Date: 2021-03-03T22:27:11+00:00

Actual source code: mg.c
petsc-3.14.5 2021-03-03
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      2: /*
      3:     Defines the multigrid preconditioner interface.
      4: */
      5: #include <petsc/private/pcmgimpl.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,PCRichardsonConvergedReason *reason)
     15: {
     16:   PC_MG          *mg = (PC_MG*)pc->data;
     17:   PC_MG_Levels   *mgc,*mglevels = *mglevelsin;
     19:   PetscInt       cycles = (mglevels->level == 1) ? 1 : (PetscInt) mglevels->cycles;

     22:   if (mglevels->eventsmoothsolve) {PetscLogEventBegin(mglevels->eventsmoothsolve,0,0,0,0);}
     23:   KSPSolve(mglevels->smoothd,mglevels->b,mglevels->x);  /* pre-smooth */
     24:   KSPCheckSolve(mglevels->smoothd,pc,mglevels->x);
     25:   if (mglevels->eventsmoothsolve) {PetscLogEventEnd(mglevels->eventsmoothsolve,0,0,0,0);}
     26:   if (mglevels->level) {  /* not the coarsest grid */
     27:     if (mglevels->eventresidual) {PetscLogEventBegin(mglevels->eventresidual,0,0,0,0);}
     28:     (*mglevels->residual)(mglevels->A,mglevels->b,mglevels->x,mglevels->r);
     29:     if (mglevels->eventresidual) {PetscLogEventEnd(mglevels->eventresidual,0,0,0,0);}

     31:     /* if on finest level and have convergence criteria set */
     32:     if (mglevels->level == mglevels->levels-1 && mg->ttol && reason) {
     33:       PetscReal rnorm;
     34:       VecNorm(mglevels->r,NORM_2,&rnorm);
     35:       if (rnorm <= mg->ttol) {
     36:         if (rnorm < mg->abstol) {
     37:           *reason = PCRICHARDSON_CONVERGED_ATOL;
     38:           PetscInfo2(pc,"Linear solver has converged. Residual norm %g is less than absolute tolerance %g\n",(double)rnorm,(double)mg->abstol);
     39:         } else {
     40:           *reason = PCRICHARDSON_CONVERGED_RTOL;
     41:           PetscInfo2(pc,"Linear solver has converged. Residual norm %g is less than relative tolerance times initial residual norm %g\n",(double)rnorm,(double)mg->ttol);
     42:         }
     43:         return(0);
     44:       }
     45:     }

     47:     mgc = *(mglevelsin - 1);
     48:     if (mglevels->eventinterprestrict) {PetscLogEventBegin(mglevels->eventinterprestrict,0,0,0,0);}
     49:     MatRestrict(mglevels->restrct,mglevels->r,mgc->b);
     50:     if (mglevels->eventinterprestrict) {PetscLogEventEnd(mglevels->eventinterprestrict,0,0,0,0);}
     51:     VecSet(mgc->x,0.0);
     52:     while (cycles--) {
     53:       PCMGMCycle_Private(pc,mglevelsin-1,reason);
     54:     }
     55:     if (mglevels->eventinterprestrict) {PetscLogEventBegin(mglevels->eventinterprestrict,0,0,0,0);}
     56:     MatInterpolateAdd(mglevels->interpolate,mgc->x,mglevels->x,mglevels->x);
     57:     if (mglevels->eventinterprestrict) {PetscLogEventEnd(mglevels->eventinterprestrict,0,0,0,0);}
     58:     if (mglevels->eventsmoothsolve) {PetscLogEventBegin(mglevels->eventsmoothsolve,0,0,0,0);}
     59:     KSPSolve(mglevels->smoothu,mglevels->b,mglevels->x);    /* post smooth */
     60:     KSPCheckSolve(mglevels->smoothu,pc,mglevels->x);
     61:     if (mglevels->eventsmoothsolve) {PetscLogEventEnd(mglevels->eventsmoothsolve,0,0,0,0);}
     62:   }
     63:   return(0);
     64: }

     66: 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)
     67: {
     68:   PC_MG          *mg        = (PC_MG*)pc->data;
     69:   PC_MG_Levels   **mglevels = mg->levels;
     71:   PC             tpc;
     72:   PetscBool      changeu,changed;
     73:   PetscInt       levels = mglevels[0]->levels,i;

     76:   /* When the DM is supplying the matrix then it will not exist until here */
     77:   for (i=0; i<levels; i++) {
     78:     if (!mglevels[i]->A) {
     79:       KSPGetOperators(mglevels[i]->smoothu,&mglevels[i]->A,NULL);
     80:       PetscObjectReference((PetscObject)mglevels[i]->A);
     81:     }
     82:   }

     84:   KSPGetPC(mglevels[levels-1]->smoothd,&tpc);
     85:   PCPreSolveChangeRHS(tpc,&changed);
     86:   KSPGetPC(mglevels[levels-1]->smoothu,&tpc);
     87:   PCPreSolveChangeRHS(tpc,&changeu);
     88:   if (!changed && !changeu) {
     89:     VecDestroy(&mglevels[levels-1]->b);
     90:     mglevels[levels-1]->b = b;
     91:   } else { /* if the smoother changes the rhs during PreSolve, we cannot use the input vector */
     92:     if (!mglevels[levels-1]->b) {
     93:       Vec *vec;

     95:       KSPCreateVecs(mglevels[levels-1]->smoothd,1,&vec,0,NULL);
     96:       mglevels[levels-1]->b = *vec;
     97:       PetscFree(vec);
     98:     }
     99:     VecCopy(b,mglevels[levels-1]->b);
    100:   }
    101:   mglevels[levels-1]->x = x;

    103:   mg->rtol   = rtol;
    104:   mg->abstol = abstol;
    105:   mg->dtol   = dtol;
    106:   if (rtol) {
    107:     /* compute initial residual norm for relative convergence test */
    108:     PetscReal rnorm;
    109:     if (zeroguess) {
    110:       VecNorm(b,NORM_2,&rnorm);
    111:     } else {
    112:       (*mglevels[levels-1]->residual)(mglevels[levels-1]->A,b,x,w);
    113:       VecNorm(w,NORM_2,&rnorm);
    114:     }
    115:     mg->ttol = PetscMax(rtol*rnorm,abstol);
    116:   } else if (abstol) mg->ttol = abstol;
    117:   else mg->ttol = 0.0;

    119:   /* since smoother is applied to full system, not just residual we need to make sure that smoothers don't
    120:      stop prematurely due to small residual */
    121:   for (i=1; i<levels; i++) {
    122:     KSPSetTolerances(mglevels[i]->smoothu,0,PETSC_DEFAULT,PETSC_DEFAULT,PETSC_DEFAULT);
    123:     if (mglevels[i]->smoothu != mglevels[i]->smoothd) {
    124:       /* For Richardson the initial guess is nonzero since it is solving in each cycle the original system not just applying as a preconditioner */
    125:       KSPSetInitialGuessNonzero(mglevels[i]->smoothd,PETSC_TRUE);
    126:       KSPSetTolerances(mglevels[i]->smoothd,0,PETSC_DEFAULT,PETSC_DEFAULT,PETSC_DEFAULT);
    127:     }
    128:   }

    130:   *reason = (PCRichardsonConvergedReason)0;
    131:   for (i=0; i<its; i++) {
    132:     PCMGMCycle_Private(pc,mglevels+levels-1,reason);
    133:     if (*reason) break;
    134:   }
    135:   if (!*reason) *reason = PCRICHARDSON_CONVERGED_ITS;
    136:   *outits = i;
    137:   if (!changed && !changeu) mglevels[levels-1]->b = NULL;
    138:   return(0);
    139: }

    141: PetscErrorCode PCReset_MG(PC pc)
    142: {
    143:   PC_MG          *mg        = (PC_MG*)pc->data;
    144:   PC_MG_Levels   **mglevels = mg->levels;
    146:   PetscInt       i,c,n;

    149:   if (mglevels) {
    150:     n = mglevels[0]->levels;
    151:     for (i=0; i<n-1; i++) {
    152:       VecDestroy(&mglevels[i+1]->r);
    153:       VecDestroy(&mglevels[i]->b);
    154:       VecDestroy(&mglevels[i]->x);
    155:       MatDestroy(&mglevels[i+1]->restrct);
    156:       MatDestroy(&mglevels[i+1]->interpolate);
    157:       MatDestroy(&mglevels[i+1]->inject);
    158:       VecDestroy(&mglevels[i+1]->rscale);
    159:     }
    160:     /* this is not null only if the smoother on the finest level
    161:        changes the rhs during PreSolve */
    162:     VecDestroy(&mglevels[n-1]->b);

    164:     for (i=0; i<n; i++) {
    165:       if (mglevels[i]->coarseSpace) for (c = 0; c < mg->Nc; ++c) {VecDestroy(&mglevels[i]->coarseSpace[c]);}
    166:       PetscFree(mglevels[i]->coarseSpace);
    167:       mglevels[i]->coarseSpace = NULL;
    168:       MatDestroy(&mglevels[i]->A);
    169:       if (mglevels[i]->smoothd != mglevels[i]->smoothu) {
    170:         KSPReset(mglevels[i]->smoothd);
    171:       }
    172:       KSPReset(mglevels[i]->smoothu);
    173:     }
    174:     mg->Nc = 0;
    175:   }
    176:   return(0);
    177: }

