Date: 2021-09-20T09:44:05+00:00

Actual source code: stoar.c
slepc-3.15.2 2021-09-20
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      1: /*
      2:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      3:    SLEPc - Scalable Library for Eigenvalue Problem Computations
      4:    Copyright (c) 2002-2021, 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 polynomial eigensolver: "stoar"

     13:    Method: S-TOAR

     15:    Algorithm:

     17:        Symmetric Two-Level Orthogonal Arnoldi.

     19:    References:

     21:        [1] C. Campos and J.E. Roman, "Restarted Q-Arnoldi-type methods
     22:            exploiting symmetry in quadratic eigenvalue problems", BIT
     23:            Numer. Math. 56(4):1213-1236, 2016.
     24: */

     26: #include <slepc/private/pepimpl.h>
     27: #include "../src/pep/impls/krylov/pepkrylov.h"
     28: #include <slepcblaslapack.h>

     30: static PetscBool  cited = PETSC_FALSE;
     31: static const char citation[] =
     32:   "@Article{slepc-stoar,\n"
     33:   "   author = \"C. Campos and J. E. Roman\",\n"
     34:   "   title = \"Restarted {Q-Arnoldi-type} methods exploiting symmetry in quadratic eigenvalue problems\",\n"
     35:   "   journal = \"{BIT} Numer. Math.\",\n"
     36:   "   volume = \"56\",\n"
     37:   "   number = \"4\",\n"
     38:   "   pages = \"1213--1236\",\n"
     39:   "   year = \"2016,\"\n"
     40:   "   doi = \"https://doi.org/10.1007/s10543-016-0601-5\"\n"
     41:   "}\n";

     43: typedef struct {
     44:   PetscReal   scal[2];
     45:   Mat         A[2];
     46:   Vec         t;
     47: } PEP_STOAR_MATSHELL;

     49: static PetscErrorCode MatMult_STOAR(Mat A,Vec x,Vec y)
     50: {
     51:   PetscErrorCode     ierr;
     52:   PEP_STOAR_MATSHELL *ctx;

     55:   MatShellGetContext(A,&ctx);
     56:   MatMult(ctx->A[0],x,y);
     57:   VecScale(y,ctx->scal[0]);
     58:   if (ctx->scal[1]) {
     59:     MatMult(ctx->A[1],x,ctx->t);
     60:     VecAXPY(y,ctx->scal[1],ctx->t);
     61:   }
     62:   return(0);
     63: }

     65: static PetscErrorCode MatDestroy_STOAR(Mat A)
     66: {
     67:   PEP_STOAR_MATSHELL *ctx;
     68:   PetscErrorCode     ierr;

     71:   MatShellGetContext(A,(void**)&ctx);
     72:   VecDestroy(&ctx->t);
     73:   PetscFree(ctx);
     74:   return(0);
     75: }

     77: PetscErrorCode PEPSTOARSetUpInnerMatrix(PEP pep,Mat *B)
     78: {
     79:   Mat                pB[4],Bs[3],D[3];
     80:   PetscInt           i,j,n,m;
     81:   PEP_STOAR_MATSHELL *ctxMat[3];
     82:   PEP_STOAR          *ctx=(PEP_STOAR*)pep->data;
     83:   PetscErrorCode     ierr;

     86:   for (i=0;i<3;i++) {
     87:     STGetMatrixTransformed(pep->st,i,&D[i]); /* D[2] = M */
     88:   }
     89:   MatGetLocalSize(D[2],&m,&n);

     91:   for (j=0;j<3;j++) {
     92:     PetscNew(ctxMat+j);
     93:     MatCreateShell(PetscObjectComm((PetscObject)pep),m,n,PETSC_DETERMINE,PETSC_DETERMINE,ctxMat[j],&Bs[j]);
     94:     MatShellSetOperation(Bs[j],MATOP_MULT,(void(*)(void))MatMult_STOAR);
     95:     MatShellSetOperation(Bs[j],MATOP_DESTROY,(void(*)(void))MatDestroy_STOAR);
     96:   }
     97:   for (i=0;i<4;i++) pB[i] = NULL;
     98:   if (ctx->alpha) {
     99:     ctxMat[0]->A[0] = D[0]; ctxMat[0]->scal[0] = ctx->alpha; ctxMat[0]->scal[1] = 0.0;
    100:     ctxMat[2]->A[0] = D[2]; ctxMat[2]->scal[0] = -ctx->alpha*pep->sfactor*pep->sfactor; ctxMat[2]->scal[1] = 0.0;
    101:     pB[0] = Bs[0]; pB[3] = Bs[2];
    102:   }
    103:   if (ctx->beta) {
    104:     i = (ctx->alpha)?1:0;
    105:     ctxMat[0]->scal[1] = 0.0;
    106:     ctxMat[0]->A[i] = D[1]; ctxMat[0]->scal[i] = -ctx->beta*pep->sfactor;
    107:     ctxMat[1]->A[0] = D[2]; ctxMat[1]->scal[0] = -ctx->beta*pep->sfactor*pep->sfactor; ctxMat[1]->scal[1] = 0.0;
    108:     pB[0] = Bs[0]; pB[1] = pB[2] = Bs[1];
    109:   }
    110:   BVCreateVec(pep->V,&ctxMat[0]->t);
    111:   MatCreateNest(PetscObjectComm((PetscObject)pep),2,NULL,2,NULL,pB,B);
    112:   for (j=0;j<3;j++) { MatDestroy(&Bs[j]); }
    113:   return(0);
    114: }

    116: PetscErrorCode PEPSetUp_STOAR(PEP pep)
    117: {
    118:   PetscErrorCode    ierr;
    119:   PetscBool         sinv,flg;
    120:   PEP_STOAR         *ctx = (PEP_STOAR*)pep->data;
    121:   PetscInt          ld,i;
    122:   PetscReal         eta;
    123:   BVOrthogType      otype;
    124:   BVOrthogBlockType obtype;

