Date: 2016-03-29T14:58:41+00:00

Actual source code: svdlapack.c
slepc-3.6.3 2016-03-29
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      1: /*
      2:    This file implements a wrapper to the LAPACK SVD subroutines.

      4:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      5:    SLEPc - Scalable Library for Eigenvalue Problem Computations
      6:    Copyright (c) 2002-2015, Universitat Politecnica de Valencia, Spain

      8:    This file is part of SLEPc.

     10:    SLEPc is free software: you can redistribute it and/or modify it under  the
     11:    terms of version 3 of the GNU Lesser General Public License as published by
     12:    the Free Software Foundation.

     14:    SLEPc  is  distributed in the hope that it will be useful, but WITHOUT  ANY
     15:    WARRANTY;  without even the implied warranty of MERCHANTABILITY or  FITNESS
     16:    FOR  A  PARTICULAR PURPOSE. See the GNU Lesser General Public  License  for
     17:    more details.

     19:    You  should have received a copy of the GNU Lesser General  Public  License
     20:    along with SLEPc. If not, see <http://www.gnu.org/licenses/>.
     21:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
     22: */

     24: #include <slepc/private/svdimpl.h>

     28: PetscErrorCode SVDSetUp_LAPACK(SVD svd)
     29: {
     31:   PetscInt       M,N;

     34:   SVDMatGetSize(svd,&M,&N);
     35:   svd->ncv = N;
     36:   if (svd->mpd) { PetscInfo(svd,"Warning: parameter mpd ignored\n"); }
     37:   svd->max_it = 1;
     38:   svd->leftbasis = PETSC_TRUE;
     39:   SVDAllocateSolution(svd,0);
     40:   DSSetType(svd->ds,DSSVD);
     41:   DSAllocate(svd->ds,PetscMax(M,N));
     42:   return(0);
     43: }

     47: PetscErrorCode SVDSolve_LAPACK(SVD svd)
     48: {
     50:   PetscInt       M,N,n,i,j,k,ld;
     51:   Mat            mat;
     52:   Vec            u,v;
     53:   PetscScalar    *pU,*pVT,*pmat,*pu,*pv,*A,*w;

     56:   DSGetLeadingDimension(svd->ds,&ld);
     57:   MatConvert(svd->OP,MATSEQDENSE,MAT_INITIAL_MATRIX,&mat);
     58:   MatGetSize(mat,&M,&N);
     59:   DSSetDimensions(svd->ds,M,N,0,0);
     60:   MatDenseGetArray(mat,&pmat);
     61:   DSGetArray(svd->ds,DS_MAT_A,&A);
     62:   for (i=0;i<M;i++)
     63:     for (j=0;j<N;j++)
     64:       A[i+j*ld] = pmat[i+j*M];
     65:   DSRestoreArray(svd->ds,DS_MAT_A,&A);
     66:   MatDenseRestoreArray(mat,&pmat);
     67:   DSSetState(svd->ds,DS_STATE_RAW);

     69:   n = PetscMin(M,N);
     70:   PetscMalloc1(n,&w);
     71:   DSSolve(svd->ds,w,NULL);
     72:   DSSort(svd->ds,w,NULL,NULL,NULL,NULL);

     74:   /* copy singular vectors */
     75:   DSGetArray(svd->ds,DS_MAT_U,&pU);
     76:   DSGetArray(svd->ds,DS_MAT_VT,&pVT);
     77:   for (i=0;i<n;i++) {
     78:     if (svd->which == SVD_SMALLEST) k = n - i - 1;
     79:     else k = i;
     80:     svd->sigma[k] = PetscRealPart(w[i]);
     81:     BVGetColumn(svd->U,k,&u);
     82:     BVGetColumn(svd->V,k,&v);
     83:     VecGetArray(u,&pu);
     84:     VecGetArray(v,&pv);
     85:     if (M>=N) {
     86:       for (j=0;j<M;j++) pu[j] = pU[i*ld+j];
     87:       for (j=0;j<N;j++) pv[j] = PetscConj(pVT[j*ld+i]);
     88:     } else {
     89:       for (j=0;j<N;j++) pu[j] = PetscConj(pVT[j*ld+i]);
     90:       for (j=0;j<M;j++) pv[j] = pU[i*ld+j];
     91:     }
     92:     VecRestoreArray(u,&pu);
     93:     VecRestoreArray(v,&pv);
     94:     BVRestoreColumn(svd->U,k,&u);
     95:     BVRestoreColumn(svd->V,k,&v);
     96:   }
     97:   DSRestoreArray(svd->ds,DS_MAT_U,&pU);
     98:   DSRestoreArray(svd->ds,DS_MAT_VT,&pVT);

    100:   svd->nconv = n;
    101:   svd->reason = SVD_CONVERGED_TOL;

    103:   MatDestroy(&mat);
    104:   PetscFree(w);
    105:   return(0);
    106: }

    110: PETSC_EXTERN PetscErrorCode SVDCreate_LAPACK(SVD svd)
    111: {
    113:   svd->ops->setup   = SVDSetUp_LAPACK;
    114:   svd->ops->solve   = SVDSolve_LAPACK;
    115:   return(0);
    116: }