The Algorithm Design Manual

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This book is ideal for the working professional who uses algorithms on a daily basis and has need for a handy reference. probably a better book to carry around in one's professional life than CLR, though it lacks some of the theoretical intensity of the Big White Book. In a mature ecosystem like Python, a lot of the time the right answer is just "pip install magiclib. In comparison to "Introduction to Algorithms" (the other algorithm book I had significant exposure to) this one is faster to read, easier to digest and more tailored towards applications.

I used the "introductory" adjective earlier as this is supposed to be an introductory book on the topic. The second part, Resources , is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography. If you want to use it as a course textbook then there are lots of exercises at the end of every chapter. A must read for any computer scientist, student or educator, who likes to invest more in ideas than the actual details. When you want to read a good introductory book about algorithms and data structures the choice comes down to two books: Introduction to Algorithms, Second Edition and this one.the book is an algorithm implementation treasure trove, and putting all of these implementations in one place was no small feat. 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. It will not only serve as a valuable undergraduate textbook, but it will also become an irreplaceable reference guide for most professionals in the area. I read the first edition of this book back in University and bought a new copy for my girlfriend who's learning programming, it's as good as I remembered.

The list of implementations, an extensive bibliography … make the book an invaluable resource for everyone interested in the subject. While computers have speeded up enormously in the past ten years - and expensive supercomputers can speed things even more - a bit of thought might speed up a program by much higher factors (or you can avoid thinking altogether by stealing one of the tried-and-trusted algorithms in the guide).Skiena covers this varied field with a mix of humour and wisdom, and he provides an excellent array of references, online code and other resources for everything he touches on. over 30 hours of audio lectures on the design and analysis of algorithms are provided, all keyed to on-line lecture notes.