Date: 2022-03-31T15:28:52+00:00

Actual source code: dspep.c
slepc-3.17.0 2022-03-31
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      1: /*
      2:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      3:    SLEPc - Scalable Library for Eigenvalue Problem Computations
      4:    Copyright (c) 2002-, Universitat Politecnica de Valencia, Spain

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

     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: {
     21:   DS_PEP         *ctx = (DS_PEP*)ds->data;
     22:   PetscInt       i;

     25:   DSAllocateMat_Private(ds,DS_MAT_X);
     26:   DSAllocateMat_Private(ds,DS_MAT_Y);
     27:   for (i=0;i<=ctx->d;i++) DSAllocateMat_Private(ds,DSMatExtra[i]);
     28:   PetscFree(ds->perm);
     29:   PetscMalloc1(ld*ctx->d,&ds->perm);
     30:   PetscLogObjectMemory((PetscObject)ds,ld*ctx->d*sizeof(PetscInt));
     31:   PetscFunctionReturn(0);
     32: }

     34: PetscErrorCode DSView_PEP(DS ds,PetscViewer viewer)
     35: {
     36:   DS_PEP            *ctx = (DS_PEP*)ds->data;
     37:   PetscViewerFormat format;
     38:   PetscInt          i;

     40:   PetscViewerGetFormat(viewer,&format);
     41:   if (format == PETSC_VIEWER_ASCII_INFO) PetscFunctionReturn(0);
     42:   if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
     43:     PetscViewerASCIIPrintf(viewer,"polynomial degree: %" PetscInt_FMT "\n",ctx->d);
     44:     PetscFunctionReturn(0);
     45:   }
     46:   for (i=0;i<=ctx->d;i++) DSViewMat(ds,viewer,DSMatExtra[i]);
     47:   if (ds->state>DS_STATE_INTERMEDIATE) DSViewMat(ds,viewer,DS_MAT_X);
     48:   PetscFunctionReturn(0);
     49: }

     51: PetscErrorCode DSVectors_PEP(DS ds,DSMatType mat,PetscInt *j,PetscReal *rnorm)
     52: {
     54:   switch (mat) {
     55:     case DS_MAT_X:
     56:       break;
     57:     case DS_MAT_Y:
     58:       break;
     59:     default:
     60:       SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_OUTOFRANGE,"Invalid mat parameter");
     61:   }
     62:   PetscFunctionReturn(0);
     63: }

     65: PetscErrorCode DSSort_PEP(DS ds,PetscScalar *wr,PetscScalar *wi,PetscScalar *rr,PetscScalar *ri,PetscInt *kout)
     66: {
     67:   DS_PEP         *ctx = (DS_PEP*)ds->data;
     68:   PetscInt       n,i,*perm,told;
     69:   PetscScalar    *A;

     71:   if (!ds->sc) PetscFunctionReturn(0);
     72:   n = ds->n*ctx->d;
     73:   A = ds->mat[DS_MAT_A];
     74:   perm = ds->perm;
     75:   for (i=0;i<n;i++) perm[i] = i;
     76:   told = ds->t;
     77:   ds->t = n;  /* force the sorting routines to consider d*n eigenvalues */
     78:   if (rr) DSSortEigenvalues_Private(ds,rr,ri,perm,PETSC_FALSE);
     79:   else DSSortEigenvalues_Private(ds,wr,wi,perm,PETSC_FALSE);
     80:   ds->t = told;  /* restore value of t */
     81:   for (i=0;i<n;i++) A[i]  = wr[perm[i]];
     82:   for (i=0;i<n;i++) wr[i] = A[i];
     83:   for (i=0;i<n;i++) A[i]  = wi[perm[i]];
     84:   for (i=0;i<n;i++) wi[i] = A[i];
     85:   DSPermuteColumnsTwo_Private(ds,0,n,ds->n,DS_MAT_X,DS_MAT_Y,perm);
     86:   PetscFunctionReturn(0);
     87: }

