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

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Weixin An, Yuanyuan Liu, Fanhua Shang, Hongying Liu
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Mathematics
Subjects:
saddle-point problem
stochastic optimization
variance reduction
asynchronous parallelism
Online Access:https://www.mdpi.com/2227-7390/13/10/1687
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2227-7390/13/10/1687

Similar Items