Work

A Compressed Gradient Tracking Method for Decentralized Optimization With Linear Convergence

Year: 2022

Type: article

Abstract: Communication compression techniques are of growing interests for solving the decentralized optimization problem under limited communication, where the global objective is to minimize the average of local cost functions over a multiagent network using only local computation and peer-to-peer communication. In this article, we propose a novel compressed gradient tracking algorithm (C-GT) that combines gradient tracking technique with communication compression. In particular, C-GT is compatible with a general class of compression operators that unifies both unbiased and biased compressors. We show that C-GT inherits the advantages of gradient tracking-based algorithms and achieves linear convergence rate for strongly convex and smooth objective functions. Numerical examples complement the theoretical findings and demonstrate the efficiency and flexibility of the proposed algorithm.

Source: IEEE Transactions on Automatic Control

DOI: 10.1109/tac.2022.3180695

Authors: Yiwei Liao, Zhuorui Li, Kun Huang, Shi Pu

Institutions: University of Science and Technology of China, Chinese University of Hong Kong, Shenzhen, Shenzhen Research Institute of Big Data, Chinese University of Hong Kong

Language: English

Topic: Distributed Control Multi-Agent Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,195

Funders: National Natural Science Foundation of China, Chinese University of Hong Kong, Shenzhen Research Institute of Big Data, Shenzhen Research Institute, City University of Hong Kong