Date: 2021-02-01T14:41:43+00:00

Actual source code: dspriv.c
slepc-3.14.2 2021-02-01
Report Typos and Errors

      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:    Private DS routines
     12: */

     14: #include <slepc/private/dsimpl.h>
     15: #include <slepcblaslapack.h>

     17: PetscErrorCode DSAllocateMatrix_Private(DS ds,DSMatType m,PetscBool isreal)
     18: {
     19:   size_t         sz;
     20:   PetscInt       n,d,nelem;
     21:   PetscBool      ispep;

     25:   PetscObjectTypeCompare((PetscObject)ds,DSPEP,&ispep);
     26:   if (ispep) {
     27:     DSPEPGetDegree(ds,&d);
     28:   }
     29:   if (ispep && (m==DS_MAT_A || m==DS_MAT_B || m==DS_MAT_W || m==DS_MAT_U || m==DS_MAT_X || m==DS_MAT_Y)) n = d*ds->ld;
     30:   else n = ds->ld;
     31:   switch (m) {
     32:     case DS_MAT_T:
     33:       nelem = 3*ds->ld;
     34:       break;
     35:     case DS_MAT_D:
     36:       nelem = ds->ld;
     37:       break;
     38:     case DS_MAT_X:
     39:       nelem = ds->ld*n;
     40:       break;
     41:     case DS_MAT_Y:
     42:       nelem = ds->ld*n;
     43:       break;
     44:     default:
     45:       nelem = n*n;
     46:   }
     47:   if (isreal) {
     48:     sz = nelem*sizeof(PetscReal);
     49:     if (ds->rmat[m]) {
     50:       PetscFree(ds->rmat[m]);
     51:     } else {
     52:       PetscLogObjectMemory((PetscObject)ds,sz);
     53:     }
     54:     PetscCalloc1(nelem,&ds->rmat[m]);
     55:   } else {
     56:     sz = nelem*sizeof(PetscScalar);
     57:     if (ds->mat[m]) {
     58:       PetscFree(ds->mat[m]);
     59:     } else {
     60:       PetscLogObjectMemory((PetscObject)ds,sz);
     61:     }
     62:     PetscCalloc1(nelem,&ds->mat[m]);
     63:   }
     64:   return(0);
     65: }

     67: PetscErrorCode DSAllocateWork_Private(DS ds,PetscInt s,PetscInt r,PetscInt i)
     68: {

     72:   if (s>ds->lwork) {
     73:     PetscFree(ds->work);
     74:     PetscMalloc1(s,&ds->work);
     75:     PetscLogObjectMemory((PetscObject)ds,(s-ds->lwork)*sizeof(PetscScalar));
     76:     ds->lwork = s;
     77:   }
     78:   if (r>ds->lrwork) {
     79:     PetscFree(ds->rwork);
     80:     PetscMalloc1(r,&ds->rwork);
     81:     PetscLogObjectMemory((PetscObject)ds,(r-ds->lrwork)*sizeof(PetscReal));
     82:     ds->lrwork = r;
     83:   }
     84:   if (i>ds->liwork) {
     85:     PetscFree(ds->iwork);
     86:     PetscMalloc1(i,&ds->iwork);
     87:     PetscLogObjectMemory((PetscObject)ds,(i-ds->liwork)*sizeof(PetscBLASInt));
     88:     ds->liwork = i;
     89:   }
     90:   return(0);
     91: }

     93: /*@C
     94:    DSViewMat - Prints one of the internal DS matrices.

     96:    Collective on ds

     98:    Input Parameters:
     99: +  ds     - the direct solver context
    100: .  viewer - visualization context
    101: -  m      - matrix to display

    103:    Note:
    104:    Works only for ascii viewers. Set the viewer in Matlab format if
    105:    want to paste into Matlab.

