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:       Mat crA;

109:       PetscCheck(!matapp, PetscObjectComm((PetscObject)pc), PETSC_ERR_SUP, "Not supported");
110:       /* TODO Turn on copy and turn off noisy if we have an exact solution
111:       PetscCall(VecCopy(mglevels->x, mglevels->crx));
112:       PetscCall(VecCopy(mglevels->b, mglevels->crb)); */
113:       PetscCall(KSPGetOperators(mglevels->cr, &crA, NULL));
114:       PetscCall(KSPSetNoisy_Private(crA, mglevels->crx));
115:       PetscCall(KSPSolve(mglevels->cr, mglevels->crb, mglevels->crx)); /* compatible relaxation */
116:       PetscCall(KSPCheckSolve(mglevels->cr, pc, mglevels->crx));
117:     }
118:     if (mglevels->eventsmoothsolve) PetscCall(PetscLogEventEnd(mglevels->eventsmoothsolve, 0, 0, 0, 0));
119:   }
120:   PetscFunctionReturn(PETSC_SUCCESS);
121: }

123: 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)
124: {
125:   PC_MG         *mg       = (PC_MG *)pc->data;
126:   PC_MG_Levels **mglevels = mg->levels;
127:   PC             tpc;
128:   PetscBool      changeu, changed;
129:   PetscInt       levels = mglevels[0]->levels, i;

131:   PetscFunctionBegin;
132:   /* When the DM is supplying the matrix then it will not exist until here */
133:   for (i = 0; i < levels; i++) {
134:     if (!mglevels[i]->A) {
135:       PetscCall(KSPGetOperators(mglevels[i]->smoothu, &mglevels[i]->A, NULL));
136:       PetscCall(PetscObjectReference((PetscObject)mglevels[i]->A));
137:     }
138:   }

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

151:       PetscCall(KSPCreateVecs(mglevels[levels - 1]->smoothd, 1, &vec, 0, NULL));
152:       mglevels[levels - 1]->b = *vec;
153:       PetscCall(PetscFree(vec));
154:     }
155:     PetscCall(VecCopy(b, mglevels[levels - 1]->b));
156:   }
157:   mglevels[levels - 1]->x = x;

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

175:   /* since smoother is applied to full system, not just residual we need to make sure that smoothers don't
176:      stop prematurely due to small residual */
177:   for (i = 1; i < levels; i++) {
178:     PetscCall(KSPSetTolerances(mglevels[i]->smoothu, 0, PETSC_CURRENT, PETSC_CURRENT, PETSC_CURRENT));
179:     if (mglevels[i]->smoothu != mglevels[i]->smoothd) {
180:       /* For Richardson the initial guess is nonzero since it is solving in each cycle the original system not just applying as a preconditioner */
181:       PetscCall(KSPSetInitialGuessNonzero(mglevels[i]->smoothd, PETSC_TRUE));
182:       PetscCall(KSPSetTolerances(mglevels[i]->smoothd, 0, PETSC_CURRENT, PETSC_CURRENT, PETSC_CURRENT));
183:     }
184:   }

186:   *reason = PCRICHARDSON_NOT_SET;
187:   for (i = 0; i < its; i++) {
188:     PetscCall(PCMGMCycle_Private(pc, mglevels + levels - 1, PETSC_FALSE, PETSC_FALSE, reason));
189:     if (*reason) break;
190:   }
191:   if (*reason == PCRICHARDSON_NOT_SET) *reason = PCRICHARDSON_CONVERGED_ITS;
192:   *outits = i;
193:   if (!changed && !changeu) mglevels[levels - 1]->b = NULL;
194:   PetscFunctionReturn(PETSC_SUCCESS);
195: }

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

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

227:     for (i = 0; i < n; i++) {
228:       PetscCall(MatDestroy(&mglevels[i]->coarseSpace));
229:       PetscCall(MatDestroy(&mglevels[i]->A));
230:       if (mglevels[i]->smoothd != mglevels[i]->smoothu) PetscCall(KSPReset(mglevels[i]->smoothd));
231:       PetscCall(KSPReset(mglevels[i]->smoothu));
232:       if (mglevels[i]->cr) PetscCall(KSPReset(mglevels[i]->cr));
233:     }
234:     mg->Nc = 0;
235:   }
236:   PetscFunctionReturn(PETSC_SUCCESS);
237: }

239: /* Implementing CR

241: 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

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

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

247:   Inj^T Inj

249: and

251:   S = I - Inj^T Inj

253: since

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

257: Brannick suggests

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

261: 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

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

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

267:   Check: || Inj P - I ||_F < tol
268:   Check: In general, Inj Inj^T = I
269: */

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

278: static PetscErrorCode CRSetup_Private(PC pc)
279: {
280:   CRContext *ctx;
281:   Mat        It;

283:   PetscFunctionBeginUser;
284:   PetscCall(PCShellGetContext(pc, &ctx));
285:   PetscCall(PCMGGetInjection(ctx->mg, ctx->l, &It));
286:   PetscCheck(It, PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_WRONGSTATE, "CR requires that injection be defined for this PCMG");
287:   PetscCall(MatCreateTranspose(It, &ctx->Inj));
288:   PetscCall(MatCreateNormal(ctx->Inj, &ctx->S));
289:   PetscCall(MatScale(ctx->S, -1.0));
290:   PetscCall(MatShift(ctx->S, 1.0));
291:   PetscFunctionReturn(PETSC_SUCCESS);
292: }

294: static PetscErrorCode CRApply_Private(PC pc, Vec x, Vec y)
295: {
296:   CRContext *ctx;

298:   PetscFunctionBeginUser;
299:   PetscCall(PCShellGetContext(pc, &ctx));
300:   PetscCall(MatMult(ctx->S, x, y));
301:   PetscFunctionReturn(PETSC_SUCCESS);
302: }

304: static PetscErrorCode CRDestroy_Private(PC pc)
305: {
306:   CRContext *ctx;

308:   PetscFunctionBeginUser;
309:   PetscCall(PCShellGetContext(pc, &ctx));
310:   PetscCall(MatDestroy(&ctx->Inj));
311:   PetscCall(MatDestroy(&ctx->S));
312:   PetscCall(PetscFree(ctx));
313:   PetscCall(PCShellSetContext(pc, NULL));
314:   PetscFunctionReturn(PETSC_SUCCESS);
315: }

317: static PetscErrorCode CreateCR_Private(PC pc, PetscInt l, PC *cr)
318: {
319:   CRContext *ctx;

321:   PetscFunctionBeginUser;
322:   PetscCall(PCCreate(PetscObjectComm((PetscObject)pc), cr));
323:   PetscCall(PetscObjectSetName((PetscObject)*cr, "S (complementary projector to injection)"));
324:   PetscCall(PetscCalloc1(1, &ctx));
325:   ctx->mg = pc;
326:   ctx->l  = l;
327:   PetscCall(PCSetType(*cr, PCSHELL));
328:   PetscCall(PCShellSetContext(*cr, ctx));
329:   PetscCall(PCShellSetApply(*cr, CRApply_Private));
330:   PetscCall(PCShellSetSetUp(*cr, CRSetup_Private));
331:   PetscCall(PCShellSetDestroy(*cr, CRDestroy_Private));
332:   PetscFunctionReturn(PETSC_SUCCESS);
333: }

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

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

350:   PetscFunctionBegin;
353:   if (mg->nlevels == levels) PetscFunctionReturn(PETSC_SUCCESS);
354:   PetscCall(PetscObjectGetComm((PetscObject)pc, &comm));
355:   if (mglevels) {
356:     mgctype = mglevels[0]->cycles;
357:     /* changing the number of levels so free up the previous stuff */
358:     PetscCall(PCReset_MG(pc));
359:     n = mglevels[0]->levels;
360:     for (i = 0; i < n; i++) {
361:       if (mglevels[i]->smoothd != mglevels[i]->smoothu) PetscCall(KSPDestroy(&mglevels[i]->smoothd));
362:       PetscCall(KSPDestroy(&mglevels[i]->smoothu));
363:       PetscCall(KSPDestroy(&mglevels[i]->cr));
364:       PetscCall(PetscFree(mglevels[i]));
365:     }
366:     PetscCall(PetscFree(mg->levels));
367:   }

369:   mg->nlevels = levels;

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

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

375:   mg->stageApply = 0;
376:   for (i = 0; i < levels; i++) {
377:     PetscCall(PetscNew(&mglevels[i]));

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

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

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

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

420:         if (i == levels - 1 && levels > 1) { // replace 'mg_finegrid_' with 'mg_levels_X_'
421:           PetscBool set;
422:           PetscCall(PetscOptionsFindPairPrefix_Private(((PetscObject)mglevels[i]->smoothd)->options, ((PetscObject)mglevels[i]->smoothd)->prefix, "-mg_fine_", NULL, NULL, &set));
423:           if (set) {
424:             if (prefix) PetscCall(PetscSNPrintf(tprefix, 128, "%smg_fine_", prefix));
425:             else PetscCall(PetscSNPrintf(tprefix, 128, "mg_fine_"));
426:             PetscCall(KSPSetOptionsPrefix(mglevels[i]->smoothd, tprefix));
427:           } else {
428:             PetscCall(PetscSNPrintf(tprefix, 128, "mg_levels_%" PetscInt_FMT "_", i));
429:             PetscCall(KSPAppendOptionsPrefix(mglevels[i]->smoothd, tprefix));
430:           }
431:         } else {
432:           PetscCall(PetscSNPrintf(tprefix, 128, "mg_levels_%" PetscInt_FMT "_", i));
433:           PetscCall(KSPAppendOptionsPrefix(mglevels[i]->smoothd, tprefix));
434:         }
435:       }
436:     }
437:     mglevels[i]->smoothu = mglevels[i]->smoothd;
438:     mg->rtol             = 0.0;
439:     mg->abstol           = 0.0;
440:     mg->dtol             = 0.0;
441:     mg->ttol             = 0.0;
442:     mg->cyclesperpcapply = 1;
443:   }
444:   mg->levels = mglevels;
445:   PetscCall(PCMGSetType(pc, mgtype));
446:   PetscFunctionReturn(PETSC_SUCCESS);
447: }

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

453:   Logically Collective

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

463:   Level: intermediate

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

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

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

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

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

490: .seealso: [](ch_ksp), `PCMGSetType()`, `PCMGGetLevels()`
491: @*/
492: PetscErrorCode PCMGSetLevels(PC pc, PetscInt levels, MPI_Comm *comms)
493: {
494:   PetscFunctionBegin;
496:   if (comms) PetscAssertPointer(comms, 3);
497:   PetscTryMethod(pc, "PCMGSetLevels_C", (PC, PetscInt, MPI_Comm *), (pc, levels, comms));
498:   PetscFunctionReturn(PETSC_SUCCESS);
499: }

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

507:   PetscFunctionBegin;
508:   PetscCall(PCReset_MG(pc));
509:   if (mglevels) {
510:     n = mglevels[0]->levels;
511:     for (i = 0; i < n; i++) {
512:       if (mglevels[i]->smoothd != mglevels[i]->smoothu) PetscCall(KSPDestroy(&mglevels[i]->smoothd));
513:       PetscCall(KSPDestroy(&mglevels[i]->smoothu));
514:       PetscCall(KSPDestroy(&mglevels[i]->cr));
515:       PetscCall(PetscFree(mglevels[i]));
516:     }
517:     PetscCall(PetscFree(mg->levels));
518:   }
519:   PetscCall(PetscFree(pc->data));
520:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGetInterpolations_C", NULL));
521:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGetCoarseOperators_C", NULL));
522:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetGalerkin_C", NULL));
523:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetLevels_C", NULL));
524:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetLevels_C", NULL));
525:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGetInterpolations_C", NULL));
526:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGetCoarseOperators_C", NULL));
527:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetAdaptInterpolation_C", NULL));
528:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetAdaptInterpolation_C", NULL));
529:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetAdaptCR_C", NULL));
530:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetAdaptCR_C", NULL));
531:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetAdaptCoarseSpaceType_C", NULL));
532:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetAdaptCoarseSpaceType_C", NULL));
533:   PetscFunctionReturn(PETSC_SUCCESS);
534: }

536: /*
537:    PCApply_MG - Runs either an additive, multiplicative, Kaskadic
538:              or full cycle of multigrid.

