- Series: Available Titles CengageNOW
- Hardcover: 847 pages
- Publisher: Brooks Cole; 8 edition (December 10, 2004)
- Language: English
- ISBN-10: 0534392008
- ISBN-13: 978-0534392000
- Product Dimensions: 8 x 1.3 x 9.2 inches
- Shipping Weight: 3.6 pounds (View shipping rates and policies)
- Average Customer Review: 66 customer reviews
- Amazon Best Sellers Rank: #353,563 in Books (See Top 100 in Books)
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Numerical Analysis (Available Titles CengageNOW) 8th Edition
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About the Author
Richard L. Burden is Emeritus Professor of Mathematics at Youngstown State University. His master's degree in mathematics and doctoral degree in mathematics, with a specialization in numerical analysis, were both awarded by Case Western Reserve University. He also earned a masters degree in computer science from the University of Pittsburgh. His mathematical interests include numerical analysis, numerical linear algebra, and mathematical statistics. Dr. Burden has been named a distinguished professor for teaching and service three times at Youngstown State University. He was also named a distinguished chair as the chair of the Department of Mathematical and Computer Sciences. He wrote the Actuarial Examinations in Numerical Analysis from 1990 until 1999.
Top customer reviews
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I picked up this book and went straight to Chapter 12. It explained everything quite concisely and had very clear descriptions and diagrams. Armed with pen and paper, I learned how to numerically solve these PDE's quite quickly. It was honestly a fun experience. Whenever there were tools that I was missing, the authors would reference the section and chapter where you could find the necessary tools.
I believe this book was written as a reference, as well as, textbook. A problem that many textbooks suffer from, is that the material is written in sequential order, with newer material depending heavily on the previous chapters. These types of books are not adept to being just picked up and read, to gather the relevant information. They require you to pretty much read all the preceding text to understand it, and who has time for that? This book is NOT like that.
You can just pick it up and easily learn from it. Unlike Numerical Recipes, this provides the method with a very clear explanation and justification for the algorithms. Numerical Recipes is good, but its purpose is not to provide detailed explanations of why and how the algorithms work.
To be able to use this text, I would suggest having taken Calc 1,2 & 3, differential equations, linear algebra class, and be comfortable with programming. I suspect that the folks complaining heavily about this text, are not very comfortable with with Calculus, linear algebra, and/or programming. If you are an undergrad and have not taken those classes or are not comfortable with the material, I can see you struggling. If you are a grad student in Math or Physics, this text will be rather refreshingly easy to read. It will help fill in the necessary gaps in your knowledge of computational work, if you have any like I did. Enjoy!
I doubt you'll be looking into any of these books unless you need a reference material for a course or something, but there wasn't significant differences between this version and the next one. But from what I understand the most recent version has enough differences that if you need this for a course, to get the newest version. But, if you are just buying this for your own sake, this is a great book/version.
The material in the book itself is a great resource, and I would argue that any CS student (or even just programmer who wants to be a bit better in his field) should know this material, that way they know how to evaluate run-time performance of a program (if nothing else). The book is fairly well understandable even if you aren't the best in math, so don't let that stop you if you are interested, there are online code snippets and evaluation programs you can try out and learn from also.
* an explanation of the mathematical tools and methods used in developing numerical algorithms, or
* a guide to the intricacies and pitfalls of implementing numerical algorithms for scientific computing.
For either of these cases, this is not the book you want. The algorithms and theorems have little motivation, and when there are several alternative algorithms to choose from there is little or no discussion of which is more appropriate under what circumstances. The pseudocode is enough to write a working version in whatever language in order to do the exercises, but it's not at all helpful for creating efficient and robust code that will be reused.
If you just want an overview of numerical analysis and the various basic algorithms this might not be a terrible choice except for the countless errors in the book, including the solutions in the back.