An accelerated first-order regularized momentum descent ascent algorithm for stochastic nonconvex-concave minimax problems

Huiling Zhang; Zi Xu

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arXiv · 2310.15448

An accelerated first-order regularized momentum descent ascent algorithm for stochastic nonconvex-concave minimax problems

Huiling Zhang·Zi Xu

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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 $\tilde{\mathcal{O}}(\varepsilon ^{-6.5})$ to obtain an $\varepsilon$-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.

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BibTeX RIS

Huiling Zhang, Zi Xu. 2023-10-24. An accelerated first-order regularized momentum descent ascent algorithm for stochastic nonconvex-concave minimax problems. https://arxiv.org/abs/2310.15448

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