Date: 2020-01-13T11:59:18+00:00

Actual source code: dsnep.c
slepc-3.12.2 2020-01-13
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      1: /*
      2:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      3:    SLEPc - Scalable Library for Eigenvalue Problem Computations
      4:    Copyright (c) 2002-2019, 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>       /*I "slepcds.h" I*/
     12: #include <slepcblaslapack.h>

     14: typedef struct {
     15:   PetscInt       nf;                 /* number of functions in f[] */
     16:   FN             f[DS_NUM_EXTRA];    /* functions defining the nonlinear operator */
     17:   PetscInt       neig;               /* number of available eigenpairs */
     18:   void           *computematrixctx;
     19:   PetscErrorCode (*computematrix)(DS,PetscScalar,PetscBool,DSMatType,void*);
     20: } DS_NEP;

     22: /*
     23:    DSNEPComputeMatrix - Build the matrix associated with a nonlinear operator
     24:    T(lambda) or its derivative T'(lambda), given the parameter lambda, where
     25:    T(lambda) = sum_i E_i*f_i(lambda). The result is written in mat.
     26: */
     27: static PetscErrorCode DSNEPComputeMatrix(DS ds,PetscScalar lambda,PetscBool deriv,DSMatType mat)
     28: {
     30:   DS_NEP         *ctx = (DS_NEP*)ds->data;
     31:   PetscScalar    *T,*E,alpha;
     32:   PetscInt       i,ld,n;
     33:   PetscBLASInt   k,inc=1;

     36:   PetscLogEventBegin(DS_Other,ds,0,0,0);
     37:   if (ctx->computematrix) {
     38:     (*ctx->computematrix)(ds,lambda,deriv,mat,ctx->computematrixctx);
     39:   } else {
     40:     DSGetDimensions(ds,&n,NULL,NULL,NULL,NULL);
     41:     DSGetLeadingDimension(ds,&ld);
     42:     PetscBLASIntCast(ld*n,&k);
     43:     DSGetArray(ds,mat,&T);
     44:     PetscArrayzero(T,k);
     45:     for (i=0;i<ctx->nf;i++) {
     46:       if (deriv) {
     47:         FNEvaluateDerivative(ctx->f[i],lambda,&alpha);
     48:       } else {
     49:         FNEvaluateFunction(ctx->f[i],lambda,&alpha);
     50:       }
     51:       E = ds->mat[DSMatExtra[i]];
     52:       PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&k,&alpha,E,&inc,T,&inc));
     53:     }
     54:     DSRestoreArray(ds,mat,&T);
     55:   }
     56:   PetscLogEventEnd(DS_Other,ds,0,0,0);
     57:   return(0);
     58: }

     60: PetscErrorCode DSAllocate_NEP(DS ds,PetscInt ld)
     61: {
     63:   DS_NEP         *ctx = (DS_NEP*)ds->data;
     64:   PetscInt       i;

     67:   if (!ctx->nf) SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_WRONGSTATE,"DSNEP requires passing some functions via DSSetFN()");
     68:   DSAllocateMat_Private(ds,DS_MAT_X);
     69:   for (i=0;i<ctx->nf;i++) {
     70:     DSAllocateMat_Private(ds,DSMatExtra[i]);
     71:   }
     72:   PetscFree(ds->perm);
     73:   PetscMalloc1(ld,&ds->perm);
     74:   PetscLogObjectMemory((PetscObject)ds,ld*sizeof(PetscInt));
     75:   return(0);
     76: }

     78: PetscErrorCode DSView_NEP(DS ds,PetscViewer viewer)
     79: {
     80:   PetscErrorCode    ierr;
     81:   DS_NEP            *ctx = (DS_NEP*)ds->data;
     82:   PetscViewerFormat format;
     83:   PetscInt          i;

     86:   PetscViewerGetFormat(viewer,&format);
     87:   PetscViewerASCIIPrintf(viewer,"number of functions: %D\n",ctx->nf);
     88:   if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) return(0);
     89:   for (i=0;i<ctx->nf;i++) {
     90:     FNView(ctx->f[i],viewer);
     91:     DSViewMat(ds,viewer,DSMatExtra[i]);
     92:   }
     93:   if (ds->state>DS_STATE_INTERMEDIATE) { DSViewMat(ds,viewer,DS_MAT_X); }
     94:   return(0);
     95: }