    127:   PEPCheckHermitian(pep);
    128:   PEPCheckQuadratic(pep);
    129:   PEPCheckShiftSinvert(pep);
    130:   /* spectrum slicing requires special treatment of default values */
    131:   if (pep->which==PEP_ALL) {
    132:     pep->ops->solve = PEPSolve_STOAR_QSlice;
    133:     pep->ops->extractvectors = NULL;
    134:     pep->ops->setdefaultst   = NULL;
    135:     PEPSetUp_STOAR_QSlice(pep);
    136:   } else {
    137:     PEPSetDimensions_Default(pep,pep->nev,&pep->ncv,&pep->mpd);
    138:     if (!ctx->lock && pep->mpd<pep->ncv) SETERRQ(PetscObjectComm((PetscObject)pep),PETSC_ERR_SUP,"Should not use mpd parameter in non-locking variant");
    139:     if (pep->max_it==PETSC_DEFAULT) pep->max_it = PetscMax(100,2*(pep->nmat-1)*pep->n/pep->ncv);
    140:     pep->ops->solve = PEPSolve_STOAR;
    141:     ld   = pep->ncv+2;
    142:     DSSetType(pep->ds,DSGHIEP);
    143:     DSSetCompact(pep->ds,PETSC_TRUE);
    144:     DSSetExtraRow(pep->ds,PETSC_TRUE);
    145:     DSAllocate(pep->ds,ld);
    146:     PEPBasisCoefficients(pep,pep->pbc);
    147:     STGetTransform(pep->st,&flg);
    148:     if (!flg) {
    149:       PetscFree(pep->solvematcoeffs);
    150:       PetscMalloc1(pep->nmat,&pep->solvematcoeffs);
    151:       PetscLogObjectMemory((PetscObject)pep,pep->nmat*sizeof(PetscScalar));
    152:       PetscObjectTypeCompare((PetscObject)pep->st,STSINVERT,&sinv);
    153:       if (sinv) {
    154:         PEPEvaluateBasis(pep,pep->target,0,pep->solvematcoeffs,NULL);
    155:       } else {
    156:         for (i=0;i<pep->nmat-1;i++) pep->solvematcoeffs[i] = 0.0;
    157:         pep->solvematcoeffs[pep->nmat-1] = 1.0;
    158:       }
    159:     }
    160:   }
    161:   if (!pep->which) { PEPSetWhichEigenpairs_Default(pep); }
    162:   PEPCheckUnsupported(pep,PEP_FEATURE_NONMONOMIAL | PEP_FEATURE_REGION);

    164:   PEPAllocateSolution(pep,2);
    165:   PEPSetWorkVecs(pep,4);
    166:   BVDestroy(&ctx->V);
    167:   BVCreateTensor(pep->V,pep->nmat-1,&ctx->V);
    168:   BVGetOrthogonalization(pep->V,&otype,NULL,&eta,&obtype);
    169:   BVSetOrthogonalization(ctx->V,otype,BV_ORTHOG_REFINE_ALWAYS,eta,obtype);
    170:   return(0);
    171: }

    173: /*
    174:   Compute a run of Lanczos iterations. dim(work)=(ctx->ld)*4
    175: */
    176: static PetscErrorCode PEPSTOARrun(PEP pep,PetscReal *a,PetscReal *b,PetscReal *omega,PetscInt k,PetscInt *M,PetscBool *breakdown,PetscBool *symmlost,Vec *t_)
    177: {
    179:   PEP_STOAR      *ctx = (PEP_STOAR*)pep->data;
    180:   PetscInt       i,j,m=*M,l,lock;
    181:   PetscInt       lds,d,ld,offq,nqt,ldds;
    182:   Vec            v=t_[0],t=t_[1],q=t_[2];
    183:   PetscReal      norm,sym=0.0,fro=0.0,*f;
    184:   PetscScalar    *y,*S,*x,sigma;
    185:   PetscBLASInt   j_,one=1;
    186:   PetscBool      lindep,flg,sinvert=PETSC_FALSE;
    187:   Mat            MS;

    190:   PetscMalloc1(*M,&y);
    191:   BVGetSizes(pep->V,NULL,NULL,&ld);
    192:   BVTensorGetDegree(ctx->V,&d);
    193:   BVGetActiveColumns(pep->V,&lock,&nqt);
    194:   lds = d*ld;
    195:   offq = ld;
    196:   DSGetLeadingDimension(pep->ds,&ldds);
    197:   *breakdown = PETSC_FALSE; /* ----- */
    198:   DSGetDimensions(pep->ds,NULL,NULL,&l,NULL,NULL);
    199:   BVSetActiveColumns(ctx->V,0,m);
    200:   BVSetActiveColumns(pep->V,0,nqt);
    201:   STGetTransform(pep->st,&flg);
    202:   if (!flg) {
    203:     /* spectral transformation handled by the solver */
    204:     PetscObjectTypeCompareAny((PetscObject)pep->st,&flg,STSINVERT,STSHIFT,"");
    205:     if (!flg) SETERRQ(PetscObjectComm((PetscObject)pep),PETSC_ERR_SUP,"ST type not supported for TOAR without transforming matrices");
    206:     PetscObjectTypeCompare((PetscObject)pep->st,STSINVERT,&sinvert);
    207:     STGetShift(pep->st,&sigma);
    208:   }
    209:   for (j=k;j<m;j++) {
    210:     /* apply operator */
    211:     BVTensorGetFactors(ctx->V,NULL,&MS);
    212:     MatDenseGetArray(MS,&S);
    213:     BVGetColumn(pep->V,nqt,&t);
    214:     BVMultVec(pep->V,1.0,0.0,v,S+j*lds);
    215:     if (!sinvert) {
    216:       STMatMult(pep->st,0,v,q);
    217:       BVMultVec(pep->V,1.0,0.0,v,S+offq+j*lds);
    218:       STMatMult(pep->st,1,v,t);
    219:       VecAXPY(q,pep->sfactor,t);
    220:       if (ctx->beta && ctx->alpha) {
    221:         STMatMult(pep->st,2,v,t);
    222:         VecAXPY(q,-pep->sfactor*pep->sfactor*ctx->beta/ctx->alpha,t);
    223:       }
    224:       STMatSolve(pep->st,q,t);
    225:       VecScale(t,-1.0/(pep->sfactor*pep->sfactor));
    226:     } else {
    227:       STMatMult(pep->st,1,v,q);
    228:       STMatMult(pep->st,2,v,t);
    229:       VecAXPY(q,sigma*pep->sfactor,t);
    230:       VecScale(q,pep->sfactor);
    231:       BVMultVec(pep->V,1.0,0.0,v,S+offq+j*lds);
    232:       STMatMult(pep->st,2,v,t);
    233:       VecAXPY(q,pep->sfactor*pep->sfactor,t);
    234:       STMatSolve(pep->st,q,t);
    235:       VecScale(t,-1.0);
    236:     }
    237:     BVRestoreColumn(pep->V,nqt,&t);

    239:     /* orthogonalize */
    240:     if (!sinvert) x = S+offq+(j+1)*lds;
    241:     else x = S+(j+1)*lds;
    242:     BVOrthogonalizeColumn(pep->V,nqt,x,&norm,&lindep);

