Date: 2016-09-29T11:12:15+00:00

Actual source code: ex8.c
slepc-3.7.3 2016-09-29
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      1: /*
      2:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      3:    SLEPc - Scalable Library for Eigenvalue Problem Computations
      4:    Copyright (c) 2002-2016, Universitat Politecnica de Valencia, Spain

      6:    This file is part of SLEPc.

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

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

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

     22: static char help[] = "Estimates the 2-norm condition number of a matrix A, that is, the ratio of the largest to the smallest singular values of A. "
     23:   "The matrix is a Grcar matrix.\n\n"
     24:   "The command line options are:\n"
     25:   "  -n <n>, where <n> = matrix dimension.\n\n";

     27: #include <slepcsvd.h>

     29: /*
     30:    This example computes the singular values of an nxn Grcar matrix,
     31:    which is a nonsymmetric Toeplitz matrix:

     33:               |  1  1  1  1               |
     34:               | -1  1  1  1  1            |
     35:               |    -1  1  1  1  1         |
     36:               |       .  .  .  .  .       |
     37:           A = |          .  .  .  .  .    |
     38:               |            -1  1  1  1  1 |
     39:               |               -1  1  1  1 |
     40:               |                  -1  1  1 |
     41:               |                     -1  1 |

     43:  */

     47: int main(int argc,char **argv)
     48: {
     49:   Mat            A;               /* Grcar matrix */
     50:   SVD            svd;             /* singular value solver context */
     51:   PetscInt       N=30,Istart,Iend,i,col[5],nconv1,nconv2;
     52:   PetscScalar    value[] = { -1, 1, 1, 1, 1 };
     53:   PetscReal      sigma_1,sigma_n;

     56:   SlepcInitialize(&argc,&argv,(char*)0,help);

     58:   PetscOptionsGetInt(NULL,NULL,"-n",&N,NULL);
     59:   PetscPrintf(PETSC_COMM_WORLD,"\nEstimate the condition number of a Grcar matrix, n=%D\n\n",N);

     61:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
     62:         Generate the matrix
     63:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

     65:   MatCreate(PETSC_COMM_WORLD,&A);
     66:   MatSetSizes(A,PETSC_DECIDE,PETSC_DECIDE,N,N);
     67:   MatSetFromOptions(A);
     68:   MatSetUp(A);

     70:   MatGetOwnershipRange(A,&Istart,&Iend);
     71:   for (i=Istart;i<Iend;i++) {
     72:     col[0]=i-1; col[1]=i; col[2]=i+1; col[3]=i+2; col[4]=i+3;
     73:     if (i==0) {
     74:       MatSetValues(A,1,&i,PetscMin(4,N-i),col+1,value+1,INSERT_VALUES);
     75:     } else {
     76:       MatSetValues(A,1,&i,PetscMin(5,N-i+1),col,value,INSERT_VALUES);
     77:     }
     78:   }

     80:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
     81:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);

     83:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
     84:              Create the singular value solver and set the solution method
     85:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

     87:   /*
     88:      Create singular value context
     89:   */
     90:   SVDCreate(PETSC_COMM_WORLD,&svd);

     92:   /*
     93:      Set operator
     94:   */
     95:   SVDSetOperator(svd,A);

     97:   /*
     98:      Set solver parameters at runtime
     99:   */
    100:   SVDSetFromOptions(svd);
    101:   SVDSetDimensions(svd,1,PETSC_DEFAULT,PETSC_DEFAULT);

    103:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    104:                       Solve the singular value problem
    105:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

    107:   /*
    108:      First request a singular value from one end of the spectrum
    109:   */
    110:   SVDSetWhichSingularTriplets(svd,SVD_LARGEST);
    111:   SVDSolve(svd);
    112:   /*
    113:      Get number of converged singular values
    114:   */
    115:   SVDGetConverged(svd,&nconv1);
    116:   /*
    117:      Get converged singular values: largest singular value is stored in sigma_1.
    118:      In this example, we are not interested in the singular vectors
    119:   */
    120:   if (nconv1 > 0) {
    121:     SVDGetSingularTriplet(svd,0,&sigma_1,NULL,NULL);
    122:   } else {
    123:     PetscPrintf(PETSC_COMM_WORLD," Unable to compute large singular value!\n\n");
    124:   }

    126:   /*
    127:      Request a singular value from the other end of the spectrum
    128:   */
    129:   SVDSetWhichSingularTriplets(svd,SVD_SMALLEST);
    130:   SVDSolve(svd);
    131:   /*
    132:      Get number of converged eigenpairs
    133:   */
    134:   SVDGetConverged(svd,&nconv2);
    135:   /*
    136:      Get converged singular values: smallest singular value is stored in sigma_n.
    137:      As before, we are not interested in the singular vectors
    138:   */
    139:   if (nconv2 > 0) {
    140:     SVDGetSingularTriplet(svd,0,&sigma_n,NULL,NULL);
    141:   } else {
    142:     PetscPrintf(PETSC_COMM_WORLD," Unable to compute small singular value!\n\n");
    143:   }

    145:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    146:                     Display solution and clean up
    147:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
    148:   if (nconv1 > 0 && nconv2 > 0) {
    149:     PetscPrintf(PETSC_COMM_WORLD," Computed singular values: sigma_1=%6f, sigma_n=%6f\n",(double)sigma_1,(double)sigma_n);
    150:     PetscPrintf(PETSC_COMM_WORLD," Estimated condition number: sigma_1/sigma_n=%6f\n\n",(double)(sigma_1/sigma_n));
    151:   }

    153:   /*
    154:      Free work space
    155:   */
    156:   SVDDestroy(&svd);
    157:   MatDestroy(&A);
    158:   SlepcFinalize();
    159:   return ierr;
    160: }