     97: PetscErrorCode DSVectors_NEP(DS ds,DSMatType mat,PetscInt *j,PetscReal *rnorm)
     98: {
    100:   if (rnorm) SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_SUP,"Not implemented yet");
    101:   switch (mat) {
    102:     case DS_MAT_X:
    103:       break;
    104:     case DS_MAT_Y:
    105:       SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_SUP,"Not implemented yet");
    106:       break;
    107:     default:
    108:       SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_OUTOFRANGE,"Invalid mat parameter");
    109:   }
    110:   return(0);
    111: }

    113: PetscErrorCode DSSort_NEP(DS ds,PetscScalar *wr,PetscScalar *wi,PetscScalar *rr,PetscScalar *ri,PetscInt *dummy)
    114: {
    116:   DS_NEP         *ctx = (DS_NEP*)ds->data;
    117:   PetscInt       n,l,i,j,k,p,*perm,told,ld=ds->ld;
    118:   PetscScalar    *A,*X,rtmp;

    121:   if (!ds->sc) return(0);
    122:   n = ds->n;
    123:   l = ds->l;
    124:   A  = ds->mat[DS_MAT_A];
    125:   perm = ds->perm;
    126:   for (i=0;i<ctx->neig;i++) perm[i] = i;
    127:   told = ds->t;
    128:   ds->t = ctx->neig;  /* force the sorting routines to consider ctx->neig eigenvalues */
    129:   if (rr) {
    130:     DSSortEigenvalues_Private(ds,rr,ri,perm,PETSC_FALSE);
    131:   } else {
    132:     DSSortEigenvalues_Private(ds,wr,NULL,perm,PETSC_FALSE);
    133:   }
    134:   ds->t = told;  /* restore value of t */
    135:   for (i=l;i<n;i++) A[i+i*ld] = wr[perm[i]];
    136:   for (i=l;i<n;i++) wr[i] = A[i+i*ld];
    137:   /* cannot use DSPermuteColumns_Private() since not all columns are filled */
    138:   X  = ds->mat[DS_MAT_X];
    139:   for (i=0;i<ctx->neig;i++) {
    140:     p = perm[i];
    141:     if (p != i) {
    142:       j = i + 1;
    143:       while (perm[j] != i) j++;
    144:       perm[j] = p; perm[i] = i;
    145:       /* swap columns i and j */
    146:       for (k=0;k<n;k++) {
    147:         rtmp  = X[k+p*ld]; X[k+p*ld] = X[k+i*ld]; X[k+i*ld] = rtmp;
    148:       }
    149:     }
    150:   }
    151:   return(0);
    152: }

    154: PetscErrorCode DSSolve_NEP_SLP(DS ds,PetscScalar *wr,PetscScalar *wi)
    155: {
    156: #if defined(SLEPC_MISSING_LAPACK_GGEV)
    158:   SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"GGEV - Lapack routine is unavailable");
    159: #else
    161:   DS_NEP         *ctx = (DS_NEP*)ds->data;
    162:   PetscScalar    *A,*B,*W,*X,*work,*alpha,*beta;
    163:   PetscScalar    norm,sigma,lambda,mu,re,re2,sone=1.0,zero=0.0;
    164:   PetscBLASInt   info,n,ld,lrwork=0,lwork,one=1;
    165:   PetscInt       it,pos,j,maxit=100,result;
    166:   PetscReal      tol;
    167: #if defined(PETSC_USE_COMPLEX)
    168:   PetscReal      *rwork;
    169: #else
    170:   PetscReal      *alphai,im,im2;
    171: #endif

