汇报标题 (Title)：Data-Driven Minimax Optimization with Expectation Constraints
汇报人 (Speaker)：郦旭东 钻研员（复旦大学大数据学院）
汇报功夫 (Time)：2023年11月7日 (周二) 09:40
汇报地址 (Place)：校本部GJ303
约请人(Inviter)：徐姿 教授
主办部门：理学院数学系
汇报提要：Attention to data-driven optimization approaches has grown significantly over recent decades, but data-driven constraints have rarely been studied. In this talk, we focus on the non-smooth convex-concave stochastic minimax regime and formulate the data-driven constraints as expectation constraints. Then, we propose a class of efficient primal-dual algorithms to tackle the minimax optimization with expectation constraints, and show that our algorithms converge at the optimal rate of $\mathcal O(\frac{1}{\sqrt{N}})$, where $N$ is the number of iterations. We also verify the practical efficiency of our algorithms by conducting numerical experiments on large-scale real-world applications.
