Date: 2022-10-01T06:39:16+00:00

Actual source code: dssvd.c
slepc-3.18.0 2022-10-01
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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 m;              /* number of columns */
     16:   PetscInt t;              /* number of rows of V after truncating */
     17: } DS_SVD;

     19: PetscErrorCode DSAllocate_SVD(DS ds,PetscInt ld)
     20: {
     21:   DSAllocateMat_Private(ds,DS_MAT_A);
     22:   DSAllocateMat_Private(ds,DS_MAT_U);
     23:   DSAllocateMat_Private(ds,DS_MAT_V);
     24:   DSAllocateMat_Private(ds,DS_MAT_T);
     25:   PetscFree(ds->perm);
     26:   PetscMalloc1(ld,&ds->perm);
     27:   return 0;
     28: }

     30: /*   0       l           k                 n-1
     31:     -----------------------------------------
     32:     |*       .           .                  |
     33:     |  *     .           .                  |
     34:     |    *   .           .                  |
     35:     |      * .           .                  |
     36:     |        o           o                  |
     37:     |          o         o                  |
     38:     |            o       o                  |
     39:     |              o     o                  |
     40:     |                o   o                  |
     41:     |                  o o                  |
     42:     |                    o x                |
     43:     |                      x x              |
     44:     |                        x x            |
     45:     |                          x x          |
     46:     |                            x x        |
     47:     |                              x x      |
     48:     |                                x x    |
     49:     |                                  x x  |
     50:     |                                    x x|
     51:     |                                      x|
     52:     -----------------------------------------
     53: */

     55: static PetscErrorCode DSSwitchFormat_SVD(DS ds)
     56: {
     57:   DS_SVD         *ctx = (DS_SVD*)ds->data;
     58:   PetscReal      *T;
     59:   PetscScalar    *A;
     60:   PetscInt       i,m=ctx->m,k=ds->k,ld=ds->ld;

     63:   /* switch from compact (arrow) to dense storage */
     64:   MatDenseGetArrayWrite(ds->omat[DS_MAT_A],&A);
     65:   DSGetArrayReal(ds,DS_MAT_T,&T);
     66:   PetscArrayzero(A,ld*ld);
     67:   for (i=0;i<k;i++) {
     68:     A[i+i*ld] = T[i];
     69:     A[i+k*ld] = T[i+ld];
     70:   }
     71:   A[k+k*ld] = T[k];
     72:   for (i=k+1;i<m;i++) {
     73:     A[i+i*ld]   = T[i];
     74:     A[i-1+i*ld] = T[i-1+ld];
     75:   }
     76:   MatDenseRestoreArrayWrite(ds->omat[DS_MAT_A],&A);
     77:   DSRestoreArrayReal(ds,DS_MAT_T,&T);
     78:   return 0;
     79: }

     81: PetscErrorCode DSView_SVD(DS ds,PetscViewer viewer)
     82: {
     83:   DS_SVD            *ctx = (DS_SVD*)ds->data;
     84:   PetscViewerFormat format;
     85:   PetscInt          i,j,r,c,m=ctx->m,rows,cols;
     86:   PetscReal         *T,value;

