Provably Accelerated Decentralized Gradient Methods Over Unbalanced Directed Graphs-Reference-Cited by-同舟云学术

Provably Accelerated Decentralized Gradient Methods Over Unbalanced Directed Graphs

Published:2024-03-22 Issue:1 Volume:34 Page:1131-1156
ISSN:1052-6234
Container-title:SIAM Journal on Optimization
language:en
Short-container-title:SIAM J. Optim.

Author:

Song Zhuoqing¹^ORCID,Shi Lei²,Pu Shi³,Yan Ming⁴

Affiliation:

1. Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China.

2. School of Mathematical Sciences, Fudan University, Shanghai Key Laboratory for Contemporary Applied Mathematics, Shanghai Artificial Intelligence Laboratory, China.

3. Co-corresponding author. School of Data Science, Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), China.

4. Co-corresponding author. School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), China.

Funder

Shanghai Science and Technology Development Foundation

National Natural Science Foundation of China

Guangdong Talent Program

Shenzhen Research Institute of Big Data

Shenzhen Science and Technology Innovation Commission

National Science Foundation

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Reference53 articles.

1. Proximal-Gradient Algorithms for Tracking Cascades Over Social Networks

2. Nonnegative Matrices in the Mathematical Sciences

3. Distributed asynchronous computation of fixed points

4. Convex Optimization: Algorithms and Complexity

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Nabla Fractional Distributed Optimization Algorithm Over Unbalanced Graphs;IEEE Control Systems Letters;2024