Date: 2021-02-01T14:42:12+00:00

Actual source code: stoar.c
slepc-3.14.2 2021-02-01
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      1: /*
      2:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      3:    SLEPc - Scalable Library for Eigenvalue Problem Computations
      4:    Copyright (c) 2002-2020, 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:     DSAllocate(pep->ds,ld);
    145:     PEPBasisCoefficients(pep,pep->pbc);
    146:     STGetTransform(pep->st,&flg);
    147:     if (!flg) {
    148:       PetscFree(pep->solvematcoeffs);
    149:       PetscMalloc1(pep->nmat,&pep->solvematcoeffs);
    150:       PetscLogObjectMemory((PetscObject)pep,pep->nmat*sizeof(PetscScalar));
    151:       PetscObjectTypeCompare((PetscObject)pep->st,STSINVERT,&sinv);
    152:       if (sinv) {
    153:         PEPEvaluateBasis(pep,pep->target,0,pep->solvematcoeffs,NULL);
    154:       } else {
    155:         for (i=0;i<pep->nmat-1;i++) pep->solvematcoeffs[i] = 0.0;
    156:         pep->solvematcoeffs[pep->nmat-1] = 1.0;
    157:       }
    158:     }
    159:   }
    160:   if (!pep->which) { PEPSetWhichEigenpairs_Default(pep); }
    161:   PEPCheckUnsupported(pep,PEP_FEATURE_NONMONOMIAL | PEP_FEATURE_REGION);

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

    172: /*
    173:   Compute a run of Lanczos iterations. dim(work)=(ctx->ld)*4
    174: */
    175: static PetscErrorCode PEPSTOARrun(PEP pep,PetscReal *a,PetscReal *b,PetscReal *omega,PetscInt k,PetscInt *M,PetscBool *breakdown,PetscBool *symmlost,Vec *t_)
    176: {
    178:   PEP_STOAR      *ctx = (PEP_STOAR*)pep->data;
    179:   PetscInt       i,j,m=*M,l,lock;
    180:   PetscInt       lds,d,ld,offq,nqt,ldds;
    181:   Vec            v=t_[0],t=t_[1],q=t_[2];
    182:   PetscReal      norm,sym=0.0,fro=0.0,*f;
    183:   PetscScalar    *y,*S,*x,sigma;
    184:   PetscBLASInt   j_,one=1;
    185:   PetscBool      lindep,flg,sinvert=PETSC_FALSE;
    186:   Mat            MS;

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    412:     /* Update l */
    413:     if (pep->reason != PEP_CONVERGED_ITERATING || breakdown) l = 0;
    414:     else {
    415:       l = PetscMax(1,(PetscInt)((nv-k)/2));
    416:       l = PetscMin(l,t);
    417:       if (!breakdown) {
    418:         DSGetArrayReal(pep->ds,DS_MAT_T,&a);
    419:         if (*(a+ldds+k+l-1)!=0) {
    420:           if (k+l<nv-1) l = l+1;
    421:           else l = l-1;
    422:         }
    423:         /* Prepare the Rayleigh quotient for restart */
    424:         DSGetArray(pep->ds,DS_MAT_Q,&Q);
    425:         DSGetArrayReal(pep->ds,DS_MAT_D,&omega);
    426:         r = a + 2*ldds;
    427:         for (j=k;j<k+l;j++) {
    428:           r[j] = PetscRealPart(Q[nv-1+j*ldds]*beta);
    429:         }
    430:         b = a+ldds;
    431:         b[k+l-1] = r[k+l-1];
    432:         omega[k+l] = omega[nv];
    433:         DSRestoreArray(pep->ds,DS_MAT_Q,&Q);
    434:         DSRestoreArrayReal(pep->ds,DS_MAT_T,&a);
    435:         DSRestoreArrayReal(pep->ds,DS_MAT_D,&omega);
    436:       }
    437:     }
    438:     nconv = k;
    439:     if (!ctx->lock && pep->reason == PEP_CONVERGED_ITERATING && !breakdown) { l += k; k = 0; } /* non-locking variant: reset no. of converged pairs */