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

551:   PetscFunctionBegin;
552:   matapp = (PetscBool)(B && X);
553:   if (mg->stageApply) PetscCall(PetscLogStagePush(mg->stageApply));
554:   /* When the DM is supplying the matrix then it will not exist until here */
555:   for (i = 0; i < levels; i++) {
556:     if (!mglevels[i]->A) {
557:       PetscCall(KSPGetOperators(mglevels[i]->smoothu, &mglevels[i]->A, NULL));
558:       PetscCall(PetscObjectReference((PetscObject)mglevels[i]->A));
559:     }
560:   }

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

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

594:         PetscCall(KSPCreateVecs(mglevels[levels - 1]->smoothd, 1, &vec, 0, NULL));
595:         mglevels[levels - 1]->b = *vec;
596:         PetscCall(PetscFree(vec));
597:       }
598:       PetscCall(VecCopy(b, mglevels[levels - 1]->b));
599:     }
600:   }
601:   if (matapp) {
602:     mglevels[levels - 1]->X = X;
603:   } else {
604:     mglevels[levels - 1]->x = x;
605:   }

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

613:     PetscCall(MatGetSize(mglevels[levels - 2]->X, NULL, &Xc));
614:     PetscCall(MatGetSize(mglevels[levels - 1]->B, NULL, &Bc));
615:     PetscCall(PetscObjectTypeCompare((PetscObject)mglevels[levels - 2]->X, ((PetscObject)mglevels[levels - 1]->X)->type_name, &flg));
616:     if (Xc != Bc || !flg) {
617:       PetscCall(MatDestroy(&mglevels[levels - 1]->R));
618:       for (i = 0; i < levels - 1; i++) {
619:         PetscCall(MatDestroy(&mglevels[i]->R));
620:         PetscCall(MatDestroy(&mglevels[i]->B));
621:         PetscCall(MatDestroy(&mglevels[i]->X));
622:       }
623:     }
624:   }

626:   if (mg->am == PC_MG_MULTIPLICATIVE) {
627:     if (matapp) PetscCall(MatZeroEntries(X));
628:     else PetscCall(VecZeroEntries(x));
629:     for (i = 0; i < mg->cyclesperpcapply; i++) PetscCall(PCMGMCycle_Private(pc, mglevels + levels - 1, transpose, matapp, NULL));
630:   } else if (mg->am == PC_MG_ADDITIVE) {
631:     PetscCall(PCMGACycle_Private(pc, mglevels, transpose, matapp));
632:   } else if (mg->am == PC_MG_KASKADE) {
633:     PetscCall(PCMGKCycle_Private(pc, mglevels, transpose, matapp));
634:   } else {
635:     PetscCall(PCMGFCycle_Private(pc, mglevels, transpose, matapp));
636:   }
637:   if (mg->stageApply) PetscCall(PetscLogStagePop());
638:   if (!changeu && !changed) {
639:     if (matapp) {
640:       mglevels[levels - 1]->B = NULL;
641:     } else {
642:       mglevels[levels - 1]->b = NULL;
643:     }
644:   }
645:   PetscFunctionReturn(PETSC_SUCCESS);
646: }

648: static PetscErrorCode PCApply_MG(PC pc, Vec b, Vec x)
649: {
650:   PetscFunctionBegin;
651:   PetscCall(PCApply_MG_Internal(pc, b, x, NULL, NULL, PETSC_FALSE));
652:   PetscFunctionReturn(PETSC_SUCCESS);
653: }

655: static PetscErrorCode PCApplyTranspose_MG(PC pc, Vec b, Vec x)
656: {
657:   PetscFunctionBegin;
658:   PetscCall(PCApply_MG_Internal(pc, b, x, NULL, NULL, PETSC_TRUE));
659:   PetscFunctionReturn(PETSC_SUCCESS);
660: }

662: static PetscErrorCode PCMatApply_MG(PC pc, Mat b, Mat x)
663: {
664:   PetscFunctionBegin;
665:   PetscCall(PCApply_MG_Internal(pc, NULL, NULL, b, x, PETSC_FALSE));
666:   PetscFunctionReturn(PETSC_SUCCESS);
667: }

669: static PetscErrorCode PCMatApplyTranspose_MG(PC pc, Mat b, Mat x)
670: {
671:   PetscFunctionBegin;
672:   PetscCall(PCApply_MG_Internal(pc, NULL, NULL, b, x, PETSC_TRUE));
673:   PetscFunctionReturn(PETSC_SUCCESS);
674: }

676: PetscErrorCode PCSetFromOptions_MG(PC pc, PetscOptionItems PetscOptionsObject)
677: {
678:   PetscInt            levels, cycles;
679:   PetscBool           flg, flg2;
680:   PC_MG              *mg = (PC_MG *)pc->data;
681:   PC_MG_Levels      **mglevels;
682:   PCMGType            mgtype;
683:   PCMGCycleType       mgctype;
684:   PCMGGalerkinType    gtype;
685:   PCMGCoarseSpaceType coarseSpaceType;

687:   PetscFunctionBegin;
688:   levels = PetscMax(mg->nlevels, 1);
689:   PetscOptionsHeadBegin(PetscOptionsObject, "Multigrid options");
690:   PetscCall(PetscOptionsInt("-pc_mg_levels", "Number of Levels", "PCMGSetLevels", levels, &levels, &flg));
691:   if (!flg && !mg->levels && pc->dm) {
692:     PetscCall(DMGetRefineLevel(pc->dm, &levels));
693:     levels++;
694:     mg->usedmfornumberoflevels = PETSC_TRUE;
695:   }
696:   PetscCall(PCMGSetLevels(pc, levels, NULL));
697:   mglevels = mg->levels;

699:   mgctype = (PCMGCycleType)mglevels[0]->cycles;
700:   PetscCall(PetscOptionsEnum("-pc_mg_cycle_type", "V cycle or for W-cycle", "PCMGSetCycleType", PCMGCycleTypes, (PetscEnum)mgctype, (PetscEnum *)&mgctype, &flg));
701:   if (flg) PetscCall(PCMGSetCycleType(pc, mgctype));
702:   coarseSpaceType = mg->coarseSpaceType;
703:   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));
704:   if (flg) PetscCall(PCMGSetAdaptCoarseSpaceType(pc, coarseSpaceType));
705:   PetscCall(PetscOptionsInt("-pc_mg_adapt_interp_n", "Size of the coarse space for adaptive interpolation", "PCMGSetCoarseSpace", mg->Nc, &mg->Nc, &flg));
706:   PetscCall(PetscOptionsBool("-pc_mg_mesp_monitor", "Monitor the multilevel eigensolver", "PCMGSetAdaptInterpolation", PETSC_FALSE, &mg->mespMonitor, &flg));
707:   flg2 = PETSC_FALSE;
708:   PetscCall(PetscOptionsBool("-pc_mg_adapt_cr", "Monitor coarse space quality using Compatible Relaxation (CR)", "PCMGSetAdaptCR", PETSC_FALSE, &flg2, &flg));
709:   if (flg) PetscCall(PCMGSetAdaptCR(pc, flg2));
710:   flg = PETSC_FALSE;
711:   PetscCall(PetscOptionsBool("-pc_mg_distinct_smoothup", "Create separate smoothup KSP and append the prefix _up", "PCMGSetDistinctSmoothUp", PETSC_FALSE, &flg, NULL));
712:   if (flg) PetscCall(PCMGSetDistinctSmoothUp(pc));
713:   PetscCall(PetscOptionsEnum("-pc_mg_galerkin", "Use Galerkin process to compute coarser operators", "PCMGSetGalerkin", PCMGGalerkinTypes, (PetscEnum)mg->galerkin, (PetscEnum *)&gtype, &flg));
714:   if (flg) PetscCall(PCMGSetGalerkin(pc, gtype));
715:   mgtype = mg->am;
716:   PetscCall(PetscOptionsEnum("-pc_mg_type", "Multigrid type", "PCMGSetType", PCMGTypes, (PetscEnum)mgtype, (PetscEnum *)&mgtype, &flg));
717:   if (flg) PetscCall(PCMGSetType(pc, mgtype));
718:   if (mg->am == PC_MG_MULTIPLICATIVE) {
719:     PetscCall(PetscOptionsInt("-pc_mg_multiplicative_cycles", "Number of cycles for each preconditioner step", "PCMGMultiplicativeSetCycles", mg->cyclesperpcapply, &cycles, &flg));
720:     if (flg) PetscCall(PCMGMultiplicativeSetCycles(pc, cycles));
721:   }
722:   flg = PETSC_FALSE;
723:   PetscCall(PetscOptionsBool("-pc_mg_log", "Log times for each multigrid level", "None", flg, &flg, NULL));
724:   if (flg) {
725:     PetscInt i;
726:     char     eventname[128];

728:     levels = mglevels[0]->levels;
729:     for (i = 0; i < levels; i++) {
730:       PetscCall(PetscSNPrintf(eventname, PETSC_STATIC_ARRAY_LENGTH(eventname), "MGSetup Level %" PetscInt_FMT, i));
731:       PetscCall(PetscLogEventRegister(eventname, ((PetscObject)pc)->classid, &mglevels[i]->eventsmoothsetup));
732:       PetscCall(PetscSNPrintf(eventname, PETSC_STATIC_ARRAY_LENGTH(eventname), "MGSmooth Level %" PetscInt_FMT, i));
733:       PetscCall(PetscLogEventRegister(eventname, ((PetscObject)pc)->classid, &mglevels[i]->eventsmoothsolve));
734:       if (i) {
735:         PetscCall(PetscSNPrintf(eventname, PETSC_STATIC_ARRAY_LENGTH(eventname), "MGResid Level %" PetscInt_FMT, i));
736:         PetscCall(PetscLogEventRegister(eventname, ((PetscObject)pc)->classid, &mglevels[i]->eventresidual));
737:         PetscCall(PetscSNPrintf(eventname, PETSC_STATIC_ARRAY_LENGTH(eventname), "MGInterp Level %" PetscInt_FMT, i));
738:         PetscCall(PetscLogEventRegister(eventname, ((PetscObject)pc)->classid, &mglevels[i]->eventinterprestrict));
739:       }
740:     }