     88:   PetscViewerGetFormat(viewer,&format);
     89:   if (format == PETSC_VIEWER_ASCII_INFO) return 0;
     90:   if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
     91:     PetscViewerASCIIPrintf(viewer,"number of columns: %" PetscInt_FMT "\n",m);
     92:     return 0;
     93:   }
     95:   if (ds->compact) {
     96:     DSGetArrayReal(ds,DS_MAT_T,&T);
     97:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
     98:     rows = ds->n;
     99:     cols = ds->extrarow? m+1: m;
    100:     if (format == PETSC_VIEWER_ASCII_MATLAB) {
    101:       PetscViewerASCIIPrintf(viewer,"%% Size = %" PetscInt_FMT " %" PetscInt_FMT "\n",rows,cols);
    102:       PetscViewerASCIIPrintf(viewer,"zzz = zeros(%" PetscInt_FMT ",3);\n",2*ds->n);
    103:       PetscViewerASCIIPrintf(viewer,"zzz = [\n");
    104:       for (i=0;i<PetscMin(ds->n,m);i++) PetscViewerASCIIPrintf(viewer,"%" PetscInt_FMT " %" PetscInt_FMT "  %18.16e\n",i+1,i+1,(double)T[i]);
    105:       for (i=0;i<cols-1;i++) {
    106:         r = PetscMax(i+2,ds->k+1);
    107:         c = i+1;
    108:         PetscViewerASCIIPrintf(viewer,"%" PetscInt_FMT " %" PetscInt_FMT "  %18.16e\n",c,r,(double)T[i+ds->ld]);
    109:       }
    110:       PetscViewerASCIIPrintf(viewer,"];\n%s = spconvert(zzz);\n",DSMatName[DS_MAT_T]);
    111:     } else {
    112:       for (i=0;i<rows;i++) {
    113:         for (j=0;j<cols;j++) {
    114:           if (i==j) value = T[i];
    115:           else if (i<ds->k && j==ds->k) value = T[PetscMin(i,j)+ds->ld];
    116:           else if (i+1==j && i>=ds->k) value = T[i+ds->ld];
    117:           else value = 0.0;
    118:           PetscViewerASCIIPrintf(viewer," %18.16e ",(double)value);
    119:         }
    120:         PetscViewerASCIIPrintf(viewer,"\n");
    121:       }
    122:     }
    123:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    124:     PetscViewerFlush(viewer);
    125:     DSRestoreArrayReal(ds,DS_MAT_T,&T);
    126:   } else DSViewMat(ds,viewer,DS_MAT_A);
    127:   if (ds->state>DS_STATE_INTERMEDIATE) {
    128:     DSViewMat(ds,viewer,DS_MAT_U);
    129:     DSViewMat(ds,viewer,DS_MAT_V);
    130:   }
    131:   return 0;
    132: }

    134: PetscErrorCode DSVectors_SVD(DS ds,DSMatType mat,PetscInt *j,PetscReal *rnorm)
    135: {
    136:   switch (mat) {
    137:     case DS_MAT_U:
    138:     case DS_MAT_V:
    139:       if (rnorm) *rnorm = 0.0;
    140:       break;
    141:     default:
    142:       SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_OUTOFRANGE,"Invalid mat parameter");
    143:   }
    144:   return 0;
    145: }

    147: PetscErrorCode DSSort_SVD(DS ds,PetscScalar *wr,PetscScalar *wi,PetscScalar *rr,PetscScalar *ri,PetscInt *k)
    148: {
    149:   DS_SVD         *ctx = (DS_SVD*)ds->data;
    150:   PetscInt       n,l,i,*perm,ld=ds->ld;
    151:   PetscScalar    *A;
    152:   PetscReal      *d;

    154:   if (!ds->sc) return 0;
    156:   l = ds->l;
    157:   n = PetscMin(ds->n,ctx->m);
    158:   DSGetArrayReal(ds,DS_MAT_T,&d);
    159:   perm = ds->perm;
    160:   if (!rr) DSSortEigenvaluesReal_Private(ds,d,perm);
    161:   else DSSortEigenvalues_Private(ds,rr,ri,perm,PETSC_FALSE);
    162:   for (i=l;i<n;i++) wr[i] = d[perm[i]];
    163:   DSPermuteBoth_Private(ds,l,n,ds->n,ctx->m,DS_MAT_U,DS_MAT_V,perm);
    164:   for (i=l;i<n;i++) d[i] = PetscRealPart(wr[i]);
    165:   if (!ds->compact) {
    166:     MatDenseGetArray(ds->omat[DS_MAT_A],&A);
    167:     for (i=l;i<n;i++) A[i+i*ld] = wr[i];
    168:     MatDenseRestoreArray(ds->omat[DS_MAT_A],&A);
    169:   }
    170:   DSRestoreArrayReal(ds,DS_MAT_T,&d);
    171:   return 0;
    172: }

