- 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: 4.3 out of 5 stars See all reviews (449 customer reviews)
- Amazon Best Sellers Rank: #9,968 in Books (See Top 100 in Books)
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
Introduction to Algorithms, 3rd Edition (MIT Press) 3rd Edition
Use the Amazon App to scan ISBNs and compare prices.
Frequently bought together
Customers who bought this item also bought
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.
Browse award-winning titles. See more
If you are a seller for this product, would you like to suggest updates through seller support?
Top Customer Reviews
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.
I recall struggling with the subject matter, despite having worked with computers since childhood and completing the requisite high-school mathematics courses. The approach taken by the authors is fairly direct; there is little hand-holding, although there are 100 pages of appendices covering pre-requisites.
I had my moments of wanting to hurl it across the room, but it remained amongst the few texts I held onto, thinking it might be useful or enlightening in the future, relative to the burden of lugging it around. I've since upgraded from the 2nd edition to the 3rd, and occasionally pick it up, read a few sections or a chapter and complete some exercises.
Each topic in the book is covered in the same way:
- Explanation of problem or concept.
- Example(s) and/or diagram(s)
- Mathematical proofs, where applicable.
- Problem set
Tip: Concrete Mathematics by Knuth is good primer, establishing the specific discrete and continuous mathematical techniques underpinning the algorithms, and filling several knowledge gaps. Also, Cormen has published a more approachable version of this text ("Algorithms Unlocked") which which be more appropriate to some.
None of the solutions to the exercises are provided in the book, but a few are available at the books website: mitpress.mit.edu/books/introduction-algorithms.
- 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.
- 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.
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.
Most Recent Customer Reviews
* A few pages have no printing on them at all
* Very thin paper, so that the printing from the back page...Read more