Logarithmically Quantized Distributed Optimization Over Dynamic Multi-Agent Networks

被引：0

作者：

Doostmohammadian, Mohammadreza ^{[1
]}

Pequito, Sergio ^{[2
]}

机构：

[1] Semnan University, Faculty of Mechanical Engineering, Mechatronics Department, Semnan,3514835331, Iran

[2] Instituto Superior Técnico, University of Lisbon, Department of Electrical and Computer Engineering, The Institute for Systems and Robotics, Lisbon,1049-001, Portugal

来源：

IEEE Control Systems Letters | 2024年 / 8卷

关键词：

Distributed optimization finds many applications in machine learning; signal processing; and control systems. In these real-world applications; the constraints of communication networks; particularly limited bandwidth; necessitate implementing quantization techniques. In this letter; we propose distributed optimization dynamics over multi-agent networks subject to logarithmically quantized data transmission. Under this condition; data exchange benefits from representing smaller values with more bits and larger values with fewer bits. As compared to uniform quantization; this allows for higher precision in representing near-optimal values and more accuracy of the distributed optimization algorithm. The proposed optimization dynamics comprise a primary state variable converging to the optimizer and an auxiliary variable tracking the objective function's gradient. Our setting accommodates dynamic network topologies; resulting in a hybrid system requiring convergence analysis using matrix perturbation theory and eigenspectrum analysis. © 2017 IEEE;

D O I：

10.1109/LCSYS.2024.3487796

中图分类号：

学科分类号：

摘要：

引用

页码：2433 / 2438

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