     89: PetscErrorCode DSSolve_PEP_QZ(DS ds,PetscScalar *wr,PetscScalar *wi)
     90: {
     91:   DS_PEP         *ctx = (DS_PEP*)ds->data;
     92:   PetscInt       i,j,k,off;
     93:   PetscScalar    *A,*B,*W,*X,*U,*Y,*E,*work,*beta;
     94:   PetscReal      *ca,*cb,*cg,norm,done=1.0;
     95:   PetscBLASInt   info,n,ld,ldd,nd,lrwork=0,lwork,one=1,zero=0,cols;
     96: #if defined(PETSC_USE_COMPLEX)
     97:   PetscReal      *rwork;
     98: #endif

    100:   if (!ds->mat[DS_MAT_A]) DSAllocateMat_Private(ds,DS_MAT_A);
    101:   if (!ds->mat[DS_MAT_B]) DSAllocateMat_Private(ds,DS_MAT_B);
    102:   if (!ds->mat[DS_MAT_W]) DSAllocateMat_Private(ds,DS_MAT_W);
    103:   if (!ds->mat[DS_MAT_U]) DSAllocateMat_Private(ds,DS_MAT_U);
    104:   PetscBLASIntCast(ds->n*ctx->d,&nd);
    105:   PetscBLASIntCast(ds->n,&n);
    106:   PetscBLASIntCast(ds->ld,&ld);
    107:   PetscBLASIntCast(ds->ld*ctx->d,&ldd);
    108: #if defined(PETSC_USE_COMPLEX)
    109:   PetscBLASIntCast(nd+2*nd,&lwork);
    110:   PetscBLASIntCast(8*nd,&lrwork);
    111: #else
    112:   PetscBLASIntCast(nd+8*nd,&lwork);
    113: #endif
    114:   DSAllocateWork_Private(ds,lwork,lrwork,0);
    115:   beta = ds->work;
    116:   work = ds->work + nd;
    117:   lwork -= nd;
    118:   A = ds->mat[DS_MAT_A];
    119:   B = ds->mat[DS_MAT_B];
    120:   W = ds->mat[DS_MAT_W];
    121:   U = ds->mat[DS_MAT_U];
    122:   X = ds->mat[DS_MAT_X];
    123:   Y = ds->mat[DS_MAT_Y];
    124:   E = ds->mat[DSMatExtra[ctx->d]];

    126:   /* build matrices A and B of the linearization */
    127:   PetscArrayzero(A,ldd*ldd);
    128:   if (!ctx->pbc) { /* monomial basis */
    129:     for (i=0;i<nd-ds->n;i++) A[i+(i+ds->n)*ldd] = 1.0;
    130:     for (i=0;i<ctx->d;i++) {
    131:       off = i*ds->n*ldd+(ctx->d-1)*ds->n;
    132:       for (j=0;j<ds->n;j++) PetscArraycpy(A+off+j*ldd,ds->mat[DSMatExtra[i]]+j*ds->ld,ds->n);
    133:     }
    134:   } else {
    135:     ca = ctx->pbc;
    136:     cb = ca+ctx->d+1;
    137:     cg = cb+ctx->d+1;
    138:     for (i=0;i<ds->n;i++) {
    139:       A[i+(i+ds->n)*ldd] = ca[0];
    140:       A[i+i*ldd] = cb[0];
    141:     }
    142:     for (;i<nd-ds->n;i++) {
    143:       j = i/ds->n;
    144:       A[i+(i+ds->n)*ldd] = ca[j];
    145:       A[i+i*ldd] = cb[j];
    146:       A[i+(i-ds->n)*ldd] = cg[j];
    147:     }
    148:     for (i=0;i<ctx->d-2;i++) {
    149:       off = i*ds->n*ldd+(ctx->d-1)*ds->n;
    150:       for (j=0;j<ds->n;j++)
    151:         for (k=0;k<ds->n;k++)
    152:           *(A+off+j*ldd+k) = *(ds->mat[DSMatExtra[i]]+j*ds->ld+k)*ca[ctx->d-1];
    153:     }
    154:     off = i*ds->n*ldd+(ctx->d-1)*ds->n;
    155:     for (j=0;j<ds->n;j++)
    156:       for (k=0;k<ds->n;k++)
    157:         *(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];
    158:     off = (++i)*ds->n*ldd+(ctx->d-1)*ds->n;
    159:     for (j=0;j<ds->n;j++)
    160:       for (k=0;k<ds->n;k++)
    161:         *(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];
    162:   }
    163:   PetscArrayzero(B,ldd*ldd);
    164:   for (i=0;i<nd-ds->n;i++) B[i+i*ldd] = 1.0;
    165:   off = (ctx->d-1)*ds->n*(ldd+1);
    166:   for (j=0;j<ds->n;j++) {
    167:     for (i=0;i<ds->n;i++) B[off+i+j*ldd] = -E[i+j*ds->ld];
    168:   }

