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Introduction to Algorithms, 3rd Edition 3rd Edition

257 customer reviews
ISBN-13: 978-0262033848
ISBN-10: 0262033844
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Editorial Reviews


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 H. Cormen is Professor of Computer Science and former Director of the Institute for Writing and Rhetoric at Dartmouth College. He is the coauthor (with Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein) of the leading textbook on computer algorithms, Introduction to Algorithms (third edition, MIT Press, 2009).

Charles E. Leiserson is Professor of Computer Science and Engineering at the Massachusetts Institute of Technology.

Ronald L. Rivest is Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology.

Clifford Stein is Professor of Industrial Engineering and Operations Research at Columbia University.


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Product Details

  • 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: 4.3 out of 5 stars  See all reviews (257 customer reviews)
  • Amazon Best Sellers Rank: #4,801 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

673 of 702 people found the following review helpful By M. Leeper on December 5, 2009
Format: Hardcover
First of all, this is the quintessential book on algorithms. If you want to learn, this is the book to get. The information in the book is awesome and it can make an excellent reference.

Students will need a very strong mathematical background and a strong arm to even think about picking up this book because the it is heavy (both physically and metaphorically). Mastery of discrete math is a must, graph theory, programming, and, combinatorics will also help.

With that said, this book falls short in one MAJOR area, explanations. Too often explanations are left out and left as exercises and there are no solutions to the exercises! Or details are replaced by ambiguous statements such as of "cleary, this works", or "it is easy to see that this ...". I get the concept of learning by doing, really I do, but there should be some kind of solutions so the student can CHECK his/her understanding of the material and sometimes the exercises are not about advanced aspects of a concept, sometimes it is the core material. Even if the solution manual only contained a simple answer without the work. Not only would it help tremendously but the purpose of doing the exercises would be preserved; that is the student getting his/her "hands dirty" and working out a problem.

For the love everything good and pure in this universe, I really wish writers of mathematical books would stop using statements like "clearly this works" or "it is easy to see", "it is obvious" etc. While that may be true for you and your brilliant circle of colleagues, everything is not always clear and obvious to your readers. Save all of that ambiguity for your research paper.
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299 of 317 people found the following review helpful By Clinton Staley on August 29, 2011
Format: Hardcover
I'm a professor of Computer Science at a respected teaching university, and have been the principal instructor of our introductory algorithms class for the past several years. I used Cormen (doesn't *everyone*?) for a year or two, but have finally relegated it to recommended-text status.

On the plus side, the text is, as my review title says, magisterial. It covers the field comprehensively and authoritatively. When one of the authors is the "R" in RSA, and others are well-known names, you can count on the text's expertise and accuracy. I've never found an error in this text.

BUT.... The pedagogy needs work. Explanations tend to jump too quickly to pure mathematical notation, and there are often insufficient concrete examples. The pseudocode has one-letter variable names that appear at times to be randomly generated :). At least the latest edition fixes what was a baffling indentation style. If you took a sample of 100 CS undergrads and asked them to learn algorithms principally from this text, I'd venture a guess that only the 10 brightest could do so. And even they'd be baffled at times.

I apologize for having to offer such an "emperor is naked" review to such a highly respected work, but it's time to consider more carefully pedagogical texts in the undergrad market.
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153 of 175 people found the following review helpful By calvinnme HALL OF FAMETOP 1000 REVIEWERVINE VOICE on November 7, 2009
Format: Hardcover
An algorithm is nothing more than a set of computational steps that transform a specific input into a desired output. From that definition, there are plenty of books on the market that are "cookbooks" of algorithms and will enable you to do just that - transform specific inputs into outputs, complete with source code, and with no real depth of understanding of your own required. However, to be a computer scientist versus a programmer, you need to know what makes an efficient algorithm, why is a particular algorithm efficient, what kinds of common data structures are involved in various computing problems, how to traverse those data structures efficiently, and a notation for analyzing various algorithms. This book will help you learn all of that. The study of the theory of algorithms is not to be undertaken lightly, and I don't recommend you attempt to self-study such a complex subject with such strong mathematical underpinnings. In fact, this book is really aimed at graduate computer science students and is often on the reading list of Ph.D. qualifying examinations in that field.

For students of graph theory, you might find your knowledge solidly supplemented by the material in chapters 22 through 26 on graph algorithms. The last section of the book, "Selected Topics", goes over various specific algorithms from many fields using the knowledge of algorithm design and analysis you have learned up to this point in the book. Throughout, the text is very clear, and there are plenty of instructive diagrams and pseudocode.

One of the most interesting parts of the book is the chapter on NP-completeness. This is the study of problems for which no efficient algorithm has ever been found. These problems are interesting for two reasons.
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61 of 70 people found the following review helpful By Emre Sermutlu on December 6, 2009
Format: Hardcover Verified Purchase
I have studied algorithms using several books, and this is by far the best. It is comprehensive (twice as thick as the average book), you can find everything you are looking for. It is pedagogical too, always starts with simpler problems. I have also used the first edition for some time, and can say that this one is much improved as a result of feedback from instructors and students. Everything from pseudocode to page layout has been touched in some way, and made easier to read and understand for the student.

The only negative thing about this book is the lack of solutions to exercises. The authors must have realized the importance of this. They published a small subset of solutions on the web, but that is inadequate.
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