mlsanityData to 14 Oct 2026

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

arXiv 2310.15448 ↗First posted 24 Oct 2023math.OC cs.LG stat.MLcited by 1

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Abstract (arXiv)

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 tildeO(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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 41 of its 59 references that are in the map. First posted on arXiv 24 Oct 2023.

Builds on

41 of 59 references.

References · 41

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29 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Single-Loop Stochastic Projected Damped Extragradient Methods for Nonconvex--(Strongly) Concave Minimax Optimizationcited by 1