - 1]->smoothd, NULL, &opsset));
878:   if (opsset) {
879:     Mat mmat;
880:     PetscCall(KSPGetOperators(mglevels[n - 1]->smoothd, NULL, &mmat));
881:     if (mmat == pc->pmat) opsset = PETSC_FALSE;
882:   }

884:   /* Create CR solvers */
885:   PetscCall(PCMGGetAdaptCR(pc, &doCR));
886:   if (doCR) {
887:     const char *prefix;

889:     PetscCall(PCGetOptionsPrefix(pc, &prefix));
890:     for (i = 1; i < n; ++i) {
891:       PC   ipc, cr;
892:       char crprefix[128];

894:       PetscCall(KSPCreate(PetscObjectComm((PetscObject)pc), &mglevels[i]->cr));
895:       PetscCall(KSPSetNestLevel(mglevels[i]->cr, pc->kspnestlevel));
896:       PetscCall(KSPSetErrorIfNotConverged(mglevels[i]->cr, PETSC_FALSE));
897:       PetscCall(PetscObjectIncrementTabLevel((PetscObject)mglevels[i]->cr, (PetscObject)pc, n - i));
898:       PetscCall(KSPSetOptionsPrefix(mglevels[i]->cr, prefix));
899:       PetscCall(PetscObjectComposedDataSetInt((PetscObject)mglevels[i]->cr, PetscMGLevelId, mglevels[i]->level));
900:       PetscCall(KSPSetType(mglevels[i]->cr, KSPCHEBYSHEV));
901:       PetscCall(KSPSetConvergenceTest(mglevels[i]->cr, KSPConvergedSkip, NULL, NULL));
902:       PetscCall(KSPSetNormType(mglevels[i]->cr, KSP_NORM_PRECONDITIONED));
903:       PetscCall(KSPGetPC(mglevels[i]->cr, &ipc));

905:       PetscCall(PCSetType(ipc, PCCOMPOSITE));
906:       PetscCall(PCCompositeSetType(ipc, PC_COMPOSITE_MULTIPLICATIVE));
907:       PetscCall(PCCompositeAddPCType(ipc, PCSOR));
908:       PetscCall(CreateCR_Private(pc, i, &cr));
909:       PetscCall(PCCompositeAddPC(ipc, cr));
910:       PetscCall(PCDestroy(&cr));

912:       PetscCall(KSPSetTolerances(mglevels[i]->cr, PETSC_CURRENT, PETSC_CURRENT, PETSC_CURRENT, mg->default_smoothd));
913:       PetscCall(KSPSetInitialGuessNonzero(mglevels[i]->cr, PETSC_TRUE));
914:       PetscCall(PetscSNPrintf(crprefix, 128, "mg_levels_%" PetscInt_FMT "_cr_", i));
915:       PetscCall(KSPAppendOptionsPrefix(mglevels[i]->cr, crprefix));
916:     }
917:   }

919:   if (!opsset) {
920:     PetscCall(PCGetUseAmat(pc, &use_amat));
921:     if (use_amat) {
922:       PetscCall(PetscInfo(pc, "Using outer operators to define finest grid operator \n  because PCMGGetSmoother(pc,nlevels-1,&ksp);KSPSetOperators(ksp,...); was not called.\n"));
923:       PetscCall(KSPSetOperators(mglevels[n - 1]->smoothd, pc->mat, pc->pmat));
924:     } else {
925:       PetscCall(PetscInfo(pc, "Using matrix (pmat) operators to define finest grid operator \n  because PCMGGetSmoother(pc,nlevels-1,&ksp);KSPSetOperators(ksp,...); was not called.\n"));
926:       PetscCall(KSPSetOperators(mglevels[n - 1]->smoothd, pc->pmat, pc->pmat));
927:     }
928:   }

930:   for (i = n - 1; i > 0; i--) {
931:     if (!(mglevels[i]->interpolate || mglevels[i]->restrct)) {
932:       missinginterpolate = PETSC_TRUE;
933:       break;
934:     }
935:   }

937:   PetscCall(KSPGetOperators(mglevels[n - 1]->smoothd, &dA, &dB));
938:   if (dA == dB) dAeqdB = PETSC_TRUE;
939:   if (mg->galerkin == PC_MG_GALERKIN_NONE || ((mg->galerkin == PC_MG_GALERKIN_PMAT || mg->galerkin == PC_MG_GALERKIN_MAT) && !dAeqdB)) {
940:     needRestricts = PETSC_TRUE; /* user must compute either mat, pmat, or both so must restrict x to coarser levels */
941:   }

943:   if (pc->dm && !pc->setupcalled) {
944:     /* finest smoother also gets DM but it is not active, independent of whether galerkin==PC_MG_GALERKIN_EXTERNAL */
945:     PetscCall(KSPSetDM(mglevels[n - 1]->smoothd, pc->dm));
946:     PetscCall(KSPSetDMActive(mglevels[n - 1]->smoothd, PETSC_FALSE));
947:     if (mglevels[n - 1]->smoothd != mglevels[n - 1]->smoothu) {
948:       PetscCall(KSPSetDM(mglevels[n - 1]->smoothu, pc->dm));
949:       PetscCall(KSPSetDMActive(mglevels[n - 1]->smoothu, PETSC_FALSE));
950:     }
951:     if (mglevels[n - 1]->cr) {
952:       PetscCall(KSPSetDM(mglevels[n - 1]->cr, pc->dm));
953:       PetscCall(KSPSetDMActive(mglevels[n - 1]->cr, PETSC_FALSE));
954:     }
955:   }