    441:     /* Update S */
    442:     DSGetMat(pep->ds,DS_MAT_Q,&MQ);
    443:     BVMultInPlace(ctx->V,MQ,pep->nconv,k+l);
    444:     MatDestroy(&MQ);

    446:     /* Copy last column of S */
    447:     BVCopyColumn(ctx->V,nv,k+l);
    448:     DSGetArrayReal(pep->ds,DS_MAT_D,&omega);
    449:     VecCreateSeq(PETSC_COMM_SELF,k+l,&vomega);
    450:     VecGetArray(vomega,&om);
    451:     for (j=0;j<k+l;j++) om[j] = omega[j];
    452:     VecRestoreArray(vomega,&om);
    453:     BVSetActiveColumns(ctx->V,0,k+l);
    454:     BVSetSignature(ctx->V,vomega);
    455:     VecDestroy(&vomega);
    456:     DSRestoreArrayReal(pep->ds,DS_MAT_D,&omega);

    458:     if (breakdown && pep->reason == PEP_CONVERGED_ITERATING) {
    459:       /* stop if breakdown */
    460:       PetscInfo2(pep,"Breakdown TOAR method (it=%D norm=%g)\n",pep->its,(double)beta);
    461:       pep->reason = PEP_DIVERGED_BREAKDOWN;
    462:     }
    463:     if (pep->reason != PEP_CONVERGED_ITERATING) l--;
    464:     BVGetActiveColumns(pep->V,NULL,&nq);
    465:     if (k+l+deg<=nq) {
    466:       BVSetActiveColumns(ctx->V,pep->nconv,k+l+1);
    467:       if (!falselock && ctx->lock) {
    468:         BVTensorCompress(ctx->V,k-pep->nconv);
    469:       } else {
    470:         BVTensorCompress(ctx->V,0);
    471:       }
    472:     }
    473:     pep->nconv = k;
    474:     PEPMonitor(pep,pep->its,nconv,pep->eigr,pep->eigi,pep->errest,nv);
    475:   }

    477:   if (pep->nconv>0) {
    478:     BVSetActiveColumns(ctx->V,0,pep->nconv);
    479:     BVGetActiveColumns(pep->V,NULL,&nq);
    480:     BVSetActiveColumns(pep->V,0,nq);
    481:     if (nq>pep->nconv) {
    482:       BVTensorCompress(ctx->V,pep->nconv);
    483:       BVSetActiveColumns(pep->V,0,pep->nconv);
    484:     }
    485:   }
    486:   STGetTransform(pep->st,&flg);
    487:   if (!flg && pep->ops->backtransform) {
    488:       (*pep->ops->backtransform)(pep);
    489:   }
    490:   if (pep->sfactor!=1.0) {
    491:     for (j=0;j<pep->nconv;j++) {
    492:       pep->eigr[j] *= pep->sfactor;
    493:       pep->eigi[j] *= pep->sfactor;
    494:     }
    495:   }
    496:   /* restore original values */
    497:   if (!flg) {
    498:     pep->target *= pep->sfactor;
    499:     STScaleShift(pep->st,pep->sfactor);
    500:   } else {
    501:     STScaleShift(pep->st,sinv?1.0/pep->sfactor:pep->sfactor);
    502:     pep->target = (sinv)?pep->target/pep->sfactor:pep->target*pep->sfactor;
    503:   }
    504:   if (pep->sfactor!=1.0) { RGPopScale(pep->rg); }

    506:   /* truncate Schur decomposition and change the state to raw so that
    507:      DSVectors() computes eigenvectors from scratch */
    508:   DSSetDimensions(pep->ds,pep->nconv,0,0,0);
    509:   DSSetState(pep->ds,DS_STATE_RAW);
    510:   return(0);
    511: }

