We introduce a new analytic framework to analyze the convergence of the Randomized Stochastic Gradient Descent Ascent (RSGDA) algorithm for stochastic minimax optimization problems. Under the so-called NC-PL condition on one of the variables, our analysis improves the state-of-the-art convergence results in the current literature and hence broadens the applicable range of the RSGDA. We also introduce a simple yet effective strategy to accelerate RSGDA , and empirically validate its efficiency on both synthetic data and real data.
@article{arxiv.2307.13880,
title = {Convergence Analysis of Randomized SGDA under NC-PL Condition for Stochastic Minimax Optimization Problems},
author = {Zehua Liu and Zenan Li and Xiaoming Yuan and Yuan Yao},
journal= {arXiv preprint arXiv:2307.13880},
year = {2023}
}