algorithms

This is a good attempt at an algorithm design manual, but from my perspective (as a professor of mathematics working in combinatorics and combinatorial optimization) the writing is simply not precise enough. I'm not saying the book is too "chatty" or "informal". A technical book can be either or both while imparting its content in a clear and unambiguous fashion. I compliment the author on the idea for this book, which I think fills a gap and is at about the right level. However, if a second edition is printed (and I hope it will be), the author should read every (technical) sentence of his text and ask the question "Am I saying exactly what I mean to say?". Unfortunately, despite knowing more often than not what the author _intends_ to say, often I have trouble convincing myself that this is what is actually said.

That having been said: this is a good reference and provides a good "meta-overview" of the basics of algorithms. The Catalog of Algorithmic Problems is a particularly useful tool for quickly locating problems that might be similar to one under consideration and determining (as of 5 years ago, anyway) the approximate state of the art.

I recommend this book, but not unreservedly.

This book fills a nice niche -- it is practical enough to be useful and accesible to professional programmers (rather than algorithms researchers or academics) but is build on solid theory as well.

Aside from this, the book has several features to recommend it:
(1) There are "war stories" scattered throughout the text. These are special sections that describe the author's experience working with algorithmic problems "in the field". These are particularly interesting because the present false starts and failures along the way to the final solution. This is a nice change from the standard model of simply presenting algorithms and proving them correct.

(2) Unlike others, such as the popular Cormen, Lieserson, Rivest and Stein text -- which is a bit advanced for beginning study -- this really is an introduction to algorithms. It is quite suitable for, say, undergradutes who have taken a couple of basic CS courses.

(3) Chapter 8 is a 250-page "Catalog of Algorithmic Problems". Here, you will find descriptions of hundreds of standard formulations for algorithmic problems along with the basic solution approaches, and -- this is what really sets it apart -- pointers to implementations of these algorithms. This is part of the overall emphasis of the book: that of understanding standard algorithms in order to avoid "reinventing the wheel".

Overall, I recommend this book for people with some programming experience that would like to take their work to the next level. Hardcore computer scientists (especially those with a more theoretical bent) may also benefit from this book, but should consider it a supplement to one of the more advanced texts, such as the above-mentioned CLRS.

I hate to give this book only 3 stars, since it is obviously so well done. But it is not a book for everyone. If you are at the advanced level, you will treasure this one.

But if you want to learn practical algorithms at a more basic level, I recommend:
Algorithms in C++, Parts 1-4: Fundamentals,
Data Structure, Sorting, Searching (3rd Edition)
by Robert Sedgewick

or

Data Structures & Algorithms in Java (Mitchell Waite Signature Series)
by Mitchell Waite, Robert Lafore

or

Data Abstraction and Problem Solving With
Java: Walls and Mirrors
by Frank M. Carrano, Janet L. Prichard