This book gives a unified overview of theory, algorithms, and practical software for eigenvalue problems. It organizes this large body of material to make it accessible for the first time to the many nonexpert users who need to choose the best state-of-the-art algorithms and software for their problems.
Using an informal decision tree, just enough theory is introduced to identify the relevant mathematical structure that determines the best algorithm for each problem. The algorithms and software at the ‘leaves’ of the decision tree range from the classical QR algorithm, which is most suitable for small dense matrices, to iterative algorithms for very large generalized eigenvalue problems. Algorithms are presented in a unified style as templates, with different levels of detail suitable for readers ranging from beginning students to experts.
Table of Contents
- A Brief Tour of Eigenproblems
- An Introduction to Iterative Projection Methods
- Hermitian Eigenvalue Problems
- Generalized Hermitian Eigenvalue Problems
- Singular Value Decomposition
- Non-Hermitian Eigenvalue Problems
- Generalized Non-Hermitian Eigenvalue Problems
- Nonlinear Eigenvalue Problems
- Common Issues
- Preconditioning Techniques
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Number of pages: 440
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