A Semi-Decentralized Tikhonov-Based Algorithm for Optimal Generalized Nash Equilibrium Selection

Conference Paper (2023)
Author(s)

E. Benenati (TU Delft - Team Sergio Grammatico)

W. Ananduta (TU Delft - Team Sergio Grammatico)

S. Grammatico (TU Delft - Team Sergio Grammatico, TU Delft - Team Bart De Schutter)

Research Group
Team Sergio Grammatico
Copyright
© 2023 E. Benenati, W. Ananduta, S. Grammatico
DOI related publication
https://doi.org/10.1109/CDC49753.2023.10383583
More Info
expand_more
Publication Year
2023
Language
English
Copyright
© 2023 E. Benenati, W. Ananduta, S. Grammatico
Research Group
Team Sergio Grammatico
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
4243-4248
ISBN (electronic)
979-8-3503-0124-3
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

To optimally select a generalized Nash equilibrium, in this paper, we consider a semi-decentralized algorithm based on a double-layer Tikhonov regularization algorithm. Technically, we extend the Tikhonov method for equilibrium selection to generalized games. Next, we couple such an algorithm with the preconditioned forward-backward splitting, which guarantees linear convergence to a solution of the inner layer problem and allows for a semi-decentralized implementation. We then establish a conceptual connection and draw a comparison between the considered algorithm and the hybrid steepest descent method, the other known distributed approach for solving the equilibrium selection problem.

Files

A_Semi-Decentralized_Tikhonov-... (pdf)
(pdf | 0.767 Mb)
- Embargo expired in 19-07-2024
License info not available