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

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 minim...

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Bibliographic Details

Published in:	Computational optimization and applications Vol. 90; no. 2; pp. 557 - 582
Main Authors:	Zhang, Huiling, Xu, Zi
Format:	Journal Article
Language:	English
Published:	New York Springer Nature B.V 01.03.2025
Subjects:	Algorithms Complexity Machine learning Methods Minimax technique Momentum Optimization Random variables Signal processing
ISSN:	0926-6003, 1573-2894
Online Access:	Get full text
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