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

745:       PetscCall(PetscLogStageGetId(sname, &mg->stageApply));
746:       if (mg->stageApply < 0) PetscCall(PetscLogStageRegister(sname, &mg->stageApply));
747:     }
748:   }
749:   PetscOptionsHeadEnd();
750:   /* Check option consistency */
751:   PetscCall(PCMGGetGalerkin(pc, &gtype));
752:   PetscCall(PCMGGetAdaptInterpolation(pc, &flg));
753:   PetscCheck(!flg || !(gtype >= PC_MG_GALERKIN_NONE), PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_INCOMP, "Must use Galerkin coarse operators when adapting the interpolator");
754:   PetscFunctionReturn(PETSC_SUCCESS);
755: }

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

762: #include <petscdraw.h>
763: PetscErrorCode PCView_MG(PC pc, PetscViewer viewer)
764: {
765:   PC_MG         *mg       = (PC_MG *)pc->data;
766:   PC_MG_Levels **mglevels = mg->levels;
767:   PetscInt       levels   = mglevels ? mglevels[0]->levels : 0, i;
768:   PetscBool      isascii, isbinary, isdraw;

770:   PetscFunctionBegin;
771:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
772:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
773:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
774:   if (isascii) {
775:     const char *cyclename = levels ? (mglevels[0]->cycles == PC_MG_CYCLE_V ? "v" : "w") : "unknown";
776:     PetscCall(PetscViewerASCIIPrintf(viewer, "  type is %s, levels=%" PetscInt_FMT " cycles=%s\n", PCMGTypes[mg->am], levels, cyclename));
777:     if (mg->am == PC_MG_MULTIPLICATIVE) PetscCall(PetscViewerASCIIPrintf(viewer, "    Cycles per PCApply=%" PetscInt_FMT "\n", mg->cyclesperpcapply));
778:     if (mg->galerkin == PC_MG_GALERKIN_BOTH) {
779:       PetscCall(PetscViewerASCIIPrintf(viewer, "    Using Galerkin computed coarse grid matrices\n"));
780:     } else if (mg->galerkin == PC_MG_GALERKIN_PMAT) {
781:       PetscCall(PetscViewerASCIIPrintf(viewer, "    Using Galerkin computed coarse grid matrices for pmat\n"));
782:     } else if (mg->galerkin == PC_MG_GALERKIN_MAT) {
783:       PetscCall(PetscViewerASCIIPrintf(viewer, "    Using Galerkin computed coarse grid matrices for mat\n"));
784:     } else if (mg->galerkin == PC_MG_GALERKIN_EXTERNAL) {
785:       PetscCall(PetscViewerASCIIPrintf(viewer, "    Using externally compute Galerkin coarse grid matrices\n"));
786:     } else {
787:       PetscCall(PetscViewerASCIIPrintf(viewer, "    Not using Galerkin computed coarse grid matrices\n"));
788:     }
789:     if (mg->view) PetscCall((*mg->view)(pc, viewer));
790:     for (i = 0; i < levels; i++) {
791:       if (i) {
792:         PetscCall(PetscViewerASCIIPrintf(viewer, "Down solver (pre-smoother) on level %" PetscInt_FMT " -------------------------------\n", i));
793:       } else {
794:         PetscCall(PetscViewerASCIIPrintf(viewer, "Coarse grid solver -- level %" PetscInt_FMT " -------------------------------\n", i));
795:       }
796:       PetscCall(PetscViewerASCIIPushTab(viewer));
797:       PetscCall(KSPView(mglevels[i]->smoothd, viewer));
798:       PetscCall(PetscViewerASCIIPopTab(viewer));
799:       if (i && mglevels[i]->smoothd == mglevels[i]->smoothu) {
800:         PetscCall(PetscViewerASCIIPrintf(viewer, "Up solver (post-smoother) same as down solver (pre-smoother)\n"));
801:       } else if (i) {
802:         PetscCall(PetscViewerASCIIPrintf(viewer, "Up solver (post-smoother) on level %" PetscInt_FMT " -------------------------------\n", i));
803:         PetscCall(PetscViewerASCIIPushTab(viewer));
804:         PetscCall(KSPView(mglevels[i]->smoothu, viewer));
805:         PetscCall(PetscViewerASCIIPopTab(viewer));
806:       }
807:       if (i && mglevels[i]->cr) {
808:         PetscCall(PetscViewerASCIIPrintf(viewer, "CR solver on level %" PetscInt_FMT " -------------------------------\n", i));
809:         PetscCall(PetscViewerASCIIPushTab(viewer));
810:         PetscCall(KSPView(mglevels[i]->cr, viewer));
811:         PetscCall(PetscViewerASCIIPopTab(viewer));
812:       }
813:     }
814:   } else if (isbinary) {
815:     for (i = levels - 1; i >= 0; i--) {
816:       PetscCall(KSPView(mglevels[i]->smoothd, viewer));
817:       if (i && mglevels[i]->smoothd != mglevels[i]->smoothu) PetscCall(KSPView(mglevels[i]->smoothu, viewer));
818:     }
819:   } else if (isdraw) {
820:     PetscDraw draw;
821:     PetscReal x, w, y, bottom, th;
822:     PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
823:     PetscCall(PetscDrawGetCurrentPoint(draw, &x, &y));
824:     PetscCall(PetscDrawStringGetSize(draw, NULL, &th));
825:     bottom = y - th;
826:     for (i = levels - 1; i >= 0; i--) {
827:       if (!mglevels[i]->smoothu || (mglevels[i]->smoothu == mglevels[i]->smoothd)) {
828:         PetscCall(PetscDrawPushCurrentPoint(draw, x, bottom));
829:         PetscCall(KSPView(mglevels[i]->smoothd, viewer));
830:         PetscCall(PetscDrawPopCurrentPoint(draw));
831:       } else {
832:         w = 0.5 * PetscMin(1.0 - x, x);
833:         PetscCall(PetscDrawPushCurrentPoint(draw, x + w, bottom));
834:         PetscCall(KSPView(mglevels[i]->smoothd, viewer));
835:         PetscCall(PetscDrawPopCurrentPoint(draw));
836:         PetscCall(PetscDrawPushCurrentPoint(draw, x - w, bottom));
837:         PetscCall(KSPView(mglevels[i]->smoothu, viewer));
838:         PetscCall(PetscDrawPopCurrentPoint(draw));
839:       }
840:       PetscCall(PetscDrawGetBoundingBox(draw, NULL, &bottom, NULL, NULL));
841:       bottom -= th;
842:     }
843:   }
844:   PetscFunctionReturn(PETSC_SUCCESS);
845: }

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

849: /*
850:     Calls setup for the KSP on each level
851: */
852: PetscErrorCode PCSetUp_MG(PC pc)
853: {
854:   PC_MG         *mg       = (PC_MG *)pc->data;
855:   PC_MG_Levels **mglevels = mg->levels;
856:   PetscInt       i, n;
857:   PC             cpc;
858:   PetscBool      dump = PETSC_FALSE, opsset, use_amat, missinginterpolate = PETSC_FALSE;
859:   Mat            dA, dB;
860:   Vec            tvec;
861:   DM            *dms;
862:   PetscViewer    viewer = NULL;
863:   PetscBool      dAeqdB = PETSC_FALSE, needRestricts = PETSC_FALSE, doCR = PETSC_FALSE;
864:   PetscBool      adaptInterpolation = mg->adaptInterpolation;

866:   PetscFunctionBegin;
867:   PetscCheck(mglevels, PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_WRONGSTATE, "Must set MG levels with PCMGSetLevels() before setting up");
868:   n = mglevels[0]->levels;
869:   /* FIX: Move this to PCSetFromOptions_MG? */
870:   if (mg->usedmfornumberoflevels) {
871:     PetscInt levels;
872:     PetscCall(DMGetRefineLevel(pc->dm, &levels));
873:     levels++;
874:     if (levels > n) { /* the problem is now being solved on a finer grid */
875:       PetscCall(PCMGSetLevels(pc, levels, NULL));
876:       n = levels;
877:       PetscCall(PCSetFromOptions(pc)); /* it is bad to call this here, but otherwise will never be called for the new hierarchy */
878:       mglevels = mg->levels;
879:     }
880:   }
881:   PetscCall(KSPGetPC(mglevels[0]->smoothd, &cpc));

883:   /* If user did not provide fine grid operators OR operator was not updated since last global KSPSetOperators() */
884:   /* so use those from global PC */
885:   /* Is this what we always want? What if user wants to keep old one? */
886:   PetscCall(KSPGetOperatorsSet(mglevels[n - 1]->smoothd, NULL, &opsset));
887:   if (opsset) {
888:     Mat mmat;
889:     PetscCall(KSPGetOperators(mglevels[n - 1]->smoothd, NULL, &mmat));
890:     if (mmat == pc->pmat) opsset = PETSC_FALSE;
891:   }

893:   /* Create CR solvers */
894:   PetscCall(PCMGGetAdaptCR(pc, &doCR));
895:   if (doCR) {
896:     const char *prefix;

898:     PetscCall(PCGetOptionsPrefix(pc, &prefix));
899:     for (i = 1; i < n; ++i) {
900:       PC   ipc, cr;
901:       char crprefix[128];

903:       PetscCall(KSPCreate(PetscObjectComm((PetscObject)pc), &mglevels[i]->cr));
904:       PetscCall(KSPSetNestLevel(mglevels[i]->cr, pc->kspnestlevel));
905:       PetscCall(KSPSetErrorIfNotConverged(mglevels[i]->cr, PETSC_FALSE));
906:       PetscCall(PetscObjectIncrementTabLevel((PetscObject)mglevels[i]->cr, (PetscObject)pc, n - i));
907:       PetscCall(KSPSetOptionsPrefix(mglevels[i]->cr, prefix));
908:       PetscCall(PetscObjectComposedDataSetInt((PetscObject)mglevels[i]->cr, PetscMGLevelId, mglevels[i]->level));
909:       PetscCall(KSPSetType(mglevels[i]->cr, KSPCHEBYSHEV));
910:       PetscCall(KSPSetConvergenceTest(mglevels[i]->cr, KSPConvergedSkip, NULL, NULL));
911:       PetscCall(KSPSetNormType(mglevels[i]->cr, KSP_NORM_PRECONDITIONED));
912:       PetscCall(KSPGetPC(mglevels[i]->cr, &ipc));

914:       PetscCall(PCSetType(ipc, PCCOMPOSITE));
915:       PetscCall(PCCompositeSetType(ipc, PC_COMPOSITE_MULTIPLICATIVE));
916:       PetscCall(PCCompositeAddPCType(ipc, PCSOR));
917:       PetscCall(CreateCR_Private(pc, i, &cr));
918:       PetscCall(PCCompositeAddPC(ipc, cr));
919:       PetscCall(PCDestroy(&cr));

921:       PetscCall(KSPSetTolerances(mglevels[i]->cr, PETSC_CURRENT, PETSC_CURRENT, PETSC_CURRENT, mg->default_smoothd));
922:       PetscCall(KSPSetInitialGuessNonzero(mglevels[i]->cr, PETSC_TRUE));
923:       PetscCall(PetscSNPrintf(crprefix, 128, "mg_levels_%" PetscInt_FMT "_cr_", i));
924:       PetscCall(KSPAppendOptionsPrefix(mglevels[i]->cr, crprefix));
925:     }
926:   }

