Local Stackelberg equilibrium seeking in generalized aggregative games

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

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)


We propose a two-layer, semi-decentralized 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 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 (PEVs).

Original languageEnglish
JournalIEEE Transactions on Automatic Control
Publication statusAccepted/In press - 6 May 2021


  • Approximation algorithms
  • Convergence
  • Cost function
  • Couplings
  • game theory
  • Games
  • hierarchical systems
  • optimization
  • Stackelberg equilibrium
  • Standards
  • Wireless networks


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