Since the first edition of this book was published in 1996, tremendous progress has been made in the scientific and engineering disciplines regarding the use of iterative methods for linear systems. The size and complexity of the new generation of linear and nonlinear systems arising in typical applications has grown. Solving the three-dimensional models of these problems using direct solvers is no longer effective. At the same time, parallel computing has penetrated these application areas as it became less expensive and standardized. Iterative methods are easier than direct solvers to implement on parallel computers but require approaches and solution algorithms that are different from classical methods.

Iterative Methods for Sparse Linear Systems, Second Edition gives an in-depth, up-to-date view of practical algorithms for solving large-scale linear systems of equations. These equations can number in the millions and are sparse in the sense that each involves only a small number of unknowns. The methods described are iterative, i.e., they provide sequences of approximations that will converge to the solution.

This new edition includes a wide range of the best methods available today. The author has added a new chapter on multigrid techniques and has updated material throughout the text, particularly the chapters on sparse matrices, Krylov subspace methods, preconditioning techniques, and parallel preconditioners. Material on older topics has been removed or shortened, numerous exercises have been added, and many typographical errors have been corrected. The updated and expanded bibliography now includes more recent works emphasizing new and important research topics in this field.

Audience
This book can be used to teach graduate-level courses on iterative methods for linear systems. Engineers and mathematicians will find its contents easily accessible, and practitioners and educators will value it as a helpful resource. The preface includes syllabi that can be used for either a semester- or quarter-length course in both mathematics and computer science.

Contents
Preface to the Second Edition; Preface to the First Edition; Chapter 1: Background in Linear Algebra; Chapter 2: Discretization of Partial Differential Equations; Chapter 3: Sparse Matrices; Chapter 4: Basic Iterative Methods; Chapter 5: Projection Methods; Chapter 6: Krylov Subspace Methods, Part I; Chapter 7: Krylov Subspace Methods, Part II; Chapter 8: Methods Related to the Normal Equations; Chapter 9: Preconditioned Iterations; Chapter 10: Preconditioning Techniques; Chapter 11: Parallel Implementations; Chapter 12: Parallel Preconditioners; Chapter 13: Multigrid Methods; Chapter 14: Domain Decomposition Methods; Bibliography; Index.

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