Numerical Methods for Large Eigenvalue Problems (Algorithms and Architectures for Advanced Scientific Computing)

This revised edition discusses numerical methods for computing eigenvalues and eigenvectors of large sparse matrices. It provides an in-depth view of the numerical methods that are applicable for solving matrix eigenvalue problems that arise in various engineering and scientific applications. Each chapter was updated by shortening or deleting outdated topics, adding topics of more recent interest, and adapting the Notes and References section.

Significant changes have been made to Chapters 6 through 8, which describe algorithms and their implementations and now include topics such as the implicit restart techniques, the Jacobi-Davidson method, and automatic multilevel substructuring.

Audience: This book is intended for researchers in applied mathematics and scientific computing as well as for practitioners interested in understanding the theory of numerical methods used for eigenvalue problems. It also can be used as a supplemental text for an advanced graduate-level course on these methods.

Contents: Chapter One: Background in Matrix Theory and Linear Algebra; Chapter Two: Sparse Matrices; Chapter Three: Perturbation Theory and Error Analysis; Chapter Four: The Tools of Spectral Approximation; Chapter Five: Subspace Iteration; Chapter Six: Krylov Subspace Methods; Chapter Seven: Filtering and Restarting Techniques; Chapter Eight: Preconditioning Techniques; Chapter Nine: Non-Standard Eigenvalue Problems; Chapter Ten: Origins of Matrix Eigenvalue Problems

This revised edition discusses numerical methods for computing the eigenvalues and eigenvectors of large sparse matrices. For researchers in applied mathematics and scientific computing, and can also be used as a supplementary text for an advanced graduate course on these methods.