A repository of matrix test data for use in comparative studies of algorithms. The matrices have been taken from problems in linear systems, least squares, and eigenvalue calculations in a wide variety of scientific and engineering disciplines. Includes search for matrices by size, mathematical properties, and keywords.

Eigenvalue analysis of non-hermitian matrices and operators can be misleading: Predictions often fail to match observations. Specifically, trouble may arise when the associated sets of eigenvectors are ill-conditioned with respect to the norm of applied interest. In the case of the familiar Euclidean or 2-norm, this means that the matrix or operator is non-normal, and the eigenvectors are not ...

Preconditioned Eigensolvers by Andrew Knyazev LINEAR ALGEBRA AND ITS APPLICATIONS Special issue on Large Scale Linear and Nonlinear Eigenvalue Problems. CALL FOR PAPERS What is preconditioned eigensolvers In one sentence those are matrix-free iterative methods for partial eigenvalue problems that take advantage of using preconditioners to accelerate convergence. A complete answer can be found ...

Bibliography on the Solution of Sparse Linear Systems and Related Areas of Computation. This bibliography is a part of the Computer Science Bibliography Collection.

This page provides a base of Bibliographic References and Web Links related to the Solution of Sparse Systems of Linear Equations, additionally providing links to other bases on Related Areas of Scientific Computing, with emphasis on Computational Linear Algebra, High Performance Computing and Mathematical Programming.