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：
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页码：2433 / 2438
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