    174:   if (!ds->mat[DS_MAT_A]) {
    175:     DSAllocateMat_Private(ds,DS_MAT_A);
    176:   }
    177:   if (!ds->mat[DS_MAT_B]) {
    178:     DSAllocateMat_Private(ds,DS_MAT_B);
    179:   }
    180:   if (!ds->mat[DS_MAT_W]) {
    181:     DSAllocateMat_Private(ds,DS_MAT_W);
    182:   }
    183:   PetscBLASIntCast(ds->n,&n);
    184:   PetscBLASIntCast(ds->ld,&ld);
    185: #if defined(PETSC_USE_COMPLEX)
    186:   PetscBLASIntCast(2*ds->n+2*ds->n,&lwork);
    187:   PetscBLASIntCast(8*ds->n,&lrwork);
    188: #else
    189:   PetscBLASIntCast(3*ds->n+8*ds->n,&lwork);
    190: #endif
    191:   DSAllocateWork_Private(ds,lwork,lrwork,0);
    192:   alpha = ds->work;
    193:   beta = ds->work + ds->n;
    194: #if defined(PETSC_USE_COMPLEX)
    195:   work = ds->work + 2*ds->n;
    196:   lwork -= 2*ds->n;
    197: #else
    198:   alphai = ds->work + 2*ds->n;
    199:   work = ds->work + 3*ds->n;
    200:   lwork -= 3*ds->n;
    201: #endif
    202:   A = ds->mat[DS_MAT_A];
    203:   B = ds->mat[DS_MAT_B];
    204:   W = ds->mat[DS_MAT_W];
    205:   X = ds->mat[DS_MAT_X];

    207:   sigma = 0.0;
    208:   if (ds->sc->comparison==SlepcCompareTargetMagnitude || ds->sc->comparison==SlepcCompareTargetReal) sigma = *(PetscScalar*)ds->sc->comparisonctx;
    209:   lambda = sigma;
    210:   tol = 1000*n*PETSC_MACHINE_EPSILON;

    212:   for (it=0;it<maxit;it++) {

    214:     /* evaluate T and T' */
    215:     DSNEPComputeMatrix(ds,lambda,PETSC_FALSE,DS_MAT_A);
    216:     if (it) {
    217:       PetscStackCallBLAS("BLASgemv",BLASgemv_("N",&n,&n,&sone,A,&ld,X,&one,&zero,X+ld,&one));
    218:       norm = BLASnrm2_(&n,X+ld,&one);
    219:       if (PetscRealPart(norm)/PetscAbsScalar(lambda)<=tol) break;
    220:     }
    221:     DSNEPComputeMatrix(ds,lambda,PETSC_TRUE,DS_MAT_B);

    223:     /* compute eigenvalue correction mu and eigenvector u */
    224: #if defined(PETSC_USE_COMPLEX)
    225:     rwork = ds->rwork;
    226:     PetscStackCallBLAS("LAPACKggev",LAPACKggev_("N","V",&n,A,&ld,B,&ld,alpha,beta,NULL,&ld,W,&ld,work,&lwork,rwork,&info));
    227: #else
    228:     PetscStackCallBLAS("LAPACKggev",LAPACKggev_("N","V",&n,A,&ld,B,&ld,alpha,alphai,beta,NULL,&ld,W,&ld,work,&lwork,&info));
    229: #endif
    230:     SlepcCheckLapackInfo("ggev",info);

    232:     /* find smallest eigenvalue */
    233:     j = 0;
    234:     if (beta[j]==0.0) re = (PetscRealPart(alpha[j])>0.0)? PETSC_MAX_REAL: PETSC_MIN_REAL;
    235:     else re = alpha[j]/beta[j];
    236: #if !defined(PETSC_USE_COMPLEX)
    237:     if (beta[j]==0.0) im = (alphai[j]>0.0)? PETSC_MAX_REAL: PETSC_MIN_REAL;
    238:     else im = alphai[j]/beta[j];
    239: #endif
    240:     pos = 0;
    241:     for (j=1;j<n;j++) {
    242:       if (beta[j]==0.0) re2 = (PetscRealPart(alpha[j])>0.0)? PETSC_MAX_REAL: PETSC_MIN_REAL;
    243:       else re2 = alpha[j]/beta[j];
    244: #if !defined(PETSC_USE_COMPLEX)
    245:       if (beta[j]==0.0) im2 = (alphai[j]>0.0)? PETSC_MAX_REAL: PETSC_MIN_REAL;
    246:       else im2 = alphai[j]/beta[j];
    247:       SlepcCompareSmallestMagnitude(re,im,re2,im2,&result,NULL);
    248: #else
    249:       SlepcCompareSmallestMagnitude(re,0.0,re2,0.0,&result,NULL);
    250: #endif
    251:       if (result > 0) {
    252:         re = re2;
    253: #if !defined(PETSC_USE_COMPLEX)
    254:         im = im2;
    255: #endif
    256:         pos = j;
    257:       }
    258:     }

