Actual source code: dspep.c

slepc-3.14.0 2020-09-30
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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: */

 11: #include <slepc/private/dsimpl.h>
 12: #include <slepcblaslapack.h>

 14: typedef struct {
 15:   PetscInt  d;              /* polynomial degree */
 16:   PetscReal *pbc;           /* polynomial basis coefficients */
 17: } DS_PEP;

 19: PetscErrorCode DSAllocate_PEP(DS ds,PetscInt ld)
 20: {
 22:   DS_PEP         *ctx = (DS_PEP*)ds->data;
 23:   PetscInt       i;

 26:   if (!ctx->d) SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_WRONGSTATE,"DSPEP requires specifying the polynomial degree via DSPEPSetDegree()");
 27:   DSAllocateMat_Private(ds,DS_MAT_X);
 28:   DSAllocateMat_Private(ds,DS_MAT_Y);
 29:   for (i=0;i<=ctx->d;i++) {
 30:     DSAllocateMat_Private(ds,DSMatExtra[i]);
 31:   }
 32:   PetscFree(ds->perm);
 33:   PetscMalloc1(ld*ctx->d,&ds->perm);
 34:   PetscLogObjectMemory((PetscObject)ds,ld*ctx->d*sizeof(PetscInt));
 35:   return(0);
 36: }

 38: PetscErrorCode DSView_PEP(DS ds,PetscViewer viewer)
 39: {
 40:   PetscErrorCode    ierr;
 41:   DS_PEP            *ctx = (DS_PEP*)ds->data;
 42:   PetscViewerFormat format;
 43:   PetscInt          i;

 46:   PetscViewerGetFormat(viewer,&format);
 47:   PetscViewerASCIIPrintf(viewer,"polynomial degree: %D\n",ctx->d);
 48:   if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) return(0);
 49:   for (i=0;i<=ctx->d;i++) {
 50:     DSViewMat(ds,viewer,DSMatExtra[i]);
 51:   }
 52:   if (ds->state>DS_STATE_INTERMEDIATE) { DSViewMat(ds,viewer,DS_MAT_X); }
 53:   return(0);
 54: }

 56: PetscErrorCode DSVectors_PEP(DS ds,DSMatType mat,PetscInt *j,PetscReal *rnorm)
 57: {
 59:   if (rnorm) SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_SUP,"Not implemented yet");
 60:   switch (mat) {
 61:     case DS_MAT_X:
 62:       break;
 63:     case DS_MAT_Y:
 64:       break;
 65:     default:
 66:       SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_OUTOFRANGE,"Invalid mat parameter");
 67:   }
 68:   return(0);
 69: }

 71: PetscErrorCode DSSort_PEP(DS ds,PetscScalar *wr,PetscScalar *wi,PetscScalar *rr,PetscScalar *ri,PetscInt *kout)
 72: {
 74:   DS_PEP         *ctx = (DS_PEP*)ds->data;
 75:   PetscInt       n,i,j,k,p,*perm,told,ld;
 76:   PetscScalar    *A,*X,*Y,rtmp,rtmp2;

 79:   if (!ds->sc) return(0);
 80:   n = ds->n*ctx->d;
 81:   A  = ds->mat[DS_MAT_A];
 82:   perm = ds->perm;
 83:   for (i=0;i<n;i++) perm[i] = i;
 84:   told = ds->t;
 85:   ds->t = n;  /* force the sorting routines to consider d*n eigenvalues */
 86:   if (rr) {
 87:     DSSortEigenvalues_Private(ds,rr,ri,perm,PETSC_FALSE);
 88:   } else {
 89:     DSSortEigenvalues_Private(ds,wr,wi,perm,PETSC_FALSE);
 90:   }
 91:   ds->t = told;  /* restore value of t */
 92:   for (i=0;i<n;i++) A[i]  = wr[perm[i]];
 93:   for (i=0;i<n;i++) wr[i] = A[i];
 94:   for (i=0;i<n;i++) A[i]  = wi[perm[i]];
 95:   for (i=0;i<n;i++) wi[i] = A[i];
 96:   /* cannot use DSPermuteColumns_Private() since matrix is not square */
 97:   ld = ds->ld;
 98:   X  = ds->mat[DS_MAT_X];
 99:   Y  = ds->mat[DS_MAT_Y];
100:   for (i=0;i<n;i++) {
101:     p = perm[i];
102:     if (p != i) {
103:       j = i + 1;
104:       while (perm[j] != i) j++;
105:       perm[j] = p; perm[i] = i;
106:       /* swap columns i and j */
107:       for (k=0;k<ds->n;k++) {
108:         rtmp  = X[k+p*ld]; X[k+p*ld] = X[k+i*ld]; X[k+i*ld] = rtmp;
109:         rtmp2 = Y[k+p*ld]; Y[k+p*ld] = Y[k+i*ld]; Y[k+i*ld] = rtmp2;
110:       }
111:     }
112:   }
113:   return(0);
114: }

