Two-timescale Extragradient for Finding Local Minimax Points
Optimization and Control
2024-04-23 v2 Machine Learning
Abstract
Minimax problems are notoriously challenging to optimize. However, we present that the two-timescale extragradient method can be a viable solution. By utilizing dynamical systems theory, we show that it converges to points that satisfy the second-order necessary condition of local minimax points, under mild conditions that the two-timescale gradient descent ascent fails to work. This work provably improves upon all previous results on finding local minimax points, by eliminating a crucial assumption that the Hessian with respect to the maximization variable is nondegenerate.
Cite
@article{arxiv.2305.16242,
title = {Two-timescale Extragradient for Finding Local Minimax Points},
author = {Jiseok Chae and Kyuwon Kim and Donghwan Kim},
journal= {arXiv preprint arXiv:2305.16242},
year = {2024}
}
Comments
40 pages, 5 figures. Published in ICLR 2024