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A Tighter Analysis of Set Cover Greedy Algorithm for Test Set

机译:测试集的集合覆盖贪婪算法的更严格分析

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摘要

Set cover greedy algorithm is a natural approximation algorithm for test set problem. This paper gives a precise and tighter analysis of approximation ratio of this algorithm. The author improves the approximation ratio 2 ln n directly derived from set cover to 1.14 lnn by applying potential function technique of derandomization method. In addition, the author gives a nontrivial lower bound (1 + α) ln n of approximation ratio, where α is a positive constant. This lower bound, together with the matching bound of information content heuristic, confirms the fact information content heuristic is slightly better than set cover greedy algorithm in worst case.
机译:集覆盖贪婪算法是测试集问题的自然近似算法。本文对该算法的逼近率进行了精确而严格的分析。通过应用去随机化方法的势函数技术,作者将直接从集合覆盖得到的近似比2 ln n提高到1.14 lnn。另外,作者给出了一个非平凡的下界(1 +α)ln n的近似比率,其中α是一个正常数。这个下限,再加上信息内容启发式算法的匹配范围,证实了事实,在最坏的情况下,信息内容启发式算法比设置覆盖贪婪算法稍好。

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