    170:   /* solve generalized eigenproblem */
    171: #if defined(PETSC_USE_COMPLEX)
    172:   rwork = ds->rwork;
    173:   PetscStackCallBLAS("LAPACKggev",LAPACKggev_("V","V",&nd,A,&ldd,B,&ldd,wr,beta,U,&ldd,W,&ldd,work,&lwork,rwork,&info));
    174: #else
    175:   PetscStackCallBLAS("LAPACKggev",LAPACKggev_("V","V",&nd,A,&ldd,B,&ldd,wr,wi,beta,U,&ldd,W,&ldd,work,&lwork,&info));
    176: #endif
    177:   SlepcCheckLapackInfo("ggev",info);

    179:   /* copy eigenvalues */
    180:   for (i=0;i<nd;i++) {
    181:     if (beta[i]==0.0) wr[i] = (PetscRealPart(wr[i])>0.0)? PETSC_MAX_REAL: PETSC_MIN_REAL;
    182:     else wr[i] /= beta[i];
    183: #if !defined(PETSC_USE_COMPLEX)
    184:     if (beta[i]==0.0) wi[i] = 0.0;
    185:     else wi[i] /= beta[i];
    186: #else
    187:     if (wi) wi[i] = 0.0;
    188: #endif
    189:   }

    191:   /* copy and normalize eigenvectors */
    192:   for (j=0;j<nd;j++) {
    193:     PetscArraycpy(X+j*ds->ld,W+j*ldd,ds->n);
    194:     PetscArraycpy(Y+j*ds->ld,U+ds->n*(ctx->d-1)+j*ldd,ds->n);
    195:   }
    196:   for (j=0;j<nd;j++) {
    197:     cols = 1;
    198:     norm = BLASnrm2_(&n,X+j*ds->ld,&one);
    199: #if !defined(PETSC_USE_COMPLEX)
    200:     if (wi[j] != 0.0) {
    201:       norm = SlepcAbsEigenvalue(norm,BLASnrm2_(&n,X+(j+1)*ds->ld,&one));
    202:       cols = 2;
    203:     }
    204: #endif
    205:     PetscStackCallBLAS("LAPACKlascl",LAPACKlascl_("G",&zero,&zero,&norm,&done,&n,&cols,X+j*ds->ld,&ld,&info));
    206:     SlepcCheckLapackInfo("lascl",info);
    207:     norm = BLASnrm2_(&n,Y+j*ds->ld,&one);
    208: #if !defined(PETSC_USE_COMPLEX)
    209:     if (wi[j] != 0.0) norm = SlepcAbsEigenvalue(norm,BLASnrm2_(&n,Y+(j+1)*ds->ld,&one));
    210: #endif
    211:     PetscStackCallBLAS("LAPACKlascl",LAPACKlascl_("G",&zero,&zero,&norm,&done,&n,&cols,Y+j*ds->ld,&ld,&info));
    212:     SlepcCheckLapackInfo("lascl",info);
    213: #if !defined(PETSC_USE_COMPLEX)
    214:     if (wi[j] != 0.0) j++;
    215: #endif
    216:   }
    217:   PetscFunctionReturn(0);
    218: }

