On Distributionally Robust Generalized Nash Games Defined over the Wasserstein Ball

Filippo Fabiani*, Barbara Franci

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

In this paper we propose an exact, deterministic, and fully continuous reformulation of generalized Nash games characterized by the presence of soft coupling constraints in the form of distributionally robust (DR) joint chance-constraints (CCs). We first rewrite the underlying uncertain game introducing mixed-integer variables to cope with DR–CCs, where the integer restriction actually amounts to a binary decision vector only, and then extend it to an equivalent deterministic problem with one additional agent handling all those introduced variables. Successively we show that, by means of a careful choice of tailored penalty functions, the extended deterministic game with additional agent can be equivalently recast in a fully continuous setting.
Original languageEnglish
Pages (from-to)298-309
Number of pages12
JournalJournal of Optimization Theory and Applications
Volume199
Issue number1
Early online date1 Jan 2023
DOIs
Publication statusPublished - Oct 2023

Keywords

  • Distributionally robust optimization
  • Exact penalty functions
  • Mixed-integer programming
  • Uncertain Nash games

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