Single-Loop Variance-Reduced Stochastic Algorithm for Nonconvex-Concave Minimax Optimization

Nonconvex-concave (NC-C) finite-sum minimax problems have broad applications in decentralized optimization and various machine learning tasks. However, the nonsmooth nature of NC-C problems makes it challenging to design effective variance reduction techniques. Existing vanilla stochastic algorithms...

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Vydané v:	Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 1 - 5
Hlavní autori:	Jiang, Xia, Zhu, Linglingzhi, Zheng, Taoli, So, Anthony Man-Cho
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 06.04.2025
Predmet:	Complexity theory Convergence Machine learning algorithms nonconvex-concave minimax optimization Numerical simulation Optimization Probabilistic logic Signal processing Signal processing algorithms single-loop Smoothing methods Speech processing stochastic algorithm variance reduction
ISSN:	2379-190X
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