    179: PetscErrorCode PCMGSetLevels_MG(PC pc,PetscInt levels,MPI_Comm *comms)
    180: {
    182:   PC_MG          *mg        = (PC_MG*)pc->data;
    183:   MPI_Comm       comm;
    184:   PC_MG_Levels   **mglevels = mg->levels;
    185:   PCMGType       mgtype     = mg->am;
    186:   PetscInt       mgctype    = (PetscInt) PC_MG_CYCLE_V;
    187:   PetscInt       i;
    188:   PetscMPIInt    size;
    189:   const char     *prefix;
    190:   PC             ipc;
    191:   PetscInt       n;

    196:   if (mg->nlevels == levels) return(0);
    197:   PetscObjectGetComm((PetscObject)pc,&comm);
    198:   if (mglevels) {
    199:     mgctype = mglevels[0]->cycles;
    200:     /* changing the number of levels so free up the previous stuff */
    201:     PCReset_MG(pc);
    202:     n    = mglevels[0]->levels;
    203:     for (i=0; i<n; i++) {
    204:       if (mglevels[i]->smoothd != mglevels[i]->smoothu) {
    205:         KSPDestroy(&mglevels[i]->smoothd);
    206:       }
    207:       KSPDestroy(&mglevels[i]->smoothu);
    208:       PetscFree(mglevels[i]);
    209:     }
    210:     PetscFree(mg->levels);
    211:   }

    213:   mg->nlevels = levels;

    215:   PetscMalloc1(levels,&mglevels);
    216:   PetscLogObjectMemory((PetscObject)pc,levels*(sizeof(PC_MG*)));

    218:   PCGetOptionsPrefix(pc,&prefix);

    220:   mg->stageApply = 0;
    221:   for (i=0; i<levels; i++) {
    222:     PetscNewLog(pc,&mglevels[i]);

    224:     mglevels[i]->level               = i;
    225:     mglevels[i]->levels              = levels;
    226:     mglevels[i]->cycles              = mgctype;
    227:     mg->default_smoothu              = 2;
    228:     mg->default_smoothd              = 2;
    229:     mglevels[i]->eventsmoothsetup    = 0;
    230:     mglevels[i]->eventsmoothsolve    = 0;
    231:     mglevels[i]->eventresidual       = 0;
    232:     mglevels[i]->eventinterprestrict = 0;

    234:     if (comms) comm = comms[i];
    235:     if (comm != MPI_COMM_NULL) {
    236:       KSPCreate(comm,&mglevels[i]->smoothd);
    237:       KSPSetErrorIfNotConverged(mglevels[i]->smoothd,pc->erroriffailure);
    238:       PetscObjectIncrementTabLevel((PetscObject)mglevels[i]->smoothd,(PetscObject)pc,levels-i);
    239:       KSPSetOptionsPrefix(mglevels[i]->smoothd,prefix);
    240:       PetscObjectComposedDataSetInt((PetscObject) mglevels[i]->smoothd, PetscMGLevelId, mglevels[i]->level);
    241:       if (i || levels == 1) {
    242:         char tprefix[128];

    244:         KSPSetType(mglevels[i]->smoothd,KSPCHEBYSHEV);
    245:         KSPSetConvergenceTest(mglevels[i]->smoothd,KSPConvergedSkip,NULL,NULL);
    246:         KSPSetNormType(mglevels[i]->smoothd,KSP_NORM_NONE);
    247:         KSPGetPC(mglevels[i]->smoothd,&ipc);
    248:         PCSetType(ipc,PCSOR);
    249:         KSPSetTolerances(mglevels[i]->smoothd,PETSC_DEFAULT,PETSC_DEFAULT,PETSC_DEFAULT, mg->default_smoothd);

    251:         PetscSNPrintf(tprefix,128,"mg_levels_%d_",(int)i);
    252:         KSPAppendOptionsPrefix(mglevels[i]->smoothd,tprefix);
    253:       } else {
    254:         KSPAppendOptionsPrefix(mglevels[0]->smoothd,"mg_coarse_");

    256:         /* coarse solve is (redundant) LU by default; set shifttype NONZERO to avoid annoying zero-pivot in LU preconditioner */
    257:         KSPSetType(mglevels[0]->smoothd,KSPPREONLY);
    258:         KSPGetPC(mglevels[0]->smoothd,&ipc);
    259:         MPI_Comm_size(comm,&size);
    260:         if (size > 1) {
    261:           PCSetType(ipc,PCREDUNDANT);
    262:         } else {
    263:           PCSetType(ipc,PCLU);
    264:         }
    265:         PCFactorSetShiftType(ipc,MAT_SHIFT_INBLOCKS);
    266:       }
    267:       PetscLogObjectParent((PetscObject)pc,(PetscObject)mglevels[i]->smoothd);
    268:     }
    269:     mglevels[i]->smoothu = mglevels[i]->smoothd;
    270:     mg->rtol             = 0.0;
    271:     mg->abstol           = 0.0;
    272:     mg->dtol             = 0.0;
    273:     mg->ttol             = 0.0;
    274:     mg->cyclesperpcapply = 1;
    275:   }
    276:   mg->levels = mglevels;
    277:   PCMGSetType(pc,mgtype);
    278:   return(0);
    279: }

    281: /*@C
    282:    PCMGSetLevels - Sets the number of levels to use with MG.
    283:    Must be called before any other MG routine.

    285:    Logically Collective on PC

    287:    Input Parameters:
    288: +  pc - the preconditioner context
    289: .  levels - the number of levels
    290: -  comms - optional communicators for each level; this is to allow solving the coarser problems
    291:            on smaller sets of processes. For processes that are not included in the computation
    292:            you must pass MPI_COMM_NULL.

    294:    Level: intermediate

    296:    Notes:
    297:      If the number of levels is one then the multigrid uses the -mg_levels prefix
    298:      for setting the level options rather than the -mg_coarse prefix.

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

    302:      The array of MPI communicators must contain MPI_COMM_NULL for those ranks that at each level
    303:      are not participating in the coarser solve. For example, with 2 levels and 1 and 2 ranks on
    304:      the two levels, rank 0 in the original communicator will pass in an array of 2 communicators
    305:      of size 2 and 1, while rank 1 in the original communicator will pass in array of 2 communicators
    306:      the first of size 2 and the second of value MPI_COMM_NULL since the rank 1 does not participate
    307:      in the coarse grid solve.

    309:      Since each coarser level may have a new MPI_Comm with fewer ranks than the previous, one
    310:      must take special care in providing the restriction and interpolation operation. We recommend
    311:      providing these as two step operations; first perform a standard restriction or interpolation on
    312:      the full number of ranks for that level and then use an MPI call to copy the resulting vector
    313:      array entries (after calls to VecGetArray()) to the smaller or larger number of ranks, not in both
    314:      cases the MPI calls must be made on the larger of the two communicators. Traditional MPI send and
    315:      recieves or MPI_AlltoAllv() could be used to do the reshuffling of the vector entries.

    318: .seealso: PCMGSetType(), PCMGGetLevels()
    319: @*/
    320: PetscErrorCode PCMGSetLevels(PC pc,PetscInt levels,MPI_Comm *comms)
    321: {

    327:   PetscTryMethod(pc,"PCMGSetLevels_C",(PC,PetscInt,MPI_Comm*),(pc,levels,comms));
    328:   return(0);
    329: }

    332: PetscErrorCode PCDestroy_MG(PC pc)
    333: {
    335:   PC_MG          *mg        = (PC_MG*)pc->data;
    336:   PC_MG_Levels   **mglevels = mg->levels;
    337:   PetscInt       i,n;

    340:   PCReset_MG(pc);
    341:   if (mglevels) {
    342:     n = mglevels[0]->levels;
    343:     for (i=0; i<n; i++) {
    344:       if (mglevels[i]->smoothd != mglevels[i]->smoothu) {
    345:         KSPDestroy(&mglevels[i]->smoothd);
    346:       }
    347:       KSPDestroy(&mglevels[i]->smoothu);
    348:       PetscFree(mglevels[i]);
    349:     }
    350:     PetscFree(mg->levels);
    351:   }
    352:   PetscFree(pc->data);
    353:   PetscObjectComposeFunction((PetscObject)pc,"PCGetInterpolations_C",NULL);
    354:   PetscObjectComposeFunction((PetscObject)pc,"PCGetCoarseOperators_C",NULL);
    355:   return(0);
    356: }

    360: extern PetscErrorCode PCMGACycle_Private(PC,PC_MG_Levels**);
    361: extern PetscErrorCode PCMGFCycle_Private(PC,PC_MG_Levels**);
    362: extern PetscErrorCode PCMGKCycle_Private(PC,PC_MG_Levels**);

    364: /*
    365:    PCApply_MG - Runs either an additive, multiplicative, Kaskadic
    366:              or full cycle of multigrid.

