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 infeasible because of computation time and memory requirements, and so iterative techniques are used instead. In recent years much research has focussed 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 from all over the world to discuss topics of current research. Areas addressed included 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 were of special interest. This volume represents the latest results of mathematical and computational research into the development and analysis of robust iterative methods for numerical linear algebra problems. This volume will be useful for both mathematicians and for those involved in applications using iterative methods.