    244:     if (!lindep) {
    245:       if (!sinvert) *(S+offq+(j+1)*lds+nqt) = norm;
    246:       else *(S+(j+1)*lds+nqt) = norm;
    247:       BVScaleColumn(pep->V,nqt,1.0/norm);
    248:       nqt++;
    249:     }
    250:     if (!sinvert) {
    251:       for (i=0;i<=nqt-1;i++) *(S+(j+1)*lds+i) = *(S+offq+j*lds+i);
    252:       if (ctx->beta && ctx->alpha) {
    253:         for (i=0;i<=nqt-1;i++) *(S+(j+1)*lds+offq+i) -= *(S+(j+1)*lds+i)*ctx->beta/ctx->alpha;
    254:       }
    255:     } else for (i=0;i<nqt;i++) *(S+(j+1)*lds+offq+i) = *(S+j*lds+i)+sigma*(*(S+(j+1)*lds+i));
    256:     BVSetActiveColumns(pep->V,0,nqt);
    257:     MatDenseRestoreArray(MS,&S);
    258:     BVTensorRestoreFactors(ctx->V,NULL,&MS);

    260:     /* level-2 orthogonalization */
    261:     BVOrthogonalizeColumn(ctx->V,j+1,y,&norm,&lindep);
    262:     a[j] = PetscRealPart(y[j]);
    263:     omega[j+1] = (norm > 0)?1.0:-1.0;
    264:     BVScaleColumn(ctx->V,j+1,1.0/norm);
    265:     b[j] = PetscAbsReal(norm);

    267:     /* check symmetry */
    268:     DSGetArrayReal(pep->ds,DS_MAT_T,&f);
    269:     if (j==k) {
    270:       for (i=l;i<j-1;i++) y[i] = PetscAbsScalar(y[i])-PetscAbsReal(f[2*ldds+i]);
    271:       for (i=0;i<l;i++) y[i] = 0.0;
    272:     }
    273:     DSRestoreArrayReal(pep->ds,DS_MAT_T,&f);
    274:     if (j>0) y[j-1] = PetscAbsScalar(y[j-1])-PetscAbsReal(b[j-1]);
    275:     PetscBLASIntCast(j,&j_);
    276:     sym = SlepcAbs(BLASnrm2_(&j_,y,&one),sym);
    277:     fro = SlepcAbs(fro,SlepcAbs(a[j],b[j]));
    278:     if (j>0) fro = SlepcAbs(fro,b[j-1]);
    279:     if (sym/fro>PetscMax(PETSC_SQRT_MACHINE_EPSILON,10*pep->tol)) {
    280:       *symmlost = PETSC_TRUE;
    281:       *M=j;
    282:       break;
    283:     }
    284:   }
    285:   BVSetActiveColumns(pep->V,lock,nqt);
    286:   BVSetActiveColumns(ctx->V,0,*M);
    287:   PetscFree(y);
    288:   return(0);
    289: }

    291: #if 0
    292: static PetscErrorCode PEPSTOARpreKConvergence(PEP pep,PetscInt nv,PetscReal *norm,Vec *w)
    293: {
    295:   PEP_STOAR      *ctx = (PEP_STOAR*)pep->data;
    296:   PetscBLASInt   n_,one=1;
    297:   PetscInt       lds=2*ctx->ld;
    298:   PetscReal      t1,t2;
    299:   PetscScalar    *S=ctx->S;

    302:   PetscBLASIntCast(nv+2,&n_);
    303:   t1 = BLASnrm2_(&n_,S+nv*2*ctx->ld,&one);
    304:   t2 = BLASnrm2_(&n_,S+(nv*2+1)*ctx->ld,&one);
    305:   *norm = SlepcAbs(t1,t2);
    306:   BVSetActiveColumns(pep->V,0,nv+2);
    307:   BVMultVec(pep->V,1.0,0.0,w[1],S+nv*lds);
    308:   STMatMult(pep->st,0,w[1],w[2]);
    309:   VecNorm(w[2],NORM_2,&t1);
    310:   BVMultVec(pep->V,1.0,0.0,w[1],S+ctx->ld+nv*lds);
    311:   STMatMult(pep->st,2,w[1],w[2]);
    312:   VecNorm(w[2],NORM_2,&t2);
    313:   t2 *= pep->sfactor*pep->sfactor;
    314:   *norm = PetscMax(*norm,SlepcAbs(t1,t2));
    315:   return(0);
    316: }
    317: #endif

    319: PetscErrorCode PEPSolve_STOAR(PEP pep)
    320: {
    322:   PEP_STOAR      *ctx = (PEP_STOAR*)pep->data;
    323:   PetscInt       j,k,l,nv=0,ld,ldds,t,nq=0;
    324:   PetscInt       nconv=0,deg=pep->nmat-1;
    325:   PetscScalar    *om,sigma;
    326:   PetscReal      beta,norm=1.0,*omega,*a,*b;
    327:   PetscBool      breakdown,symmlost=PETSC_FALSE,sinv=PETSC_FALSE,falselock=PETSC_TRUE,flg;
    328:   Mat            MQ,A;
    329:   Vec            vomega;

    332:   PetscCitationsRegister(citation,&cited);
    333:   PEPSTOARSetUpInnerMatrix(pep,&A);
    334:   BVSetMatrix(ctx->V,A,PETSC_TRUE);
    335:   MatDestroy(&A);
    336:   if (ctx->lock) {
    337:     /* undocumented option to use a cheaper locking instead of the true locking */
    338:     PetscOptionsGetBool(NULL,NULL,"-pep_stoar_falselocking",&falselock,NULL);
    339:   }
    340:   BVGetSizes(pep->V,NULL,NULL,&ld);
    341:   STGetShift(pep->st,&sigma);
    342:   STGetTransform(pep->st,&flg);
    343:   if (pep->sfactor!=1.0) {
    344:     if (!flg) {
    345:       pep->target /= pep->sfactor;
    346:       RGPushScale(pep->rg,1.0/pep->sfactor);
    347:       STScaleShift(pep->st,1.0/pep->sfactor);
    348:       sigma /= pep->sfactor;
    349:     } else {
    350:       PetscObjectTypeCompare((PetscObject)pep->st,STSINVERT,&sinv);
    351:       pep->target = sinv?pep->target*pep->sfactor:pep->target/pep->sfactor;
    352:       RGPushScale(pep->rg,sinv?pep->sfactor:1.0/pep->sfactor);
    353:       STScaleShift(pep->st,sinv?pep->sfactor:1.0/pep->sfactor);
    354:     }
    355:   }
    356:   if (flg) sigma = 0.0;

