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On-line Access: 2021-11-15

Received: 2020-11-08

Revision Accepted: 2021-02-15

Crosschecked: 2021-04-01

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Bihao Sun


Huaqing Li


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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.11 P.1463-1476


A distributed stochastic optimization algorithm with gradient-tracking and distributed heavy-ball acceleration

Author(s):  Bihao Sun, Jinhui Hu, Dawen Xia, Huaqing Li

Affiliation(s):  Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China; more

Corresponding email(s):   huaqingli@swu.edu.cn

Key Words:  Distributed optimization, High-performance algorithm, Multi-agent system, Machine-learning problem, Stochastic gradient

Bihao Sun, Jinhui Hu, Dawen Xia, Huaqing Li. A distributed stochastic optimization algorithm with gradient-tracking and distributed heavy-ball acceleration[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(11): 1463-1476.

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author="Bihao Sun, Jinhui Hu, Dawen Xia, Huaqing Li",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T A distributed stochastic optimization algorithm with gradient-tracking and distributed heavy-ball acceleration
%A Bihao Sun
%A Jinhui Hu
%A Dawen Xia
%A Huaqing Li
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 11
%P 1463-1476
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000615

T1 - A distributed stochastic optimization algorithm with gradient-tracking and distributed heavy-ball acceleration
A1 - Bihao Sun
A1 - Jinhui Hu
A1 - Dawen Xia
A1 - Huaqing Li
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 11
SP - 1463
EP - 1476
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000615

distributed optimization has been well developed in recent years due to its wide applications in machine learning and signal processing. In this paper, we focus on investigating distributed optimization to minimize a global objective. The objective is a sum of smooth and strongly convex local cost functions which are distributed over an undirected network of n nodes. In contrast to existing works, we apply a distributed heavy-ball term to improve the convergence performance of the proposed algorithm. To accelerate the convergence of existing distributed stochastic first-order gradient methods, a momentum term is combined with a gradient-tracking technique. It is shown that the proposed algorithm has better acceleration ability than GT-SAGA without increasing the complexity. Extensive experiments on real-world datasets verify the effectiveness and correctness of the proposed algorithm.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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