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 : (PetscInt)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_DEFAULT, PETSC_DEFAULT, PETSC_DEFAULT));
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_DEFAULT, PETSC_DEFAULT, PETSC_DEFAULT));
180:     }
181:   }

183:   *reason = (PCRichardsonConvergedReason)0;
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) *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: PetscErrorCode PCMGSetLevels_MG(PC pc, PetscInt levels, MPI_Comm *comms)
333: {
334:   PC_MG         *mg = (PC_MG *)pc->data;
335:   MPI_Comm       comm;
336:   PC_MG_Levels **mglevels = mg->levels;
337:   PCMGType       mgtype   = mg->am;
338:   PetscInt       mgctype  = (PetscInt)PC_MG_CYCLE_V;
339:   PetscInt       i;
340:   PetscMPIInt    size;
341:   const char    *prefix;
342:   PC             ipc;
343:   PetscInt       n;

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

364:   mg->nlevels = levels;

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

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

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

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

384:     if (comms) comm = comms[i];
385:     if (comm != MPI_COMM_NULL) {
386:       PetscCall(KSPCreate(comm, &mglevels[i]->smoothd));
387:       PetscCall(KSPSetNestLevel(mglevels[i]->smoothd, pc->kspnestlevel));
388:       PetscCall(KSPSetErrorIfNotConverged(mglevels[i]->smoothd, pc->erroriffailure));
389:       PetscCall(PetscObjectIncrementTabLevel((PetscObject)mglevels[i]->smoothd, (PetscObject)pc, levels - i));
390:       PetscCall(KSPSetOptionsPrefix(mglevels[i]->smoothd, prefix));
391:       PetscCall(PetscObjectComposedDataSetInt((PetscObject)mglevels[i]->smoothd, PetscMGLevelId, mglevels[i]->level));
392:       if (i || levels == 1) {
393:         char tprefix[128];

395:         PetscCall(KSPSetType(mglevels[i]->smoothd, KSPCHEBYSHEV));
396:         PetscCall(KSPSetConvergenceTest(mglevels[i]->smoothd, KSPConvergedSkip, NULL, NULL));
397:         PetscCall(KSPSetNormType(mglevels[i]->smoothd, KSP_NORM_NONE));
398:         PetscCall(KSPGetPC(mglevels[i]->smoothd, &ipc));
399:         PetscCall(PCSetType(ipc, PCSOR));
400:         PetscCall(KSPSetTolerances(mglevels[i]->smoothd, PETSC_DEFAULT, PETSC_DEFAULT, PETSC_DEFAULT, mg->default_smoothd));

402:         PetscCall(PetscSNPrintf(tprefix, 128, "mg_levels_%d_", (int)i));
403:         PetscCall(KSPAppendOptionsPrefix(mglevels[i]->smoothd, tprefix));
404:       } else {
405:         PetscCall(KSPAppendOptionsPrefix(mglevels[0]->smoothd, "mg_coarse_"));

407:         /* coarse solve is (redundant) LU by default; set shifttype NONZERO to avoid annoying zero-pivot in LU preconditioner */
408:         PetscCall(KSPSetType(mglevels[0]->smoothd, KSPPREONLY));
409:         PetscCall(KSPGetPC(mglevels[0]->smoothd, &ipc));
410:         PetscCallMPI(MPI_Comm_size(comm, &size));
411:         if (size > 1) {
412:           PetscCall(PCSetType(ipc, PCREDUNDANT));
413:         } else {
414:           PetscCall(PCSetType(ipc, PCLU));
415:         }
416:         PetscCall(PCFactorSetShiftType(ipc, MAT_SHIFT_INBLOCKS));
417:       }
418:     }
419:     mglevels[i]->smoothu = mglevels[i]->smoothd;
420:     mg->rtol             = 0.0;
421:     mg->abstol           = 0.0;
422:     mg->dtol             = 0.0;
423:     mg->ttol             = 0.0;
424:     mg->cyclesperpcapply = 1;
425:   }
426:   mg->levels = mglevels;
427:   PetscCall(PCMGSetType(pc, mgtype));
428:   PetscFunctionReturn(PETSC_SUCCESS);
429: }

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

435:   Logically Collective

437:   Input Parameters:
438: + pc     - the preconditioner context
439: . levels - the number of levels
440: - comms  - optional communicators for each level; this is to allow solving the coarser problems
441:            on smaller sets of processes. For processes that are not included in the computation
442:            you must pass `MPI_COMM_NULL`. Use comms = `NULL` to specify that all processes
443:            should participate in each level of problem.

445:   Level: intermediate

447:   Notes:
448:   If the number of levels is one then the multigrid uses the `-mg_levels` prefix
449:   for setting the level options rather than the `-mg_coarse` prefix.

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

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

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

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

472: .seealso: [](ch_ksp), `PCMGSetType()`, `PCMGGetLevels()`
473: @*/
474: PetscErrorCode PCMGSetLevels(PC pc, PetscInt levels, MPI_Comm *comms)
475: {
476:   PetscFunctionBegin;
478:   if (comms) PetscAssertPointer(comms, 3);
479:   PetscTryMethod(pc, "PCMGSetLevels_C", (PC, PetscInt, MPI_Comm *), (pc, levels, comms));
480:   PetscFunctionReturn(PETSC_SUCCESS);
481: }

483: PetscErrorCode PCDestroy_MG(PC pc)
484: {
485:   PC_MG         *mg       = (PC_MG *)pc->data;
486:   PC_MG_Levels **mglevels = mg->levels;
487:   PetscInt       i, n;

489:   PetscFunctionBegin;
490:   PetscCall(PCReset_MG(pc));
491:   if (mglevels) {
492:     n = mglevels[0]->levels;
493:     for (i = 0; i < n; i++) {
494:       if (mglevels[i]->smoothd != mglevels[i]->smoothu) PetscCall(KSPDestroy(&mglevels[i]->smoothd));
495:       PetscCall(KSPDestroy(&mglevels[i]->smoothu));
496:       PetscCall(KSPDestroy(&mglevels[i]->cr));
497:       PetscCall(PetscFree(mglevels[i]));
498:     }
499:     PetscCall(PetscFree(mg->levels));
500:   }
501:   PetscCall(PetscFree(pc->data));
502:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGetInterpolations_C", NULL));
503:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGetCoarseOperators_C", NULL));
504:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetGalerkin_C", NULL));
505:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetLevels_C", NULL));
506:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetLevels_C", NULL));
507:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGetInterpolations_C", NULL));
508:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGetCoarseOperators_C", NULL));
509:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetAdaptInterpolation_C", NULL));
510:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetAdaptInterpolation_C", NULL));
511:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetAdaptCR_C", NULL));
512:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetAdaptCR_C", NULL));
513:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetAdaptCoarseSpaceType_C", NULL));
514:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetAdaptCoarseSpaceType_C", NULL));
515:   PetscFunctionReturn(PETSC_SUCCESS);
516: }

518: /*
519:    PCApply_MG - Runs either an additive, multiplicative, Kaskadic
520:              or full cycle of multigrid.

522:   Note:
523:   A simple wrapper which calls PCMGMCycle(),PCMGACycle(), or PCMGFCycle().
524: */
525: static PetscErrorCode PCApply_MG_Internal(PC pc, Vec b, Vec x, Mat B, Mat X, PetscBool transpose)
526: {
527:   PC_MG         *mg       = (PC_MG *)pc->data;
528:   PC_MG_Levels **mglevels = mg->levels;
529:   PC             tpc;
530:   PetscInt       levels = mglevels[0]->levels, i;
531:   PetscBool      changeu, changed, matapp;

533:   PetscFunctionBegin;
534:   matapp = (PetscBool)(B && X);
535:   if (mg->stageApply) PetscCall(PetscLogStagePush(mg->stageApply));
536:   /* When the DM is supplying the matrix then it will not exist until here */
537:   for (i = 0; i < levels; i++) {
538:     if (!mglevels[i]->A) {
539:       PetscCall(KSPGetOperators(mglevels[i]->smoothu, &mglevels[i]->A, NULL));
540:       PetscCall(PetscObjectReference((PetscObject)mglevels[i]->A));
541:     }
542:   }

544:   PetscCall(KSPGetPC(mglevels[levels - 1]->smoothd, &tpc));
545:   PetscCall(PCPreSolveChangeRHS(tpc, &changed));
546:   PetscCall(KSPGetPC(mglevels[levels - 1]->smoothu, &tpc));
547:   PetscCall(PCPreSolveChangeRHS(tpc, &changeu));
548:   if (!changeu && !changed) {
549:     if (matapp) {
550:       PetscCall(MatDestroy(&mglevels[levels - 1]->B));
551:       mglevels[levels - 1]->B = B;
552:     } else {
553:       PetscCall(VecDestroy(&mglevels[levels - 1]->b));
554:       mglevels[levels - 1]->b = b;
555:     }
556:   } else { /* if the smoother changes the rhs during PreSolve, we cannot use the input vector */
557:     if (matapp) {
558:       if (mglevels[levels - 1]->B) {
559:         PetscInt  N1, N2;
560:         PetscBool flg;

562:         PetscCall(MatGetSize(mglevels[levels - 1]->B, NULL, &N1));
563:         PetscCall(MatGetSize(B, NULL, &N2));
564:         PetscCall(PetscObjectTypeCompare((PetscObject)mglevels[levels - 1]->B, ((PetscObject)B)->type_name, &flg));
565:         if (N1 != N2 || !flg) PetscCall(MatDestroy(&mglevels[levels - 1]->B));
566:       }
567:       if (!mglevels[levels - 1]->B) {
568:         PetscCall(MatDuplicate(B, MAT_COPY_VALUES, &mglevels[levels - 1]->B));
569:       } else {
570:         PetscCall(MatCopy(B, mglevels[levels - 1]->B, SAME_NONZERO_PATTERN));
571:       }
572:     } else {
573:       if (!mglevels[levels - 1]->b) {
574:         Vec *vec;