957:   /*
958:    Skipping if user has provided all interpolation/restriction needed (since DM might not be able to produce them (when coming from SNES/TS)
959:    Skipping for externally managed hierarchy (such as ML and GAMG). Cleaner logic here would be great. Wrap ML/GAMG as DMs?
960:   */
961:   if (missinginterpolate && mg->galerkin != PC_MG_GALERKIN_EXTERNAL && !pc->setupcalled) {
962:     /* first see if we can compute a coarse space */
963:     if (mg->coarseSpaceType == PCMG_ADAPT_GDSW) {
964:       for (i = n - 2; i > -1; i--) {
965:         if (!mglevels[i + 1]->restrct && !mglevels[i + 1]->interpolate) {
966:           PetscCall(PCMGComputeCoarseSpace_Internal(pc, i + 1, mg->coarseSpaceType, mg->Nc, NULL, &mglevels[i + 1]->coarseSpace));
967:           PetscCall(PCMGSetInterpolation(pc, i + 1, mglevels[i + 1]->coarseSpace));
968:         }
969:       }
970:     } else { /* construct the interpolation from the DMs */
971:       Mat p;
972:       Vec rscale;
973:       PetscCall(PetscMalloc1(n, &dms));
974:       dms[n - 1] = pc->dm;
975:       /* Separately create them so we do not get DMKSP interference between levels */
976:       for (i = n - 2; i > -1; i--) PetscCall(DMCoarsen(dms[i + 1], MPI_COMM_NULL, &dms[i]));
977:       for (i = n - 2; i > -1; i--) {
978:         DMKSP     kdm;
979:         PetscBool dmhasrestrict, dmhasinject;
980:         PetscCall(KSPSetDM(mglevels[i]->smoothd, dms[i]));
981:         if (!needRestricts) PetscCall(KSPSetDMActive(mglevels[i]->smoothd, PETSC_FALSE));
982:         if (mglevels[i]->smoothd != mglevels[i]->smoothu) {
983:           PetscCall(KSPSetDM(mglevels[i]->smoothu, dms[i]));
984:           if (!needRestricts) PetscCall(KSPSetDMActive(mglevels[i]->smoothu, PETSC_FALSE));
985:         }
986:         if (mglevels[i]->cr) {
987:           PetscCall(KSPSetDM(mglevels[i]->cr, dms[i]));
988:           if (!needRestricts) PetscCall(KSPSetDMActive(mglevels[i]->cr, PETSC_FALSE));
989:         }
990:         PetscCall(DMGetDMKSPWrite(dms[i], &kdm));
991:         /* Ugly hack so that the next KSPSetUp() will use the RHS that we set. A better fix is to change dmActive to take
992:          * a bitwise OR of computing the matrix, RHS, and initial iterate. */
993:         kdm->ops->computerhs = NULL;
994:         kdm->rhsctx          = NULL;
995:         if (!mglevels[i + 1]->interpolate) {
996:           PetscCall(DMCreateInterpolation(dms[i], dms[i + 1], &p, &rscale));
997:           PetscCall(PCMGSetInterpolation(pc, i + 1, p));
998:           if (rscale) PetscCall(PCMGSetRScale(pc, i + 1, rscale));
999:           PetscCall(VecDestroy(&rscale));
1000:           PetscCall(MatDestroy(&p));
1001:         }
1002:         PetscCall(DMHasCreateRestriction(dms[i], &dmhasrestrict));
1003:         if (dmhasrestrict && !mglevels[i + 1]->restrct) {
1004:           PetscCall(DMCreateRestriction(dms[i], dms[i + 1], &p));
1005:           PetscCall(PCMGSetRestriction(pc, i + 1, p));
1006:           PetscCall(MatDestroy(&p));
1007:         }
1008:         PetscCall(DMHasCreateInjection(dms[i], &dmhasinject));
1009:         if (dmhasinject && !mglevels[i + 1]->inject) {
1010:           PetscCall(DMCreateInjection(dms[i], dms[i + 1], &p));
1011:           PetscCall(PCMGSetInjection(pc, i + 1, p));
1012:           PetscCall(MatDestroy(&p));
1013:         }
1014:       }

1016:       for (i = n - 2; i > -1; i--) PetscCall(DMDestroy(&dms[i]));
1017:       PetscCall(PetscFree(dms));
1018:     }
1019:   }

1021:   if (mg->galerkin < PC_MG_GALERKIN_NONE) {
1022:     Mat       A, B;
1023:     PetscBool doA = PETSC_FALSE, doB = PETSC_FALSE;
1024:     MatReuse  reuse = MAT_INITIAL_MATRIX;

1026:     if (mg->galerkin == PC_MG_GALERKIN_PMAT || mg->galerkin == PC_MG_GALERKIN_BOTH) doB = PETSC_TRUE;
1027:     if (mg->galerkin == PC_MG_GALERKIN_MAT || (mg->galerkin == PC_MG_GALERKIN_BOTH && dA != dB)) doA = PETSC_TRUE;
1028:     if (pc->setupcalled) reuse = MAT_REUSE_MATRIX;
1029:     for (i = n - 2; i > -1; i--) {
1030:       PetscCheck(mglevels[i + 1]->restrct || mglevels[i + 1]->interpolate, PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_WRONGSTATE, "Must provide interpolation or restriction for each MG level except level 0");
1031:       if (!mglevels[i + 1]->interpolate) PetscCall(PCMGSetInterpolation(pc, i + 1, mglevels[i + 1]->restrct));
1032:       if (!mglevels[i + 1]->restrct) PetscCall(PCMGSetRestriction(pc, i + 1, mglevels[i + 1]->interpolate));
1033:       if (reuse == MAT_REUSE_MATRIX) PetscCall(KSPGetOperators(mglevels[i]->smoothd, &A, &B));
1034:       if (doA) PetscCall(MatGalerkin(mglevels[i + 1]->restrct, dA, mglevels[i + 1]->interpolate, reuse, 1.0, &A));
1035:       if (doB) PetscCall(MatGalerkin(mglevels[i + 1]->restrct, dB, mglevels[i + 1]->interpolate, reuse, 1.0, &B));
1036:       /* the management of the PetscObjectReference() and PetscObjecDereference() below is rather delicate */
1037:       if (!doA && dAeqdB) {
1038:         if (reuse == MAT_INITIAL_MATRIX) PetscCall(PetscObjectReference((PetscObject)B));
1039:         A = B;
1040:       } else if (!doA && reuse == MAT_INITIAL_MATRIX) {
1041:         PetscCall(KSPGetOperators(mglevels[i]->smoothd, &A, NULL));
1042:         PetscCall(PetscObjectReference((PetscObject)A));
1043:       }
1044:       if (!doB && dAeqdB) {
1045:         if (reuse == MAT_INITIAL_MATRIX) PetscCall(PetscObjectReference((PetscObject)A));
1046:         B = A;
1047:       } else if (!doB && reuse == MAT_INITIAL_MATRIX) {
1048:         PetscCall(KSPGetOperators(mglevels[i]->smoothd, NULL, &B));
1049:         PetscCall(PetscObjectReference((PetscObject)B));
1050:       }
1051:       if (reuse == MAT_INITIAL_MATRIX) {
1052:         PetscCall(KSPSetOperators(mglevels[i]->smoothd, A, B));
1053:         PetscCall(PetscObjectDereference((PetscObject)A));
1054:         PetscCall(PetscObjectDereference((PetscObject)B));
1055:       }
1056:       dA = A;
1057:       dB = B;
1058:     }
1059:   }

1061:   /* Adapt interpolation matrices */
1062:   if (adaptInterpolation) {
1063:     for (i = 0; i < n; ++i) {
1064:       if (!mglevels[i]->coarseSpace) PetscCall(PCMGComputeCoarseSpace_Internal(pc, i, mg->coarseSpaceType, mg->Nc, !i ? NULL : mglevels[i - 1]->coarseSpace, &mglevels[i]->coarseSpace));
1065:       if (i) PetscCall(PCMGAdaptInterpolator_Internal(pc, i, mglevels[i - 1]->smoothu, mglevels[i]->smoothu, mglevels[i - 1]->coarseSpace, mglevels[i]->coarseSpace));
1066:     }
1067:     for (i = n - 2; i > -1; --i) PetscCall(PCMGRecomputeLevelOperators_Internal(pc, i));
1068:   }

1070:   if (needRestricts && pc->dm) {
1071:     for (i = n - 2; i >= 0; i--) {
1072:       DM  dmfine, dmcoarse;
1073:       Mat Restrict, Inject;
1074:       Vec rscale;
1075:       PetscCall(KSPGetDM(mglevels[i + 1]->smoothd, &dmfine));
1076:       PetscCall(KSPGetDM(mglevels[i]->smoothd, &dmcoarse));
1077:       PetscCall(PCMGGetRestriction(pc, i + 1, &Restrict));
1078:       PetscCall(PCMGGetRScale(pc, i + 1, &rscale));
1079:       PetscCall(PCMGGetInjection(pc, i + 1, &Inject));
1080:       PetscCall(DMRestrict(dmfine, Restrict, rscale, Inject, dmcoarse));
1081:     }
1082:   }

