lsvd.cc

ARPACK is a collection of Fortran77 subroutines designed to solve large scale eigenvalue problems.

/*
   ARPACK++ v1.0 8/1/1997
   c++ interface to ARPACK code.

   MODULE LSVD.cc.
   Example program that illustrates how to determine the condition
   number of a matrix using arpack++ to find its largest and smallest
   singular values.

   1) Problem description:

      In this example, Arpack++ is called to solve the symmetric problem:

                             (A'*A)*v = sigma*v

      where A is an m by n real matrix.
      This formulation is appropriate when m >= n.
      The roles of A and A' must be reversed in the case that m < n.

   2) Data structure used to represent the matrix:

      {nnzA, irowA, pcolA, valA}: matrix A data in CSC format.

   3) Included header files:

      File             Contents
      -----------      --------------------------------------------
      lnmatrxv.h       RectangularMatrix, a function that generates
                       matrix A in CSC format.
      arlnsmat.h       The ARluNonSymMatrix class definition.
      arssym.h         The ARSymStdEig class definition.

   4) ARPACK Authors:

      Richard Lehoucq
      Kristyn Maschhoff
      Danny Sorensen
      Chao Yang
      Dept. of Computational & Applied Mathematics
      Rice University
      Houston, Texas
*/

#include "arssym.h"
#include "lnmatrxv.h"
#include "arlnsmat.h"
#include <math.h>


main()
{

  // Defining variables;

  int     m;          // Number of rows in A.
  int     n;          // Number of columns in A.
  int     nnz;        // Number of nonzero elements in A.
  int*    irow;       // pointer to an array that stores the row
                      // indices of the nonzeros in A.
  int*    pcol;       // pointer to an array of pointers to the
                      // beginning of each column of A in valA.
  double* valA;       // pointer to an array that stores the
                      // nonzero elements of A.
  double  cond;       // Condition number of A.
  double* svalue = new double[6];

  // Creating a retangular matrix with m = 200 and n = 100.

  n = 100;
  RetangularMatrix(n, m, nnz, valA, irow, pcol);

  // Using ARluNonSymMatrix to store matrix information and to
  // perform the product A'Ax (LU decomposition is not used).

  ARluNonSymMatrix<double> A(m, n, nnz, valA, irow, pcol);

  // Defining what we need: eigenvalues from both ends of the spectrum.

  ARSymStdEig<double, ARluNonSymMatrix<double> >
    dprob(n, 6L, &A, &ARluNonSymMatrix<double>::MultMtMv, "BE", 20L);

  // Finding eigenvalues.

  dprob.Eigenvalues(svalue);

  // Calculating singular values.

  svalue[0] = sqrt(svalue[0]);
  svalue[5] = sqrt(svalue[5]);

  // Obtaining the condition number.

  cond = svalue[5]/svalue[0];

  // Printing some information about the problem.

  cout << endl << "Testing ARPACK++ class ARSymStdEig" << endl;
  cout << "Obtaining singular values by solving (A'*A)*v = sigma*v" << endl;
  cout << "  greatest singular value: " << svalue[5] << endl;
  cout << "  smallest singular value: " << svalue[0] << endl;
  cout << "  condition number of A  : " << cond << endl;
  cout << "MATLAB solution:" << endl;
  cout << "  greatest singular value:  9.89757224207690 \n";
  cout << "  smallest singular value:  1.41683937261247 \n";
  cout << "  condition number of A  :  6.98566995906319 \n";

} // main.