Numerical Methods for Large Eigenvalue Problems
Numerical Methods for Large Eigenvalue Problems
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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 Each chapter was updated by shortening or deleting outdated topics, adding topics of more recent interest and adapting the Notes and References section.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.
- Offers an in-depth view of the common numerical methods
- Discusses both theoretical aspects of the methods as well as practical implementations
- Thoroughly revised and updated version of a classic first published in 1991
Product details
May 2011Paperback
9781611970722
340 pages
228 × 152 × 15 mm
0.4kg
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Table of Contents
- Preface to the Classics Edition
- Preface
- 1. Background in matrix theory and linear algebra
- 2. Sparse matrices
- 3. Perturbation theory and error analysis
- 4. The tools of spectral approximation
- 5. Subspace iteration
- 6. Krylov subspace methods
- 7. Filtering and restarting techniques
- 8. Preconditioning techniques
- 9. Non-standard eigenvalue problems
- 10. Origins of matrix eigenvalue problems
- References
- Index.