1084:   if (!pc->setupcalled) {
1085:     for (i = 0; i < n; i++) PetscCall(KSPSetFromOptions(mglevels[i]->smoothd));
1086:     for (i = 1; i < n; i++) {
1087:       if (mglevels[i]->smoothu && (mglevels[i]->smoothu != mglevels[i]->smoothd)) PetscCall(KSPSetFromOptions(mglevels[i]->smoothu));
1088:       if (mglevels[i]->cr) PetscCall(KSPSetFromOptions(mglevels[i]->cr));
1089:     }
1090:     /* insure that if either interpolation or restriction is set the other one is set */
1091:     for (i = 1; i < n; i++) {
1092:       PetscCall(PCMGGetInterpolation(pc, i, NULL));
1093:       PetscCall(PCMGGetRestriction(pc, i, NULL));
1094:     }
1095:     for (i = 0; i < n - 1; i++) {
1096:       if (!mglevels[i]->b) {
1097:         Vec *vec;
1098:         PetscCall(KSPCreateVecs(mglevels[i]->smoothd, 1, &vec, 0, NULL));
1099:         PetscCall(PCMGSetRhs(pc, i, *vec));
1100:         PetscCall(VecDestroy(vec));
1101:         PetscCall(PetscFree(vec));
1102:       }
1103:       if (!mglevels[i]->r && i) {
1104:         PetscCall(VecDuplicate(mglevels[i]->b, &tvec));
1105:         PetscCall(PCMGSetR(pc, i, tvec));
1106:         PetscCall(VecDestroy(&tvec));
1107:       }
1108:       if (!mglevels[i]->x) {
1109:         PetscCall(VecDuplicate(mglevels[i]->b, &tvec));
1110:         PetscCall(PCMGSetX(pc, i, tvec));
1111:         PetscCall(VecDestroy(&tvec));
1112:       }
1113:       if (doCR) {
1114:         PetscCall(VecDuplicate(mglevels[i]->b, &mglevels[i]->crx));
1115:         PetscCall(VecDuplicate(mglevels[i]->b, &mglevels[i]->crb));
1116:         PetscCall(VecZeroEntries(mglevels[i]->crb));
1117:       }
1118:     }
1119:     if (n != 1 && !mglevels[n - 1]->r) {
1120:       /* PCMGSetR() on the finest level if user did not supply it */
1121:       Vec *vec;
1122:       PetscCall(KSPCreateVecs(mglevels[n - 1]->smoothd, 1, &vec, 0, NULL));
1123:       PetscCall(PCMGSetR(pc, n - 1, *vec));
1124:       PetscCall(VecDestroy(vec));
1125:       PetscCall(PetscFree(vec));
1126:     }
1127:     if (doCR) {
1128:       PetscCall(VecDuplicate(mglevels[n - 1]->r, &mglevels[n - 1]->crx));
1129:       PetscCall(VecDuplicate(mglevels[n - 1]->r, &mglevels[n - 1]->crb));
1130:       PetscCall(VecZeroEntries(mglevels[n - 1]->crb));
1131:     }
1132:   }

1134:   if (pc->dm) {
1135:     /* need to tell all the coarser levels to rebuild the matrix using the DM for that level */
1136:     for (i = 0; i < n - 1; i++) {
1137:       if (mglevels[i]->smoothd->setupstage != KSP_SETUP_NEW) mglevels[i]->smoothd->setupstage = KSP_SETUP_NEWMATRIX;
1138:     }
1139:   }
1140:   // We got here (PCSetUp_MG) because the matrix has changed, which means the smoother needs to be set up again (e.g.,
1141:   // new diagonal for Jacobi). Setting it here allows it to be logged under PCSetUp rather than deep inside a PCApply.
1142:   if (mglevels[n - 1]->smoothd->setupstage != KSP_SETUP_NEW) mglevels[n - 1]->smoothd->setupstage = KSP_SETUP_NEWMATRIX;

1144:   for (i = 1; i < n; i++) {
1145:     if (mglevels[i]->smoothu == mglevels[i]->smoothd || mg->am == PC_MG_FULL || mg->am == PC_MG_KASKADE || mg->cyclesperpcapply > 1) {
1146:       /* if doing only down then initial guess is zero */
1147:       PetscCall(KSPSetInitialGuessNonzero(mglevels[i]->smoothd, PETSC_TRUE));
1148:     }
1149:     if (mglevels[i]->cr) PetscCall(KSPSetInitialGuessNonzero(mglevels[i]->cr, PETSC_TRUE));
1150:     if (mglevels[i]->eventsmoothsetup) PetscCall(PetscLogEventBegin(mglevels[i]->eventsmoothsetup, 0, 0, 0, 0));
1151:     PetscCall(KSPSetUp(mglevels[i]->smoothd));
1152:     if (mglevels[i]->smoothd->reason) pc->failedreason = PC_SUBPC_ERROR;
1153:     if (mglevels[i]->eventsmoothsetup) PetscCall(PetscLogEventEnd(mglevels[i]->eventsmoothsetup, 0, 0, 0, 0));
1154:     if (!mglevels[i]->residual) {
1155:       Mat mat;
1156:       PetscCall(KSPGetOperators(mglevels[i]->smoothd, &mat, NULL));
1157:       PetscCall(PCMGSetResidual(pc, i, PCMGResidualDefault, mat));
1158:     }
1159:     if (!mglevels[i]->residualtranspose) {
1160:       Mat mat;
1161:       PetscCall(KSPGetOperators(mglevels[i]->smoothd, &mat, NULL));
1162:       PetscCall(PCMGSetResidualTranspose(pc, i, PCMGResidualTransposeDefault, mat));
1163:     }
1164:   }
1165:   for (i = 1; i < n; i++) {
1166:     if (mglevels[i]->smoothu && mglevels[i]->smoothu != mglevels[i]->smoothd) {
1167:       Mat downmat, downpmat;

1169:       /* check if operators have been set for up, if not use down operators to set them */
1170:       PetscCall(KSPGetOperatorsSet(mglevels[i]->smoothu, &opsset, NULL));
1171:       if (!opsset) {
1172:         PetscCall(KSPGetOperators(mglevels[i]->smoothd, &downmat, &downpmat));
1173:         PetscCall(KSPSetOperators(mglevels[i]->smoothu, downmat, downpmat));
1174:       }

1176:       PetscCall(KSPSetInitialGuessNonzero(mglevels[i]->smoothu, PETSC_TRUE));
1177:       if (mglevels[i]->eventsmoothsetup) PetscCall(PetscLogEventBegin(mglevels[i]->eventsmoothsetup, 0, 0, 0, 0));
1178:       PetscCall(KSPSetUp(mglevels[i]->smoothu));
1179:       if (mglevels[i]->smoothu->reason) pc->failedreason = PC_SUBPC_ERROR;
1180:       if (mglevels[i]->eventsmoothsetup) PetscCall(PetscLogEventEnd(mglevels[i]->eventsmoothsetup, 0, 0, 0, 0));
1181:     }
1182:     if (mglevels[i]->cr) {
1183:       Mat downmat, downpmat;

1185:       /* check if operators have been set for up, if not use down operators to set them */
1186:       PetscCall(KSPGetOperatorsSet(mglevels[i]->cr, &opsset, NULL));
1187:       if (!opsset) {
1188:         PetscCall(KSPGetOperators(mglevels[i]->smoothd, &downmat, &downpmat));
1189:         PetscCall(KSPSetOperators(mglevels[i]->cr, downmat, downpmat));
1190:       }

1192:       PetscCall(KSPSetInitialGuessNonzero(mglevels[i]->cr, PETSC_TRUE));
1193:       if (mglevels[i]->eventsmoothsetup) PetscCall(PetscLogEventBegin(mglevels[i]->eventsmoothsetup, 0, 0, 0, 0));
1194:       PetscCall(KSPSetUp(mglevels[i]->cr));
1195:       if (mglevels[i]->cr->reason) pc->failedreason = PC_SUBPC_ERROR;
1196:       if (mglevels[i]->eventsmoothsetup) PetscCall(PetscLogEventEnd(mglevels[i]->eventsmoothsetup, 0, 0, 0, 0));
1197:     }
1198:   }

1200:   if (mglevels[0]->eventsmoothsetup) PetscCall(PetscLogEventBegin(mglevels[0]->eventsmoothsetup, 0, 0, 0, 0));
1201:   PetscCall(KSPSetUp(mglevels[0]->smoothd));
1202:   if (mglevels[0]->smoothd->reason) pc->failedreason = PC_SUBPC_ERROR;
1203:   if (mglevels[0]->eventsmoothsetup) PetscCall(PetscLogEventEnd(mglevels[0]->eventsmoothsetup, 0, 0, 0, 0));

1205:   /*
1206:      Dump the interpolation/restriction matrices plus the
1207:    Jacobian/stiffness on each level. This allows MATLAB users to
1208:    easily check if the Galerkin condition A_c = R A_f R^T is satisfied.