    174: PetscErrorCode DSUpdateExtraRow_SVD(DS ds)
    175: {
    176:   DS_SVD            *ctx = (DS_SVD*)ds->data;
    177:   PetscInt          i;
    178:   PetscBLASInt      n=0,m=0,ld,incx=1;
    179:   PetscScalar       *A,*x,*y,one=1.0,zero=0.0;
    180:   PetscReal         *T,*e,beta;
    181:   const PetscScalar *U;

    184:   PetscBLASIntCast(ds->n,&n);
    185:   PetscBLASIntCast(ctx->m,&m);
    186:   PetscBLASIntCast(ds->ld,&ld);
    187:   MatDenseGetArrayRead(ds->omat[DS_MAT_U],&U);
    188:   if (ds->compact) {
    189:     DSGetArrayReal(ds,DS_MAT_T,&T);
    190:     e = T+ld;
    191:     beta = e[m-1];   /* in compact, we assume all entries are zero except the last one */
    192:     for (i=0;i<n;i++) e[i] = PetscRealPart(beta*U[n-1+i*ld]);
    193:     ds->k = m;
    194:     DSRestoreArrayReal(ds,DS_MAT_T,&T);
    195:   } else {
    196:     MatDenseGetArray(ds->omat[DS_MAT_A],&A);
    197:     DSAllocateWork_Private(ds,2*ld,0,0);
    198:     x = ds->work;
    199:     y = ds->work+ld;
    200:     for (i=0;i<n;i++) x[i] = PetscConj(A[i+m*ld]);
    201:     PetscCallBLAS("BLASgemv",BLASgemv_("C",&n,&n,&one,U,&ld,x,&incx,&zero,y,&incx));
    202:     for (i=0;i<n;i++) A[i+m*ld] = PetscConj(y[i]);
    203:     ds->k = m;
    204:     MatDenseRestoreArray(ds->omat[DS_MAT_A],&A);
    205:   }
    206:   MatDenseRestoreArrayRead(ds->omat[DS_MAT_U],&U);
    207:   return 0;
    208: }

    210: PetscErrorCode DSTruncate_SVD(DS ds,PetscInt n,PetscBool trim)
    211: {
    212:   PetscInt    i,ld=ds->ld,l=ds->l;
    213:   PetscScalar *A;
    214:   DS_SVD      *ctx = (DS_SVD*)ds->data;

    216:   if (!ds->compact && ds->extrarow) MatDenseGetArray(ds->omat[DS_MAT_A],&A);
    217:   if (trim) {
    218:     if (!ds->compact && ds->extrarow) {   /* clean extra column */
    219:       for (i=l;i<ds->n;i++) A[i+ctx->m*ld] = 0.0;
    220:     }
    221:     ds->l  = 0;
    222:     ds->k  = 0;
    223:     ds->n  = n;
    224:     ctx->m = n;
    225:     ds->t  = ds->n;   /* truncated length equal to the new dimension */
    226:     ctx->t = ctx->m;  /* must also keep the previous dimension of V */
    227:   } else {
    228:     if (!ds->compact && ds->extrarow && ds->k==ds->n) {
    229:       /* copy entries of extra column to the new position, then clean last row */
    230:       for (i=l;i<n;i++) A[i+n*ld] = A[i+ctx->m*ld];
    231:       for (i=l;i<ds->n;i++) A[i+ctx->m*ld] = 0.0;
    232:     }
    233:     ds->k  = (ds->extrarow)? n: 0;
    234:     ds->t  = ds->n;   /* truncated length equal to previous dimension */
    235:     ctx->t = ctx->m;  /* must also keep the previous dimension of V */
    236:     ds->n  = n;
    237:     ctx->m = n;
    238:   }
    239:   if (!ds->compact && ds->extrarow) MatDenseRestoreArray(ds->omat[DS_MAT_A],&A);
    240:   return 0;
    241: }

    243: PetscErrorCode DSSolve_SVD_DC(DS ds,PetscScalar *wr,PetscScalar *wi)
    244: {
    245:   DS_SVD         *ctx = (DS_SVD*)ds->data;
    246:   PetscInt       i,j;
    247:   PetscBLASInt   n1,m1,info,l = 0,n = 0,m = 0,nm,ld,off,lwork;
    248:   PetscScalar    *A,*U,*V,*W,qwork;
    249:   PetscReal      *d,*e,*Ur,*Vr;