    260: #if !defined(PETSC_USE_COMPLEX)
    261:     if (im!=0.0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"DSNEP found a complex eigenvalue; try rerunning with complex scalars");
    262: #endif
    263:     mu = alpha[pos]/beta[pos];
    264:     PetscArraycpy(X,W+pos*ld,n);
    265:     norm = BLASnrm2_(&n,X,&one);
    266:     norm = 1.0/norm;
    267:     PetscStackCallBLAS("BLASscal",BLASscal_(&n,&norm,X,&one));

    269:     /* correct eigenvalue approximation */
    270:     lambda = lambda - mu;
    271:   }

    273:   if (it==maxit) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_CONV_FAILED,"DSNEP did not converge");
    274:   ctx->neig = 1;
    275:   wr[0] = lambda;
    276:   if (wi) wi[0] = 0.0;
    277:   return(0);
    278: #endif
    279: }

    281: PetscErrorCode DSSynchronize_NEP(DS ds,PetscScalar eigr[],PetscScalar eigi[])
    282: {
    284:   PetscInt       k=0;
    285:   PetscMPIInt    n,rank,size,off=0;

    288:   if (ds->state>=DS_STATE_CONDENSED) k += ds->n;
    289:   if (eigr) k += 1;
    290:   if (eigi) k += 1;
    291:   DSAllocateWork_Private(ds,k,0,0);
    292:   PetscMPIIntCast(k*sizeof(PetscScalar),&size);
    293:   PetscMPIIntCast(ds->n,&n);
    294:   MPI_Comm_rank(PetscObjectComm((PetscObject)ds),&rank);
    295:   if (!rank) {
    296:     if (ds->state>=DS_STATE_CONDENSED) {
    297:       MPI_Pack(ds->mat[DS_MAT_X],n,MPIU_SCALAR,ds->work,size,&off,PetscObjectComm((PetscObject)ds));
    298:     }
    299:     if (eigr) {
    300:       MPI_Pack(eigr,1,MPIU_SCALAR,ds->work,size,&off,PetscObjectComm((PetscObject)ds));
    301:     }
    302:     if (eigi) {
    303:       MPI_Pack(eigi,1,MPIU_SCALAR,ds->work,size,&off,PetscObjectComm((PetscObject)ds));
    304:     }
    305:   }
    306:   MPI_Bcast(ds->work,size,MPI_BYTE,0,PetscObjectComm((PetscObject)ds));
    307:   if (rank) {
    308:     if (ds->state>=DS_STATE_CONDENSED) {
    309:       MPI_Unpack(ds->work,size,&off,ds->mat[DS_MAT_X],n,MPIU_SCALAR,PetscObjectComm((PetscObject)ds));
    310:     }
    311:     if (eigr) {
    312:       MPI_Unpack(ds->work,size,&off,eigr,1,MPIU_SCALAR,PetscObjectComm((PetscObject)ds));
    313:     }
    314:     if (eigi) {
    315:       MPI_Unpack(ds->work,size,&off,eigi,1,MPIU_SCALAR,PetscObjectComm((PetscObject)ds));
    316:     }
    317:   }
    318:   return(0);
    319: }

    321: static PetscErrorCode DSNEPSetFN_NEP(DS ds,PetscInt n,FN fn[])
    322: {
    324:   DS_NEP         *ctx = (DS_NEP*)ds->data;
    325:   PetscInt       i;

