Второе издание полезной книги, выложенное автором на его сайте.
http://www-users.cs.umn.edu/~saad/books.html.
This is the second edition of a book published almost two decades ago by Manchester University Press (See below). The book is published by SIAM.
SIAM, 2011 / xvi + 276 pages / Softcover / ISBN 978-1-611970-72-2.
Contents.
Preface to the Classics Edition; Preface;
Background in Matrix Theory and Linear Algebra;
Sparse Matrices;
Perturbation Theory and Error Analysis;
The Tools of Spectral Approximation;
Subspace Iteration;
Krylov Subspace Methods;
Filtering and Restarting Techniques;
Preconditioning Techniques;
Non-Standard Eigenvalue Problems;
Origins of Matrix Eigenvalue Problems;
References; Index.
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 Each chapter was updated by shortening or deleting outdated topics, adding topics of more recent interest, and adapting the Notes and References section.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.
About the Author.
Yousef Saad is a College of Science and Engineering distinguished professor in the Department of Computer Science at the University of Minnesota. His current research interests include numerical linear algebra, sparse matrix computations, iterative methods, parallel computing, numerical methods for electronic structure, and data analysis. He is a Fellow of SIAM and the AAAS.
