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arXiv 1010.2885cs.DS
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鲁棒分割和最短路径的改进近似

Improved approximations for robust mincut and shortest path

Valentin Polishchuk, Mikko Sysikaski

摘要:

在两阶段鲁棒优化中,问题的解决方案内置的两个阶段:在第一阶段,展示部分,不一定可行的解决方案。然后,对手从一组预定义的场景中选择“最坏”的场景。在第二阶段中,第一阶段的解决方案被扩展为对于所选择的场景是可行的。第二阶段的成本高于第一阶段,目标是尽量减少两个阶段的总成本。给出了鲁棒mincut问题的2-近似算法和鲁棒最短路问题的(γ+2)-近似算法,其中γ是Steiner树的近似比.这改进了来自[Golovin,Goyal和Ravi.未雨绸缪:需求鲁棒最小割和最短路径问题的改进近似算法。STACS 2006]。此外,我们的鲁棒最短路径的解决方案比以前的更简单,更有效;这是通过更直接的算法和分析来实现的,而不是使用一些标准的需求鲁棒优化技术。

英文摘要:

In two-stage robust optimization the solution to a problem is built in two stages: In the first stage a partial, not necessarily feasible, solution is exhibited. Then the adversary chooses the "worst" scenario from a predefined set of scenarios. In the second stage, the first-stage solution is extended to become feasible for the chosen scenario. The costs at the second stage are larger than at the first one, and the objective is to minimize the total cost paid in the two stages. We give a 2-approximation algorithm for the robust mincut problem and a (γ+2)-approximation for the robust shortest path problem, where γ is the approximation ratio for the Steiner tree. This improves the factors (1+\sqrt2) and 2(γ+2) from [Golovin, Goyal and Ravi. Pay today for a rainy day: Improved approximation algorithms for demand-robust min-cut and shortest path problems. STACS 2006]. In addition, our solution for robust shortest path is simpler and more efficient than the earlier ones; this is achieved by a more direct algorithm and analysis, not using some of the standard demand-robust optimization techniques.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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