116: PetscErrorCode DSSolve_PEP_QZ(DS ds,PetscScalar *wr,PetscScalar *wi)
117: {
119:   DS_PEP         *ctx = (DS_PEP*)ds->data;
120:   PetscInt       i,j,k,off;
121:   PetscScalar    *A,*B,*W,*X,*U,*Y,*E,*work,*beta,norm;
122:   PetscReal      *ca,*cb,*cg;
123:   PetscBLASInt   info,n,ldd,nd,lrwork=0,lwork,one=1;
124: #if defined(PETSC_USE_COMPLEX)
125:   PetscReal      *rwork;
126: #else
127:   PetscScalar    norm0;
128: #endif

131:   if (!ds->mat[DS_MAT_A]) {
132:     DSAllocateMat_Private(ds,DS_MAT_A);
133:   }
134:   if (!ds->mat[DS_MAT_B]) {
135:     DSAllocateMat_Private(ds,DS_MAT_B);
136:   }
137:   if (!ds->mat[DS_MAT_W]) {
138:     DSAllocateMat_Private(ds,DS_MAT_W);
139:   }
140:   if (!ds->mat[DS_MAT_U]) {
141:     DSAllocateMat_Private(ds,DS_MAT_U);
142:   }
143:   PetscBLASIntCast(ds->n*ctx->d,&nd);
144:   PetscBLASIntCast(ds->n,&n);
145:   PetscBLASIntCast(ds->ld*ctx->d,&ldd);
146: #if defined(PETSC_USE_COMPLEX)
147:   PetscBLASIntCast(nd+2*nd,&lwork);
148:   PetscBLASIntCast(8*nd,&lrwork);
149: #else
150:   PetscBLASIntCast(nd+8*nd,&lwork);
151: #endif
152:   DSAllocateWork_Private(ds,lwork,lrwork,0);
153:   beta = ds->work;
154:   work = ds->work + nd;
155:   lwork -= nd;
156:   A = ds->mat[DS_MAT_A];
157:   B = ds->mat[DS_MAT_B];
158:   W = ds->mat[DS_MAT_W];
159:   U = ds->mat[DS_MAT_U];
160:   X = ds->mat[DS_MAT_X];
161:   Y = ds->mat[DS_MAT_Y];
162:   E = ds->mat[DSMatExtra[ctx->d]];

