Date: 2022-11-02T11:47:23+00:00

Actual source code: pciss.c
slepc-3.18.1 2022-11-02
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      1: /*
      2:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      3:    SLEPc - Scalable Library for Eigenvalue Problem Computations
      4:    Copyright (c) 2002-, Universitat Politecnica de Valencia, Spain

      6:    This file is part of SLEPc.
      7:    SLEPc is distributed under a 2-clause BSD license (see LICENSE).
      8:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      9: */
     10: /*
     11:    SLEPc eigensolver: "ciss"

     13:    Method: Contour Integral Spectral Slicing

     15:    Algorithm:

     17:        Contour integral based on Sakurai-Sugiura method to construct a
     18:        subspace, with various eigenpair extractions (Rayleigh-Ritz,
     19:        explicit moment).

     21:    Based on code contributed by Y. Maeda, T. Sakurai.

     23:    References:

     25:        [1] J. Asakura, T. Sakurai, H. Tadano, T. Ikegami, K. Kimura, "A
     26:            numerical method for polynomial eigenvalue problems using contour
     27:            integral", Japan J. Indust. Appl. Math. 27:73-90, 2010.
     28: */

     30: #include <slepc/private/pepimpl.h>
     31: #include <slepc/private/slepccontour.h>

     33: typedef struct {
     34:   /* parameters */
     35:   PetscInt          N;             /* number of integration points (32) */
     36:   PetscInt          L;             /* block size (16) */
     37:   PetscInt          M;             /* moment degree (N/4 = 4) */
     38:   PetscReal         delta;         /* threshold of singular value (1e-12) */
     39:   PetscInt          L_max;         /* maximum number of columns of the source matrix V */
     40:   PetscReal         spurious_threshold; /* discard spurious eigenpairs */
     41:   PetscBool         isreal;        /* T(z) is real for real z */
     42:   PetscInt          npart;         /* number of partitions */
     43:   PetscInt          refine_inner;
     44:   PetscInt          refine_blocksize;
     45:   PEPCISSExtraction extraction;
     46:   /* private data */
     47:   SlepcContourData  contour;
     48:   PetscReal         *sigma;        /* threshold for numerical rank */
     49:   PetscScalar       *weight;
     50:   PetscScalar       *omega;
     51:   PetscScalar       *pp;
     52:   BV                V;
     53:   BV                S;
     54:   BV                Y;
     55:   PetscBool         useconj;
     56:   Mat               J,*Psplit;     /* auxiliary matrices */
     57:   BV                pV;
     58:   PetscObjectId     rgid;
     59:   PetscObjectState  rgstate;
     60: } PEP_CISS;

     62: static PetscErrorCode PEPComputeFunction(PEP pep,PetscScalar lambda,Mat T,Mat P,PetscBool deriv)
     63: {
     64:   PetscInt         i;
     65:   PetscScalar      *coeff;
     66:   Mat              *A,*K;
     67:   MatStructure     str,strp;
     68:   PEP_CISS         *ctx = (PEP_CISS*)pep->data;
     69:   SlepcContourData contour = ctx->contour;

     71:   A = (contour->pA)?contour->pA:pep->A;
     72:   K = (contour->pP)?contour->pP:ctx->Psplit;
     73:   PetscMalloc1(pep->nmat,&coeff);
     74:   if (deriv) PEPEvaluateBasisDerivative(pep,lambda,0,coeff,NULL);
     75:   else PEPEvaluateBasis(pep,lambda,0,coeff,NULL);
     76:   STGetMatStructure(pep->st,&str);
     77:   MatZeroEntries(T);
     78:   if (!deriv && T != P) {
     79:     STGetSplitPreconditionerInfo(pep->st,NULL,&strp);
     80:     MatZeroEntries(P);
     81:   }
     82:   i = deriv?1:0;
     83:   for (;i<pep->nmat;i++) {
     84:     MatAXPY(T,coeff[i],A[i],str);
     85:     if (!deriv && T != P) MatAXPY(P,coeff[i],K[i],strp);
     86:   }
     87:   PetscFree(coeff);
     88:   return 0;
     89: }

     91: /*
     92:   Set up KSP solvers for every integration point
     93: */
     94: static PetscErrorCode PEPCISSSetUp(PEP pep,Mat T,Mat P)
     95: {
     96:   PEP_CISS         *ctx = (PEP_CISS*)pep->data;
     97:   SlepcContourData contour;
     98:   PetscInt         i,p_id;
     99:   Mat              Amat,Pmat;

    101:   if (!ctx->contour || !ctx->contour->ksp) PEPCISSGetKSPs(pep,NULL,NULL);
    102:   contour = ctx->contour;
    103:   PetscAssert(ctx->contour && ctx->contour->ksp,PetscObjectComm((PetscObject)pep),PETSC_ERR_PLIB,"Something went wrong with PEPCISSGetKSPs()");
    104:   for (i=0;i<contour->npoints;i++) {
    105:     p_id = i*contour->subcomm->n + contour->subcomm->color;
    106:     MatDuplicate(T,MAT_DO_NOT_COPY_VALUES,&Amat);
    107:     if (T != P) MatDuplicate(P,MAT_DO_NOT_COPY_VALUES,&Pmat); else Pmat = Amat;
    108:     PEPComputeFunction(pep,ctx->omega[p_id],Amat,Pmat,PETSC_FALSE);
    109:     PEP_KSPSetOperators(contour->ksp[i],Amat,Pmat);
    110:     MatDestroy(&Amat);
    111:     if (T != P) MatDestroy(&Pmat);
    112:   }
    113:   return 0;
    114: }

    116: /*
    117:   Y_i = F(z_i)^{-1}Fp(z_i)V for every integration point, Y=[Y_i] is in the context
    118: */
    119: static PetscErrorCode PEPCISSSolve(PEP pep,Mat dT,BV V,PetscInt L_start,PetscInt L_end)
    120: {
    121:   PEP_CISS         *ctx = (PEP_CISS*)pep->data;
    122:   SlepcContourData contour;
    123:   PetscInt         i,p_id;
    124:   Mat              MV,BMV=NULL,MC;

    126:   contour = ctx->contour;
    127:   BVSetActiveColumns(V,L_start,L_end);
    128:   BVGetMat(V,&MV);
    129:   for (i=0;i<contour->npoints;i++) {
    130:     p_id = i*contour->subcomm->n + contour->subcomm->color;
    131:     PEPComputeFunction(pep,ctx->omega[p_id],dT,NULL,PETSC_TRUE);
    132:     BVSetActiveColumns(ctx->Y,i*ctx->L+L_start,i*ctx->L+L_end);
    133:     BVGetMat(ctx->Y,&MC);
    134:     if (!i) {
    135:       MatProductCreate(dT,MV,NULL,&BMV);
    136:       MatProductSetType(BMV,MATPRODUCT_AB);
    137:       MatProductSetFromOptions(BMV);
    138:       MatProductSymbolic(BMV);
    139:     }
    140:     MatProductNumeric(BMV);
    141:     KSPMatSolve(contour->ksp[i],BMV,MC);
    142:     BVRestoreMat(ctx->Y,&MC);
    143:   }
    144:   MatDestroy(&BMV);
    145:   BVRestoreMat(V,&MV);
    146:   return 0;
    147: }