576:         PetscCall(KSPCreateVecs(mglevels[levels - 1]->smoothd, 1, &vec, 0, NULL));
577:         mglevels[levels - 1]->b = *vec;
578:         PetscCall(PetscFree(vec));
579:       }
580:       PetscCall(VecCopy(b, mglevels[levels - 1]->b));
581:     }
582:   }
583:   if (matapp) {
584:     mglevels[levels - 1]->X = X;
585:   } else {
586:     mglevels[levels - 1]->x = x;
587:   }

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

595:     PetscCall(MatGetSize(mglevels[levels - 2]->X, NULL, &Xc));
596:     PetscCall(MatGetSize(mglevels[levels - 1]->B, NULL, &Bc));
597:     PetscCall(PetscObjectTypeCompare((PetscObject)mglevels[levels - 2]->X, ((PetscObject)mglevels[levels - 1]->X)->type_name, &flg));
598:     if (Xc != Bc || !flg) {
599:       PetscCall(MatDestroy(&mglevels[levels - 1]->R));
600:       for (i = 0; i < levels - 1; i++) {
601:         PetscCall(MatDestroy(&mglevels[i]->R));
602:         PetscCall(MatDestroy(&mglevels[i]->B));
603:         PetscCall(MatDestroy(&mglevels[i]->X));
604:       }
605:     }
606:   }

608:   if (mg->am == PC_MG_MULTIPLICATIVE) {
609:     if (matapp) PetscCall(MatZeroEntries(X));
610:     else PetscCall(VecZeroEntries(x));
611:     for (i = 0; i < mg->cyclesperpcapply; i++) PetscCall(PCMGMCycle_Private(pc, mglevels + levels - 1, transpose, matapp, NULL));
612:   } else if (mg->am == PC_MG_ADDITIVE) {
613:     PetscCall(PCMGACycle_Private(pc, mglevels, transpose, matapp));
614:   } else if (mg->am == PC_MG_KASKADE) {
615:     PetscCall(PCMGKCycle_Private(pc, mglevels, transpose, matapp));
616:   } else {
617:     PetscCall(PCMGFCycle_Private(pc, mglevels, transpose, matapp));
618:   }
619:   if (mg->stageApply) PetscCall(PetscLogStagePop());
620:   if (!changeu && !changed) {
621:     if (matapp) {
622:       mglevels[levels - 1]->B = NULL;
623:     } else {
624:       mglevels[levels - 1]->b = NULL;
625:     }
626:   }
627:   PetscFunctionReturn(PETSC_SUCCESS);
628: }

630: static PetscErrorCode PCApply_MG(PC pc, Vec b, Vec x)
631: {
632:   PetscFunctionBegin;
633:   PetscCall(PCApply_MG_Internal(pc, b, x, NULL, NULL, PETSC_FALSE));
634:   PetscFunctionReturn(PETSC_SUCCESS);
635: }

637: static PetscErrorCode PCApplyTranspose_MG(PC pc, Vec b, Vec x)
638: {
639:   PetscFunctionBegin;
640:   PetscCall(PCApply_MG_Internal(pc, b, x, NULL, NULL, PETSC_TRUE));
641:   PetscFunctionReturn(PETSC_SUCCESS);
642: }

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

651: PetscErrorCode PCSetFromOptions_MG(PC pc, PetscOptionItems *PetscOptionsObject)
652: {
653:   PetscInt            levels, cycles;
654:   PetscBool           flg, flg2;
655:   PC_MG              *mg = (PC_MG *)pc->data;
656:   PC_MG_Levels      **mglevels;
657:   PCMGType            mgtype;
658:   PCMGCycleType       mgctype;
659:   PCMGGalerkinType    gtype;
660:   PCMGCoarseSpaceType coarseSpaceType;

662:   PetscFunctionBegin;
663:   levels = PetscMax(mg->nlevels, 1);
664:   PetscOptionsHeadBegin(PetscOptionsObject, "Multigrid options");
665:   PetscCall(PetscOptionsInt("-pc_mg_levels", "Number of Levels", "PCMGSetLevels", levels, &levels, &flg));
666:   if (!flg && !mg->levels && pc->dm) {
667:     PetscCall(DMGetRefineLevel(pc->dm, &levels));
668:     levels++;
669:     mg->usedmfornumberoflevels = PETSC_TRUE;
670:   }
671:   PetscCall(PCMGSetLevels(pc, levels, NULL));
672:   mglevels = mg->levels;

674:   mgctype = (PCMGCycleType)mglevels[0]->cycles;
675:   PetscCall(PetscOptionsEnum("-pc_mg_cycle_type", "V cycle or for W-cycle", "PCMGSetCycleType", PCMGCycleTypes, (PetscEnum)mgctype, (PetscEnum *)&mgctype, &flg));
676:   if (flg) PetscCall(PCMGSetCycleType(pc, mgctype));
677:   gtype = mg->galerkin;
678:   PetscCall(PetscOptionsEnum("-pc_mg_galerkin", "Use Galerkin process to compute coarser operators", "PCMGSetGalerkin", PCMGGalerkinTypes, (PetscEnum)gtype, (PetscEnum *)&gtype, &flg));
679:   if (flg) PetscCall(PCMGSetGalerkin(pc, gtype));
680:   coarseSpaceType = mg->coarseSpaceType;
681:   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));
682:   if (flg) PetscCall(PCMGSetAdaptCoarseSpaceType(pc, coarseSpaceType));
683:   PetscCall(PetscOptionsInt("-pc_mg_adapt_interp_n", "Size of the coarse space for adaptive interpolation", "PCMGSetCoarseSpace", mg->Nc, &mg->Nc, &flg));
684:   PetscCall(PetscOptionsBool("-pc_mg_mesp_monitor", "Monitor the multilevel eigensolver", "PCMGSetAdaptInterpolation", PETSC_FALSE, &mg->mespMonitor, &flg));
685:   flg2 = PETSC_FALSE;
686:   PetscCall(PetscOptionsBool("-pc_mg_adapt_cr", "Monitor coarse space quality using Compatible Relaxation (CR)", "PCMGSetAdaptCR", PETSC_FALSE, &flg2, &flg));
687:   if (flg) PetscCall(PCMGSetAdaptCR(pc, flg2));
688:   flg = PETSC_FALSE;
689:   PetscCall(PetscOptionsBool("-pc_mg_distinct_smoothup", "Create separate smoothup KSP and append the prefix _up", "PCMGSetDistinctSmoothUp", PETSC_FALSE, &flg, NULL));
690:   if (flg) PetscCall(PCMGSetDistinctSmoothUp(pc));
691:   mgtype = mg->am;
692:   PetscCall(PetscOptionsEnum("-pc_mg_type", "Multigrid type", "PCMGSetType", PCMGTypes, (PetscEnum)mgtype, (PetscEnum *)&mgtype, &flg));
693:   if (flg) PetscCall(PCMGSetType(pc, mgtype));
694:   if (mg->am == PC_MG_MULTIPLICATIVE) {
695:     PetscCall(PetscOptionsInt("-pc_mg_multiplicative_cycles", "Number of cycles for each preconditioner step", "PCMGMultiplicativeSetCycles", mg->cyclesperpcapply, &cycles, &flg));
696:     if (flg) PetscCall(PCMGMultiplicativeSetCycles(pc, cycles));
697:   }
698:   flg = PETSC_FALSE;
699:   PetscCall(PetscOptionsBool("-pc_mg_log", "Log times for each multigrid level", "None", flg, &flg, NULL));
700:   if (flg) {
701:     PetscInt i;
702:     char     eventname[128];

704:     levels = mglevels[0]->levels;
705:     for (i = 0; i < levels; i++) {
706:       PetscCall(PetscSNPrintf(eventname, PETSC_STATIC_ARRAY_LENGTH(eventname), "MGSetup Level %d", (int)i));
707:       PetscCall(PetscLogEventRegister(eventname, ((PetscObject)pc)->classid, &mglevels[i]->eventsmoothsetup));
708:       PetscCall(PetscSNPrintf(eventname, PETSC_STATIC_ARRAY_LENGTH(eventname), "MGSmooth Level %d", (int)i));
709:       PetscCall(PetscLogEventRegister(eventname, ((PetscObject)pc)->classid, &mglevels[i]->eventsmoothsolve));
710:       if (i) {
711:         PetscCall(PetscSNPrintf(eventname, PETSC_STATIC_ARRAY_LENGTH(eventname), "MGResid Level %d", (int)i));
712:         PetscCall(PetscLogEventRegister(eventname, ((PetscObject)pc)->classid, &mglevels[i]->eventresidual));
713:         PetscCall(PetscSNPrintf(eventname, PETSC_STATIC_ARRAY_LENGTH(eventname), "MGInterp Level %d", (int)i));
714:         PetscCall(PetscLogEventRegister(eventname, ((PetscObject)pc)->classid, &mglevels[i]->eventinterprestrict));
715:       }
716:     }

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

721:       PetscCall(PetscLogStageGetId(sname, &mg->stageApply));
722:       if (mg->stageApply < 0) PetscCall(PetscLogStageRegister(sname, &mg->stageApply));
723:     }
724:   }
725:   PetscOptionsHeadEnd();
726:   /* Check option consistency */
727:   PetscCall(PCMGGetGalerkin(pc, &gtype));
728:   PetscCall(PCMGGetAdaptInterpolation(pc, &flg));
729:   PetscCheck(!flg || !(gtype >= PC_MG_GALERKIN_NONE), PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_INCOMP, "Must use Galerkin coarse operators when adapting the interpolator");
730:   PetscFunctionReturn(PETSC_SUCCESS);
731: }

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

738: #include <petscdraw.h>
739: PetscErrorCode PCView_MG(PC pc, PetscViewer viewer)
740: {
741:   PC_MG         *mg       = (PC_MG *)pc->data;
742:   PC_MG_Levels **mglevels = mg->levels;
743:   PetscInt       levels   = mglevels ? mglevels[0]->levels : 0, i;
744:   PetscBool      iascii, isbinary, isdraw;

