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The Matrix Eigenvalue Problem: GR and Krylov Subspace Methods

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$105.50
Order Code:
OT101

 Product Description

by David S. Watkins

-

2007 / x + 442 pages / Softcover / ISBN 978-0-898716-41-2 / List Price $105.50 / SIAM Member Price $73.85 / Order Code OT101

Keywords: eigenvalue, QR algorithm, Krylov subspace methods, structured eigenproblem, product eigenproblem

Table of Contents
Preface
Index
Sample Chapter

"This is an excellent exposition of the state of the art in eigenvalue computations. It systematically combines the theory and the computational methods for structured and unstructured problems in a unique framework." – Volker Mehrmann, Technische UniversitŠt Berlin

This book presents the first in-depth, complete, and unified theoretical discussion of the two most important classes of algorithms for solving matrix eigenvalue problems: QR-like algorithms for dense problems and Krylov subspace methods for sparse problems. The author discusses the theory of the generic GR algorithm, including special cases (for example, QR, SR, HR), and the development of Krylov subspace methods. Also addressed are a generic Krylov process and the Arnoldi and various Lanczos algorithms, which are obtained as special cases. The chapter on product eigenvalue problems provides further unification, showing that the generalized eigenvalue problem, the singular value decomposition problem, and other product eigenvalue problems can all be viewed as standard eigenvalue problems.

The author provides theoretical and computational exercises in which the student is guided, step by step, to the results. Some of the exercises refer to a collection of MATLAB™ programs compiled by the author that are available on a Web site that supplements the book.

Audience
Readers of this book are expected to be familiar with the basic ideas of linear algebra and to have had some experience with matrix computations. This book is intended for graduate students in numerical linear algebra. It will also be useful as a reference for researchers in the area and for users of eigenvalue codes who seek a better understanding of the methods they are using.

About the Author
David S. Watkins is professor of mathematics at Washington State University. His research interests are in numerical analysis, numerical linear algebra, and scientific computing, with an emphasis on eigenvalue problems with Hamiltonian, symplectic, and other special structures. His most recent work has involved the numerical solution of large Schršdinger eigenvalue problems in the study of the nonlinear optical response of materials. He is the author of Fundamentals of Matrix Computations, Second Edition (Wiley, 2002) and over 80 mathematical and scientific articles.



ISBN: 9780898716412

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