    220: PetscErrorCode DSSynchronize_PEP(DS ds,PetscScalar eigr[],PetscScalar eigi[])
    221: {
    222:   DS_PEP         *ctx = (DS_PEP*)ds->data;
    223:   PetscInt       ld=ds->ld,k=0;
    224:   PetscMPIInt    ldnd,rank,off=0,size,dn;

    226:   if (ds->state>=DS_STATE_CONDENSED) k += 2*ctx->d*ds->n*ld;
    227:   if (eigr) k += ctx->d*ds->n;
    228:   if (eigi) k += ctx->d*ds->n;
    229:   DSAllocateWork_Private(ds,k,0,0);
    230:   PetscMPIIntCast(k*sizeof(PetscScalar),&size);
    231:   PetscMPIIntCast(ds->n*ctx->d*ld,&ldnd);
    232:   PetscMPIIntCast(ctx->d*ds->n,&dn);
    233:   MPI_Comm_rank(PetscObjectComm((PetscObject)ds),&rank);
    234:   if (!rank) {
    235:     if (ds->state>=DS_STATE_CONDENSED) {
    236:       MPI_Pack(ds->mat[DS_MAT_X],ldnd,MPIU_SCALAR,ds->work,size,&off,PetscObjectComm((PetscObject)ds));
    237:       MPI_Pack(ds->mat[DS_MAT_Y],ldnd,MPIU_SCALAR,ds->work,size,&off,PetscObjectComm((PetscObject)ds));
    238:     }
    239:     if (eigr) MPI_Pack(eigr,dn,MPIU_SCALAR,ds->work,size,&off,PetscObjectComm((PetscObject)ds));
    240: #if !defined(PETSC_USE_COMPLEX)
    241:     if (eigi) MPI_Pack(eigi,dn,MPIU_SCALAR,ds->work,size,&off,PetscObjectComm((PetscObject)ds));
    242: #endif
    243:   }
    244:   MPI_Bcast(ds->work,size,MPI_BYTE,0,PetscObjectComm((PetscObject)ds));
    245:   if (rank) {
    246:     if (ds->state>=DS_STATE_CONDENSED) {
    247:       MPI_Unpack(ds->work,size,&off,ds->mat[DS_MAT_X],ldnd,MPIU_SCALAR,PetscObjectComm((PetscObject)ds));
    248:       MPI_Unpack(ds->work,size,&off,ds->mat[DS_MAT_Y],ldnd,MPIU_SCALAR,PetscObjectComm((PetscObject)ds));
    249:     }
    250:     if (eigr) MPI_Unpack(ds->work,size,&off,eigr,dn,MPIU_SCALAR,PetscObjectComm((PetscObject)ds));
    251: #if !defined(PETSC_USE_COMPLEX)
    252:     if (eigi) MPI_Unpack(ds->work,size,&off,eigi,dn,MPIU_SCALAR,PetscObjectComm((PetscObject)ds));
    253: #endif
    254:   }
    255:   PetscFunctionReturn(0);
    256: }

    258: static PetscErrorCode DSPEPSetDegree_PEP(DS ds,PetscInt d)
    259: {
    260:   DS_PEP *ctx = (DS_PEP*)ds->data;

    264:   ctx->d = d;
    265:   PetscFunctionReturn(0);
    266: }

    268: /*@
    269:    DSPEPSetDegree - Sets the polynomial degree for a DSPEP.

    271:    Logically Collective on ds

    273:    Input Parameters:
    274: +  ds - the direct solver context
    275: -  d  - the degree

    277:    Level: intermediate

    279: .seealso: DSPEPGetDegree()
    280: @*/
    281: PetscErrorCode DSPEPSetDegree(DS ds,PetscInt d)
    282: {
    285:   PetscTryMethod(ds,"DSPEPSetDegree_C",(DS,PetscInt),(ds,d));
    286:   PetscFunctionReturn(0);
    287: }

    289: static PetscErrorCode DSPEPGetDegree_PEP(DS ds,PetscInt *d)
    290: {
    291:   DS_PEP *ctx = (DS_PEP*)ds->data;

    293:   *d = ctx->d;
    294:   PetscFunctionReturn(0);
    295: }

    297: /*@
    298:    DSPEPGetDegree - Returns the polynomial degree for a DSPEP.

