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内容説明
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. This book also addresses a generic Krylov process and the Arnoldi and various Lanczos algorithms, which are obtained as special cases. Theoretical and computational exercises guide students, step by step, to the results. Downloadable MATLAB programs, compiled by the author, are available on a supplementary Web site. 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. Ideal for graduate students, or as a reference book for researchers and users of eigenvalue codes.
著者について
David S. Watkins is professor of mathematics at Washington State University.
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5つ星のうち 5.0
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5つ星のうち 5.0
The most up-to-date textbook for matrix eigenvalue problems
2008/8/23
By M. C. Chen
- (Amazon.com)
形式:ペーパーバック
After working on the reduction of IC interconnect networks for months, I am eager to find a book about matrix eigenvalue computations. Although there are several famous bibles in this field, like "The Algebraic Eigenvalue Problem" by James Wilkinson, they are not so up-to-date that some important topics like product eigenvalue problems are not covered. Also, matrices with special structures require dedicated algortihms to compute their eigenvalues efficiently. This book might be the best book which covers all the latest eigenvalue problems in a very traceable way.
The other major advantage of this book is its well-designed exercises. Many theorems are proved in the exercises with step-by-step guidance. Actually, the exercises demonsrtate how the autohr proves/solves a theorem/problem. I indeed gain a lot of insight from the exercises! I recommend this book to anyone who wants to implement matrix eigenvalue computation algorithms by yourslef.
The other major advantage of this book is its well-designed exercises. Many theorems are proved in the exercises with step-by-step guidance. Actually, the exercises demonsrtate how the autohr proves/solves a theorem/problem. I indeed gain a lot of insight from the exercises! I recommend this book to anyone who wants to implement matrix eigenvalue computation algorithms by yourslef.
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5つ星のうち 5.0
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