具體描述
Thomas H.Cormen
達特茅斯學院計算機科學係副教授
Charles E.Leiserson
麻省理工學院計算機科學與電氣工程係教授
Ronald L.Rivest
麻省理工學院計算機科學係Andrew與Erna Viterbi具名教授
Clifford Stein
哥倫比亞大學工業工程與運籌學副教授
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, and substantial additions to the chapter on recurrences (now called "Divide-and-Conquer"). 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.
用戶評價
##You can program for ten years to become a top programmer. Or you can program for two years and take algorithms lessons.
評分 評分##推薦殿堂級的算法工具書《算法導論》，講解瞭常見的算法和數據結構，分類細緻且論證通俗；內容包含排序和遞歸、廣度優先與生成樹、綫性規劃以及諸多窮盡智力而不能讀懂的算法。文字深入淺齣且沒有學術教條，例題相當具有範式意義；再也不必為瞭一個算法的邏輯意義和代碼實現去...
評分##php和JavaScript都不適閤寫算法，then i digest... i'm gonna pick up the good parts along the way instead of pure learning
評分##php和JavaScript都不適閤寫算法，then i digest... i'm gonna pick up the good parts along the way instead of pure learning
評分 評分 評分##總之很有收獲
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