    252:   PetscBLASIntCast(ds->n,&n);
    253:   PetscBLASIntCast(ctx->m,&m);
    254:   PetscBLASIntCast(ds->l,&l);
    255:   PetscBLASIntCast(ds->ld,&ld);
    256:   n1 = n-l;     /* n1 = size of leading block, excl. locked + size of trailing block */
    257:   m1 = m-l;
    258:   off = l+l*ld;
    259:   MatDenseGetArray(ds->omat[DS_MAT_A],&A);
    260:   MatDenseGetArrayWrite(ds->omat[DS_MAT_U],&U);
    261:   MatDenseGetArrayWrite(ds->omat[DS_MAT_V],&V);
    262:   DSGetArrayReal(ds,DS_MAT_T,&d);
    263:   e = d+ld;
    264:   PetscArrayzero(U,ld*ld);
    265:   for (i=0;i<l;i++) U[i+i*ld] = 1.0;
    266:   PetscArrayzero(V,ld*ld);
    267:   for (i=0;i<l;i++) V[i+i*ld] = 1.0;

    269:   if (ds->state>DS_STATE_RAW) {
    270:     /* solve bidiagonal SVD problem */
    271:     for (i=0;i<l;i++) wr[i] = d[i];
    272: #if defined(PETSC_USE_COMPLEX)
    273:     DSAllocateWork_Private(ds,0,3*n1*n1+4*n1+2*ld*ld,8*n1);
    274:     Ur = ds->rwork+3*n1*n1+4*n1;
    275:     Vr = ds->rwork+3*n1*n1+4*n1+ld*ld;
    276: #else
    277:     DSAllocateWork_Private(ds,0,3*n1*n1+4*n1+ld*ld,8*n1);
    278:     Ur = U;
    279:     Vr = ds->rwork+3*n1*n1+4*n1;
    280: #endif
    281:     PetscCallBLAS("LAPACKbdsdc",LAPACKbdsdc_("U","I",&n1,d+l,e+l,Ur+off,&ld,Vr+off,&ld,NULL,NULL,ds->rwork,ds->iwork,&info));
    282:     SlepcCheckLapackInfo("bdsdc",info);
    283:     for (i=l;i<n;i++) {
    284:       for (j=l;j<n;j++) {
    285: #if defined(PETSC_USE_COMPLEX)
    286:         U[i+j*ld] = Ur[i+j*ld];
    287: #endif
    288:         V[i+j*ld] = PetscConj(Vr[j+i*ld]);  /* transpose VT returned by Lapack */
    289:       }
    290:     }
    291:   } else {
    292:     /* solve general rectangular SVD problem */
    293:     DSAllocateMat_Private(ds,DS_MAT_W);
    294:     MatDenseGetArrayWrite(ds->omat[DS_MAT_W],&W);
    295:     if (ds->compact) DSSwitchFormat_SVD(ds);
    296:     for (i=0;i<l;i++) wr[i] = d[i];
    297:     nm = PetscMin(n,m);
    298:     DSAllocateWork_Private(ds,0,0,8*nm);
    299:     lwork = -1;
    300: #if defined(PETSC_USE_COMPLEX)
    301:     DSAllocateWork_Private(ds,0,5*nm*nm+7*nm,0);
    302:     PetscCallBLAS("LAPACKgesdd",LAPACKgesdd_("A",&n1,&m1,A+off,&ld,d+l,U+off,&ld,W+off,&ld,&qwork,&lwork,ds->rwork,ds->iwork,&info));
    303: #else
    304:     PetscCallBLAS("LAPACKgesdd",LAPACKgesdd_("A",&n1,&m1,A+off,&ld,d+l,U+off,&ld,W+off,&ld,&qwork,&lwork,ds->iwork,&info));
    305: #endif
    306:     SlepcCheckLapackInfo("gesdd",info);
    307:     PetscBLASIntCast((PetscInt)PetscRealPart(qwork),&lwork);
    308:     DSAllocateWork_Private(ds,lwork,0,0);
    309: #if defined(PETSC_USE_COMPLEX)
    310:     PetscCallBLAS("LAPACKgesdd",LAPACKgesdd_("A",&n1,&m1,A+off,&ld,d+l,U+off,&ld,W+off,&ld,ds->work,&lwork,ds->rwork,ds->iwork,&info));
    311: #else
    312:     PetscCallBLAS("LAPACKgesdd",LAPACKgesdd_("A",&n1,&m1,A+off,&ld,d+l,U+off,&ld,W+off,&ld,ds->work,&lwork,ds->iwork,&info));
    313: #endif
    314:     SlepcCheckLapackInfo("gesdd",info);
    315:     for (i=l;i<m;i++) {
    316:       for (j=l;j<m;j++) V[i+j*ld] = PetscConj(W[j+i*ld]);  /* transpose VT returned by Lapack */
    317:     }
    318:     MatDenseRestoreArrayWrite(ds->omat[DS_MAT_W],&W);
    319:   }
    320:   for (i=l;i<PetscMin(ds->n,ctx->m);i++) wr[i] = d[i];

