Geodesic
Distributed aggregative optimization with quantized communication
Kybernetika, Tome 58 (2022) no. 1, pp. 123-144 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

Voir la notice de l'article

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.
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.
DOI : 10.14736/kyb-2022-1-0123
Classification : 68W15, 90C33
Keywords: distributed aggregative optimization; multi-agent network; quantized communication; linear convergence rate 
@article{10_14736_kyb_2022_1_0123,
     author = {Chen, Ziqin and Liang, Shu},
     title = {Distributed aggregative optimization with quantized communication},
     journal = {Kybernetika},
     pages = {123--144},
     year = {2022},
     volume = {58},
     number = {1},
     doi = {10.14736/kyb-2022-1-0123},
     mrnumber = {4405950},
     zbl = {07511614},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.14736/kyb-2022-1-0123/}
}
TY  - JOUR
AU  - Chen, Ziqin
AU  - Liang, Shu
TI  - Distributed aggregative optimization with quantized communication
JO  - Kybernetika
PY  - 2022
SP  - 123
EP  - 144
VL  - 58
IS  - 1
UR  - http://geodesic.mathdoc.fr/articles/10.14736/kyb-2022-1-0123/
DO  - 10.14736/kyb-2022-1-0123
LA  - en
ID  - 10_14736_kyb_2022_1_0123
ER  - 
%0 Journal Article
%A Chen, Ziqin
%A Liang, Shu
%T Distributed aggregative optimization with quantized communication
%J Kybernetika
%D 2022
%P 123-144
%V 58
%N 1
%U http://geodesic.mathdoc.fr/articles/10.14736/kyb-2022-1-0123/
%R 10.14736/kyb-2022-1-0123
%G en
%F 10_14736_kyb_2022_1_0123
Chen, Ziqin; Liang, Shu. Distributed aggregative optimization with quantized communication. Kybernetika, Tome 58 (2022) no. 1, pp. 123-144. doi: 10.14736/kyb-2022-1-0123

Cité par Sources :