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|Title:||Gradient-free nash equilibrium seeking in N-cluster games with uncoordinated constant step-sizes||Authors:||Pang, Yipeng
|Keywords:||Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering||Issue Date:||2022||Source:||Pang, Y. & Hu, G. (2022). Gradient-free nash equilibrium seeking in N-cluster games with uncoordinated constant step-sizes. 2022 IEEE 61st Conference on Decision and Control (CDC), 3815-3820. https://dx.doi.org/10.1109/CDC51059.2022.9992991||Abstract:||This work investigates a problem of simultaneous global cost minimization and Nash equilibrium seeking, which commonly exists in N-cluster non-cooperative games. Specifically, the players in the same cluster collaborate to minimize a global cost function, being a summation of their individual cost functions, and jointly play a non-cooperative game with other clusters as players. For the problem settings, we suppose that the explicit analytical expressions of the players' local cost functions are unknown, but the function values can be measured. We propose a gradient-free Nash equilibrium seeking algorithm by a synthesis of Gaussian smoothing techniques and gradient tracking. Furthermore, instead of using the uniform coordinated step-size, we allow the players across different clusters to choose different constant step-sizes. When the largest step-size is sufficiently small, we prove a linear convergence of the players' actions to a neighborhood of the unique Nash equilibrium under a strongly monotone game mapping condition, with the error gap being propotional to the largest step-size and the smoothing parameter. The performance of the proposed algorithm is validated by numerical simulations.||URI:||https://hdl.handle.net/10356/162428||DOI:||10.1109/CDC51059.2022.9992991||Rights:||© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/CDC51059.2022.9992991.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Conference Papers|
Updated on Feb 6, 2023
Updated on Feb 6, 2023
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