Numerical methods for large eigenvalue problems
by Saad, Y.
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 Each chapter was updated by shortening or deleting outdated topics, adding topics of more recent interest, and adapting the Notes and References section.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 th…
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- Full title :Numerical methods for large eigenvalue problems / Yousef Saad.
Summary
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 Each chapter was updated by shortening or deleting outdated topics, adding topics of more recent interest, and adapting the Notes and References section.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.
Read More ↓Edition Statements
- Publication Date:©2011.
- Edition:Rev. ed
Publication Statements
- Publisher(s):Society for Industrial and Applied Mathematics
- Place of Publication:Philadelphia
- Language:eng
- Publication Date:©2011.
The Physical Object
- Description:1 online resource (xvi, 276 pages).
Edition Identifiers
- ISBN:9781611970739, 1611970733
- oclc:ocn757103227
- LC Classification:QA188
- Dewey Decimal:512.9/436
Table of Contents
Preface to the Classics edition -- Preface -- Chapter 1. Background in matrix theory and linear algebra -- Chapter 2. Sparse matrices -- Chapter 3. Perturbation theory and error analysis -- Chapter 4. The tools of spectral approximation -- Chapter 5. Subspace iteration -- Chapter 6. Krylov subspace methods -- Chapter 7. Filtering and restarting techniques -- Chapter 8. Preconditioning techniques -- Chapter 9. Non-standard eigenvalue problems -- Chapter 10. Origins of matrix eigenvalue problems -- References -- Index.
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