    368:   Note:
    369:   A simple wrapper which calls PCMGMCycle(),PCMGACycle(), or PCMGFCycle().
    370: */
    371: static PetscErrorCode PCApply_MG(PC pc,Vec b,Vec x)
    372: {
    373:   PC_MG          *mg        = (PC_MG*)pc->data;
    374:   PC_MG_Levels   **mglevels = mg->levels;
    376:   PC             tpc;
    377:   PetscInt       levels = mglevels[0]->levels,i;
    378:   PetscBool      changeu,changed;

    381:   if (mg->stageApply) {PetscLogStagePush(mg->stageApply);}
    382:   /* When the DM is supplying the matrix then it will not exist until here */
    383:   for (i=0; i<levels; i++) {
    384:     if (!mglevels[i]->A) {
    385:       KSPGetOperators(mglevels[i]->smoothu,&mglevels[i]->A,NULL);
    386:       PetscObjectReference((PetscObject)mglevels[i]->A);
    387:     }
    388:   }

    390:   KSPGetPC(mglevels[levels-1]->smoothd,&tpc);
    391:   PCPreSolveChangeRHS(tpc,&changed);
    392:   KSPGetPC(mglevels[levels-1]->smoothu,&tpc);
    393:   PCPreSolveChangeRHS(tpc,&changeu);
    394:   if (!changeu && !changed) {
    395:     VecDestroy(&mglevels[levels-1]->b);
    396:     mglevels[levels-1]->b = b;
    397:   } else { /* if the smoother changes the rhs during PreSolve, we cannot use the input vector */
    398:     if (!mglevels[levels-1]->b) {
    399:       Vec *vec;

    401:       KSPCreateVecs(mglevels[levels-1]->smoothd,1,&vec,0,NULL);
    402:       mglevels[levels-1]->b = *vec;
    403:       PetscFree(vec);
    404:     }
    405:     VecCopy(b,mglevels[levels-1]->b);
    406:   }
    407:   mglevels[levels-1]->x = x;

    409:   if (mg->am == PC_MG_MULTIPLICATIVE) {
    410:     VecSet(x,0.0);
    411:     for (i=0; i<mg->cyclesperpcapply; i++) {
    412:       PCMGMCycle_Private(pc,mglevels+levels-1,NULL);
    413:     }
    414:   } else if (mg->am == PC_MG_ADDITIVE) {
    415:     PCMGACycle_Private(pc,mglevels);
    416:   } else if (mg->am == PC_MG_KASKADE) {
    417:     PCMGKCycle_Private(pc,mglevels);
    418:   } else {
    419:     PCMGFCycle_Private(pc,mglevels);
    420:   }
    421:   if (mg->stageApply) {PetscLogStagePop();}
    422:   if (!changeu && !changed) mglevels[levels-1]->b = NULL;
    423:   return(0);
    424: }

    427: PetscErrorCode PCSetFromOptions_MG(PetscOptionItems *PetscOptionsObject,PC pc)
    428: {
    429:   PetscErrorCode   ierr;
    430:   PetscInt         levels,cycles;
    431:   PetscBool        flg, flg2;
    432:   PC_MG            *mg = (PC_MG*)pc->data;
    433:   PC_MG_Levels     **mglevels;
    434:   PCMGType         mgtype;
    435:   PCMGCycleType    mgctype;
    436:   PCMGGalerkinType gtype;

    439:   levels = PetscMax(mg->nlevels,1);
    440:   PetscOptionsHead(PetscOptionsObject,"Multigrid options");
    441:   PetscOptionsInt("-pc_mg_levels","Number of Levels","PCMGSetLevels",levels,&levels,&flg);
    442:   if (!flg && !mg->levels && pc->dm) {
    443:     DMGetRefineLevel(pc->dm,&levels);
    444:     levels++;
    445:     mg->usedmfornumberoflevels = PETSC_TRUE;
    446:   }
    447:   PCMGSetLevels(pc,levels,NULL);
    448:   mglevels = mg->levels;

    450:   mgctype = (PCMGCycleType) mglevels[0]->cycles;
    451:   PetscOptionsEnum("-pc_mg_cycle_type","V cycle or for W-cycle","PCMGSetCycleType",PCMGCycleTypes,(PetscEnum)mgctype,(PetscEnum*)&mgctype,&flg);
    452:   if (flg) {
    453:     PCMGSetCycleType(pc,mgctype);
    454:   }
    455:   gtype = mg->galerkin;
    456:   PetscOptionsEnum("-pc_mg_galerkin","Use Galerkin process to compute coarser operators","PCMGSetGalerkin",PCMGGalerkinTypes,(PetscEnum)gtype,(PetscEnum*)&gtype,&flg);
    457:   if (flg) {
    458:     PCMGSetGalerkin(pc,gtype);
    459:   }
    460:   flg2 = PETSC_FALSE;
    461:   PetscOptionsBool("-pc_mg_adapt_interp","Adapt interpolation using some coarse space","PCMGSetAdaptInterpolation",PETSC_FALSE,&flg2,&flg);
    462:   if (flg) {PCMGSetAdaptInterpolation(pc, flg2);}
    463:   PetscOptionsInt("-pc_mg_adapt_interp_n","Size of the coarse space for adaptive interpolation","PCMGSetCoarseSpace",mg->Nc,&mg->Nc,&flg);
    464:   PetscOptionsEnum("-pc_mg_adapt_interp_coarse_space","Type of coarse space: polynomial, harmonic, eigenvector, generalized_eigenvector","PCMGSetAdaptCoarseSpaceType",PCMGCoarseSpaceTypes,(PetscEnum)mg->coarseSpaceType,(PetscEnum*)&mg->coarseSpaceType,&flg);
    465:   PetscOptionsBool("-pc_mg_mesp_monitor","Monitor the multilevel eigensolver","PCMGSetAdaptInterpolation",PETSC_FALSE,&mg->mespMonitor,&flg);
    466:   flg = PETSC_FALSE;
    467:   PetscOptionsBool("-pc_mg_distinct_smoothup","Create separate smoothup KSP and append the prefix _up","PCMGSetDistinctSmoothUp",PETSC_FALSE,&flg,NULL);
    468:   if (flg) {
    469:     PCMGSetDistinctSmoothUp(pc);
    470:   }
    471:   mgtype = mg->am;
    472:   PetscOptionsEnum("-pc_mg_type","Multigrid type","PCMGSetType",PCMGTypes,(PetscEnum)mgtype,(PetscEnum*)&mgtype,&flg);
    473:   if (flg) {
    474:     PCMGSetType(pc,mgtype);
    475:   }
    476:   if (mg->am == PC_MG_MULTIPLICATIVE) {
    477:     PetscOptionsInt("-pc_mg_multiplicative_cycles","Number of cycles for each preconditioner step","PCMGMultiplicativeSetCycles",mg->cyclesperpcapply,&cycles,&flg);
    478:     if (flg) {
    479:       PCMGMultiplicativeSetCycles(pc,cycles);
    480:     }
    481:   }
    482:   flg  = PETSC_FALSE;
    483:   PetscOptionsBool("-pc_mg_log","Log times for each multigrid level","None",flg,&flg,NULL);
    484:   if (flg) {
    485:     PetscInt i;
    486:     char     eventname[128];

    488:     levels = mglevels[0]->levels;
    489:     for (i=0; i<levels; i++) {
    490:       sprintf(eventname,"MGSetup Level %d",(int)i);
    491:       PetscLogEventRegister(eventname,((PetscObject)pc)->classid,&mglevels[i]->eventsmoothsetup);
    492:       sprintf(eventname,"MGSmooth Level %d",(int)i);
    493:       PetscLogEventRegister(eventname,((PetscObject)pc)->classid,&mglevels[i]->eventsmoothsolve);
    494:       if (i) {
    495:         sprintf(eventname,"MGResid Level %d",(int)i);
    496:         PetscLogEventRegister(eventname,((PetscObject)pc)->classid,&mglevels[i]->eventresidual);
    497:         sprintf(eventname,"MGInterp Level %d",(int)i);
    498:         PetscLogEventRegister(eventname,((PetscObject)pc)->classid,&mglevels[i]->eventinterprestrict);
    499:       }
    500:     }

    502: #if defined(PETSC_USE_LOG)
    503:     {
    504:       const char    *sname = "MG Apply";
    505:       PetscStageLog stageLog;
    506:       PetscInt      st;

    508:       PetscLogGetStageLog(&stageLog);
    509:       for (st = 0; st < stageLog->numStages; ++st) {
    510:         PetscBool same;

    512:         PetscStrcmp(stageLog->stageInfo[st].name, sname, &same);
    513:         if (same) mg->stageApply = st;
    514:       }
    515:       if (!mg->stageApply) {
    516:         PetscLogStageRegister(sname, &mg->stageApply);
    517:       }
    518:     }
    519: #endif
    520:   }
    521:   PetscOptionsTail();
    522:   /* Check option consistency */
    523:   PCMGGetGalerkin(pc, &gtype);
    524:   PCMGGetAdaptInterpolation(pc, &flg);
    525:   if (flg && (gtype >= PC_MG_GALERKIN_NONE)) SETERRQ(PetscObjectComm((PetscObject) pc), PETSC_ERR_ARG_INCOMP, "Must use Galerkin coarse operators when adapting the interpolator");
    526:   return(0);
    527: }

    529: const char *const PCMGTypes[] = {"MULTIPLICATIVE","ADDITIVE","FULL","KASKADE","PCMGType","PC_MG",NULL};
    530: const char *const PCMGCycleTypes[] = {"invalid","v","w","PCMGCycleType","PC_MG_CYCLE",NULL};
    531: const char *const PCMGGalerkinTypes[] = {"both","pmat","mat","none","external","PCMGGalerkinType","PC_MG_GALERKIN",NULL};
    532: const char *const PCMGCoarseSpaceTypes[] = {"polynomial","harmonic","eigenvector","generalized_eigenvector","PCMGCoarseSpaceType","PCMG_POLYNOMIAL",NULL};

    534: #include <petscdraw.h>
    535: PetscErrorCode PCView_MG(PC pc,PetscViewer viewer)
    536: {
    537:   PC_MG          *mg        = (PC_MG*)pc->data;
    538:   PC_MG_Levels   **mglevels = mg->levels;
    540:   PetscInt       levels = mglevels ? mglevels[0]->levels : 0,i;
    541:   PetscBool      iascii,isbinary,isdraw;

