2013 / xxi + 756 pages / Hardcover / ISBN: 978-1-4214-0794-4 / List Price $70.00 / SIAM Member Price $49.00 / Order Code JH01
Published by Johns Hopkins University Press. Distributed by SIAM.
"A mine of insight and information and a provocation to thought; the annotated bibliographies are helpful to those wishing to explore further. One could not ask for more, and the book should be considered a resounding success."
—Bulletin of the Institute of Mathematics and Its Applications, reviewing a previous edition
A comprehensive treatment of numerical linear algebra from the standpoint of both theory and practice.
The fourth edition of Gene H. Golub and Charles F. Van Loan's classic is an essential reference for computational scientists and engineers in addition to researchers in the numerical linear algebra community. Anyone whose work requires the solution to a matrix problem and an appreciation of its mathematical properties will find this text useful and engaging.
This revision is a cover-to-cover expansion and renovation of the third edition. It now includes an introduction to tensor computations and brand new sections on
• fast transforms
• parallel LU
• discrete Poisson solvers
• structured linear equation problems
• structured eigenvalue problems
• large-scale SVD methods
• polynomial eigenvalue problems
Matrix Computations is packed with challenging problems, insightful derivations, and pointers to the literature—everything needed to become a matrix-savvy developer of numerical methods and software.
Now in its fourth edition, Golub and Van Loan’s widely recognized classic should be a welcome text for anyone working on problems related to computer science or mathematics.
SIREV Book Review
About the Authors
Gene H. Golub (1932-2007) was a professor emeritus and former director of scientific computing and computational mathematics at Stanford University.
Charles F. Van Loan is a professor of computer science at Cornell University, where he is the Joseph C. Ford Professor of Engineering.
1: Matrix Multiplication;
2: Matrix Analysis;
3: General Linear Systems;
4: Special Linear Systems;
5: Orthogonalization and Least Squares;
6: Modified Least Squares Problems and Methods;
7: Unsymmetric Eigenvalue Problems;
8: Symmetric Eigenvalue Problems;
9: Functions of Matrices;
10: Large Sparse Eigenvalue Problems;
11: Large Sparse Linear System Problems;
12: Special Topics;
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