In this work, we study the problem of monotone non-submodular maximization with partition matroid constraint. Although a generalization of this problem has been studied in literature, our work focuses on leveraging properties of partition matroid constraint to (1) propose algorithms with theoretical bound and efficient query complexity; and (2) provide better analysis on theoretical performance guarantee of some existing techniques. We further investigate those algorithms' performance in two applications: Boosting Influence Spread and Video Summarization. Experiments show our algorithms return comparative results to the state-of-the-art algorithms while taking much fewer queries.

翻译：在这项工作中,我们研究了单质单质非子元最大化与分割型机器人限制的问题。尽管在文献中已经研究了这一问题的概括性,但我们的工作重点是利用分割型机器人限制的特性:(1) 提出具有理论约束和有效查询复杂性的算法;(2) 更好地分析某些现有技术的理论性能保障。我们进一步调查了这些算法在两个应用中的性能:促进影响扩散和视频总结。实验显示我们的算法返回到最先进的算法的比较结果,同时较少进行查询。