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With sample problems and mathematical proofs demonstrating the correctness of each algorithm, this book is ideal as a textbook for classroom study, but its reach doesn't end there. The authors do a fine job of explaining each algorithm. (Reference sections on basic mathematical notation will help readers bridge the gap, but it will help to have some math background to appreciate the full achievement of this handsome hardcover volume.) Every algorithm is presented in pseudo-code, which can be implemented in any computer language, including C/C++ and Java. This ecumenical approach is one of the book's strengths. When it comes to sorting and common data structures, from basic linked lists to trees (including binary trees, red-black, and B-trees), this title really shines, with clear diagrams that show algorithms in operation. Even if you just glance over the mathematical notation here, you can definitely benefit from this text in other ways.

The book moves forward with more advanced algorithms that implement strategies for solving more complicated problems (including dynamic programming techniques, greedy algorithms, and amortized analysis). Algorithms for graphing problems (used in such real-world business problems as optimizing flight schedules or flow through pipelines) come next. In each case, the authors provide the best from current research in each topic, along with sample solutions.

This text closes with a grab bag of useful algorithms including matrix operations and linear programming, evaluating polynomials, and the well-known Fast Fourier Transformation (FFT) (useful in signal processing and engineering). Final sections on "NP-complete" problems, like the well-known traveling salesman problem, show off that while not all problems have a demonstrably final and best answer, algorithms that generate acceptable approximate solutions can still be used to generate useful, real-world answers.

Throughout this text, the authors anchor their discussion of algorithms with current examples drawn from molecular biology (like the Human Genome Project), business, and engineering. Each section ends with short discussions of related historical material, often discussing original research in each area of algorithms. On the whole, they argue successfully that algorithms are a "technology" just like hardware and software that can be used to write better software that does more, with better performance. Along with classic books on algorithms (like Donald Knuth's three-volume set, The Art of Computer Programming), this title sets a new standard for compiling the best research in algorithms. For any experienced developer, regardless of their chosen language, this text deserves a close look for extending the range and performance of real-world software. --Richard Dragan

Topics covered: Overview of algorithms (including algorithms as a technology); designing and analyzing algorithms; asymptotic notation; recurrences and recursion; probabilistic analysis and randomized algorithms; heapsort algorithms; priority queues; quicksort algorithms; linear time sorting (including radix and bucket sort); medians and order statistics (including minimum and maximum); introduction to data structures (stacks, queues, linked lists, and rooted trees); hash tables (including hash functions); binary search trees; red-black trees; augmenting data structures for custom applications; dynamic programming explained (including assembly-line scheduling, matrix-chain multiplication, and optimal binary search trees); greedy algorithms (including Huffman codes and task-scheduling problems); amortized analysis (the accounting and potential methods); advanced data structures (including B-trees, binomial and Fibonacci heaps, representing disjoint sets in data structures); graph algorithms (representing graphs, minimum spanning trees, single-source shortest paths, all-pairs shortest paths, and maximum flow algorithms); sorting networks; matrix operations; linear programming (standard and slack forms); polynomials and the Fast Fourier Transformation (FFT); number theoretic algorithms (including greatest common divisor, modular arithmetic, the Chinese remainder theorem, RSA public-key encryption, primality testing, integer factorization); string matching; computational geometry (including finding the convex hull); NP-completeness (including sample real-world NP-complete problems and their insolvability); approximation algorithms for NP-complete problems (including the traveling salesman problem); reference sections for summations and other mathematical notation, sets, relations, functions, graphs and trees, as well as counting and probability backgrounder (plus geometric and binomial distributions).

Average Customer Rating:

good condition	Customer Rating:
this book was kept up...no pages missing was a great deal for me, it beats buyin the book for full price

Faily good timely delivery	Customer Rating:
I received the book in a fairly good amount of time. I will just note that this book did not come with it's companion CD. I expected it to have it's companion cd. There was not mention as to whether or not the book had it's companion cd.

The best textbook on algorithms, but it is not a programming book.

Customer Rating:

I used this book for a graduate level Algorithms course, and I really liked it. It is packed full of content on a wide range of topics. While this book does provide some high-level implementations of algorithms in pseudo-code, you will not find any examples of how to program algorithms in this book. That's really not what this book is meant for anyways. I found the reading to be easier than Knuth on similar topics, but you still need to have sufficient mathematical background in order to keep up (statistics, discrete math, some calculus). Also, unlike many technical books I've read recently, I did not find any mistakes, not even a typo. Some people are not aware that the MIT Open Courseware website has some excellent free video course lectures that use this book. I highly recommend at least viewing the first three or four of those lectures if you are new to this topic because they compliment this book very well. Make sure you understand the first four chapters of this book before moving on to other topics. Also, since it had been a while since I had the math as an undergraduate, I was relieved to learn that this book had several appendices that provided a review of the math topics required by the book. The only negative about the book is that it does not provide answers to any of the exercises at the end of the chapters, so you really need to use this book in conjunction with a course in order to be able to check your progress and how well you are learning the information. If you're not using this book with a course, check the MIT Open Courseware website that I mentioned previously. It has some sample assignments you can use.

This version has NO CD

Customer Rating:

There are three versions of the second edition, only one of which has the CD: The first one is published by the MIT Press, with the title "Introduction to Algorithms". This one has no CD. This is the one Amazon currently carries, so if you buy from Amazon, you get no CD. The second one is published by McGraw-Hill, also with the title "Introduction to Algorithms". This one also has no CD. The third one is published by McGraw-Hill too, but has the title "Introduction to Algorithms and Java CD-ROM". This is the one with the CD. It's much more expensive than the other two. The CD in the third version contains implementations of the algorithms in Java. To find someone that carries the CD version, search for this ISBN-13 number: 9780072970548 , or for "Introduction to Algorithms and Java CD-ROM" . Note: Some listings that come up for the ISBN number will not be the correct version. Look carefully for "and Java CD-ROM" before buying.

excellent resource

Customer Rating:

This book is (in my opinion) an essential resource of common computer science algorithms. It covers a broad range of different algorithm topics and I found the explanations by the authors extremely helpful and simple to understand (both with simple and advanced topics). It does attempt to cover as many algorithm topics as possible, so some topics may not be covered in fine detail (it does not spend as much time on runtime analysis as other books, or spend much time on NP problems). It is perfect for someone taking an algorithms class (grad or undergrad), or someone looking to broaden their algorithm knowledge. I only wish there was some form of solutions guide to help verify answers to review questions.