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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• pseudospectra• 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.
- Sales Rank: #302788 in eBooks
- Published on: 2012-12-27
- Released on: 2012-12-27
- Format: Kindle eBook
Review
Problems, solutions, and discussions of the formulas, methods and literature surrounding matrix computations make for a reference that is specific and well detailed: perfect for any college-level math collection appealing to engineers.
(Midwest Book Review)Written for scientists and engineers, Matrix Computations, fourth edition provides comprehensive coverage of numerical linear algebra. Anyone whose work requires the solution to a matrix problem and an appreciation of mathematical properties will find this book to be an indispensable tool.
(MathWorks) Review
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) About the Author
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.
Most helpful customer reviews
0 of 0 people found the following review helpful.
reference, systematic
By Katarzyna
The book has errors. I do not mean typos, but errors in programs and algorithms. I used two previous editions and order the newest hoping that it would be more error-free. What is disappointing, this book is a great source of reference and some codes and methods are really hard to find anywhere else, and are never explained as systematically. The style is not the easiest to digest, but once you passed enough numerical analysis classes, you mostly need a reference, and how attractive is this: everything in one book? I appreciate switching from fortran to matlab.
0 of 0 people found the following review helpful.
This is an amazing resource for understanding the numerical problems associated with implementations ...
By Dave Eberly
This is an amazing resource for understanding the numerical problems associated with implementations of matrix algorithms. I use it regularly for writing source code for solving problems that require the concepts discussed in the book.
16 of 17 people found the following review helpful.
A classic book that got even better
By Amazon Customer
This is now the best and most up-to-date textbook for numerical linear algebra at the graduate level, which I have been teaching for seven years. In the past, I always liked its third edition as a reference book, but hesitated to use it as a textbook, because it was difficult for students to study. The fourth edition reads much clearer, and I also like the new organization of the chapters very much. The fourth edition is much more friendly for self-study by matured students. I highly recommend it to students and practitioners who already have some basic understanding of linear algebra and would like to learn more about numerical algorithms in linear algebra for computational sciences.
A word of caution: This book is not meant to be an introduction to linear algebra for self-study or at the undergraduate level. There are good books specifically for that purpose, such as Linear Algebra and Its Applications by Gilbert Strang.
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