    513: PetscErrorCode PEPSetFromOptions_STOAR(PetscOptionItems *PetscOptionsObject,PEP pep)
    514: {
    516:   PetscBool      flg,lock,b,f1,f2,f3;
    517:   PetscInt       i,j,k;
    518:   PetscReal      array[2]={0,0};
    519:   PEP_STOAR      *ctx = (PEP_STOAR*)pep->data;

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

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

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

    531:     i = 1;
    532:     j = k = PETSC_DECIDE;
    533:     PetscOptionsInt("-pep_stoar_nev","Number of eigenvalues to compute in each subsolve (only for spectrum slicing)","PEPSTOARSetDimensions",20,&i,&f1);
    534:     PetscOptionsInt("-pep_stoar_ncv","Number of basis vectors in each subsolve (only for spectrum slicing)","PEPSTOARSetDimensions",40,&j,&f2);
    535:     PetscOptionsInt("-pep_stoar_mpd","Maximum dimension of projected problem in each subsolve (only for spectrum slicing)","PEPSTOARSetDimensions",40,&k,&f3);
    536:     if (f1 || f2 || f3) { PEPSTOARSetDimensions(pep,i,j,k); }

    538:     k = 2;
    539:     PetscOptionsRealArray("-pep_stoar_linearization","Parameters of the linearization","PEPSTOARSetLinearization",array,&k,&flg);
    540:     if (flg) {
    541:       PEPSTOARSetLinearization(pep,array[0],array[1]);
    542:     }

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

    548:   PetscOptionsTail();
    549:   return(0);
    550: }

    552: static PetscErrorCode PEPSTOARSetLocking_STOAR(PEP pep,PetscBool lock)
    553: {
    554:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    557:   ctx->lock = lock;
    558:   return(0);
    559: }

    561: /*@
    562:    PEPSTOARSetLocking - Choose between locking and non-locking variants of
    563:    the STOAR method.

    565:    Logically Collective on pep

    567:    Input Parameters:
    568: +  pep  - the eigenproblem solver context
    569: -  lock - true if the locking variant must be selected

    571:    Options Database Key:
    572: .  -pep_stoar_locking - Sets the locking flag

    574:    Notes:
    575:    The default is to lock converged eigenpairs when the method restarts.
    576:    This behaviour can be changed so that all directions are kept in the
    577:    working subspace even if already converged to working accuracy (the
    578:    non-locking variant).

    580:    Level: advanced

    582: .seealso: PEPSTOARGetLocking()
    583: @*/
    584: PetscErrorCode PEPSTOARSetLocking(PEP pep,PetscBool lock)
    585: {

    591:   PetscTryMethod(pep,"PEPSTOARSetLocking_C",(PEP,PetscBool),(pep,lock));
    592:   return(0);
    593: }

    595: static PetscErrorCode PEPSTOARGetLocking_STOAR(PEP pep,PetscBool *lock)
    596: {
    597:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    600:   *lock = ctx->lock;
    601:   return(0);
    602: }

    604: /*@
    605:    PEPSTOARGetLocking - Gets the locking flag used in the STOAR method.

    607:    Not Collective

    609:    Input Parameter:
    610: .  pep - the eigenproblem solver context

    612:    Output Parameter:
    613: .  lock - the locking flag

    615:    Level: advanced

    617: .seealso: PEPSTOARSetLocking()
    618: @*/
    619: PetscErrorCode PEPSTOARGetLocking(PEP pep,PetscBool *lock)
    620: {

    626:   PetscUseMethod(pep,"PEPSTOARGetLocking_C",(PEP,PetscBool*),(pep,lock));
    627:   return(0);
    628: }

    630: static PetscErrorCode PEPSTOARGetInertias_STOAR(PEP pep,PetscInt *n,PetscReal **shifts,PetscInt **inertias)
    631: {
    633:   PetscInt       i,numsh;
    634:   PEP_STOAR      *ctx = (PEP_STOAR*)pep->data;
    635:   PEP_SR         sr = ctx->sr;

