Nicely written book. Easy to follow. The authors presented the algorithms very well. A minor complain would be that some of the proofs are too informal for my liking, and some proofs could be simpler. I would recommend this book to anyone who wants an introduction to the theory of computation. Another nice thing is the wide margin. This certainly encourages active learning.

best undergraduate handbook about algorithms i've seen so far.
examples are much less artificial than in cormen (introduction to algorithms)

This is a great text as it teaches you the major techniques for designing efficient algortihms and how to solve real-life problems using algorithms rather than just presenting polished but inscrutable solutions to abstract problems

With algorithms not a strength of mine (although I am a computer scientist student), I was quite happy that our professor switched to this book away from the MIT Press book. The first few chapters were exactly what I needed in order to finally get a grasp of key algorithm analysis and design concepts. As the book progressed to more difficult chapters, their explanations became less and less clear and seemed to rely more on text than on nice diagrams and graphics. A few complaints I have about the book: no mention of the Master Method in the dynamic programming chapter, NP-Complete explanation confusing, and no published errata that I could find. Although I have a few complaints about the book, it is one of the better books for being introduced to algorithms. It needs a little tuning now and then as some explanations are not so clear, but you'll find even more complicated explinations in other algorithm books.

I use this book in a course and I am immensely pleased with it. I must point out, though, that this is a very theoretical book. There is not much code and the authors use rather high-level psuedo code for what's there. However, it is great at explaining concepts and what the generic algorithms actually do. So it does its job quite brilliantly. If you need more code, you might want to pick up the Sedewick books, although the code could be better commented and organized if you asked me.

This book is not concise; also the pseudocode syntax used in the book is annoying.

If you enjoy reading for the sake of reading then you might enjoy this book.There are better books out there by Knuth, Corman and Aho that cover the same topics as this one.

The text offers an interesting blend of rigour and informality. The numerous proofs in each chapter have that rigour. Yet what may be more important is how the text remains accessible to a primarily undergraduate audience.

The book is not just a compendium of common algorithms in computer science, and proofs about them. The authors place a stronger emphasis on motivating how to develop an intuitive understanding of the problems that the algorithms address, and of how to shape new algorithms. Or, possibly, apply or modify existing algorithms to new problems.

If you compare the text to Knuth's classic "Art of Computer Programming", then you might find Kleinberg and Tardos more accessible. (At least for undergraduate readership.)

Also, the extensive exercises at the end of each chapter often have contexts germane to the Web. For example, the links in web pages are used to motivate problems in graph theory, where we have directed (unidirectional) graphs, due to the one way nature of links. More generally, the recent, contextual nature of the problems may appeal to some students. Knuth had many exercises listed in his books, but they can be too abstract for most students.

The text also has an interesting chapter on NP problems. The authors address a very practical situation. Even if you find that you have a problem that is NP complete, it is not necessarily the end of the story. For real life reasons, you may have to find an approximate solution that is computationally feasible to evaluate. The chapter offers suggestions and examples that may be of help. (More formal texts might merely stop at proving NP completeness.)

The flow in this book is excellent. The authors do a great job in organizing this book in logical chapter. The chapters are organized into techniques to find solutions to particular problems, like for example, Greedy Algorithms, Divide and Conquer, and Dynamic Programming.

Each chapter contains a few representative problems of the technique or topic discussed. These are discussed in great detail, which is helpful to initially grasp the concepts. Furthermore, the end of each chapter contains a number of solved exercises. These are written up in less detail than the chapter problems, because they are usually slight variations or applications of the representative problems. I found these to be very helpful to me, as to build up a stronger grasp of the problem at hand.

Furthemore, the progressive search for a solution, such as for the Weighted Interval Scheduling problem using dynamic programming, is essential to understanding the process through which we can find such algorithms. The book is well written, in a clear, understandable language. The supplementary chapters on Basics of Algorithm Analysis and Graph Theory are a great started for people who have not been exposed to those concepts previously.

Network flows are covered extensively with their applications. I suppose this section of the course was enhanced because our instructor's research interests are Network Flows and she threw example after example at us. There are a great number of problems at the end of this chapter to practice.

(...)
One of the strenghs of this book, is that when the authors determine the running time of a particular algorithm, they write about how to implement it, with which data structures and why. Although it is assumed that data structures are common knowledge for the reader, this type of analysis is helpful for further understanding of such structures.

All in all, this is a great textbook for an introductory course in the design of algorithms.

This is a great book for an undergraduate level introduction to design and analysis of algorithms. It has been used as the textbook for the undergrad intro to algorithms course at Cornell University for the last 3 years, and I have personally used this book as both a student and a TA. It covers a broad range of topics and gives tons of examples for each topic. This is especially helpful for topics like NP-completeness, where the problems are like the animals in a zoo, they come in all sorts of varieties and it's not obvious how they relate to each other. The beginning student needs a lot of examples and practice to learn the techniques of constructing reductions and proving their correctness.

Of course, this book isn't perfect. Students still do come to my office hours to chat and stuff.