The Cut-and-Play Algorithm: Computing Nash Equilibria via Outer Approximations

We introduce Cut-and-Play, a practically efficient algorithm for computing Nash equilibria in simultaneous noncooperative games where players decide via nonconvex and possibly unbounded optimization problems with separable payoff functions. Our algorithm exploits an intrinsic relationship between the equilibria of the original nonconvex game and the ones of a convexified counterpart. In practice, Cut-and-Play formulates a series of convex approximations of the game and iteratively refines them with cutting planes and branching operations. Our algorithm does not require convexity or continuity of the player’s optimization problems and can be integrated with existing optimization software. We test Cut-and-Play on two families of challenging nonconvex games involving discrete decisions and bilevel problems, and we empirically demonstrate that it efficiently computes equilibria while outperforming existing game-specific algorithms.

Funding: G. Dragotto, A. Lodi, and S. Sankaranarayanan thank the Canada Excellence Research Chair in “Data Science for Real-time Decision-making” at Polytechnique Montreal for support.

Supplemental Material: All supplemental materials are available at https://doi.org/10.1287/opre.2023.0327.