    358:   /* Get the starting Arnoldi vector */
    359:   BVTensorBuildFirstColumn(ctx->V,pep->nini);
    360:   DSGetArrayReal(pep->ds,DS_MAT_D,&omega);
    361:   VecCreateSeq(PETSC_COMM_SELF,1,&vomega);
    362:   BVSetActiveColumns(ctx->V,0,1);
    363:   BVGetSignature(ctx->V,vomega);
    364:   VecGetArray(vomega,&om);
    365:   omega[0] = PetscRealPart(om[0]);
    366:   VecRestoreArray(vomega,&om);
    367:   DSRestoreArrayReal(pep->ds,DS_MAT_D,&omega);
    368:   VecDestroy(&vomega);

    370:   /* Restart loop */
    371:   l = 0;
    372:   DSGetLeadingDimension(pep->ds,&ldds);
    373:   while (pep->reason == PEP_CONVERGED_ITERATING) {
    374:     pep->its++;
    375:     DSGetArrayReal(pep->ds,DS_MAT_T,&a);
    376:     b = a+ldds;
    377:     DSGetArrayReal(pep->ds,DS_MAT_D,&omega);

    379:     /* Compute an nv-step Lanczos factorization */
    380:     nv = PetscMin(pep->nconv+pep->mpd,pep->ncv);
    381:     PEPSTOARrun(pep,a,b,omega,pep->nconv+l,&nv,&breakdown,&symmlost,pep->work);
    382:     beta = b[nv-1];
    383:     if (symmlost && nv==pep->nconv+l) {
    384:       pep->reason = PEP_DIVERGED_SYMMETRY_LOST;
    385:       pep->nconv = nconv;
    386:       if (falselock || !ctx->lock) {
    387:        BVSetActiveColumns(ctx->V,0,pep->nconv);
    388:        BVTensorCompress(ctx->V,0);
    389:       }
    390:       break;
    391:     }
    392:     DSRestoreArrayReal(pep->ds,DS_MAT_T,&a);
    393:     DSRestoreArrayReal(pep->ds,DS_MAT_D,&omega);
    394:     DSSetDimensions(pep->ds,nv,0,pep->nconv,pep->nconv+l);
    395:     if (l==0) {
    396:       DSSetState(pep->ds,DS_STATE_INTERMEDIATE);
    397:     } else {
    398:       DSSetState(pep->ds,DS_STATE_RAW);
    399:     }

    401:     /* Solve projected problem */
    402:     DSSolve(pep->ds,pep->eigr,pep->eigi);
    403:     DSSort(pep->ds,pep->eigr,pep->eigi,NULL,NULL,NULL);
    404:     DSUpdateExtraRow(pep->ds);
    405:     DSSynchronize(pep->ds,pep->eigr,pep->eigi);

    407:     /* Check convergence */
    408:     /* PEPSTOARpreKConvergence(pep,nv,&norm,pep->work);*/
    409:     norm = 1.0;
    410:     DSGetDimensions(pep->ds,NULL,NULL,NULL,NULL,&t);
    411:     PEPKrylovConvergence(pep,PETSC_FALSE,pep->nconv,t-pep->nconv,PetscAbsReal(beta)*norm,&k);
    412:     (*pep->stopping)(pep,pep->its,pep->max_it,k,pep->nev,&pep->reason,pep->stoppingctx);

    414:     /* Update l */
    415:     if (pep->reason != PEP_CONVERGED_ITERATING || breakdown) l = 0;
    416:     else {
    417:       l = PetscMax(1,(PetscInt)((nv-k)/2));
    418:       l = PetscMin(l,t);
    419:       DSGetTruncateSize(pep->ds,k,t,&l);
    420:       if (!breakdown) {
    421:         /* Prepare the Rayleigh quotient for restart */
    422:         DSTruncate(pep->ds,k+l,PETSC_FALSE);
    423:       }
    424:     }
    425:     nconv = k;
    426:     if (!ctx->lock && pep->reason == PEP_CONVERGED_ITERATING && !breakdown) { l += k; k = 0; } /* non-locking variant: reset no. of converged pairs */
    427:     if (l) { PetscInfo1(pep,"Preparing to restart keeping l=%D vectors\n",l); }

    429:     /* Update S */
    430:     DSGetMat(pep->ds,DS_MAT_Q,&MQ);
    431:     BVMultInPlace(ctx->V,MQ,pep->nconv,k+l);
    432:     MatDestroy(&MQ);

    434:     /* Copy last column of S */
    435:     BVCopyColumn(ctx->V,nv,k+l);
    436:     DSGetArrayReal(pep->ds,DS_MAT_D,&omega);
    437:     VecCreateSeq(PETSC_COMM_SELF,k+l,&vomega);
    438:     VecGetArray(vomega,&om);
    439:     for (j=0;j<k+l;j++) om[j] = omega[j];
    440:     VecRestoreArray(vomega,&om);
    441:     BVSetActiveColumns(ctx->V,0,k+l);
    442:     BVSetSignature(ctx->V,vomega);
    443:     VecDestroy(&vomega);
    444:     DSRestoreArrayReal(pep->ds,DS_MAT_D,&omega);

    446:     if (breakdown && pep->reason == PEP_CONVERGED_ITERATING) {
    447:       /* stop if breakdown */
    448:       PetscInfo2(pep,"Breakdown TOAR method (it=%D norm=%g)\n",pep->its,(double)beta);
    449:       pep->reason = PEP_DIVERGED_BREAKDOWN;
    450:     }
    451:     if (pep->reason != PEP_CONVERGED_ITERATING) l--;
    452:     BVGetActiveColumns(pep->V,NULL,&nq);
    453:     if (k+l+deg<=nq) {
    454:       BVSetActiveColumns(ctx->V,pep->nconv,k+l+1);
    455:       if (!falselock && ctx->lock) {
    456:         BVTensorCompress(ctx->V,k-pep->nconv);
    457:       } else {
    458:         BVTensorCompress(ctx->V,0);
    459:       }
    460:     }
    461:     pep->nconv = k;
    462:     PEPMonitor(pep,pep->its,nconv,pep->eigr,pep->eigi,pep->errest,nv);
    463:   }

