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...

Celý popis

Podrobná bibliografia

Vydané v:	Computational optimization and applications Ročník 90; číslo 2; s. 557 - 582
Hlavní autori:	Zhang, Huiling, Xu, Zi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer Nature B.V 01.03.2025
Predmet:	Algorithms Complexity Machine learning Methods Minimax technique Momentum Optimization Random variables Signal processing
ISSN:	0926-6003, 1573-2894
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!