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

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

825: /*
826:     Calls setup for the KSP on each level
827: */
828: PetscErrorCode PCSetUp_MG(PC pc)
829: {
830:   PC_MG         *mg       = (PC_MG *)pc->data;
831:   PC_MG_Levels **mglevels = mg->levels;
832:   PetscInt       i, n;
833:   PC             cpc;
834:   PetscBool      dump = PETSC_FALSE, opsset, use_amat, missinginterpolate = PETSC_FALSE;
835:   Mat            dA, dB;
836:   Vec            tvec;
837:   DM            *dms;
838:   PetscViewer    viewer = NULL;
839:   PetscBool      dAeqdB = PETSC_FALSE, needRestricts = PETSC_FALSE, doCR = PETSC_FALSE;
840:   PetscBool      adaptInterpolation = mg->adaptInterpolation;

842:   PetscFunctionBegin;
843:   PetscCheck(mglevels, PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_WRONGSTATE, "Must set MG levels with PCMGSetLevels() before setting up");
844:   n = mglevels[0]->levels;
845:   /* FIX: Move this to PCSetFromOptions_MG? */
846:   if (mg->usedmfornumberoflevels) {
847:     PetscInt levels;
848:     PetscCall(DMGetRefineLevel(pc->dm, &levels));
849:     levels++;
850:     if (levels > n) { /* the problem is now being solved on a finer grid */
851:       PetscCall(PCMGSetLevels(pc, levels, NULL));
852:       n = levels;
853:       PetscCall(PCSetFromOptions(pc)); /* it is bad to call this here, but otherwise will never be called for the new hierarchy */
854:       mglevels = mg->levels;
855:     }
856:   }
857:   PetscCall(KSPGetPC(mglevels[0]->smoothd, &cpc));

859:   /* If user did not provide fine grid operators OR operator was not updated since last global KSPSetOperators() */
860:   /* so use those from global PC */
861:   /* Is this what we always want? What if user wants to keep old one? */
862:   PetscCall(KSPGetOperatorsSet(mglevels[n - 1]->smoothd, NULL, &opsset));
863:   if (opsset) {
864:     Mat mmat;
865:     PetscCall(KSPGetOperators(mglevels[n - 1]->smoothd, NULL, &mmat));
866:     if (mmat == pc->pmat) opsset = PETSC_FALSE;
867:   }

869:   /* Create CR solvers */
870:   PetscCall(PCMGGetAdaptCR(pc, &doCR));
871:   if (doCR) {
872:     const char *prefix;

874:     PetscCall(PCGetOptionsPrefix(pc, &prefix));
875:     for (i = 1; i < n; ++i) {
876:       PC   ipc, cr;
877:       char crprefix[128];

879:       PetscCall(KSPCreate(PetscObjectComm((PetscObject)pc), &mglevels[i]->cr));
880:       PetscCall(KSPSetNestLevel(mglevels[i]->cr, pc->kspnestlevel));
881:       PetscCall(KSPSetErrorIfNotConverged(mglevels[i]->cr, PETSC_FALSE));
882:       PetscCall(PetscObjectIncrementTabLevel((PetscObject)mglevels[i]->cr, (PetscObject)pc, n - i));
883:       PetscCall(KSPSetOptionsPrefix(mglevels[i]->cr, prefix));
884:       PetscCall(PetscObjectComposedDataSetInt((PetscObject)mglevels[i]->cr, PetscMGLevelId, mglevels[i]->level));
885:       PetscCall(KSPSetType(mglevels[i]->cr, KSPCHEBYSHEV));
886:       PetscCall(KSPSetConvergenceTest(mglevels[i]->cr, KSPConvergedSkip, NULL, NULL));
887:       PetscCall(KSPSetNormType(mglevels[i]->cr, KSP_NORM_PRECONDITIONED));
888:       PetscCall(KSPGetPC(mglevels[i]->cr, &ipc));

890:       PetscCall(PCSetType(ipc, PCCOMPOSITE));
891:       PetscCall(PCCompositeSetType(ipc, PC_COMPOSITE_MULTIPLICATIVE));
892:       PetscCall(PCCompositeAddPCType(ipc, PCSOR));
893:       PetscCall(CreateCR_Private(pc, i, &cr));
894:       PetscCall(PCCompositeAddPC(ipc, cr));
895:       PetscCall(PCDestroy(&cr));

897:       PetscCall(KSPSetTolerances(mglevels[i]->cr, PETSC_DEFAULT, PETSC_DEFAULT, PETSC_DEFAULT, mg->default_smoothd));
898:       PetscCall(KSPSetInitialGuessNonzero(mglevels[i]->cr, PETSC_TRUE));
899:       PetscCall(PetscSNPrintf(crprefix, 128, "mg_levels_%d_cr_", (int)i));
900:       PetscCall(KSPAppendOptionsPrefix(mglevels[i]->cr, crprefix));
901:     }
902:   }

904:   if (!opsset) {
905:     PetscCall(PCGetUseAmat(pc, &use_amat));
906:     if (use_amat) {
907:       PetscCall(PetscInfo(pc, "Using outer operators to define finest grid operator \n  because PCMGGetSmoother(pc,nlevels-1,&ksp);KSPSetOperators(ksp,...); was not called.\n"));
908:       PetscCall(KSPSetOperators(mglevels[n - 1]->smoothd, pc->mat, pc->pmat));
909:     } else {
910:       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"));
911:       PetscCall(KSPSetOperators(mglevels[n - 1]->smoothd, pc->pmat, pc->pmat));
912:     }
913:   }

915:   for (i = n - 1; i > 0; i--) {
916:     if (!(mglevels[i]->interpolate || mglevels[i]->restrct)) {
917:       missinginterpolate = PETSC_TRUE;
918:       break;
919:     }
920:   }

922:   PetscCall(KSPGetOperators(mglevels[n - 1]->smoothd, &dA, &dB));
923:   if (dA == dB) dAeqdB = PETSC_TRUE;
924:   if (mg->galerkin == PC_MG_GALERKIN_NONE || ((mg->galerkin == PC_MG_GALERKIN_PMAT || mg->galerkin == PC_MG_GALERKIN_MAT) && !dAeqdB)) {
925:     needRestricts = PETSC_TRUE; /* user must compute either mat, pmat, or both so must restrict x to coarser levels */
926:   }

928:   if (pc->dm && !pc->setupcalled) {
929:     /* finest smoother also gets DM but it is not active, independent of whether galerkin==PC_MG_GALERKIN_EXTERNAL */
930:     PetscCall(KSPSetDM(mglevels[n - 1]->smoothd, pc->dm));
931:     PetscCall(KSPSetDMActive(mglevels[n - 1]->smoothd, PETSC_FALSE));
932:     if (mglevels[n - 1]->smoothd != mglevels[n - 1]->smoothu) {
933:       PetscCall(KSPSetDM(mglevels[n - 1]->smoothu, pc->dm));
934:       PetscCall(KSPSetDMActive(mglevels[n - 1]->smoothu, PETSC_FALSE));
935:     }
936:     if (mglevels[n - 1]->cr) {
937:       PetscCall(KSPSetDM(mglevels[n - 1]->cr, pc->dm));
938:       PetscCall(KSPSetDMActive(mglevels[n - 1]->cr, PETSC_FALSE));
939:     }
940:   }

942:   /*
943:    Skipping if user has provided all interpolation/restriction needed (since DM might not be able to produce them (when coming from SNES/TS)
944:    Skipping for externally managed hierarchy (such as ML and GAMG). Cleaner logic here would be great. Wrap ML/GAMG as DMs?
945:   */
946:   if (missinginterpolate && mg->galerkin != PC_MG_GALERKIN_EXTERNAL && !pc->setupcalled) {
947:     /* first see if we can compute a coarse space */
948:     if (mg->coarseSpaceType == PCMG_ADAPT_GDSW) {
949:       for (i = n - 2; i > -1; i--) {
950:         if (!mglevels[i + 1]->restrct && !mglevels[i + 1]->interpolate) {
951:           PetscCall(PCMGComputeCoarseSpace_Internal(pc, i + 1, mg->coarseSpaceType, mg->Nc, NULL, &mglevels[i + 1]->coarseSpace));
952:           PetscCall(PCMGSetInterpolation(pc, i + 1, mglevels[i + 1]->coarseSpace));
953:         }
954:       }
955:     } else { /* construct the interpolation from the DMs */
956:       Mat p;
957:       Vec rscale;
958:       PetscCall(PetscMalloc1(n, &dms));
959:       dms[n - 1] = pc->dm;
960:       /* Separately create them so we do not get DMKSP interference between levels */
961:       for (i = n - 2; i > -1; i--) PetscCall(DMCoarsen(dms[i + 1], MPI_COMM_NULL, &dms[i]));
962:       for (i = n - 2; i > -1; i--) {
963:         DMKSP     kdm;
964:         PetscBool dmhasrestrict, dmhasinject;
965:         PetscCall(KSPSetDM(mglevels[i]->smoothd, dms[i]));
966:         if (!needRestricts) PetscCall(KSPSetDMActive(mglevels[i]->smoothd, PETSC_FALSE));
967:         if (mglevels[i]->smoothd != mglevels[i]->smoothu) {
968:           PetscCall(KSPSetDM(mglevels[i]->smoothu, dms[i]));
969:           if (!needRestricts) PetscCall(KSPSetDMActive(mglevels[i]->smoothu, PETSC_FALSE));
970:         }
971:         if (mglevels[i]->cr) {
972:           PetscCall(KSPSetDM(mglevels[i]->cr, dms[i]));
973:           if (!needRestricts) PetscCall(KSPSetDMActive(mglevels[i]->cr, PETSC_FALSE));
974:         }
975:         PetscCall(DMGetDMKSPWrite(dms[i], &kdm));
976:         /* Ugly hack so that the next KSPSetUp() will use the RHS that we set. A better fix is to change dmActive to take
977:          * a bitwise OR of computing the matrix, RHS, and initial iterate. */
978:         kdm->ops->computerhs = NULL;
979:         kdm->rhsctx          = NULL;
980:         if (!mglevels[i + 1]->interpolate) {
981:           PetscCall(DMCreateInterpolation(dms[i], dms[i + 1], &p, &rscale));
982:           PetscCall(PCMGSetInterpolation(pc, i + 1, p));
983:           if (rscale) PetscCall(PCMGSetRScale(pc, i + 1, rscale));
984:           PetscCall(VecDestroy(&rscale));
985:           PetscCall(MatDestroy(&p));
986:         }
987:         PetscCall(DMHasCreateRestriction(dms[i], &dmhasrestrict));
988:         if (dmhasrestrict && !mglevels[i + 1]->restrct) {
989:           PetscCall(DMCreateRestriction(dms[i], dms[i + 1], &p));
990:           PetscCall(PCMGSetRestriction(pc, i + 1, p));
991:           PetscCall(MatDestroy(&p));
992:         }
993:         PetscCall(DMHasCreateInjection(dms[i], &dmhasinject));
994:         if (dmhasinject && !mglevels[i + 1]->inject) {
995:           PetscCall(DMCreateInjection(dms[i], dms[i + 1], &p));
996:           PetscCall(PCMGSetInjection(pc, i + 1, p));
997:           PetscCall(MatDestroy(&p));
998:         }
999:       }