1210:    Only support one or the other at the same time.
1211:   */
1212: #if defined(PETSC_USE_SOCKET_VIEWER)
1213:   PetscCall(PetscOptionsGetBool(((PetscObject)pc)->options, ((PetscObject)pc)->prefix, "-pc_mg_dump_matlab", &dump, NULL));
1214:   if (dump) viewer = PETSC_VIEWER_SOCKET_(PetscObjectComm((PetscObject)pc));
1215:   dump = PETSC_FALSE;
1216: #endif
1217:   PetscCall(PetscOptionsGetBool(((PetscObject)pc)->options, ((PetscObject)pc)->prefix, "-pc_mg_dump_binary", &dump, NULL));
1218:   if (dump) viewer = PETSC_VIEWER_BINARY_(PetscObjectComm((PetscObject)pc));

1220:   if (viewer) {
1221:     for (i = 1; i < n; i++) PetscCall(MatView(mglevels[i]->restrct, viewer));
1222:     for (i = 0; i < n; i++) {
1223:       PetscCall(KSPGetPC(mglevels[i]->smoothd, &pc));
1224:       PetscCall(MatView(pc->mat, viewer));
1225:     }
1226:   }
1227:   PetscFunctionReturn(PETSC_SUCCESS);
1228: }

1230: PetscErrorCode PCMGGetLevels_MG(PC pc, PetscInt *levels)
1231: {
1232:   PC_MG *mg = (PC_MG *)pc->data;

1234:   PetscFunctionBegin;
1235:   *levels = mg->nlevels;
1236:   PetscFunctionReturn(PETSC_SUCCESS);
1237: }

1239: /*@
1240:   PCMGGetLevels - Gets the number of levels to use with `PCMG`.

1242:   Not Collective

1244:   Input Parameter:
1245: . pc - the preconditioner context

1247:   Output Parameter:
1248: . levels - the number of levels

1250:   Level: advanced

1252: .seealso: [](ch_ksp), `PCMG`, `PCMGSetLevels()`
1253: @*/
1254: PetscErrorCode PCMGGetLevels(PC pc, PetscInt *levels)
1255: {
1256:   PetscFunctionBegin;
1258:   PetscAssertPointer(levels, 2);
1259:   *levels = 0;
1260:   PetscTryMethod(pc, "PCMGGetLevels_C", (PC, PetscInt *), (pc, levels));
1261:   PetscFunctionReturn(PETSC_SUCCESS);
1262: }

1264: /*@
1265:   PCMGGetGridComplexity - compute operator and grid complexity of the `PCMG` hierarchy

1267:   Input Parameter:
1268: . pc - the preconditioner context

1270:   Output Parameters:
1271: + gc - grid complexity = sum_i(n_i) / n_0
1272: - oc - operator complexity = sum_i(nnz_i) / nnz_0

1274:   Level: advanced

1276:   Note:
1277:   This is often call the operator complexity in multigrid literature

1279: .seealso: [](ch_ksp), `PCMG`, `PCMGGetLevels()`, `PCMGSetLevels()`
1280: @*/
1281: PetscErrorCode PCMGGetGridComplexity(PC pc, PetscReal *gc, PetscReal *oc)
1282: {
1283:   PC_MG         *mg       = (PC_MG *)pc->data;
1284:   PC_MG_Levels **mglevels = mg->levels;
1285:   PetscInt       lev, N;
1286:   PetscLogDouble nnz0 = 0, sgc = 0, soc = 0, n0 = 0;
1287:   MatInfo        info;

1289:   PetscFunctionBegin;
1291:   if (gc) PetscAssertPointer(gc, 2);
1292:   if (oc) PetscAssertPointer(oc, 3);
1293:   if (!pc->setupcalled) {
1294:     if (gc) *gc = 0;
1295:     if (oc) *oc = 0;
1296:     PetscFunctionReturn(PETSC_SUCCESS);
1297:   }
1298:   PetscCheck(mg->nlevels > 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MG has no levels");
1299:   for (lev = 0; lev < mg->nlevels; lev++) {
1300:     Mat dB;
1301:     PetscCall(KSPGetOperators(mglevels[lev]->smoothd, NULL, &dB));
1302:     PetscCall(MatGetInfo(dB, MAT_GLOBAL_SUM, &info)); /* global reduction */
1303:     PetscCall(MatGetSize(dB, &N, NULL));
1304:     sgc += N;
1305:     soc += info.nz_used;
1306:     if (lev == mg->nlevels - 1) {
1307:       nnz0 = info.nz_used;
1308:       n0   = N;
1309:     }
1310:   }
1311:   PetscCheck(n0 > 0 && gc, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Number for grid points on finest level is not available");
1312:   *gc = (PetscReal)(sgc / n0);
1313:   if (nnz0 > 0 && oc) *oc = (PetscReal)(soc / nnz0);
1314:   PetscFunctionReturn(PETSC_SUCCESS);
1315: }

1317: /*@
1318:   PCMGSetType - Determines the form of multigrid to use, either
1319:   multiplicative, additive, full, or the Kaskade algorithm.

1321:   Logically Collective

1323:   Input Parameters:
1324: + pc   - the preconditioner context
1325: - form - multigrid form, one of `PC_MG_MULTIPLICATIVE`, `PC_MG_ADDITIVE`, `PC_MG_FULL`, `PC_MG_KASKADE`

1327:   Options Database Key:
1328: . -pc_mg_type <form> - Sets <form>, one of multiplicative, additive, full, kaskade

1330:   Level: advanced

1332: .seealso: [](ch_ksp), `PCMGType`, `PCMG`, `PCMGGetLevels()`, `PCMGSetLevels()`, `PCMGGetType()`, `PCMGCycleType`
1333: @*/
1334: PetscErrorCode PCMGSetType(PC pc, PCMGType form)
1335: {
1336:   PC_MG *mg = (PC_MG *)pc->data;

1338:   PetscFunctionBegin;
1341:   mg->am = form;
1342:   if (form == PC_MG_MULTIPLICATIVE) pc->ops->applyrichardson = PCApplyRichardson_MG;
1343:   else pc->ops->applyrichardson = NULL;
1344:   PetscFunctionReturn(PETSC_SUCCESS);
1345: }

1347: /*@
1348:   PCMGGetType - Finds the form of multigrid the `PCMG` is using  multiplicative, additive, full, or the Kaskade algorithm.

