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Applied Numerical Linear Algebra

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by James W. Demmel


1997 / xii + 419 pages / Softcover / ISBN: 978-0-898713-89-3 / List Price $82.50 / SIAM Member Price $57.75 / Order Code OT56

"Jim Demmel's book on applied numerical linear algebra is a wonderful text blending together the mathematical basis, good numerical software, and practical knowledge for solving real problems. It is destined to be a classic." --Jack Dongarra, Department of Computer Science, University of Tennessee, Knoxville.

"This book has many unprecedented features as a graduate textbook and research reference book on numerical linear algebra and matrix computations. Many topics appear for the first time in a graduate textbook, such as single precision iterative refinement, relative perturbation theory, full-version of divide-and-conquer method, high precision Jacobi method, connection of QR method and the Toda lattice and so on. ÉIt is astonishing to what extent this book, by means of systematic and easily understandable exposition, has succeeded in making clear the state of the art of numerical linear algebra theory, methods and analysis which we numerical analysts consider the lively frontier of our current work." --Zhaoujun Bai, University of Kentucky.

"This is an excellent graduate-level textbook for people who want to learn or teach the state of the art of numerical linear algebra. It covers systematically all the fundamental topics in theory, as well as software implementation. The book is very easy to use in the classroom since it provides pointers, in the book and on the author's home page, to lots of available Matlab and LAPACK routines, and it has a large number of homework problems marked with Easy, Medium and Hard. The book requires the students to have a stronger background in linear algebra than most other engineering books on numerical linear algebra." --Xia-Chuan Cai, Department of Computer Science, University of Colorado.

"Demmel's book covers the state of the art tools of numerical linear algebra. He tells us how they work and why they work so well. He also gives many references to recent research work. He avoids including everything, so the book is still easy to read." --Martin H. Gutknecht, IPS Supercomputing in Zurich, Switzerland.

Designed for use by 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 recommendations of which algorithms to use in a variety of practical situations.

If you are looking for a textbook that

  • teaches state-of-the-art techniques for solving linear algebra problems,
  • covers the most important methods for dense and sparse problems,
  • presents both the mathematical background and good software techniques,
  • is self-contained, assuming only a good undergraduate background in linear algebra

then this is the book for you.

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.

Material either not available elsewhere, or presented quite differently in other textbooks, includes a discussion of the impact of modern cache-based computer memories on algorithm design;

  • frequent recommendations and pointers in the text to the best software currently available, including a detailed performance comparison of state-of-the-art software for eigenvalue and least squares problems, and a description of sparse direct solvers for serial and parallel machines;
  • a discussion of iterative methods ranging from Jacobi's method to multigrid and domain decomposition, with performance comparisons on a model problem;
  • a great deal of Matlab-based software, available on the Web, which either implements algorithms presented in the book, produces the figures in the book, or is used in homework problems;
  • numerical examples drawn from fields ranging from mechanical vibrations to computational geometry;
  • high-accuracy algorithms for solving linear systems and eigenvalue problems, along with tighter "relative" error bounds;
  • dynamical systems interpretations of some eigenvalue algorithms.

Demmel discusses several current research topics, making students aware of both the lively research taking place and connections to other parts of numerical analysis, mathematics, and computer science. Some of this material is developed in questions at the end of each chapter, which are marked Easy, Medium, or Hard according to their difficulty. Some questions are straightforward, supplying proofs of lemmas used in the text. Others are more difficult theoretical or computing problems. Questions involving significant amounts of programming are marked Programming. The computing questions mainly involve Matlab programming, and others involve retrieving, using, and perhaps modifying LAPACK code from NETLIB.

Recommended as a first year graduate textbook for a numerical linear algebra course.

Preface; Chapter 1: Introduction. Basic Notation; Standard Problems of Numerical Linear Algebra; General Techniques; Example: Polynomial Evaluation; Floating Point Arithmetic; Polynomial Evaluation Revisited; Vector and Matrix Norms; References and Other Topics for Chapter 1; Questions for Chapter 1; Chapter 2: Linear Equation Solving. Introduction; Perturbation Theory; Gaussian Elimination; Error Analysis; Improving the Accuracy of a Solution; Blocking Algorithms for Higher Performance; Special Linear Systems; References and Other Topics for Chapter 2; Questions for Chapter 2; Chapter 3: Linear Least Squares Problems. Introduction; Matrix Factorizations That Solve the Linear Least Squares Problem; Perturbation Theory for the Least Squares Problem; Orthogonal Matrices; Rank Deficient Least Squares Problems; Performance Comparison of Methods for Solving Least Squares Problems; Reference and Other Topics for Chapter 3; Questions for Chapter 3; Chapter 4: Nonsymmetric Eigenvalue Problems. Introduction; Canonical Forms; Perturbation Theory; Algorithms for the Nonsymmetric Eigenproblem; Other Nonsymmetric Eigenvalue Problems; Summary; References and Other Topics for Chapter 4; Questions for Chapter 4; Chapter 5: The Symmetric Eigenproblem and Singular Value Decomposition. Introduction; Perturbation Theory; Algorithms for the Symmetric Eigenproblem; Algorithms for the Singular Value Decomposition; Differential Equations and Eigenvalue Problems; References and Other Topics for Chapter 5; Questions for Chapter 5; Chapter 6: Iterative Methods for Linear Systems. Introduction; On-line Help for Iterative Methods; Poisson's Equation; Summary of Methods for Solving Poisson's Equation; Basic Iterative Methods; Krylov Subspace Methods; Fast Fourier Transform; Block Cyclic Reduction; Multigrid; Domain Decomposition; References and Other Topics for Chapter 6; Questions for Chapter 6; Chapter 7: Iterative Methods for Eigenvalue Problems. Introduction. The Rayleigh-Ritz Method; The Lanczos Algorithm in Exact Arithmetic; The Lanczos Algorithm in Floating Point Arithmetic; The Lanczos Algorithm with Selective Orthogonalization; Beyond Selective Orthogonalization; Iterative Algorithms for the Nonsymmetric Eigenproblem; References and Other Topics for Chapter 7; Questions for Chapter 7; Bibliography; Index.

ISBN: 9780898713893

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