    107:    Level: developer

    109: .seealso: DSView()
    110: @*/
    111: PetscErrorCode DSViewMat(DS ds,PetscViewer viewer,DSMatType m)
    112: {
    113:   PetscErrorCode    ierr;
    114:   PetscInt          i,j,rows,cols;
    115:   PetscScalar       *v;
    116:   PetscViewerFormat format;
    117: #if defined(PETSC_USE_COMPLEX)
    118:   PetscBool         allreal = PETSC_TRUE;
    119: #endif

    124:   DSCheckValidMat(ds,m,3);
    125:   if (!viewer) {
    126:     PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)ds),&viewer);
    127:   }
    130:   PetscViewerGetFormat(viewer,&format);
    131:   if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) return(0);
    132:   PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    133:   DSMatGetSize(ds,m,&rows,&cols);
    134: #if defined(PETSC_USE_COMPLEX)
    135:   /* determine if matrix has all real values */
    136:   v = ds->mat[m];
    137:   for (i=0;i<rows;i++)
    138:     for (j=0;j<cols;j++)
    139:       if (PetscImaginaryPart(v[i+j*ds->ld])) { allreal = PETSC_FALSE; break; }
    140: #endif
    141:   if (format == PETSC_VIEWER_ASCII_MATLAB) {
    142:     PetscViewerASCIIPrintf(viewer,"%% Size = %D %D\n",rows,cols);
    143:     PetscViewerASCIIPrintf(viewer,"%s = [\n",DSMatName[m]);
    144:   } else {
    145:     PetscViewerASCIIPrintf(viewer,"Matrix %s =\n",DSMatName[m]);
    146:   }

    148:   for (i=0;i<rows;i++) {
    149:     v = ds->mat[m]+i;
    150:     for (j=0;j<cols;j++) {
    151: #if defined(PETSC_USE_COMPLEX)
    152:       if (allreal) {
    153:         PetscViewerASCIIPrintf(viewer,"%18.16e ",(double)PetscRealPart(*v));
    154:       } else {
    155:         PetscViewerASCIIPrintf(viewer,"%18.16e%+18.16ei ",(double)PetscRealPart(*v),(double)PetscImaginaryPart(*v));
    156:       }
    157: #else
    158:       PetscViewerASCIIPrintf(viewer,"%18.16e ",(double)*v);
    159: #endif
    160:       v += ds->ld;
    161:     }
    162:     PetscViewerASCIIPrintf(viewer,"\n");
    163:   }

    165:   if (format == PETSC_VIEWER_ASCII_MATLAB) {
    166:     PetscViewerASCIIPrintf(viewer,"];\n");
    167:   }
    168:   PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    169:   PetscViewerFlush(viewer);
    170:   return(0);
    171: }

    173: PetscErrorCode DSSortEigenvalues_Private(DS ds,PetscScalar *wr,PetscScalar *wi,PetscInt *perm,PetscBool isghiep)
    174: {
    176:   PetscScalar    re,im,wi0;
    177:   PetscInt       n,i,j,result,tmp1,tmp2=0,d=1;

    180:   n = ds->t;   /* sort only first t pairs if truncated */
    181:   /* insertion sort */
    182:   i=ds->l+1;
    183: #if !defined(PETSC_USE_COMPLEX)
    184:   if (wi && wi[perm[i-1]]!=0.0) i++; /* initial value is complex */
    185: #else
    186:   if (isghiep && PetscImaginaryPart(wr[perm[i-1]])!=0.0) i++;
    187: #endif
    188:   for (;i<n;i+=d) {
    189:     re = wr[perm[i]];
    190:     if (wi) im = wi[perm[i]];
    191:     else im = 0.0;
    192:     tmp1 = perm[i];
    193: #if !defined(PETSC_USE_COMPLEX)
    194:     if (im!=0.0) { d = 2; tmp2 = perm[i+1]; }
    195:     else d = 1;
    196: #else
    197:     if (isghiep && PetscImaginaryPart(re)!=0.0) { d = 2; tmp2 = perm[i+1]; }
    198:     else d = 1;
    199: #endif
    200:     j = i-1;
    201:     if (wi) wi0 = wi[perm[j]];
    202:     else wi0 = 0.0;
    203:     SlepcSCCompare(ds->sc,re,im,wr[perm[j]],wi0,&result);
    204:     while (result<0 && j>=ds->l) {
    205:       perm[j+d] = perm[j];
    206:       j--;
    207: #if !defined(PETSC_USE_COMPLEX)
    208:       if (wi && wi[perm[j+1]]!=0)
    209: #else
    210:       if (isghiep && PetscImaginaryPart(wr[perm[j+1]])!=0)
    211: #endif
    212:         { perm[j+d] = perm[j]; j--; }

