Artículo
Tracking-Based Distributed Equilibrium Seeking for Aggregative Games
Autor/es | Carnevale, Guido
Fabiani, Filippo Fele, Filiberto Margellos, Kostas Notarstefano, Giuseppe |
Departamento | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2024-02 |
Fecha de depósito | 2024-06-28 |
Publicado en |
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Resumen | We propose fully-distributed algorithms for Nash equilibrium seeking in aggregative games over networks. We first consider the case where local constraints are present and we design an algorithm combining, for each agent, ... We propose fully-distributed algorithms for Nash equilibrium seeking in aggregative games over networks. We first consider the case where local constraints are present and we design an algorithm combining, for each agent, (i) the projected pseudo-gradient descent and (ii) a tracking mechanism to locally reconstruct the aggregative variable. To handle coupling constraints arising in generalized settings, we propose another distributed algorithm based on (i) a recently emerged augmented primal-dual scheme and (ii) two tracking mechanisms to reconstruct, for each agent, both the aggregative variable and the coupling constraint satisfaction. Leveraging tools from singular perturbations analysis, we prove linear convergence to the Nash equilibrium for both schemes. Finally, we run extensive numerical simulations to confirm the effectiveness of our methods and compare them with state-of-the-art distributed equilibrium-seeking algorithms. |
Agencias financiadoras | Ministerio de Asuntos Exteriores y Cooperación Internacional. Italia Ministerio de Ciencia, Innovación y Universidades. España Agencia Estatal de Investigación. España European Union (UE) European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) |
Identificador del proyecto | BR22GR01
RYC2021-033960-I PID2022-142946NA-I00 |
Cita | Carnevale, G., Fabiani, F., Fele, F., Margellos, K. y Notarstefano, G. (2024). Tracking-Based Distributed Equilibrium Seeking for Aggregative Games. IEEE Transactions on Automatic Control. https://doi.org/10.1109/TAC.2024.3368967. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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IEEE_2024_Carnevale_Tracking_OA.pdf | 1.820Mb | [PDF] | Ver/ | Versión publicada |