同时逼近在组合优化中的应用

A. Frank, É. Tardos
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引用次数: 21

摘要

提出了一种预处理算法,使某些多项式算法成为强多项式。的运行时间的一些已知的组合优化算法的大小取决于目标函数w。我们的预处理算法代替积分价值w w的多项式的大小是有限大小的组合结构和收益率相同的最优解集是w。作为应用,我们展示现有的多项式算法寻找最大重量集团在一个完美的图和最小成本子模块流问题强烈的多项式。该方法依赖于Lovász的同时逼近算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An application of simultaneous approximation in combinatorial optimization
We present a preprocessing algorithm to make certain polynomial algorithms strongly polynomial. The running time of some of the known combinatorial optimization algorithms depends on the size of the objective function w. Our preprocessing algorithm replaces w by an integral valued w whose size is polynomially bounded in the size of the combinatorial structure and which yields the same set of optimal solutions as w. As applications we show how existing polynomial algorithms for finding the maximum weight clique in a perfect graph and for the minimum cost submodular flow problem can be made strongly polynomial. The method relies on Lovász's simultaneous approximation algorithm.
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