928:   if (!opsset) {
929:     PetscCall(PCGetUseAmat(pc, &use_amat));
930:     if (use_amat) {
931:       PetscCall(PetscInfo(pc, "Using outer operators to define finest grid operator \n  because PCMGGetSmoother(pc,nlevels-1,&ksp);KSPSetOperators(ksp,...); was not called.\n"));
932:       PetscCall(KSPSetOperators(mglevels[n - 1]->smoothd, pc->mat, pc->pmat));
933:     } else {
934:       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"));
935:       PetscCall(KSPSetOperators(mglevels[n - 1]->smoothd, pc->pmat, pc->pmat));
936:     }
937:   }

939:   for (i = n - 1; i > 0; i--) {
940:     if (!(mglevels[i]->interpolate || mglevels[i]->restrct)) {
941:       missinginterpolate = PETSC_TRUE;
942:       break;
943:     }
944:   }

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

950:   if (pc->dm && !pc->setupcalled) {
951:     /* finest smoother also gets DM but it is not active, independent of whether galerkin==PC_MG_GALERKIN_EXTERNAL */
952:     PetscCall(KSPSetDM(mglevels[n - 1]->smoothd, pc->dm));
953:     PetscCall(KSPSetDMActive(mglevels[n - 1]->smoothd, PETSC_FALSE));
954:     if (mglevels[n - 1]->smoothd != mglevels[n - 1]->smoothu) {
955:       PetscCall(KSPSetDM(mglevels[n - 1]->smoothu, pc->dm));
956:       PetscCall(KSPSetDMActive(mglevels[n - 1]->smoothu, PETSC_FALSE));
957:     }
958:     if (mglevels[n - 1]->cr) {
959:       PetscCall(KSPSetDM(mglevels[n - 1]->cr, pc->dm));
960:       PetscCall(KSPSetDMActive(mglevels[n - 1]->cr, PETSC_FALSE));
961:     }
962:   }

964:   /*
965:    Skipping if user has provided all interpolation/restriction needed (since DM might not be able to produce them (when coming from SNES/TS)
966:    Skipping for externally managed hierarchy (such as ML and GAMG). Cleaner logic here would be great. Wrap ML/GAMG as DMs?
967:   */
968:   if (missinginterpolate && mg->galerkin != PC_MG_GALERKIN_EXTERNAL && !pc->setupcalled) {
969:     /* first see if we can compute a coarse space */
970:     if (mg->coarseSpaceType == PCMG_ADAPT_GDSW) {
971:       for (i = n - 2; i > -1; i--) {
972:         if (!mglevels[i + 1]->restrct && !mglevels[i + 1]->interpolate) {
973:           PetscCall(PCMGComputeCoarseSpace_Internal(pc, i + 1, mg->coarseSpaceType, mg->Nc, NULL, &mglevels[i + 1]->coarseSpace));
974:           PetscCall(PCMGSetInterpolation(pc, i + 1, mglevels[i + 1]->coarseSpace));
975:         }
976:       }
977:     } else { /* construct the interpolation from the DMs */
978:       Mat p;
979:       Vec rscale;
980:       PetscCall(PetscMalloc1(n, &dms));
981:       dms[n - 1] = pc->dm;
982:       /* Separately create them so we do not get DMKSP interference between levels */
983:       for (i = n - 2; i > -1; i--) PetscCall(DMCoarsen(dms[i + 1], MPI_COMM_NULL, &dms[i]));
984:       for (i = n - 2; i > -1; i--) {
985:         DMKSP     kdm;
986:         PetscBool dmhasrestrict, dmhasinject;
987:         PetscCall(KSPSetDM(mglevels[i]->smoothd, dms[i]));
988:         if (!needRestricts) PetscCall(KSPSetDMActive(mglevels[i]->smoothd, PETSC_FALSE));
989:         if (mglevels[i]->smoothd != mglevels[i]->smoothu) {
990:           PetscCall(KSPSetDM(mglevels[i]->smoothu, dms[i]));
991:           if (!needRestricts) PetscCall(KSPSetDMActive(mglevels[i]->smoothu, PETSC_FALSE));
992:         }
993:         if (mglevels[i]->cr) {
994:           PetscCall(KSPSetDM(mglevels[i]->cr, dms[i]));
995:           if (!needRestricts) PetscCall(KSPSetDMActive(mglevels[i]->cr, PETSC_FALSE));
996:         }
997:         PetscCall(DMGetDMKSPWrite(dms[i], &kdm));
998:         /* Ugly hack so that the next KSPSetUp() will use the RHS that we set. A better fix is to change dmActive to take
999:          * a bitwise OR of computing the matrix, RHS, and initial iterate. */
1000:         kdm->ops->computerhs = NULL;
1001:         kdm->rhsctx          = NULL;
1002:         if (!mglevels[i + 1]->interpolate) {
1003:           PetscCall(DMCreateInterpolation(dms[i], dms[i + 1], &p, &rscale));
1004:           PetscCall(PCMGSetInterpolation(pc, i + 1, p));
1005:           if (rscale) PetscCall(PCMGSetRScale(pc, i + 1, rscale));
1006:           PetscCall(VecDestroy(&rscale));
1007:           PetscCall(MatDestroy(&p));
1008:         }
1009:         PetscCall(DMHasCreateRestriction(dms[i], &dmhasrestrict));
1010:         if (dmhasrestrict && !mglevels[i + 1]->restrct) {
1011:           PetscCall(DMCreateRestriction(dms[i], dms[i + 1], &p));
1012:           PetscCall(PCMGSetRestriction(pc, i + 1, p));
1013:           PetscCall(MatDestroy(&p));
1014:         }
1015:         PetscCall(DMHasCreateInjection(dms[i], &dmhasinject));
1016:         if (dmhasinject && !mglevels[i + 1]->inject) {
1017:           PetscCall(DMCreateInjection(dms[i], dms[i + 1], &p));
1018:           PetscCall(PCMGSetInjection(pc, i + 1, p));
1019:           PetscCall(MatDestroy(&p));
1020:         }
1021:       }

1023:       for (i = n - 2; i > -1; i--) PetscCall(DMDestroy(&dms[i]));
1024:       PetscCall(PetscFree(dms));
1025:     }
1026:   }

1028:   if (mg->galerkin < PC_MG_GALERKIN_NONE) {
1029:     Mat       A, B;
1030:     PetscBool doA = PETSC_FALSE, doB = PETSC_FALSE;
1031:     MatReuse  reuse = MAT_INITIAL_MATRIX;

1033:     if (mg->galerkin == PC_MG_GALERKIN_PMAT || mg->galerkin == PC_MG_GALERKIN_BOTH) doB = PETSC_TRUE;
1034:     if (mg->galerkin == PC_MG_GALERKIN_MAT || (mg->galerkin == PC_MG_GALERKIN_BOTH && dA != dB)) doA = PETSC_TRUE;
1035:     if (pc->setupcalled) reuse = MAT_REUSE_MATRIX;
1036:     for (i = n - 2; i > -1; i--) {
1037:       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");
1038:       if (!mglevels[i + 1]->interpolate) PetscCall(PCMGSetInterpolation(pc, i + 1, mglevels[i + 1]->restrct));
1039:       if (!mglevels[i + 1]->restrct) PetscCall(PCMGSetRestriction(pc, i + 1, mglevels[i + 1]->interpolate));
1040:       if (reuse == MAT_REUSE_MATRIX) PetscCall(KSPGetOperators(mglevels[i]->smoothd, &A, &B));
1041:       if (doA) PetscCall(MatGalerkin(mglevels[i + 1]->restrct, dA, mglevels[i + 1]->interpolate, reuse, 1.0, &A));
1042:       if (doB) PetscCall(MatGalerkin(mglevels[i + 1]->restrct, dB, mglevels[i + 1]->interpolate, reuse, 1.0, &B));
1043:       /* the management of the PetscObjectReference() and PetscObjecDereference() below is rather delicate */
1044:       if (!doA && dAeqdB) {
1045:         if (reuse == MAT_INITIAL_MATRIX) PetscCall(PetscObjectReference((PetscObject)B));
1046:         A = B;
1047:       } else if (!doA && reuse == MAT_INITIAL_MATRIX) {
1048:         PetscCall(KSPGetOperators(mglevels[i]->smoothd, &A, NULL));
1049:         PetscCall(PetscObjectReference((PetscObject)A));
1050:       }
1051:       if (!doB && dAeqdB) {
1052:         if (reuse == MAT_INITIAL_MATRIX) PetscCall(PetscObjectReference((PetscObject)A));
1053:         B = A;
1054:       } else if (!doB && reuse == MAT_INITIAL_MATRIX) {
1055:         PetscCall(KSPGetOperators(mglevels[i]->smoothd, NULL, &B));
1056:         PetscCall(PetscObjectReference((PetscObject)B));
1057:       }
1058:       if (reuse == MAT_INITIAL_MATRIX) {
1059:         PetscCall(KSPSetOperators(mglevels[i]->smoothd, A, B));
1060:         PetscCall(PetscObjectDereference((PetscObject)A));
1061:         PetscCall(PetscObjectDereference((PetscObject)B));
1062:       }
1063:       dA = A;
1064:       dB = B;
1065:     }
1066:   }

1068:   /* Adapt interpolation matrices */
1069:   if (adaptInterpolation) {
1070:     for (i = 0; i < n; ++i) {
1071:       if (!mglevels[i]->coarseSpace) PetscCall(PCMGComputeCoarseSpace_Internal(pc, i, mg->coarseSpaceType, mg->Nc, !i ? NULL : mglevels[i - 1]->coarseSpace, &mglevels[i]->coarseSpace));
1072:       if (i) PetscCall(PCMGAdaptInterpolator_Internal(pc, i, mglevels[i - 1]->smoothu, mglevels[i]->smoothu, mglevels[i - 1]->coarseSpace, mglevels[i]->coarseSpace));
1073:     }
1074:     for (i = n - 2; i > -1; --i) PetscCall(PCMGRecomputeLevelOperators_Internal(pc, i));
1075:   }

1077:   if (needRestricts && pc->dm) {
1078:     for (i = n - 2; i >= 0; i--) {
1079:       DM  dmfine, dmcoarse;
1080:       Mat Restrict, Inject;
1081:       Vec rscale;
1082:       PetscCall(KSPGetDM(mglevels[i + 1]->smoothd, &dmfine));
1083:       PetscCall(KSPGetDM(mglevels[i]->smoothd, &dmcoarse));
1084:       PetscCall(PCMGGetRestriction(pc, i + 1, &Restrict));
1085:       PetscCall(PCMGGetRScale(pc, i + 1, &rscale));
1086:       PetscCall(PCMGGetInjection(pc, i + 1, &Inject));
1087:       PetscCall(DMRestrict(dmfine, Restrict, rscale, Inject, dmcoarse));
1088:     }
1089:   }

