An accelerated first-order regularized momentum descent ascent algorithm for stochastic nonconvex-concave minimax problems
Optimization and Control
2024-10-16 v2 Machine Learning
Machine Learning
Abstract
Stochastic nonconvex minimax problems have attracted wide attention in machine learning, signal processing and many other fields in recent years. In this paper, we propose an accelerated first-order regularized momentum descent ascent algorithm (FORMDA) for solving stochastic nonconvex-concave minimax problems. The iteration complexity of the algorithm is proved to be to obtain an -stationary point, which achieves the best-known complexity bound for single-loop algorithms to solve the stochastic nonconvex-concave minimax problems under the stationarity of the objective function.
Cite
@article{arxiv.2310.15448,
title = {An accelerated first-order regularized momentum descent ascent algorithm for stochastic nonconvex-concave minimax problems},
author = {Huiling Zhang and Zi Xu},
journal= {arXiv preprint arXiv:2310.15448},
year = {2024}
}