    544:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    545:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
    546:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
    547:   if (iascii) {
    548:     const char *cyclename = levels ? (mglevels[0]->cycles == PC_MG_CYCLE_V ? "v" : "w") : "unknown";
    549:     PetscViewerASCIIPrintf(viewer,"  type is %s, levels=%D cycles=%s\n", PCMGTypes[mg->am],levels,cyclename);
    550:     if (mg->am == PC_MG_MULTIPLICATIVE) {
    551:       PetscViewerASCIIPrintf(viewer,"    Cycles per PCApply=%d\n",mg->cyclesperpcapply);
    552:     }
    553:     if (mg->galerkin == PC_MG_GALERKIN_BOTH) {
    554:       PetscViewerASCIIPrintf(viewer,"    Using Galerkin computed coarse grid matrices\n");
    555:     } else if (mg->galerkin == PC_MG_GALERKIN_PMAT) {
    556:       PetscViewerASCIIPrintf(viewer,"    Using Galerkin computed coarse grid matrices for pmat\n");
    557:     } else if (mg->galerkin == PC_MG_GALERKIN_MAT) {
    558:       PetscViewerASCIIPrintf(viewer,"    Using Galerkin computed coarse grid matrices for mat\n");
    559:     } else if (mg->galerkin == PC_MG_GALERKIN_EXTERNAL) {
    560:       PetscViewerASCIIPrintf(viewer,"    Using externally compute Galerkin coarse grid matrices\n");
    561:     } else {
    562:       PetscViewerASCIIPrintf(viewer,"    Not using Galerkin computed coarse grid matrices\n");
    563:     }
    564:     if (mg->view){
    565:       (*mg->view)(pc,viewer);
    566:     }
    567:     for (i=0; i<levels; i++) {
    568:       if (!i) {
    569:         PetscViewerASCIIPrintf(viewer,"Coarse grid solver -- level -------------------------------\n",i);
    570:       } else {
    571:         PetscViewerASCIIPrintf(viewer,"Down solver (pre-smoother) on level %D -------------------------------\n",i);
    572:       }
    573:       PetscViewerASCIIPushTab(viewer);
    574:       KSPView(mglevels[i]->smoothd,viewer);
    575:       PetscViewerASCIIPopTab(viewer);
    576:       if (i && mglevels[i]->smoothd == mglevels[i]->smoothu) {
    577:         PetscViewerASCIIPrintf(viewer,"Up solver (post-smoother) same as down solver (pre-smoother)\n");
    578:       } else if (i) {
    579:         PetscViewerASCIIPrintf(viewer,"Up solver (post-smoother) on level %D -------------------------------\n",i);
    580:         PetscViewerASCIIPushTab(viewer);
    581:         KSPView(mglevels[i]->smoothu,viewer);
    582:         PetscViewerASCIIPopTab(viewer);
    583:       }
    584:     }
    585:   } else if (isbinary) {
    586:     for (i=levels-1; i>=0; i--) {
    587:       KSPView(mglevels[i]->smoothd,viewer);
    588:       if (i && mglevels[i]->smoothd != mglevels[i]->smoothu) {
    589:         KSPView(mglevels[i]->smoothu,viewer);
    590:       }
    591:     }
    592:   } else if (isdraw) {
    593:     PetscDraw draw;
    594:     PetscReal x,w,y,bottom,th;
    595:     PetscViewerDrawGetDraw(viewer,0,&draw);
    596:     PetscDrawGetCurrentPoint(draw,&x,&y);
    597:     PetscDrawStringGetSize(draw,NULL,&th);
    598:     bottom = y - th;
    599:     for (i=levels-1; i>=0; i--) {
    600:       if (!mglevels[i]->smoothu || (mglevels[i]->smoothu == mglevels[i]->smoothd)) {
    601:         PetscDrawPushCurrentPoint(draw,x,bottom);
    602:         KSPView(mglevels[i]->smoothd,viewer);
    603:         PetscDrawPopCurrentPoint(draw);
    604:       } else {
    605:         w    = 0.5*PetscMin(1.0-x,x);
    606:         PetscDrawPushCurrentPoint(draw,x+w,bottom);
    607:         KSPView(mglevels[i]->smoothd,viewer);
    608:         PetscDrawPopCurrentPoint(draw);
    609:         PetscDrawPushCurrentPoint(draw,x-w,bottom);
    610:         KSPView(mglevels[i]->smoothu,viewer);
    611:         PetscDrawPopCurrentPoint(draw);
    612:       }
    613:       PetscDrawGetBoundingBox(draw,NULL,&bottom,NULL,NULL);
    614:       bottom -= th;
    615:     }
    616:   }
    617:   return(0);
    618: }

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

    622: /*
    623:     Calls setup for the KSP on each level
    624: */
    625: PetscErrorCode PCSetUp_MG(PC pc)
    626: {
    627:   PC_MG          *mg        = (PC_MG*)pc->data;
    628:   PC_MG_Levels   **mglevels = mg->levels;
    630:   PetscInt       i,n;
    631:   PC             cpc;
    632:   PetscBool      dump = PETSC_FALSE,opsset,use_amat,missinginterpolate = PETSC_FALSE;
    633:   Mat            dA,dB;
    634:   Vec            tvec;
    635:   DM             *dms;
    636:   PetscViewer    viewer = NULL;
    637:   PetscBool      dAeqdB = PETSC_FALSE, needRestricts = PETSC_FALSE;

    640:   if (!mglevels) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_WRONGSTATE,"Must set MG levels with PCMGSetLevels() before setting up");
    641:   n = mglevels[0]->levels;
    642:   /* FIX: Move this to PCSetFromOptions_MG? */
    643:   if (mg->usedmfornumberoflevels) {
    644:     PetscInt levels;
    645:     DMGetRefineLevel(pc->dm,&levels);
    646:     levels++;
    647:     if (levels > n) { /* the problem is now being solved on a finer grid */
    648:       PCMGSetLevels(pc,levels,NULL);
    649:       n        = levels;
    650:       PCSetFromOptions(pc); /* it is bad to call this here, but otherwise will never be called for the new hierarchy */
    651:       mglevels = mg->levels;
    652:     }
    653:   }
    654:   KSPGetPC(mglevels[0]->smoothd,&cpc);

    657:   /* If user did not provide fine grid operators OR operator was not updated since last global KSPSetOperators() */
    658:   /* so use those from global PC */
    659:   /* Is this what we always want? What if user wants to keep old one? */
    660:   KSPGetOperatorsSet(mglevels[n-1]->smoothd,NULL,&opsset);
    661:   if (opsset) {
    662:     Mat mmat;
    663:     KSPGetOperators(mglevels[n-1]->smoothd,NULL,&mmat);
    664:     if (mmat == pc->pmat) opsset = PETSC_FALSE;
    665:   }

    667:   if (!opsset) {
    668:     PCGetUseAmat(pc,&use_amat);
    669:     if (use_amat) {
    670:       PetscInfo(pc,"Using outer operators to define finest grid operator \n  because PCMGGetSmoother(pc,nlevels-1,&ksp);KSPSetOperators(ksp,...); was not called.\n");
    671:       KSPSetOperators(mglevels[n-1]->smoothd,pc->mat,pc->pmat);
    672:     } else {
    673:       PetscInfo(pc,"Using matrix (pmat) operators to define finest grid operator \n  because PCMGGetSmoother(pc,nlevels-1,&ksp);KSPSetOperators(ksp,...); was not called.\n");
    674:       KSPSetOperators(mglevels[n-1]->smoothd,pc->pmat,pc->pmat);
    675:     }
    676:   }

    678:   for (i=n-1; i>0; i--) {
    679:     if (!(mglevels[i]->interpolate || mglevels[i]->restrct)) {
    680:       missinginterpolate = PETSC_TRUE;
    681:       continue;
    682:     }
    683:   }

    685:   KSPGetOperators(mglevels[n-1]->smoothd,&dA,&dB);
    686:   if (dA == dB) dAeqdB = PETSC_TRUE;
    687:   if ((mg->galerkin == PC_MG_GALERKIN_NONE) || (((mg->galerkin == PC_MG_GALERKIN_PMAT) || (mg->galerkin == PC_MG_GALERKIN_MAT)) && !dAeqdB)) {
    688:     needRestricts = PETSC_TRUE;  /* user must compute either mat, pmat, or both so must restrict x to coarser levels */
    689:   }

    692:   /*
    693:    Skipping if user has provided all interpolation/restriction needed (since DM might not be able to produce them (when coming from SNES/TS)
    694:    Skipping for galerkin==2 (externally managed hierarchy such as ML and GAMG). Cleaner logic here would be great. Wrap ML/GAMG as DMs?
    695:   */
    696:   if (missinginterpolate && pc->dm && mg->galerkin != PC_MG_GALERKIN_EXTERNAL && !pc->setupcalled) {
    697:         /* construct the interpolation from the DMs */
    698:     Mat p;
    699:     Vec rscale;
    700:     PetscMalloc1(n,&dms);
    701:     dms[n-1] = pc->dm;
    702:     /* Separately create them so we do not get DMKSP interference between levels */
    703:     for (i=n-2; i>-1; i--) {DMCoarsen(dms[i+1],MPI_COMM_NULL,&dms[i]);}
    704:         /*
    705:            Force the mat type of coarse level operator to be AIJ because usually we want to use LU for coarse level.
    706:            Notice that it can be overwritten by -mat_type because KSPSetUp() reads command line options.
    707:            But it is safe to use -dm_mat_type.

