The Algorithm Design Manual

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One of Skiena's "war stories" tells of making a program run 30,000 times faster by using a better-designed algorithm. It is really intended as a set of pointers to get you started on where to look up details for approaches to a particular problem, and in that respect succeeds quite well (and has a lot of up-to-date references).

This newly expanded and updated second edition of the best-selling classic continues to take the "mystery" out of designing algorithms, and analyzing their efficacy and efficiency. The second part, Resources , is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography. With its aid, most programmers could go from writing code that merely works to writing code that is thousands of times more efficient and reliable: this book will earn some people fortunes.If you want to use it as a course textbook then there are lots of exercises at the end of every chapter.

The second half of the book is collection of short essays on various kinds of problems and sketches of techniques to handle them. To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. If you were a professional programmer trying to earn a living, you would want to do even better: programme the computer to find the best way of finding a route (fastest, smallest, most accurate, suitable for wide loads, avoiding speed cameras .the book is an algorithm-implementation treasure trove, and putting all of these implementations in one place was no small feat.

Each of the topics is treated in a readable informal style with lots of asides and accounts of personal experiences - ‘war stories’ in implementing algorithms. This book is a practical, example-driven book on computer science algorithms, which is very readable and has a wealth of ready-to-use examples. More than any other book it helped me understand just how astonishingly commonplace … graph problems are -- they should be part of every working programmer’s toolkit. But I found the text around it was written so you could skim the hard stuff, get the idea, and keep going.I also skipped all the problems in chapter 10, which dealt with NP hard problems and approximate algos and more proofy ones about reducing problems down to satisfiability. But regardless, the reason for the 3 stars is that this book tries to straddle the middle of being a practical interview prep book and being a proof heavy, theoretical Algo book. A lot of people might enjoy this, but I feel that having the mathematical understanding of algorithms and the proofs to back it up will greatly increase your understanding of the material. It is essential for scientists, engineers, and any professionals who aim to solve problems, with a noticeable emphasis on real problems. The list of implementations [and] extensive bibliography make the book an invaluable resource for everyone interested in the subject.

For those who are new to the book, its main aim remains to provide a comprehensive catalogue of algorithms and background resources. He is recipient of the ONR Young Investigator Award and the IEEE Computer Science and Engineering Undergraduate Teaching Award.

If I were to go back in time, I'd probably pick either a 100% practical Algo book or something like CLRS for very rigorous understanding.