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Introduction to Algorithms 3ed Hardcover – 15 Sep 2009

by Thomas Cormen (Author), Charles Leiserson (Author), Ronald Rivest (Author),

4 ratings

in Computer Algorithms Textbooks

ISBN-13: 978-0262033848 ISBN-10: 9780262033848 Edition: 3^rd

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Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, and substantial additions to the chapter on recurrence (now called ''Divide-and-Conquer''). It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks. Many new exercises and problems have been added for this edition.As of the third edition, this textbook is published exclusively by the MIT Press.

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Product details

Hardcover: 1320 pages
Publisher: The MIT Press; 3 edition (15 September 2009)
Language: English
ISBN-10: 9780262033848
ISBN-13: 978-0262033848
ASIN: 0262033844
Product Dimensions: 23.1 x 20.8 x 5.2 cm
Boxed-product Weight: 2.3 Kg
Average Customer Review: 4 customer reviews
Amazon Bestsellers Rank: 3,533 in Books (See Top 100 in Books)
- #17 in Management Information Systems
- #2 in Computer Algorithm Date Structures
- #1 in Computer Algorithms Textbooks

Product description

Review

'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 VirginiaThe MIT Press'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 UniversityDaniel Spielman' 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 UniversityThe MIT Press

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.

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Awesome

26 September 2019

Format: HardcoverVerified Purchase

I've had my eye on this book for a very long time, but it's always been above $100 for just the paperback version.

This "hardcover" version, can barely be called a hardcover. It's just thicker, but still flexible card stock, not hard in the sense of other books that tend to be rigid (almost like wood). The only comparison I can think of is that it's similar to the card stock board game boxes are made of.

This book is always in the top 10 of recommended computer science books. I'm really hoping something a little more approachable comes along though. I don't like the code examples given, or some of the text that features heavy use of equations, which are difficult to keep in your head all at once.

While I commend the authors ability to write concise, meaningful explanations. There are some areas that could have been expanded on a little more to aid in understanding. I did find myself reading paragraphs multiple times to get their full meaning. So, in some areas it can be a difficult read, and so it helps to sometimes draw yourself a little diagram on what's happening.

In saying that, if you're a pro at mathematics, you'll probably do fine with this book. I'm not, so I struggled a little.

One person found this helpful

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Young Chul Sim

Near Last chance to have real book.

30 August 2019

Format: HardcoverVerified Purchase

Still having a book gives me better chance to focus on contents. When it comes with near classic material it is worth
to have. Our education system let understand things superficially. If you have a book, you might have slight more chance to read again and agian until the knowledg and wisdome in the book becomes part of your mind.

This kind of near classical master pieces in the book market is disappearing. I think it is a really good chace to collect
Computer Science Classic book. These kind of books are worth to keep until the end of my programming career.

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Shaon Diwakar

Proof heavy - great for deep theory, not for interview prep

2 August 2018

Format: PaperbackVerified Purchase

Overall, this book is aimed at University level students studying Computer Science, or Engineering. While the treatment of most subjects is handled with care and depth, if you are looking for a quick backgrounder on AVL trees, or Tries etc you may have better luck elsewhere.

That said, this book is a fantastic resource for anyone looking for a complete treatment of data structures and algorithms. It sets the standard for this field, and I believe, is a required resource on every engineers bookshelf.

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Customer Profiles 2

Five Stars

27 September 2014

Format: Kindle Edition

Great one

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Most helpful customer reviews on Amazon.com

Amazon.com: 388 reviews

Matthew

Good, But Still Very Complex!

21 March 2016 - Published on Amazon.com

Format: HardcoverVerified Purchase

For background, I am a not-so-sharp CS undergrad that used this book for an intro. to algorithms class. I've done linear algebra, struggled my way through a "calculus" probability course, and enjoyed implementing many typical data structures. My learning style relies on simple examples (especially visual) accompanied by a concise explanation. Here are my thoughts:

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.