    709:            The mat type should not be hardcoded like this, we need to find a better way.
    710:     DMSetMatType(dms[0],MATAIJ);
    711:     */
    712:     for (i=n-2; i>-1; i--) {
    713:       DMKSP     kdm;
    714:       PetscBool dmhasrestrict, dmhasinject;
    715:       KSPSetDM(mglevels[i]->smoothd,dms[i]);
    716:       if (!needRestricts) {KSPSetDMActive(mglevels[i]->smoothd,PETSC_FALSE);}
    717:       if (mglevels[i]->smoothd != mglevels[i]->smoothu) {
    718:         KSPSetDM(mglevels[i]->smoothu,dms[i]);
    719:         if (!needRestricts) {KSPSetDMActive(mglevels[i]->smoothu,PETSC_FALSE);}
    720:       }
    721:       DMGetDMKSPWrite(dms[i],&kdm);
    722:       /* Ugly hack so that the next KSPSetUp() will use the RHS that we set. A better fix is to change dmActive to take
    723:        * a bitwise OR of computing the matrix, RHS, and initial iterate. */
    724:       kdm->ops->computerhs = NULL;
    725:       kdm->rhsctx          = NULL;
    726:       if (!mglevels[i+1]->interpolate) {
    727:         DMCreateInterpolation(dms[i],dms[i+1],&p,&rscale);
    728:         PCMGSetInterpolation(pc,i+1,p);
    729:         if (rscale) {PCMGSetRScale(pc,i+1,rscale);}
    730:         VecDestroy(&rscale);
    731:         MatDestroy(&p);
    732:       }
    733:       DMHasCreateRestriction(dms[i],&dmhasrestrict);
    734:       if (dmhasrestrict && !mglevels[i+1]->restrct){
    735:         DMCreateRestriction(dms[i],dms[i+1],&p);
    736:         PCMGSetRestriction(pc,i+1,p);
    737:         MatDestroy(&p);
    738:       }
    739:       DMHasCreateInjection(dms[i],&dmhasinject);
    740:       if (dmhasinject && !mglevels[i+1]->inject){
    741:         DMCreateInjection(dms[i],dms[i+1],&p);
    742:         PCMGSetInjection(pc,i+1,p);
    743:         MatDestroy(&p);
    744:       }
    745:     }

    747:     for (i=n-2; i>-1; i--) {DMDestroy(&dms[i]);}
    748:     PetscFree(dms);
    749:   }

    751:   if (pc->dm && !pc->setupcalled) {
    752:     /* finest smoother also gets DM but it is not active, independent of whether galerkin==PC_MG_GALERKIN_EXTERNAL */
    753:     KSPSetDM(mglevels[n-1]->smoothd,pc->dm);
    754:     KSPSetDMActive(mglevels[n-1]->smoothd,PETSC_FALSE);
    755:     if (mglevels[n-1]->smoothd != mglevels[n-1]->smoothu) {
    756:       KSPSetDM(mglevels[n-1]->smoothu,pc->dm);
    757:       KSPSetDMActive(mglevels[n-1]->smoothu,PETSC_FALSE);
    758:     }
    759:   }

    761:   if (mg->galerkin < PC_MG_GALERKIN_NONE) {
    762:     Mat       A,B;
    763:     PetscBool doA = PETSC_FALSE,doB = PETSC_FALSE;
    764:     MatReuse  reuse = MAT_INITIAL_MATRIX;

    766:     if ((mg->galerkin == PC_MG_GALERKIN_PMAT) || (mg->galerkin == PC_MG_GALERKIN_BOTH)) doB = PETSC_TRUE;
    767:     if ((mg->galerkin == PC_MG_GALERKIN_MAT) || ((mg->galerkin == PC_MG_GALERKIN_BOTH) && (dA != dB))) doA = PETSC_TRUE;
    768:     if (pc->setupcalled) reuse = MAT_REUSE_MATRIX;
    769:     for (i=n-2; i>-1; i--) {
    770:       if (!mglevels[i+1]->restrct && !mglevels[i+1]->interpolate) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_WRONGSTATE,"Must provide interpolation or restriction for each MG level except level 0");
    771:       if (!mglevels[i+1]->interpolate) {
    772:         PCMGSetInterpolation(pc,i+1,mglevels[i+1]->restrct);
    773:       }
    774:       if (!mglevels[i+1]->restrct) {
    775:         PCMGSetRestriction(pc,i+1,mglevels[i+1]->interpolate);
    776:       }
    777:       if (reuse == MAT_REUSE_MATRIX) {
    778:         KSPGetOperators(mglevels[i]->smoothd,&A,&B);
    779:       }
    780:       if (doA) {
    781:         MatGalerkin(mglevels[i+1]->restrct,dA,mglevels[i+1]->interpolate,reuse,1.0,&A);
    782:       }
    783:       if (doB) {
    784:         MatGalerkin(mglevels[i+1]->restrct,dB,mglevels[i+1]->interpolate,reuse,1.0,&B);
    785:       }
    786:       /* the management of the PetscObjectReference() and PetscObjecDereference() below is rather delicate */
    787:       if (!doA && dAeqdB) {
    788:         if (reuse == MAT_INITIAL_MATRIX) {PetscObjectReference((PetscObject)B);}
    789:         A = B;
    790:       } else if (!doA && reuse == MAT_INITIAL_MATRIX) {
    791:         KSPGetOperators(mglevels[i]->smoothd,&A,NULL);
    792:         PetscObjectReference((PetscObject)A);
    793:       }
    794:       if (!doB && dAeqdB) {
    795:         if (reuse == MAT_INITIAL_MATRIX) {PetscObjectReference((PetscObject)A);}
    796:         B = A;
    797:       } else if (!doB && reuse == MAT_INITIAL_MATRIX) {
    798:         KSPGetOperators(mglevels[i]->smoothd,NULL,&B);
    799:         PetscObjectReference((PetscObject)B);
    800:       }
    801:       if (reuse == MAT_INITIAL_MATRIX) {
    802:         KSPSetOperators(mglevels[i]->smoothd,A,B);
    803:         PetscObjectDereference((PetscObject)A);
    804:         PetscObjectDereference((PetscObject)B);
    805:       }
    806:       dA = A;
    807:       dB = B;
    808:     }
    809:   }

    812:   /* Adapt interpolation matrices */
    813:   if (mg->adaptInterpolation) {
    814:     mg->Nc = mg->Nc < 0 ? 6 : mg->Nc; /* Default to 6 modes */
    815:     for (i = 0; i < n; ++i) {
    816:       PCMGComputeCoarseSpace_Internal(pc, i, mg->coarseSpaceType, mg->Nc, !i ? NULL : mglevels[i-1]->coarseSpace, &mglevels[i]->coarseSpace);
    817:       if (i) {PCMGAdaptInterpolator_Internal(pc, i, mglevels[i-1]->smoothu, mglevels[i]->smoothu, mg->Nc, mglevels[i-1]->coarseSpace, mglevels[i]->coarseSpace);}
    818:     }
    819:     for (i = n-2; i > -1; --i) {
    820:       PCMGRecomputeLevelOperators_Internal(pc, i);
    821:     }
    822:   }

    824:   if (needRestricts && pc->dm) {
    825:     for (i=n-2; i>=0; i--) {
    826:       DM  dmfine,dmcoarse;
    827:       Mat Restrict,Inject;
    828:       Vec rscale;
    829:       KSPGetDM(mglevels[i+1]->smoothd,&dmfine);
    830:       KSPGetDM(mglevels[i]->smoothd,&dmcoarse);
    831:       PCMGGetRestriction(pc,i+1,&Restrict);
    832:       PCMGGetRScale(pc,i+1,&rscale);
    833:       PCMGGetInjection(pc,i+1,&Inject);
    834:       DMRestrict(dmfine,Restrict,rscale,Inject,dmcoarse);
    835:     }
    836:   }

    838:   if (!pc->setupcalled) {
    839:     for (i=0; i<n; i++) {
    840:       KSPSetFromOptions(mglevels[i]->smoothd);
    841:     }
    842:     for (i=1; i<n; i++) {
    843:       if (mglevels[i]->smoothu && (mglevels[i]->smoothu != mglevels[i]->smoothd)) {
    844:         KSPSetFromOptions(mglevels[i]->smoothu);
    845:       }
    846:     }
    847:     /* insure that if either interpolation or restriction is set the other other one is set */
    848:     for (i=1; i<n; i++) {
    849:       PCMGGetInterpolation(pc,i,NULL);
    850:       PCMGGetRestriction(pc,i,NULL);
    851:     }
    852:     for (i=0; i<n-1; i++) {
    853:       if (!mglevels[i]->b) {
    854:         Vec *vec;
    855:         KSPCreateVecs(mglevels[i]->smoothd,1,&vec,0,NULL);
    856:         PCMGSetRhs(pc,i,*vec);
    857:         VecDestroy(vec);
    858:         PetscFree(vec);
    859:       }
    860:       if (!mglevels[i]->r && i) {
    861:         VecDuplicate(mglevels[i]->b,&tvec);
    862:         PCMGSetR(pc,i,tvec);
    863:         VecDestroy(&tvec);
    864:       }
    865:       if (!mglevels[i]->x) {
    866:         VecDuplicate(mglevels[i]->b,&tvec);
    867:         PCMGSetX(pc,i,tvec);
    868:         VecDestroy(&tvec);
    869:       }
    870:     }
    871:     if (n != 1 && !mglevels[n-1]->r) {
    872:       /* PCMGSetR() on the finest level if user did not supply it */
    873:       Vec *vec;
    874:       KSPCreateVecs(mglevels[n-1]->smoothd,1,&vec,0,NULL);
    875:       PCMGSetR(pc,n-1,*vec);
    876:       VecDestroy(vec);
    877:       PetscFree(vec);
    878:     }
    879:   }