    149: PetscErrorCode PEPSetUp_CISS(PEP pep)
    150: {
    151:   PEP_CISS         *ctx = (PEP_CISS*)pep->data;
    152:   SlepcContourData contour;
    153:   PetscInt         i,nwork,nsplit;
    154:   PetscBool        istrivial,isellipse,flg;
    155:   PetscObjectId    id;
    156:   PetscObjectState state;
    157:   Vec              v0;

    159:   if (pep->ncv==PETSC_DEFAULT) pep->ncv = ctx->L_max*ctx->M;
    160:   else {
    161:     ctx->L_max = pep->ncv/ctx->M;
    162:     if (!ctx->L_max) {
    163:       ctx->L_max = 1;
    164:       pep->ncv = ctx->L_max*ctx->M;
    165:     }
    166:   }
    167:   ctx->L = PetscMin(ctx->L,ctx->L_max);
    168:   if (pep->max_it==PETSC_DEFAULT) pep->max_it = 5;
    169:   if (pep->mpd==PETSC_DEFAULT) pep->mpd = pep->ncv;
    170:   if (!pep->which) pep->which = PEP_ALL;
    172:   PEPCheckUnsupported(pep,PEP_FEATURE_STOPPING);
    173:   PEPCheckIgnored(pep,PEP_FEATURE_SCALE);

    175:   /* check region */
    176:   RGIsTrivial(pep->rg,&istrivial);
    178:   RGGetComplement(pep->rg,&flg);
    180:   PetscObjectTypeCompare((PetscObject)pep->rg,RGELLIPSE,&isellipse);

    183:   /* if the region has changed, then reset contour data */
    184:   PetscObjectGetId((PetscObject)pep->rg,&id);
    185:   PetscObjectStateGet((PetscObject)pep->rg,&state);
    186:   if (ctx->rgid && (id != ctx->rgid || state != ctx->rgstate)) {
    187:     SlepcContourDataDestroy(&ctx->contour);
    188:     PetscInfo(pep,"Resetting the contour data structure due to a change of region\n");
    189:     ctx->rgid = id; ctx->rgstate = state;
    190:   }

    192:   /* create contour data structure */
    193:   if (!ctx->contour) {
    194:     RGCanUseConjugates(pep->rg,ctx->isreal,&ctx->useconj);
    195:     SlepcContourDataCreate(ctx->useconj?ctx->N/2:ctx->N,ctx->npart,(PetscObject)pep,&ctx->contour);
    196:   }

    198:   PEPAllocateSolution(pep,0);
    199:   if (ctx->weight) PetscFree4(ctx->weight,ctx->omega,ctx->pp,ctx->sigma);
    200:   PetscMalloc4(ctx->N,&ctx->weight,ctx->N,&ctx->omega,ctx->N,&ctx->pp,ctx->L_max*ctx->M,&ctx->sigma);

    202:   /* allocate basis vectors */
    203:   BVDestroy(&ctx->S);
    204:   BVDuplicateResize(pep->V,ctx->L*ctx->M,&ctx->S);
    205:   BVDestroy(&ctx->V);
    206:   BVDuplicateResize(pep->V,ctx->L,&ctx->V);

    208:   /* check if a user-defined split preconditioner has been set */
    209:   STGetSplitPreconditionerInfo(pep->st,&nsplit,NULL);
    210:   if (nsplit) {
    211:     PetscFree(ctx->Psplit);
    212:     PetscMalloc1(nsplit,&ctx->Psplit);
    213:     for (i=0;i<nsplit;i++) STGetSplitPreconditionerTerm(pep->st,i,&ctx->Psplit[i]);
    214:   }

    216:   contour = ctx->contour;
    217:   SlepcContourRedundantMat(contour,pep->nmat,pep->A,ctx->Psplit);
    218:   if (!ctx->J) MatDuplicate(contour->pA?contour->pA[0]:pep->A[0],MAT_DO_NOT_COPY_VALUES,&ctx->J);
    219:   if (contour->pA) {
    220:     BVGetColumn(ctx->V,0,&v0);
    221:     SlepcContourScatterCreate(contour,v0);
    222:     BVRestoreColumn(ctx->V,0,&v0);
    223:     BVDestroy(&ctx->pV);
    224:     BVCreate(PetscObjectComm((PetscObject)contour->xsub),&ctx->pV);
    225:     BVSetSizesFromVec(ctx->pV,contour->xsub,pep->n);
    226:     BVSetFromOptions(ctx->pV);
    227:     BVResize(ctx->pV,ctx->L,PETSC_FALSE);
    228:   }

    230:   BVDestroy(&ctx->Y);
    231:   if (contour->pA) {
    232:     BVCreate(PetscObjectComm((PetscObject)contour->xsub),&ctx->Y);
    233:     BVSetSizesFromVec(ctx->Y,contour->xsub,pep->n);
    234:     BVSetFromOptions(ctx->Y);
    235:     BVResize(ctx->Y,contour->npoints*ctx->L,PETSC_FALSE);
    236:   } else BVDuplicateResize(pep->V,contour->npoints*ctx->L,&ctx->Y);

    238:   if (ctx->extraction == PEP_CISS_EXTRACTION_HANKEL) DSSetType(pep->ds,DSGNHEP);
    239:   else if (ctx->extraction == PEP_CISS_EXTRACTION_CAA) DSSetType(pep->ds,DSNHEP);
    240:   else {
    241:     DSSetType(pep->ds,DSPEP);
    242:     DSPEPSetDegree(pep->ds,pep->nmat-1);
    243:     DSPEPSetCoefficients(pep->ds,pep->pbc);
    244:   }
    245:   DSAllocate(pep->ds,pep->ncv);
    246:   nwork = 2;
    247:   PEPSetWorkVecs(pep,nwork);
    248:   return 0;
    249: }

    251: PetscErrorCode PEPSolve_CISS(PEP pep)
    252: {
    253:   PEP_CISS         *ctx = (PEP_CISS*)pep->data;
    254:   SlepcContourData contour = ctx->contour;
    255:   Mat              X,M,E,T,P;
    256:   PetscInt         i,j,ld,L_add=0,nv=0,L_base=ctx->L,inner,*inside,nsplit;
    257:   PetscScalar      *Mu,*H0,*H1,*rr,*temp,center;
    258:   PetscReal        error,max_error,radius,rgscale,est_eig,eta;
    259:   PetscBool        isellipse,*fl1;
    260:   Vec              si;
    261:   SlepcSC          sc;
    262:   PetscRandom      rand;

