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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题材组织的还不错，不过例题的讲解没有vazirani那本清楚
评分还是比较全的 可以再加一些property testing的模型 比如 monotonicity, regularity lemma, triangle freeness 之类的
评分个人觉得比Vijay的写得好。
评分个人觉得比Vijay的写得好。
评分题材组织的还不错，不过例题的讲解没有vazirani那本清楚
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