1091:   if (!pc->setupcalled) {
1092:     for (i = 0; i < n; i++) PetscCall(KSPSetFromOptions(mglevels[i]->smoothd));
1093:     for (i = 1; i < n; i++) {
1094:       if (mglevels[i]->smoothu && (mglevels[i]->smoothu != mglevels[i]->smoothd)) PetscCall(KSPSetFromOptions(mglevels[i]->smoothu));
1095:       if (mglevels[i]->cr) PetscCall(KSPSetFromOptions(mglevels[i]->cr));
1096:     }
1097:     /* insure that if either interpolation or restriction is set the other one is set */
1098:     for (i = 1; i < n; i++) {
1099:       PetscCall(PCMGGetInterpolation(pc, i, NULL));
1100:       PetscCall(PCMGGetRestriction(pc, i, NULL));
1101:     }
1102:     for (i = 0; i < n - 1; i++) {
1103:       if (!mglevels[i]->b) {
1104:         Vec *vec;
1105:         PetscCall(KSPCreateVecs(mglevels[i]->smoothd, 1, &vec, 0, NULL));
1106:         PetscCall(PCMGSetRhs(pc, i, *vec));
1107:         PetscCall(VecDestroy(vec));
1108:         PetscCall(PetscFree(vec));
1109:       }
1110:       if (!mglevels[i]->r && i) {
1111:         PetscCall(VecDuplicate(mglevels[i]->b, &tvec));
1112:         PetscCall(PCMGSetR(pc, i, tvec));
1113:         PetscCall(VecDestroy(&tvec));
1114:       }
1115:       if (!mglevels[i]->x) {
1116:         PetscCall(VecDuplicate(mglevels[i]->b, &tvec));
1117:         PetscCall(PCMGSetX(pc, i, tvec));
1118:         PetscCall(VecDestroy(&tvec));
1119:       }
1120:       if (doCR) {
1121:         PetscCall(VecDuplicate(mglevels[i]->b, &mglevels[i]->crx));
1122:         PetscCall(VecDuplicate(mglevels[i]->b, &mglevels[i]->crb));
1123:         PetscCall(VecZeroEntries(mglevels[i]->crb));
1124:       }
1125:     }
1126:     if (n != 1 && !mglevels[n - 1]->r) {
1127:       /* PCMGSetR() on the finest level if user did not supply it */
1128:       Vec *vec;
1129:       PetscCall(KSPCreateVecs(mglevels[n - 1]->smoothd, 1, &vec, 0, NULL));
1130:       PetscCall(PCMGSetR(pc, n - 1, *vec));
1131:       PetscCall(VecDestroy(vec));
1132:       PetscCall(PetscFree(vec));
1133:     }
1134:     if (doCR) {
1135:       PetscCall(VecDuplicate(mglevels[n - 1]->r, &mglevels[n - 1]->crx));
1136:       PetscCall(VecDuplicate(mglevels[n - 1]->r, &mglevels[n - 1]->crb));
1137:       PetscCall(VecZeroEntries(mglevels[n - 1]->crb));
1138:     }
1139:   }

1141:   if (pc->dm) {
1142:     /* need to tell all the coarser levels to rebuild the matrix using the DM for that level */
1143:     for (i = 0; i < n - 1; i++) {
1144:       if (mglevels[i]->smoothd->setupstage != KSP_SETUP_NEW) mglevels[i]->smoothd->setupstage = KSP_SETUP_NEWMATRIX;
1145:     }
1146:   }
1147:   // We got here (PCSetUp_MG) because the matrix has changed, which means the smoother needs to be set up again (e.g.,
1148:   // new diagonal for Jacobi). Setting it here allows it to be logged under PCSetUp rather than deep inside a PCApply.
1149:   if (mglevels[n - 1]->smoothd->setupstage != KSP_SETUP_NEW) mglevels[n - 1]->smoothd->setupstage = KSP_SETUP_NEWMATRIX;

1151:   for (i = 1; i < n; i++) {
1152:     if (mglevels[i]->smoothu == mglevels[i]->smoothd || mg->am == PC_MG_FULL || mg->am == PC_MG_KASKADE || mg->cyclesperpcapply > 1) {
1153:       /* if doing only down then initial guess is zero */
1154:       PetscCall(KSPSetInitialGuessNonzero(mglevels[i]->smoothd, PETSC_TRUE));
1155:     }
1156:     if (mglevels[i]->cr) PetscCall(KSPSetInitialGuessNonzero(mglevels[i]->cr, PETSC_TRUE));
1157:     if (mglevels[i]->eventsmoothsetup) PetscCall(PetscLogEventBegin(mglevels[i]->eventsmoothsetup, 0, 0, 0, 0));
1158:     PetscCall(KSPSetUp(mglevels[i]->smoothd));
1159:     if (mglevels[i]->smoothd->reason) pc->failedreason = PC_SUBPC_ERROR;
1160:     if (mglevels[i]->eventsmoothsetup) PetscCall(PetscLogEventEnd(mglevels[i]->eventsmoothsetup, 0, 0, 0, 0));
1161:     if (!mglevels[i]->residual) {
1162:       Mat mat;
1163:       PetscCall(KSPGetOperators(mglevels[i]->smoothd, &mat, NULL));
1164:       PetscCall(PCMGSetResidual(pc, i, PCMGResidualDefault, mat));
1165:     }
1166:     if (!mglevels[i]->residualtranspose) {
1167:       Mat mat;
1168:       PetscCall(KSPGetOperators(mglevels[i]->smoothd, &mat, NULL));
1169:       PetscCall(PCMGSetResidualTranspose(pc, i, PCMGResidualTransposeDefault, mat));
1170:     }
1171:   }
1172:   for (i = 1; i < n; i++) {
1173:     if (mglevels[i]->smoothu && mglevels[i]->smoothu != mglevels[i]->smoothd) {
1174:       Mat downmat, downpmat;

1176:       /* check if operators have been set for up, if not use down operators to set them */
1177:       PetscCall(KSPGetOperatorsSet(mglevels[i]->smoothu, &opsset, NULL));
1178:       if (!opsset) {
1179:         PetscCall(KSPGetOperators(mglevels[i]->smoothd, &downmat, &downpmat));
1180:         PetscCall(KSPSetOperators(mglevels[i]->smoothu, downmat, downpmat));
1181:       }

1183:       PetscCall(KSPSetInitialGuessNonzero(mglevels[i]->smoothu, PETSC_TRUE));
1184:       if (mglevels[i]->eventsmoothsetup) PetscCall(PetscLogEventBegin(mglevels[i]->eventsmoothsetup, 0, 0, 0, 0));
1185:       PetscCall(KSPSetUp(mglevels[i]->smoothu));
1186:       if (mglevels[i]->smoothu->reason) pc->failedreason = PC_SUBPC_ERROR;
1187:       if (mglevels[i]->eventsmoothsetup) PetscCall(PetscLogEventEnd(mglevels[i]->eventsmoothsetup, 0, 0, 0, 0));
1188:     }
1189:     if (mglevels[i]->cr) {
1190:       Mat downmat, downpmat;

1192:       /* check if operators have been set for up, if not use down operators to set them */
1193:       PetscCall(KSPGetOperatorsSet(mglevels[i]->cr, &opsset, NULL));
1194:       if (!opsset) {
1195:         PetscCall(KSPGetOperators(mglevels[i]->smoothd, &downmat, &downpmat));
1196:         PetscCall(KSPSetOperators(mglevels[i]->cr, downmat, downpmat));
1197:       }

1199:       PetscCall(KSPSetInitialGuessNonzero(mglevels[i]->cr, PETSC_TRUE));
1200:       if (mglevels[i]->eventsmoothsetup) PetscCall(PetscLogEventBegin(mglevels[i]->eventsmoothsetup, 0, 0, 0, 0));
1201:       PetscCall(KSPSetUp(mglevels[i]->cr));
1202:       if (mglevels[i]->cr->reason) pc->failedreason = PC_SUBPC_ERROR;
1203:       if (mglevels[i]->eventsmoothsetup) PetscCall(PetscLogEventEnd(mglevels[i]->eventsmoothsetup, 0, 0, 0, 0));
1204:     }
1205:   }

1207:   if (mglevels[0]->eventsmoothsetup) PetscCall(PetscLogEventBegin(mglevels[0]->eventsmoothsetup, 0, 0, 0, 0));
1208:   PetscCall(KSPSetUp(mglevels[0]->smoothd));
1209:   if (mglevels[0]->smoothd->reason) pc->failedreason = PC_SUBPC_ERROR;
1210:   if (mglevels[0]->eventsmoothsetup) PetscCall(PetscLogEventEnd(mglevels[0]->eventsmoothsetup, 0, 0, 0, 0));

1212:   /*
1213:      Dump the interpolation/restriction matrices plus the
1214:    Jacobian/stiffness on each level. This allows MATLAB users to
1215:    easily check if the Galerkin condition A_c = R A_f R^T is satisfied.

1217:    Only support one or the other at the same time.
1218:   */
1219: #if defined(PETSC_USE_SOCKET_VIEWER)
1220:   PetscCall(PetscOptionsGetBool(((PetscObject)pc)->options, ((PetscObject)pc)->prefix, "-pc_mg_dump_matlab", &dump, NULL));
1221:   if (dump) viewer = PETSC_VIEWER_SOCKET_(PetscObjectComm((PetscObject)pc));
1222:   dump = PETSC_FALSE;
1223: #endif
1224:   PetscCall(PetscOptionsGetBool(((PetscObject)pc)->options, ((PetscObject)pc)->prefix, "-pc_mg_dump_binary", &dump, NULL));
1225:   if (dump) viewer = PETSC_VIEWER_BINARY_(PetscObjectComm((PetscObject)pc));

1227:   if (viewer) {
1228:     for (i = 1; i < n; i++) PetscCall(MatView(mglevels[i]->restrct, viewer));
1229:     for (i = 0; i < n; i++) {
1230:       PetscCall(KSPGetPC(mglevels[i]->smoothd, &pc));
1231:       PetscCall(MatView(pc->mat, viewer));
1232:     }
1233:   }
1234:   PetscFunctionReturn(PETSC_SUCCESS);
1235: }

1237: PetscErrorCode PCMGGetLevels_MG(PC pc, PetscInt *levels)
1238: {
1239:   PC_MG *mg = (PC_MG *)pc->data;

1241:   PetscFunctionBegin;
1242:   *levels = mg->nlevels;
1243:   PetscFunctionReturn(PETSC_SUCCESS);
1244: }

1246: /*@
1247:   PCMGGetLevels - Gets the number of levels to use with `PCMG`.