    638:   if (!pep->state) SETERRQ(PetscObjectComm((PetscObject)pep),PETSC_ERR_ARG_WRONGSTATE,"Must call PEPSetUp() first");
    639:   if (!ctx->sr) SETERRQ(PetscObjectComm((PetscObject)pep),PETSC_ERR_ARG_WRONGSTATE,"Only available in interval computations, see PEPSetInterval()");
    640:   switch (pep->state) {
    641:   case PEP_STATE_INITIAL:
    642:     break;
    643:   case PEP_STATE_SETUP:
    644:     if (n) *n = 2;
    645:     if (shifts) {
    646:       PetscMalloc1(2,shifts);
    647:       (*shifts)[0] = pep->inta;
    648:       (*shifts)[1] = pep->intb;
    649:     }
    650:     if (inertias) {
    651:       PetscMalloc1(2,inertias);
    652:       (*inertias)[0] = (sr->dir==1)?sr->inertia0:sr->inertia1;
    653:       (*inertias)[1] = (sr->dir==1)?sr->inertia1:sr->inertia0;
    654:     }
    655:     break;
    656:   case PEP_STATE_SOLVED:
    657:   case PEP_STATE_EIGENVECTORS:
    658:     numsh = ctx->nshifts;
    659:     if (n) *n = numsh;
    660:     if (shifts) {
    661:       PetscMalloc1(numsh,shifts);
    662:       for (i=0;i<numsh;i++) (*shifts)[i] = ctx->shifts[i];
    663:     }
    664:     if (inertias) {
    665:       PetscMalloc1(numsh,inertias);
    666:       for (i=0;i<numsh;i++) (*inertias)[i] = ctx->inertias[i];
    667:     }
    668:     break;
    669:   }
    670:   return(0);
    671: }

    673: /*@C
    674:    PEPSTOARGetInertias - Gets the values of the shifts and their
    675:    corresponding inertias in case of doing spectrum slicing for a
    676:    computational interval.

    678:    Not Collective

    680:    Input Parameter:
    681: .  pep - the eigenproblem solver context

    683:    Output Parameters:
    684: +  n        - number of shifts, including the endpoints of the interval
    685: .  shifts   - the values of the shifts used internally in the solver
    686: -  inertias - the values of the inertia in each shift

    688:    Notes:
    689:    If called after PEPSolve(), all shifts used internally by the solver are
    690:    returned (including both endpoints and any intermediate ones). If called
    691:    before PEPSolve() and after PEPSetUp() then only the information of the
    692:    endpoints of subintervals is available.

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

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

    699:    Fortran Notes:
    700:    The calling sequence from Fortran is
    701: .vb
    702:    PEPSTOARGetInertias(pep,n,shifts,inertias,ierr)
    703:    integer n
    704:    double precision shifts(*)
    705:    integer inertias(*)
    706: .ve
    707:    The arrays should be at least of length n. The value of n can be determined
    708:    by an initial call
    709: .vb
    710:    PEPSTOARGetInertias(pep,n,PETSC_NULL_REAL,PETSC_NULL_INTEGER,ierr)
    711: .ve

    713:    Level: advanced

    715: .seealso: PEPSetInterval()
    716: @*/
    717: PetscErrorCode PEPSTOARGetInertias(PEP pep,PetscInt *n,PetscReal **shifts,PetscInt **inertias)
    718: {

    724:   PetscUseMethod(pep,"PEPSTOARGetInertias_C",(PEP,PetscInt*,PetscReal**,PetscInt**),(pep,n,shifts,inertias));
    725:   return(0);
    726: }

    728: static PetscErrorCode PEPSTOARSetDetectZeros_STOAR(PEP pep,PetscBool detect)
    729: {
    730:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    733:   ctx->detect = detect;
    734:   pep->state  = PEP_STATE_INITIAL;
    735:   return(0);
    736: }

    738: /*@
    739:    PEPSTOARSetDetectZeros - Sets a flag to enforce detection of
    740:    zeros during the factorizations throughout the spectrum slicing computation.

