TEST_EIGEN
Test Matrices for Eigenvalue Analysis


TEST_EIGEN is a C++ library which generates eigenvalue tests.

The current version of the code can only generate a symmetric matrix with eigenvalues distributed according to a normal distribution whose mean and standard deviation are specified by the user (subroutine SYMM_TEST).

Licensing:

The computer code and data files described and made available on this web page are distributed under the GNU LGPL license.

Languages:

TEST_EIGEN is available in a C version and a C++ version and a FORTRAN77 version and a FORTRAN90 version and a MATLAB version.

Related Data and Programs:

EISPACK, a C++ library which carries out eigenvalue computations. It includes a function to compute the singular value decomposition (SVD) of a rectangular matrix. superseded by LAPACK;

JACOBI_EIGENVALUE, a C++ library which implements the Jacobi iteration for the iterative determination of the eigenvalues and eigenvectors of a real symmetric matrix.

POWER_METHOD, a C++ library which carries out the power method for finding a dominant eigenvalue and its eigenvector.

TEST_MAT, a C++ library which defines test matrices.

TOMS343, a FORTRAN77 library which computes the eigenvalues and eigenvectors of a general real matrix;
this is a FORTRAN77 version of ACM TOMS algorithm 343.

TOMS384, a FORTRAN77 library which computes the eigenvalues and eigenvectors of a symmetric matrix;
this is a FORTRAN77 version of ACM TOMS algorithm 384.

Reference:

  1. Robert Gregory, David Karney,
    A Collection of Matrices for Testing Computational Algorithms,
    Wiley, 1969,
    ISBN: 0882756494,
    LC: QA263.G68.
  2. Pete Stewart,
    Efficient Generation of Random Orthogonal Matrices With an Application to Condition Estimators,
    SIAM Journal on Numerical Analysis,
    Volume 17, Number 3, June 1980, pages 403-409.

Source Code:

Examples and Tests:

List of Routines:

You can go up one level to the C++ source codes.


Last revised on 22 February 2012.