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.
具體描述
讀後感
評分
評分
評分
評分
用戶評價
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
评分個人覺得是近似算法最好的教材。
评分還是比較全的 可以再加一些property testing的模型 比如 monotonicity, regularity lemma, triangle freeness 之類的
相關圖書
本站所有內容均為互聯網搜索引擎提供的公開搜索信息，本站不存儲任何數據與內容，任何內容與數據均與本站無關，如有需要請聯繫相關搜索引擎包括但不限於百度，google,bing,sogou 等
© 2025 qciss.net All Rights Reserved. 小哈圖書下載中心 版权所有