The problem of maximizing a normalized monotone non-submodular set function subject to a cardinality constraint arises in the context of extracting information from massive streaming data. In this paper, we present four streaming algorithms for this problem by utilizing the concept of diminishing-return ratio. We analyze these algorithms to obtain the corresponding approximation ratios, which generalize the previous results for the submodular case. The numerical experiments show that our algorithms have better solution quality and competitive running time when compared to an existing algorithm.

中文翻译：

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海量数据流上的非亚模最大化

受制于基数约束的最大化标准化单调非子模集合函数的问题出现在从大量流数据中提取信息的情况下。在本文中，我们利用递减收益率的概念提出了针对此问题的四种流算法。我们分析这些算法以获得相应的近似率，从而将先前的结果推广到亚模数情况。数值实验表明，与现有算法相比，我们的算法具有更好的解决方案质量和运行时间。