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| Matrix Computations (Johns Hopkins Studies in Mathematical Sciences)(3rd Edition) | 
 enlarge | Authors: Gene H. Golub, Charles F. Van Loan
Publisher: The Johns Hopkins University Press
Category: Book
List Price: $48.00
Buy New: $29.57
You Save: $18.43 (38%)
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Avg. Customer Rating:  (26 reviews)
Sales Rank: 64478
Languages: English (Original Language), English (Unknown), English (Published)
Media: Paperback
Edition: 3rd
Number Of Items: 1
Pages: 728
Shipping Weight (lbs): 3
Dimensions (in): 9.2 x 6.1 x 1.4
ISBN: 0801854148
Dewey Decimal Number: 512.9434
EAN: 9780801854149
ASIN: 0801854148
Publication Date: October 15, 1996
Availability: Usually ships in 1-2 business days
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| Customer Reviews:
 Don't judge a book by its cover (or name)...best book on the subject! October 30, 2006
6 out of 7 found this review helpful
This book looks like it should be both dry and difficult to read. After all, it is a comprehensive reference on numerical linear algebra. However...it is absolutely outstanding! I cannot emphasize how informative, useful, and fun this book is!
The thing I like most about this book is its clarity...it's so easy to read. Golub again and again focuses on helping the reader make the transition between theory and algorithm--in both directions. Although many books help the reader understand theory and then create code from it, Golub's book is one of the few I have ever encountered that actively cultivates the ability to look at an algorithm and understand it in theoretical terms. To anyone who has ever tried to optimize code in a complex project, this is an immensely useful skill that is often overlooked in programming and mathematics classes alike. Many books will help you understand the theory behind things, but this is the only book I've ever found that not only strikes the perfect balance between theory and practice, but provides a sufficiently strong bridge between the two to be actually useful.
The book is structured in such a way that it is easy to skip around, reading sections in as much detail as is needed. Golub avoids the tight logical dependencies that plague most modern math texts, making this book an oustanding reference. And although this book probably gets more use as a reference, I think it would actually make an excellent textbook, and it is certainly useful for self-study. I find it clearer at times than Trefethen's textbook "Numerical Linear Algebra", and it is certainly more comprehensive.
A classic October 2, 2006
11 out of 12 found this review helpful
In certain ways, this book has been both a bane and a boon to my career as a computational mathematician. Way back in 1989, I had the mixed experience of taking a course in Numerical Analysis from Brian Smith at the University of New Mexico. Prof. Smith taught that course exclusively from this book (actually, from the 2nd edition). As a college sophomore, I was terribly out of my depth, but I managed to do okay. Later, I had the opportunity to study under Gene Golub at Stanford, although I was certainly not one of his better students :) Naturally, Prof. Golub also taught pretty much exclusively from this book, by the way, he is a gifted mathematician and wonderful instructor, and a real gentleman. Between these experiences, I'd say I became extremely familar with the contents of this book.
Okay, back to the actual book. If you've got a numerical linear algebra problem to solve, and you don't know which NAG or Matlab routine to use, or simiarly can't figure out why your Numerical Recipes ripped-off code is blowing up on a certain matrix, well, you'll find the reason in this book. The main issue is that you've got to know what you're looking for in order to find it, and that's kind of the kernel of the problem. Some reviewers have stated that the writing is terse, that it is too rigorous, etc. I don't really agree with these reviews, but I agree that it is not for the casual reader who wants a quick answer to the question of "how do I invert this thing". The book spends a lot of time with subtle details such as convergence and stability, and in my experience, these excellent treatments are wasted on most would-be users who are really just looking for a numerical silver bullet, which, of course, just doesn't exist. I find that the book is an invaluable reference, when I have a problem like this "okay, I've got a small Vandermonde matrix that may or may not be singular. What's the quickest algorithm to get a stable result?" Usually what happens is I read this book to understand the method, and then go looking for an existing implementation, usually in Matlab or NAG. I guess the bottom line is that if you're looking for a "recipe" you won't find them here. If you're looking for insight and understanding into a numerical method, you will find it here, but you'll have to work to eek out an implementation if you really feel that you need to write one.
The standard reference August 18, 2006
15 out of 16 found this review helpful
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
 Misbound June 30, 2006
6 out of 11 found this review helpful
I ordered this book new but it was misbound, apparently during publication. In my first order, the book was missing the end of Chapter 12, had two indices, and had the final references buried in the middle of another chapter; the replacement book from Amazon had similar faults -- the end of chapter 12 was again missing, there was no index, and chapter four was in the place of the end of chapter 12.
I suggest you wait until Amazon gets a new batch before ordering. How you can be sure it's not the same batch I ordered from, I'm not sure. Good luck. I fervently awaited the arrival of this book, but in its current form is useless.
 Might be a good reference June 16, 2006
1 out of 2 found this review helpful
but not a good book to learn methods. I guess a book explaining the ideas of main methods in an easy way will help.
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