On inexact stochastic splitting methods for a class of nonconvex composite optimization problems with relative error-Reference-Cited by-同舟云学术

On inexact stochastic splitting methods for a class of nonconvex composite optimization problems with relative error

Published:2022-07-19 Issue:1 Volume:38 Page:1-33
ISSN:1055-6788
Container-title:Optimization Methods and Software
language:en
Short-container-title:Optimization Methods and Software

Author:

Hu Jia¹,Han Congying¹²,Guo Tiande¹²,Zhao Tong¹²

Affiliation:

1. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, People's Republic of China

2. Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, People's Republic of China

Funder

National key research and development program of China

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Informa UK Limited

Subject

Applied Mathematics,Control and Optimization,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/10556788.2022.2091562

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4. On Proximal Subgradient Splitting Method for Minimizing the sum of two Nonsmooth Convex Functions

5. A. Defazio, F. Bach, and S. Lacoste-Julien, SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives, in Advances in Neural Information Processing Systems, Curran Associates, Montréal, QC, 2014, pp. 1646–1654.