    881:   if (pc->dm) {
    882:     /* need to tell all the coarser levels to rebuild the matrix using the DM for that level */
    883:     for (i=0; i<n-1; i++) {
    884:       if (mglevels[i]->smoothd->setupstage != KSP_SETUP_NEW) mglevels[i]->smoothd->setupstage = KSP_SETUP_NEWMATRIX;
    885:     }
    886:   }

    888:   for (i=1; i<n; i++) {
    889:     if (mglevels[i]->smoothu == mglevels[i]->smoothd || mg->am == PC_MG_FULL || mg->am == PC_MG_KASKADE || mg->cyclesperpcapply > 1){
    890:       /* if doing only down then initial guess is zero */
    891:       KSPSetInitialGuessNonzero(mglevels[i]->smoothd,PETSC_TRUE);
    892:     }
    893:     if (mglevels[i]->eventsmoothsetup) {PetscLogEventBegin(mglevels[i]->eventsmoothsetup,0,0,0,0);}
    894:     KSPSetUp(mglevels[i]->smoothd);
    895:     if (mglevels[i]->smoothd->reason == KSP_DIVERGED_PC_FAILED) {
    896:       pc->failedreason = PC_SUBPC_ERROR;
    897:     }
    898:     if (mglevels[i]->eventsmoothsetup) {PetscLogEventEnd(mglevels[i]->eventsmoothsetup,0,0,0,0);}
    899:     if (!mglevels[i]->residual) {
    900:       Mat mat;
    901:       KSPGetOperators(mglevels[i]->smoothd,&mat,NULL);
    902:       PCMGSetResidual(pc,i,PCMGResidualDefault,mat);
    903:     }
    904:   }
    905:   for (i=1; i<n; i++) {
    906:     if (mglevels[i]->smoothu && mglevels[i]->smoothu != mglevels[i]->smoothd) {
    907:       Mat downmat,downpmat;

    909:       /* check if operators have been set for up, if not use down operators to set them */
    910:       KSPGetOperatorsSet(mglevels[i]->smoothu,&opsset,NULL);
    911:       if (!opsset) {
    912:         KSPGetOperators(mglevels[i]->smoothd,&downmat,&downpmat);
    913:         KSPSetOperators(mglevels[i]->smoothu,downmat,downpmat);
    914:       }

    916:       KSPSetInitialGuessNonzero(mglevels[i]->smoothu,PETSC_TRUE);
    917:       if (mglevels[i]->eventsmoothsetup) {PetscLogEventBegin(mglevels[i]->eventsmoothsetup,0,0,0,0);}
    918:       KSPSetUp(mglevels[i]->smoothu);
    919:       if (mglevels[i]->smoothu->reason == KSP_DIVERGED_PC_FAILED) {
    920:         pc->failedreason = PC_SUBPC_ERROR;
    921:       }
    922:       if (mglevels[i]->eventsmoothsetup) {PetscLogEventEnd(mglevels[i]->eventsmoothsetup,0,0,0,0);}
    923:     }
    924:   }

    926:   if (mglevels[0]->eventsmoothsetup) {PetscLogEventBegin(mglevels[0]->eventsmoothsetup,0,0,0,0);}
    927:   KSPSetUp(mglevels[0]->smoothd);
    928:   if (mglevels[0]->smoothd->reason == KSP_DIVERGED_PC_FAILED) {
    929:     pc->failedreason = PC_SUBPC_ERROR;
    930:   }
    931:   if (mglevels[0]->eventsmoothsetup) {PetscLogEventEnd(mglevels[0]->eventsmoothsetup,0,0,0,0);}

    933:   /*
    934:      Dump the interpolation/restriction matrices plus the
    935:    Jacobian/stiffness on each level. This allows MATLAB users to
    936:    easily check if the Galerkin condition A_c = R A_f R^T is satisfied.

    938:    Only support one or the other at the same time.
    939:   */
    940: #if defined(PETSC_USE_SOCKET_VIEWER)
    941:   PetscOptionsGetBool(((PetscObject)pc)->options,((PetscObject)pc)->prefix,"-pc_mg_dump_matlab",&dump,NULL);
    942:   if (dump) viewer = PETSC_VIEWER_SOCKET_(PetscObjectComm((PetscObject)pc));
    943:   dump = PETSC_FALSE;
    944: #endif
    945:   PetscOptionsGetBool(((PetscObject)pc)->options,((PetscObject)pc)->prefix,"-pc_mg_dump_binary",&dump,NULL);
    946:   if (dump) viewer = PETSC_VIEWER_BINARY_(PetscObjectComm((PetscObject)pc));

    948:   if (viewer) {
    949:     for (i=1; i<n; i++) {
    950:       MatView(mglevels[i]->restrct,viewer);
    951:     }
    952:     for (i=0; i<n; i++) {
    953:       KSPGetPC(mglevels[i]->smoothd,&pc);
    954:       MatView(pc->mat,viewer);
    955:     }
    956:   }
    957:   return(0);
    958: }

    960: /* -------------------------------------------------------------------------------------*/

    962: PetscErrorCode PCMGGetLevels_MG(PC pc, PetscInt *levels)
    963: {
    964:   PC_MG *mg = (PC_MG *) pc->data;

    967:   *levels = mg->nlevels;
    968:   return(0);
    969: }

    971: /*@
    972:    PCMGGetLevels - Gets the number of levels to use with MG.

    974:    Not Collective

    976:    Input Parameter:
    977: .  pc - the preconditioner context

    979:    Output parameter:
    980: .  levels - the number of levels

    982:    Level: advanced

    984: .seealso: PCMGSetLevels()
    985: @*/
    986: PetscErrorCode PCMGGetLevels(PC pc,PetscInt *levels)
    987: {

    993:   *levels = 0;
    994:   PetscTryMethod(pc,"PCMGGetLevels_C",(PC,PetscInt*),(pc,levels));
    995:   return(0);
    996: }

    998: /*@
    999:    PCMGSetType - Determines the form of multigrid to use:
    1000:    multiplicative, additive, full, or the Kaskade algorithm.

    1002:    Logically Collective on PC

    1004:    Input Parameters:
    1005: +  pc - the preconditioner context
    1006: -  form - multigrid form, one of PC_MG_MULTIPLICATIVE, PC_MG_ADDITIVE,
    1007:    PC_MG_FULL, PC_MG_KASKADE

    1009:    Options Database Key:
    1010: .  -pc_mg_type <form> - Sets <form>, one of multiplicative,
    1011:    additive, full, kaskade

    1013:    Level: advanced

    1015: .seealso: PCMGSetLevels()
    1016: @*/
    1017: PetscErrorCode  PCMGSetType(PC pc,PCMGType form)
    1018: {
    1019:   PC_MG *mg = (PC_MG*)pc->data;

    1024:   mg->am = form;
    1025:   if (form == PC_MG_MULTIPLICATIVE) pc->ops->applyrichardson = PCApplyRichardson_MG;
    1026:   else pc->ops->applyrichardson = NULL;
    1027:   return(0);
    1028: }

    1030: /*@
    1031:    PCMGGetType - Determines the form of multigrid to use:
    1032:    multiplicative, additive, full, or the Kaskade algorithm.

    1034:    Logically Collective on PC

    1036:    Input Parameter:
    1037: .  pc - the preconditioner context

    1039:    Output Parameter:
    1040: .  type - one of PC_MG_MULTIPLICATIVE, PC_MG_ADDITIVE,PC_MG_FULL, PC_MG_KASKADE

    1043:    Level: advanced

    1045: .seealso: PCMGSetLevels()
    1046: @*/
    1047: PetscErrorCode  PCMGGetType(PC pc,PCMGType *type)
    1048: {
    1049:   PC_MG *mg = (PC_MG*)pc->data;

    1053:   *type = mg->am;
    1054:   return(0);
    1055: }

    1057: /*@
    1058:    PCMGSetCycleType - Sets the type cycles to use.  Use PCMGSetCycleTypeOnLevel() for more
    1059:    complicated cycling.

