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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010183261 | QA184 D45 1997 | Open Access Book | Book | Searching... |
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Summary
Summary
Designed for first-year graduate students from a variety of engineering and scientific disciplines, this comprehensive textbook covers the solution of linear systems, least squares problems, eigenvalue problems, and the singular value decomposition. The author, who helped design the widely used LAPACK and ScaLAPACK linear algebra libraries, draws on this experience to present state-of-the-art techniques for these problems, including recommending which algorithms to use in various practical situations. Algorithms are derived in a mathematically illuminating way, including condition numbers and error bounds. Direct and iterative algorithms, suitable for dense and sparse matrices, are discussed. Algorithm design for modern computer architectures, where moving data is often more expensive than arithmetic operations, is discussed in detail, using LAPACK as an illustration. There are many numerical examples throughout the text and in the problems at the ends of chapters, most of which are written in MATLAB and are freely available on the Web.
Author Notes
James Demmel is a Professor in the Computer Science Division and Mathematics Department at the University of California, Berkeley. He was elected to the National Academy of Engineering in 1999.
Reviews 1
Choice Review
Demmel (computer science, Univ. of California, Berkeley) is best known for his work on the LAPACK and ScaLAPACK libraries of numerical linear algebra software (E. Anderson, Z. Bai, C. Bischof, J. Demmel, and J. Dongarra, LAPACK Users' Guide, 2nd ed., 1995; L.S. Blackford, J. Choi, A. Cleary, E. D'Azevedo, and J. Demmel, ScaLAPACK User's Guide, 1997). Demmel writes at a graduate level on numerical linear algebra. Topics include iterative and direct methods for linear systems of equations, least squares problems, eigenvalue problems, and singular value problems. The strongest aspect of the book is its balanced introduction to theory, algorithms, and available software. Compare Demmel with the standard work by G. Golub and C. Van Loan, Matrix Computations (3rd ed., 1996). These latter authors cover a very wide range of topics in a way that makes their book a very useful reference. Demmel offers a smaller number of topics but focuses on the most important, and provides a more readable introduction for beginners. Graduates through professionals. B. Borchers New Mexico Institute of Mining and Technology
Table of Contents
| Preface |
| 1 Introduction |
| 2 Linear equation solving |
| 3 Linear least squares problems |
| 4 Nonsymmetric Eigenvalue problems |
| 5 The symmetric Eigenproblem and singular value decomposition |
| 6 Iterative methods for linear systems |
| 7 Iterative methods for Eigenvalue problems |
| Bibliography |
| Index |