    328:   if (n<=0) SETERRQ1(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_OUTOFRANGE,"Must have one or more functions, you have %D",n);
    329:   if (n>DS_NUM_EXTRA) SETERRQ2(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_OUTOFRANGE,"Too many functions, you specified %D but the limit is %D",n,DS_NUM_EXTRA);
    330:   if (ds->ld) { PetscInfo(ds,"DSNEPSetFN() called after DSAllocate()\n"); }
    331:   for (i=0;i<n;i++) {
    332:     PetscObjectReference((PetscObject)fn[i]);
    333:   }
    334:   for (i=0;i<ctx->nf;i++) {
    335:     FNDestroy(&ctx->f[i]);
    336:   }
    337:   for (i=0;i<n;i++) ctx->f[i] = fn[i];
    338:   ctx->nf = n;
    339:   return(0);
    340: }

    342: /*@
    343:    DSNEPSetFN - Sets a number of functions that define the nonlinear
    344:    eigenproblem.

    346:    Collective on ds

    348:    Input Parameters:
    349: +  ds - the direct solver context
    350: .  n  - number of functions
    351: -  fn - array of functions

    353:    Notes:
    354:    The nonlinear eigenproblem is defined in terms of the split nonlinear
    355:    operator T(lambda) = sum_i A_i*f_i(lambda).

    357:    This function must be called before DSAllocate(). Then DSAllocate()
    358:    will allocate an extra matrix A_i per each function, that can be
    359:    filled in the usual way.

    361:    Level: advanced

    363: .seealso: DSNEPGetFN(), DSAllocate()
    364:  @*/
    365: PetscErrorCode DSNEPSetFN(DS ds,PetscInt n,FN fn[])
    366: {
    367:   PetscInt       i;

    374:   for (i=0;i<n;i++) {
    377:   }
    378:   PetscTryMethod(ds,"DSNEPSetFN_C",(DS,PetscInt,FN[]),(ds,n,fn));
    379:   return(0);
    380: }

    382: static PetscErrorCode DSNEPGetFN_NEP(DS ds,PetscInt k,FN *fn)
    383: {
    384:   DS_NEP *ctx = (DS_NEP*)ds->data;

    387:   if (k<0 || k>=ctx->nf) SETERRQ1(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_OUTOFRANGE,"k must be between 0 and %D",ctx->nf-1);
    388:   *fn = ctx->f[k];
    389:   return(0);
    390: }

    392: /*@
    393:    DSNEPGetFN - Gets the functions associated with the nonlinear DS.

    395:    Not collective, though parallel FNs are returned if the DS is parallel

    397:    Input Parameter:
    398: +  ds - the direct solver context
    399: -  k  - the index of the requested function (starting in 0)

    401:    Output Parameter:
    402: .  fn - the function

    404:    Level: advanced

    406: .seealso: DSNEPSetFN()
    407: @*/
    408: PetscErrorCode DSNEPGetFN(DS ds,PetscInt k,FN *fn)
    409: {

    415:   PetscUseMethod(ds,"DSNEPGetFN_C",(DS,PetscInt,FN*),(ds,k,fn));
    416:   return(0);
    417: }

    419: static PetscErrorCode DSNEPGetNumFN_NEP(DS ds,PetscInt *n)
    420: {
    421:   DS_NEP *ctx = (DS_NEP*)ds->data;

    424:   *n = ctx->nf;
    425:   return(0);
    426: }

    428: /*@
    429:    DSNEPGetNumFN - Returns the number of functions stored internally by
    430:    the DS.

    432:    Not collective

    434:    Input Parameter:
    435: .  ds - the direct solver context

    437:    Output Parameters:
    438: .  n - the number of functions passed in DSNEPSetFN()

    440:    Level: advanced

    442: .seealso: DSNEPSetFN()
    443: @*/
    444: PetscErrorCode DSNEPGetNumFN(DS ds,PetscInt *n)
    445: {

    451:   PetscUseMethod(ds,"DSNEPGetNumFN_C",(DS,PetscInt*),(ds,n));
    452:   return(0);
    453: }

    455: static PetscErrorCode DSNEPSetComputeMatrixFunction_NEP(DS ds,PetscErrorCode (*fun)(DS,PetscScalar,PetscBool,DSMatType,void*),void *ctx)
    456: {
    457:   DS_NEP *dsctx = (DS_NEP*)ds->data;

    460:   dsctx->computematrix    = fun;
    461:   dsctx->computematrixctx = ctx;
    462:   return(0);
    463: }

    465: /*@
    466:    DSNEPSetComputeMatrixFunction - Sets a user-provided subroutine to compute
    467:    the matrices T(lambda) or T'(lambda).