1350:   Logically Collective

1352:   Input Parameter:
1353: . pc - the preconditioner context

1355:   Output Parameter:
1356: . type - one of `PC_MG_MULTIPLICATIVE`, `PC_MG_ADDITIVE`, `PC_MG_FULL`, `PC_MG_KASKADE`, `PCMGCycleType`

1358:   Level: advanced

1360: .seealso: [](ch_ksp), `PCMGType`, `PCMG`, `PCMGGetLevels()`, `PCMGSetLevels()`, `PCMGSetType()`
1361: @*/
1362: PetscErrorCode PCMGGetType(PC pc, PCMGType *type)
1363: {
1364:   PC_MG *mg = (PC_MG *)pc->data;

1366:   PetscFunctionBegin;
1368:   *type = mg->am;
1369:   PetscFunctionReturn(PETSC_SUCCESS);
1370: }

1372: /*@
1373:   PCMGSetCycleType - Sets the type cycles to use.  Use `PCMGSetCycleTypeOnLevel()` for more
1374:   complicated cycling.

1376:   Logically Collective

1378:   Input Parameters:
1379: + pc - the multigrid context
1380: - n  - either `PC_MG_CYCLE_V` or `PC_MG_CYCLE_W`

1382:   Options Database Key:
1383: . -pc_mg_cycle_type <v,w> - provide the cycle desired

1385:   Level: advanced

1387: .seealso: [](ch_ksp), `PCMG`, `PCMGSetCycleTypeOnLevel()`, `PCMGType`, `PCMGCycleType`
1388: @*/
1389: PetscErrorCode PCMGSetCycleType(PC pc, PCMGCycleType n)
1390: {
1391:   PC_MG         *mg       = (PC_MG *)pc->data;
1392:   PC_MG_Levels **mglevels = mg->levels;
1393:   PetscInt       i, levels;

1395:   PetscFunctionBegin;
1398:   PetscCheck(mglevels, PetscObjectComm((PetscObject)pc), PETSC_ERR_ORDER, "Must set MG levels with PCMGSetLevels() before calling");
1399:   levels = mglevels[0]->levels;
1400:   for (i = 0; i < levels; i++) mglevels[i]->cycles = n;
1401:   PetscFunctionReturn(PETSC_SUCCESS);
1402: }

1404: /*@
1405:   PCMGMultiplicativeSetCycles - Sets the number of cycles to use for each preconditioner step
1406:   of multigrid when `PCMGType` is `PC_MG_MULTIPLICATIVE`

1408:   Logically Collective

1410:   Input Parameters:
1411: + pc - the multigrid context
1412: - n  - number of cycles (default is 1)

1414:   Options Database Key:
1415: . -pc_mg_multiplicative_cycles n - set the number of cycles

1417:   Level: advanced

1419:   Note:
1420:   This is not associated with setting a v or w cycle, that is set with `PCMGSetCycleType()`

1422: .seealso: [](ch_ksp), `PCMGSetCycleTypeOnLevel()`, `PCMGSetCycleType()`, `PCMGCycleType`, `PCMGType`
1423: @*/
1424: PetscErrorCode PCMGMultiplicativeSetCycles(PC pc, PetscInt n)
1425: {
1426:   PC_MG *mg = (PC_MG *)pc->data;

1428:   PetscFunctionBegin;
1431:   mg->cyclesperpcapply = n;
1432:   PetscFunctionReturn(PETSC_SUCCESS);
1433: }

1435: static PetscErrorCode PCMGSetGalerkin_MG(PC pc, PCMGGalerkinType use)
1436: {
1437:   PC_MG *mg = (PC_MG *)pc->data;

1439:   PetscFunctionBegin;
1440:   mg->galerkin = use;
1441:   PetscFunctionReturn(PETSC_SUCCESS);
1442: }

1444: /*@
1445:   PCMGSetGalerkin - Causes the coarser grid matrices to be computed from the
1446:   finest grid via the Galerkin process: A_i-1 = r_i * A_i * p_i

1448:   Logically Collective

1450:   Input Parameters:
1451: + pc  - the multigrid context
1452: - use - one of `PC_MG_GALERKIN_BOTH`, `PC_MG_GALERKIN_PMAT`, `PC_MG_GALERKIN_MAT`, or `PC_MG_GALERKIN_NONE`

1454:   Options Database Key:
1455: . -pc_mg_galerkin <both,pmat,mat,none> - set the matrices to form via the Galerkin process

1457:   Level: intermediate

1459:   Note:
1460:   Some codes that use `PCMG` such as `PCGAMG` use Galerkin internally while constructing the hierarchy and thus do not
1461:   use the `PCMG` construction of the coarser grids.

1463: .seealso: [](ch_ksp), `PCMG`, `PCMGGetGalerkin()`, `PCMGGalerkinType`
1464: @*/
1465: PetscErrorCode PCMGSetGalerkin(PC pc, PCMGGalerkinType use)
1466: {
1467:   PetscFunctionBegin;
1469:   PetscTryMethod(pc, "PCMGSetGalerkin_C", (PC, PCMGGalerkinType), (pc, use));
1470:   PetscFunctionReturn(PETSC_SUCCESS);
1471: }

1473: /*@
1474:   PCMGGetGalerkin - Checks if Galerkin multigrid is being used, i.e. A_i-1 = r_i * A_i * p_i

1476:   Not Collective

1478:   Input Parameter:
1479: . pc - the multigrid context

1481:   Output Parameter:
1482: . galerkin - one of `PC_MG_GALERKIN_BOTH`,`PC_MG_GALERKIN_PMAT`,`PC_MG_GALERKIN_MAT`, `PC_MG_GALERKIN_NONE`, or `PC_MG_GALERKIN_EXTERNAL`

1484:   Level: intermediate

1486: .seealso: [](ch_ksp), `PCMG`, `PCMGSetGalerkin()`, `PCMGGalerkinType`
1487: @*/
1488: PetscErrorCode PCMGGetGalerkin(PC pc, PCMGGalerkinType *galerkin)
1489: {
1490:   PC_MG *mg = (PC_MG *)pc->data;

1492:   PetscFunctionBegin;
1494:   *galerkin = mg->galerkin;
1495:   PetscFunctionReturn(PETSC_SUCCESS);
1496: }

1498: static PetscErrorCode PCMGSetAdaptInterpolation_MG(PC pc, PetscBool adapt)
1499: {
1500:   PC_MG *mg = (PC_MG *)pc->data;

1502:   PetscFunctionBegin;
1503:   mg->adaptInterpolation = adapt;
1504:   PetscFunctionReturn(PETSC_SUCCESS);
1505: }

1507: static PetscErrorCode PCMGGetAdaptInterpolation_MG(PC pc, PetscBool *adapt)
1508: {
1509:   PC_MG *mg = (PC_MG *)pc->data;

1511:   PetscFunctionBegin;
1512:   *adapt = mg->adaptInterpolation;
1513:   PetscFunctionReturn(PETSC_SUCCESS);
1514: }

1516: static PetscErrorCode PCMGSetAdaptCoarseSpaceType_MG(PC pc, PCMGCoarseSpaceType ctype)
1517: {
1518:   PC_MG *mg = (PC_MG *)pc->data;

1520:   PetscFunctionBegin;
1521:   mg->adaptInterpolation = ctype != PCMG_ADAPT_NONE ? PETSC_TRUE : PETSC_FALSE;
1522:   mg->coarseSpaceType    = ctype;
1523:   PetscCall(PCMGSetGalerkin(pc, PC_MG_GALERKIN_BOTH));
1524:   PetscFunctionReturn(PETSC_SUCCESS);
1525: }

1527: static PetscErrorCode PCMGGetAdaptCoarseSpaceType_MG(PC pc, PCMGCoarseSpaceType *ctype)
1528: {
1529:   PC_MG *mg = (PC_MG *)pc->data;

1531:   PetscFunctionBegin;
1532:   *ctype = mg->coarseSpaceType;
1533:   PetscFunctionReturn(PETSC_SUCCESS);
1534: }

1536: static PetscErrorCode PCMGSetAdaptCR_MG(PC pc, PetscBool cr)
1537: {
1538:   PC_MG *mg = (PC_MG *)pc->data;

1540:   PetscFunctionBegin;
1541:   mg->compatibleRelaxation = cr;
1542:   PetscFunctionReturn(PETSC_SUCCESS);
1543: }

1545: static PetscErrorCode PCMGGetAdaptCR_MG(PC pc, PetscBool *cr)
1546: {
1547:   PC_MG *mg = (PC_MG *)pc->data;

1549:   PetscFunctionBegin;
1550:   *cr = mg->compatibleRelaxation;
1551:   PetscFunctionReturn(PETSC_SUCCESS);
1552: }

1554: /*@
1555:   PCMGSetAdaptCoarseSpaceType - Set the type of adaptive coarse space.

