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A Consensus-based Algorithm for Non-convex Multiplayer Games.

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Title: A Consensus-based Algorithm for Non-convex Multiplayer Games.
Authors: Chenchene, Enis1 (AUTHOR) enis.chenchene@univie.ac.at, Huang, Hui2 (AUTHOR) hui.huang@uni-graz.at, Qiu, Jinniao3 (AUTHOR) jinniao.qiu@ucalgary.ca
Source: Journal of Optimization Theory & Applications. Aug2025, Vol. 206 Issue 2, p1-30. 30p.
Abstract: In this paper, we present a novel consensus-based zeroth-order algorithm tailored for non-convex multiplayer games. The proposed method leverages a metaheuristic approach using concepts from swarm intelligence to reliably identify global Nash equilibria. We utilize a group of interacting particles, each agreeing on a specific consensus point, asymptotically converging to the corresponding optimal strategy. This paradigm permits a passage to the mean-field limit, allowing us to establish convergence guarantees under appropriate assumptions regarding initialization and objective functions. Finally, we conduct a series of numerical experiments to unveil the dependency of the proposed method on its parameters and apply it to solve a nonlinear Cournot oligopoly game involving multiple goods. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Optimization Theory & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 185645953
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  Data: A Consensus-based Algorithm for Non-convex Multiplayer Games.
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  Data: <searchLink fieldCode="AR" term="%22Chenchene%2C+Enis%22">Chenchene, Enis</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> enis.chenchene@univie.ac.at</i><br /><searchLink fieldCode="AR" term="%22Huang%2C+Hui%22">Huang, Hui</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> hui.huang@uni-graz.at</i><br /><searchLink fieldCode="AR" term="%22Qiu%2C+Jinniao%22">Qiu, Jinniao</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> jinniao.qiu@ucalgary.ca</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Optimization+Theory+%26+Applications%22">Journal of Optimization Theory & Applications</searchLink>. Aug2025, Vol. 206 Issue 2, p1-30. 30p.
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this paper, we present a novel consensus-based zeroth-order algorithm tailored for non-convex multiplayer games. The proposed method leverages a metaheuristic approach using concepts from swarm intelligence to reliably identify global Nash equilibria. We utilize a group of interacting particles, each agreeing on a specific consensus point, asymptotically converging to the corresponding optimal strategy. This paradigm permits a passage to the mean-field limit, allowing us to establish convergence guarantees under appropriate assumptions regarding initialization and objective functions. Finally, we conduct a series of numerical experiments to unveil the dependency of the proposed method on its parameters and apply it to solve a nonlinear Cournot oligopoly game involving multiple goods. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Optimization Theory & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10957-025-02719-z
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      – Code: eng
        Text: English
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              Text: Aug2025
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              Y: 2025
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