    469:    Logically Collective on ds

    471:    Input Parameters:
    472: +  ds  - the direct solver context
    473: .  fun - a pointer to the user function
    474: -  ctx - a context pointer (the last parameter to the user function)

    476:    Calling Sequence of fun:
    477: $   fun(DS ds,PetscScalar lambda,PetscBool deriv,DSMatType mat,void *ctx)

    479: +   ds     - the direct solver object
    480: .   lambda - point where T(lambda) or T'(lambda) must be evaluated
    481: .   deriv  - if true compute T'(lambda), otherwise compute T(lambda)
    482: .   mat    - the DS matrix where the result must be stored
    483: -   ctx    - optional context, as set by DSNEPSetComputeMatrixFunction()

    485:    Note:
    486:    The result is computed as T(lambda) = sum_i E_i*f_i(lambda), and similarly
    487:    for the derivative.

    489:    Level: developer
    490: @*/
    491: PetscErrorCode DSNEPSetComputeMatrixFunction(DS ds,PetscErrorCode (*fun)(DS,PetscScalar,PetscBool,DSMatType,void*),void *ctx)
    492: {

    497:   PetscTryMethod(ds,"DSNEPSetComputeMatrixFunction_C",(DS,PetscErrorCode (*)(DS,PetscScalar,PetscBool,DSMatType,void*),void*),(ds,fun,ctx));
    498:   return(0);
    499: }

    501: PetscErrorCode DSDestroy_NEP(DS ds)
    502: {
    504:   DS_NEP         *ctx = (DS_NEP*)ds->data;
    505:   PetscInt       i;

    508:   for (i=0;i<ctx->nf;i++) {
    509:     FNDestroy(&ctx->f[i]);
    510:   }
    511:   PetscFree(ds->data);
    512:   PetscObjectComposeFunction((PetscObject)ds,"DSNEPSetFN_C",NULL);
    513:   PetscObjectComposeFunction((PetscObject)ds,"DSNEPGetFN_C",NULL);
    514:   PetscObjectComposeFunction((PetscObject)ds,"DSNEPGetNumFN_C",NULL);
    515:   PetscObjectComposeFunction((PetscObject)ds,"DSNEPSetComputeMatrixFunction_C",NULL);
    516:   return(0);
    517: }

    519: SLEPC_EXTERN PetscErrorCode DSCreate_NEP(DS ds)
    520: {
    521:   DS_NEP         *ctx;

    525:   PetscNewLog(ds,&ctx);
    526:   ds->data = (void*)ctx;

    528:   ds->ops->allocate      = DSAllocate_NEP;
    529:   ds->ops->view          = DSView_NEP;
    530:   ds->ops->vectors       = DSVectors_NEP;
    531:   ds->ops->solve[0]      = DSSolve_NEP_SLP;
    532:   ds->ops->sort          = DSSort_NEP;
    533:   ds->ops->synchronize   = DSSynchronize_NEP;
    534:   ds->ops->destroy       = DSDestroy_NEP;
    535:   PetscObjectComposeFunction((PetscObject)ds,"DSNEPSetFN_C",DSNEPSetFN_NEP);
    536:   PetscObjectComposeFunction((PetscObject)ds,"DSNEPGetFN_C",DSNEPGetFN_NEP);
    537:   PetscObjectComposeFunction((PetscObject)ds,"DSNEPGetNumFN_C",DSNEPGetNumFN_NEP);
    538:   PetscObjectComposeFunction((PetscObject)ds,"DSNEPSetComputeMatrixFunction_C",DSNEPSetComputeMatrixFunction_NEP);
    539:   return(0);
    540: }