164:   /* build matrices A and B of the linearization */
165:   PetscArrayzero(A,ldd*ldd);
166:   if (!ctx->pbc) { /* monomial basis */
167:     for (i=0;i<nd-ds->n;i++) A[i+(i+ds->n)*ldd] = 1.0;
168:     for (i=0;i<ctx->d;i++) {
169:       off = i*ds->n*ldd+(ctx->d-1)*ds->n;
170:       for (j=0;j<ds->n;j++) {
171:         PetscArraycpy(A+off+j*ldd,ds->mat[DSMatExtra[i]]+j*ds->ld,ds->n);
172:       }
173:     }
174:   } else {
175:     ca = ctx->pbc;
176:     cb = ca+ctx->d+1;
177:     cg = cb+ctx->d+1;
178:     for (i=0;i<ds->n;i++) {
179:       A[i+(i+ds->n)*ldd] = ca[0];
180:       A[i+i*ldd] = cb[0];
181:     }
182:     for (;i<nd-ds->n;i++) {
183:       j = i/ds->n;
184:       A[i+(i+ds->n)*ldd] = ca[j];
185:       A[i+i*ldd] = cb[j];
186:       A[i+(i-ds->n)*ldd] = cg[j];
187:     }
188:     for (i=0;i<ctx->d-2;i++) {
189:       off = i*ds->n*ldd+(ctx->d-1)*ds->n;
190:       for (j=0;j<ds->n;j++)
191:         for (k=0;k<ds->n;k++)
192:           *(A+off+j*ldd+k) = *(ds->mat[DSMatExtra[i]]+j*ds->ld+k)*ca[ctx->d-1];
193:     }
194:     off = i*ds->n*ldd+(ctx->d-1)*ds->n;
195:     for (j=0;j<ds->n;j++)
196:       for (k=0;k<ds->n;k++)
197:         *(A+off+j*ldd+k) = *(ds->mat[DSMatExtra[i]]+j*ds->ld+k)*ca[ctx->d-1]-E[j*ds->ld+k]*cg[ctx->d-1];
198:     off = (++i)*ds->n*ldd+(ctx->d-1)*ds->n;
199:     for (j=0;j<ds->n;j++)
200:       for (k=0;k<ds->n;k++)
201:         *(A+off+j*ldd+k) = *(ds->mat[DSMatExtra[i]]+j*ds->ld+k)*ca[ctx->d-1]-E[j*ds->ld+k]*cb[ctx->d-1];
202:   }
203:   PetscArrayzero(B,ldd*ldd);
204:   for (i=0;i<nd-ds->n;i++) B[i+i*ldd] = 1.0;
205:   off = (ctx->d-1)*ds->n*(ldd+1);
206:   for (j=0;j<ds->n;j++) {
207:     for (i=0;i<ds->n;i++) B[off+i+j*ldd] = -E[i+j*ds->ld];
208:   }

210:   /* solve generalized eigenproblem */
211: #if defined(PETSC_USE_COMPLEX)
212:   rwork = ds->rwork;
213:   PetscStackCallBLAS("LAPACKggev",LAPACKggev_("V","V",&nd,A,&ldd,B,&ldd,wr,beta,U,&ldd,W,&ldd,work,&lwork,rwork,&info));
214: #else
215:   PetscStackCallBLAS("LAPACKggev",LAPACKggev_("V","V",&nd,A,&ldd,B,&ldd,wr,wi,beta,U,&ldd,W,&ldd,work,&lwork,&info));
216: #endif
217:   SlepcCheckLapackInfo("ggev",info);

219:   /* copy eigenvalues */
220:   for (i=0;i<nd;i++) {
221:     if (beta[i]==0.0) wr[i] = (PetscRealPart(wr[i])>0.0)? PETSC_MAX_REAL: PETSC_MIN_REAL;
222:     else wr[i] /= beta[i];
223: #if !defined(PETSC_USE_COMPLEX)
224:     if (beta[i]==0.0) wi[i] = 0.0;
225:     else wi[i] /= beta[i];
226: #else
227:     if (wi) wi[i] = 0.0;
228: #endif
229:   }