    264:   DSSetFromOptions(pep->ds);
    265:   DSGetSlepcSC(pep->ds,&sc);
    266:   sc->comparison    = SlepcCompareLargestMagnitude;
    267:   sc->comparisonctx = NULL;
    268:   sc->map           = NULL;
    269:   sc->mapobj        = NULL;
    270:   DSGetLeadingDimension(pep->ds,&ld);
    271:   RGComputeQuadrature(pep->rg,RG_QUADRULE_TRAPEZOIDAL,ctx->N,ctx->omega,ctx->pp,ctx->weight);
    272:   STGetSplitPreconditionerInfo(pep->st,&nsplit,NULL);
    273:   if (contour->pA) {
    274:     T = contour->pA[0];
    275:     P = nsplit? contour->pP[0]: T;
    276:   } else {
    277:     T = pep->A[0];
    278:     P = nsplit? ctx->Psplit[0]: T;
    279:   }
    280:   PEPCISSSetUp(pep,T,P);
    281:   BVSetActiveColumns(ctx->V,0,ctx->L);
    282:   BVSetRandomSign(ctx->V);
    283:   BVGetRandomContext(ctx->V,&rand);
    284:   if (contour->pA) BVScatter(ctx->V,ctx->pV,contour->scatterin,contour->xdup);
    285:   PEPCISSSolve(pep,ctx->J,(contour->pA)?ctx->pV:ctx->V,0,ctx->L);
    286:   PetscObjectTypeCompare((PetscObject)pep->rg,RGELLIPSE,&isellipse);
    287:   if (isellipse) {
    288:     BVTraceQuadrature(ctx->Y,ctx->V,ctx->L,ctx->L,ctx->weight,contour->scatterin,contour->subcomm,contour->npoints,ctx->useconj,&est_eig);
    289:     PetscInfo(pep,"Estimated eigenvalue count: %f\n",(double)est_eig);
    290:     eta = PetscPowReal(10.0,-PetscLog10Real(pep->tol)/ctx->N);
    291:     L_add = PetscMax(0,(PetscInt)PetscCeilReal((est_eig*eta)/ctx->M)-ctx->L);
    292:     if (L_add>ctx->L_max-ctx->L) {
    293:       PetscInfo(pep,"Number of eigenvalues inside the contour path may be too large\n");
    294:       L_add = ctx->L_max-ctx->L;
    295:     }
    296:   }
    297:   /* Updates L after estimate the number of eigenvalue */
    298:   if (L_add>0) {
    299:     PetscInfo(pep,"Changing L %" PetscInt_FMT " -> %" PetscInt_FMT " by Estimate #Eig\n",ctx->L,ctx->L+L_add);
    300:     BVCISSResizeBases(ctx->S,contour->pA?ctx->pV:ctx->V,ctx->Y,ctx->L,ctx->L+L_add,ctx->M,contour->npoints);
    301:     BVSetActiveColumns(ctx->V,ctx->L,ctx->L+L_add);
    302:     BVSetRandomSign(ctx->V);
    303:     if (contour->pA) BVScatter(ctx->V,ctx->pV,contour->scatterin,contour->xdup);
    304:     ctx->L += L_add;
    305:     PEPCISSSolve(pep,ctx->J,(contour->pA)?ctx->pV:ctx->V,ctx->L-L_add,ctx->L);
    306:   }

    308:   PetscMalloc2(ctx->L*ctx->L*ctx->M*2,&Mu,ctx->L*ctx->M*ctx->L*ctx->M,&H0);
    309:   for (i=0;i<ctx->refine_blocksize;i++) {
    310:     BVDotQuadrature(ctx->Y,(contour->pA)?ctx->pV:ctx->V,Mu,ctx->M,ctx->L,ctx->L,ctx->weight,ctx->pp,contour->subcomm,contour->npoints,ctx->useconj);
    311:     CISS_BlockHankel(Mu,0,ctx->L,ctx->M,H0);
    312:     PetscLogEventBegin(PEP_CISS_SVD,pep,0,0,0);
    313:     SlepcCISS_BH_SVD(H0,ctx->L*ctx->M,ctx->delta,ctx->sigma,&nv);
    314:     PetscLogEventEnd(PEP_CISS_SVD,pep,0,0,0);
    315:     if (ctx->sigma[0]<=ctx->delta || nv < ctx->L*ctx->M || ctx->L == ctx->L_max) break;
    316:     L_add = L_base;
    317:     if (ctx->L+L_add>ctx->L_max) L_add = ctx->L_max-ctx->L;
    318:     PetscInfo(pep,"Changing L %" PetscInt_FMT " -> %" PetscInt_FMT " by SVD(H0)\n",ctx->L,ctx->L+L_add);
    319:     BVCISSResizeBases(ctx->S,contour->pA?ctx->pV:ctx->V,ctx->Y,ctx->L,ctx->L+L_add,ctx->M,contour->npoints);
    320:     BVSetActiveColumns(ctx->V,ctx->L,ctx->L+L_add);
    321:     BVSetRandomSign(ctx->V);
    322:     if (contour->pA) BVScatter(ctx->V,ctx->pV,contour->scatterin,contour->xdup);
    323:     ctx->L += L_add;
    324:     PEPCISSSolve(pep,ctx->J,(contour->pA)?ctx->pV:ctx->V,ctx->L-L_add,ctx->L);
    325:     if (L_add) {
    326:       PetscFree2(Mu,H0);
    327:       PetscMalloc2(ctx->L*ctx->L*ctx->M*2,&Mu,ctx->L*ctx->M*ctx->L*ctx->M,&H0);
    328:     }
    329:   }

    331:   RGGetScale(pep->rg,&rgscale);
    332:   RGEllipseGetParameters(pep->rg,&center,&radius,NULL);

    334:   if (ctx->extraction == PEP_CISS_EXTRACTION_HANKEL) PetscMalloc1(ctx->L*ctx->M*ctx->L*ctx->M,&H1);