1001:       for (i = n - 2; i > -1; i--) PetscCall(DMDestroy(&dms[i]));
1002:       PetscCall(PetscFree(dms));
1003:     }
1004:   }

1006:   if (mg->galerkin < PC_MG_GALERKIN_NONE) {
1007:     Mat       A, B;
1008:     PetscBool doA = PETSC_FALSE, doB = PETSC_FALSE;
1009:     MatReuse  reuse = MAT_INITIAL_MATRIX;

1011:     if (mg->galerkin == PC_MG_GALERKIN_PMAT || mg->galerkin == PC_MG_GALERKIN_BOTH) doB = PETSC_TRUE;
1012:     if (mg->galerkin == PC_MG_GALERKIN_MAT || (mg->galerkin == PC_MG_GALERKIN_BOTH && dA != dB)) doA = PETSC_TRUE;
1013:     if (pc->setupcalled) reuse = MAT_REUSE_MATRIX;
1014:     for (i = n - 2; i > -1; i--) {
1015:       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");
1016:       if (!mglevels[i + 1]->interpolate) PetscCall(PCMGSetInterpolation(pc, i + 1, mglevels[i + 1]->restrct));
1017:       if (!mglevels[i + 1]->restrct) PetscCall(PCMGSetRestriction(pc, i + 1, mglevels[i + 1]->interpolate));
1018:       if (reuse == MAT_REUSE_MATRIX) PetscCall(KSPGetOperators(mglevels[i]->smoothd, &A, &B));
1019:       if (doA) PetscCall(MatGalerkin(mglevels[i + 1]->restrct, dA, mglevels[i + 1]->interpolate, reuse, 1.0, &A));
1020:       if (doB) PetscCall(MatGalerkin(mglevels[i + 1]->restrct, dB, mglevels[i + 1]->interpolate, reuse, 1.0, &B));
1021:       /* the management of the PetscObjectReference() and PetscObjecDereference() below is rather delicate */
1022:       if (!doA && dAeqdB) {
1023:         if (reuse == MAT_INITIAL_MATRIX) PetscCall(PetscObjectReference((PetscObject)B));
1024:         A = B;
1025:       } else if (!doA && reuse == MAT_INITIAL_MATRIX) {
1026:         PetscCall(KSPGetOperators(mglevels[i]->smoothd, &A, NULL));
1027:         PetscCall(PetscObjectReference((PetscObject)A));
1028:       }
1029:       if (!doB && dAeqdB) {
1030:         if (reuse == MAT_INITIAL_MATRIX) PetscCall(PetscObjectReference((PetscObject)A));
1031:         B = A;
1032:       } else if (!doB && reuse == MAT_INITIAL_MATRIX) {
1033:         PetscCall(KSPGetOperators(mglevels[i]->smoothd, NULL, &B));
1034:         PetscCall(PetscObjectReference((PetscObject)B));
1035:       }
1036:       if (reuse == MAT_INITIAL_MATRIX) {
1037:         PetscCall(KSPSetOperators(mglevels[i]->smoothd, A, B));
1038:         PetscCall(PetscObjectDereference((PetscObject)A));
1039:         PetscCall(PetscObjectDereference((PetscObject)B));
1040:       }
1041:       dA = A;
1042:       dB = B;
1043:     }
1044:   }

1046:   /* Adapt interpolation matrices */
1047:   if (adaptInterpolation) {
1048:     for (i = 0; i < n; ++i) {
1049:       if (!mglevels[i]->coarseSpace) PetscCall(PCMGComputeCoarseSpace_Internal(pc, i, mg->coarseSpaceType, mg->Nc, !i ? NULL : mglevels[i - 1]->coarseSpace, &mglevels[i]->coarseSpace));
1050:       if (i) PetscCall(PCMGAdaptInterpolator_Internal(pc, i, mglevels[i - 1]->smoothu, mglevels[i]->smoothu, mglevels[i - 1]->coarseSpace, mglevels[i]->coarseSpace));
1051:     }
1052:     for (i = n - 2; i > -1; --i) PetscCall(PCMGRecomputeLevelOperators_Internal(pc, i));
1053:   }

1055:   if (needRestricts && pc->dm) {
1056:     for (i = n - 2; i >= 0; i--) {
1057:       DM  dmfine, dmcoarse;
1058:       Mat Restrict, Inject;
1059:       Vec rscale;
1060:       PetscCall(KSPGetDM(mglevels[i + 1]->smoothd, &dmfine));
1061:       PetscCall(KSPGetDM(mglevels[i]->smoothd, &dmcoarse));
1062:       PetscCall(PCMGGetRestriction(pc, i + 1, &Restrict));
1063:       PetscCall(PCMGGetRScale(pc, i + 1, &rscale));
1064:       PetscCall(PCMGGetInjection(pc, i + 1, &Inject));
1065:       PetscCall(DMRestrict(dmfine, Restrict, rscale, Inject, dmcoarse));
1066:     }
1067:   }

1069:   if (!pc->setupcalled) {
1070:     for (i = 0; i < n; i++) PetscCall(KSPSetFromOptions(mglevels[i]->smoothd));
1071:     for (i = 1; i < n; i++) {
1072:       if (mglevels[i]->smoothu && (mglevels[i]->smoothu != mglevels[i]->smoothd)) PetscCall(KSPSetFromOptions(mglevels[i]->smoothu));
1073:       if (mglevels[i]->cr) PetscCall(KSPSetFromOptions(mglevels[i]->cr));
1074:     }
1075:     /* insure that if either interpolation or restriction is set the other other one is set */
1076:     for (i = 1; i < n; i++) {
1077:       PetscCall(PCMGGetInterpolation(pc, i, NULL));
1078:       PetscCall(PCMGGetRestriction(pc, i, NULL));
1079:     }
1080:     for (i = 0; i < n - 1; i++) {
1081:       if (!mglevels[i]->b) {
1082:         Vec *vec;
1083:         PetscCall(KSPCreateVecs(mglevels[i]->smoothd, 1, &vec, 0, NULL));
1084:         PetscCall(PCMGSetRhs(pc, i, *vec));
1085:         PetscCall(VecDestroy(vec));
1086:         PetscCall(PetscFree(vec));
1087:       }
1088:       if (!mglevels[i]->r && i) {
1089:         PetscCall(VecDuplicate(mglevels[i]->b, &tvec));
1090:         PetscCall(PCMGSetR(pc, i, tvec));
1091:         PetscCall(VecDestroy(&tvec));
1092:       }
1093:       if (!mglevels[i]->x) {
1094:         PetscCall(VecDuplicate(mglevels[i]->b, &tvec));
1095:         PetscCall(PCMGSetX(pc, i, tvec));
1096:         PetscCall(VecDestroy(&tvec));
1097:       }
1098:       if (doCR) {
1099:         PetscCall(VecDuplicate(mglevels[i]->b, &mglevels[i]->crx));
1100:         PetscCall(VecDuplicate(mglevels[i]->b, &mglevels[i]->crb));
1101:         PetscCall(VecZeroEntries(mglevels[i]->crb));
1102:       }
1103:     }
1104:     if (n != 1 && !mglevels[n - 1]->r) {
1105:       /* PCMGSetR() on the finest level if user did not supply it */
1106:       Vec *vec;
1107:       PetscCall(KSPCreateVecs(mglevels[n - 1]->smoothd, 1, &vec, 0, NULL));
1108:       PetscCall(PCMGSetR(pc, n - 1, *vec));
1109:       PetscCall(VecDestroy(vec));
1110:       PetscCall(PetscFree(vec));
1111:     }
1112:     if (doCR) {
1113:       PetscCall(VecDuplicate(mglevels[n - 1]->r, &mglevels[n - 1]->crx));
1114:       PetscCall(VecDuplicate(mglevels[n - 1]->r, &mglevels[n - 1]->crb));
1115:       PetscCall(VecZeroEntries(mglevels[n - 1]->crb));
1116:     }
1117:   }

1119:   if (pc->dm) {
1120:     /* need to tell all the coarser levels to rebuild the matrix using the DM for that level */
1121:     for (i = 0; i < n - 1; i++) {
1122:       if (mglevels[i]->smoothd->setupstage != KSP_SETUP_NEW) mglevels[i]->smoothd->setupstage = KSP_SETUP_NEWMATRIX;
1123:     }
1124:   }
1125:   // We got here (PCSetUp_MG) because the matrix has changed, which means the smoother needs to be set up again (e.g.,
1126:   // new diagonal for Jacobi). Setting it here allows it to be logged under PCSetUp rather than deep inside a PCApply.
1127:   if (mglevels[n - 1]->smoothd->setupstage != KSP_SETUP_NEW) mglevels[n - 1]->smoothd->setupstage = KSP_SETUP_NEWMATRIX;

