动态划分拟阵约束下子集选择的Pareto优化
Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints
摘要：
在本研究中，我们考虑了在分割拟阵约束下的次模或单调离散目标函数的子集选择问题，其中阈值是动态的。我们专注于POMC，一个简单的帕累托优化方法，已被证明是有效的，这样的问题。我们的分析从奇异约束问题出发，并扩展到多个约束的问题。我们表明，以前的结果POMC的性能也适用于多个约束。对随机无向最大割问题的实验研究表明，POMC算法与经典的带重启策略的贪心算法相比具有很强的竞争力。
英文摘要：
In this study, we consider the subset selection problems with submodular or monotone discrete objective functions under partition matroid constraints where the thresholds are dynamic. We focus on POMC, a simple Pareto optimization approach that has been shown to be effective on such problems. Our analysis departs from singular constraint problems and extends to problems of multiple constraints. We show that previous results of POMC's performance also hold for multiple constraints. Our experimental investigations on random undirected maxcut problems demonstrate POMC's competitiveness against the classical GREEDY algorithm with restart strategy.
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