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Written by top researchers, this text blends theory and practice. It covers the modern topics of parallel algorithms, concurrency and recurrency. A McGraw-Hill/MIT Press collaboration, the text is designed for both the instructor and the student. It offers a flexible organization with self-contained chapters, and it provides an introduction to the necessary mathematical analysis. Introduction to Algorithms contains sections that gently introduce mathematical techniques for students who may need help. This material takes students at an elementary level of mathematical sophistication and raises them to a level allowing them to solve algorithmic problems. Simple, easy-to-do exercises, as well as more thoughtful, step-by-step case-generated problems are included. The book features standard analytic notation and includes trimmed-down, easy-to-read pseudocode.
- Sales Rank: #860292 in Books
- Published on: 1990-03-01
- Original language: English
- Number of items: 1
- Dimensions: 10.25" h x 8.25" w x 2.00" l,
- Binding: Hardcover
- 768 pages
Amazon.com Review
If you had to buy just one text on algorithms, Introduction to Algorithms is a magnificent choice. The book begins by considering the mathematical foundations of the analysis of algorithms and maintains this mathematical rigor throughout the work. The tools developed in these opening sections are then applied to sorting, data structures, graphs, and a variety of selected algorithms including computational geometry, string algorithms, parallel models of computation, fast Fourier transforms (FFTs), and more.
This book's strength lies in its encyclopedic range, clear exposition, and powerful analysis. Pseudo-code explanation of the algorithms coupled with proof of their accuracy makes this book is a great resource on the basic tools used to analyze the performance of algorithms.
About the Author
Thomas H. Cormen is Professor of Computer Science and former Director of the Institute for Writing and Rhetoric at Dartmouth College.
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.
Most helpful customer reviews
11 of 11 people found the following review helpful.
Very in-depth text but lacks accessibility, clarity, & inspiration
By Asr
I just finished an undergrad-level college course that used this book (3rd edition). Extremely in-depth on the topics that it covers, using formal mathematical notation throughout to present the algorithms. As many others have said, it's very math-heavy and I'd recommend that anyone thinking about learning from this book should already have a strong background in especially discrete math, but should also have some familiarity with single-variable calculus and probability. The appendices do a brief math review (on discrete math & probability) but are not a substitute for learning the math for the first time.
The book is essentially best described as a detailed, mathematically-oriented analysis of algorithmic theory and covers a wide variety of topics. Proofs of various concepts are built up with lemmas throughout as well, and many of the exercises in the book often ask for a proof to show that something is correct. The authors do provide a Solutions file online, but for only some exercises up through chapter 26 when it has 35 chapters.
The book more than "combines rigor and comprehensiveness" as it says on the back, and it will almost certainly be an indispensable reference in the future for anyone planning to work (or is already working) in a computer science-related field, but it also has some notable drawbacks as well, not the least of which is the title. A genuine "Introduction" to algorithms would've been either more accessible (with less math) or substantially shorter (at approximately half the page count or less), or both. As it is, the book's title could have more accurately reflected its density with something more like "Algorithm Theory In Depth" or "Algorithm Analysis". Fortunately the book includes an extensive index that lists out nearly every sub-topic that it covers for quick reference.
Some of the notable drawbacks include:
- It often shows that it was written by 4 different authors, as some topics are explained better than others (the graph algorithms overall were explained well) while others lacked sufficient clarity (recurrence relations, sorting in general, greedy algorithms, amortized analysis, NP completeness).
- Like many other academia-oriented textbooks, it suffers from overuse of the infamous pretentious phrase "it is easy to see," paticularly when it's not easy to see what the author is talking about.
- Chapter 4 suffers from a lack of explanation on solving recurrences by the recursion-tree and substitution methods, with insufficient examples.
- No exercises or content to help with actually implementing an algorithm in a programming language, because putting an algorithm into practice should be just as important as the theory.
- The authors write in the preface that the book is aimed at teachers, students, professionals, & their colleagues (!). A book that attempts to cater to both students and the authors' colleagues at the same time would appear to be paradoxical, right?
- Dry, dispassionate wording that fails to inspire the reader to continue reading the text, and continue learning past this book as well.
Despite the drawbacks it's a good, very dense & in-depth reference on the subject, but for those learning algorithms for the first time, I'd recommend one of these other books first that explain the concepts more clearly in plain English and then coming back to CLRS afterwards. Just pick the one based on the language you're most familiar with. I own copies of all of these books btw (a few in digital formats, not all of them in print) and can personally speak to their quality in offering clearer, easier-to-understand explanations of algorithms.
- Data Structures In C, by Noel Kalicharan
- Data Structures and Algorithms in C++ (3rd Edition), by Adam Drozdek (can't speak to the newer 4th Edition)
- Data Structures and Algorithms in Java (2nd Edition), by Robert Lafore
- Algorithms (4th Edition), by Robert Sedgewick & Kevin Wayne (uses Java)
- Problem Solving with Algorithms and Data Structures Using Python SECOND EDITION, by Bradley Miller & David Ranum
If you're a college student that has to buy this book for your algorithms class like I had to, there are a variety of free resources online that should make the subject easier to learn along the way, like the MIT Open CourseWare resources on algorithms (course numbers 6.006 and 6.046), StackOverflow, GeeksforGeeks, and the sheer quantity of lecture slides available online at other colleges & universities. Coursera regularly has a free class on the subject as well.
467 of 490 people found the following review helpful.
Magisterial, and impenetrable
By Clinton Staley
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.
5 of 5 people found the following review helpful.
That book everyone knows as CLRS.
By AJay
This is the quintessential text for algorithms and many universities use this book in their "Computer Science 101" course. It was required in my first year at the University of Toronto (late 90's). It presents a treatment of the most common algorithms and the techniques needed to analyze them.
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.
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