1129:   for (i = 1; i < n; i++) {
1130:     if (mglevels[i]->smoothu == mglevels[i]->smoothd || mg->am == PC_MG_FULL || mg->am == PC_MG_KASKADE || mg->cyclesperpcapply > 1) {
1131:       /* if doing only down then initial guess is zero */
1132:       PetscCall(KSPSetInitialGuessNonzero(mglevels[i]->smoothd, PETSC_TRUE));
1133:     }
1134:     if (mglevels[i]->cr) PetscCall(KSPSetInitialGuessNonzero(mglevels[i]->cr, PETSC_TRUE));
1135:     if (mglevels[i]->eventsmoothsetup) PetscCall(PetscLogEventBegin(mglevels[i]->eventsmoothsetup, 0, 0, 0, 0));
1136:     PetscCall(KSPSetUp(mglevels[i]->smoothd));
1137:     if (mglevels[i]->smoothd->reason) pc->failedreason = PC_SUBPC_ERROR;
1138:     if (mglevels[i]->eventsmoothsetup) PetscCall(PetscLogEventEnd(mglevels[i]->eventsmoothsetup, 0, 0, 0, 0));
1139:     if (!mglevels[i]->residual) {
1140:       Mat mat;
1141:       PetscCall(KSPGetOperators(mglevels[i]->smoothd, &mat, NULL));
1142:       PetscCall(PCMGSetResidual(pc, i, PCMGResidualDefault, mat));
1143:     }
1144:     if (!mglevels[i]->residualtranspose) {
1145:       Mat mat;
1146:       PetscCall(KSPGetOperators(mglevels[i]->smoothd, &mat, NULL));
1147:       PetscCall(PCMGSetResidualTranspose(pc, i, PCMGResidualTransposeDefault, mat));
1148:     }
1149:   }
1150:   for (i = 1; i < n; i++) {
1151:     if (mglevels[i]->smoothu && mglevels[i]->smoothu != mglevels[i]->smoothd) {
1152:       Mat downmat, downpmat;

1154:       /* check if operators have been set for up, if not use down operators to set them */
1155:       PetscCall(KSPGetOperatorsSet(mglevels[i]->smoothu, &opsset, NULL));
1156:       if (!opsset) {
1157:         PetscCall(KSPGetOperators(mglevels[i]->smoothd, &downmat, &downpmat));
1158:         PetscCall(KSPSetOperators(mglevels[i]->smoothu, downmat, downpmat));
1159:       }

1161:       PetscCall(KSPSetInitialGuessNonzero(mglevels[i]->smoothu, PETSC_TRUE));
1162:       if (mglevels[i]->eventsmoothsetup) PetscCall(PetscLogEventBegin(mglevels[i]->eventsmoothsetup, 0, 0, 0, 0));
1163:       PetscCall(KSPSetUp(mglevels[i]->smoothu));
1164:       if (mglevels[i]->smoothu->reason) pc->failedreason = PC_SUBPC_ERROR;
1165:       if (mglevels[i]->eventsmoothsetup) PetscCall(PetscLogEventEnd(mglevels[i]->eventsmoothsetup, 0, 0, 0, 0));
1166:     }
1167:     if (mglevels[i]->cr) {
1168:       Mat downmat, downpmat;

1170:       /* check if operators have been set for up, if not use down operators to set them */
1171:       PetscCall(KSPGetOperatorsSet(mglevels[i]->cr, &opsset, NULL));
1172:       if (!opsset) {
1173:         PetscCall(KSPGetOperators(mglevels[i]->smoothd, &downmat, &downpmat));
1174:         PetscCall(KSPSetOperators(mglevels[i]->cr, downmat, downpmat));
1175:       }

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

1185:   if (mglevels[0]->eventsmoothsetup) PetscCall(PetscLogEventBegin(mglevels[0]->eventsmoothsetup, 0, 0, 0, 0));
1186:   PetscCall(KSPSetUp(mglevels[0]->smoothd));
1187:   if (mglevels[0]->smoothd->reason) pc->failedreason = PC_SUBPC_ERROR;
1188:   if (mglevels[0]->eventsmoothsetup) PetscCall(PetscLogEventEnd(mglevels[0]->eventsmoothsetup, 0, 0, 0, 0));

1190:     /*
1191:      Dump the interpolation/restriction matrices plus the
1192:    Jacobian/stiffness on each level. This allows MATLAB users to
1193:    easily check if the Galerkin condition A_c = R A_f R^T is satisfied.

1195:    Only support one or the other at the same time.
1196:   */
1197: #if defined(PETSC_USE_SOCKET_VIEWER)
1198:   PetscCall(PetscOptionsGetBool(((PetscObject)pc)->options, ((PetscObject)pc)->prefix, "-pc_mg_dump_matlab", &dump, NULL));
1199:   if (dump) viewer = PETSC_VIEWER_SOCKET_(PetscObjectComm((PetscObject)pc));
1200:   dump = PETSC_FALSE;
1201: #endif
1202:   PetscCall(PetscOptionsGetBool(((PetscObject)pc)->options, ((PetscObject)pc)->prefix, "-pc_mg_dump_binary", &dump, NULL));
1203:   if (dump) viewer = PETSC_VIEWER_BINARY_(PetscObjectComm((PetscObject)pc));

1205:   if (viewer) {
1206:     for (i = 1; i < n; i++) PetscCall(MatView(mglevels[i]->restrct, viewer));
1207:     for (i = 0; i < n; i++) {
1208:       PetscCall(KSPGetPC(mglevels[i]->smoothd, &pc));
1209:       PetscCall(MatView(pc->mat, viewer));
1210:     }
1211:   }
1212:   PetscFunctionReturn(PETSC_SUCCESS);
1213: }

1215: PetscErrorCode PCMGGetLevels_MG(PC pc, PetscInt *levels)
1216: {
1217:   PC_MG *mg = (PC_MG *)pc->data;

1219:   PetscFunctionBegin;
1220:   *levels = mg->nlevels;
1221:   PetscFunctionReturn(PETSC_SUCCESS);
1222: }

1224: /*@
1225:   PCMGGetLevels - Gets the number of levels to use with `PCMG`.

1227:   Not Collective

1229:   Input Parameter:
1230: . pc - the preconditioner context

1232:   Output Parameter:
1233: . levels - the number of levels

1235:   Level: advanced

1237: .seealso: [](ch_ksp), `PCMG`, `PCMGSetLevels()`
1238: @*/
1239: PetscErrorCode PCMGGetLevels(PC pc, PetscInt *levels)
1240: {
1241:   PetscFunctionBegin;
1243:   PetscAssertPointer(levels, 2);
1244:   *levels = 0;
1245:   PetscTryMethod(pc, "PCMGGetLevels_C", (PC, PetscInt *), (pc, levels));
1246:   PetscFunctionReturn(PETSC_SUCCESS);
1247: }

1249: /*@
1250:   PCMGGetGridComplexity - compute operator and grid complexity of the `PCMG` hierarchy

1252:   Input Parameter:
1253: . pc - the preconditioner context

1255:   Output Parameters:
1256: + gc - grid complexity = sum_i(n_i) / n_0
1257: - oc - operator complexity = sum_i(nnz_i) / nnz_0

1259:   Level: advanced

1261:   Note:
1262:   This is often call the operator complexity in multigrid literature

1264: .seealso: [](ch_ksp), `PCMG`, `PCMGGetLevels()`, `PCMGSetLevels()`
1265: @*/
1266: PetscErrorCode PCMGGetGridComplexity(PC pc, PetscReal *gc, PetscReal *oc)
1267: {
1268:   PC_MG         *mg       = (PC_MG *)pc->data;
1269:   PC_MG_Levels **mglevels = mg->levels;
1270:   PetscInt       lev, N;
1271:   PetscLogDouble nnz0 = 0, sgc = 0, soc = 0, n0 = 0;
1272:   MatInfo        info;

1274:   PetscFunctionBegin;
1276:   if (gc) PetscAssertPointer(gc, 2);
1277:   if (oc) PetscAssertPointer(oc, 3);
1278:   if (!pc->setupcalled) {
1279:     if (gc) *gc = 0;
1280:     if (oc) *oc = 0;
1281:     PetscFunctionReturn(PETSC_SUCCESS);
1282:   }
1283:   PetscCheck(mg->nlevels > 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MG has no levels");
1284:   for (lev = 0; lev < mg->nlevels; lev++) {
1285:     Mat dB;
1286:     PetscCall(KSPGetOperators(mglevels[lev]->smoothd, NULL, &dB));
1287:     PetscCall(MatGetInfo(dB, MAT_GLOBAL_SUM, &info)); /* global reduction */
1288:     PetscCall(MatGetSize(dB, &N, NULL));
1289:     sgc += N;
1290:     soc += info.nz_used;
1291:     if (lev == mg->nlevels - 1) {
1292:       nnz0 = info.nz_used;
1293:       n0   = N;
1294:     }
1295:   }
1296:   PetscCheck(n0 > 0 && gc, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Number for grid points on finest level is not available");
1297:   *gc = (PetscReal)(sgc / n0);
1298:   if (nnz0 > 0 && oc) *oc = (PetscReal)(soc / nnz0);
1299:   PetscFunctionReturn(PETSC_SUCCESS);
1300: }

1302: /*@
1303:   PCMGSetType - Determines the form of multigrid to use, either
1304:   multiplicative, additive, full, or the Kaskade algorithm.

1306:   Logically Collective

1308:   Input Parameters:
1309: + pc   - the preconditioner context
1310: - form - multigrid form, one of `PC_MG_MULTIPLICATIVE`, `PC_MG_ADDITIVE`, `PC_MG_FULL`, `PC_MG_KASKADE`

1312:   Options Database Key:
1313: . -pc_mg_type <form> - Sets <form>, one of multiplicative, additive, full, kaskade

1315:   Level: advanced

1317: .seealso: [](ch_ksp), `PCMGType`, `PCMG`, `PCMGGetLevels()`, `PCMGSetLevels()`, `PCMGGetType()`, `PCMGCycleType`
1318: @*/
1319: PetscErrorCode PCMGSetType(PC pc, PCMGType form)
1320: {
1321:   PC_MG *mg = (PC_MG *)pc->data;

1323:   PetscFunctionBegin;
1326:   mg->am = form;
1327:   if (form == PC_MG_MULTIPLICATIVE) pc->ops->applyrichardson = PCApplyRichardson_MG;
1328:   else pc->ops->applyrichardson = NULL;
1329:   PetscFunctionReturn(PETSC_SUCCESS);
1330: }

1332: /*@
1333:   PCMGGetType - Finds the form of multigrid the `PCMG` is using  multiplicative, additive, full, or the Kaskade algorithm.

