- Series: MIT Press
- Hardcover: 1312 pages
- Publisher: The MIT Press; 3rd edition (July 31, 2009)
- Language: English
- ISBN-10: 0262033844
- ISBN-13: 978-0262033848
- Product Dimensions: 8 x 1.8 x 9 inches
- Shipping Weight: 4.7 pounds (View shipping rates and policies)
- Average Customer Review: 456 customer reviews
- Amazon Best Sellers Rank: #4,637 in Books (See Top 100 in Books)
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Introduction to Algorithms, 3rd Edition (MIT Press) 3rd Edition
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As an educator and researcher in the field of algorithms for over two decades, I can unequivocally say that the Cormen et al book is the best textbook that I have ever seen on this subject. It offers an incisive, encyclopedic, and modern treatment of algorithms, and our department will continue to use it for teaching at both the graduate and undergraduate levels, as well as a reliable research reference.(Gabriel Robins, Department of Computer Science, University of Virginia)
Introduction to Algorithms, the 'bible' of the field, is a comprehensive textbook covering the full spectrum of modern algorithms: from the fastest algorithms and data structures to polynomial-time algorithms for seemingly intractable problems, from classical algorithms in graph theory to special algorithms for string matching, computational geometry, and number theory. The revised third edition notably adds a chapter on van Emde Boas trees, one of the most useful data structures, and on multithreaded algorithms, a topic of increasing importance.(Daniel Spielman, Department of Computer Science, Yale University)
About the Author
Thomas Cormen is Professor of Computer Science at Dartmouth College. Charles Leiserson is Professor of Computer Science and Engineering at MIT. Ronald L. Rivest is Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT. Clifford Stein is Professor of Industrial Engineering and Operations Research at Columbia University.
Top customer reviews
It does a good job at explaining use cases for algorithms and comparing similar algorithms without spending too much time on it.
The book could use a reference for a lot of the symbols. I don't think that the book requires much prerequisite knowledge, but I've been guessing at what some of the symbols mean. This is a very heavy book, both literally and figuratively. I hate suggesting more content to add, but I think a few more pages to clarify what is used on every page would be valuable.
While the book definitely is a good book and is the go-to book for algorithms courses, it actually is more of a graduate level book.
As my professor explains it, it is a very mathy book and is not suited well for undergraduate (even though he made us undergraduates get it...), it's only use in undergrad is the fact you can get it while in undergrad and take it with you to graduate school.
So what makes undergraduate different from graduate to make this book suited for graduate level courses?
Undergraduate: Don't care about proofs or the math part, just wanna know the algorithms at a basic understanding without knowing the reason the algorithm even works at the math level. Most professors can just teach the material straight up no book for undergraduate courses honestly, the professors got PHDs they can give undergraduate level explanations on the fly.
Graduate: You are required to give mathematical proofs in graduate level courses, and are expected to know the algorithm at the deepest math level. Because of the work load, this is where this book shines because the professor cannot spend everyday till midnight teaching each student how to prove every algorithm, so this book is very well suited for graduate level because it is VERY math oriented.
This is a book that focuses on the math of the algorithm, but that's not entirely bad because undergraduates still may be interested in that stuff, my course just doesn't care about the proofs because there already is a graduate course for the ones in the Master's program.
As for the actual content and how easy it is to understand for an undergraduate... Well I do plan to go for PHD and this book has been very helpful for that because I am motivated to take the next step. I catch a snag once in a while on trying to understand the math part, but no pain no gain!
I only rated 4 stars because I haven't read the whole book yet, so giving a 5 star would be a bit awkward....
This book is impressive! It covers a lot of subject matter and is clearly worded. However, you're going to get lost because this often reads more like a reference manual than a conversation that appeals to intuition. You'll be pushed into analyzing algorithms for theoretical data structures that you fuzzily remember (if at all). But, nonetheless, throw enough man hours into this book and you will learn concrete approaches to determining just how hard you're making the computer work.
My biggest criticism is that, as an *introduction*, this book doesn't do the best job at warming up readers to new tools and methodologies. This is an 'eh, just push them into the deep end' kind of approach to learning.
Most recent customer reviews
My algorithms class was hard (what algorithms class isn't), and I'm not always an ideal student.Read more
* A few pages have no printing on them at all
* Very thin paper, so that the printing from the back page...Read more