English

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.

Keywords

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

R2 v1 2026-06-28T10:46:20.437Z