    742:    Logically Collective on pep

    744:    Input Parameters:
    745: +  pep    - the eigenproblem solver context
    746: -  detect - check for zeros

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

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

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

    759:    Level: advanced

    761: .seealso: PEPSetInterval()
    762: @*/
    763: PetscErrorCode PEPSTOARSetDetectZeros(PEP pep,PetscBool detect)
    764: {

    770:   PetscTryMethod(pep,"PEPSTOARSetDetectZeros_C",(PEP,PetscBool),(pep,detect));
    771:   return(0);
    772: }

    774: static PetscErrorCode PEPSTOARGetDetectZeros_STOAR(PEP pep,PetscBool *detect)
    775: {
    776:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    779:   *detect = ctx->detect;
    780:   return(0);
    781: }

    783: /*@
    784:    PEPSTOARGetDetectZeros - Gets the flag that enforces zero detection
    785:    in spectrum slicing.

    787:    Not Collective

    789:    Input Parameter:
    790: .  pep - the eigenproblem solver context

    792:    Output Parameter:
    793: .  detect - whether zeros detection is enforced during factorizations

    795:    Level: advanced

    797: .seealso: PEPSTOARSetDetectZeros()
    798: @*/
    799: PetscErrorCode PEPSTOARGetDetectZeros(PEP pep,PetscBool *detect)
    800: {

    806:   PetscUseMethod(pep,"PEPSTOARGetDetectZeros_C",(PEP,PetscBool*),(pep,detect));
    807:   return(0);
    808: }

    810: static PetscErrorCode PEPSTOARSetLinearization_STOAR(PEP pep,PetscReal alpha,PetscReal beta)
    811: {
    812:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    815:   if (beta==0.0 && alpha==0.0) SETERRQ(PetscObjectComm((PetscObject)pep),PETSC_ERR_ARG_WRONG,"Parameters alpha and beta cannot be zero simultaneously");
    816:   ctx->alpha = alpha;
    817:   ctx->beta  = beta;
    818:   return(0);
    819: }

    821: /*@
    822:    PEPSTOARSetLinearization - Set the coefficients that define
    823:    the linearization of a quadratic eigenproblem.

    825:    Logically Collective on pep

    827:    Input Parameters:
    828: +  pep   - polynomial eigenvalue solver
    829: .  alpha - first parameter of the linearization
    830: -  beta  - second parameter of the linearization

    832:    Options Database Key:
    833: .  -pep_stoar_linearization <alpha,beta> - Sets the coefficients

    835:    Notes:
    836:    Cannot pass zero for both alpha and beta. The default values are
    837:    alpha=1 and beta=0.

    839:    Level: advanced

    841: .seealso: PEPSTOARGetLinearization()
    842: @*/
    843: PetscErrorCode PEPSTOARSetLinearization(PEP pep,PetscReal alpha,PetscReal beta)
    844: {

    851:   PetscTryMethod(pep,"PEPSTOARSetLinearization_C",(PEP,PetscReal,PetscReal),(pep,alpha,beta));
    852:   return(0);
    853: }

    855: static PetscErrorCode PEPSTOARGetLinearization_STOAR(PEP pep,PetscReal *alpha,PetscReal *beta)
    856: {
    857:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    860:   if (alpha) *alpha = ctx->alpha;
    861:   if (beta)  *beta  = ctx->beta;
    862:   return(0);
    863: }

    865: /*@
    866:    PEPSTOARGetLinearization - Returns the coefficients that define
    867:    the linearization of a quadratic eigenproblem.