    214:       if (j>=ds->l) {
    215:         if (wi) wi0 = wi[perm[j]];
    216:         else wi0 = 0.0;
    217:         SlepcSCCompare(ds->sc,re,im,wr[perm[j]],wi0,&result);
    218:       }
    219:     }
    220:     perm[j+1] = tmp1;
    221:     if (d==2) perm[j+2] = tmp2;
    222:   }
    223:   return(0);
    224: }

    226: PetscErrorCode DSSortEigenvaluesReal_Private(DS ds,PetscReal *eig,PetscInt *perm)
    227: {
    229:   PetscScalar    re;
    230:   PetscInt       i,j,result,tmp,l,n;

    233:   n = ds->t;   /* sort only first t pairs if truncated */
    234:   l = ds->l;
    235:   /* insertion sort */
    236:   for (i=l+1;i<n;i++) {
    237:     re = eig[perm[i]];
    238:     j = i-1;
    239:     SlepcSCCompare(ds->sc,re,0.0,eig[perm[j]],0.0,&result);
    240:     while (result<0 && j>=l) {
    241:       tmp = perm[j]; perm[j] = perm[j+1]; perm[j+1] = tmp; j--;
    242:       if (j>=l) {
    243:         SlepcSCCompare(ds->sc,re,0.0,eig[perm[j]],0.0,&result);
    244:       }
    245:     }
    246:   }
    247:   return(0);
    248: }

    250: /*
    251:   DSCopyMatrix_Private - Copies the trailing block of a matrix (from
    252:   rows/columns l to n).
    253: */
    254: PetscErrorCode DSCopyMatrix_Private(DS ds,DSMatType dst,DSMatType src)
    255: {
    257:   PetscInt    j,m,off,ld;
    258:   PetscScalar *S,*D;

    261:   ld  = ds->ld;
    262:   m   = ds->n-ds->l;
    263:   off = ds->l+ds->l*ld;
    264:   S   = ds->mat[src];
    265:   D   = ds->mat[dst];
    266:   for (j=0;j<m;j++) {
    267:     PetscArraycpy(D+off+j*ld,S+off+j*ld,m);
    268:   }
    269:   return(0);
    270: }

    272: PetscErrorCode DSPermuteColumns_Private(DS ds,PetscInt l,PetscInt n,DSMatType mat,PetscInt *perm)
    273: {
    274:   PetscInt    i,j,k,p,ld;
    275:   PetscScalar *Q,rtmp;

    278:   ld = ds->ld;
    279:   Q  = ds->mat[mat];
    280:   for (i=l;i<n;i++) {
    281:     p = perm[i];
    282:     if (p != i) {
    283:       j = i + 1;
    284:       while (perm[j] != i) j++;
    285:       perm[j] = p; perm[i] = i;
    286:       /* swap columns i and j */
    287:       for (k=0;k<n;k++) {
    288:         rtmp = Q[k+p*ld]; Q[k+p*ld] = Q[k+i*ld]; Q[k+i*ld] = rtmp;
    289:       }
    290:     }
    291:   }
    292:   return(0);
    293: }

    295: PetscErrorCode DSPermuteRows_Private(DS ds,PetscInt l,PetscInt n,DSMatType mat,PetscInt *perm)
    296: {
    297:   PetscInt    i,j,m=ds->m,k,p,ld;
    298:   PetscScalar *Q,rtmp;

    301:   if (!m) SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_WRONG,"m was not set");
    302:   ld = ds->ld;
    303:   Q  = ds->mat[mat];
    304:   for (i=l;i<n;i++) {
    305:     p = perm[i];
    306:     if (p != i) {
    307:       j = i + 1;
    308:       while (perm[j] != i) j++;
    309:       perm[j] = p; perm[i] = i;
    310:       /* swap rows i and j */
    311:       for (k=0;k<m;k++) {
    312:         rtmp = Q[p+k*ld]; Q[p+k*ld] = Q[i+k*ld]; Q[i+k*ld] = rtmp;
    313:       }
    314:     }
    315:   }
    316:   return(0);
    317: }

