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Title: Distributed aggregative optimization with quantized communication (English)
Author: Chen, Ziqin
Author: Liang, Shu
Language: English
Journal: Kybernetika
ISSN: 0023-5954 (print)
ISSN: 1805-949X (online)
Volume: 58
Issue: 1
Year: 2022
Pages: 123-144
Summary lang: English
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Category: math
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Summary: In this paper, we focus on an aggregative optimization problem under the communication bottleneck. The aggregative optimization is to minimize the sum of local cost functions. Each cost function depends on not only local state variables but also the sum of functions of global state variables. The goal is to solve the aggregative optimization problem through distributed computation and local efficient communication over a network of agents without a central coordinator. Using the variable tracking method to seek the global state variables and the quantization scheme to reduce the communication cost spent in the optimization process, we develop a novel distributed quantized algorithm, called D-QAGT, to track the optimal variables with finite bits communication. Although quantization may lose transmitting information, our algorithm can still achieve the exact optimal solution with linear convergence rate. Simulation experiments on an optimal placement problem is carried out to verify the correctness of the theoretical results. (English)
Keyword: distributed aggregative optimization
Keyword: multi-agent network
Keyword: quantized communication
Keyword: linear convergence rate
MSC: 68W15
MSC: 90C33
idZBL: Zbl 07511614
idMR: MR4405950
DOI: 10.14736/kyb-2022-1-0123
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Date available: 2022-04-08T07:56:14Z
Last updated: 2022-08-11
Stable URL: http://hdl.handle.net/10338.dmlcz/149605
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