    869:    Not Collective

    871:    Input Parameter:
    872: .  pep  - polynomial eigenvalue solver

    874:    Output Parameters:
    875: +  alpha - the first parameter of the linearization
    876: -  beta  - the second parameter of the linearization

    878:    Level: advanced

    880: .seealso: PEPSTOARSetLinearization()
    881: @*/
    882: PetscErrorCode PEPSTOARGetLinearization(PEP pep,PetscReal *alpha,PetscReal *beta)
    883: {

    888:   PetscUseMethod(pep,"PEPSTOARGetLinearization_C",(PEP,PetscReal*,PetscReal*),(pep,alpha,beta));
    889:   return(0);
    890: }

    892: static PetscErrorCode PEPSTOARSetDimensions_STOAR(PEP pep,PetscInt nev,PetscInt ncv,PetscInt mpd)
    893: {
    894:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    897:   if (nev<1) SETERRQ(PetscObjectComm((PetscObject)pep),PETSC_ERR_ARG_OUTOFRANGE,"Illegal value of nev. Must be > 0");
    898:   ctx->nev = nev;
    899:   if (ncv == PETSC_DECIDE || ncv == PETSC_DEFAULT) {
    900:     ctx->ncv = PETSC_DEFAULT;
    901:   } else {
    902:     if (ncv<1) SETERRQ(PetscObjectComm((PetscObject)pep),PETSC_ERR_ARG_OUTOFRANGE,"Illegal value of ncv. Must be > 0");
    903:     ctx->ncv = ncv;
    904:   }
    905:   if (mpd == PETSC_DECIDE || mpd == PETSC_DEFAULT) {
    906:     ctx->mpd = PETSC_DEFAULT;
    907:   } else {
    908:     if (mpd<1) SETERRQ(PetscObjectComm((PetscObject)pep),PETSC_ERR_ARG_OUTOFRANGE,"Illegal value of mpd. Must be > 0");
    909:     ctx->mpd = mpd;
    910:   }
    911:   pep->state = PEP_STATE_INITIAL;
    912:   return(0);
    913: }

    915: /*@
    916:    PEPSTOARSetDimensions - Sets the dimensions used for each subsolve
    917:    step in case of doing spectrum slicing for a computational interval.
    918:    The meaning of the parameters is the same as in PEPSetDimensions().

    920:    Logically Collective on pep

    922:    Input Parameters:
    923: +  pep - the eigenproblem solver context
    924: .  nev - number of eigenvalues to compute
    925: .  ncv - the maximum dimension of the subspace to be used by the subsolve
    926: -  mpd - the maximum dimension allowed for the projected problem

    928:    Options Database Key:
    929: +  -eps_stoar_nev <nev> - Sets the number of eigenvalues
    930: .  -eps_stoar_ncv <ncv> - Sets the dimension of the subspace
    931: -  -eps_stoar_mpd <mpd> - Sets the maximum projected dimension

    933:    Level: advanced

    935: .seealso: PEPSTOARGetDimensions(), PEPSetDimensions(), PEPSetInterval()
    936: @*/
    937: PetscErrorCode PEPSTOARSetDimensions(PEP pep,PetscInt nev,PetscInt ncv,PetscInt mpd)
    938: {

    946:   PetscTryMethod(pep,"PEPSTOARSetDimensions_C",(PEP,PetscInt,PetscInt,PetscInt),(pep,nev,ncv,mpd));
    947:   return(0);
    948: }

    950: static PetscErrorCode PEPSTOARGetDimensions_STOAR(PEP pep,PetscInt *nev,PetscInt *ncv,PetscInt *mpd)
    951: {
    952:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    955:   if (nev) *nev = ctx->nev;
    956:   if (ncv) *ncv = ctx->ncv;
    957:   if (mpd) *mpd = ctx->mpd;
    958:   return(0);
    959: }

    961: /*@
    962:    PEPSTOARGetDimensions - Gets the dimensions used for each subsolve
    963:    step in case of doing spectrum slicing for a computational interval.

