The Matrix Eigenvalue Problem
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The Matrix Eigenvalue Problem
An in-depth, theoretical discussion of the two most important classes of algorithms for solving matrix eigenvalue problems.
Numerical Methods for Large Eigenvalue Problems
This revised edition discusses numerical methods for computing the 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 Each chapter was updated by shortening or deleting outdated topics, adding topics of more recent interest and adapting the Notes and References section.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.
Numerical Methods for General and Structured Eigenvalue Problems
Author: Daniel Kressner
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-01-20
This book is about computing eigenvalues, eigenvectors, and invariant subspaces of matrices. Treatment includes generalized and structured eigenvalue problems and all vital aspects of eigenvalue computations. A unique feature is the detailed treatment of structured eigenvalue problems, providing insight on accuracy and efficiency gains to be expected from algorithms that take the structure of a matrix into account.