    1061:    Logically Collective on PC

    1063:    Input Parameters:
    1064: +  pc - the multigrid context
    1065: -  n - either PC_MG_CYCLE_V or PC_MG_CYCLE_W

    1067:    Options Database Key:
    1068: .  -pc_mg_cycle_type <v,w> - provide the cycle desired

    1070:    Level: advanced

    1072: .seealso: PCMGSetCycleTypeOnLevel()
    1073: @*/
    1074: PetscErrorCode  PCMGSetCycleType(PC pc,PCMGCycleType n)
    1075: {
    1076:   PC_MG        *mg        = (PC_MG*)pc->data;
    1077:   PC_MG_Levels **mglevels = mg->levels;
    1078:   PetscInt     i,levels;

    1083:   if (!mglevels) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ORDER,"Must set MG levels with PCMGSetLevels() before calling");
    1084:   levels = mglevels[0]->levels;
    1085:   for (i=0; i<levels; i++) mglevels[i]->cycles = n;
    1086:   return(0);
    1087: }

    1089: /*@
    1090:    PCMGMultiplicativeSetCycles - Sets the number of cycles to use for each preconditioner step
    1091:          of multigrid when PCMGType of PC_MG_MULTIPLICATIVE is used

    1093:    Logically Collective on PC

    1095:    Input Parameters:
    1096: +  pc - the multigrid context
    1097: -  n - number of cycles (default is 1)

    1099:    Options Database Key:
    1100: .  -pc_mg_multiplicative_cycles n

    1102:    Level: advanced

    1104:    Notes:
    1105:     This is not associated with setting a v or w cycle, that is set with PCMGSetCycleType()

    1107: .seealso: PCMGSetCycleTypeOnLevel(), PCMGSetCycleType()
    1108: @*/
    1109: PetscErrorCode  PCMGMultiplicativeSetCycles(PC pc,PetscInt n)
    1110: {
    1111:   PC_MG        *mg        = (PC_MG*)pc->data;

    1116:   mg->cyclesperpcapply = n;
    1117:   return(0);
    1118: }

    1120: PetscErrorCode PCMGSetGalerkin_MG(PC pc,PCMGGalerkinType use)
    1121: {
    1122:   PC_MG *mg = (PC_MG*)pc->data;

    1125:   mg->galerkin = use;
    1126:   return(0);
    1127: }

    1129: /*@
    1130:    PCMGSetGalerkin - Causes the coarser grid matrices to be computed from the
    1131:       finest grid via the Galerkin process: A_i-1 = r_i * A_i * p_i

    1133:    Logically Collective on PC

    1135:    Input Parameters:
    1136: +  pc - the multigrid context
    1137: -  use - one of PC_MG_GALERKIN_BOTH,PC_MG_GALERKIN_PMAT,PC_MG_GALERKIN_MAT, or PC_MG_GALERKIN_NONE

    1139:    Options Database Key:
    1140: .  -pc_mg_galerkin <both,pmat,mat,none>

    1142:    Level: intermediate

    1144:    Notes:
    1145:     Some codes that use PCMG such as PCGAMG use Galerkin internally while constructing the hierarchy and thus do not
    1146:      use the PCMG construction of the coarser grids.

    1148: .seealso: PCMGGetGalerkin(), PCMGGalerkinType

    1150: @*/
    1151: PetscErrorCode PCMGSetGalerkin(PC pc,PCMGGalerkinType use)
    1152: {

    1157:   PetscTryMethod(pc,"PCMGSetGalerkin_C",(PC,PCMGGalerkinType),(pc,use));
    1158:   return(0);
    1159: }

    1161: /*@
    1162:    PCMGGetGalerkin - Checks if Galerkin multigrid is being used, i.e.
    1163:       A_i-1 = r_i * A_i * p_i

    1165:    Not Collective

    1167:    Input Parameter:
    1168: .  pc - the multigrid context

    1170:    Output Parameter:
    1171: .  galerkin - one of PC_MG_GALERKIN_BOTH,PC_MG_GALERKIN_PMAT,PC_MG_GALERKIN_MAT, PC_MG_GALERKIN_NONE, or PC_MG_GALERKIN_EXTERNAL

    1173:    Level: intermediate

    1175: .seealso: PCMGSetGalerkin(), PCMGGalerkinType

    1177: @*/
    1178: PetscErrorCode  PCMGGetGalerkin(PC pc,PCMGGalerkinType  *galerkin)
    1179: {
    1180:   PC_MG *mg = (PC_MG*)pc->data;

    1184:   *galerkin = mg->galerkin;
    1185:   return(0);
    1186: }

    1188: PetscErrorCode PCMGSetAdaptInterpolation_MG(PC pc, PetscBool adapt)
    1189: {
    1190:   PC_MG *mg = (PC_MG *) pc->data;

    1193:   mg->adaptInterpolation = adapt;
    1194:   return(0);
    1195: }

    1197: PetscErrorCode PCMGGetAdaptInterpolation_MG(PC pc, PetscBool *adapt)
    1198: {
    1199:   PC_MG *mg = (PC_MG *) pc->data;

    1202:   *adapt = mg->adaptInterpolation;
    1203:   return(0);
    1204: }

    1206: /*@
    1207:   PCMGSetAdaptInterpolation - Adapt the interpolator based upon a vector space which should be accurately captured by the next coarser mesh, and thus accurately interpolated.

    1209:   Logically Collective on PC

    1211:   Input Parameters:
    1212: + pc    - the multigrid context
    1213: - adapt - flag for adaptation of the interpolator

    1215:   Options Database Keys:
    1216: + -pc_mg_adapt_interp                     - Turn on adaptation
    1217: . -pc_mg_adapt_interp_n <int>             - The number of modes to use, should be divisible by dimension
    1218: - -pc_mg_adapt_interp_coarse_space <type> - The type of coarse space: polynomial, harmonic, eigenvector, generalized_eigenvector

    1220:   Level: intermediate

    1222: .keywords: MG, set, Galerkin
    1223: .seealso: PCMGGetAdaptInterpolation(), PCMGSetGalerkin()
    1224: @*/
    1225: PetscErrorCode PCMGSetAdaptInterpolation(PC pc, PetscBool adapt)
    1226: {

    1231:   PetscTryMethod(pc,"PCMGSetAdaptInterpolation_C",(PC,PetscBool),(pc,adapt));
    1232:   return(0);
    1233: }

    1235: /*@
    1236:   PCMGGetAdaptInterpolation - Get the flag to adapt the interpolator based upon a vector space which should be accurately captured by the next coarser mesh, and thus accurately interpolated.

    1238:   Not collective

    1240:   Input Parameter:
    1241: . pc    - the multigrid context

    1243:   Output Parameter:
    1244: . adapt - flag for adaptation of the interpolator

    1246:   Level: intermediate

    1248: .keywords: MG, set, Galerkin
    1249: .seealso: PCMGSetAdaptInterpolation(), PCMGSetGalerkin()
    1250: @*/
    1251: PetscErrorCode PCMGGetAdaptInterpolation(PC pc, PetscBool *adapt)
    1252: {

    1258:   PetscUseMethod(pc,"PCMGGetAdaptInterpolation_C",(PC,PetscBool*),(pc,adapt));
    1259:   return(0);
    1260: }

    1262: /*@
    1263:    PCMGSetNumberSmooth - Sets the number of pre and post-smoothing steps to use
    1264:    on all levels.  Use PCMGDistinctSmoothUp() to create separate up and down smoothers if you want different numbers of
    1265:    pre- and post-smoothing steps.

    1267:    Logically Collective on PC

    1269:    Input Parameters:
    1270: +  mg - the multigrid context
    1271: -  n - the number of smoothing steps

    1273:    Options Database Key:
    1274: .  -mg_levels_ksp_max_it <n> - Sets number of pre and post-smoothing steps

    1276:    Level: advanced

    1278:    Notes:
    1279:     this does not set a value on the coarsest grid, since we assume that
    1280:     there is no separate smooth up on the coarsest grid.

    1282: .seealso: PCMGSetDistinctSmoothUp()
    1283: @*/
    1284: PetscErrorCode  PCMGSetNumberSmooth(PC pc,PetscInt n)
    1285: {
    1286:   PC_MG          *mg        = (PC_MG*)pc->data;
    1287:   PC_MG_Levels   **mglevels = mg->levels;
    1289:   PetscInt       i,levels;

    1294:   if (!mglevels) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ORDER,"Must set MG levels with PCMGSetLevels() before calling");
    1295:   levels = mglevels[0]->levels;

    1297:   for (i=1; i<levels; i++) {
    1298:     KSPSetTolerances(mglevels[i]->smoothu,PETSC_DEFAULT,PETSC_DEFAULT,PETSC_DEFAULT,n);
    1299:     KSPSetTolerances(mglevels[i]->smoothd,PETSC_DEFAULT,PETSC_DEFAULT,PETSC_DEFAULT,n);
    1300:     mg->default_smoothu = n;
    1301:     mg->default_smoothd = n;
    1302:   }
    1303:   return(0);
    1304: }

    1306: /*@
    1307:    PCMGSetDistinctSmoothUp - sets the up (post) smoother to be a separate KSP from the down (pre) smoother on all levels
    1308:        and adds the suffix _up to the options name

    1310:    Logically Collective on PC

    1312:    Input Parameters:
    1313: .  pc - the preconditioner context

    1315:    Options Database Key:
    1316: .  -pc_mg_distinct_smoothup

    1318:    Level: advanced

    1320:    Notes:
    1321:     this does not set a value on the coarsest grid, since we assume that
    1322:     there is no separate smooth up on the coarsest grid.