1557:   Adapts or creates the interpolator based upon a vector space which should be accurately captured by the next coarser mesh, and thus accurately interpolated.

1559:   Logically Collective

1561:   Input Parameters:
1562: + pc    - the multigrid context
1563: - ctype - the type of coarse space

1565:   Options Database Keys:
1566: + -pc_mg_adapt_interp_n <int>             - The number of modes to use
1567: - -pc_mg_adapt_interp_coarse_space <type> - The type of coarse space: none, `polynomial`, `harmonic`, `eigenvector`, `generalized_eigenvector`, `gdsw`

1569:   Level: intermediate

1571:   Note:
1572:   Requires a `DM` with specific functionality be attached to the `PC`.

1574: .seealso: [](ch_ksp), `PCMG`, `PCMGCoarseSpaceType`, `PCMGGetAdaptCoarseSpaceType()`, `PCMGSetGalerkin()`, `PCMGSetAdaptInterpolation()`, `DM`
1575: @*/
1576: PetscErrorCode PCMGSetAdaptCoarseSpaceType(PC pc, PCMGCoarseSpaceType ctype)
1577: {
1578:   PetscFunctionBegin;
1581:   PetscTryMethod(pc, "PCMGSetAdaptCoarseSpaceType_C", (PC, PCMGCoarseSpaceType), (pc, ctype));
1582:   PetscFunctionReturn(PETSC_SUCCESS);
1583: }

1585: /*@
1586:   PCMGGetAdaptCoarseSpaceType - Get the type of adaptive coarse space.

1588:   Not Collective

1590:   Input Parameter:
1591: . pc - the multigrid context

1593:   Output Parameter:
1594: . ctype - the type of coarse space

1596:   Level: intermediate

1598: .seealso: [](ch_ksp), `PCMG`, `PCMGCoarseSpaceType`, `PCMGSetAdaptCoarseSpaceType()`, `PCMGSetGalerkin()`, `PCMGSetAdaptInterpolation()`
1599: @*/
1600: PetscErrorCode PCMGGetAdaptCoarseSpaceType(PC pc, PCMGCoarseSpaceType *ctype)
1601: {
1602:   PetscFunctionBegin;
1604:   PetscAssertPointer(ctype, 2);
1605:   PetscUseMethod(pc, "PCMGGetAdaptCoarseSpaceType_C", (PC, PCMGCoarseSpaceType *), (pc, ctype));
1606:   PetscFunctionReturn(PETSC_SUCCESS);
1607: }

1609: /* MATT: REMOVE? */
1610: /*@
1611:   PCMGSetAdaptInterpolation - Adapt the interpolator based upon a vector space which should be accurately captured by the next coarser mesh, and thus accurately interpolated.

1613:   Logically Collective

1615:   Input Parameters:
1616: + pc    - the multigrid context
1617: - adapt - flag for adaptation of the interpolator

1619:   Options Database Keys:
1620: + -pc_mg_adapt_interp                     - Turn on adaptation
1621: . -pc_mg_adapt_interp_n <int>             - The number of modes to use, should be divisible by dimension
1622: - -pc_mg_adapt_interp_coarse_space <type> - The type of coarse space: polynomial, harmonic, eigenvector, generalized_eigenvector

1624:   Level: intermediate

1626: .seealso: [](ch_ksp), `PCMG`, `PCMGGetAdaptInterpolation()`, `PCMGSetGalerkin()`, `PCMGGetAdaptCoarseSpaceType()`, `PCMGSetAdaptCoarseSpaceType()`
1627: @*/
1628: PetscErrorCode PCMGSetAdaptInterpolation(PC pc, PetscBool adapt)
1629: {
1630:   PetscFunctionBegin;
1632:   PetscTryMethod(pc, "PCMGSetAdaptInterpolation_C", (PC, PetscBool), (pc, adapt));
1633:   PetscFunctionReturn(PETSC_SUCCESS);
1634: }

1636: /*@
1637:   PCMGGetAdaptInterpolation - Get the flag to adapt the interpolator based upon a vector space which should be accurately captured by the next coarser mesh,
1638:   and thus accurately interpolated.

1640:   Not Collective

1642:   Input Parameter:
1643: . pc - the multigrid context

1645:   Output Parameter:
1646: . adapt - flag for adaptation of the interpolator

1648:   Level: intermediate

1650: .seealso: [](ch_ksp), `PCMG`, `PCMGSetAdaptInterpolation()`, `PCMGSetGalerkin()`, `PCMGGetAdaptCoarseSpaceType()`, `PCMGSetAdaptCoarseSpaceType()`
1651: @*/
1652: PetscErrorCode PCMGGetAdaptInterpolation(PC pc, PetscBool *adapt)
1653: {
1654:   PetscFunctionBegin;
1656:   PetscAssertPointer(adapt, 2);
1657:   PetscUseMethod(pc, "PCMGGetAdaptInterpolation_C", (PC, PetscBool *), (pc, adapt));
1658:   PetscFunctionReturn(PETSC_SUCCESS);
1659: }

1661: /*@
1662:   PCMGSetAdaptCR - Monitor the coarse space quality using an auxiliary solve with compatible relaxation.

1664:   Logically Collective

1666:   Input Parameters:
1667: + pc - the multigrid context
1668: - cr - flag for compatible relaxation

1670:   Options Database Key:
1671: . -pc_mg_adapt_cr - Turn on compatible relaxation

1673:   Level: intermediate

1675: .seealso: [](ch_ksp), `PCMG`, `PCMGGetAdaptCR()`, `PCMGSetAdaptInterpolation()`, `PCMGSetGalerkin()`, `PCMGGetAdaptCoarseSpaceType()`, `PCMGSetAdaptCoarseSpaceType()`
1676: @*/
1677: PetscErrorCode PCMGSetAdaptCR(PC pc, PetscBool cr)
1678: {
1679:   PetscFunctionBegin;
1681:   PetscTryMethod(pc, "PCMGSetAdaptCR_C", (PC, PetscBool), (pc, cr));
1682:   PetscFunctionReturn(PETSC_SUCCESS);
1683: }

1685: /*@
1686:   PCMGGetAdaptCR - Get the flag to monitor coarse space quality using an auxiliary solve with compatible relaxation.

