Discrete optimization problems are everywhere, from traditional operations research planning (scheduling, facility location and network design); to computer science databases; to advertising issues in viral marketing. Yet most such problems are NP-hard; unless P = NP, there are no efficient algorithms to find optimal solutions. This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. The book is organized around central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization. Each chapter in the first section is devoted to a single algorithmic technique applied to several different problems, with more sophisticated treatment in the second section. The book also covers methods for proving that optimization problems are hard to approximate. Designed as a textbook for graduate-level algorithm courses, it will also serve as a reference for researchers interested in the heuristic solution of discrete optimization problems.
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個人覺得是近似算法最好的教材。
评分題材組織的還不錯，不過例題的講解沒有vazirani那本清楚
评分圖書館發現此書，閱讀，大贊，遂從ebay上30刀購來舊書一本；說是舊書，其實跟新書沒區彆，隻是側麵有些黑罷瞭；兩位作者都是大牛級人物，寫得深入淺齣，受益匪淺
评分圖書館發現此書，閱讀，大贊，遂從ebay上30刀購來舊書一本；說是舊書，其實跟新書沒區彆，隻是側麵有些黑罷瞭；兩位作者都是大牛級人物，寫得深入淺齣，受益匪淺
评分題材組織的還不錯，不過例題的講解沒有vazirani那本清楚
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