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Introduction to Algorithms, third edition by [Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein]

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Thomas H. Cormen

Clifford Stein

Introduction to Algorithms, third edition Kindle Edition

by Thomas H. Cormen (Author), Charles E. Leiserson (Author), Ronald L. Rivest (Author), Format: Kindle Edition

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The latest edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-based flow.

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, substantial additions to the chapter on recurrence (now called “Divide-and-Conquer”), and an appendix on matrices. 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 exercises and problems have been added for this edition. The international paperback edition is no longer available; the hardcover is available worldwide.

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2059 pages
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English
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The MIT Press
Publication date

July 31, 2009
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15688 KB
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Editorial Reviews

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"In light of the explosive growth in the amount of data and the diversity of computing applications, efficient algorithms are needed now more than ever. This beautifully written, thoughtfully organized book is the definitive introductory book on the design and analysis of algorithms. The first half offers an effective method to teach and study algorithms; the second half then engages more advanced readers and curious students with compelling material on both the possibilities and the challenges in this fascinating field."--Shang-Hua Teng, University of Southern California

""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

"As an educator and researcher in the field of algorithms for over two decades, I can unequivocally say that the Cormen 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 --This text refers to an alternate kindle_edition edition.

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. --This text refers to an alternate kindle_edition edition.

Product details

ASIN ‏ : ‎ B08FH8N996
Publisher ‏ : ‎ The MIT Press (July 31, 2009)
Publication date ‏ : ‎ July 31, 2009
Language ‏ : ‎ English
File size ‏ : ‎ 15688 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 2059 pages
Lending ‏ : ‎ Not Enabled

Best Sellers Rank: #182,624 in Kindle Store (See Top 100 in Kindle Store)
- #24 in Algorithm Programming
- #101 in Programming Algorithms
- #303 in Tech Culture & Computer Literacy

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Ronald L. Rivest
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Ronald Linn Rivest (/rɪˈvɛst/; born May 6, 1947) is a cryptographer and an Institute Professor at MIT. He is a member of MIT's Department of Electrical Engineering and Computer Science (EECS) and a member of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He was a member of the Election Assistance Commission's Technical Guidelines Development Committee, tasked with assisting the EAC in drafting the Voluntary Voting System Guidelines.
Rivest is one of the inventors of the RSA algorithm (along with Adi Shamir and Len Adleman). He is the inventor of the symmetric key encryption algorithms RC2, RC4, RC5, and co-inventor of RC6. The "RC" stands for "Rivest Cipher", or alternatively, "Ron's Code". (RC3 was broken at RSA Security during development; similarly, RC1 was never published.) He also authored the MD2, MD4, MD5 and MD6 cry.ptographic hash functions. In 2006, he published his invention of the ThreeBallot voting system, a voting system that incorporates the ability for the voter to discern that their vote was counted while still protecting their voter privacy. Most importantly, this system does not rely on cryptography at all. Stating "Our democracy is too important," he simultaneously placed ThreeBallot in the public domain.
Bio from Wikipedia, the free encyclopedia. Photo by Ronald L. Rivest (Own work) [CC BY-SA 4.0 (http://creativecommons.org/licenses/by-sa/4.0)], via Wikimedia Commons.
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Charles E. Leiserson
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Charles E. Leiserson is Professor of Computer Science and Engineering at the Massachusetts Institute of Technology.
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Clifford Stein
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Clifford Seth Stein (born December 14, 1965), a computer scientist, is a professor of industrial engineering and operations research at Columbia University in New York, NY, where he also holds an appointment in the Department of Computer Science. Stein is chair of the Industrial Engineering and Operations Research Department at Columbia University. Prior to joining Columbia, Stein was a professor at Dartmouth College in New Hampshire.
Stein's research interests include the design and analysis of algorithms, combinatorial optimization, operations research, network algorithms, scheduling, algorithm engineering and computational biology.
Stein has published many influential papers in the leading conferences and journals in his fields of research, and has occupied a variety of editorial positions including in the journals ACM Transactions on Algorithms, Mathematical Programming, Journal of Algorithms, SIAM Journal on Discrete Mathematics and Operations Research Letters. His work has been funded by the National Science Foundation and the Sloan Foundation. As of November 1, 2015, his publications have been cited over 46,000 times, and he has an h-index of 42.
Stein is the winner of several prestigious awards including an NSF Career Award, an Alfred Sloan Research Fellowship and the Karen Wetterhahn Award for Distinguished Creative or Scholarly Achievement. He is also the co-author of two textbooks:
Introduction to Algorithms, with T. Cormen, C. Leiserson and R. Rivest, which is currently the best-selling textbook in algorithms and has been translated into 8 languages. About 39,500 of Stein's 46,000 citations are made to this book.
Discrete Math for Computer Science, with Ken Bogart and Scot Drysdale, which is a new textbook that covers discrete math at an undergraduate level.
Stein earned his B.S.E. from Princeton University in 1987, a Master of Science from The Massachusetts Institute of Technology in 1989, and a PhD also from the Massachusetts Institute of Technology in 1992.
In recent years, Stein has built up close ties with the Norwegian research community which earned him an honorary doctorate from the University of Oslo (May 2010).
Bio from Wikipedia, the free encyclopedia. Photo by Sergio01 (Own work) [CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0) or GFDL (http://www.gnu.org/copyleft/fdl.html)], via Wikimedia Commons.
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Thomas H. Cormen
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Thomas H. Cormen is Emeritus Professor and former Chair of the Dartmouth College Department of Computer Science and former director of the Dartmouth College Institute for Writing and Rhetoric. He received the B.S.E. degree in Electrical Engineering and Computer Science from Princeton University in 1978 and the S.M. and Ph.D. degrees in Electrical Engineering and Computer Science from MIT in 1986 and 1993, respectively. He is coauthor of the leading textbook on computer algorithms, Introduction to Algorithms, which he wrote with Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. The book, now in its fourth edition, has been translated into several languages. He is also the author of Algorithms Unlocked, a gentle introduction to understanding computer algorithms and how they relate to real-world problems.
Outside computer science, Cormen likes skating (inline and nordic), paddling, and cooking and eating barbecue. He considers himself the world's worst electrician who has a Ph.D. in electrical engineering.
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thomas scanlan

