Recent Advances in Iterative Methods

Abstract

The solution of very large sparse or structured linear algebra problems is an integral part of many scientific computations. Direct methods for solving such problems are often difficult because of computation time and memory requirements, and so iterative techniques are used instead. In recent years, much research has focused on the efficient solution of large systems of linear equations, least squares problems, and eigenvalue problems using iterative methods. This volume on iterative methods for sparse and structured problems brings together researchers to discuss topics of current research. Areas addressed include the development of efficient iterative techniques for solving nonsymmetric linear systems and eigenvalue problems, estimating the convergence rate of such algorithms, and constructing efficient preconditioners for special classes of matrices such as Toeplitz and Hankel matrices. Iteration strategies and preconditioners that could exploit parallelism are of special interest.