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

共 50 条

[21] Distributed Estimation in Multi-Agent Networks
Sankar, Lalitha
Poor, H. Vincent
2012 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY PROCEEDINGS (ISIT), 2012, : 329 - 333
[22] Distributed Boosting Variational Inference Algorithm Over Multi-Agent Networks
An, Xibin
Hu, Chen
Liu, Gang
Wang, Minghao
IEEE ACCESS, 2020, 8 : 195645 - 195654
[23] Dynamic distributed constraint optimization using multi-agent reinforcement learning
Maryam Shokoohi
Mohsen Afsharchi
Hamed Shah-Hoseini
Soft Computing, 2022, 26 : 3601 - 3629
[24] Dynamic distributed constraint optimization using multi-agent reinforcement learning
Shokoohi, Maryam
Afsharchi, Mohsen
Shah-Hoseini, Hamed
SOFT COMPUTING, 2022, 26 (08) : 3601 - 3629
[25] Distributed Detection and Mitigation of Biasing Attacks Over Multi-Agent Networks
Doostmohammadian, Mohammadreza
Zarrabi, Houman
Rabiee, Hamid R.
Khan, Usman A.
Charalambous, Themistoklis
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2021, 8 (04): : 3465 - 3477
[26] Distributed Subgradient Algorithm for Multi-agent Optimization with Uncoordinated Dynamic Stepsizes
Ren, Xiaoxing
Li, Dewei
Xi, Yugeng
Shao, Haibin
2020 IEEE 16TH INTERNATIONAL CONFERENCE ON CONTROL & AUTOMATION (ICCA), 2020, : 199 - 204
[27] Distributed constrained optimization for multi-agent networks with nonsmooth objective functions
Chen, Gang
Yang, Qing
SYSTEMS & CONTROL LETTERS, 2019, 124 : 60 - 67
[28] Distributed Online Optimization for Multi-Agent Networks With Coupled Inequality Constraints
Li, Xiuxian
Yi, Xinlei
Xie, Lihua
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2021, 66 (08) : 3575 - 3591
[29] Distributed Heterogeneous Multi-Agent Networks Optimization with Nonconvex Velocity Constraints
Mo, Lipo
Hu, Haokun
Yu, Yongguang
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2020, 357 (11): : 7139 - 7158
[30] Distributed Optimization for Mixed-Integer Consensus in Multi-Agent Networks
Liu, Zonglin
Stursberg, Olaf
2022 EUROPEAN CONTROL CONFERENCE (ECC), 2022, : 2196 - 2202

← 1 2 3 4 5 →