    336:   while (pep->reason == PEP_CONVERGED_ITERATING) {
    337:     pep->its++;
    338:     for (inner=0;inner<=ctx->refine_inner;inner++) {
    339:       if (ctx->extraction == PEP_CISS_EXTRACTION_HANKEL) {
    340:         BVDotQuadrature(ctx->Y,(contour->pA)?ctx->pV:ctx->V,Mu,ctx->M,ctx->L,ctx->L,ctx->weight,ctx->pp,contour->subcomm,contour->npoints,ctx->useconj);
    341:         CISS_BlockHankel(Mu,0,ctx->L,ctx->M,H0);
    342:         PetscLogEventBegin(PEP_CISS_SVD,pep,0,0,0);
    343:         SlepcCISS_BH_SVD(H0,ctx->L*ctx->M,ctx->delta,ctx->sigma,&nv);
    344:         PetscLogEventEnd(PEP_CISS_SVD,pep,0,0,0);
    345:       } else {
    346:         BVSumQuadrature(ctx->S,ctx->Y,ctx->M,ctx->L,ctx->L,ctx->weight,ctx->pp,contour->scatterin,contour->subcomm,contour->npoints,ctx->useconj);
    347:         /* compute SVD of S */
    348:         BVSVDAndRank(ctx->S,ctx->M,ctx->L,ctx->delta,(ctx->extraction==PEP_CISS_EXTRACTION_CAA)?BV_SVD_METHOD_QR_CAA:BV_SVD_METHOD_QR,H0,ctx->sigma,&nv);
    349:       }
    350:       PetscInfo(pep,"Estimated rank: nv = %" PetscInt_FMT "\n",nv);
    351:       if (ctx->sigma[0]>ctx->delta && nv==ctx->L*ctx->M && inner!=ctx->refine_inner) {
    352:         BVSumQuadrature(ctx->S,ctx->Y,ctx->M,ctx->L,ctx->L,ctx->weight,ctx->pp,contour->scatterin,contour->subcomm,contour->npoints,ctx->useconj);
    353:         BVSetActiveColumns(ctx->S,0,ctx->L);
    354:         BVSetActiveColumns(ctx->V,0,ctx->L);
    355:         BVCopy(ctx->S,ctx->V);
    356:         if (contour->pA) BVScatter(ctx->V,ctx->pV,contour->scatterin,contour->xdup);
    357:         PEPCISSSolve(pep,ctx->J,(contour->pA)?ctx->pV:ctx->V,0,ctx->L);
    358:       } else break;
    359:     }
    360:     pep->nconv = 0;
    361:     if (nv == 0) { pep->reason = PEP_CONVERGED_TOL; break; }
    362:     else {
    363:       /* Extracting eigenpairs */
    364:       DSSetDimensions(pep->ds,nv,0,0);
    365:       DSSetState(pep->ds,DS_STATE_RAW);
    366:       if (ctx->extraction == PEP_CISS_EXTRACTION_HANKEL) {
    367:         CISS_BlockHankel(Mu,0,ctx->L,ctx->M,H0);
    368:         CISS_BlockHankel(Mu,1,ctx->L,ctx->M,H1);
    369:         DSGetArray(pep->ds,DS_MAT_A,&temp);
    370:         for (j=0;j<nv;j++)
    371:           for (i=0;i<nv;i++)
    372:             temp[i+j*ld] = H1[i+j*ctx->L*ctx->M];
    373:         DSRestoreArray(pep->ds,DS_MAT_A,&temp);
    374:         DSGetArray(pep->ds,DS_MAT_B,&temp);
    375:         for (j=0;j<nv;j++)
    376:           for (i=0;i<nv;i++)
    377:             temp[i+j*ld] = H0[i+j*ctx->L*ctx->M];
    378:         DSRestoreArray(pep->ds,DS_MAT_B,&temp);
    379:       } else if (ctx->extraction == PEP_CISS_EXTRACTION_CAA) {
    380:         BVSetActiveColumns(ctx->S,0,nv);
    381:         DSGetArray(pep->ds,DS_MAT_A,&temp);
    382:         for (i=0;i<nv;i++) PetscArraycpy(temp+i*ld,H0+i*nv,nv);
    383:         DSRestoreArray(pep->ds,DS_MAT_A,&temp);
    384:       } else {
    385:         BVSetActiveColumns(ctx->S,0,nv);
    386:         for (i=0;i<pep->nmat;i++) {
    387:           DSGetMat(pep->ds,DSMatExtra[i],&E);
    388:           BVMatProject(ctx->S,pep->A[i],ctx->S,E);
    389:           DSRestoreMat(pep->ds,DSMatExtra[i],&E);
    390:         }
    391:         nv = (pep->nmat-1)*nv;
    392:       }
    393:       DSSolve(pep->ds,pep->eigr,pep->eigi);
    394:       DSSynchronize(pep->ds,pep->eigr,pep->eigi);
    395:       if (ctx->extraction == PEP_CISS_EXTRACTION_CAA || ctx->extraction == PEP_CISS_EXTRACTION_HANKEL) {
    396:         for (i=0;i<nv;i++) {
    397:           pep->eigr[i] = (pep->eigr[i]*radius+center)*rgscale;
    398:         }
    399:       }
    400:       PetscMalloc3(nv,&fl1,nv,&inside,nv,&rr);
    401:       DSVectors(pep->ds,DS_MAT_X,NULL,NULL);
    402:       DSGetMat(pep->ds,DS_MAT_X,&X);
    403:       SlepcCISS_isGhost(X,nv,ctx->sigma,ctx->spurious_threshold,fl1);
    404:       DSRestoreMat(pep->ds,DS_MAT_X,&X);
    405:       RGCheckInside(pep->rg,nv,pep->eigr,pep->eigi,inside);
    406:       for (i=0;i<nv;i++) {
    407:         if (fl1[i] && inside[i]>=0) {
    408:           rr[i] = 1.0;
    409:           pep->nconv++;
    410:         } else rr[i] = 0.0;
    411:       }
    412:       DSSort(pep->ds,pep->eigr,pep->eigi,rr,NULL,&pep->nconv);
    413:       DSSynchronize(pep->ds,pep->eigr,pep->eigi);
    414:       if (ctx->extraction == PEP_CISS_EXTRACTION_CAA || ctx->extraction == PEP_CISS_EXTRACTION_HANKEL) {
    415:         for (i=0;i<nv;i++) pep->eigr[i] = (pep->eigr[i]*radius+center)*rgscale;
    416:       }
    417:       PetscFree3(fl1,inside,rr);
    418:       BVSetActiveColumns(pep->V,0,nv);
    419:       DSVectors(pep->ds,DS_MAT_X,NULL,NULL);
    420:       if (ctx->extraction == PEP_CISS_EXTRACTION_HANKEL) {
    421:         BVSumQuadrature(ctx->S,ctx->Y,ctx->M,ctx->L,ctx->L,ctx->weight,ctx->pp,contour->scatterin,contour->subcomm,contour->npoints,ctx->useconj);
    422:         BVSetActiveColumns(ctx->S,0,nv);
    423:         BVCopy(ctx->S,pep->V);
    424:         DSGetMat(pep->ds,DS_MAT_X,&X);
    425:         BVMultInPlace(ctx->S,X,0,pep->nconv);
    426:         BVMultInPlace(pep->V,X,0,pep->nconv);
    427:         DSRestoreMat(pep->ds,DS_MAT_X,&X);
    428:       } else {
    429:         DSGetMat(pep->ds,DS_MAT_X,&X);
    430:         BVMultInPlace(ctx->S,X,0,pep->nconv);
    431:         DSRestoreMat(pep->ds,DS_MAT_X,&X);
    432:         BVSetActiveColumns(ctx->S,0,pep->nconv);
    433:         BVCopy(ctx->S,pep->V);
    434:       }
    435:       max_error = 0.0;
    436:       for (i=0;i<pep->nconv;i++) {
    437:         BVGetColumn(pep->V,i,&si);
    438:         VecNormalize(si,NULL);
    439:         PEPComputeResidualNorm_Private(pep,pep->eigr[i],0,si,NULL,pep->work,&error);
    440:         (*pep->converged)(pep,pep->eigr[i],0,error,&error,pep->convergedctx);
    441:         BVRestoreColumn(pep->V,i,&si);
    442:         max_error = PetscMax(max_error,error);
    443:       }
    444:       if (max_error <= pep->tol) pep->reason = PEP_CONVERGED_TOL;
    445:       else if (pep->its > pep->max_it) pep->reason = PEP_DIVERGED_ITS;
    446:       else {
    447:         if (pep->nconv > ctx->L) nv = pep->nconv;
    448:         else if (ctx->L > nv) nv = ctx->L;
    449:         nv = PetscMin(nv,ctx->L*ctx->M);
    450:         MatCreateSeqDense(PETSC_COMM_SELF,nv,ctx->L,NULL,&M);
    451:         MatSetRandom(M,rand);
    452:         BVSetActiveColumns(ctx->S,0,nv);
    453:         BVMultInPlace(ctx->S,M,0,ctx->L);
    454:         MatDestroy(&M);
    455:         BVSetActiveColumns(ctx->S,0,ctx->L);
    456:         BVSetActiveColumns(ctx->V,0,ctx->L);
    457:         BVCopy(ctx->S,ctx->V);
    458:         if (contour->pA) BVScatter(ctx->V,ctx->pV,contour->scatterin,contour->xdup);
    459:         PEPCISSSolve(pep,ctx->J,(contour->pA)?ctx->pV:ctx->V,0,ctx->L);
    460:       }
    461:     }
    462:   }
    463:   PetscFree2(Mu,H0);
    464:   if (ctx->extraction == PEP_CISS_EXTRACTION_HANKEL) PetscFree(H1);
    465:   return 0;
    466: }

