Extremum Seeking Tracking for Derivative-free Distributed Optimization
Abstract
In this paper, we deal with a network of agents that want to cooperatively minimize the sum of local cost functions depending on a common decision variable. We consider the challenging scenario in which objective functions are unknown and agents have only access to local measurements of their local functions. We propose a novel distributed algorithm that combines a recent gradient tracking policy with an extremum seeking technique to estimate the global descent direction. The joint use of these two techniques results in a distributed optimization scheme that provides arbitrarily accurate solution estimates through the combination of Lyapunov and averaging analysis approaches with consensus theory. We perform numerical simulations in a personalized optimization framework to corroborate the theoretical results.
Cite
@article{arxiv.2110.04234,
title = {Extremum Seeking Tracking for Derivative-free Distributed Optimization},
author = {Nicola Mimmo and Guido Carnevale and Andrea Testa and Giuseppe Notarstefano},
journal= {arXiv preprint arXiv:2110.04234},
year = {2024}
}