Helpful text, but not sufficient as sole resource

Reviewed in the United States on January 31, 2019

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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.

70 people found this helpful

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CyrixLord

very mathy

Reviewed in the United States on October 30, 2018

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yes, yes, it's an algorithm book but it looks like they expect you to be fluent in math so don't expect a primer here. great at making me looks smart to people who look at my bookshelf. I found 'easier' descriptions of common algorithms online but If you already know math like a boss, this would be pretty helpful. Better bring some water because its also pretty dry reading.

41 people found this helpful

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Ben Bitdiddle

Text is pretty good but has pedagogical weaknesses

Reviewed in the United States on October 6, 2016

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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.

92 people found this helpful

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Matthew

Good, But Still Very Complex!

Reviewed in the United States on March 21, 2016

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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.

182 people found this helpful

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E

Covered More Than Professor Recommended Text

Reviewed in the United States on July 28, 2013

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Fantastic - I love the author's tone and clear explanations, hoping between math and theory to plain English and examples.

I got it to supplement my professor's original recommendation (for COS 485/585 at the University of Southern Maine), which was Namipour Kumarss's 'Foundations of Algorithms'. It definitely covered more of the class than the text, because we already had alternative sources in addition to 'Foundations'.

Get the PDF if possible, because it's faster to search it doesn't follow the same organization of your class.

If you can, get the solution manual as well - it's worth working out the problems in addition to your other homework.

To anyone studying algorithms, good luck! Don't forget there are also great video resources as well.

2 people found this helpful

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james

Happy with my purchase

Reviewed in the United States on March 27, 2021

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I'm only several dozen pages into this textbook, but I'm able to tell you that this textbook is professionally written, and contains a wealth of information. This is a textbook that should be used at top-tier universities, and if a newer version comes out, I will definitely buy this textbook again.

One person found this helpful

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Liz DW

One of my favorites

Reviewed in the United States on May 7, 2021

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This is one of my favorite textbooks. I used the copy my school's library had for quite a while, and when I graduated I bought a copy for myself. I recommend it to anyone wanting to learn more about algorithms.

One person found this helpful

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Top reviews from other countries

Yashas

Received A Xerox Copy

Reviewed in India on January 8, 2020

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Firstly, this is not the international edition. It's the Eastern economy edition. And the book I received, sold by "law book world", seems like a xerox copy. Doesn't seem legit. Disappointed about that.

But I got it for a price of 900 Rs. Still, it's not correct for the seller to ship a Xerox copy. And the quality of the xerox is not good as well because in many pages, the ink is too light and almost not readable.

Coming to the book itself, it's a great book and no doubt it. Recommend this book to anyone who wants to study algorithms.

42 people found this helpful

Vikas Yadav

Seems like Copy of book instead of original book.

Reviewed in India on November 8, 2020

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It seems like amazon provided copy version of book. Even the figures are not clear.

30 people found this helpful

DanF

Book is missing of pages

Reviewed in the United Kingdom on March 24, 2019

Verified Purchase

I have bought this book years ago but only now I have realised that over 100 pages are missing (I usually use an electronic version of it). You clearly can see from the images that it is not possible seeing the missing pages from the side of the tome that means the book is was originally faulty. Over £32! That is what you call a proper SCAM!

8 people found this helpful

Sam

This is THE textbook on algorithms

Reviewed in the United Kingdom on February 28, 2018

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This is the definitive textbook on algorithms, and something no Computer Science student or researcher should ever be without.

That said, the approach, whilst detailed, is at times quite dry and heavy going, and could perhaps be broken up a little more to keep the reader refreshed and focused.

9 people found this helpful

Steven Warriner

Tends to student textbook rather than cookbook of algorithms

Reviewed in the United Kingdom on December 3, 2018

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Comprehensive and large selection of algorithms with emphasis on analysis of the runtime of the algorithms. Written as student textbook so some of elaboration and analysis is presented as exercises to be completed. The algorithms themselves are clearly described and given in pseudo-code, but there is no difficulty transcribing them into an actual language.

On balance given 5* for comprehensive coverage of algorithms and clear descriptions - but don't expect a pure cookbook of algorithms that can be typed in or downloaded.

3 people found this helpful

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