Extremum Seeking Tracking for Derivative-free Distributed Optimization
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866913573301649408 |
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| author | Mimmo, Nicola Carnevale, Guido Testa, Andrea Notarstefano, Giuseppe |
| author_facet | Mimmo, Nicola Carnevale, Guido Testa, Andrea Notarstefano, Giuseppe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2110_04234 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Extremum Seeking Tracking for Derivative-free Distributed Optimization Mimmo, Nicola Carnevale, Guido Testa, Andrea Notarstefano, Giuseppe Optimization and Control Systems and Control 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. |
| title | Extremum Seeking Tracking for Derivative-free Distributed Optimization |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2110.04234 |