Efficient Algorithms for Smooth Minimax Optimization

TL;DR

This paper introduces improved first-order algorithms for smooth minimax problems, achieving faster convergence rates in both strongly convex and nonconvex settings, with practical implications for finite nonconvex minimax problems.

Contribution

It proposes a new algorithm combining Mirror-Prox and Nesterov's AGD, improving convergence rates for smooth minimax optimization in both convex and nonconvex cases.

Findings

Achieves $ ilde{O}(1/k^2)$ convergence for strongly convex cases.

Provides $ ilde{O}(1/k^{1/3})$ rate for nonconvex stationary points.

Establishes a convergence rate of $O(m( ext{log } m)^{3/2}/k^{1/3})$ for finite nonconvex minimax problems.

Abstract

This paper studies first order methods for solving smooth minimax optimization problems $\min_x \max_y g(x,y)$ where $g(\cdot,\cdot)$ is smooth and $g(x,\cdot)$ is concave for each $x$. In terms of $g(\cdot,y)$, we consider two settings -- strongly convex and nonconvex -- and improve upon the best known rates in both. For strongly-convex $g(\cdot, y),\ \forall y$, we propose a new algorithm combining Mirror-Prox and Nesterov's AGD, and show that it can find global optimum in $\tilde{O}(1/k^2)$ iterations, improving over current state-of-the-art rate of $O(1/k)$. We use this result along with an inexact proximal point method to provide $\tilde{O}(1/k^{1/3})$ rate for finding stationary points in the nonconvex setting where $g(\cdot, y)$ can be nonconvex. This improves over current best-known rate of $O(1/k^{1/5})$. Finally, we instantiate our result for finite nonconvex minimax problems, i.e., $\min_x \max_{1\leq i\leq m} f_i(x)$, with nonconvex $f_i(\cdot)$, to obtain convergence rate of $O(m(\log m)^{3/2}/k^{1/3})$ total gradient evaluations for finding a stationary point.