    965:    Not Collective

    967:    Input Parameter:
    968: .  pep - the eigenproblem solver context

    970:    Output Parameters:
    971: +  nev - number of eigenvalues to compute
    972: .  ncv - the maximum dimension of the subspace to be used by the subsolve
    973: -  mpd - the maximum dimension allowed for the projected problem

    975:    Level: advanced

    977: .seealso: PEPSTOARSetDimensions()
    978: @*/
    979: PetscErrorCode PEPSTOARGetDimensions(PEP pep,PetscInt *nev,PetscInt *ncv,PetscInt *mpd)
    980: {

    985:   PetscUseMethod(pep,"PEPSTOARGetDimensions_C",(PEP,PetscInt*,PetscInt*,PetscInt*),(pep,nev,ncv,mpd));
    986:   return(0);
    987: }

    989: static PetscErrorCode PEPSTOARSetCheckEigenvalueType_STOAR(PEP pep,PetscBool checket)
    990: {
    991:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    994:   ctx->checket = checket;
    995:   pep->state   = PEP_STATE_INITIAL;
    996:   return(0);
    997: }

    999: /*@
    1000:    PEPSTOARSetCheckEigenvalueType - Sets a flag to check that all the eigenvalues
    1001:    obtained throughout the spectrum slicing computation have the same definite type.

    1003:    Logically Collective on pep

    1005:    Input Parameters:
    1006: +  pep     - the eigenproblem solver context
    1007: -  checket - check eigenvalue type

    1009:    Options Database Key:
    1010: .  -pep_stoar_check_eigenvalue_type - Check eigenvalue type; this takes an optional
    1011:    bool value (0/1/no/yes/true/false)

    1013:    Notes:
    1014:    This option is relevant only for spectrum slicing computations, but it is
    1015:    ignored if the problem type is PEP_HYPERBOLIC.

    1017:    This flag is turned on by default, to guarantee that the computed eigenvalues
    1018:    have the same type (otherwise the computed solution might be wrong). But since
    1019:    the check is computationally quite expensive, the check may be turned off if
    1020:    the user knows for sure that all eigenvalues in the requested interval have
    1021:    the same type.

    1023:    Level: advanced

    1025: .seealso: PEPSetProblemType(), PEPSetInterval()
    1026: @*/
    1027: PetscErrorCode PEPSTOARSetCheckEigenvalueType(PEP pep,PetscBool checket)
    1028: {

    1034:   PetscTryMethod(pep,"PEPSTOARSetCheckEigenvalueType_C",(PEP,PetscBool),(pep,checket));
    1035:   return(0);
    1036: }

    1038: static PetscErrorCode PEPSTOARGetCheckEigenvalueType_STOAR(PEP pep,PetscBool *checket)
    1039: {
    1040:   PEP_STOAR *ctx = (PEP_STOAR*)pep->data;

    1043:   *checket = ctx->checket;
    1044:   return(0);
    1045: }

    1047: /*@
    1048:    PEPSTOARGetCheckEigenvalueType - Gets the flag for the eigenvalue type
    1049:    check in spectrum slicing.

    1051:    Not Collective

    1053:    Input Parameter:
    1054: .  pep - the eigenproblem solver context

    1056:    Output Parameter:
    1057: .  checket - whether eigenvalue type must be checked during spectrum slcing

    1059:    Level: advanced

    1061: .seealso: PEPSTOARSetCheckEigenvalueType()
    1062: @*/
    1063: PetscErrorCode PEPSTOARGetCheckEigenvalueType(PEP pep,PetscBool *checket)
    1064: {

    1070:   PetscUseMethod(pep,"PEPSTOARGetCheckEigenvalueType_C",(PEP,PetscBool*),(pep,checket));
    1071:   return(0);
    1072: }

    1074: PetscErrorCode PEPView_STOAR(PEP pep,PetscViewer viewer)
    1075: {
    1077:   PEP_STOAR      *ctx = (PEP_STOAR*)pep->data;
    1078:   PetscBool      isascii;