231:   /* copy and normalize eigenvectors */
232:   for (j=0;j<nd;j++) {
233:     PetscArraycpy(X+j*ds->ld,W+j*ldd,ds->n);
234:     PetscArraycpy(Y+j*ds->ld,U+ds->n*(ctx->d-1)+j*ldd,ds->n);
235:   }
236:   for (j=0;j<nd;j++) {
237: #if !defined(PETSC_USE_COMPLEX)
238:     if (wi[j] != 0.0) {
239:       norm = BLASnrm2_(&n,X+j*ds->ld,&one);
240:       norm0 = BLASnrm2_(&n,X+(j+1)*ds->ld,&one);
241:       norm = 1.0/SlepcAbsEigenvalue(norm,norm0);
242:       PetscStackCallBLAS("BLASscal",BLASscal_(&n,&norm,X+j*ds->ld,&one));
243:       PetscStackCallBLAS("BLASscal",BLASscal_(&n,&norm,X+(j+1)*ds->ld,&one));
244:       norm = BLASnrm2_(&n,Y+j*ds->ld,&one);
245:       norm0 = BLASnrm2_(&n,Y+(j+1)*ds->ld,&one);
246:       norm = 1.0/SlepcAbsEigenvalue(norm,norm0);
247:       PetscStackCallBLAS("BLASscal",BLASscal_(&n,&norm,Y+j*ds->ld,&one));
248:       PetscStackCallBLAS("BLASscal",BLASscal_(&n,&norm,Y+(j+1)*ds->ld,&one));
249:       j++;
250:     } else
251: #endif
252:     {
253:       norm = 1.0/BLASnrm2_(&n,X+j*ds->ld,&one);
254:       PetscStackCallBLAS("BLASscal",BLASscal_(&n,&norm,X+j*ds->ld,&one));
255:       norm = 1.0/BLASnrm2_(&n,Y+j*ds->ld,&one);
256:       PetscStackCallBLAS("BLASscal",BLASscal_(&n,&norm,Y+j*ds->ld,&one));
257:     }
258:   }
259:   return(0);
260: }

262: PetscErrorCode DSSynchronize_PEP(DS ds,PetscScalar eigr[],PetscScalar eigi[])
263: {
265:   DS_PEP         *ctx = (DS_PEP*)ds->data;
266:   PetscInt       ld=ds->ld,k=0;
267:   PetscMPIInt    ldnd,rank,off=0,size,dn;

270:   if (ds->state>=DS_STATE_CONDENSED) k += 2*ctx->d*ds->n*ld;
271:   if (eigr) k += ctx->d*ds->n;
272:   if (eigi) k += ctx->d*ds->n;
273:   DSAllocateWork_Private(ds,k,0,0);
274:   PetscMPIIntCast(k*sizeof(PetscScalar),&size);
275:   PetscMPIIntCast(ds->n*ctx->d*ld,&ldnd);
276:   PetscMPIIntCast(ctx->d*ds->n,&dn);
277:   MPI_Comm_rank(PetscObjectComm((PetscObject)ds),&rank);
278:   if (!rank) {
279:     if (ds->state>=DS_STATE_CONDENSED) {
280:       MPI_Pack(ds->mat[DS_MAT_X],ldnd,MPIU_SCALAR,ds->work,size,&off,PetscObjectComm((PetscObject)ds));
281:       MPI_Pack(ds->mat[DS_MAT_Y],ldnd,MPIU_SCALAR,ds->work,size,&off,PetscObjectComm((PetscObject)ds));
282:     }
283:     if (eigr) {
284:       MPI_Pack(eigr,dn,MPIU_SCALAR,ds->work,size,&off,PetscObjectComm((PetscObject)ds));
285:     }
286:     if (eigi) {
287:       MPI_Pack(eigi,dn,MPIU_SCALAR,ds->work,size,&off,PetscObjectComm((PetscObject)ds));
288:     }
289:   }
290:   MPI_Bcast(ds->work,size,MPI_BYTE,0,PetscObjectComm((PetscObject)ds));
291:   if (rank) {
292:     if (ds->state>=DS_STATE_CONDENSED) {
293:       MPI_Unpack(ds->work,size,&off,ds->mat[DS_MAT_X],ldnd,MPIU_SCALAR,PetscObjectComm((PetscObject)ds));
294:       MPI_Unpack(ds->work,size,&off,ds->mat[DS_MAT_Y],ldnd,MPIU_SCALAR,PetscObjectComm((PetscObject)ds));
295:     }
296:     if (eigr) {
297:       MPI_Unpack(ds->work,size,&off,eigr,dn,MPIU_SCALAR,PetscObjectComm((PetscObject)ds));
298:     }
299:     if (eigi) {
300:       MPI_Unpack(ds->work,size,&off,eigi,dn,MPIU_SCALAR,PetscObjectComm((PetscObject)ds));
301:     }
302:   }
303:   return(0);
304: }

306: static PetscErrorCode DSPEPSetDegree_PEP(DS ds,PetscInt d)
307: {
308:   DS_PEP *ctx = (DS_PEP*)ds->data;

311:   if (d<0) SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_OUTOFRANGE,"The degree must be a non-negative integer");
312:   if (d>=DS_NUM_EXTRA) SETERRQ1(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_OUTOFRANGE,"Only implemented for polynomials of degree at most %D",DS_NUM_EXTRA-1);
313:   ctx->d = d;
314:   return(0);
315: }

317: /*@
318:    DSPEPSetDegree - Sets the polynomial degree for a DSPEP.