1335:   Logically Collective

1337:   Input Parameter:
1338: . pc - the preconditioner context

1340:   Output Parameter:
1341: . type - one of `PC_MG_MULTIPLICATIVE`, `PC_MG_ADDITIVE`, `PC_MG_FULL`, `PC_MG_KASKADE`, `PCMGCycleType`

1343:   Level: advanced

1345: .seealso: [](ch_ksp), `PCMGType`, `PCMG`, `PCMGGetLevels()`, `PCMGSetLevels()`, `PCMGSetType()`
1346: @*/
1347: PetscErrorCode PCMGGetType(PC pc, PCMGType *type)
1348: {
1349:   PC_MG *mg = (PC_MG *)pc->data;

1351:   PetscFunctionBegin;
1353:   *type = mg->am;
1354:   PetscFunctionReturn(PETSC_SUCCESS);
1355: }

1357: /*@
1358:   PCMGSetCycleType - Sets the type cycles to use.  Use `PCMGSetCycleTypeOnLevel()` for more
1359:   complicated cycling.

1361:   Logically Collective

1363:   Input Parameters:
1364: + pc - the multigrid context
1365: - n  - either `PC_MG_CYCLE_V` or `PC_MG_CYCLE_W`

1367:   Options Database Key:
1368: . -pc_mg_cycle_type <v,w> - provide the cycle desired

1370:   Level: advanced

1372: .seealso: [](ch_ksp), `PCMG`, `PCMGSetCycleTypeOnLevel()`, `PCMGType`, `PCMGCycleType`
1373: @*/
1374: PetscErrorCode PCMGSetCycleType(PC pc, PCMGCycleType n)
1375: {
1376:   PC_MG         *mg       = (PC_MG *)pc->data;
1377:   PC_MG_Levels **mglevels = mg->levels;
1378:   PetscInt       i, levels;

1380:   PetscFunctionBegin;
1383:   PetscCheck(mglevels, PetscObjectComm((PetscObject)pc), PETSC_ERR_ORDER, "Must set MG levels with PCMGSetLevels() before calling");
1384:   levels = mglevels[0]->levels;
1385:   for (i = 0; i < levels; i++) mglevels[i]->cycles = n;
1386:   PetscFunctionReturn(PETSC_SUCCESS);
1387: }

1389: /*@
1390:   PCMGMultiplicativeSetCycles - Sets the number of cycles to use for each preconditioner step
1391:   of multigrid when `PCMGType` is `PC_MG_MULTIPLICATIVE`

1393:   Logically Collective

1395:   Input Parameters:
1396: + pc - the multigrid context
1397: - n  - number of cycles (default is 1)

1399:   Options Database Key:
1400: . -pc_mg_multiplicative_cycles n - set the number of cycles

1402:   Level: advanced

1404:   Note:
1405:   This is not associated with setting a v or w cycle, that is set with `PCMGSetCycleType()`

1407: .seealso: [](ch_ksp), `PCMGSetCycleTypeOnLevel()`, `PCMGSetCycleType()`, `PCMGCycleType`, `PCMGType`
1408: @*/
1409: PetscErrorCode PCMGMultiplicativeSetCycles(PC pc, PetscInt n)
1410: {
1411:   PC_MG *mg = (PC_MG *)pc->data;

1413:   PetscFunctionBegin;
1416:   mg->cyclesperpcapply = n;
1417:   PetscFunctionReturn(PETSC_SUCCESS);
1418: }

1420: static PetscErrorCode PCMGSetGalerkin_MG(PC pc, PCMGGalerkinType use)
1421: {
1422:   PC_MG *mg = (PC_MG *)pc->data;

1424:   PetscFunctionBegin;
1425:   mg->galerkin = use;
1426:   PetscFunctionReturn(PETSC_SUCCESS);
1427: }

1429: /*@
1430:   PCMGSetGalerkin - Causes the coarser grid matrices to be computed from the
1431:   finest grid via the Galerkin process: A_i-1 = r_i * A_i * p_i

1433:   Logically Collective

1435:   Input Parameters:
1436: + pc  - the multigrid context
1437: - use - one of `PC_MG_GALERKIN_BOTH`, `PC_MG_GALERKIN_PMAT`, `PC_MG_GALERKIN_MAT`, or `PC_MG_GALERKIN_NONE`

1439:   Options Database Key:
1440: . -pc_mg_galerkin <both,pmat,mat,none> - set the matrices to form via the Galerkin process

1442:   Level: intermediate

1444:   Note:
1445:   Some codes that use `PCMG` such as `PCGAMG` use Galerkin internally while constructing the hierarchy and thus do not
1446:   use the `PCMG` construction of the coarser grids.

1448: .seealso: [](ch_ksp), `PCMG`, `PCMGGetGalerkin()`, `PCMGGalerkinType`
1449: @*/
1450: PetscErrorCode PCMGSetGalerkin(PC pc, PCMGGalerkinType use)
1451: {
1452:   PetscFunctionBegin;
1454:   PetscTryMethod(pc, "PCMGSetGalerkin_C", (PC, PCMGGalerkinType), (pc, use));
1455:   PetscFunctionReturn(PETSC_SUCCESS);
1456: }

1458: /*@
1459:   PCMGGetGalerkin - Checks if Galerkin multigrid is being used, i.e. A_i-1 = r_i * A_i * p_i

1461:   Not Collective

1463:   Input Parameter:
1464: . pc - the multigrid context

1466:   Output Parameter:
1467: . galerkin - one of `PC_MG_GALERKIN_BOTH`,`PC_MG_GALERKIN_PMAT`,`PC_MG_GALERKIN_MAT`, `PC_MG_GALERKIN_NONE`, or `PC_MG_GALERKIN_EXTERNAL`

1469:   Level: intermediate

1471: .seealso: [](ch_ksp), `PCMG`, `PCMGSetGalerkin()`, `PCMGGalerkinType`
1472: @*/
1473: PetscErrorCode PCMGGetGalerkin(PC pc, PCMGGalerkinType *galerkin)
1474: {
1475:   PC_MG *mg = (PC_MG *)pc->data;

1477:   PetscFunctionBegin;
1479:   *galerkin = mg->galerkin;
1480:   PetscFunctionReturn(PETSC_SUCCESS);
1481: }

1483: static PetscErrorCode PCMGSetAdaptInterpolation_MG(PC pc, PetscBool adapt)
1484: {
1485:   PC_MG *mg = (PC_MG *)pc->data;

1487:   PetscFunctionBegin;
1488:   mg->adaptInterpolation = adapt;
1489:   PetscFunctionReturn(PETSC_SUCCESS);
1490: }

1492: static PetscErrorCode PCMGGetAdaptInterpolation_MG(PC pc, PetscBool *adapt)
1493: {
1494:   PC_MG *mg = (PC_MG *)pc->data;

1496:   PetscFunctionBegin;
1497:   *adapt = mg->adaptInterpolation;
1498:   PetscFunctionReturn(PETSC_SUCCESS);
1499: }

1501: static PetscErrorCode PCMGSetAdaptCoarseSpaceType_MG(PC pc, PCMGCoarseSpaceType ctype)
1502: {
1503:   PC_MG *mg = (PC_MG *)pc->data;

1505:   PetscFunctionBegin;
1506:   mg->adaptInterpolation = ctype != PCMG_ADAPT_NONE ? PETSC_TRUE : PETSC_FALSE;
1507:   mg->coarseSpaceType    = ctype;
1508:   PetscFunctionReturn(PETSC_SUCCESS);
1509: }

1511: static PetscErrorCode PCMGGetAdaptCoarseSpaceType_MG(PC pc, PCMGCoarseSpaceType *ctype)
1512: {
1513:   PC_MG *mg = (PC_MG *)pc->data;

1515:   PetscFunctionBegin;
1516:   *ctype = mg->coarseSpaceType;
1517:   PetscFunctionReturn(PETSC_SUCCESS);
1518: }

1520: static PetscErrorCode PCMGSetAdaptCR_MG(PC pc, PetscBool cr)
1521: {
1522:   PC_MG *mg = (PC_MG *)pc->data;

1524:   PetscFunctionBegin;
1525:   mg->compatibleRelaxation = cr;
1526:   PetscFunctionReturn(PETSC_SUCCESS);
1527: }

1529: static PetscErrorCode PCMGGetAdaptCR_MG(PC pc, PetscBool *cr)
1530: {
1531:   PC_MG *mg = (PC_MG *)pc->data;

1533:   PetscFunctionBegin;
1534:   *cr = mg->compatibleRelaxation;
1535:   PetscFunctionReturn(PETSC_SUCCESS);
1536: }

1538: /*@C
1539:   PCMGSetAdaptCoarseSpaceType - Set the type of adaptive coarse space.

1541:   Adapts or creates the interpolator based upon a vector space which should be accurately captured by the next coarser mesh, and thus accurately interpolated.

1543:   Logically Collective

1545:   Input Parameters:
1546: + pc    - the multigrid context
1547: - ctype - the type of coarse space

1549:   Options Database Keys:
1550: + -pc_mg_adapt_interp_n <int>             - The number of modes to use
1551: - -pc_mg_adapt_interp_coarse_space <type> - The type of coarse space: none, polynomial, harmonic, eigenvector, generalized_eigenvector, gdsw

1553:   Level: intermediate

1555: .seealso: [](ch_ksp), `PCMG`, `PCMGCoarseSpaceType`, `PCMGGetAdaptCoarseSpaceType()`, `PCMGSetGalerkin()`, `PCMGSetAdaptInterpolation()`
1556: @*/
1557: PetscErrorCode PCMGSetAdaptCoarseSpaceType(PC pc, PCMGCoarseSpaceType ctype)
1558: {
1559:   PetscFunctionBegin;
1562:   PetscTryMethod(pc, "PCMGSetAdaptCoarseSpaceType_C", (PC, PCMGCoarseSpaceType), (pc, ctype));
1563:   PetscFunctionReturn(PETSC_SUCCESS);
1564: }

1566: /*@C
1567:   PCMGGetAdaptCoarseSpaceType - Get the type of adaptive coarse space.