    300:    Not collective

    302:    Input Parameter:
    303: .  ds - the direct solver context

    305:    Output Parameters:
    306: .  d - the degree

    308:    Level: intermediate

    310: .seealso: DSPEPSetDegree()
    311: @*/
    312: PetscErrorCode DSPEPGetDegree(DS ds,PetscInt *d)
    313: {
    316:   PetscUseMethod(ds,"DSPEPGetDegree_C",(DS,PetscInt*),(ds,d));
    317:   PetscFunctionReturn(0);
    318: }

    320: static PetscErrorCode DSPEPSetCoefficients_PEP(DS ds,PetscReal *pbc)
    321: {
    322:   DS_PEP         *ctx = (DS_PEP*)ds->data;
    323:   PetscInt       i;

    326:   if (ctx->pbc) PetscFree(ctx->pbc);
    327:   PetscMalloc1(3*(ctx->d+1),&ctx->pbc);
    328:   for (i=0;i<3*(ctx->d+1);i++) ctx->pbc[i] = pbc[i];
    329:   ds->state = DS_STATE_RAW;
    330:   PetscFunctionReturn(0);
    331: }

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

    336:    Logically Collective on ds

    338:    Input Parameters:
    339: +  ds  - the direct solver context
    340: -  pbc - the polynomial basis coefficients

    342:    Notes:
    343:    This function is required only in the case of a polynomial specified in a
    344:    non-monomial basis, to provide the coefficients that will be used
    345:    during the linearization, multiplying the identity blocks on the three main
    346:    diagonal blocks. Depending on the polynomial basis (Chebyshev, Legendre, ...)
    347:    the coefficients must be different.

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

    355:    Level: advanced

    357: .seealso: DSPEPGetCoefficients(), DSPEPSetDegree()
    358: @*/
    359: PetscErrorCode DSPEPSetCoefficients(DS ds,PetscReal *pbc)
    360: {
    362:   PetscTryMethod(ds,"DSPEPSetCoefficients_C",(DS,PetscReal*),(ds,pbc));
    363:   PetscFunctionReturn(0);
    364: }

    366: static PetscErrorCode DSPEPGetCoefficients_PEP(DS ds,PetscReal **pbc)
    367: {
    368:   DS_PEP         *ctx = (DS_PEP*)ds->data;
    369:   PetscInt       i;

    372:   PetscCalloc1(3*(ctx->d+1),pbc);
    373:   if (ctx->pbc) for (i=0;i<3*(ctx->d+1);i++) (*pbc)[i] = ctx->pbc[i];
    374:   else for (i=0;i<ctx->d+1;i++) (*pbc)[i] = 1.0;
    375:   PetscFunctionReturn(0);
    376: }

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

    381:    Not collective

    383:    Input Parameter:
    384: .  ds - the direct solver context

    386:    Output Parameters:
    387: .  pbc - the polynomial basis coefficients

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

    392:    Fortran Notes:
    393:    The calling sequence from Fortran is
    394: .vb
    395:    DSPEPGetCoefficients(eps,pbc,ierr)
    396:    double precision pbc(d+1) output
    397: .ve

    399:    Level: advanced

    401: .seealso: DSPEPSetCoefficients()
    402: @*/
    403: PetscErrorCode DSPEPGetCoefficients(DS ds,PetscReal **pbc)
    404: {
    407:   PetscUseMethod(ds,"DSPEPGetCoefficients_C",(DS,PetscReal**),(ds,pbc));
    408:   PetscFunctionReturn(0);
    409: }

    411: PetscErrorCode DSDestroy_PEP(DS ds)
    412: {
    413:   DS_PEP         *ctx = (DS_PEP*)ds->data;