    465:   if (pep->nconv>0) {
    466:     BVSetActiveColumns(ctx->V,0,pep->nconv);
    467:     BVGetActiveColumns(pep->V,NULL,&nq);
    468:     BVSetActiveColumns(pep->V,0,nq);
    469:     if (nq>pep->nconv) {
    470:       BVTensorCompress(ctx->V,pep->nconv);
    471:       BVSetActiveColumns(pep->V,0,pep->nconv);
    472:     }
    473:   }
    474:   STGetTransform(pep->st,&flg);
    475:   if (!flg && pep->ops->backtransform) {
    476:       (*pep->ops->backtransform)(pep);
    477:   }
    478:   if (pep->sfactor!=1.0) {
    479:     for (j=0;j<pep->nconv;j++) {
    480:       pep->eigr[j] *= pep->sfactor;
    481:       pep->eigi[j] *= pep->sfactor;
    482:     }
    483:   }
    484:   /* restore original values */
    485:   if (!flg) {
    486:     pep->target *= pep->sfactor;
    487:     STScaleShift(pep->st,pep->sfactor);
    488:   } else {
    489:     STScaleShift(pep->st,sinv?1.0/pep->sfactor:pep->sfactor);
    490:     pep->target = (sinv)?pep->target/pep->sfactor:pep->target*pep->sfactor;
    491:   }
    492:   if (pep->sfactor!=1.0) { RGPopScale(pep->rg); }

    494:   DSTruncate(pep->ds,pep->nconv,PETSC_TRUE);
    495:   return(0);
    496: }

    498: PetscErrorCode PEPSetFromOptions_STOAR(PetscOptionItems *PetscOptionsObject,PEP pep)
    499: {
    501:   PetscBool      flg,lock,b,f1,f2,f3;
    502:   PetscInt       i,j,k;
    503:   PetscReal      array[2]={0,0};
    504:   PEP_STOAR      *ctx = (PEP_STOAR*)pep->data;

    507:   PetscOptionsHead(PetscOptionsObject,"PEP STOAR Options");

    509:     PetscOptionsBool("-pep_stoar_locking","Choose between locking and non-locking variants","PEPSTOARSetLocking",PETSC_FALSE,&lock,&flg);
    510:     if (flg) { PEPSTOARSetLocking(pep,lock); }

    512:     b = ctx->detect;
    513:     PetscOptionsBool("-pep_stoar_detect_zeros","Check zeros during factorizations at interval boundaries","PEPSTOARSetDetectZeros",ctx->detect,&b,&flg);
    514:     if (flg) { PEPSTOARSetDetectZeros(pep,b); }

    516:     i = 1;
    517:     j = k = PETSC_DECIDE;
    518:     PetscOptionsInt("-pep_stoar_nev","Number of eigenvalues to compute in each subsolve (only for spectrum slicing)","PEPSTOARSetDimensions",20,&i,&f1);
    519:     PetscOptionsInt("-pep_stoar_ncv","Number of basis vectors in each subsolve (only for spectrum slicing)","PEPSTOARSetDimensions",40,&j,&f2);
    520:     PetscOptionsInt("-pep_stoar_mpd","Maximum dimension of projected problem in each subsolve (only for spectrum slicing)","PEPSTOARSetDimensions",40,&k,&f3);
    521:     if (f1 || f2 || f3) { PEPSTOARSetDimensions(pep,i,j,k); }

    523:     k = 2;
    524:     PetscOptionsRealArray("-pep_stoar_linearization","Parameters of the linearization","PEPSTOARSetLinearization",array,&k,&flg);
    525:     if (flg) {
    526:       PEPSTOARSetLinearization(pep,array[0],array[1]);
    527:     }

    529:     b = ctx->checket;
    530:     PetscOptionsBool("-pep_stoar_check_eigenvalue_type","Check eigenvalue type during spectrum slicing","PEPSTOARSetCheckEigenvalueType",ctx->checket,&b,&flg);
    531:     if (flg) { PEPSTOARSetCheckEigenvalueType(pep,b); }

    533:   PetscOptionsTail();
    534:   return(0);
    535: }

    537: static PetscErrorCode PEPSTOARSetLocking_STOAR(PEP pep,PetscBool lock)
    538: {
    539:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    542:   ctx->lock = lock;
    543:   return(0);
    544: }

    546: /*@
    547:    PEPSTOARSetLocking - Choose between locking and non-locking variants of
    548:    the STOAR method.

    550:    Logically Collective on pep

    552:    Input Parameters:
    553: +  pep  - the eigenproblem solver context
    554: -  lock - true if the locking variant must be selected

    556:    Options Database Key:
    557: .  -pep_stoar_locking - Sets the locking flag

    559:    Notes:
    560:    The default is to lock converged eigenpairs when the method restarts.
    561:    This behaviour can be changed so that all directions are kept in the
    562:    working subspace even if already converged to working accuracy (the
    563:    non-locking variant).

    565:    Level: advanced

    567: .seealso: PEPSTOARGetLocking()
    568: @*/
    569: PetscErrorCode PEPSTOARSetLocking(PEP pep,PetscBool lock)
    570: {

    576:   PetscTryMethod(pep,"PEPSTOARSetLocking_C",(PEP,PetscBool),(pep,lock));
    577:   return(0);
    578: }

    580: static PetscErrorCode PEPSTOARGetLocking_STOAR(PEP pep,PetscBool *lock)
    581: {
    582:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    585:   *lock = ctx->lock;
    586:   return(0);
    587: }

    589: /*@
    590:    PEPSTOARGetLocking - Gets the locking flag used in the STOAR method.

    592:    Not Collective

    594:    Input Parameter:
    595: .  pep - the eigenproblem solver context

    597:    Output Parameter:
    598: .  lock - the locking flag

    600:    Level: advanced

    602: .seealso: PEPSTOARSetLocking()
    603: @*/
    604: PetscErrorCode PEPSTOARGetLocking(PEP pep,PetscBool *lock)
    605: {

    611:   PetscUseMethod(pep,"PEPSTOARGetLocking_C",(PEP,PetscBool*),(pep,lock));
    612:   return(0);
    613: }

    615: static PetscErrorCode PEPSTOARGetInertias_STOAR(PEP pep,PetscInt *n,PetscReal **shifts,PetscInt **inertias)
    616: {
    618:   PetscInt       i,numsh;
    619:   PEP_STOAR      *ctx = (PEP_STOAR*)pep->data;
    620:   PEP_SR         sr = ctx->sr;

