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 or nonsymmetric matrix of arbitrary size, with eigenvalues distributed according to a normal distribution whose mean and standard deviation are specified by the user (subroutines R8SYMM_GEN and R8NSYMM_GEN).


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


TEST_EIGEN is available in a C version and a C++ version and a FORTRAN90 version and a MATLAB version and a Python 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 superseded by LAPACK;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.


  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 09 March 2018.