    415:   if (ctx->pbc) PetscFree(ctx->pbc);
    416:   PetscFree(ds->data);
    417:   PetscObjectComposeFunction((PetscObject)ds,"DSPEPSetDegree_C",NULL);
    418:   PetscObjectComposeFunction((PetscObject)ds,"DSPEPGetDegree_C",NULL);
    419:   PetscObjectComposeFunction((PetscObject)ds,"DSPEPSetCoefficients_C",NULL);
    420:   PetscObjectComposeFunction((PetscObject)ds,"DSPEPGetCoefficients_C",NULL);
    421:   PetscFunctionReturn(0);
    422: }

    424: PetscErrorCode DSMatGetSize_PEP(DS ds,DSMatType t,PetscInt *rows,PetscInt *cols)
    425: {
    426:   DS_PEP *ctx = (DS_PEP*)ds->data;

    429:   *rows = ds->n;
    430:   if (t==DS_MAT_A || t==DS_MAT_B || t==DS_MAT_W || t==DS_MAT_U) *rows *= ctx->d;
    431:   *cols = ds->n;
    432:   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;
    433:   PetscFunctionReturn(0);
    434: }

    436: /*MC
    437:    DSPEP - Dense Polynomial Eigenvalue Problem.

    439:    Level: beginner

    441:    Notes:
    442:    The problem is expressed as P(lambda)*x = 0, where P(.) is a matrix
    443:    polynomial of degree d. The eigenvalues lambda are the arguments
    444:    returned by DSSolve().

    446:    The degree of the polynomial, d, can be set with DSPEPSetDegree(), with
    447:    the first d+1 extra matrices of the DS defining the matrix polynomial. By
    448:    default, the polynomial is expressed in the monomial basis, but a
    449:    different basis can be used by setting the corresponding coefficients
    450:    via DSPEPSetCoefficients().

    452:    The problem is solved via linearization, by building a pencil (A,B) of
    453:    size p*n and solving the corresponding GNHEP.

    455:    Used DS matrices:
    456: +  DS_MAT_Ex - coefficients of the matrix polynomial
    457: .  DS_MAT_A  - (workspace) first matrix of the linearization
    458: .  DS_MAT_B  - (workspace) second matrix of the linearization
    459: .  DS_MAT_W  - (workspace) right eigenvectors of the linearization
    460: -  DS_MAT_U  - (workspace) left eigenvectors of the linearization

    462:    Implemented methods:
    463: .  0 - QZ iteration on the linearization (_ggev)

    465: .seealso: DSCreate(), DSSetType(), DSType, DSPEPSetDegree(), DSPEPSetCoefficients()
    466: M*/
    467: SLEPC_EXTERN PetscErrorCode DSCreate_PEP(DS ds)
    468: {
    469:   DS_PEP         *ctx;

    471:   PetscNewLog(ds,&ctx);
    472:   ds->data = (void*)ctx;

    474:   ds->ops->allocate      = DSAllocate_PEP;
    475:   ds->ops->view          = DSView_PEP;
    476:   ds->ops->vectors       = DSVectors_PEP;
    477:   ds->ops->solve[0]      = DSSolve_PEP_QZ;
    478:   ds->ops->sort          = DSSort_PEP;
    479:   ds->ops->synchronize   = DSSynchronize_PEP;
    480:   ds->ops->destroy       = DSDestroy_PEP;
    481:   ds->ops->matgetsize    = DSMatGetSize_PEP;
    482:   PetscObjectComposeFunction((PetscObject)ds,"DSPEPSetDegree_C",DSPEPSetDegree_PEP);
    483:   PetscObjectComposeFunction((PetscObject)ds,"DSPEPGetDegree_C",DSPEPGetDegree_PEP);
    484:   PetscObjectComposeFunction((PetscObject)ds,"DSPEPSetCoefficients_C",DSPEPSetCoefficients_PEP);
    485:   PetscObjectComposeFunction((PetscObject)ds,"DSPEPGetCoefficients_C",DSPEPGetCoefficients_PEP);
    486:   PetscFunctionReturn(0);
    487: }