Hedging uncertainty: Approximation algorithms for stochastic optimization problems
成果类型：
Article
署名作者：
Ravi, R.; Sinha, Amitabh
署名单位：
Carnegie Mellon University; University of Michigan System; University of Michigan
刊物名称：
MATHEMATICAL PROGRAMMING
ISSN/ISSBN：
0025-5610
DOI：
10.1007/s10107-005-0673-5
发表日期：
2006
页码：
97-114
关键词：
摘要：
We study two-stage, finite-scenario stochastic versions of several combinatorial optimization problems, and provide nearly tight approximation algorithms for them. Our problems range from the graph-theoretic (shortest path, vertex cover, facility location) to set-theoretic (set cover, bin packing), and contain representatives with different approximation ratios. The approximation ratio of the stochastic variant of a typical problem is found to be of the same order of magnitude as its deterministic counterpart. Furthermore, we show that common techniques for designing approximation algorithms such as LP rounding, the primal-dual method, and the greedy algorithm, can be adapted to obtain these results.