320:    Logically Collective on ds

322:    Input Parameters:
323: +  ds - the direct solver context
324: -  d  - the degree

326:    Level: intermediate

328: .seealso: DSPEPGetDegree()
329: @*/
330: PetscErrorCode DSPEPSetDegree(DS ds,PetscInt d)
331: {

337:   PetscTryMethod(ds,"DSPEPSetDegree_C",(DS,PetscInt),(ds,d));
338:   return(0);
339: }

341: static PetscErrorCode DSPEPGetDegree_PEP(DS ds,PetscInt *d)
342: {
343:   DS_PEP *ctx = (DS_PEP*)ds->data;

346:   *d = ctx->d;
347:   return(0);
348: }

350: /*@
351:    DSPEPGetDegree - Returns the polynomial degree for a DSPEP.

353:    Not collective

355:    Input Parameter:
356: .  ds - the direct solver context

358:    Output Parameters:
359: .  d - the degree

361:    Level: intermediate

363: .seealso: DSPEPSetDegree()
364: @*/
365: PetscErrorCode DSPEPGetDegree(DS ds,PetscInt *d)
366: {

372:   PetscUseMethod(ds,"DSPEPGetDegree_C",(DS,PetscInt*),(ds,d));
373:   return(0);
374: }

376: static PetscErrorCode DSPEPSetCoefficients_PEP(DS ds,PetscReal *pbc)
377: {
379:   DS_PEP         *ctx = (DS_PEP*)ds->data;
380:   PetscInt       i;

383:   if (!ctx->d) SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_WRONGSTATE,"Must first specify the polynomial degree via DSPEPSetDegree()");
384:   if (ctx->pbc) { PetscFree(ctx->pbc); }
385:   PetscMalloc1(3*(ctx->d+1),&ctx->pbc);
386:   for (i=0;i<3*(ctx->d+1);i++) ctx->pbc[i] = pbc[i];
387:   ds->state = DS_STATE_RAW;
388:   return(0);
389: }

391: /*@C
392:    DSPEPSetCoefficients - Sets the polynomial basis coefficients for a DSPEP.

394:    Logically Collective on ds

396:    Input Parameters:
397: +  ds  - the direct solver context
398: -  pbc - the polynomial basis coefficients

400:    Notes:
401:    This function is required only in the case of a polynomial specified in a
402:    non-monomial basis, to provide the coefficients that will be used
403:    during the linearization, multiplying the identity blocks on the three main
404:    diagonal blocks. Depending on the polynomial basis (Chebyshev, Legendre, ...)
405:    the coefficients must be different.

407:    There must be a total of 3*(d+1) coefficients, where d is the degree of the
408:    polynomial. The coefficients are arranged in three groups: alpha, beta, and
409:    gamma, according to the definition of the three-term recurrence. In the case
410:    of the monomial basis, alpha=1 and beta=gamma=0, in which case it is not
411:    necessary to invoke this function.

413:    Level: advanced

415: .seealso: DSPEPGetCoefficients(), DSPEPSetDegree()
416: @*/
417: PetscErrorCode DSPEPSetCoefficients(DS ds,PetscReal *pbc)
418: {

423:   PetscTryMethod(ds,"DSPEPSetCoefficients_C",(DS,PetscReal*),(ds,pbc));
424:   return(0);
425: }

427: static PetscErrorCode DSPEPGetCoefficients_PEP(DS ds,PetscReal **pbc)
428: {
430:   DS_PEP         *ctx = (DS_PEP*)ds->data;
431:   PetscInt       i;

434:   if (!ctx->d) SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_WRONGSTATE,"Must first specify the polynomial degree via DSPEPSetDegree()");
435:   PetscCalloc1(3*(ctx->d+1),pbc);
436:   if (ctx->pbc) for (i=0;i<3*(ctx->d+1);i++) (*pbc)[i] = ctx->pbc[i];
437:   else for (i=0;i<ctx->d+1;i++) (*pbc)[i] = 1.0;
438:   return(0);
439: }

441: /*@C
442:    DSPEPGetCoefficients - Returns the polynomial basis coefficients for a DSPEP.