    623:   if (!pep->state) SETERRQ(PetscObjectComm((PetscObject)pep),PETSC_ERR_ARG_WRONGSTATE,"Must call PEPSetUp() first");
    624:   if (!ctx->sr) SETERRQ(PetscObjectComm((PetscObject)pep),PETSC_ERR_ARG_WRONGSTATE,"Only available in interval computations, see PEPSetInterval()");
    625:   switch (pep->state) {
    626:   case PEP_STATE_INITIAL:
    627:     break;
    628:   case PEP_STATE_SETUP:
    629:     if (n) *n = 2;
    630:     if (shifts) {
    631:       PetscMalloc1(2,shifts);
    632:       (*shifts)[0] = pep->inta;
    633:       (*shifts)[1] = pep->intb;
    634:     }
    635:     if (inertias) {
    636:       PetscMalloc1(2,inertias);
    637:       (*inertias)[0] = (sr->dir==1)?sr->inertia0:sr->inertia1;
    638:       (*inertias)[1] = (sr->dir==1)?sr->inertia1:sr->inertia0;
    639:     }
    640:     break;
    641:   case PEP_STATE_SOLVED:
    642:   case PEP_STATE_EIGENVECTORS:
    643:     numsh = ctx->nshifts;
    644:     if (n) *n = numsh;
    645:     if (shifts) {
    646:       PetscMalloc1(numsh,shifts);
    647:       for (i=0;i<numsh;i++) (*shifts)[i] = ctx->shifts[i];
    648:     }
    649:     if (inertias) {
    650:       PetscMalloc1(numsh,inertias);
    651:       for (i=0;i<numsh;i++) (*inertias)[i] = ctx->inertias[i];
    652:     }
    653:     break;
    654:   }
    655:   return(0);
    656: }

    658: /*@C
    659:    PEPSTOARGetInertias - Gets the values of the shifts and their
    660:    corresponding inertias in case of doing spectrum slicing for a
    661:    computational interval.

    663:    Not Collective

    665:    Input Parameter:
    666: .  pep - the eigenproblem solver context

    668:    Output Parameters:
    669: +  n        - number of shifts, including the endpoints of the interval
    670: .  shifts   - the values of the shifts used internally in the solver
    671: -  inertias - the values of the inertia in each shift

    673:    Notes:
    674:    If called after PEPSolve(), all shifts used internally by the solver are
    675:    returned (including both endpoints and any intermediate ones). If called
    676:    before PEPSolve() and after PEPSetUp() then only the information of the
    677:    endpoints of subintervals is available.

    679:    This function is only available for spectrum slicing runs.

    681:    The returned arrays should be freed by the user. Can pass NULL in any of
    682:    the two arrays if not required.

    684:    Fortran Notes:
    685:    The calling sequence from Fortran is
    686: .vb
    687:    PEPSTOARGetInertias(pep,n,shifts,inertias,ierr)
    688:    integer n
    689:    double precision shifts(*)
    690:    integer inertias(*)
    691: .ve
    692:    The arrays should be at least of length n. The value of n can be determined
    693:    by an initial call
    694: .vb
    695:    PEPSTOARGetInertias(pep,n,PETSC_NULL_REAL,PETSC_NULL_INTEGER,ierr)
    696: .ve

    698:    Level: advanced

    700: .seealso: PEPSetInterval()
    701: @*/
    702: PetscErrorCode PEPSTOARGetInertias(PEP pep,PetscInt *n,PetscReal **shifts,PetscInt **inertias)
    703: {

    709:   PetscUseMethod(pep,"PEPSTOARGetInertias_C",(PEP,PetscInt*,PetscReal**,PetscInt**),(pep,n,shifts,inertias));
    710:   return(0);
    711: }

    713: static PetscErrorCode PEPSTOARSetDetectZeros_STOAR(PEP pep,PetscBool detect)
    714: {
    715:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    718:   ctx->detect = detect;
    719:   pep->state  = PEP_STATE_INITIAL;
    720:   return(0);
    721: }

    723: /*@
    724:    PEPSTOARSetDetectZeros - Sets a flag to enforce detection of
    725:    zeros during the factorizations throughout the spectrum slicing computation.

    727:    Logically Collective on pep

    729:    Input Parameters:
    730: +  pep    - the eigenproblem solver context
    731: -  detect - check for zeros

    733:    Options Database Key:
    734: .  -pep_stoar_detect_zeros - Check for zeros; this takes an optional
    735:    bool value (0/1/no/yes/true/false)

    737:    Notes:
    738:    A zero in the factorization indicates that a shift coincides with an eigenvalue.

    740:    This flag is turned off by default, and may be necessary in some cases.
    741:    This feature currently requires an external package for factorizations
    742:    with support for zero detection, e.g. MUMPS.

    744:    Level: advanced

    746: .seealso: PEPSetInterval()
    747: @*/
    748: PetscErrorCode PEPSTOARSetDetectZeros(PEP pep,PetscBool detect)
    749: {

    755:   PetscTryMethod(pep,"PEPSTOARSetDetectZeros_C",(PEP,PetscBool),(pep,detect));
    756:   return(0);
    757: }

    759: static PetscErrorCode PEPSTOARGetDetectZeros_STOAR(PEP pep,PetscBool *detect)
    760: {
    761:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    764:   *detect = ctx->detect;
    765:   return(0);
    766: }

    768: /*@
    769:    PEPSTOARGetDetectZeros - Gets the flag that enforces zero detection
    770:    in spectrum slicing.

    772:    Not Collective

    774:    Input Parameter:
    775: .  pep - the eigenproblem solver context

    777:    Output Parameter:
    778: .  detect - whether zeros detection is enforced during factorizations

    780:    Level: advanced

    782: .seealso: PEPSTOARSetDetectZeros()
    783: @*/
    784: PetscErrorCode PEPSTOARGetDetectZeros(PEP pep,PetscBool *detect)
    785: {

    791:   PetscUseMethod(pep,"PEPSTOARGetDetectZeros_C",(PEP,PetscBool*),(pep,detect));
    792:   return(0);
    793: }

    795: static PetscErrorCode PEPSTOARSetLinearization_STOAR(PEP pep,PetscReal alpha,PetscReal beta)
    796: {
    797:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    800:   if (beta==0.0 && alpha==0.0) SETERRQ(PetscObjectComm((PetscObject)pep),PETSC_ERR_ARG_WRONG,"Parameters alpha and beta cannot be zero simultaneously");
    801:   ctx->alpha = alpha;
    802:   ctx->beta  = beta;
    803:   return(0);
    804: }

    806: /*@
    807:    PEPSTOARSetLinearization - Set the coefficients that define
    808:    the linearization of a quadratic eigenproblem.