    319: PetscErrorCode DSPermuteBoth_Private(DS ds,PetscInt l,PetscInt n,DSMatType mat1,DSMatType mat2,PetscInt *perm)
    320: {
    321:   PetscInt    i,j,m=ds->m,k,p,ld;
    322:   PetscScalar *U,*VT,rtmp;

    325:   if (!m) SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_WRONG,"m was not set");
    326:   ld = ds->ld;
    327:   U  = ds->mat[mat1];
    328:   VT = ds->mat[mat2];
    329:   for (i=l;i<n;i++) {
    330:     p = perm[i];
    331:     if (p != i) {
    332:       j = i + 1;
    333:       while (perm[j] != i) j++;
    334:       perm[j] = p; perm[i] = i;
    335:       /* swap columns i and j of U */
    336:       for (k=0;k<n;k++) {
    337:         rtmp = U[k+p*ld]; U[k+p*ld] = U[k+i*ld]; U[k+i*ld] = rtmp;
    338:       }
    339:       /* swap rows i and j of VT */
    340:       for (k=0;k<m;k++) {
    341:         rtmp = VT[p+k*ld]; VT[p+k*ld] = VT[i+k*ld]; VT[i+k*ld] = rtmp;
    342:       }
    343:     }
    344:   }
    345:   return(0);
    346: }

    348: /*@
    349:    DSSetIdentity - Copy the identity (a diagonal matrix with ones) on the
    350:    active part of a matrix.

    352:    Logically Collective on ds

    354:    Input Parameters:
    355: +  ds  - the direct solver context
    356: -  mat - the matrix to modify

    358:    Level: intermediate
    359: @*/
    360: PetscErrorCode DSSetIdentity(DS ds,DSMatType mat)
    361: {
    363:   PetscScalar    *x;
    364:   PetscInt       i,ld,n,l;

    369:   DSCheckValidMat(ds,mat,2);

    371:   DSGetDimensions(ds,&n,NULL,&l,NULL,NULL);
    372:   DSGetLeadingDimension(ds,&ld);
    373:   PetscLogEventBegin(DS_Other,ds,0,0,0);
    374:   DSGetArray(ds,mat,&x);
    375:   PetscArrayzero(&x[ld*l],ld*(n-l));
    376:   for (i=l;i<n;i++) x[i+i*ld] = 1.0;
    377:   DSRestoreArray(ds,mat,&x);
    378:   PetscLogEventEnd(DS_Other,ds,0,0,0);
    379:   return(0);
    380: }

    382: /*@C
    383:    DSOrthogonalize - Orthogonalize the columns of a matrix.

    385:    Logically Collective on ds

    387:    Input Parameters:
    388: +  ds   - the direct solver context
    389: .  mat  - a matrix
    390: -  cols - number of columns to orthogonalize (starting from column zero)

    392:    Output Parameter:
    393: .  lindcols - (optional) number of linearly independent columns of the matrix

    395:    Level: developer

    397: .seealso: DSPseudoOrthogonalize()
    398: @*/
    399: PetscErrorCode DSOrthogonalize(DS ds,DSMatType mat,PetscInt cols,PetscInt *lindcols)
    400: {
    402:   PetscInt       n,l,ld;
    403:   PetscBLASInt   ld_,rA,cA,info,ltau,lw;
    404:   PetscScalar    *A,*tau,*w,saux,dummy;

    408:   DSCheckAlloc(ds,1);
    410:   DSCheckValidMat(ds,mat,2);

    413:   DSGetDimensions(ds,&n,NULL,&l,NULL,NULL);
    414:   DSGetLeadingDimension(ds,&ld);
    415:   n = n - l;
    416:   if (cols > n) SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_WRONG,"Invalid number of columns");
    417:   if (n == 0 || cols == 0) return(0);

