Numerical linear algebra with applications using MATLAB by William Ford.

By: Ford, William [author]Material type: TextTextPublisher: London, UK Elsevier/AP, Academic Press is an imprint of Elsevier 2015Description: axxvi, 602 pages illustrations 29 cmContent type: atext Media type: unmediated Carrier type: volumeISBN: 9780123944351Subject(s): Algebras, Linear | Numerical calculations | MATLAB -- Problems, exercises, etcDDC classification: M 512.5 F711 2015
Contents:
1. Matrices -- 2. Linear equations -- 3. Subspaces -- 4. Determinants -- 5. Eigenvalues and eigenvectors -- 6. Orthogonal vectors and matrices -- 7. Vector and matrix norms -- 8. Floating point arithmetic -- 9. Algorithms -- 10. Conditioning of problems and stability of algorithms -- 11. Gaussian elimination and the LU decomposition -- 12. Linear system applications -- 13. Important special systems -- 14. Gram-Schmidt orthonormalization -- 15. The singular value decomposition -- 16. Least-square problems -- 17. Implementing the QR decomposition -- 18. The algebraic eigenvalue problem -- 19. The symmetric eigenvalue problem -- 20. Basic iterative methods -- 21. Krylov subspace methods -- 22. Large sparse eigenvalue problems -- 23. Computing the singular value decomposition -- A. Complex numbers -- B. Mathematical induction -- C. Chebyshev polynominals.
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Item type Current library Collection Call number Copy number Status Date due Barcode
Book Book JHCSC - Main Campus Library
REF M 512.5 F711 2015 (Browse shelf(Opens below)) 1 Available 10870m

Includes bibliographical references and index.

1. Matrices -- 2. Linear equations -- 3. Subspaces -- 4. Determinants -- 5. Eigenvalues and eigenvectors -- 6. Orthogonal vectors and matrices -- 7. Vector and matrix norms -- 8. Floating point arithmetic -- 9. Algorithms -- 10. Conditioning of problems and stability of algorithms -- 11. Gaussian elimination and the LU decomposition -- 12. Linear system applications -- 13. Important special systems -- 14. Gram-Schmidt orthonormalization -- 15. The singular value decomposition -- 16. Least-square problems -- 17. Implementing the QR decomposition -- 18. The algebraic eigenvalue problem -- 19. The symmetric eigenvalue problem -- 20. Basic iterative methods -- 21. Krylov subspace methods -- 22. Large sparse eigenvalue problems -- 23. Computing the singular value decomposition -- A. Complex numbers -- B. Mathematical induction -- C. Chebyshev polynominals.

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