    322:   /* create diagonal matrix as a result */
    323:   if (ds->compact) PetscArrayzero(e,n-1);
    324:   else {
    325:     for (i=l;i<m;i++) PetscArrayzero(A+l+i*ld,n-l);
    326:     for (i=l;i<n;i++) A[i+i*ld] = d[i];
    327:   }
    328:   MatDenseRestoreArray(ds->omat[DS_MAT_A],&A);
    329:   MatDenseRestoreArrayWrite(ds->omat[DS_MAT_U],&U);
    330:   MatDenseRestoreArrayWrite(ds->omat[DS_MAT_V],&V);
    331:   DSRestoreArrayReal(ds,DS_MAT_T,&d);
    332:   return 0;
    333: }

    335: PetscErrorCode DSSynchronize_SVD(DS ds,PetscScalar eigr[],PetscScalar eigi[])
    336: {
    337:   PetscInt       ld=ds->ld,l=ds->l,k=0,kr=0;
    338:   PetscMPIInt    n,rank,off=0,size,ldn,ld3;
    339:   PetscScalar    *A,*U,*V;
    340:   PetscReal      *T;

    342:   if (ds->compact) kr = 3*ld;
    343:   else k = (ds->n-l)*ld;
    344:   if (ds->state>DS_STATE_RAW) k += 2*(ds->n-l)*ld;
    345:   if (eigr) k += ds->n-l;
    346:   DSAllocateWork_Private(ds,k+kr,0,0);
    347:   PetscMPIIntCast(k*sizeof(PetscScalar)+kr*sizeof(PetscReal),&size);
    348:   PetscMPIIntCast(ds->n-l,&n);
    349:   PetscMPIIntCast(ld*(ds->n-l),&ldn);
    350:   PetscMPIIntCast(3*ld,&ld3);
    351:   if (ds->compact) DSGetArrayReal(ds,DS_MAT_T,&T);
    352:   else MatDenseGetArray(ds->omat[DS_MAT_A],&A);
    353:   if (ds->state>DS_STATE_RAW) {
    354:     MatDenseGetArray(ds->omat[DS_MAT_U],&U);
    355:     MatDenseGetArray(ds->omat[DS_MAT_V],&V);
    356:   }
    357:   MPI_Comm_rank(PetscObjectComm((PetscObject)ds),&rank);
    358:   if (!rank) {
    359:     if (ds->compact) MPI_Pack(T,ld3,MPIU_REAL,ds->work,size,&off,PetscObjectComm((PetscObject)ds));
    360:     else MPI_Pack(A+l*ld,ldn,MPIU_SCALAR,ds->work,size,&off,PetscObjectComm((PetscObject)ds));
    361:     if (ds->state>DS_STATE_RAW) {
    362:       MPI_Pack(U+l*ld,ldn,MPIU_SCALAR,ds->work,size,&off,PetscObjectComm((PetscObject)ds));
    363:       MPI_Pack(V+l*ld,ldn,MPIU_SCALAR,ds->work,size,&off,PetscObjectComm((PetscObject)ds));
    364:     }
    365:     if (eigr) MPI_Pack(eigr+l,n,MPIU_SCALAR,ds->work,size,&off,PetscObjectComm((PetscObject)ds));
    366:   }
    367:   MPI_Bcast(ds->work,size,MPI_BYTE,0,PetscObjectComm((PetscObject)ds));
    368:   if (rank) {
    369:     if (ds->compact) MPI_Unpack(ds->work,size,&off,T,ld3,MPIU_REAL,PetscObjectComm((PetscObject)ds));