    468: static PetscErrorCode PEPCISSSetSizes_CISS(PEP pep,PetscInt ip,PetscInt bs,PetscInt ms,PetscInt npart,PetscInt bsmax,PetscBool realmats)
    469: {
    470:   PEP_CISS       *ctx = (PEP_CISS*)pep->data;
    471:   PetscInt       oN,oL,oM,oLmax,onpart;
    472:   PetscMPIInt    size;

    474:   oN = ctx->N;
    475:   if (ip == PETSC_DECIDE || ip == PETSC_DEFAULT) {
    476:     if (ctx->N!=32) { ctx->N =32; ctx->M = ctx->N/4; }
    477:   } else {
    480:     if (ctx->N!=ip) { ctx->N = ip; ctx->M = ctx->N/4; }
    481:   }
    482:   oL = ctx->L;
    483:   if (bs == PETSC_DECIDE || bs == PETSC_DEFAULT) {
    484:     ctx->L = 16;
    485:   } else {
    487:     ctx->L = bs;
    488:   }
    489:   oM = ctx->M;
    490:   if (ms == PETSC_DECIDE || ms == PETSC_DEFAULT) {
    491:     ctx->M = ctx->N/4;
    492:   } else {
    495:     ctx->M = PetscMax(ms,2);
    496:   }
    497:   onpart = ctx->npart;
    498:   if (npart == PETSC_DECIDE || npart == PETSC_DEFAULT) {
    499:     ctx->npart = 1;
    500:   } else {
    501:     MPI_Comm_size(PetscObjectComm((PetscObject)pep),&size);
    503:     ctx->npart = npart;
    504:   }
    505:   oLmax = ctx->L_max;
    506:   if (bsmax == PETSC_DECIDE || bsmax == PETSC_DEFAULT) {
    507:     ctx->L_max = 64;
    508:   } else {
    510:     ctx->L_max = PetscMax(bsmax,ctx->L);
    511:   }
    512:   if (onpart != ctx->npart || oN != ctx->N || realmats != ctx->isreal) {
    513:     SlepcContourDataDestroy(&ctx->contour);
    514:     PetscInfo(pep,"Resetting the contour data structure due to a change of parameters\n");
    515:     pep->state = PEP_STATE_INITIAL;
    516:   }
    517:   ctx->isreal = realmats;
    518:   if (oL != ctx->L || oM != ctx->M || oLmax != ctx->L_max) pep->state = PEP_STATE_INITIAL;
    519:   return 0;
    520: }

    522: /*@
    523:    PEPCISSSetSizes - Sets the values of various size parameters in the CISS solver.

    525:    Logically Collective on pep

    527:    Input Parameters:
    528: +  pep   - the polynomial eigensolver context
    529: .  ip    - number of integration points
    530: .  bs    - block size
    531: .  ms    - moment size
    532: .  npart - number of partitions when splitting the communicator
    533: .  bsmax - max block size
    534: -  realmats - all coefficient matrices of P(.) are real

    536:    Options Database Keys:
    537: +  -pep_ciss_integration_points - Sets the number of integration points
    538: .  -pep_ciss_blocksize - Sets the block size
    539: .  -pep_ciss_moments - Sets the moment size
    540: .  -pep_ciss_partitions - Sets the number of partitions
    541: .  -pep_ciss_maxblocksize - Sets the maximum block size
    542: -  -pep_ciss_realmats - all coefficient matrices of P(.) are real

    544:    Notes:
    545:    The default number of partitions is 1. This means the internal KSP object is shared
    546:    among all processes of the PEP communicator. Otherwise, the communicator is split
    547:    into npart communicators, so that npart KSP solves proceed simultaneously.

    549:    Level: advanced

    551: .seealso: PEPCISSGetSizes()
    552: @*/
    553: PetscErrorCode PEPCISSSetSizes(PEP pep,PetscInt ip,PetscInt bs,PetscInt ms,PetscInt npart,PetscInt bsmax,PetscBool realmats)
    554: {
    562:   PetscTryMethod(pep,"PEPCISSSetSizes_C",(PEP,PetscInt,PetscInt,PetscInt,PetscInt,PetscInt,PetscBool),(pep,ip,bs,ms,npart,bsmax,realmats));
    563:   return 0;
    564: }

    566: static PetscErrorCode PEPCISSGetSizes_CISS(PEP pep,PetscInt *ip,PetscInt *bs,PetscInt *ms,PetscInt *npart,PetscInt *bsmax,PetscBool *realmats)
    567: {
    568:   PEP_CISS *ctx = (PEP_CISS*)pep->data;

    570:   if (ip) *ip = ctx->N;
    571:   if (bs) *bs = ctx->L;
    572:   if (ms) *ms = ctx->M;
    573:   if (npart) *npart = ctx->npart;
    574:   if (bsmax) *bsmax = ctx->L_max;
    575:   if (realmats) *realmats = ctx->isreal;
    576:   return 0;
    577: }

    579: /*@
    580:    PEPCISSGetSizes - Gets the values of various size parameters in the CISS solver.

    582:    Not Collective

    584:    Input Parameter:
    585: .  pep - the polynomial eigensolver context

    587:    Output Parameters:
    588: +  ip    - number of integration points
    589: .  bs    - block size
    590: .  ms    - moment size
    591: .  npart - number of partitions when splitting the communicator
    592: .  bsmax - max block size
    593: -  realmats - all coefficient matrices of P(.) are real

    595:    Level: advanced

    597: .seealso: PEPCISSSetSizes()
    598: @*/
    599: PetscErrorCode PEPCISSGetSizes(PEP pep,PetscInt *ip,PetscInt *bs,PetscInt *ms,PetscInt *npart,PetscInt *bsmax,PetscBool *realmats)
    600: {
    602:   PetscUseMethod(pep,"PEPCISSGetSizes_C",(PEP,PetscInt*,PetscInt*,PetscInt*,PetscInt*,PetscInt*,PetscBool*),(pep,ip,bs,ms,npart,bsmax,realmats));
    603:   return 0;
    604: }

    606: static PetscErrorCode PEPCISSSetThreshold_CISS(PEP pep,PetscReal delta,PetscReal spur)
    607: {
    608:   PEP_CISS *ctx = (PEP_CISS*)pep->data;

    610:   if (delta == PETSC_DEFAULT) {
    611:     ctx->delta = SLEPC_DEFAULT_TOL*1e-4;
    612:   } else {
    614:     ctx->delta = delta;
    615:   }
    616:   if (spur == PETSC_DEFAULT) {
    617:     ctx->spurious_threshold = PetscSqrtReal(SLEPC_DEFAULT_TOL);
    618:   } else {
    620:     ctx->spurious_threshold = spur;
    621:   }
    622:   return 0;
    623: }

    625: /*@
    626:    PEPCISSSetThreshold - Sets the values of various threshold parameters in
    627:    the CISS solver.

