Actual source code: stoar.c

slepc-3.14.1 2020-12-08
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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: }