Actual source code: test7.c

      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: static char help[] = "Test DSSVD.\n\n";

     13: #include <slepcds.h>

     15: int main(int argc,char **argv)
     16: {
     17:   DS             ds;
     18:   SlepcSC        sc;
     19:   PetscReal      sigma,rnorm,aux;
     20:   PetscScalar    *A,*U,*w,d;
     21:   PetscInt       i,j,k,n=15,m=10,m1,ld;
     22:   PetscViewer    viewer;
     23:   PetscBool      verbose,extrarow;

     25:   PetscFunctionBeginUser;
     26:   PetscCall(SlepcInitialize(&argc,&argv,NULL,help));
     27:   PetscCall(PetscOptionsGetInt(NULL,NULL,"-n",&n,NULL));
     28:   PetscCall(PetscOptionsGetInt(NULL,NULL,"-m",&m,NULL));
     29:   k = PetscMin(n,m);
     30:   PetscCall(PetscPrintf(PETSC_COMM_WORLD,"Solve a Dense System of type SVD - dimension %" PetscInt_FMT "x%" PetscInt_FMT ".\n",n,m));
     31:   PetscCall(PetscOptionsHasName(NULL,NULL,"-verbose",&verbose));
     32:   PetscCall(PetscOptionsHasName(NULL,NULL,"-extrarow",&extrarow));

     34:   /* Create DS object */
     35:   PetscCall(DSCreate(PETSC_COMM_WORLD,&ds));
     36:   PetscCall(DSSetType(ds,DSSVD));
     37:   PetscCall(DSSetFromOptions(ds));
     38:   ld = PetscMax(n,m)+2;  /* test leading dimension larger than n */
     39:   PetscCall(DSAllocate(ds,ld));
     40:   PetscCall(DSSetDimensions(ds,n,0,0));
     41:   PetscCall(DSSVDSetDimensions(ds,m));
     42:   PetscCall(DSSVDGetDimensions(ds,&m1));
     43:   PetscCheck(m1==m,PETSC_COMM_WORLD,PETSC_ERR_PLIB,"Inconsistent dimension value");
     44:   PetscCall(DSSetExtraRow(ds,extrarow));

     46:   /* Set up viewer */
     47:   PetscCall(PetscViewerASCIIGetStdout(PETSC_COMM_WORLD,&viewer));
     48:   PetscCall(PetscViewerPushFormat(viewer,PETSC_VIEWER_ASCII_INFO_DETAIL));
     49:   PetscCall(DSView(ds,viewer));
     50:   PetscCall(PetscViewerPopFormat(viewer));

     52:   /* Fill with a rectangular Toeplitz matrix */
     53:   PetscCall(DSGetArray(ds,DS_MAT_A,&A));
     54:   for (i=0;i<k;i++) A[i+i*ld]=1.0;
     55:   for (j=1;j<3;j++) {
     56:     for (i=0;i<m-j;i++) {
     57:       if ((i+j)<m) A[i+(i+j)*ld]=(PetscScalar)(j+1);
     58:     }
     59:   }
     60:   for (j=1;j<n/2;j++) {
     61:     for (i=0;i<n-j;i++) {
     62:       if ((i+j)<n && i<m) A[(i+j)+i*ld]=-1.0;
     63:     }
     64:   }
     65:   if (extrarow) { A[n-2+m*ld]=1.0; A[n-1+m*ld]=1.0; }  /* really an extra column */
     66:   PetscCall(DSRestoreArray(ds,DS_MAT_A,&A));
     67:   PetscCall(DSSetState(ds,DS_STATE_RAW));
     68:   if (verbose) {
     69:     PetscCall(PetscViewerPushFormat(viewer,PETSC_VIEWER_ASCII_MATLAB));
     70:     PetscCall(PetscPrintf(PETSC_COMM_WORLD,"Initial - - - - - - - - -\n"));
     71:   }
     72:   PetscCall(DSView(ds,viewer));

     74:   /* Solve */
     75:   PetscCall(PetscMalloc1(k,&w));
     76:   PetscCall(DSGetSlepcSC(ds,&sc));
     77:   sc->comparison    = SlepcCompareLargestReal;
     78:   sc->comparisonctx = NULL;
     79:   sc->map           = NULL;
     80:   sc->mapobj        = NULL;
     81:   PetscCall(DSSolve(ds,w,NULL));
     82:   PetscCall(DSSort(ds,w,NULL,NULL,NULL,NULL));
     83:   if (extrarow) PetscCall(DSUpdateExtraRow(ds));
     84:   if (verbose) {
     85:     PetscCall(PetscPrintf(PETSC_COMM_WORLD,"After solve - - - - - - - - -\n"));
     86:     PetscCall(DSView(ds,viewer));
     87:   }

     89:   /* Print singular values */
     90:   PetscCall(PetscPrintf(PETSC_COMM_WORLD,"Computed singular values =\n"));
     91:   for (i=0;i<k;i++) {
     92:     sigma = PetscRealPart(w[i]);
     93:     PetscCall(PetscViewerASCIIPrintf(viewer,"  %.5f\n",(double)sigma));
     94:   }

     96:   if (extrarow) {
     97:     /* Check that extra column is correct */
     98:     PetscCall(DSGetArray(ds,DS_MAT_A,&A));
     99:     PetscCall(DSGetArray(ds,DS_MAT_U,&U));
    100:     d = 0.0;
    101:     for (i=0;i<n;i++) d += A[i+m*ld]-U[n-2+i*ld]-U[n-1+i*ld];
    102:     if (PetscAbsScalar(d)>10*PETSC_MACHINE_EPSILON) PetscCall(PetscPrintf(PETSC_COMM_WORLD,"Warning: there is a mismatch in the extra row of %g\n",(double)PetscAbsScalar(d)));
    103:     PetscCall(DSRestoreArray(ds,DS_MAT_A,&A));
    104:     PetscCall(DSRestoreArray(ds,DS_MAT_U,&U));
    105:   }

    107:   /* Singular vectors */
    108:   PetscCall(DSVectors(ds,DS_MAT_U,NULL,NULL));  /* all singular vectors */
    109:   j = 0;
    110:   rnorm = 0.0;
    111:   PetscCall(DSGetArray(ds,DS_MAT_U,&U));
    112:   for (i=0;i<n;i++) {
    113:     aux = PetscAbsScalar(U[i+j*ld]);
    114:     rnorm += aux*aux;
    115:   }
    116:   PetscCall(DSRestoreArray(ds,DS_MAT_U,&U));
    117:   rnorm = PetscSqrtReal(rnorm);
    118:   PetscCall(PetscPrintf(PETSC_COMM_WORLD,"Norm of 1st U vector = %.3f\n",(double)rnorm));

    120:   PetscCall(PetscFree(w));
    121:   PetscCall(DSDestroy(&ds));
    122:   PetscCall(SlepcFinalize());
    123:   return 0;
    124: }

    126: /*TEST

    128:    test:
    129:       args: -ds_method {{0 1}}
    130:       suffix: 1
    131:       filter: grep -v "solving the problem"
    132:       requires: !single

    134:    test:
    135:       suffix: 2
    136:       args: -extrarow -ds_method {{0 1}}
    137:       filter: grep -v "solving the problem"
    138:       requires: !single

    140: TEST*/