Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization

Understand

We study the problem of finding a near-stationary point for smooth minimax optimization.

• The recently proposed extra anchored gradient (EAG) methods achieve the optimal convergence rate for the convex-concave minimax problem in the deterministic setting.
• However, the direct extension of EAG to stochastic optimization is not efficient.
• In this paper, we design a novel stochastic algorithm called Recursive Anchored IteratioN (RAIN).