    370:     else MPI_Unpack(ds->work,size,&off,A+l*ld,ldn,MPIU_SCALAR,PetscObjectComm((PetscObject)ds));
    371:     if (ds->state>DS_STATE_RAW) {
    372:       MPI_Unpack(ds->work,size,&off,U+l*ld,ldn,MPIU_SCALAR,PetscObjectComm((PetscObject)ds));
    373:       MPI_Unpack(ds->work,size,&off,V+l*ld,ldn,MPIU_SCALAR,PetscObjectComm((PetscObject)ds));
    374:     }
    375:     if (eigr) MPI_Unpack(ds->work,size,&off,eigr+l,n,MPIU_SCALAR,PetscObjectComm((PetscObject)ds));
    376:   }
    377:   if (ds->compact) DSRestoreArrayReal(ds,DS_MAT_T,&T);
    378:   MatDenseRestoreArray(ds->omat[DS_MAT_A],&A);
    379:   if (ds->state>DS_STATE_RAW) {
    380:     MatDenseRestoreArray(ds->omat[DS_MAT_U],&U);
    381:     MatDenseRestoreArray(ds->omat[DS_MAT_V],&V);
    382:   }
    383:   return 0;
    384: }

    386: PetscErrorCode DSMatGetSize_SVD(DS ds,DSMatType t,PetscInt *rows,PetscInt *cols)
    387: {
    388:   DS_SVD *ctx = (DS_SVD*)ds->data;

    391:   switch (t) {
    392:     case DS_MAT_A:
    393:       *rows = ds->n;
    394:       *cols = ds->extrarow? ctx->m+1: ctx->m;
    395:       break;
    396:     case DS_MAT_T:
    397:       *rows = ds->n;
    398:       *cols = PetscDefined(USE_COMPLEX)? 2: 3;
    399:       break;
    400:     case DS_MAT_U:
    401:       *rows = ds->state==DS_STATE_TRUNCATED? ds->t: ds->n;
    402:       *cols = ds->n;
    403:       break;
    404:     case DS_MAT_V:
    405:       *rows = ds->state==DS_STATE_TRUNCATED? ctx->t: ctx->m;
    406:       *cols = ctx->m;
    407:       break;
    408:     default:
    409:       SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_OUTOFRANGE,"Invalid t parameter");
    410:   }
    411:   return 0;
    412: }

    414: static PetscErrorCode DSSVDSetDimensions_SVD(DS ds,PetscInt m)
    415: {
    416:   DS_SVD *ctx = (DS_SVD*)ds->data;

    418:   DSCheckAlloc(ds,1);
    419:   if (m==PETSC_DECIDE || m==PETSC_DEFAULT) {
    420:     ctx->m = ds->ld;
    421:   } else {
    423:     ctx->m = m;
    424:   }
    425:   return 0;
    426: }

    428: /*@
    429:    DSSVDSetDimensions - Sets the number of columns for a DSSVD.

    431:    Logically Collective on ds

    433:    Input Parameters:
    434: +  ds - the direct solver context
    435: -  m  - the number of columns

    437:    Notes:
    438:    This call is complementary to DSSetDimensions(), to provide a dimension
    439:    that is specific to this DS type.

