Optimization in Open Networks via Dual Averaging
This addresses the challenge of maintaining optimization performance in dynamic networks for applications like vehicle fleets or sensor networks, but it appears incremental as it builds on existing online optimization techniques.
The paper tackled the problem of distributed optimization in open networks where agents can join or leave at any time, proposing a decentralized asynchronous method that achieves convergence.
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.