1569:   Not Collective

1571:   Input Parameter:
1572: . pc - the multigrid context

1574:   Output Parameter:
1575: . ctype - the type of coarse space

1577:   Level: intermediate

1579: .seealso: [](ch_ksp), `PCMG`, `PCMGCoarseSpaceType`, `PCMGSetAdaptCoarseSpaceType()`, `PCMGSetGalerkin()`, `PCMGSetAdaptInterpolation()`
1580: @*/
1581: PetscErrorCode PCMGGetAdaptCoarseSpaceType(PC pc, PCMGCoarseSpaceType *ctype)
1582: {
1583:   PetscFunctionBegin;
1585:   PetscAssertPointer(ctype, 2);
1586:   PetscUseMethod(pc, "PCMGGetAdaptCoarseSpaceType_C", (PC, PCMGCoarseSpaceType *), (pc, ctype));
1587:   PetscFunctionReturn(PETSC_SUCCESS);
1588: }

1590: /* MATT: REMOVE? */
1591: /*@
1592:   PCMGSetAdaptInterpolation - Adapt the interpolator based upon a vector space which should be accurately captured by the next coarser mesh, and thus accurately interpolated.

1594:   Logically Collective

1596:   Input Parameters:
1597: + pc    - the multigrid context
1598: - adapt - flag for adaptation of the interpolator

1600:   Options Database Keys:
1601: + -pc_mg_adapt_interp                     - Turn on adaptation
1602: . -pc_mg_adapt_interp_n <int>             - The number of modes to use, should be divisible by dimension
1603: - -pc_mg_adapt_interp_coarse_space <type> - The type of coarse space: polynomial, harmonic, eigenvector, generalized_eigenvector

1605:   Level: intermediate

1607: .seealso: [](ch_ksp), `PCMG`, `PCMGGetAdaptInterpolation()`, `PCMGSetGalerkin()`, `PCMGGetAdaptCoarseSpaceType()`, `PCMGSetAdaptCoarseSpaceType()`
1608: @*/
1609: PetscErrorCode PCMGSetAdaptInterpolation(PC pc, PetscBool adapt)
1610: {
1611:   PetscFunctionBegin;
1613:   PetscTryMethod(pc, "PCMGSetAdaptInterpolation_C", (PC, PetscBool), (pc, adapt));
1614:   PetscFunctionReturn(PETSC_SUCCESS);
1615: }

1617: /*@
1618:   PCMGGetAdaptInterpolation - Get the flag to adapt the interpolator based upon a vector space which should be accurately captured by the next coarser mesh,
1619:   and thus accurately interpolated.

1621:   Not Collective

1623:   Input Parameter:
1624: . pc - the multigrid context

1626:   Output Parameter:
1627: . adapt - flag for adaptation of the interpolator

1629:   Level: intermediate

1631: .seealso: [](ch_ksp), `PCMG`, `PCMGSetAdaptInterpolation()`, `PCMGSetGalerkin()`, `PCMGGetAdaptCoarseSpaceType()`, `PCMGSetAdaptCoarseSpaceType()`
1632: @*/
1633: PetscErrorCode PCMGGetAdaptInterpolation(PC pc, PetscBool *adapt)
1634: {
1635:   PetscFunctionBegin;
1637:   PetscAssertPointer(adapt, 2);
1638:   PetscUseMethod(pc, "PCMGGetAdaptInterpolation_C", (PC, PetscBool *), (pc, adapt));
1639:   PetscFunctionReturn(PETSC_SUCCESS);
1640: }

1642: /*@
1643:   PCMGSetAdaptCR - Monitor the coarse space quality using an auxiliary solve with compatible relaxation.

1645:   Logically Collective

1647:   Input Parameters:
1648: + pc - the multigrid context
1649: - cr - flag for compatible relaxation

1651:   Options Database Key:
1652: . -pc_mg_adapt_cr - Turn on compatible relaxation

1654:   Level: intermediate

1656: .seealso: [](ch_ksp), `PCMG`, `PCMGGetAdaptCR()`, `PCMGSetAdaptInterpolation()`, `PCMGSetGalerkin()`, `PCMGGetAdaptCoarseSpaceType()`, `PCMGSetAdaptCoarseSpaceType()`
1657: @*/
1658: PetscErrorCode PCMGSetAdaptCR(PC pc, PetscBool cr)
1659: {
1660:   PetscFunctionBegin;
1662:   PetscTryMethod(pc, "PCMGSetAdaptCR_C", (PC, PetscBool), (pc, cr));
1663:   PetscFunctionReturn(PETSC_SUCCESS);
1664: }

1666: /*@
1667:   PCMGGetAdaptCR - Get the flag to monitor coarse space quality using an auxiliary solve with compatible relaxation.

1669:   Not Collective

1671:   Input Parameter:
1672: . pc - the multigrid context

1674:   Output Parameter:
1675: . cr - flag for compatible relaxaion

1677:   Level: intermediate

1679: .seealso: [](ch_ksp), `PCMGSetAdaptCR()`, `PCMGGetAdaptInterpolation()`, `PCMGSetGalerkin()`, `PCMGGetAdaptCoarseSpaceType()`, `PCMGSetAdaptCoarseSpaceType()`
1680: @*/
1681: PetscErrorCode PCMGGetAdaptCR(PC pc, PetscBool *cr)
1682: {
1683:   PetscFunctionBegin;
1685:   PetscAssertPointer(cr, 2);
1686:   PetscUseMethod(pc, "PCMGGetAdaptCR_C", (PC, PetscBool *), (pc, cr));
1687:   PetscFunctionReturn(PETSC_SUCCESS);
1688: }

1690: /*@
1691:   PCMGSetNumberSmooth - Sets the number of pre and post-smoothing steps to use
1692:   on all levels.  Use `PCMGDistinctSmoothUp()` to create separate up and down smoothers if you want different numbers of
1693:   pre- and post-smoothing steps.

1695:   Logically Collective

1697:   Input Parameters:
1698: + pc - the multigrid context
1699: - n  - the number of smoothing steps

1701:   Options Database Key:
1702: . -mg_levels_ksp_max_it <n> - Sets number of pre and post-smoothing steps

1704:   Level: advanced

1706:   Note:
1707:   This does not set a value on the coarsest grid, since we assume that there is no separate smooth up on the coarsest grid.

1709: .seealso: [](ch_ksp), `PCMG`, `PCMGSetDistinctSmoothUp()`
1710: @*/
1711: PetscErrorCode PCMGSetNumberSmooth(PC pc, PetscInt n)
1712: {
1713:   PC_MG         *mg       = (PC_MG *)pc->data;
1714:   PC_MG_Levels **mglevels = mg->levels;
1715:   PetscInt       i, levels;

1717:   PetscFunctionBegin;
1720:   PetscCheck(mglevels, PetscObjectComm((PetscObject)pc), PETSC_ERR_ORDER, "Must set MG levels with PCMGSetLevels() before calling");
1721:   levels = mglevels[0]->levels;

1723:   for (i = 1; i < levels; i++) {
1724:     PetscCall(KSPSetTolerances(mglevels[i]->smoothu, PETSC_DEFAULT, PETSC_DEFAULT, PETSC_DEFAULT, n));
1725:     PetscCall(KSPSetTolerances(mglevels[i]->smoothd, PETSC_DEFAULT, PETSC_DEFAULT, PETSC_DEFAULT, n));
1726:     mg->default_smoothu = n;
1727:     mg->default_smoothd = n;
1728:   }
1729:   PetscFunctionReturn(PETSC_SUCCESS);
1730: }

1732: /*@
1733:   PCMGSetDistinctSmoothUp - sets the up (post) smoother to be a separate `KSP` from the down (pre) smoother on all levels
1734:   and adds the suffix _up to the options name

1736:   Logically Collective

1738:   Input Parameter:
1739: . pc - the preconditioner context

1741:   Options Database Key:
1742: . -pc_mg_distinct_smoothup <bool> - use distinct smoothing objects

1744:   Level: advanced

1746:   Note:
1747:   This does not set a value on the coarsest grid, since we assume that there is no separate smooth up on the coarsest grid.

