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Introduction to Algorithms Second Edition
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Rating: 4.17/5 from 110 reviews Very usable algorithm reference...Complete, thorough...Quote from a previous review: Instead of touching on new technologies, such as AI, graphics, or anything else remotely relevant to today's demands on programmers and designers, this book, faithful to its MIT roots, gives a pompous, eggheaded distortion to the field of computers as a whole. Its focus is mainly on such trivialities as algorithm analysis, offering about 10 pages of proofs for each simple assertion. The points that the authors hope to make have no relevance whatsoever in a world in which processor power, not meticulous code optimization, reigns. ---------- I've had Cormen (one of the authors) as a professor in class, and my algorithms class uses this book, so admittedly my view might be a bit biased. But if you read the above (quoted) review, you might have gotten the wrong impression about this book. Cormen et. al. *intentionally* left "AI and graphics" algorithms to other authors; this isn't the place to cover those topics enough to do them justice. And as someone who has actually read the book, each proof is *not* 10 pages long. The examples are usually quite good, and concisely (if thoroughly explained). Finally, prof. Cormen always explains to his intro CS students why the study of algorithms is important, even as computers get faster and faster: some problems, poorly implemented, just *will not* run as well on a machine of today compared to a much older machine running a better algorithm. There will *always* be a justified place for the study and analysis of algorithms. Had the previous reviewer actually had met Prof. Cormen, he wouldn't be able to write the book off with the title of "pompous" or "eggheaded" either... Concise and Clear, No. Why not both?There are a number of reviewers who proclaim that the language agnostic nature of this book, in addition to it's erudite tone, more than compensate for the (artificial) learning curve. I beg to differ. Being an actuary, I recognize that this book's code snippets are written in a variation of APL. APL is hardly a self-evident programming language (read-only is a more accurate description). While computer science cognoscenti might decry spoon-feeding, there's nothing wrong with being *concise and clear* concurrently. Indeed, the truly great books in the hard sciences are both easy to read and rigorous at the same time. If pedants would get off of their high horses for a moment, they would probably admit this much (heck, who wouldn't?). Rigor at the expense of clarity may appeal to intellectual snobs (who live for the material, god help them), and clarity at the expense of rigor may appeal to beginners, but WHY NOT HAVE BOTH? While this book covers a good deal of ground, it does so at the expense of clarity. A canonical book would have both rigor and clarity, and this book doesn't. It's as simple as that. The sordid truth about this book is that Professors tend to assign it as reading material with the expectation that students will rely primarily on class notes and then use the book as a reference of sorts (or as a source of homework problems). Most of the graduate courses that I've taken follow this approach. Having said all this, academia is essentially a small and sterile refuge for people who couldn't hack the real world. Take your courses, if you must, and then go out and get a life. In the end, most journals end up in the waste basket. Your time on this planet is short, don't waste it cloistered in a library! trust me on this... | ||||||||||||||||||
As a textbook, or for self-study, the book excels in clarity, coverage, and precision. It never lets go of the big picture, but still has every bit of detail you would want in an introductory text, with mathematical proofs of interesting properties of the algorithms introduced (and the first 6 chapters explain any mathematics you need).
As a reference, the book is immensely useful. Because almost any other algorithm book has a reasonable size, they must make a choice of what to cover. This book avoids the choice, and is therefore much thicker.
Of course, one book can't cover everything, the authors limit themselves to fundamental algorithms, anything related to parsing, semantic analysis, rewrite-systems, theorem-proving, computational biology, numerical analysis, computer graphics, machine-learning, signal-processing, or a number of other topics is better dealt with in a more specialized book. Still the coverage is unusually broad, and you would be hard pressed to find a more complete reference.
If you only want one book on algorithms, this is the book you want. If one algorithm-book isn't enough, you would still want this one, because it would be hard to even find a collection of books covering the same material. Besides, it will give you more shelf-space for other more advanced texts :-)