Improving the Transient Times for Distributed Stochastic Gradient Methods-Reference-Cited by-同舟云学术

Improving the Transient Times for Distributed Stochastic Gradient Methods

Published:2022 Issue: Volume: Page:1-16
ISSN:0018-9286
Container-title:IEEE Transactions on Automatic Control
language:
Short-container-title:IEEE Trans. Automat. Contr.

Author:

Huang Kun¹^ORCID,Pu Shi²^ORCID

Affiliation:

1. School of Data Science, Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, China

2. School of Data Science, Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), The Chinese University of Hong Kong, Shenzhen, China

Funder

Shenzhen Research Institute of Big Data

National Natural Science Foundation of China

Shenzhen Science and Technology Program

Shenzhen Institute of Artificial Intelligence and Robotics for Society

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9/4601496/09865230.pdf?arnumber=9865230

Cited by 5 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. Distributed Stochastic Optimization Under a General Variance Condition;IEEE Transactions on Automatic Control;2024-09

2. Achieving Linear Speedup with Network-Independent Learning Rates in Decentralized Stochastic Optimization;2023 62nd IEEE Conference on Decision and Control (CDC);2023-12-13

3. A Stochastic Second-Order Proximal Method for Distributed Optimization;IEEE Control Systems Letters;2023

4. Distributed Random Reshuffling Over Networks;IEEE Transactions on Signal Processing;2023

5. On the Role of Data Homogeneity in Multi-Agent Non-convex Stochastic Optimization;2022 IEEE 61st Conference on Decision and Control (CDC);2022-12-06