Numerical Linear Algebra with Applications: Using MATLAB and Octave
Autor William Ford, David Stapletonen Limba Engleză Paperback – aug 2025
Appropriate for advanced undergraduate and early graduate courses on numerical linear algebra, this useful textbook explores numerous applications to engineering and science.
- Features six introductory chapters to provide the required background for readers without coursework in applied or theoretical linear algebra
- Offers a through discussion of the algorithms necessary for the accurate computation of the solution to the most frequently occurring problems in numerical linear algebra
- Provides illustrative examples from engineering and science applications
- Includes online teaching support for qualified instructors (Solutions Manual, PowerPoint Slides) and study materials for students (Text examples, Algorithms)
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Specificații
ISBN-13: 9780443134760
ISBN-10: 0443134766
Pagini: 500
Dimensiuni: 216 x 276 mm
Ediția:2
Editura: ELSEVIER SCIENCE
ISBN-10: 0443134766
Pagini: 500
Dimensiuni: 216 x 276 mm
Ediția:2
Editura: ELSEVIER SCIENCE
Cuprins
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 decomposition
15. The singular value decomposition
16. Least-squares problems
17. Implementing the QR factorization
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
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 decomposition
15. The singular value decomposition
16. Least-squares problems
17. Implementing the QR factorization
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