Accelerated Stochastic Peaceman–Rachford Method for Empirical Risk Minimization-Reference-Cited by-同舟云学术

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Accelerated Stochastic Peaceman–Rachford Method for Empirical Risk Minimization

Published:2023-03-31 Issue:4 Volume:11 Page:783-807
ISSN:2194-668X
Container-title:Journal of the Operations Research Society of China
language:en
Short-container-title:J. Oper. Res. Soc. China

Author:

Bai Jian-Chao^ORCID,Bian Feng-Miao,Chang Xiao-Kai,Du Lin

Funder

National Natural Science Foundation of China

Postdoctoral Research Foundation of China

Guangdong Basic and Applied Basic Research Foundation

Publisher

Springer Science and Business Media LLC

Subject

Management Science and Operations Research

Link

https://link.springer.com/content/pdf/10.1007/s40305-023-00470-8.pdf

Reference29 articles.

1. Bai, J., Han, D., Sun, H., Zhang, H.: Convergence on a symmetric accelerated stochastic ADMM with larger stepsizes. CSIAM Trans. Appl. Math. 3, 448–479 (2022)

2. Bai, J., Hager, W., Zhang, H.: An inexact accelerated stochastic ADMM for separable convex optimization. Comput. Optim. Appl. 81, 479–518 (2022)

3. Bian, F., Liang, J., Zhang, X.: A stochastic alternating direction method of multipliers for non-smooth and non-convex optimization. Inverse Probl. 37, 075009 (2021)

4. Kim, S., Xing, E.: Statistical estimation of correlated genome associations to a quantitative trait network. PLOS Genet. 5, 1–18 (2009)

5. Li, H., Liu, N., Ma, X. et al.: ADMM-based weight pruning for real-time deep learning acceleration on mobile devices. In: Proceedings of the 2019 on great lakes symposium on VLSI, pp. 501–506 (2019)

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