1249:   Not Collective

1251:   Input Parameter:
1252: . pc - the preconditioner context

1254:   Output Parameter:
1255: . levels - the number of levels

1257:   Level: advanced

1259: .seealso: [](ch_ksp), `PCMG`, `PCMGSetLevels()`
1260: @*/
1261: PetscErrorCode PCMGGetLevels(PC pc, PetscInt *levels)
1262: {
1263:   PetscFunctionBegin;
1265:   PetscAssertPointer(levels, 2);
1266:   *levels = 0;
1267:   PetscTryMethod(pc, "PCMGGetLevels_C", (PC, PetscInt *), (pc, levels));
1268:   PetscFunctionReturn(PETSC_SUCCESS);
1269: }

1271: /*@
1272:   PCMGGetGridComplexity - compute operator and grid complexity of the `PCMG` hierarchy

1274:   Input Parameter:
1275: . pc - the preconditioner context

1277:   Output Parameters:
1278: + gc - grid complexity = sum_i(n_i) / n_0
1279: - oc - operator complexity = sum_i(nnz_i) / nnz_0

1281:   Level: advanced

1283:   Note:
1284:   This is often call the operator complexity in multigrid literature

1286: .seealso: [](ch_ksp), `PCMG`, `PCMGGetLevels()`, `PCMGSetLevels()`
1287: @*/
1288: PetscErrorCode PCMGGetGridComplexity(PC pc, PetscReal *gc, PetscReal *oc)
1289: {
1290:   PC_MG         *mg       = (PC_MG *)pc->data;
1291:   PC_MG_Levels **mglevels = mg->levels;
1292:   PetscInt       lev, N;
1293:   PetscLogDouble nnz0 = 0, sgc = 0, soc = 0, n0 = 0;
1294:   MatInfo        info;

1296:   PetscFunctionBegin;
1298:   if (gc) PetscAssertPointer(gc, 2);
1299:   if (oc) PetscAssertPointer(oc, 3);
1300:   if (!pc->setupcalled) {
1301:     if (gc) *gc = 0;
1302:     if (oc) *oc = 0;
1303:     PetscFunctionReturn(PETSC_SUCCESS);
1304:   }
1305:   PetscCheck(mg->nlevels > 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MG has no levels");
1306:   for (lev = 0; lev < mg->nlevels; lev++) {
1307:     Mat dB;
1308:     PetscCall(KSPGetOperators(mglevels[lev]->smoothd, NULL, &dB));
1309:     PetscCall(MatGetInfo(dB, MAT_GLOBAL_SUM, &info)); /* global reduction */
1310:     PetscCall(MatGetSize(dB, &N, NULL));
1311:     sgc += N;
1312:     soc += info.nz_used;
1313:     if (lev == mg->nlevels - 1) {
1314:       nnz0 = info.nz_used;
1315:       n0   = N;
1316:     }
1317:   }
1318:   PetscCheck(n0 > 0 && gc, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Number for grid points on finest level is not available");
1319:   *gc = (PetscReal)(sgc / n0);
1320:   if (nnz0 > 0 && oc) *oc = (PetscReal)(soc / nnz0);
1321:   PetscFunctionReturn(PETSC_SUCCESS);
1322: }

1324: /*@
1325:   PCMGSetType - Determines the form of multigrid to use, either
1326:   multiplicative, additive, full, or the Kaskade algorithm.

1328:   Logically Collective

1330:   Input Parameters:
1331: + pc   - the preconditioner context
1332: - form - multigrid form, one of `PC_MG_MULTIPLICATIVE`, `PC_MG_ADDITIVE`, `PC_MG_FULL`, `PC_MG_KASKADE`

1334:   Options Database Key:
1335: . -pc_mg_type <form> - Sets <form>, one of multiplicative, additive, full, kaskade

1337:   Level: advanced

1339: .seealso: [](ch_ksp), `PCMGType`, `PCMG`, `PCMGGetLevels()`, `PCMGSetLevels()`, `PCMGGetType()`, `PCMGCycleType`
1340: @*/
1341: PetscErrorCode PCMGSetType(PC pc, PCMGType form)
1342: {
1343:   PC_MG *mg = (PC_MG *)pc->data;

1345:   PetscFunctionBegin;
1348:   mg->am = form;
1349:   if (form == PC_MG_MULTIPLICATIVE) pc->ops->applyrichardson = PCApplyRichardson_MG;
1350:   else pc->ops->applyrichardson = NULL;
1351:   PetscFunctionReturn(PETSC_SUCCESS);
1352: }

1354: /*@
1355:   PCMGGetType - Finds the form of multigrid the `PCMG` is using  multiplicative, additive, full, or the Kaskade algorithm.

1357:   Logically Collective

1359:   Input Parameter:
1360: . pc - the preconditioner context

1362:   Output Parameter:
1363: . type - one of `PC_MG_MULTIPLICATIVE`, `PC_MG_ADDITIVE`, `PC_MG_FULL`, `PC_MG_KASKADE`, `PCMGCycleType`

1365:   Level: advanced

1367: .seealso: [](ch_ksp), `PCMGType`, `PCMG`, `PCMGGetLevels()`, `PCMGSetLevels()`, `PCMGSetType()`
1368: @*/
1369: PetscErrorCode PCMGGetType(PC pc, PCMGType *type)
1370: {
1371:   PC_MG *mg = (PC_MG *)pc->data;

1373:   PetscFunctionBegin;
1375:   *type = mg->am;
1376:   PetscFunctionReturn(PETSC_SUCCESS);
1377: }

1379: /*@
1380:   PCMGSetCycleType - Sets the type cycles to use.  Use `PCMGSetCycleTypeOnLevel()` for more
1381:   complicated cycling.

1383:   Logically Collective

1385:   Input Parameters:
1386: + pc - the multigrid context
1387: - n  - either `PC_MG_CYCLE_V` or `PC_MG_CYCLE_W`

1389:   Options Database Key:
1390: . -pc_mg_cycle_type <v,w> - provide the cycle desired

1392:   Level: advanced

1394: .seealso: [](ch_ksp), `PCMG`, `PCMGSetCycleTypeOnLevel()`, `PCMGType`, `PCMGCycleType`
1395: @*/
1396: PetscErrorCode PCMGSetCycleType(PC pc, PCMGCycleType n)
1397: {
1398:   PC_MG         *mg       = (PC_MG *)pc->data;
1399:   PC_MG_Levels **mglevels = mg->levels;
1400:   PetscInt       i, levels;

1402:   PetscFunctionBegin;
1405:   PetscCheck(mglevels, PetscObjectComm((PetscObject)pc), PETSC_ERR_ORDER, "Must set MG levels with PCMGSetLevels() before calling");
1406:   levels = mglevels[0]->levels;
1407:   for (i = 0; i < levels; i++) mglevels[i]->cycles = n;
1408:   PetscFunctionReturn(PETSC_SUCCESS);
1409: }

1411: /*@
1412:   PCMGMultiplicativeSetCycles - Sets the number of cycles to use for each preconditioner step
1413:   of multigrid when `PCMGType` is `PC_MG_MULTIPLICATIVE`

1415:   Logically Collective

1417:   Input Parameters:
1418: + pc - the multigrid context
1419: - n  - number of cycles (default is 1)

1421:   Options Database Key:
1422: . -pc_mg_multiplicative_cycles n - set the number of cycles

1424:   Level: advanced

1426:   Note:
1427:   This is not associated with setting a v or w cycle, that is set with `PCMGSetCycleType()`

1429: .seealso: [](ch_ksp), `PCMGSetCycleTypeOnLevel()`, `PCMGSetCycleType()`, `PCMGCycleType`, `PCMGType`
1430: @*/
1431: PetscErrorCode PCMGMultiplicativeSetCycles(PC pc, PetscInt n)
1432: {
1433:   PC_MG *mg = (PC_MG *)pc->data;

1435:   PetscFunctionBegin;
1438:   mg->cyclesperpcapply = n;
1439:   PetscFunctionReturn(PETSC_SUCCESS);
1440: }

1442: static PetscErrorCode PCMGSetGalerkin_MG(PC pc, PCMGGalerkinType use)
1443: {
1444:   PC_MG *mg = (PC_MG *)pc->data;

1446:   PetscFunctionBegin;
1447:   mg->galerkin = use;
1448:   PetscFunctionReturn(PETSC_SUCCESS);
1449: }

1451: /*@
1452:   PCMGSetGalerkin - Causes the coarser grid matrices to be computed from the
1453:   finest grid via the Galerkin process: A_i-1 = r_i * A_i * p_i

1455:   Logically Collective

1457:   Input Parameters:
1458: + pc  - the multigrid context
1459: - use - one of `PC_MG_GALERKIN_BOTH`, `PC_MG_GALERKIN_PMAT`, `PC_MG_GALERKIN_MAT`, or `PC_MG_GALERKIN_NONE`

1461:   Options Database Key:
1462: . -pc_mg_galerkin <both,pmat,mat,none> - set the matrices to form via the Galerkin process

1464:   Level: intermediate

1466:   Note:
1467:   Some codes that use `PCMG` such as `PCGAMG` use Galerkin internally while constructing the hierarchy and thus do not
1468:   use the `PCMG` construction of the coarser grids.

1470: .seealso: [](ch_ksp), `PCMG`, `PCMGGetGalerkin()`, `PCMGGalerkinType`
1471: @*/
1472: PetscErrorCode PCMGSetGalerkin(PC pc, PCMGGalerkinType use)
1473: {
1474:   PetscFunctionBegin;
1476:   PetscTryMethod(pc, "PCMGSetGalerkin_C", (PC, PCMGGalerkinType), (pc, use));
1477:   PetscFunctionReturn(PETSC_SUCCESS);
1478: }

1480: /*@
1481:   PCMGGetGalerkin - Checks if Galerkin multigrid is being used, i.e. A_i-1 = r_i * A_i * p_i

1483:   Not Collective

1485:   Input Parameter:
1486: . pc - the multigrid context

1488:   Output Parameter:
1489: . galerkin - one of `PC_MG_GALERKIN_BOTH`,`PC_MG_GALERKIN_PMAT`,`PC_MG_GALERKIN_MAT`, `PC_MG_GALERKIN_NONE`, or `PC_MG_GALERKIN_EXTERNAL`

1491:   Level: intermediate

1493: .seealso: [](ch_ksp), `PCMG`, `PCMGSetGalerkin()`, `PCMGGalerkinType`
1494: @*/
1495: PetscErrorCode PCMGGetGalerkin(PC pc, PCMGGalerkinType *galerkin)
1496: {
1497:   PC_MG *mg = (PC_MG *)pc->data;

1499:   PetscFunctionBegin;
1501:   *galerkin = mg->galerkin;
1502:   PetscFunctionReturn(PETSC_SUCCESS);
1503: }

1505: static PetscErrorCode PCMGSetAdaptInterpolation_MG(PC pc, PetscBool adapt)
1506: {
1507:   PC_MG *mg = (PC_MG *)pc->data;

1509:   PetscFunctionBegin;
1510:   mg->adaptInterpolation = adapt;
1511:   PetscFunctionReturn(PETSC_SUCCESS);
1512: }

1514: static PetscErrorCode PCMGGetAdaptInterpolation_MG(PC pc, PetscBool *adapt)
1515: {
1516:   PC_MG *mg = (PC_MG *)pc->data;

1518:   PetscFunctionBegin;
1519:   *adapt = mg->adaptInterpolation;
1520:   PetscFunctionReturn(PETSC_SUCCESS);
1521: }

1523: static PetscErrorCode PCMGSetAdaptCoarseSpaceType_MG(PC pc, PCMGCoarseSpaceType ctype)
1524: {
1525:   PC_MG *mg = (PC_MG *)pc->data;

1527:   PetscFunctionBegin;
1528:   mg->adaptInterpolation = ctype != PCMG_ADAPT_NONE ? PETSC_TRUE : PETSC_FALSE;
1529:   mg->coarseSpaceType    = ctype;
1530:   PetscCall(PCMGSetGalerkin(pc, PC_MG_GALERKIN_BOTH));
1531:   PetscFunctionReturn(PETSC_SUCCESS);
1532: }

1534: static PetscErrorCode PCMGGetAdaptCoarseSpaceType_MG(PC pc, PCMGCoarseSpaceType *ctype)
1535: {
1536:   PC_MG *mg = (PC_MG *)pc->data;

1538:   PetscFunctionBegin;
1539:   *ctype = mg->coarseSpaceType;
1540:   PetscFunctionReturn(PETSC_SUCCESS);
1541: }

1543: static PetscErrorCode PCMGSetAdaptCR_MG(PC pc, PetscBool cr)
1544: {
1545:   PC_MG *mg = (PC_MG *)pc->data;

1547:   PetscFunctionBegin;
1548:   mg->compatibleRelaxation = cr;
1549:   PetscFunctionReturn(PETSC_SUCCESS);
1550: }

1552: static PetscErrorCode PCMGGetAdaptCR_MG(PC pc, PetscBool *cr)
1553: {
1554:   PC_MG *mg = (PC_MG *)pc->data;

1556:   PetscFunctionBegin;
1557:   *cr = mg->compatibleRelaxation;
1558:   PetscFunctionReturn(PETSC_SUCCESS);
1559: }

1561: /*@
1562:   PCMGSetAdaptCoarseSpaceType - Set the type of adaptive coarse space.