    419:   PetscLogEventBegin(DS_Other,ds,0,0,0);
    420:   PetscFPTrapPush(PETSC_FP_TRAP_OFF);
    421:   DSGetArray(ds,mat,&A);
    422:   PetscBLASIntCast(PetscMin(cols,n),&ltau);
    423:   PetscBLASIntCast(ld,&ld_);
    424:   PetscBLASIntCast(n,&rA);
    425:   PetscBLASIntCast(cols,&cA);
    426:   lw = -1;
    427:   PetscStackCallBLAS("LAPACKgeqrf",LAPACKgeqrf_(&rA,&cA,A,&ld_,&dummy,&saux,&lw,&info));
    428:   SlepcCheckLapackInfo("geqrf",info);
    429:   lw = (PetscBLASInt)PetscRealPart(saux);
    430:   DSAllocateWork_Private(ds,lw+ltau,0,0);
    431:   tau = ds->work;
    432:   w = &tau[ltau];
    433:   PetscStackCallBLAS("LAPACKgeqrf",LAPACKgeqrf_(&rA,&cA,&A[ld*l+l],&ld_,tau,w,&lw,&info));
    434:   SlepcCheckLapackInfo("geqrf",info);
    435:   PetscStackCallBLAS("LAPACKorgqr",LAPACKorgqr_(&rA,&ltau,&ltau,&A[ld*l+l],&ld_,tau,w,&lw,&info));
    436:   SlepcCheckLapackInfo("orgqr",info);
    437:   if (lindcols) *lindcols = ltau;

    439:   PetscFPTrapPop();
    440:   PetscLogEventEnd(DS_Other,ds,0,0,0);
    441:   DSRestoreArray(ds,mat,&A);
    442:   PetscObjectStateIncrease((PetscObject)ds);
    443:   return(0);
    444: }

    446: /*
    447:   Compute C <- a*A*B + b*C, where
    448:     ldC, the leading dimension of C,
    449:     ldA, the leading dimension of A,
    450:     rA, cA, rows and columns of A,
    451:     At, if true use the transpose of A instead,
    452:     ldB, the leading dimension of B,
    453:     rB, cB, rows and columns of B,
    454:     Bt, if true use the transpose of B instead
    455: */
    456: static PetscErrorCode SlepcMatDenseMult(PetscScalar *C,PetscInt _ldC,PetscScalar b,PetscScalar a,const PetscScalar *A,PetscInt _ldA,PetscInt rA,PetscInt cA,PetscBool At,const PetscScalar *B,PetscInt _ldB,PetscInt rB,PetscInt cB,PetscBool Bt)
    457: {
    459:   PetscInt       tmp;
    460:   PetscBLASInt   m, n, k, ldA = _ldA, ldB = _ldB, ldC = _ldC;
    461:   const char     *N = "N", *T = "C", *qA = N, *qB = N;

    464:   if ((rA == 0) || (cB == 0)) return(0);

    469:   /* Transpose if needed */
    470:   if (At) tmp = rA, rA = cA, cA = tmp, qA = T;
    471:   if (Bt) tmp = rB, rB = cB, cB = tmp, qB = T;

    473:   /* Check size */
    474:   if (cA != rB) SETERRQ(PETSC_COMM_SELF,1,"Matrix dimensions do not match");

    476:   /* Do stub */
    477:   if ((rA == 1) && (cA == 1) && (cB == 1)) {
    478:     if (!At && !Bt) *C = *A * *B;
    479:     else if (At && !Bt) *C = PetscConj(*A) * *B;
    480:     else if (!At && Bt) *C = *A * PetscConj(*B);
    481:     else *C = PetscConj(*A) * PetscConj(*B);
    482:     m = n = k = 1;
    483:   } else {
    484:     m = rA; n = cB; k = cA;
    485:     PetscStackCallBLAS("BLASgemm",BLASgemm_(qA,qB,&m,&n,&k,&a,(PetscScalar*)A,&ldA,(PetscScalar*)B,&ldB,&b,C,&ldC));
    486:   }

    488:   PetscLogFlops(2.0*m*n*k);
    489:   return(0);
    490: }

    492: /*@C
    493:    DSPseudoOrthogonalize - Orthogonalize the columns of a matrix with Modified
    494:    Gram-Schmidt in an indefinite inner product space defined by a signature.

    496:    Logically Collective on ds

    498:    Input Parameters:
    499: +  ds   - the direct solver context
    500: .  mat  - the matrix
    501: .  cols - number of columns to orthogonalize (starting from column zero)
    502: -  s    - the signature that defines the inner product

    504:    Output Parameters:
    505: +  lindcols - (optional) linearly independent columns of the matrix
    506: -  ns   - (optional) the new signature of the vectors

    508:    Note:
    509:    After the call the matrix satisfies A'*s*A = ns.

