Rating: 
Summary: Great!!
Review: Simply great! A must buy for professionals and college students.
Rating:
Summary: The authoritative introduction to the study of algorithms
Review: Thorough and good organization give readers clear appreciation for the study of algorithms. Ideal and necessary companion for anyone serious in learning about algorithms. Requires mathematic background. Prior exposure to concepts of mathematic proof and induction will be helpful
Rating:
Summary: Great book! A must for each serious programmer.
Review: This is complete and very thorough book about algorithms (sorting, balanced trees, and much more) and programming techniques such as dynamic programming. Don't be misleaded by the word "introduction" in the title. The book goes much far than an introduction. I must say although, that some topics could be explained in simpler ways. This is especially true for the topic of NP-complete problems. Despite this, it is very good and highly recommended book
Rating: 
Summary: Big on scope, Brutal on math.
Review: This book covers a lot of ground in its discussions of algorithms, but if the reader doesn't have an excellent grasp of mathematics, it will surely cause severe pain and suffering. To complement this book, a companion mathematics book with exercises in items such as summations and logarithms is highly recommended
Rating:
Summary: A non-trivial algorithms text.
Review: This is an excellent book on algorithms that runs very deep and wide. I would say "introduction" could be a bit misleading. The more mathematically inclined you are the more appropriate and enjoyable this book will be for you. It is at the advanced undergraduate to graduate level
Rating:
Summary: An excellent beginning as well as a handy reference
Review: Introduction to Algorithms is most comprehensive book on programming
and algorithms that I have ever seen. It covers everything you
could possibly want to know; from quick sort to FFT to
heaps to computational geometry, it has it all. Rigorous
mathematical proofs of the running times of the algorithms
are discussed, but are easily disregarded if one wants just
the algorithms. Introduction to Algorithms is a must-have for
any programmer.
Rating:
Summary: An excellent text & reference.
Review: Used it as a text in a graduate class. Found it very
comprehensive and thorough. The problems at the end of chapters provide tremendous insight.
Rating: 
Summary: It's a standard, but...
Review: I have deep respect for math and people who can make sense out of it, but I am really slow thinker and this book simply overwelmed me. I don't know what is so wrong with having some problems answered. I learned to solve them by going through step by step examples, which this book lacks. After "Single Variable Calculus" by Stewart and "Discrete Mathematics" by Epp, this book looks quite arrogant. Each page like a statement: "You stupid, don't touch me!" I did not like pseudo-code and I did not find it's clear and helpful. This book has a lot of good stuff, but I don't believe that making things hard makes them more important and full of sense. After all , I think everything can be divided into simple ideas , and then explained. Like those "divide and conquer" algorithms that authors describe. It's pitty that they themselves don't practice what they preach.Don't buy it unless you're forced. Get Robert Sedgewick's books. They balance well math and programming and have nicely done code snippets.
Rating: 
Summary: Concise and Clear, No. Why not both?
Review: There are a number of reviewers who proclaim that the language agnostic nature of this book, in addition to it's erudite tone, more than compensate for the (artificial) learning curve. I beg to differ. Being an actuary, I recognize that this book's code snippets are written in a variation of APL. APL is hardly a self-evident programming language (read-only is a more accurate description). While computer science cognoscenti might decry spoon-feeding, there's nothing wrong with being *concise and clear* concurrently. Indeed, the truly great books in the hard sciences are both easy to read and rigorous at the same time. If pedants would get off of their high horses for a moment, they would probably admit this much (heck, who wouldn't?). Rigor at the expense of clarity may appeal to intellectual snobs (who live for the material, god help them), and clarity at the expense of rigor may appeal to beginners, but WHY NOT HAVE BOTH? While this book covers a good deal of ground, it does so at the expense of clarity. A canonical book would have both rigor and clarity, and this book doesn't. It's as simple as that. The sordid truth about this book is that Professors tend to assign it as reading material with the expectation that students will rely primarily on class notes and then use the book as a reference of sorts (or as a source of homework problems). Most of the graduate courses that I've taken follow this approach. Having said all this, academia is essentially a small and sterile refuge for people who couldn't hack the real world. Take your courses, if you must, and then go out and get a life. In the end, most journals end up in the waste basket. Your time on this planet is short, don't waste it cloistered in a library! trust me on this...
Rating: 
Summary: Not easy to understand
Review: Although there is some great analysis of algorithms in this text, the text does not do a very good job of TEACHING one how to perform the analysis. I really did not like the fact that answers to the exercises are not available anywhere. I learn best by seeing examples done step by step and this book does not provide any solutions or even offer a separate solutions text. The authors seem to feel that the instructor should provide solutions, well I don't know about other Universities, but my professor does not provide solutions in class and the TAs are not very strong with this type of difficult material. Which leaves me to scour the Internet and other texts to try and understand the topics when I could have gained the same understanding by examining the solutions to the exercises.
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