    629:    Logically Collective on pep

    631:    Input Parameters:
    632: +  pep   - the polynomial eigensolver context
    633: .  delta - threshold for numerical rank
    634: -  spur  - spurious threshold (to discard spurious eigenpairs)

    636:    Options Database Keys:
    637: +  -pep_ciss_delta - Sets the delta
    638: -  -pep_ciss_spurious_threshold - Sets the spurious threshold

    640:    Level: advanced

    642: .seealso: PEPCISSGetThreshold()
    643: @*/
    644: PetscErrorCode PEPCISSSetThreshold(PEP pep,PetscReal delta,PetscReal spur)
    645: {
    649:   PetscTryMethod(pep,"PEPCISSSetThreshold_C",(PEP,PetscReal,PetscReal),(pep,delta,spur));
    650:   return 0;
    651: }

    653: static PetscErrorCode PEPCISSGetThreshold_CISS(PEP pep,PetscReal *delta,PetscReal *spur)
    654: {
    655:   PEP_CISS *ctx = (PEP_CISS*)pep->data;

    657:   if (delta) *delta = ctx->delta;
    658:   if (spur)  *spur = ctx->spurious_threshold;
    659:   return 0;
    660: }

    662: /*@
    663:    PEPCISSGetThreshold - Gets the values of various threshold parameters in
    664:    the CISS solver.

    666:    Not Collective

    668:    Input Parameter:
    669: .  pep - the polynomial eigensolver context

    671:    Output Parameters:
    672: +  delta - threshold for numerical rank
    673: -  spur  - spurious threshold (to discard spurious eigenpairs)

    675:    Level: advanced

    677: .seealso: PEPCISSSetThreshold()
    678: @*/
    679: PetscErrorCode PEPCISSGetThreshold(PEP pep,PetscReal *delta,PetscReal *spur)
    680: {
    682:   PetscUseMethod(pep,"PEPCISSGetThreshold_C",(PEP,PetscReal*,PetscReal*),(pep,delta,spur));
    683:   return 0;
    684: }

    686: static PetscErrorCode PEPCISSSetRefinement_CISS(PEP pep,PetscInt inner,PetscInt blsize)
    687: {
    688:   PEP_CISS *ctx = (PEP_CISS*)pep->data;

    690:   if (inner == PETSC_DEFAULT) {
    691:     ctx->refine_inner = 0;
    692:   } else {
    694:     ctx->refine_inner = inner;
    695:   }
    696:   if (blsize == PETSC_DEFAULT) {
    697:     ctx->refine_blocksize = 0;
    698:   } else {
    700:     ctx->refine_blocksize = blsize;
    701:   }
    702:   return 0;
    703: }

    705: /*@
    706:    PEPCISSSetRefinement - Sets the values of various refinement parameters
    707:    in the CISS solver.

    709:    Logically Collective on pep

    711:    Input Parameters:
    712: +  pep    - the polynomial eigensolver context
    713: .  inner  - number of iterative refinement iterations (inner loop)
    714: -  blsize - number of iterative refinement iterations (blocksize loop)

    716:    Options Database Keys:
    717: +  -pep_ciss_refine_inner - Sets number of inner iterations
    718: -  -pep_ciss_refine_blocksize - Sets number of blocksize iterations

    720:    Level: advanced

    722: .seealso: PEPCISSGetRefinement()
    723: @*/
    724: PetscErrorCode PEPCISSSetRefinement(PEP pep,PetscInt inner,PetscInt blsize)
    725: {
    729:   PetscTryMethod(pep,"PEPCISSSetRefinement_C",(PEP,PetscInt,PetscInt),(pep,inner,blsize));
    730:   return 0;
    731: }

    733: static PetscErrorCode PEPCISSGetRefinement_CISS(PEP pep,PetscInt *inner,PetscInt *blsize)
    734: {
    735:   PEP_CISS *ctx = (PEP_CISS*)pep->data;

    737:   if (inner)  *inner = ctx->refine_inner;
    738:   if (blsize) *blsize = ctx->refine_blocksize;
    739:   return 0;
    740: }

    742: /*@
    743:    PEPCISSGetRefinement - Gets the values of various refinement parameters
    744:    in the CISS solver.

    746:    Not Collective

    748:    Input Parameter:
    749: .  pep - the polynomial eigensolver context

    751:    Output Parameters:
    752: +  inner  - number of iterative refinement iterations (inner loop)
    753: -  blsize - number of iterative refinement iterations (blocksize loop)

    755:    Level: advanced

    757: .seealso: PEPCISSSetRefinement()
    758: @*/
    759: PetscErrorCode PEPCISSGetRefinement(PEP pep, PetscInt *inner, PetscInt *blsize)
    760: {
    762:   PetscUseMethod(pep,"PEPCISSGetRefinement_C",(PEP,PetscInt*,PetscInt*),(pep,inner,blsize));
    763:   return 0;
    764: }

    766: static PetscErrorCode PEPCISSSetExtraction_CISS(PEP pep,PEPCISSExtraction extraction)
    767: {
    768:   PEP_CISS *ctx = (PEP_CISS*)pep->data;

    770:   if (ctx->extraction != extraction) {
    771:     ctx->extraction = extraction;
    772:     pep->state      = PEP_STATE_INITIAL;
    773:   }
    774:   return 0;
    775: }

    777: /*@
    778:    PEPCISSSetExtraction - Sets the extraction technique used in the CISS solver.

    780:    Logically Collective on pep

    782:    Input Parameters:
    783: +  pep        - the polynomial eigensolver context
    784: -  extraction - the extraction technique

    786:    Options Database Key:
    787: .  -pep_ciss_extraction - Sets the extraction technique (either 'ritz', 'hankel' or 'caa')

    789:    Notes:
    790:    By default, the Rayleigh-Ritz extraction is used (PEP_CISS_EXTRACTION_RITZ).

    792:    If the 'hankel' or the 'caa' option is specified (PEP_CISS_EXTRACTION_HANKEL or
    793:    PEP_CISS_EXTRACTION_CAA), then the Block Hankel method, or the Communication-avoiding
    794:    Arnoldi method, respectively, is used for extracting eigenpairs.

    796:    Level: advanced

    798: .seealso: PEPCISSGetExtraction(), PEPCISSExtraction
    799: @*/
    800: PetscErrorCode PEPCISSSetExtraction(PEP pep,PEPCISSExtraction extraction)
    801: {
    804:   PetscTryMethod(pep,"PEPCISSSetExtraction_C",(PEP,PEPCISSExtraction),(pep,extraction));
    805:   return 0;
    806: }

    808: static PetscErrorCode PEPCISSGetExtraction_CISS(PEP pep,PEPCISSExtraction *extraction)
    809: {
    810:   PEP_CISS *ctx = (PEP_CISS*)pep->data;

    812:   *extraction = ctx->extraction;
    813:   return 0;
    814: }

    816: /*@
    817:    PEPCISSGetExtraction - Gets the extraction technique used in the CISS solver.

