dsymshf.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 DSymShf.cc.
   Example program that illustrates how to solve a real symmetric
   dense standard eigenvalue problem in shift and invert mode using 
   the ARluSymStdEig class.

   1) Problem description:

      In this example we try to solve A*x = x*lambda in shift and  
      invert mode, where A is derived from the standard central 
      difference discretization of the 2-dimensional Laplacian on 
      the unit square with zero Dirichlet boundary conditions.

   2) Data structure used to represent matrix A:

      Although A is very sparse in this example, it is stored
      here as a dense symmetric matrix. The lower triangular part
      of A is stored, by columns, in the vector A.

   3) Library called by this example:

      The LAPACK package is called by ARluSymStdEig to solve
      some linear systems involving (A-sigma*I). This is needed to
      implement the shift and invert strategy.

   4) Included header files:

      File             Contents
      -----------      --------------------------------------------
      dsmatrxa.h       DenseMatrixA, a function that generates
                       matrix A.
      ardsmat.h        The ARdsSymMatrix class definition.
      ardssym.h        The ARluSymStdEig class definition.
      lsymsol.h        The Solution function.

   5) ARPACK Authors:

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

#include "dsmatrxa.h"
#include "ardsmat.h"
#include "ardssym.h"
#include "lsymsol.h"


main()
{

  // Defining variables;

  int     nx;
  int     n;       // Dimension of the problem.
  double* A;       // Pointer to an array that stores the elements of A.

  // Creating a 100x100 matrix.

  nx  = 10;
  DenseMatrixA(nx, n, A);
  ARdsSymMatrix<double> matrix(n, A);

  // Defining what we need: the four eigenvectors of A nearest to 1.0.

  ARluSymStdEig<double> dprob(4L, matrix, 1.0);

  // Finding eigenvalues and eigenvectors.

  dprob.FindEigenvectors();

  // Printing solution.

  Solution(matrix, dprob);

} // main