1688:   Not Collective

1690:   Input Parameter:
1691: . pc - the multigrid context

1693:   Output Parameter:
1694: . cr - flag for compatible relaxaion

1696:   Level: intermediate

1698: .seealso: [](ch_ksp), `PCMGSetAdaptCR()`, `PCMGGetAdaptInterpolation()`, `PCMGSetGalerkin()`, `PCMGGetAdaptCoarseSpaceType()`, `PCMGSetAdaptCoarseSpaceType()`
1699: @*/
1700: PetscErrorCode PCMGGetAdaptCR(PC pc, PetscBool *cr)
1701: {
1702:   PetscFunctionBegin;
1704:   PetscAssertPointer(cr, 2);
1705:   PetscUseMethod(pc, "PCMGGetAdaptCR_C", (PC, PetscBool *), (pc, cr));
1706:   PetscFunctionReturn(PETSC_SUCCESS);
1707: }

1709: /*@
1710:   PCMGSetNumberSmooth - Sets the number of pre and post-smoothing steps to use
1711:   on all levels.  Use `PCMGDistinctSmoothUp()` to create separate up and down smoothers if you want different numbers of
1712:   pre- and post-smoothing steps.

1714:   Logically Collective

1716:   Input Parameters:
1717: + pc - the multigrid context
1718: - n  - the number of smoothing steps

1720:   Options Database Key:
1721: . -mg_levels_ksp_max_it <n> - Sets number of pre and post-smoothing steps

1723:   Level: advanced

1725:   Note:
1726:   This does not set a value on the coarsest grid, since we assume that there is no separate smooth up on the coarsest grid.

1728: .seealso: [](ch_ksp), `PCMG`, `PCMGSetDistinctSmoothUp()`
1729: @*/
1730: PetscErrorCode PCMGSetNumberSmooth(PC pc, PetscInt n)
1731: {
1732:   PC_MG         *mg       = (PC_MG *)pc->data;
1733:   PC_MG_Levels **mglevels = mg->levels;
1734:   PetscInt       i, levels;

1736:   PetscFunctionBegin;
1739:   PetscCheck(mglevels, PetscObjectComm((PetscObject)pc), PETSC_ERR_ORDER, "Must set MG levels with PCMGSetLevels() before calling");
1740:   levels = mglevels[0]->levels;

1742:   for (i = 1; i < levels; i++) {
1743:     PetscCall(KSPSetTolerances(mglevels[i]->smoothu, PETSC_CURRENT, PETSC_CURRENT, PETSC_CURRENT, n));
1744:     PetscCall(KSPSetTolerances(mglevels[i]->smoothd, PETSC_CURRENT, PETSC_CURRENT, PETSC_CURRENT, n));
1745:     mg->default_smoothu = n;
1746:     mg->default_smoothd = n;
1747:   }
1748:   PetscFunctionReturn(PETSC_SUCCESS);
1749: }

1751: /*@
1752:   PCMGSetDistinctSmoothUp - sets the up (post) smoother to be a separate `KSP` from the down (pre) smoother on all levels
1753:   and adds the suffix _up to the options name

1755:   Logically Collective

1757:   Input Parameter:
1758: . pc - the preconditioner context

1760:   Options Database Key:
1761: . -pc_mg_distinct_smoothup <bool> - use distinct smoothing objects

1763:   Level: advanced

1765:   Note:
1766:   This does not set a value on the coarsest grid, since we assume that there is no separate smooth up on the coarsest grid.

1768: .seealso: [](ch_ksp), `PCMG`, `PCMGSetNumberSmooth()`
1769: @*/
1770: PetscErrorCode PCMGSetDistinctSmoothUp(PC pc)
1771: {
1772:   PC_MG         *mg       = (PC_MG *)pc->data;
1773:   PC_MG_Levels **mglevels = mg->levels;
1774:   PetscInt       i, levels;
1775:   KSP            subksp;

1777:   PetscFunctionBegin;
1779:   PetscCheck(mglevels, PetscObjectComm((PetscObject)pc), PETSC_ERR_ORDER, "Must set MG levels with PCMGSetLevels() before calling");
1780:   levels = mglevels[0]->levels;

1782:   for (i = 1; i < levels; i++) {
1783:     const char *prefix = NULL;
1784:     /* make sure smoother up and down are different */
1785:     PetscCall(PCMGGetSmootherUp(pc, i, &subksp));
1786:     PetscCall(KSPGetOptionsPrefix(mglevels[i]->smoothd, &prefix));
1787:     PetscCall(KSPSetOptionsPrefix(subksp, prefix));
1788:     PetscCall(KSPAppendOptionsPrefix(subksp, "up_"));
1789:   }
1790:   PetscFunctionReturn(PETSC_SUCCESS);
1791: }

1793: /* No new matrices are created, and the coarse operator matrices are the references to the original ones */
1794: static PetscErrorCode PCGetInterpolations_MG(PC pc, PetscInt *num_levels, Mat *interpolations[])
1795: {
1796:   PC_MG         *mg       = (PC_MG *)pc->data;
1797:   PC_MG_Levels **mglevels = mg->levels;
1798:   Mat           *mat;
1799:   PetscInt       l;

1801:   PetscFunctionBegin;
1802:   PetscCheck(mglevels, PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_WRONGSTATE, "Must set MG levels before calling");
1803:   PetscCall(PetscMalloc1(mg->nlevels, &mat));
1804:   for (l = 1; l < mg->nlevels; l++) {
1805:     mat[l - 1] = mglevels[l]->interpolate;
1806:     PetscCall(PetscObjectReference((PetscObject)mat[l - 1]));
1807:   }
1808:   *num_levels     = mg->nlevels;
1809:   *interpolations = mat;
1810:   PetscFunctionReturn(PETSC_SUCCESS);
1811: }

1813: /* No new matrices are created, and the coarse operator matrices are the references to the original ones */
1814: static PetscErrorCode PCGetCoarseOperators_MG(PC pc, PetscInt *num_levels, Mat *coarseOperators[])
1815: {
1816:   PC_MG         *mg       = (PC_MG *)pc->data;
1817:   PC_MG_Levels **mglevels = mg->levels;
1818:   PetscInt       l;
1819:   Mat           *mat;

1821:   PetscFunctionBegin;
1822:   PetscCheck(mglevels, PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_WRONGSTATE, "Must set MG levels before calling");
1823:   PetscCall(PetscMalloc1(mg->nlevels, &mat));
1824:   for (l = 0; l < mg->nlevels - 1; l++) {
1825:     PetscCall(KSPGetOperators(mglevels[l]->smoothd, NULL, &mat[l]));
1826:     PetscCall(PetscObjectReference((PetscObject)mat[l]));
1827:   }
1828:   *num_levels      = mg->nlevels;
1829:   *coarseOperators = mat;
1830:   PetscFunctionReturn(PETSC_SUCCESS);
1831: }

1833: /*@C
1834:   PCMGRegisterCoarseSpaceConstructor -  Adds a method to the `PCMG` package for coarse space construction.

1836:   Not Collective, No Fortran Support

1838:   Input Parameters:
1839: + name     - name of the constructor
1840: - function - constructor routine

1842:   Calling sequence of `function`:
1843: + pc        - The `PC` object
1844: . l         - The multigrid level, 0 is the coarse level
1845: . dm        - The `DM` for this level
1846: . smooth    - The level smoother
1847: . Nc        - The size of the coarse space
1848: . initGuess - Basis for an initial guess for the space
1849: - coarseSp  - A basis for the computed coarse space

1851:   Level: advanced

1853:   Developer Notes:
1854:   How come this is not used by `PCGAMG`?

