Buch, Englisch, 628 Seiten, Format (B × H): 224 mm x 289 mm, Gewicht: 1786 g
Buch, Englisch, 628 Seiten, Format (B × H): 224 mm x 289 mm, Gewicht: 1786 g
ISBN: 978-0-12-394435-1
Verlag: ACADEMIC PRESS
The text consists of six introductory chapters that thoroughly provide the required background for those who have not taken a course in applied or theoretical linear algebra. It explains in great detail the algorithms necessary for the accurate computation of the solution to the most frequently occurring problems in numerical linear algebra. In addition to examples from engineering and science applications, proofs of required results are provided without leaving out critical details. The Preface suggests ways in which the book can be used with or without an intensive study of proofs.
This book will be a useful reference for graduate or advanced undergraduate students in engineering, science, and mathematics. It will also appeal to professionals in engineering and science, such as practicing engineers who want to see how numerical linear algebra problems can be solved using a programming language such as MATLAB, MAPLE, or Mathematica.
Zielgruppe
<p>Graduate or advanced undergraduate students in engineering, science, and mathematics, professionals in engineering and science, such as practicing engineers who want to see how numerical linear algebra problems can be solved using a programming language such as MATLAB, MAPLE, or Mathematica</p>
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
- Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
- Mathematik | Informatik Mathematik Mathematik Interdisziplinär Computeralgebra
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Numerische Mathematik
Weitere Infos & Material
1. Matrices 2. Linear equations3. Subspaces4. Determinants5. Eigenvalues and eigenvectors6. Orthogonal vectors and matrices7. Vector and matrix norms8. Floating point arithmetic9. Algorithms10. Conditioning of problems and stability of algorithms11. Gaussian elimination and the LU decomposition12. Linear system applications13. Important special systems14. Gram-Schmidt decomposition15. The singular value decomposition16. Least-squares problems17. Implementing the QR factorization18. The algebraic eigenvalue problem19. The symmetric eigenvalue problem20. Basic iterative methods21. Krylov subspace methods22. Large sparse eigenvalue problems23. Computing the singular value decompositionAppendix A. Complex numbersAppendix B. Mathematical inductionAppendix C. Chebyshev polynomials