1564:   Adapts or creates the interpolator based upon a vector space which should be accurately captured by the next coarser mesh, and thus accurately interpolated.

1566:   Logically Collective

1568:   Input Parameters:
1569: + pc    - the multigrid context
1570: - ctype - the type of coarse space

1572:   Options Database Keys:
1573: + -pc_mg_adapt_interp_n <int>             - The number of modes to use
1574: - -pc_mg_adapt_interp_coarse_space <type> - The type of coarse space: none, `polynomial`, `harmonic`, `eigenvector`, `generalized_eigenvector`, `gdsw`

1576:   Level: intermediate

1578:   Note:
1579:   Requires a `DM` with specific functionality be attached to the `PC`.

1581: .seealso: [](ch_ksp), `PCMG`, `PCMGCoarseSpaceType`, `PCMGGetAdaptCoarseSpaceType()`, `PCMGSetGalerkin()`, `PCMGSetAdaptInterpolation()`, `DM`
1582: @*/
1583: PetscErrorCode PCMGSetAdaptCoarseSpaceType(PC pc, PCMGCoarseSpaceType ctype)
1584: {
1585:   PetscFunctionBegin;
1588:   PetscTryMethod(pc, "PCMGSetAdaptCoarseSpaceType_C", (PC, PCMGCoarseSpaceType), (pc, ctype));
1589:   PetscFunctionReturn(PETSC_SUCCESS);
1590: }

1592: /*@
1593:   PCMGGetAdaptCoarseSpaceType - Get the type of adaptive coarse space.

1595:   Not Collective

1597:   Input Parameter:
1598: . pc - the multigrid context

1600:   Output Parameter:
1601: . ctype - the type of coarse space

1603:   Level: intermediate

1605: .seealso: [](ch_ksp), `PCMG`, `PCMGCoarseSpaceType`, `PCMGSetAdaptCoarseSpaceType()`, `PCMGSetGalerkin()`, `PCMGSetAdaptInterpolation()`
1606: @*/
1607: PetscErrorCode PCMGGetAdaptCoarseSpaceType(PC pc, PCMGCoarseSpaceType *ctype)
1608: {
1609:   PetscFunctionBegin;
1611:   PetscAssertPointer(ctype, 2);
1612:   PetscUseMethod(pc, "PCMGGetAdaptCoarseSpaceType_C", (PC, PCMGCoarseSpaceType *), (pc, ctype));
1613:   PetscFunctionReturn(PETSC_SUCCESS);
1614: }

1616: /* MATT: REMOVE? */
1617: /*@
1618:   PCMGSetAdaptInterpolation - Adapt the interpolator based upon a vector space which should be accurately captured by the next coarser mesh, and thus accurately interpolated.

1620:   Logically Collective

1622:   Input Parameters:
1623: + pc    - the multigrid context
1624: - adapt - flag for adaptation of the interpolator

1626:   Options Database Keys:
1627: + -pc_mg_adapt_interp                     - Turn on adaptation
1628: . -pc_mg_adapt_interp_n <int>             - The number of modes to use, should be divisible by dimension
1629: - -pc_mg_adapt_interp_coarse_space <type> - The type of coarse space: polynomial, harmonic, eigenvector, generalized_eigenvector

1631:   Level: intermediate

1633: .seealso: [](ch_ksp), `PCMG`, `PCMGGetAdaptInterpolation()`, `PCMGSetGalerkin()`, `PCMGGetAdaptCoarseSpaceType()`, `PCMGSetAdaptCoarseSpaceType()`
1634: @*/
1635: PetscErrorCode PCMGSetAdaptInterpolation(PC pc, PetscBool adapt)
1636: {
1637:   PetscFunctionBegin;
1639:   PetscTryMethod(pc, "PCMGSetAdaptInterpolation_C", (PC, PetscBool), (pc, adapt));
1640:   PetscFunctionReturn(PETSC_SUCCESS);
1641: }

1643: /*@
1644:   PCMGGetAdaptInterpolation - Get the flag to adapt the interpolator based upon a vector space which should be accurately captured by the next coarser mesh,
1645:   and thus accurately interpolated.

1647:   Not Collective

1649:   Input Parameter:
1650: . pc - the multigrid context

1652:   Output Parameter:
1653: . adapt - flag for adaptation of the interpolator

1655:   Level: intermediate

1657: .seealso: [](ch_ksp), `PCMG`, `PCMGSetAdaptInterpolation()`, `PCMGSetGalerkin()`, `PCMGGetAdaptCoarseSpaceType()`, `PCMGSetAdaptCoarseSpaceType()`
1658: @*/
1659: PetscErrorCode PCMGGetAdaptInterpolation(PC pc, PetscBool *adapt)
1660: {
1661:   PetscFunctionBegin;
1663:   PetscAssertPointer(adapt, 2);
1664:   PetscUseMethod(pc, "PCMGGetAdaptInterpolation_C", (PC, PetscBool *), (pc, adapt));
1665:   PetscFunctionReturn(PETSC_SUCCESS);
1666: }

1668: /*@
1669:   PCMGSetAdaptCR - Monitor the coarse space quality using an auxiliary solve with compatible relaxation.

1671:   Logically Collective

1673:   Input Parameters:
1674: + pc - the multigrid context
1675: - cr - flag for compatible relaxation

1677:   Options Database Key:
1678: . -pc_mg_adapt_cr - Turn on compatible relaxation

1680:   Level: intermediate

1682: .seealso: [](ch_ksp), `PCMG`, `PCMGGetAdaptCR()`, `PCMGSetAdaptInterpolation()`, `PCMGSetGalerkin()`, `PCMGGetAdaptCoarseSpaceType()`, `PCMGSetAdaptCoarseSpaceType()`
1683: @*/
1684: PetscErrorCode PCMGSetAdaptCR(PC pc, PetscBool cr)
1685: {
1686:   PetscFunctionBegin;
1688:   PetscTryMethod(pc, "PCMGSetAdaptCR_C", (PC, PetscBool), (pc, cr));
1689:   PetscFunctionReturn(PETSC_SUCCESS);
1690: }

1692: /*@
1693:   PCMGGetAdaptCR - Get the flag to monitor coarse space quality using an auxiliary solve with compatible relaxation.

1695:   Not Collective

1697:   Input Parameter:
1698: . pc - the multigrid context

1700:   Output Parameter:
1701: . cr - flag for compatible relaxaion

1703:   Level: intermediate

1705: .seealso: [](ch_ksp), `PCMGSetAdaptCR()`, `PCMGGetAdaptInterpolation()`, `PCMGSetGalerkin()`, `PCMGGetAdaptCoarseSpaceType()`, `PCMGSetAdaptCoarseSpaceType()`
1706: @*/
1707: PetscErrorCode PCMGGetAdaptCR(PC pc, PetscBool *cr)
1708: {
1709:   PetscFunctionBegin;
1711:   PetscAssertPointer(cr, 2);
1712:   PetscUseMethod(pc, "PCMGGetAdaptCR_C", (PC, PetscBool *), (pc, cr));
1713:   PetscFunctionReturn(PETSC_SUCCESS);
1714: }

1716: /*@
1717:   PCMGSetNumberSmooth - Sets the number of pre and post-smoothing steps to use
1718:   on all levels.  Use `PCMGDistinctSmoothUp()` to create separate up and down smoothers if you want different numbers of
1719:   pre- and post-smoothing steps.

1721:   Logically Collective

1723:   Input Parameters:
1724: + pc - the multigrid context
1725: - n  - the number of smoothing steps

1727:   Options Database Key:
1728: . -mg_levels_ksp_max_it <n> - Sets number of pre and post-smoothing steps

1730:   Level: advanced

1732:   Note:
1733:   This does not set a value on the coarsest grid, since we assume that there is no separate smooth up on the coarsest grid.

1735: .seealso: [](ch_ksp), `PCMG`, `PCMGSetDistinctSmoothUp()`
1736: @*/
1737: PetscErrorCode PCMGSetNumberSmooth(PC pc, PetscInt n)
1738: {
1739:   PC_MG         *mg       = (PC_MG *)pc->data;
1740:   PC_MG_Levels **mglevels = mg->levels;
1741:   PetscInt       i, levels;

1743:   PetscFunctionBegin;
1746:   PetscCheck(mglevels, PetscObjectComm((PetscObject)pc), PETSC_ERR_ORDER, "Must set MG levels with PCMGSetLevels() before calling");
1747:   levels = mglevels[0]->levels;

1749:   for (i = 1; i < levels; i++) {
1750:     PetscCall(KSPSetTolerances(mglevels[i]->smoothu, PETSC_CURRENT, PETSC_CURRENT, PETSC_CURRENT, n));
1751:     PetscCall(KSPSetTolerances(mglevels[i]->smoothd, PETSC_CURRENT, PETSC_CURRENT, PETSC_CURRENT, n));
1752:     mg->default_smoothu = n;
1753:     mg->default_smoothd = n;
1754:   }
1755:   PetscFunctionReturn(PETSC_SUCCESS);
1756: }

1758: /*@
1759:   PCMGSetDistinctSmoothUp - sets the up (post) smoother to be a separate `KSP` from the down (pre) smoother on all levels
1760:   and adds the suffix _up to the options name

1762:   Logically Collective

1764:   Input Parameter:
1765: . pc - the preconditioner context

1767:   Options Database Key:
1768: . -pc_mg_distinct_smoothup <bool> - use distinct smoothing objects

1770:   Level: advanced

1772:   Note:
1773:   This does not set a value on the coarsest grid, since we assume that there is no separate smooth up on the coarsest grid.