100 people found this helpful

Ben Bitdiddle

Text is pretty good but has pedagogical weaknesses

7 October 2016 - Published on Amazon.com

Format: HardcoverVerified Purchase

I think this is a pretty good book that is easy to read if you have a strong background in proof-based math.

Highlights:
- The introduction (Chapters 1-4) is really good and does a good job setting up all the fundamental concepts of algorithms. I think a lot of people tend to skip over introductions because they think they know all of it already, but this is an introduction that I recommend reading the whole way through.
- The book is a pretty light read (none of the math is too difficult) and each chapter is a good length.
- I think the material on dynamic programming and greedy algorithms was particularly enlightening, and if you read it the whole way through you actually learn how to prove that greedy algorithms work, instead of just being like "let's use a greedy algorithm because it seems right"
- I was able to copy a lot of CLRS code almost verbatim in my programming interviews and pass them.
- The figures are really well done and informative.

Drawbacks:
- The pseudocode has a lot of one-letter variable names, and while this follows the tradition of pure math, it also makes understanding the algorithms more difficult than it should be.
- Sometimes the pseudocode is not the "easiest" possible pseudocode (for example, merge sort), and I think it would be better if the authors presented a simpler version of the pseudocode first and then extended it to the optimal version. But then I guess CLRS would be even longer than it already is.
- The arrays are 1-indexed, which makes it trickier to convert to code. Also there are some sections of the textbook (the counting sort section) where some of the arrays are 0-indexed and other arrays are 1-indexed, which is just weird.
- I think the material on graphs, particularly the derivations, could be done in a more engaging and intuitive way. The derivations in Chapters 22-24 were a long series of small uninteresting lemmas, instead of a small number of harder, more insightful theorems. I found derivations elsewhere on the internet that were a lot more interesting and built more intuition about why the procedures worked. I feel like the rest of the book is pretty good though, so maybe all the graph stuff was written by a separate person who is not very good at explaining things.

39 people found this helpful

thomas scanlan

Helpful text, but not sufficient as sole resource

1 February 2019 - Published on Amazon.com

Format: HardcoverVerified Purchase

My process for studying this text is to read a section at a time and walk through the examples illustrated. I often struggle with the math that is being demonstrated as a proof for the algorithm. I then research an actual example of the topic discussed and watch a youtube video that demonstrates it in application.

I then re-read the section and take notes. I find that I feel somewhat lost at first when the book introduces a topic that I am unfamiliar with, but after reviewing it from a high level (youtube video) it helps me understand the algorithm on a surface level. Once I understand it in its simplest terms, the proofs become much simpler and they make a lot of sense.

I think to get everything out of this text you should be comfortable with data-structures, linear algebra and discrete mathematics. I found discrete math and linear algebra to be difficult courses, but this text is increasing my confidence in how much I had learned in those courses.

Great text but at times I feel lost. I wish the examples were more comprehensive at times.

12 people found this helpful

Top-Notch Content, Poor Binding

4 June 2016 - Published on Amazon.com

Format: HardcoverVerified Purchase

This is probably the most well known and most used textbook on the subject, and with good reason. An excellent resource, covering just about everything you need to know for a good understanding of Algorithms. (side tip, my friends in the industry call this the "How To Pass a Google Interview" book).

My only complaint is that the binding has completely stated disintegrating after only 9 weeks of use. All of chapters 15 and 16 are completely falling out of my copy (and this is getting worse). Very disappointing as I plan on using it for a long time.

22 people found this helpful

Monday Monkey

Thorough, Rigorous, Maybe not Best for Most Beginners

1 November 2015 - Published on Amazon.com

Format: HardcoverVerified Purchase

I'm reviewing this for what it is: An academic text that also serves as a collective document of algorithms for the community (computer science, etc.). This is not a beginner text. It's a first-class algorithms book that is very academically rigorous. That's not a put-down.

Great reference and great read when you need to get out of the box and think creatively.

For a more approachable intro to algorithms, check out Sedgewick's book. That book also has a companion website and Coursera course. it's hard to go wrong with that combo.

20 people found this helpful