论文标题
改进的近似算法，用于在凸集上最大化DR-Submodular函数
An improved approximation algorithm for maximizing a DR-submodular function over a convex set
论文作者
论文摘要
受一般凸组的最大化功能最大化是由组合优化和机器学习中的许多应用引起的NP固定问题。尽管在这种一般环境下设计有效的近似算法是非常需要的，在这种一般环境下，目标函数既不单调的，也不是可行的集合，但我们的主要贡献是呈现具有低于价值的甲骨文模型的0.25-Approximation Frank-Wolfe算法类型的算法类型。
Maximizing a DR-submodular function subject to a general convex set is an NP-hard problem arising from many applications in combinatorial optimization and machine learning. While it is highly desirable to design efficient approximation algorithms under this general setting where neither the objective function is monotonic nor the feasible set is down-closed, our main contribution is to present a 0.25-approximation Frank-Wolfe type of algorithm with a sub-exponential time-complexity under the value oracle model.
