Introduction to Algorithms, 3rd Edition (MIT Press) 3rd Edition

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.

A great book should deliver in two areas; it should challenge and it should inform. The challenge is there, no doubt. However in some ways it fails to inform the reader. The authors should really think about releasing a students solution manual to help students learn better. I take away two stars for the reasons stated about.

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.

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. The first being that even though an efficient algorithm has never been found, there is no proof that one cannot exist. Second, if an efficient algorithm exists for one of them, then an efficient algorithm exists for all. Thus, if you are ever called upon to write an efficient algorithm for an NP-complete problem, you will be involved in a long fruitless search if you do not recognize the problem as NP-complete. If you can show the problem is NP-complete, you can go about producing an algorithm that gives a good solution, but not the best possible solution. This kind of knowledge is what separates a computer scientist from a mere programmer, and is one of many reasons to study this book's contents. I highly recommend this book to anyone who truly wants to be called a computer scientist.

To get the most from this book you should already be familiar with discrete mathematics and combinatorics, as this book makes heavy use of these subjects. Because this book contains no solutions to any of the exercises, might I suggest "Problems on Algorithms" by Ian Parberry as a companion to this book. It has a little bit of tutorial and a lot of exercises, many unsolved, but some with hints and others with solutions. Also, for more basic material, you might look at "Schaum's Outline of Discrete Mathematics". It's very inexpensive and can almost stand alone as a tutorial on the mathematics you need to know to succeed at understanding this book. The table of contents is not shown for this third edition of the book, so I supply that information next:

Table of Contents
Preface
I Foundations
1 The Role of Algorithms in Computing
2 Getting Started
3 Growth of Functions
4 Recurrences
5 Probabilistic Analysis and Randomized Algorithms
II Sorting and Order Statistics
6 Heapsort
7 Quicksort
8 Sorting in Linear Time
9 Medians and Order Statistics
III Data Structures
10 Elementary Data Structures
11 Hash Table
12 Binary Search Trees
13 Red-Black Trees
14 Augmenting Data Structures
IV Advanced Design and Analysis Techniques
15 Dynamic Programming
16 Greedy Algorithms
17 Amortized Analysis
V Advanced Data Structures
18 B-Trees
19 Binomial Heaps
20 Fibonacci Heaps
21 Data Structures for Disjoint Sets
VI Graph Algorithms
22 Elementary Graph Algorithms
23 Minimum Spanning Trees
24 Single-Source Shortest Paths
25 All-Pairs Shortest Paths
26 Maximum Flow
VII Selected Topics
27 Sorting Networks
28 Matrix Operations
29 Linear Programming
30 Polynomials and the FFT
31 Number-Theoretic Algorithms
32 String Matching
33 Computational Geometry
34 NP Completeness
35 Approximation Algorithms