Work

Extremum Seeking Tracking for Derivative-Free Distributed Optimization

Year: 2024

Type: article

Abstract: In this article, 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.

Source: IEEE Transactions on Control of Network Systems

DOI: 10.1109/tcns.2024.3510368

Authors: Nicola Mimmo, Guido Carnevale, Andrea Testa, Giuseppe Notarstefano

Institution: University of Bologna

Language: English

Topic: Extremum Seeking Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,495

Funder: Ministero degli Affari Esteri e della Cooperazione Internazionale