NashOpt - A Python Library for Computing Generalized Nash Equilibria
This provides a practical tool for researchers and practitioners in game theory and control systems to solve complex equilibrium problems, though it is incremental as it builds on existing mathematical formulations.
The authors introduced NashOpt, a Python library for computing generalized Nash equilibria in noncooperative games with shared constraints, handling nonlinear and linear-quadratic cases via methods like nonlinear least-squares and mixed-integer linear programming. They demonstrated its capabilities on examples such as linear quadratic regulation and model predictive control, making it available as open-source software.
NashOpt is an open-source Python library for computing and designing generalized Nash equilibria (GNEs) in noncooperative games with shared constraints and real-valued decision variables. The library exploits the joint Karush-Kuhn-Tucker (KKT) conditions of all players to handle both general nonlinear GNEs and linear-quadratic games, including their variational versions. Nonlinear games are solved via nonlinear least-squares formulations, relying on JAX for automatic differentiation. Linear-quadratic GNEs are reformulated as mixed-integer linear programs, enabling efficient computation of multiple equilibria. The framework also supports inverse-game and Stackelberg game-design problems. The capabilities of NashOpt are demonstrated through several examples, including noncooperative game-theoretic control problems of linear quadratic regulation and model predictive control. The library is available at https://github.com/bemporad/nashopt