444:    Not collective

446:    Input Parameter:
447: .  ds - the direct solver context

449:    Output Parameters:
450: .  pbc - the polynomial basis coefficients

452:    Note:
453:    The returned array has length 3*(d+1) and should be freed by the user.

455:    Fortran Note:
456:    The calling sequence from Fortran is
457: .vb
458:    DSPEPGetCoefficients(eps,pbc,ierr)
459:    double precision pbc(d+1) output
460: .ve

462:    Level: advanced

464: .seealso: DSPEPSetCoefficients()
465: @*/
466: PetscErrorCode DSPEPGetCoefficients(DS ds,PetscReal **pbc)
467: {

473:   PetscUseMethod(ds,"DSPEPGetCoefficients_C",(DS,PetscReal**),(ds,pbc));
474:   return(0);
475: }

477: PetscErrorCode DSDestroy_PEP(DS ds)
478: {
480:   DS_PEP         *ctx = (DS_PEP*)ds->data;

483:   if (ctx->pbc) { PetscFree(ctx->pbc); }
484:   PetscFree(ds->data);
485:   PetscObjectComposeFunction((PetscObject)ds,"DSPEPSetDegree_C",NULL);
486:   PetscObjectComposeFunction((PetscObject)ds,"DSPEPGetDegree_C",NULL);
487:   PetscObjectComposeFunction((PetscObject)ds,"DSPEPSetCoefficients_C",NULL);
488:   PetscObjectComposeFunction((PetscObject)ds,"DSPEPGetCoefficients_C",NULL);
489:   return(0);
490: }

492: PetscErrorCode DSMatGetSize_PEP(DS ds,DSMatType t,PetscInt *rows,PetscInt *cols)
493: {
494:   DS_PEP *ctx = (DS_PEP*)ds->data;

497:   if (!ctx->d) SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_WRONGSTATE,"DSPEP requires specifying the polynomial degree via DSPEPSetDegree()");
498:   *rows = ds->n;
499:   if (t==DS_MAT_A || t==DS_MAT_B || t==DS_MAT_W || t==DS_MAT_U) *rows *= ctx->d;
500:   *cols = ds->n;
501:   if (t==DS_MAT_A || t==DS_MAT_B || t==DS_MAT_W || t==DS_MAT_U || t==DS_MAT_X || t==DS_MAT_Y) *cols *= ctx->d;
502:   return(0);
503: }

505: SLEPC_EXTERN PetscErrorCode DSCreate_PEP(DS ds)
506: {
507:   DS_PEP         *ctx;

511:   PetscNewLog(ds,&ctx);
512:   ds->data = (void*)ctx;

514:   ds->ops->allocate      = DSAllocate_PEP;
515:   ds->ops->view          = DSView_PEP;
516:   ds->ops->vectors       = DSVectors_PEP;
517:   ds->ops->solve[0]      = DSSolve_PEP_QZ;
518:   ds->ops->sort          = DSSort_PEP;
519:   ds->ops->synchronize   = DSSynchronize_PEP;
520:   ds->ops->destroy       = DSDestroy_PEP;
521:   ds->ops->matgetsize    = DSMatGetSize_PEP;
522:   PetscObjectComposeFunction((PetscObject)ds,"DSPEPSetDegree_C",DSPEPSetDegree_PEP);
523:   PetscObjectComposeFunction((PetscObject)ds,"DSPEPGetDegree_C",DSPEPGetDegree_PEP);
524:   PetscObjectComposeFunction((PetscObject)ds,"DSPEPSetCoefficients_C",DSPEPSetCoefficients_PEP);
525:   PetscObjectComposeFunction((PetscObject)ds,"DSPEPGetCoefficients_C",DSPEPGetCoefficients_PEP);
526:   return(0);
527: }