Optimization in Open Networks via Dual Averaging
Optimization and Control
2021-10-19 v2 Machine Learning
Multiagent Systems
Abstract
In networks of autonomous agents (e.g., fleets of vehicles, scattered sensors), the problem of minimizing the sum of the agents' local functions has received a lot of interest. We tackle here this distributed optimization problem in the case of open networks when agents can join and leave the network at any time. Leveraging recent online optimization techniques, we propose and analyze the convergence of a decentralized asynchronous optimization method for open networks.
Cite
@article{arxiv.2105.13348,
title = {Optimization in Open Networks via Dual Averaging},
author = {Yu-Guan Hsieh and Franck Iutzeler and Jérôme Malick and Panayotis Mertikopoulos},
journal= {arXiv preprint arXiv:2105.13348},
year = {2021}
}
Comments
In 60th IEEE Conference on Decision and Control (CDC 2021); 7 pages, 1 figure