    810:    Logically Collective on pep

    812:    Input Parameters:
    813: +  pep   - polynomial eigenvalue solver
    814: .  alpha - first parameter of the linearization
    815: -  beta  - second parameter of the linearization

    817:    Options Database Key:
    818: .  -pep_stoar_linearization <alpha,beta> - Sets the coefficients

    820:    Notes:
    821:    Cannot pass zero for both alpha and beta. The default values are
    822:    alpha=1 and beta=0.

    824:    Level: advanced

    826: .seealso: PEPSTOARGetLinearization()
    827: @*/
    828: PetscErrorCode PEPSTOARSetLinearization(PEP pep,PetscReal alpha,PetscReal beta)
    829: {

    836:   PetscTryMethod(pep,"PEPSTOARSetLinearization_C",(PEP,PetscReal,PetscReal),(pep,alpha,beta));
    837:   return(0);
    838: }

    840: static PetscErrorCode PEPSTOARGetLinearization_STOAR(PEP pep,PetscReal *alpha,PetscReal *beta)
    841: {
    842:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    845:   if (alpha) *alpha = ctx->alpha;
    846:   if (beta)  *beta  = ctx->beta;
    847:   return(0);
    848: }

    850: /*@
    851:    PEPSTOARGetLinearization - Returns the coefficients that define
    852:    the linearization of a quadratic eigenproblem.

    854:    Not Collective

    856:    Input Parameter:
    857: .  pep  - polynomial eigenvalue solver

    859:    Output Parameters:
    860: +  alpha - the first parameter of the linearization
    861: -  beta  - the second parameter of the linearization

    863:    Level: advanced

    865: .seealso: PEPSTOARSetLinearization()
    866: @*/
    867: PetscErrorCode PEPSTOARGetLinearization(PEP pep,PetscReal *alpha,PetscReal *beta)
    868: {

    873:   PetscUseMethod(pep,"PEPSTOARGetLinearization_C",(PEP,PetscReal*,PetscReal*),(pep,alpha,beta));
    874:   return(0);
    875: }

    877: static PetscErrorCode PEPSTOARSetDimensions_STOAR(PEP pep,PetscInt nev,PetscInt ncv,PetscInt mpd)
    878: {
    879:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    882:   if (nev<1) SETERRQ(PetscObjectComm((PetscObject)pep),PETSC_ERR_ARG_OUTOFRANGE,"Illegal value of nev. Must be > 0");
    883:   ctx->nev = nev;
    884:   if (ncv == PETSC_DECIDE || ncv == PETSC_DEFAULT) {
    885:     ctx->ncv = PETSC_DEFAULT;
    886:   } else {
    887:     if (ncv<1) SETERRQ(PetscObjectComm((PetscObject)pep),PETSC_ERR_ARG_OUTOFRANGE,"Illegal value of ncv. Must be > 0");
    888:     ctx->ncv = ncv;
    889:   }
    890:   if (mpd == PETSC_DECIDE || mpd == PETSC_DEFAULT) {
    891:     ctx->mpd = PETSC_DEFAULT;
    892:   } else {
    893:     if (mpd<1) SETERRQ(PetscObjectComm((PetscObject)pep),PETSC_ERR_ARG_OUTOFRANGE,"Illegal value of mpd. Must be > 0");
    894:     ctx->mpd = mpd;
    895:   }
    896:   pep->state = PEP_STATE_INITIAL;
    897:   return(0);
    898: }

    900: /*@
    901:    PEPSTOARSetDimensions - Sets the dimensions used for each subsolve
    902:    step in case of doing spectrum slicing for a computational interval.
    903:    The meaning of the parameters is the same as in PEPSetDimensions().

    905:    Logically Collective on pep

    907:    Input Parameters:
    908: +  pep - the eigenproblem solver context
    909: .  nev - number of eigenvalues to compute
    910: .  ncv - the maximum dimension of the subspace to be used by the subsolve
    911: -  mpd - the maximum dimension allowed for the projected problem

    913:    Options Database Key:
    914: +  -eps_stoar_nev <nev> - Sets the number of eigenvalues
    915: .  -eps_stoar_ncv <ncv> - Sets the dimension of the subspace
    916: -  -eps_stoar_mpd <mpd> - Sets the maximum projected dimension

    918:    Level: advanced

    920: .seealso: PEPSTOARGetDimensions(), PEPSetDimensions(), PEPSetInterval()
    921: @*/
    922: PetscErrorCode PEPSTOARSetDimensions(PEP pep,PetscInt nev,PetscInt ncv,PetscInt mpd)
    923: {

    931:   PetscTryMethod(pep,"PEPSTOARSetDimensions_C",(PEP,PetscInt,PetscInt,PetscInt),(pep,nev,ncv,mpd));
    932:   return(0);
    933: }

    935: static PetscErrorCode PEPSTOARGetDimensions_STOAR(PEP pep,PetscInt *nev,PetscInt *ncv,PetscInt *mpd)
    936: {
    937:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    940:   if (nev) *nev = ctx->nev;
    941:   if (ncv) *ncv = ctx->ncv;
    942:   if (mpd) *mpd = ctx->mpd;
    943:   return(0);
    944: }

    946: /*@
    947:    PEPSTOARGetDimensions - Gets the dimensions used for each subsolve
    948:    step in case of doing spectrum slicing for a computational interval.

    950:    Not Collective

    952:    Input Parameter:
    953: .  pep - the eigenproblem solver context

    955:    Output Parameters:
    956: +  nev - number of eigenvalues to compute
    957: .  ncv - the maximum dimension of the subspace to be used by the subsolve
    958: -  mpd - the maximum dimension allowed for the projected problem

    960:    Level: advanced

    962: .seealso: PEPSTOARSetDimensions()
    963: @*/
    964: PetscErrorCode PEPSTOARGetDimensions(PEP pep,PetscInt *nev,PetscInt *ncv,PetscInt *mpd)
    965: {

    970:   PetscUseMethod(pep,"PEPSTOARGetDimensions_C",(PEP,PetscInt*,PetscInt*,PetscInt*),(pep,nev,ncv,mpd));
    971:   return(0);
    972: }

    974: static PetscErrorCode PEPSTOARSetCheckEigenvalueType_STOAR(PEP pep,PetscBool checket)
    975: {
    976:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    979:   ctx->checket = checket;
    980:   pep->state   = PEP_STATE_INITIAL;
    981:   return(0);
    982: }

    984: /*@
    985:    PEPSTOARSetCheckEigenvalueType - Sets a flag to check that all the eigenvalues
    986:    obtained throughout the spectrum slicing computation have the same definite type.

