Extremum Seeking Tracking for Derivative-Free Distributed Optimization

Nicola Mimmo

doi:10.1109/TCNS.2024.3510368

Extremum Seeking Tracking for Derivative-Free Distributed Optimization

2024-01-01

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Giuseppe Notarstefano

DOI：10.1109/TCNS.2024.3510368

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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.

Keyword:

Optimization

Distributed algorithms

Network systems

Costs

Control systems

Protocols

Cost function

Convergence

Vectors

Standards

Distributed processing

gradient methods

optimization

Extremum Seeking Tracking for Derivative-Free Distributed Optimization

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