    1324: .seealso: PCMGSetNumberSmooth()
    1325: @*/
    1326: PetscErrorCode  PCMGSetDistinctSmoothUp(PC pc)
    1327: {
    1328:   PC_MG          *mg        = (PC_MG*)pc->data;
    1329:   PC_MG_Levels   **mglevels = mg->levels;
    1331:   PetscInt       i,levels;
    1332:   KSP            subksp;

    1336:   if (!mglevels) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ORDER,"Must set MG levels with PCMGSetLevels() before calling");
    1337:   levels = mglevels[0]->levels;

    1339:   for (i=1; i<levels; i++) {
    1340:     const char *prefix = NULL;
    1341:     /* make sure smoother up and down are different */
    1342:     PCMGGetSmootherUp(pc,i,&subksp);
    1343:     KSPGetOptionsPrefix(mglevels[i]->smoothd,&prefix);
    1344:     KSPSetOptionsPrefix(subksp,prefix);
    1345:     KSPAppendOptionsPrefix(subksp,"up_");
    1346:   }
    1347:   return(0);
    1348: }

    1350: /* No new matrices are created, and the coarse operator matrices are the references to the original ones */
    1351: PetscErrorCode  PCGetInterpolations_MG(PC pc,PetscInt *num_levels,Mat *interpolations[])
    1352: {
    1353:   PC_MG          *mg        = (PC_MG*)pc->data;
    1354:   PC_MG_Levels   **mglevels = mg->levels;
    1355:   Mat            *mat;
    1356:   PetscInt       l;

    1360:   if (!mglevels) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_WRONGSTATE,"Must set MG levels before calling");
    1361:   PetscMalloc1(mg->nlevels,&mat);
    1362:   for (l=1; l< mg->nlevels; l++) {
    1363:     mat[l-1] = mglevels[l]->interpolate;
    1364:     PetscObjectReference((PetscObject)mat[l-1]);
    1365:   }
    1366:   *num_levels = mg->nlevels;
    1367:   *interpolations = mat;
    1368:   return(0);
    1369: }

    1371: /* No new matrices are created, and the coarse operator matrices are the references to the original ones */
    1372: PetscErrorCode  PCGetCoarseOperators_MG(PC pc,PetscInt *num_levels,Mat *coarseOperators[])
    1373: {
    1374:   PC_MG          *mg        = (PC_MG*)pc->data;
    1375:   PC_MG_Levels   **mglevels = mg->levels;
    1376:   PetscInt       l;
    1377:   Mat            *mat;

    1381:   if (!mglevels) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_WRONGSTATE,"Must set MG levels before calling");
    1382:   PetscMalloc1(mg->nlevels,&mat);
    1383:   for (l=0; l<mg->nlevels-1; l++) {
    1384:     KSPGetOperators(mglevels[l]->smoothd,NULL,&(mat[l]));
    1385:     PetscObjectReference((PetscObject)mat[l]);
    1386:   }
    1387:   *num_levels = mg->nlevels;
    1388:   *coarseOperators = mat;
    1389:   return(0);
    1390: }

    1392: /*@C
    1393:   PCMGRegisterCoarseSpaceConstructor -  Adds a method to the PCMG package for coarse space construction.

    1395:   Not collective

    1397:   Input Parameters:
    1398: + name     - name of the constructor
    1399: - function - constructor routine

    1401:   Notes:
    1402:   Calling sequence for the routine:
    1403: $ my_csp(PC pc, PetscInt l, DM dm, KSP smooth, PetscInt Nc, const Vec initGuess[], Vec **coarseSp)
    1404: $   pc        - The PC object
    1405: $   l         - The multigrid level, 0 is the coarse level
    1406: $   dm        - The DM for this level
    1407: $   smooth    - The level smoother
    1408: $   Nc        - The size of the coarse space
    1409: $   initGuess - Basis for an initial guess for the space
    1410: $   coarseSp  - A basis for the computed coarse space

    1412:   Level: advanced

    1414: .seealso: PCMGGetCoarseSpaceConstructor(), PCRegister()
    1415: @*/
    1416: PetscErrorCode PCMGRegisterCoarseSpaceConstructor(const char name[], PetscErrorCode (*function)(PC, PetscInt, DM, KSP, PetscInt, const Vec[], Vec **))
    1417: {

    1421:   PCInitializePackage();
    1422:   PetscFunctionListAdd(&PCMGCoarseList,name,function);
    1423:   return(0);
    1424: }

    1426: /*@C
    1427:   PCMGGetCoarseSpaceConstructor -  Returns the given coarse space construction method.

    1429:   Not collective

    1431:   Input Parameter:
    1432: . name     - name of the constructor

    1434:   Output Parameter:
    1435: . function - constructor routine

    1437:   Notes:
    1438:   Calling sequence for the routine:
    1439: $ my_csp(PC pc, PetscInt l, DM dm, KSP smooth, PetscInt Nc, const Vec initGuess[], Vec **coarseSp)
    1440: $   pc        - The PC object
    1441: $   l         - The multigrid level, 0 is the coarse level
    1442: $   dm        - The DM for this level
    1443: $   smooth    - The level smoother
    1444: $   Nc        - The size of the coarse space
    1445: $   initGuess - Basis for an initial guess for the space
    1446: $   coarseSp  - A basis for the computed coarse space

    1448:   Level: advanced

    1450: .seealso: PCMGRegisterCoarseSpaceConstructor(), PCRegister()
    1451: @*/
    1452: PetscErrorCode PCMGGetCoarseSpaceConstructor(const char name[], PetscErrorCode (**function)(PC, PetscInt, DM, KSP, PetscInt, const Vec[], Vec **))
    1453: {

    1457:   PetscFunctionListFind(PCMGCoarseList,name,function);
    1458:   return(0);
    1459: }

    1461: /* ----------------------------------------------------------------------------------------*/

    1463: /*MC
    1464:    PCMG - Use multigrid preconditioning. This preconditioner requires you provide additional
    1465:     information about the coarser grid matrices and restriction/interpolation operators.

    1467:    Options Database Keys:
    1468: +  -pc_mg_levels <nlevels> - number of levels including finest
    1469: .  -pc_mg_cycle_type <v,w> - provide the cycle desired
    1470: .  -pc_mg_type <additive,multiplicative,full,kaskade> - multiplicative is the default
    1471: .  -pc_mg_log - log information about time spent on each level of the solver
    1472: .  -pc_mg_distinct_smoothup - configure up (after interpolation) and down (before restriction) smoothers separately (with different options prefixes)
    1473: .  -pc_mg_galerkin <both,pmat,mat,none> - use Galerkin process to compute coarser operators, i.e. Acoarse = R A R'
    1474: .  -pc_mg_multiplicative_cycles - number of cycles to use as the preconditioner (defaults to 1)
    1475: .  -pc_mg_dump_matlab - dumps the matrices for each level and the restriction/interpolation matrices
    1476:                         to the Socket viewer for reading from MATLAB.
    1477: -  -pc_mg_dump_binary - dumps the matrices for each level and the restriction/interpolation matrices
    1478:                         to the binary output file called binaryoutput

    1480:    Notes:
    1481:     If one uses a Krylov method such GMRES or CG as the smoother then one must use KSPFGMRES, KSPGCR, or KSPRICHARDSON as the outer Krylov method

    1483:        When run with a single level the smoother options are used on that level NOT the coarse grid solver options

    1485:        When run with KSPRICHARDSON the convergence test changes slightly if monitor is turned on. The iteration count may change slightly. This
    1486:        is because without monitoring the residual norm is computed WITHIN each multigrid cycle on the finest level after the pre-smoothing
    1487:        (because the residual has just been computed for the multigrid algorithm and is hence available for free) while with monitoring the
    1488:        residual is computed at the end of each cycle.

    1490:    Level: intermediate

    1492: .seealso:  PCCreate(), PCSetType(), PCType (for list of available types), PC, PCMGType, PCEXOTIC, PCGAMG, PCML, PCHYPRE
    1493:            PCMGSetLevels(), PCMGGetLevels(), PCMGSetType(), PCMGSetCycleType(),
    1494:            PCMGSetDistinctSmoothUp(), PCMGGetCoarseSolve(), PCMGSetResidual(), PCMGSetInterpolation(),
    1495:            PCMGSetRestriction(), PCMGGetSmoother(), PCMGGetSmootherUp(), PCMGGetSmootherDown(),
    1496:            PCMGSetCycleTypeOnLevel(), PCMGSetRhs(), PCMGSetX(), PCMGSetR()
    1497: M*/

    1499: PETSC_EXTERN PetscErrorCode PCCreate_MG(PC pc)
    1500: {
    1501:   PC_MG          *mg;

    1505:   PetscNewLog(pc,&mg);
    1506:   pc->data     = (void*)mg;
    1507:   mg->nlevels  = -1;
    1508:   mg->am       = PC_MG_MULTIPLICATIVE;
    1509:   mg->galerkin = PC_MG_GALERKIN_NONE;
    1510:   mg->adaptInterpolation = PETSC_FALSE;
    1511:   mg->Nc                 = -1;
    1512:   mg->eigenvalue         = -1;

    1514:   pc->useAmat = PETSC_TRUE;

    1516:   pc->ops->apply          = PCApply_MG;
    1517:   pc->ops->setup          = PCSetUp_MG;
    1518:   pc->ops->reset          = PCReset_MG;
    1519:   pc->ops->destroy        = PCDestroy_MG;
    1520:   pc->ops->setfromoptions = PCSetFromOptions_MG;
    1521:   pc->ops->view           = PCView_MG;

    1523:   PetscObjectComposedDataRegister(&mg->eigenvalue);
    1524:   PetscObjectComposeFunction((PetscObject)pc,"PCMGSetGalerkin_C",PCMGSetGalerkin_MG);
    1525:   PetscObjectComposeFunction((PetscObject)pc,"PCMGGetLevels_C",PCMGGetLevels_MG);
    1526:   PetscObjectComposeFunction((PetscObject)pc,"PCMGSetLevels_C",PCMGSetLevels_MG);
    1527:   PetscObjectComposeFunction((PetscObject)pc,"PCGetInterpolations_C",PCGetInterpolations_MG);
    1528:   PetscObjectComposeFunction((PetscObject)pc,"PCGetCoarseOperators_C",PCGetCoarseOperators_MG);
    1529:   PetscObjectComposeFunction((PetscObject)pc,"PCMGSetAdaptInterpolation_C",PCMGSetAdaptInterpolation_MG);
    1530:   PetscObjectComposeFunction((PetscObject)pc,"PCMGGetAdaptInterpolation_C",PCMGGetAdaptInterpolation_MG);
    1531:   return(0);
    1532: }