1775: .seealso: [](ch_ksp), `PCMG`, `PCMGSetNumberSmooth()`
1776: @*/
1777: PetscErrorCode PCMGSetDistinctSmoothUp(PC pc)
1778: {
1779:   PC_MG         *mg       = (PC_MG *)pc->data;
1780:   PC_MG_Levels **mglevels = mg->levels;
1781:   PetscInt       i, levels;
1782:   KSP            subksp;

1784:   PetscFunctionBegin;
1786:   PetscCheck(mglevels, PetscObjectComm((PetscObject)pc), PETSC_ERR_ORDER, "Must set MG levels with PCMGSetLevels() before calling");
1787:   levels = mglevels[0]->levels;

1789:   for (i = 1; i < levels; i++) {
1790:     const char *prefix = NULL;
1791:     /* make sure smoother up and down are different */
1792:     PetscCall(PCMGGetSmootherUp(pc, i, &subksp));
1793:     PetscCall(KSPGetOptionsPrefix(mglevels[i]->smoothd, &prefix));
1794:     PetscCall(KSPSetOptionsPrefix(subksp, prefix));
1795:     PetscCall(KSPAppendOptionsPrefix(subksp, "up_"));
1796:   }
1797:   PetscFunctionReturn(PETSC_SUCCESS);
1798: }

1800: /* No new matrices are created, and the coarse operator matrices are the references to the original ones */
1801: static PetscErrorCode PCGetInterpolations_MG(PC pc, PetscInt *num_levels, Mat *interpolations[])
1802: {
1803:   PC_MG         *mg       = (PC_MG *)pc->data;
1804:   PC_MG_Levels **mglevels = mg->levels;
1805:   Mat           *mat;
1806:   PetscInt       l;

1808:   PetscFunctionBegin;
1809:   PetscCheck(mglevels, PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_WRONGSTATE, "Must set MG levels before calling");
1810:   PetscCall(PetscMalloc1(mg->nlevels, &mat));
1811:   for (l = 1; l < mg->nlevels; l++) {
1812:     mat[l - 1] = mglevels[l]->interpolate;
1813:     PetscCall(PetscObjectReference((PetscObject)mat[l - 1]));
1814:   }
1815:   *num_levels     = mg->nlevels;
1816:   *interpolations = mat;
1817:   PetscFunctionReturn(PETSC_SUCCESS);
1818: }

1820: /* No new matrices are created, and the coarse operator matrices are the references to the original ones */
1821: static PetscErrorCode PCGetCoarseOperators_MG(PC pc, PetscInt *num_levels, Mat *coarseOperators[])
1822: {
1823:   PC_MG         *mg       = (PC_MG *)pc->data;
1824:   PC_MG_Levels **mglevels = mg->levels;
1825:   PetscInt       l;
1826:   Mat           *mat;

1828:   PetscFunctionBegin;
1829:   PetscCheck(mglevels, PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_WRONGSTATE, "Must set MG levels before calling");
1830:   PetscCall(PetscMalloc1(mg->nlevels, &mat));
1831:   for (l = 0; l < mg->nlevels - 1; l++) {
1832:     PetscCall(KSPGetOperators(mglevels[l]->smoothd, NULL, &mat[l]));
1833:     PetscCall(PetscObjectReference((PetscObject)mat[l]));
1834:   }
1835:   *num_levels      = mg->nlevels;
1836:   *coarseOperators = mat;
1837:   PetscFunctionReturn(PETSC_SUCCESS);
1838: }

1840: /*@C
1841:   PCMGRegisterCoarseSpaceConstructor -  Adds a method to the `PCMG` package for coarse space construction.

1843:   Not Collective, No Fortran Support

1845:   Input Parameters:
1846: + name     - name of the constructor
1847: - function - constructor routine, see `PCMGCoarseSpaceConstructorFn`

1849:   Level: advanced

1851:   Developer Notes:
1852:   This does not appear to be used anywhere

1854: .seealso: [](ch_ksp), `PCMGCoarseSpaceConstructorFn`, `PCMG`, `PCMGGetCoarseSpaceConstructor()`, `PCRegister()`
1855: @*/
1856: PetscErrorCode PCMGRegisterCoarseSpaceConstructor(const char name[], PCMGCoarseSpaceConstructorFn *function)
1857: {
1858:   PetscFunctionBegin;
1859:   PetscCall(PCInitializePackage());
1860:   PetscCall(PetscFunctionListAdd(&PCMGCoarseList, name, function));
1861:   PetscFunctionReturn(PETSC_SUCCESS);
1862: }

1864: /*@C
1865:   PCMGGetCoarseSpaceConstructor -  Returns the given coarse space construction method.

1867:   Not Collective, No Fortran Support

1869:   Input Parameter:
1870: . name - name of the constructor

1872:   Output Parameter:
1873: . function - constructor routine

1875:   Level: advanced

1877: .seealso: [](ch_ksp), `PCMGCoarseSpaceConstructorFn`, `PCMG`, `PCMGRegisterCoarseSpaceConstructor()`, `PCRegister()`
1878: @*/
1879: PetscErrorCode PCMGGetCoarseSpaceConstructor(const char name[], PCMGCoarseSpaceConstructorFn **function)
1880: {
1881:   PetscFunctionBegin;
1882:   PetscCall(PetscFunctionListFind(PCMGCoarseList, name, function));
1883:   PetscFunctionReturn(PETSC_SUCCESS);
1884: }

1886: /*MC
1887:    PCMG - Use multigrid preconditioning. This preconditioner requires you provide additional
1888:     information about the coarser grid matrices and restriction/interpolation operators.

1890:    Options Database Keys:
1891: +  -pc_mg_levels <nlevels>                            - number of levels including finest
1892: .  -pc_mg_cycle_type <v,w>                            - provide the cycle desired
1893: .  -pc_mg_type <additive,multiplicative,full,kaskade> - multiplicative is the default
1894: .  -pc_mg_log                                         - log information about time spent on each level of the solver
1895: .  -pc_mg_distinct_smoothup                           - configure up (after interpolation) and down (before restriction) smoothers separately (with different options prefixes)
1896: .  -pc_mg_galerkin <both,pmat,mat,none>               - use Galerkin process to compute coarser operators, i.e. Acoarse = R A R'
1897: .  -pc_mg_multiplicative_cycles                        - number of cycles to use as the preconditioner (defaults to 1)
1898: .  -pc_mg_dump_matlab                                  - dumps the matrices for each level and the restriction/interpolation matrices
1899:                                                          to a `PETSCVIEWERSOCKET` for reading from MATLAB.
1900: -  -pc_mg_dump_binary                                  -dumps the matrices for each level and the restriction/interpolation matrices
1901:                                                         to the binary output file called binaryoutput

1903:    Level: intermediate

1905:    Notes:
1906:    The Krylov solver (if any) and preconditioner (smoother) and their parameters are controlled from the options database with the standard
1907:    options database keywords prefixed with `-mg_levels_` to affect all the levels but the coarsest, which is controlled with `-mg_coarse_`,
1908:    and the finest where `-mg_fine_` can override `-mg_levels_`.  One can set different preconditioners etc on specific levels with the prefix
1909:    `-mg_levels_n_` where `n` is the level number (zero being the coarse level. For example
1910: .vb
1911:    -mg_levels_ksp_type gmres -mg_levels_pc_type bjacobi -mg_coarse_pc_type svd -mg_levels_2_pc_type sor
1912: .ve
1913:    These options also work for controlling the smoothers etc inside `PCGAMG`

1915:    If one uses a Krylov method such `KSPGMRES` or `KSPCG` as the smoother than one must use `KSPFGMRES`, `KSPGCR`, or `KSPRICHARDSON` as the outer Krylov method

1917:    When run with a single level the smoother options are used on that level NOT the coarse grid solver options

1919:    When run with `KSPRICHARDSON` the convergence test changes slightly if monitor is turned on. The iteration count may change slightly. This
1920:    is because without monitoring the residual norm is computed WITHIN each multigrid cycle on the finest level after the pre-smoothing
1921:    (because the residual has just been computed for the multigrid algorithm and is hence available for free) while with monitoring the
1922:    residual is computed at the end of each cycle.

1924: .seealso: [](sec_mg), `PCCreate()`, `PCSetType()`, `PCType`, `PC`, `PCMGType`, `PCEXOTIC`, `PCGAMG`, `PCML`, `PCHYPRE`
1925:           `PCMGSetLevels()`, `PCMGGetLevels()`, `PCMGSetType()`, `PCMGSetCycleType()`,
1926:           `PCMGSetDistinctSmoothUp()`, `PCMGGetCoarseSolve()`, `PCMGSetResidual()`, `PCMGSetInterpolation()`,
1927:           `PCMGSetRestriction()`, `PCMGGetSmoother()`, `PCMGGetSmootherUp()`, `PCMGGetSmootherDown()`,
1928:           `PCMGSetCycleTypeOnLevel()`, `PCMGSetRhs()`, `PCMGSetX()`, `PCMGSetR()`,
1929:           `PCMGSetAdaptCR()`, `PCMGGetAdaptInterpolation()`, `PCMGSetGalerkin()`, `PCMGGetAdaptCoarseSpaceType()`, `PCMGSetAdaptCoarseSpaceType()`
1930: M*/

1932: PETSC_EXTERN PetscErrorCode PCCreate_MG(PC pc)
1933: {
1934:   PC_MG *mg;

1936:   PetscFunctionBegin;
1937:   PetscCall(PetscNew(&mg));
1938:   pc->data               = mg;
1939:   mg->nlevels            = -1;
1940:   mg->am                 = PC_MG_MULTIPLICATIVE;
1941:   mg->galerkin           = PC_MG_GALERKIN_NONE;
1942:   mg->adaptInterpolation = PETSC_FALSE;
1943:   mg->Nc                 = -1;
1944:   mg->eigenvalue         = -1;

1946:   pc->useAmat = PETSC_TRUE;

1948:   pc->ops->apply             = PCApply_MG;
1949:   pc->ops->applytranspose    = PCApplyTranspose_MG;
1950:   pc->ops->matapply          = PCMatApply_MG;
1951:   pc->ops->matapplytranspose = PCMatApplyTranspose_MG;
1952:   pc->ops->setup             = PCSetUp_MG;
1953:   pc->ops->reset             = PCReset_MG;
1954:   pc->ops->destroy           = PCDestroy_MG;
1955:   pc->ops->setfromoptions    = PCSetFromOptions_MG;
1956:   pc->ops->view              = PCView_MG;

1958:   PetscCall(PetscObjectComposedDataRegister(&mg->eigenvalue));
1959:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetGalerkin_C", PCMGSetGalerkin_MG));
1960:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetLevels_C", PCMGGetLevels_MG));
1961:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetLevels_C", PCMGSetLevels_MG));
1962:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGetInterpolations_C", PCGetInterpolations_MG));
1963:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCGetCoarseOperators_C", PCGetCoarseOperators_MG));
1964:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetAdaptInterpolation_C", PCMGSetAdaptInterpolation_MG));
1965:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetAdaptInterpolation_C", PCMGGetAdaptInterpolation_MG));
1966:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetAdaptCR_C", PCMGSetAdaptCR_MG));
1967:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetAdaptCR_C", PCMGGetAdaptCR_MG));
1968:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGSetAdaptCoarseSpaceType_C", PCMGSetAdaptCoarseSpaceType_MG));
1969:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCMGGetAdaptCoarseSpaceType_C", PCMGGetAdaptCoarseSpaceType_MG));
1970:   PetscFunctionReturn(PETSC_SUCCESS);
1971: }