Work

Local Stackelberg Equilibrium Seeking in Generalized Aggregative Games

Year: 2021

Type: article

Abstract: We propose a two-layer, semidecentralized algorithm to compute a local solution to the Stackelberg equilibrium problem in aggregative games with coupling constraints. Specifically, we focus on a single-leader, multiple-follower problem, and after equivalently recasting the Stackelberg game as a mathematical program with complementarity constraints (MPCC), we iteratively convexify a regularized version of the MPCC as the inner problem, whose solution generates a sequence of feasible descent directions for the original MPCC. Thus, by pursuing a descent direction at every outer iteration, we establish convergence to a local Stackelberg equilibrium. Finally, the proposed algorithm is tested on a numerical case study, a hierarchical instance of the charging coordination problem of plug-in electric vehicles.

Source: IEEE Transactions on Automatic Control

DOI: 10.1109/tac.2021.3077874

Authors: Filippo Fabiani, Mohammad Amin Tajeddini, Hamed Kebriaei, Sergio Grammatico

Institutions: University of Oxford, University of Tehran, Delft University of Technology

Language: English

Topic: Game Theory and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

Funders: UK Research and Innovation, Innovate UK

Award: G4609290022