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arXiv · 2110.04234

Extremum Seeking Tracking for Derivative-free Distributed Optimization

Nicola Mimmo·Guido Carnevale·Andrea Testa·Giuseppe Notarstefano
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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.

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BibTeXRIS

Nicola Mimmo, Guido Carnevale, Andrea Testa, Giuseppe Notarstefano. 2021-10-08. Extremum Seeking Tracking for Derivative-free Distributed Optimization. https://arxiv.org/abs/2110.04234

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