Stochastic Variance Reduced Primal–Dual Hybrid Gradient Methods for Saddle-Point Problems

Recently, many stochastic Alternating Direction Methods of Multipliers (ADMMs) have been proposed to solve large-scale machine learning problems. However, for large-scale saddle-point problems, the state-of-the-art (SOTA) stochastic ADMMs still have high per-iteration costs. On the other hand, the s...

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Главные авторы:	Weixin An, Yuanyuan Liu, Fanhua Shang, Hongying Liu
Формат:	Artigo
Язык:	Inglês
Опубликовано:	MDPI AG 2025-05-01
Серии:	Mathematics
Предметы:	saddle-point problem stochastic optimization variance reduction asynchronous parallelism
Online-ссылка:	https://www.mdpi.com/2227-7390/13/10/1687
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