    441:    Level: intermediate

    443: .seealso: DSSVDGetDimensions(), DSSetDimensions()
    444: @*/
    445: PetscErrorCode DSSVDSetDimensions(DS ds,PetscInt m)
    446: {
    449:   PetscTryMethod(ds,"DSSVDSetDimensions_C",(DS,PetscInt),(ds,m));
    450:   return 0;
    451: }

    453: static PetscErrorCode DSSVDGetDimensions_SVD(DS ds,PetscInt *m)
    454: {
    455:   DS_SVD *ctx = (DS_SVD*)ds->data;

    457:   *m = ctx->m;
    458:   return 0;
    459: }

    461: /*@
    462:    DSSVDGetDimensions - Returns the number of columns for a DSSVD.

    464:    Not collective

    466:    Input Parameter:
    467: .  ds - the direct solver context

    469:    Output Parameters:
    470: .  m - the number of columns

    472:    Level: intermediate

    474: .seealso: DSSVDSetDimensions()
    475: @*/
    476: PetscErrorCode DSSVDGetDimensions(DS ds,PetscInt *m)
    477: {
    480:   PetscUseMethod(ds,"DSSVDGetDimensions_C",(DS,PetscInt*),(ds,m));
    481:   return 0;
    482: }

    484: PetscErrorCode DSDestroy_SVD(DS ds)
    485: {
    486:   PetscFree(ds->data);
    487:   PetscObjectComposeFunction((PetscObject)ds,"DSSVDSetDimensions_C",NULL);
    488:   PetscObjectComposeFunction((PetscObject)ds,"DSSVDGetDimensions_C",NULL);
    489:   return 0;
    490: }

    492: /*MC
    493:    DSSVD - Dense Singular Value Decomposition.

    495:    Level: beginner

    497:    Notes:
    498:    The problem is expressed as A = U*Sigma*V', where A is rectangular in
    499:    general, with n rows and m columns. Sigma is a diagonal matrix whose diagonal
    500:    elements are the arguments of DSSolve(). After solve, A is overwritten
    501:    with Sigma.

    503:    The orthogonal (or unitary) matrices of left and right singular vectors, U
    504:    and V, have size n and m, respectively. The number of columns m must
    505:    be specified via DSSVDSetDimensions().

    507:    If the DS object is in the intermediate state, A is assumed to be in upper
    508:    bidiagonal form (possibly with an arrow) and is stored in compact format
    509:    on matrix T. Otherwise, no particular structure is assumed. The compact
    510:    storage is implemented for the square case only, m=n. The extra row should
    511:    be interpreted in this case as an extra column.

    513:    Used DS matrices:
    514: +  DS_MAT_A - problem matrix
    515: -  DS_MAT_T - upper bidiagonal matrix

    517:    Implemented methods:
    518: .  0 - Divide and Conquer (_bdsdc or _gesdd)

    520: .seealso: DSCreate(), DSSetType(), DSType, DSSVDSetDimensions()
    521: M*/
    522: SLEPC_EXTERN PetscErrorCode DSCreate_SVD(DS ds)
    523: {
    524:   DS_SVD         *ctx;

    526:   PetscNew(&ctx);
    527:   ds->data = (void*)ctx;

    529:   ds->ops->allocate      = DSAllocate_SVD;
    530:   ds->ops->view          = DSView_SVD;
    531:   ds->ops->vectors       = DSVectors_SVD;
    532:   ds->ops->solve[0]      = DSSolve_SVD_DC;
    533:   ds->ops->sort          = DSSort_SVD;
    534:   ds->ops->synchronize   = DSSynchronize_SVD;
    535:   ds->ops->truncate      = DSTruncate_SVD;
    536:   ds->ops->update        = DSUpdateExtraRow_SVD;
    537:   ds->ops->destroy       = DSDestroy_SVD;
    538:   ds->ops->matgetsize    = DSMatGetSize_SVD;
    539:   PetscObjectComposeFunction((PetscObject)ds,"DSSVDSetDimensions_C",DSSVDSetDimensions_SVD);
    540:   PetscObjectComposeFunction((PetscObject)ds,"DSSVDGetDimensions_C",DSSVDGetDimensions_SVD);
    541:   return 0;
    542: }