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

• URL: http://arxiv.org/abs/2310.15448v2
• Date: Tue, 15 Oct 2024 08:51:21 GMT
• Title: An accelerated first-order regularized momentum descent ascent algorithm for stochastic nonconvex-concave minimax problems
• Authors: Huiling Zhang, Zi Xu,
• Abstract summary: We have an accelerated regularized momentum descent ascent algorithm (FORMDA) for solving non-concave mini problems. The best complexity for bound algorithms to solve non-concave minimax problems under the station of objectivearity function.
• Score: 0.4910937238451484
• License:
• 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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