1856: .seealso: [](ch_ksp), `PCMG`, `PCMGGetCoarseSpaceConstructor()`, `PCRegister()`
1857: @*/
1858: PetscErrorCode PCMGRegisterCoarseSpaceConstructor(const char name[], PetscErrorCode (*function)(PC pc, PetscInt l, DM dm, KSP smooth, PetscInt Nc, Mat initGuess, Mat *coarseSp))
1859: {
1860:   PetscFunctionBegin;
1861:   PetscCall(PCInitializePackage());
1862:   PetscCall(PetscFunctionListAdd(&PCMGCoarseList, name, function));
1863:   PetscFunctionReturn(PETSC_SUCCESS);
1864: }

1866: /*@C
1867:   PCMGGetCoarseSpaceConstructor -  Returns the given coarse space construction method.

1869:   Not Collective, No Fortran Support

1871:   Input Parameter:
1872: . name - name of the constructor

1874:   Output Parameter:
1875: . function - constructor routine

1877:   Level: advanced

1879: .seealso: [](ch_ksp), `PCMG`, `PCMGRegisterCoarseSpaceConstructor()`, `PCRegister()`
1880: @*/
1881: PetscErrorCode PCMGGetCoarseSpaceConstructor(const char name[], PetscErrorCode (**function)(PC, PetscInt, DM, KSP, PetscInt, Mat, Mat *))
1882: {
1883:   PetscFunctionBegin;
1884:   PetscCall(PetscFunctionListFind(PCMGCoarseList, name, function));
1885:   PetscFunctionReturn(PETSC_SUCCESS);
1886: }

1888: /*MC
1889:    PCMG - Use multigrid preconditioning. This preconditioner requires you provide additional
1890:     information about the coarser grid matrices and restriction/interpolation operators.

1892:    Options Database Keys:
1893: +  -pc_mg_levels <nlevels>                            - number of levels including finest
1894: .  -pc_mg_cycle_type <v,w>                            - provide the cycle desired
1895: .  -pc_mg_type <additive,multiplicative,full,kaskade> - multiplicative is the default
1896: .  -pc_mg_log                                         - log information about time spent on each level of the solver
1897: .  -pc_mg_distinct_smoothup                           - configure up (after interpolation) and down (before restriction) smoothers separately (with different options prefixes)
1898: .  -pc_mg_galerkin <both,pmat,mat,none>               - use Galerkin process to compute coarser operators, i.e. Acoarse = R A R'
1899: .  -pc_mg_multiplicative_cycles                        - number of cycles to use as the preconditioner (defaults to 1)
1900: .  -pc_mg_dump_matlab                                  - dumps the matrices for each level and the restriction/interpolation matrices
1901:                                                          to a `PETSCVIEWERSOCKET` for reading from MATLAB.
1902: -  -pc_mg_dump_binary                                  -dumps the matrices for each level and the restriction/interpolation matrices
1903:                                                         to the binary output file called binaryoutput

1905:    Level: intermediate

1907:    Notes:
1908:    The Krylov solver (if any) and preconditioner (smoother) and their parameters are controlled from the options database with the standard
1909:    options database keywords prefixed with `-mg_levels_` to affect all the levels but the coarsest, which is controlled with `-mg_coarse_`,
1910:    and the finest where `-mg_fine_` can override `-mg_levels_`.  One can set different preconditioners etc on specific levels with the prefix
1911:    `-mg_levels_n_` where `n` is the level number (zero being the coarse level. For example
1912: .vb
1913:    -mg_levels_ksp_type gmres -mg_levels_pc_type bjacobi -mg_coarse_pc_type svd -mg_levels_2_pc_type sor
1914: .ve
1915:    These options also work for controlling the smoothers etc inside `PCGAMG`

1917:    If one uses a Krylov method such `KSPGMRES` or `KSPCG` as the smoother then one must use `KSPFGMRES`, `KSPGCR`, or `KSPRICHARDSON` as the outer Krylov method

1919:    When run with a single level the smoother options are used on that level NOT the coarse grid solver options

1921:    When run with `KSPRICHARDSON` the convergence test changes slightly if monitor is turned on. The iteration count may change slightly. This
1922:    is because without monitoring the residual norm is computed WITHIN each multigrid cycle on the finest level after the pre-smoothing
1923:    (because the residual has just been computed for the multigrid algorithm and is hence available for free) while with monitoring the
1924:    residual is computed at the end of each cycle.

1926: .seealso: [](sec_mg), `PCCreate()`, `PCSetType()`, `PCType`, `PC`, `PCMGType`, `PCEXOTIC`, `PCGAMG`, `PCML`, `PCHYPRE`
1927:           `PCMGSetLevels()`, `PCMGGetLevels()`, `PCMGSetType()`, `PCMGSetCycleType()`,
1928:           `PCMGSetDistinctSmoothUp()`, `PCMGGetCoarseSolve()`, `PCMGSetResidual()`, `PCMGSetInterpolation()`,
1929:           `PCMGSetRestriction()`, `PCMGGetSmoother()`, `PCMGGetSmootherUp()`, `PCMGGetSmootherDown()`,
1930:           `PCMGSetCycleTypeOnLevel()`, `PCMGSetRhs()`, `PCMGSetX()`, `PCMGSetR()`,
1931:           `PCMGSetAdaptCR()`, `PCMGGetAdaptInterpolation()`, `PCMGSetGalerkin()`, `PCMGGetAdaptCoarseSpaceType()`, `PCMGSetAdaptCoarseSpaceType()`
1932: M*/

1934: PETSC_EXTERN PetscErrorCode PCCreate_MG(PC pc)
1935: {
1936:   PC_MG *mg;

1938:   PetscFunctionBegin;
1939:   PetscCall(PetscNew(&mg));
1940:   pc->data               = mg;
1941:   mg->nlevels            = -1;
1942:   mg->am                 = PC_MG_MULTIPLICATIVE;
1943:   mg->galerkin           = PC_MG_GALERKIN_NONE;
1944:   mg->adaptInterpolation = PETSC_FALSE;
1945:   mg->Nc                 = -1;
1946:   mg->eigenvalue         = -1;

1948:   pc->useAmat = PETSC_TRUE;

1950:   pc->ops->apply          = PCApply_MG;
1951:   pc->ops->applytranspose = PCApplyTranspose_MG;
1952:   pc->ops->matapply       = PCMatApply_MG;
1953:   pc->ops->setup          = PCSetUp_MG;
1954:   pc->ops->reset          = PCReset_MG;
1955:   pc->ops->destroy        = PCDestroy_MG;
1956:   pc->ops->setfromoptions = PCSetFromOptions_MG;
1957:   pc->ops->view           = PCView_MG;

1959:   PetscCall(PetscObjectComposedDataRegister(&mg->eigenvalue));
1960:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetGalerkin_C", PCMGSetGalerkin_MG));
1961:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetLevels_C", PCMGGetLevels_MG));
1962:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetLevels_C", PCMGSetLevels_MG));
1963:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGetInterpolations_C", PCGetInterpolations_MG));
1964:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGetCoarseOperators_C", PCGetCoarseOperators_MG));
1965:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetAdaptInterpolation_C", PCMGSetAdaptInterpolation_MG));
1966:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetAdaptInterpolation_C", PCMGGetAdaptInterpolation_MG));
1967:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetAdaptCR_C", PCMGSetAdaptCR_MG));
1968:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetAdaptCR_C", PCMGGetAdaptCR_MG));
1969:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetAdaptCoarseSpaceType_C", PCMGSetAdaptCoarseSpaceType_MG));
1970:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetAdaptCoarseSpaceType_C", PCMGGetAdaptCoarseSpaceType_MG));
1971:   PetscFunctionReturn(PETSC_SUCCESS);
1972: }