    1081:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&isascii);
    1082:   if (isascii) {
    1083:     PetscViewerASCIIPrintf(viewer,"  using the %slocking variant\n",ctx->lock?"":"non-");
    1084:     PetscViewerASCIIPrintf(viewer,"  linearization parameters: alpha=%g beta=%g\n",(double)ctx->alpha,(double)ctx->beta);
    1085:     if (pep->which==PEP_ALL && !ctx->hyperbolic) {
    1086:       PetscViewerASCIIPrintf(viewer,"  checking eigenvalue type: %s\n",ctx->checket?"enabled":"disabled");
    1087:     }
    1088:   }
    1089:   return(0);
    1090: }

    1092: PetscErrorCode PEPReset_STOAR(PEP pep)
    1093: {

    1097:   if (pep->which==PEP_ALL) {
    1098:     PEPReset_STOAR_QSlice(pep);
    1099:   }
    1100:   return(0);
    1101: }

    1103: PetscErrorCode PEPDestroy_STOAR(PEP pep)
    1104: {
    1106:   PEP_STOAR      *ctx = (PEP_STOAR*)pep->data;

    1109:   BVDestroy(&ctx->V);
    1110:   PetscFree(pep->data);
    1111:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetLocking_C",NULL);
    1112:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetLocking_C",NULL);
    1113:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetDetectZeros_C",NULL);
    1114:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetDetectZeros_C",NULL);
    1115:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetInertias_C",NULL);
    1116:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetDimensions_C",NULL);
    1117:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetDimensions_C",NULL);
    1118:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetLinearization_C",NULL);
    1119:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetLinearization_C",NULL);
    1120:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetCheckEigenvalueType_C",NULL);
    1121:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetCheckEigenvalueType_C",NULL);
    1122:   return(0);
    1123: }

    1125: SLEPC_EXTERN PetscErrorCode PEPCreate_STOAR(PEP pep)
    1126: {
    1128:   PEP_STOAR      *ctx;

    1131:   PetscNewLog(pep,&ctx);
    1132:   pep->data = (void*)ctx;

    1134:   pep->lineariz = PETSC_TRUE;
    1135:   ctx->lock     = PETSC_TRUE;
    1136:   ctx->nev      = 1;
    1137:   ctx->ncv      = PETSC_DEFAULT;
    1138:   ctx->mpd      = PETSC_DEFAULT;
    1139:   ctx->alpha    = 1.0;
    1140:   ctx->beta     = 0.0;
    1141:   ctx->checket  = PETSC_TRUE;

    1143:   pep->ops->setup          = PEPSetUp_STOAR;
    1144:   pep->ops->setfromoptions = PEPSetFromOptions_STOAR;
    1145:   pep->ops->destroy        = PEPDestroy_STOAR;
    1146:   pep->ops->view           = PEPView_STOAR;
    1147:   pep->ops->backtransform  = PEPBackTransform_Default;
    1148:   pep->ops->computevectors = PEPComputeVectors_Default;
    1149:   pep->ops->extractvectors = PEPExtractVectors_TOAR;
    1150:   pep->ops->reset          = PEPReset_STOAR;

    1152:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetLocking_C",PEPSTOARSetLocking_STOAR);
    1153:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetLocking_C",PEPSTOARGetLocking_STOAR);
    1154:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetDetectZeros_C",PEPSTOARSetDetectZeros_STOAR);
    1155:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetDetectZeros_C",PEPSTOARGetDetectZeros_STOAR);
    1156:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetInertias_C",PEPSTOARGetInertias_STOAR);
    1157:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetDimensions_C",PEPSTOARGetDimensions_STOAR);
    1158:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetDimensions_C",PEPSTOARSetDimensions_STOAR);
    1159:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetLinearization_C",PEPSTOARSetLinearization_STOAR);
    1160:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetLinearization_C",PEPSTOARGetLinearization_STOAR);
    1161:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetCheckEigenvalueType_C",PEPSTOARSetCheckEigenvalueType_STOAR);
    1162:   PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetCheckEigenvalueType_C",PEPSTOARGetCheckEigenvalueType_STOAR);
    1163:   return(0);
    1164: }