    819:    Not Collective

    821:    Input Parameter:
    822: .  pep - the polynomial eigensolver context

    824:    Output Parameters:
    825: .  extraction - extraction technique

    827:    Level: advanced

    829: .seealso: PEPCISSSetExtraction() PEPCISSExtraction
    830: @*/
    831: PetscErrorCode PEPCISSGetExtraction(PEP pep,PEPCISSExtraction *extraction)
    832: {
    835:   PetscUseMethod(pep,"PEPCISSGetExtraction_C",(PEP,PEPCISSExtraction*),(pep,extraction));
    836:   return 0;
    837: }

    839: static PetscErrorCode PEPCISSGetKSPs_CISS(PEP pep,PetscInt *nsolve,KSP **ksp)
    840: {
    841:   PEP_CISS         *ctx = (PEP_CISS*)pep->data;
    842:   SlepcContourData contour;
    843:   PetscInt         i,nsplit;
    844:   PC               pc;
    845:   MPI_Comm         child;

    847:   if (!ctx->contour) {  /* initialize contour data structure first */
    848:     RGCanUseConjugates(pep->rg,ctx->isreal,&ctx->useconj);
    849:     SlepcContourDataCreate(ctx->useconj?ctx->N/2:ctx->N,ctx->npart,(PetscObject)pep,&ctx->contour);
    850:   }
    851:   contour = ctx->contour;
    852:   if (!contour->ksp) {
    853:     PetscMalloc1(contour->npoints,&contour->ksp);
    854:     PEPGetST(pep,&pep->st);
    855:     STGetSplitPreconditionerInfo(pep->st,&nsplit,NULL);
    856:     PetscSubcommGetChild(contour->subcomm,&child);
    857:     for (i=0;i<contour->npoints;i++) {
    858:       KSPCreate(child,&contour->ksp[i]);
    859:       PetscObjectIncrementTabLevel((PetscObject)contour->ksp[i],(PetscObject)pep,1);
    860:       KSPSetOptionsPrefix(contour->ksp[i],((PetscObject)pep)->prefix);
    861:       KSPAppendOptionsPrefix(contour->ksp[i],"pep_ciss_");
    862:       PetscObjectSetOptions((PetscObject)contour->ksp[i],((PetscObject)pep)->options);
    863:       KSPSetErrorIfNotConverged(contour->ksp[i],PETSC_TRUE);
    864:       KSPSetTolerances(contour->ksp[i],SlepcDefaultTol(pep->tol),PETSC_DEFAULT,PETSC_DEFAULT,PETSC_DEFAULT);
    865:       KSPGetPC(contour->ksp[i],&pc);
    866:       if (nsplit) {
    867:         KSPSetType(contour->ksp[i],KSPBCGS);
    868:         PCSetType(pc,PCBJACOBI);
    869:       } else {
    870:         KSPSetType(contour->ksp[i],KSPPREONLY);
    871:         PCSetType(pc,PCLU);
    872:       }
    873:     }
    874:   }
    875:   if (nsolve) *nsolve = contour->npoints;
    876:   if (ksp)    *ksp    = contour->ksp;
    877:   return 0;
    878: }

    880: /*@C
    881:    PEPCISSGetKSPs - Retrieve the array of linear solver objects associated with
    882:    the CISS solver.

    884:    Not Collective

    886:    Input Parameter:
    887: .  pep - polynomial eigenvalue solver

    889:    Output Parameters:
    890: +  nsolve - number of solver objects
    891: -  ksp - array of linear solver object

    893:    Notes:
    894:    The number of KSP solvers is equal to the number of integration points divided by
    895:    the number of partitions. This value is halved in the case of real matrices with
    896:    a region centered at the real axis.

    898:    Level: advanced

    900: .seealso: PEPCISSSetSizes()
    901: @*/
    902: PetscErrorCode PEPCISSGetKSPs(PEP pep,PetscInt *nsolve,KSP **ksp)
    903: {
    905:   PetscUseMethod(pep,"PEPCISSGetKSPs_C",(PEP,PetscInt*,KSP**),(pep,nsolve,ksp));
    906:   return 0;
    907: }

    909: PetscErrorCode PEPReset_CISS(PEP pep)
    910: {
    911:   PEP_CISS       *ctx = (PEP_CISS*)pep->data;

    913:   BVDestroy(&ctx->S);
    914:   BVDestroy(&ctx->V);
    915:   BVDestroy(&ctx->Y);
    916:   SlepcContourDataReset(ctx->contour);
    917:   MatDestroy(&ctx->J);
    918:   BVDestroy(&ctx->pV);
    919:   PetscFree(ctx->Psplit);
    920:   return 0;
    921: }

    923: PetscErrorCode PEPSetFromOptions_CISS(PEP pep,PetscOptionItems *PetscOptionsObject)
    924: {
    925:   PEP_CISS          *ctx = (PEP_CISS*)pep->data;
    926:   PetscReal         r1,r2;
    927:   PetscInt          i,i1,i2,i3,i4,i5,i6,i7;
    928:   PetscBool         b1,flg,flg2,flg3,flg4,flg5,flg6;
    929:   PEPCISSExtraction extraction;

    931:   PetscOptionsHeadBegin(PetscOptionsObject,"PEP CISS Options");

    933:     PEPCISSGetSizes(pep,&i1,&i2,&i3,&i4,&i5,&b1);
    934:     PetscOptionsInt("-pep_ciss_integration_points","Number of integration points","PEPCISSSetSizes",i1,&i1,&flg);
    935:     PetscOptionsInt("-pep_ciss_blocksize","Block size","PEPCISSSetSizes",i2,&i2,&flg2);
    936:     PetscOptionsInt("-pep_ciss_moments","Moment size","PEPCISSSetSizes",i3,&i3,&flg3);
    937:     PetscOptionsInt("-pep_ciss_partitions","Number of partitions","PEPCISSSetSizes",i4,&i4,&flg4);
    938:     PetscOptionsInt("-pep_ciss_maxblocksize","Maximum block size","PEPCISSSetSizes",i5,&i5,&flg5);
    939:     PetscOptionsBool("-pep_ciss_realmats","True if all coefficient matrices of P(.) are real","PEPCISSSetSizes",b1,&b1,&flg6);
    940:     if (flg || flg2 || flg3 || flg4 || flg5 || flg6) PEPCISSSetSizes(pep,i1,i2,i3,i4,i5,b1);

    942:     PEPCISSGetThreshold(pep,&r1,&r2);
    943:     PetscOptionsReal("-pep_ciss_delta","Threshold for numerical rank","PEPCISSSetThreshold",r1,&r1,&flg);
    944:     PetscOptionsReal("-pep_ciss_spurious_threshold","Threshold for the spurious eigenpairs","PEPCISSSetThreshold",r2,&r2,&flg2);
    945:     if (flg || flg2) PEPCISSSetThreshold(pep,r1,r2);

    947:     PEPCISSGetRefinement(pep,&i6,&i7);
    948:     PetscOptionsInt("-pep_ciss_refine_inner","Number of inner iterative refinement iterations","PEPCISSSetRefinement",i6,&i6,&flg);
    949:     PetscOptionsInt("-pep_ciss_refine_blocksize","Number of blocksize iterative refinement iterations","PEPCISSSetRefinement",i7,&i7,&flg2);
    950:     if (flg || flg2) PEPCISSSetRefinement(pep,i6,i7);

    952:     PetscOptionsEnum("-pep_ciss_extraction","Extraction technique","PEPCISSSetExtraction",PEPCISSExtractions,(PetscEnum)ctx->extraction,(PetscEnum*)&extraction,&flg);
    953:     if (flg) PEPCISSSetExtraction(pep,extraction);

    955:   PetscOptionsHeadEnd();

