论文标题
敏感性分析亚辅导功能最大化
Sensitivity Analysis of Submodular Function Maximization
论文作者
论文摘要
我们研究了最近引入的关于单调量子次数最大化的最差敏感性和基数约束$ k $的想法，该$ k $捕获了输出参数在输入中删除元素的删除程度。我们发现，对于大类算法，即使有界曲率，也无法使用$ o（k）$的非平凡灵敏度，并且这些结果也存在于分布式框架中。但是，我们还表明，在制度$ k =ω（n）$中，我们可以获得$ o（1）$灵敏度，以获得足够低的曲率。
We study the recently introduced idea of worst-case sensitivity for monotone submodular maximization with cardinality constraint $k$, which captures the degree to which the output argument changes on deletion of an element in the input. We find that for large classes of algorithms that non-trivial sensitivity of $o(k)$ is not possible, even with bounded curvature, and that these results also hold in the distributed framework. However, we also show that in the regime $k = Ω(n)$ that we can obtain $O(1)$ sensitivity for sufficiently low curvature.