    988:    Logically Collective on pep

    990:    Input Parameters:
    991: +  pep     - the eigenproblem solver context
    992: -  checket - check eigenvalue type

    994:    Options Database Key:
    995: .  -pep_stoar_check_eigenvalue_type - Check eigenvalue type; this takes an optional
    996:    bool value (0/1/no/yes/true/false)

    998:    Notes:
    999:    This option is relevant only for spectrum slicing computations, but it is
    1000:    ignored if the problem type is PEP_HYPERBOLIC.

    1002:    This flag is turned on by default, to guarantee that the computed eigenvalues
    1003:    have the same type (otherwise the computed solution might be wrong). But since
    1004:    the check is computationally quite expensive, the check may be turned off if
    1005:    the user knows for sure that all eigenvalues in the requested interval have
    1006:    the same type.

    1008:    Level: advanced

    1010: .seealso: PEPSetProblemType(), PEPSetInterval()
    1011: @*/
    1012: PetscErrorCode PEPSTOARSetCheckEigenvalueType(PEP pep,PetscBool checket)
    1013: {

    1019:   PetscTryMethod(pep,"PEPSTOARSetCheckEigenvalueType_C",(PEP,PetscBool),(pep,checket));
    1020:   return(0);
    1021: }

    1023: static PetscErrorCode PEPSTOARGetCheckEigenvalueType_STOAR(PEP pep,PetscBool *checket)
    1024: {
    1025:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    1028:   *checket = ctx->checket;
    1029:   return(0);
    1030: }

    1032: /*@
    1033:    PEPSTOARGetCheckEigenvalueType - Gets the flag for the eigenvalue type
    1034:    check in spectrum slicing.

    1036:    Not Collective

    1038:    Input Parameter:
    1039: .  pep - the eigenproblem solver context

    1041:    Output Parameter:
    1042: .  checket - whether eigenvalue type must be checked during spectrum slcing

    1044:    Level: advanced

    1046: .seealso: PEPSTOARSetCheckEigenvalueType()
    1047: @*/
    1048: PetscErrorCode PEPSTOARGetCheckEigenvalueType(PEP pep,PetscBool *checket)
    1049: {

    1055:   PetscUseMethod(pep,"PEPSTOARGetCheckEigenvalueType_C",(PEP,PetscBool*),(pep,checket));
    1056:   return(0);
    1057: }

    1059: PetscErrorCode PEPView_STOAR(PEP pep,PetscViewer viewer)
    1060: {
    1062:   PEP_STOAR      *ctx = (PEP_STOAR*)pep->data;
    1063:   PetscBool      isascii;

    1066:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&isascii);
    1067:   if (isascii) {
    1068:     PetscViewerASCIIPrintf(viewer,"  using the %slocking variant\n",ctx->lock?"":"non-");
    1069:     PetscViewerASCIIPrintf(viewer,"  linearization parameters: alpha=%g beta=%g\n",(double)ctx->alpha,(double)ctx->beta);
    1070:     if (pep->which==PEP_ALL && !ctx->hyperbolic) {
    1071:       PetscViewerASCIIPrintf(viewer,"  checking eigenvalue type: %s\n",ctx->checket?"enabled":"disabled");
    1072:     }
    1073:   }
    1074:   return(0);
    1075: }

    1077: PetscErrorCode PEPReset_STOAR(PEP pep)
    1078: {

    1082:   if (pep->which==PEP_ALL) {
    1083:     PEPReset_STOAR_QSlice(pep);
    1084:   }
    1085:   return(0);
    1086: }

    1088: PetscErrorCode PEPDestroy_STOAR(PEP pep)
    1089: {
    1091:   PEP_STOAR      *ctx = (PEP_STOAR*)pep->data;

    1094:   BVDestroy(&ctx->V);
    1095:   PetscFree(pep->data);
    1096:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetLocking_C",NULL);
    1097:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetLocking_C",NULL);
    1098:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetDetectZeros_C",NULL);
    1099:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetDetectZeros_C",NULL);
    1100:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetInertias_C",NULL);
    1101:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetDimensions_C",NULL);
    1102:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetDimensions_C",NULL);
    1103:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetLinearization_C",NULL);
    1104:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetLinearization_C",NULL);
    1105:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetCheckEigenvalueType_C",NULL);
    1106:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetCheckEigenvalueType_C",NULL);
    1107:   return(0);
    1108: }

    1110: SLEPC_EXTERN PetscErrorCode PEPCreate_STOAR(PEP pep)
    1111: {
    1113:   PEP_STOAR      *ctx;

    1116:   PetscNewLog(pep,&ctx);
    1117:   pep->data = (void*)ctx;

    1119:   pep->lineariz = PETSC_TRUE;
    1120:   ctx->lock     = PETSC_TRUE;
    1121:   ctx->nev      = 1;
    1122:   ctx->ncv      = PETSC_DEFAULT;
    1123:   ctx->mpd      = PETSC_DEFAULT;
    1124:   ctx->alpha    = 1.0;
    1125:   ctx->beta     = 0.0;
    1126:   ctx->checket  = PETSC_TRUE;

    1128:   pep->ops->setup          = PEPSetUp_STOAR;
    1129:   pep->ops->setfromoptions = PEPSetFromOptions_STOAR;
    1130:   pep->ops->destroy        = PEPDestroy_STOAR;
    1131:   pep->ops->view           = PEPView_STOAR;
    1132:   pep->ops->backtransform  = PEPBackTransform_Default;
    1133:   pep->ops->computevectors = PEPComputeVectors_Default;
    1134:   pep->ops->extractvectors = PEPExtractVectors_TOAR;
    1135:   pep->ops->reset          = PEPReset_STOAR;

    1137:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetLocking_C",PEPSTOARSetLocking_STOAR);
    1138:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetLocking_C",PEPSTOARGetLocking_STOAR);
    1139:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetDetectZeros_C",PEPSTOARSetDetectZeros_STOAR);
    1140:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetDetectZeros_C",PEPSTOARGetDetectZeros_STOAR);
    1141:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetInertias_C",PEPSTOARGetInertias_STOAR);
    1142:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetDimensions_C",PEPSTOARGetDimensions_STOAR);
    1143:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetDimensions_C",PEPSTOARSetDimensions_STOAR);
    1144:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetLinearization_C",PEPSTOARSetLinearization_STOAR);
    1145:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetLinearization_C",PEPSTOARGetLinearization_STOAR);
    1146:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetCheckEigenvalueType_C",PEPSTOARSetCheckEigenvalueType_STOAR);
    1147:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetCheckEigenvalueType_C",PEPSTOARGetCheckEigenvalueType_STOAR);
    1148:   return(0);
    1149: }