    957:   if (!pep->rg) PEPGetRG(pep,&pep->rg);
    958:   RGSetFromOptions(pep->rg); /* this is necessary here to set useconj */
    959:   if (!ctx->contour || !ctx->contour->ksp) PEPCISSGetKSPs(pep,NULL,NULL);
    960:   PetscAssert(ctx->contour && ctx->contour->ksp,PetscObjectComm((PetscObject)pep),PETSC_ERR_PLIB,"Something went wrong with PEPCISSGetKSPs()");
    961:   for (i=0;i<ctx->contour->npoints;i++) KSPSetFromOptions(ctx->contour->ksp[i]);
    962:   PetscSubcommSetFromOptions(ctx->contour->subcomm);
    963:   return 0;
    964: }

    966: PetscErrorCode PEPDestroy_CISS(PEP pep)
    967: {
    968:   PEP_CISS       *ctx = (PEP_CISS*)pep->data;

    970:   SlepcContourDataDestroy(&ctx->contour);
    971:   PetscFree4(ctx->weight,ctx->omega,ctx->pp,ctx->sigma);
    972:   PetscFree(pep->data);
    973:   PetscObjectComposeFunction((PetscObject)pep,"PEPCISSSetSizes_C",NULL);
    974:   PetscObjectComposeFunction((PetscObject)pep,"PEPCISSGetSizes_C",NULL);
    975:   PetscObjectComposeFunction((PetscObject)pep,"PEPCISSSetThreshold_C",NULL);
    976:   PetscObjectComposeFunction((PetscObject)pep,"PEPCISSGetThreshold_C",NULL);
    977:   PetscObjectComposeFunction((PetscObject)pep,"PEPCISSSetRefinement_C",NULL);
    978:   PetscObjectComposeFunction((PetscObject)pep,"PEPCISSGetRefinement_C",NULL);
    979:   PetscObjectComposeFunction((PetscObject)pep,"PEPCISSSetExtraction_C",NULL);
    980:   PetscObjectComposeFunction((PetscObject)pep,"PEPCISSGetExtraction_C",NULL);
    981:   PetscObjectComposeFunction((PetscObject)pep,"PEPCISSGetKSPs_C",NULL);
    982:   return 0;
    983: }

    985: PetscErrorCode PEPView_CISS(PEP pep,PetscViewer viewer)
    986: {
    987:   PEP_CISS       *ctx = (PEP_CISS*)pep->data;
    988:   PetscBool      isascii;
    989:   PetscViewer    sviewer;

    991:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&isascii);
    992:   if (isascii) {
    993:     PetscViewerASCIIPrintf(viewer,"  sizes { integration points: %" PetscInt_FMT ", block size: %" PetscInt_FMT ", moment size: %" PetscInt_FMT ", partitions: %" PetscInt_FMT ", maximum block size: %" PetscInt_FMT " }\n",ctx->N,ctx->L,ctx->M,ctx->npart,ctx->L_max);
    994:     if (ctx->isreal) PetscViewerASCIIPrintf(viewer,"  exploiting symmetry of integration points\n");
    995:     PetscViewerASCIIPrintf(viewer,"  threshold { delta: %g, spurious threshold: %g }\n",(double)ctx->delta,(double)ctx->spurious_threshold);
    996:     PetscViewerASCIIPrintf(viewer,"  iterative refinement  { inner: %" PetscInt_FMT ", blocksize: %" PetscInt_FMT " }\n",ctx->refine_inner, ctx->refine_blocksize);
    997:     PetscViewerASCIIPrintf(viewer,"  extraction: %s\n",PEPCISSExtractions[ctx->extraction]);
    998:     if (!ctx->contour || !ctx->contour->ksp) PEPCISSGetKSPs(pep,NULL,NULL);
    999:     PetscAssert(ctx->contour && ctx->contour->ksp,PetscObjectComm((PetscObject)pep),PETSC_ERR_PLIB,"Something went wrong with PEPCISSGetKSPs()");
    1000:     PetscViewerASCIIPushTab(viewer);
    1001:     if (ctx->npart>1 && ctx->contour->subcomm) {
    1002:       PetscViewerGetSubViewer(viewer,ctx->contour->subcomm->child,&sviewer);
    1003:       if (!ctx->contour->subcomm->color) KSPView(ctx->contour->ksp[0],sviewer);
    1004:       PetscViewerFlush(sviewer);
    1005:       PetscViewerRestoreSubViewer(viewer,ctx->contour->subcomm->child,&sviewer);
    1006:       PetscViewerFlush(viewer);
    1007:       /* extra call needed because of the two calls to PetscViewerASCIIPushSynchronized() in PetscViewerGetSubViewer() */
    1008:       PetscViewerASCIIPopSynchronized(viewer);
    1009:     } else KSPView(ctx->contour->ksp[0],viewer);
    1010:     PetscViewerASCIIPopTab(viewer);
    1011:   }
    1012:   return 0;
    1013: }

    1015: SLEPC_EXTERN PetscErrorCode PEPCreate_CISS(PEP pep)
    1016: {
    1017:   PEP_CISS       *ctx = (PEP_CISS*)pep->data;

    1019:   PetscNew(&ctx);
    1020:   pep->data = ctx;
    1021:   /* set default values of parameters */
    1022:   ctx->N                  = 32;
    1023:   ctx->L                  = 16;
    1024:   ctx->M                  = ctx->N/4;
    1025:   ctx->delta              = SLEPC_DEFAULT_TOL*1e-4;
    1026:   ctx->L_max              = 64;
    1027:   ctx->spurious_threshold = PetscSqrtReal(SLEPC_DEFAULT_TOL);
    1028:   ctx->isreal             = PETSC_FALSE;
    1029:   ctx->npart              = 1;

    1031:   pep->ops->solve          = PEPSolve_CISS;
    1032:   pep->ops->setup          = PEPSetUp_CISS;
    1033:   pep->ops->setfromoptions = PEPSetFromOptions_CISS;
    1034:   pep->ops->reset          = PEPReset_CISS;
    1035:   pep->ops->destroy        = PEPDestroy_CISS;
    1036:   pep->ops->view           = PEPView_CISS;

    1038:   PetscObjectComposeFunction((PetscObject)pep,"PEPCISSSetSizes_C",PEPCISSSetSizes_CISS);
    1039:   PetscObjectComposeFunction((PetscObject)pep,"PEPCISSGetSizes_C",PEPCISSGetSizes_CISS);
    1040:   PetscObjectComposeFunction((PetscObject)pep,"PEPCISSSetThreshold_C",PEPCISSSetThreshold_CISS);
    1041:   PetscObjectComposeFunction((PetscObject)pep,"PEPCISSGetThreshold_C",PEPCISSGetThreshold_CISS);
    1042:   PetscObjectComposeFunction((PetscObject)pep,"PEPCISSSetRefinement_C",PEPCISSSetRefinement_CISS);
    1043:   PetscObjectComposeFunction((PetscObject)pep,"PEPCISSGetRefinement_C",PEPCISSGetRefinement_CISS);
    1044:   PetscObjectComposeFunction((PetscObject)pep,"PEPCISSSetExtraction_C",PEPCISSSetExtraction_CISS);
    1045:   PetscObjectComposeFunction((PetscObject)pep,"PEPCISSGetExtraction_C",PEPCISSGetExtraction_CISS);
    1046:   PetscObjectComposeFunction((PetscObject)pep,"PEPCISSGetKSPs_C",PEPCISSGetKSPs_CISS);
    1047:   return 0;
    1048: }