    511:    Level: developer

    513: .seealso: DSOrthogonalize()
    514: @*/
    515: PetscErrorCode DSPseudoOrthogonalize(DS ds,DSMatType mat,PetscInt cols,PetscReal *s,PetscInt *lindcols,PetscReal *ns)
    516: {
    518:   PetscInt       i,j,k,l,n,ld;
    519:   PetscBLASInt   one=1,rA_;
    520:   PetscScalar    alpha,*A,*A_,*m,*h,nr0;
    521:   PetscReal      nr_o,nr,*ns_;

    525:   DSCheckAlloc(ds,1);
    527:   DSCheckValidMat(ds,mat,2);
    530:   DSGetDimensions(ds,&n,NULL,&l,NULL,NULL);
    531:   DSGetLeadingDimension(ds,&ld);
    532:   n = n - l;
    533:   if (cols > n) SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_WRONG,"Invalid number of columns");
    534:   if (n == 0 || cols == 0) return(0);
    535:   PetscBLASIntCast(n,&rA_);
    536:   DSGetArray(ds,mat,&A_);
    537:   A = &A_[ld*l+l];
    538:   DSAllocateWork_Private(ds,n+cols,ns?0:cols,0);
    539:   m = ds->work;
    540:   h = &m[n];
    541:   ns_ = ns ? ns : ds->rwork;
    542:   PetscLogEventBegin(DS_Other,ds,0,0,0);
    543:   for (i=0; i<cols; i++) {
    544:     /* m <- diag(s)*A[i] */
    545:     for (k=0; k<n; k++) m[k] = s[k]*A[k+i*ld];
    546:     /* nr_o <- mynorm(A[i]'*m), mynorm(x) = sign(x)*sqrt(|x|) */
    547:     SlepcMatDenseMult(&nr0,1,0.0,1.0,&A[ld*i],ld,n,1,PETSC_TRUE,m,n,n,1,PETSC_FALSE);
    548:     nr = nr_o = PetscSign(PetscRealPart(nr0))*PetscSqrtReal(PetscAbsScalar(nr0));
    549:     for (j=0; j<3 && i>0; j++) {
    550:       /* h <- A[0:i-1]'*m */
    551:       SlepcMatDenseMult(h,i,0.0,1.0,A,ld,n,i,PETSC_TRUE,m,n,n,1,PETSC_FALSE);
    552:       /* h <- diag(ns)*h */
    553:       for (k=0; k<i; k++) h[k] *= ns_[k];
    554:       /* A[i] <- A[i] - A[0:i-1]*h */
    555:       SlepcMatDenseMult(&A[ld*i],ld,1.0,-1.0,A,ld,n,i,PETSC_FALSE,h,i,i,1,PETSC_FALSE);
    556:       /* m <- diag(s)*A[i] */
    557:       for (k=0; k<n; k++) m[k] = s[k]*A[k+i*ld];
    558:       /* nr_o <- mynorm(A[i]'*m) */
    559:       SlepcMatDenseMult(&nr0,1,0.0,1.0,&A[ld*i],ld,n,1,PETSC_TRUE,m,n,n,1,PETSC_FALSE);
    560:       nr = PetscSign(PetscRealPart(nr0))*PetscSqrtReal(PetscAbsScalar(nr0));
    561:       if (PetscAbs(nr) < PETSC_MACHINE_EPSILON) SETERRQ(PETSC_COMM_SELF,1,"Linear dependency detected");
    562:       if (PetscAbs(nr) > 0.7*PetscAbs(nr_o)) break;
    563:       nr_o = nr;
    564:     }
    565:     ns_[i] = PetscSign(nr);
    566:     /* A[i] <- A[i]/|nr| */
    567:     alpha = 1.0/PetscAbs(nr);
    568:     PetscStackCallBLAS("BLASscal",BLASscal_(&rA_,&alpha,&A[i*ld],&one));
    569:   }
    570:   PetscLogEventEnd(DS_Other,ds,0,0,0);
    571:   DSRestoreArray(ds,mat,&A_);
    572:   PetscObjectStateIncrease((PetscObject)ds);
    573:   if (lindcols) *lindcols = cols;
    574:   return(0);
    575: }