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Matrix Computations (Johns Hopkins Studies in Mathematical Sciences)(3rd Edition)

Download Ebook Matrix Computations (Johns Hopkins Studies in Mathematical Sciences)(3rd Edition)

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Review

A wealth of material, some old and classical, some new and still subject to debate. It will be a valuable reference source for workers in numerical linear algebra as well as a challenge to students. (SIAM Review)In purely academic terms the reader with an interest in matrix computations will find this book to be 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)The authors have rewritten and clarified many of the proofs and derivations from the first edition. They have also added new topics such as Arnoldi iteration, domain decomposition methods, and hyperbolic downdating. Clearly the second edition is an invaluable reference book that should be in every university library. With the new proofs and derivations, it should remain the text of choice for graduate courses in matrix computations (Image: Bulletin of the International Linear Algebra Society)

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About the Author

Gene H. Golub is professor of computer science at Stanford University. Charles F. Van Loan is professor of computer science at Cornell University.

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Product details

Paperback: 728 pages

Publisher: Johns Hopkins University Press; 3rd edition (October 15, 1996)

Language: English

ISBN-10: 0801854148

ISBN-13: 978-0801854149

Product Dimensions:

6.1 x 1.3 x 9.2 inches

Shipping Weight: 2.2 pounds

Average Customer Review:

4.5 out of 5 stars

37 customer reviews

Amazon Best Sellers Rank:

#816,441 in Books (See Top 100 in Books)

Classic of the genre. Details all important numerical techniques of linear algebra. Rigorous enough for an academic yet practical enough for those working in the industry. Includes pseudocode snippets that can be easily translated into any modern language (although why would you? This has been implemented in MATLAB, Octave and R a long time ago...) Highly and unconditionally recommended.

Good manual for numerical analysis but I wish it had more of a breakdown of the steps or examples which demonstrate the building blocks to get to the model used.

One of the most thorough and erudite books on numerical linear analysis available. The authors, Golub and Van Loan, are pioneers in matrix analysis. The review is encyclopedic. This is a great book to demonstrate the finer points of the art and to provide a bird's eye view of the subject. If you can't articulate differences in speed and accuracy between the LU decomposition, the QR decomposition, bidiagonalization, Householder reflections and Givens rotations, and the venerable singular value decomposition then this book is for you.My first copy has been worn out. This review was triggered when a replacement copy was ordered.

First, this isn't Numerical Recipes. If you're looking for cut&paste code, you're just looking in the wrong place. This is for people who need a deep understanding of the computational issues, and are going to put a lot of time into an implementation. It's for people who are completely at ease with linear algebra, standard matrix-oriented problems, and dense mathematical notation.Despite its demand for a reader well versed in theory, this really is about practice. It's about the nasty effects of finite-precision arithmetic, about specific ways of minimizing the harm they cause. These techniques take full advantage of any special features in the problem, including banding and symmetry. This also deals briefly with caching issues, which are even more important now than when this book was written. Cache data can get to the processor in 1-10 cycles, in a modern workstation processor, but main memory access costs 100-1000 cycles. TLB misses can cost many thousands of cycles, even when data is already in memory. Clearly, good data structures and well-orgnized access patterns can make a huge difference, but one that is mentioned only briefly. The section on parallel computation is brief and helpful, but overdue for review. The authors could never have foreseen today's multi-(thread, core, processor) systems, Blue Gene, or clusters.Still, this is an indispensable reference for someone in the thick of numerical computation. Most programmers would do better, in lots of ways, usingn the GNU Scientific Library or one of the other production-quality packages out there. They don't always do the job, though. Emerging architectures, include hardware threading and reconfigurable computing, need new implementations of even well-known algorithms. If you have big mathematical problems and machines too exotic for the standard tools, you're on your own. Numerical computing is such a large topic that no one book can possibly cover it all. In the end, though, many other problems reduce to linear systems, and that's where this comes in. It may not be theonly book you'll need, but you'll need it.//wiredweird

The classic, what else to say?!

Delivery – greatThe book was wonderful as I expected, and I really enjoyed it.I got what I wanted, and I got everything I needed.What more do you need?I recommended it to my friends.

I used this book to teach me some of the finer points of matrix operations, and continue to reference it. In fact, in my field (geophysics), this book is the standard reference for matrix operations in journal publications. I have not found anything better, nor anything that even comes close.

A classic by a legend!

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Matrix Computations (Johns Hopkins Studies in Mathematical Sciences)(3rd Edition) PDF

Matrix Computations (Johns Hopkins Studies in Mathematical Sciences)(3rd Edition) PDF