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. 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. 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. The 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.--jacket. Preface; 1. Preliminary Material; 2. Basic Theory Of Eigensystems; 3. Elimination; 4. Iteration; 5. Convergence; 6. The Generalized Eigenvalue Problem; 7. Inside The Bulge; 8. Product Eigenvalue Problems; 9. Krylov Subspace Methods; Bibliography; Index. David S. Watkins. Ot101--spine. Bkot0101--p. [4] Of Cover. Includes Bibliographical References (p. 421-436) And Index. 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.
