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Introduction to Algorithms (MIT Press) (英語) ペーパーバック – 2009/7/31
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A new edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-based flow.
Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.
The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, substantial additions to the chapter on recurrence (now called "Divide-and-Conquer"), and an appendix on matrices. It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks. Many new exercises and problems have been added for this edition. As of the third edition, this textbook is published exclusively by the MIT Press.
As an educator and researcher in the field of algorithms for over two decades, I can unequivocally say that the Cormen et al book is the best textbook that I have ever seen on this subject. It offers an incisive, encyclopedic, and modern treatment of algorithms, and our department will continue to use it for teaching at both the graduate and undergraduate levels, as well as a reliable research reference.(Gabriel Robins, Department of Computer Science, University of Virginia)
Introduction to Algorithms, the 'bible' of the field, is a comprehensive textbook covering the full spectrum of modern algorithms: from the fastest algorithms and data structures to polynomial-time algorithms for seemingly intractable problems, from classical algorithms in graph theory to special algorithms for string matching, computational geometry, and number theory. The revised third edition notably adds a chapter on van Emde Boas trees, one of the most useful data structures, and on multithreaded algorithms, a topic of increasing importance.(Daniel Spielman, Department of Computer Science, Yale University) 商品の説明をすべて表示する
もちろんこの本さえ読めば良いという生ぬるい世界ではないが，まずはこの本でもって，計算機科学に関する基礎を頭に叩き込もう．そして，よりdeepな教科書，たとえば Garey & Johnson の Computers and Intractability や Navarro & Raffinot の Flexible Pattern Matching in Strings を読むなどして補完していけば良い．
This book is impressive! It covers a lot of subject matter and is clearly worded. However, you're going to get lost because this often reads more like a reference manual than a conversation that appeals to intuition. You'll be pushed into analyzing algorithms for theoretical data structures that you fuzzily remember (if at all). But, nonetheless, throw enough man hours into this book and you will learn concrete approaches to determining just how hard you're making the computer work.
My biggest criticism is that, as an *introduction*, this book doesn't do the best job at warming up readers to new tools and methodologies. This is an 'eh, just push them into the deep end' kind of approach to learning.
While the book definitely is a good book and is the go-to book for algorithms courses, it actually is more of a graduate level book.
As my professor explains it, it is a very mathy book and is not suited well for undergraduate (even though he made us undergraduates get it...), it's only use in undergrad is the fact you can get it while in undergrad and take it with you to graduate school.
So what makes undergraduate different from graduate to make this book suited for graduate level courses?
Undergraduate: Don't care about proofs or the math part, just wanna know the algorithms at a basic understanding without knowing the reason the algorithm even works at the math level. Most professors can just teach the material straight up no book for undergraduate courses honestly, the professors got PHDs they can give undergraduate level explanations on the fly.
Graduate: You are required to give mathematical proofs in graduate level courses, and are expected to know the algorithm at the deepest math level. Because of the work load, this is where this book shines because the professor cannot spend everyday till midnight teaching each student how to prove every algorithm, so this book is very well suited for graduate level because it is VERY math oriented.
This is a book that focuses on the math of the algorithm, but that's not entirely bad because undergraduates still may be interested in that stuff, my course just doesn't care about the proofs because there already is a graduate course for the ones in the Master's program.
As for the actual content and how easy it is to understand for an undergraduate... Well I do plan to go for PHD and this book has been very helpful for that because I am motivated to take the next step. I catch a snag once in a while on trying to understand the math part, but no pain no gain!
I only rated 4 stars because I haven't read the whole book yet, so giving a 5 star would be a bit awkward....