English

A first-order augmented Lagrangian method for constrained minimax optimization

Optimization and Control 2024-10-29 v3 Machine Learning Numerical Analysis Numerical Analysis Machine Learning

Abstract

In this paper we study a class of constrained minimax problems. In particular, we propose a first-order augmented Lagrangian method for solving them, whose subproblems turn out to be a much simpler structured minimax problem and are suitably solved by a first-order method developed in this paper. Under some suitable assumptions, an \emph{operation complexity} of O(ε4logε1)O(\varepsilon^{-4}\log\varepsilon^{-1}), measured by its fundamental operations, is established for the first-order augmented Lagrangian method for finding an ε\varepsilon-KKT solution of the constrained minimax problems.

Keywords

Cite

@article{arxiv.2301.02060,
  title  = {A first-order augmented Lagrangian method for constrained minimax optimization},
  author = {Zhaosong Lu and Sanyou Mei},
  journal= {arXiv preprint arXiv:2301.02060},
  year   = {2024}
}

Comments

Accepted by Mathematical Programming

R2 v1 2026-06-28T08:03:45.784Z