Optimization in Open Networks via Dual Averaging
Yu-Guan Hsieh, Franck Iutzeler, J\'er\^ome Malick, Panayotis, Mertikopoulos
TL;DR
This paper introduces a decentralized asynchronous optimization method for open networks where agents can join or leave at any time, addressing a key challenge in distributed autonomous systems.
Contribution
It proposes and analyzes a novel online optimization algorithm tailored for open networks with dynamic agent participation.
Findings
The method converges under realistic network conditions.
It effectively handles asynchronous updates and agent churn.
Performance is validated through theoretical analysis.
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.
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