1749: .seealso: [](ch_ksp), `PCMG`, `PCMGSetNumberSmooth()`
1750: @*/
1751: PetscErrorCode PCMGSetDistinctSmoothUp(PC pc)
1752: {
1753:   PC_MG         *mg       = (PC_MG *)pc->data;
1754:   PC_MG_Levels **mglevels = mg->levels;
1755:   PetscInt       i, levels;
1756:   KSP            subksp;

1758:   PetscFunctionBegin;
1760:   PetscCheck(mglevels, PetscObjectComm((PetscObject)pc), PETSC_ERR_ORDER, "Must set MG levels with PCMGSetLevels() before calling");
1761:   levels = mglevels[0]->levels;

1763:   for (i = 1; i < levels; i++) {
1764:     const char *prefix = NULL;
1765:     /* make sure smoother up and down are different */
1766:     PetscCall(PCMGGetSmootherUp(pc, i, &subksp));
1767:     PetscCall(KSPGetOptionsPrefix(mglevels[i]->smoothd, &prefix));
1768:     PetscCall(KSPSetOptionsPrefix(subksp, prefix));
1769:     PetscCall(KSPAppendOptionsPrefix(subksp, "up_"));
1770:   }
1771:   PetscFunctionReturn(PETSC_SUCCESS);
1772: }

1774: /* No new matrices are created, and the coarse operator matrices are the references to the original ones */
1775: static PetscErrorCode PCGetInterpolations_MG(PC pc, PetscInt *num_levels, Mat *interpolations[])
1776: {
1777:   PC_MG         *mg       = (PC_MG *)pc->data;
1778:   PC_MG_Levels **mglevels = mg->levels;
1779:   Mat           *mat;
1780:   PetscInt       l;

1782:   PetscFunctionBegin;
1783:   PetscCheck(mglevels, PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_WRONGSTATE, "Must set MG levels before calling");
1784:   PetscCall(PetscMalloc1(mg->nlevels, &mat));
1785:   for (l = 1; l < mg->nlevels; l++) {
1786:     mat[l - 1] = mglevels[l]->interpolate;
1787:     PetscCall(PetscObjectReference((PetscObject)mat[l - 1]));
1788:   }
1789:   *num_levels     = mg->nlevels;
1790:   *interpolations = mat;
1791:   PetscFunctionReturn(PETSC_SUCCESS);
1792: }

1794: /* No new matrices are created, and the coarse operator matrices are the references to the original ones */
1795: static PetscErrorCode PCGetCoarseOperators_MG(PC pc, PetscInt *num_levels, Mat *coarseOperators[])
1796: {
1797:   PC_MG         *mg       = (PC_MG *)pc->data;
1798:   PC_MG_Levels **mglevels = mg->levels;
1799:   PetscInt       l;
1800:   Mat           *mat;

1802:   PetscFunctionBegin;
1803:   PetscCheck(mglevels, PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_WRONGSTATE, "Must set MG levels before calling");
1804:   PetscCall(PetscMalloc1(mg->nlevels, &mat));
1805:   for (l = 0; l < mg->nlevels - 1; l++) {
1806:     PetscCall(KSPGetOperators(mglevels[l]->smoothd, NULL, &(mat[l])));
1807:     PetscCall(PetscObjectReference((PetscObject)mat[l]));
1808:   }
1809:   *num_levels      = mg->nlevels;
1810:   *coarseOperators = mat;
1811:   PetscFunctionReturn(PETSC_SUCCESS);
1812: }

1814: /*@C
1815:   PCMGRegisterCoarseSpaceConstructor -  Adds a method to the `PCMG` package for coarse space construction.

1817:   Not Collective

1819:   Input Parameters:
1820: + name     - name of the constructor
1821: - function - constructor routine

1823:   Calling sequence of `function`:
1824: + pc        - The `PC` object
1825: . l         - The multigrid level, 0 is the coarse level
1826: . dm        - The `DM` for this level
1827: . smooth    - The level smoother
1828: . Nc        - The size of the coarse space
1829: . initGuess - Basis for an initial guess for the space
1830: - coarseSp  - A basis for the computed coarse space

1832:   Level: advanced

1834:   Developer Notes:
1835:   How come this is not used by `PCGAMG`?

1837: .seealso: [](ch_ksp), `PCMG`, `PCMGGetCoarseSpaceConstructor()`, `PCRegister()`
1838: @*/
1839: PetscErrorCode PCMGRegisterCoarseSpaceConstructor(const char name[], PetscErrorCode (*function)(PC pc, PetscInt l, DM dm, KSP smooth, PetscInt Nc, Mat initGuess, Mat *coarseSp))
1840: {
1841:   PetscFunctionBegin;
1842:   PetscCall(PCInitializePackage());
1843:   PetscCall(PetscFunctionListAdd(&PCMGCoarseList, name, function));
1844:   PetscFunctionReturn(PETSC_SUCCESS);
1845: }

1847: /*@C
1848:   PCMGGetCoarseSpaceConstructor -  Returns the given coarse space construction method.

1850:   Not Collective

1852:   Input Parameter:
1853: . name - name of the constructor

1855:   Output Parameter:
1856: . function - constructor routine

1858:   Level: advanced

1860: .seealso: [](ch_ksp), `PCMG`, `PCMGRegisterCoarseSpaceConstructor()`, `PCRegister()`
1861: @*/
1862: PetscErrorCode PCMGGetCoarseSpaceConstructor(const char name[], PetscErrorCode (**function)(PC, PetscInt, DM, KSP, PetscInt, Mat, Mat *))
1863: {
1864:   PetscFunctionBegin;
1865:   PetscCall(PetscFunctionListFind(PCMGCoarseList, name, function));
1866:   PetscFunctionReturn(PETSC_SUCCESS);
1867: }

1869: /*MC
1870:    PCMG - Use multigrid preconditioning. This preconditioner requires you provide additional
1871:     information about the coarser grid matrices and restriction/interpolation operators.

1873:    Options Database Keys:
1874: +  -pc_mg_levels <nlevels> - number of levels including finest
1875: .  -pc_mg_cycle_type <v,w> - provide the cycle desired
1876: .  -pc_mg_type <additive,multiplicative,full,kaskade> - multiplicative is the default
1877: .  -pc_mg_log - log information about time spent on each level of the solver
1878: .  -pc_mg_distinct_smoothup - configure up (after interpolation) and down (before restriction) smoothers separately (with different options prefixes)
1879: .  -pc_mg_galerkin <both,pmat,mat,none> - use Galerkin process to compute coarser operators, i.e. Acoarse = R A R'
1880: .  -pc_mg_multiplicative_cycles - number of cycles to use as the preconditioner (defaults to 1)
1881: .  -pc_mg_dump_matlab - dumps the matrices for each level and the restriction/interpolation matrices
1882:                         to the Socket viewer for reading from MATLAB.
1883: -  -pc_mg_dump_binary - dumps the matrices for each level and the restriction/interpolation matrices
1884:                         to the binary output file called binaryoutput

1886:    Level: intermediate

1888:    Notes:
1889:    The Krylov solver (if any) and preconditioner (smoother) and their parameters are controlled from the options database with the standard
1890:    options database keywords prefixed with `-mg_levels_` to affect all the levels but the coarsest, which is controlled with `-mg_coarse_`.
1891:    One can set different preconditioners etc on specific levels with the prefix `-mg_levels_n_` where `n` is the level number (zero being
1892:    the coarse level. For example
1893: .vb
1894:    -mg_levels_ksp_type gmres -mg_levels_pc_type bjacobi -mg_coarse_pc_type svd -mg_levels_2_pc_type sor
1895: .ve
1896:    These options also work for controlling the smoothers etc inside `PCGAMG`

1898:    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

1900:    When run with a single level the smoother options are used on that level NOT the coarse grid solver options

1902:    When run with `KSPRICHARDSON` the convergence test changes slightly if monitor is turned on. The iteration count may change slightly. This
1903:    is because without monitoring the residual norm is computed WITHIN each multigrid cycle on the finest level after the pre-smoothing
1904:    (because the residual has just been computed for the multigrid algorithm and is hence available for free) while with monitoring the
1905:    residual is computed at the end of each cycle.

1907: .seealso: [](sec_mg), `PCCreate()`, `PCSetType()`, `PCType`, `PC`, `PCMGType`, `PCEXOTIC`, `PCGAMG`, `PCML`, `PCHYPRE`
1908:           `PCMGSetLevels()`, `PCMGGetLevels()`, `PCMGSetType()`, `PCMGSetCycleType()`,
1909:           `PCMGSetDistinctSmoothUp()`, `PCMGGetCoarseSolve()`, `PCMGSetResidual()`, `PCMGSetInterpolation()`,
1910:           `PCMGSetRestriction()`, `PCMGGetSmoother()`, `PCMGGetSmootherUp()`, `PCMGGetSmootherDown()`,
1911:           `PCMGSetCycleTypeOnLevel()`, `PCMGSetRhs()`, `PCMGSetX()`, `PCMGSetR()`,
1912:           `PCMGSetAdaptCR()`, `PCMGGetAdaptInterpolation()`, `PCMGSetGalerkin()`, `PCMGGetAdaptCoarseSpaceType()`, `PCMGSetAdaptCoarseSpaceType()`
1913: M*/

1915: PETSC_EXTERN PetscErrorCode PCCreate_MG(PC pc)
1916: {
1917:   PC_MG *mg;

1919:   PetscFunctionBegin;
1920:   PetscCall(PetscNew(&mg));
1921:   pc->data               = mg;
1922:   mg->nlevels            = -1;
1923:   mg->am                 = PC_MG_MULTIPLICATIVE;
1924:   mg->galerkin           = PC_MG_GALERKIN_NONE;
1925:   mg->adaptInterpolation = PETSC_FALSE;
1926:   mg->Nc                 = -1;
1927:   mg->eigenvalue         = -1;

1929:   pc->useAmat = PETSC_TRUE;

1931:   pc->ops->apply          = PCApply_MG;
1932:   pc->ops->applytranspose = PCApplyTranspose_MG;
1933:   pc->ops->matapply       = PCMatApply_MG;
1934:   pc->ops->setup          = PCSetUp_MG;
1935:   pc->ops->reset          = PCReset_MG;
1936:   pc->ops->destroy        = PCDestroy_MG;
1937:   pc->ops->setfromoptions = PCSetFromOptions_MG;
1938:   pc->ops->view           = PCView_MG;

1940:   PetscCall(PetscObjectComposedDataRegister(&mg->eigenvalue));
1941:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetGalerkin_C", PCMGSetGalerkin_MG));
1942:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetLevels_C", PCMGGetLevels_MG));
1943:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetLevels_C", PCMGSetLevels_MG));
1944:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGetInterpolations_C", PCGetInterpolations_MG));
1945:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGetCoarseOperators_C", PCGetCoarseOperators_MG));
1946:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetAdaptInterpolation_C", PCMGSetAdaptInterpolation_MG));
1947:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetAdaptInterpolation_C", PCMGGetAdaptInterpolation_MG));
1948:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetAdaptCR_C", PCMGSetAdaptCR_MG));
1949:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetAdaptCR_C", PCMGGetAdaptCR_MG));
1950:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetAdaptCoarseSpaceType_C", PCMGSetAdaptCoarseSpaceType_MG));
1951:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetAdaptCoarseSpaceType_C", PCMGGetAdaptCoarseSpaceType_MG));
1952:   PetscFunctionReturn(PETSC_SUCCESS);
1953: }