A Jacobi-type Newton method for Nash equilibrium problems with descent guarantees

TL;DR

This paper introduces a new Newton-based algorithm for solving two-player Nash equilibrium problems that guarantees descent directions and improves robustness by considering game dynamics and prediction-based strategies.

Contribution

The paper proposes a Jacobi-type Newton method with descent guarantees that incorporates game dynamics and prediction, enhancing convergence and robustness over standard approaches.

Findings

Algorithm guarantees descent directions for each player.

Method improves convergence robustness in non-convex settings.

Numerical experiments demonstrate superior performance over existing strategies.

Abstract

A common strategy for solving an unconstrained two-player Nash equilibrium problem with continuous variables is applying Newton's method to the system of nonlinear equations obtained by the corresponding first-order necessary optimality conditions. However, when taking into account the game dynamics, it is not clear what is the goal of each player when considering that they are taking their current decision following Newton's iterates. In this paper we provide an interpretation for Newton's iterate in view of the game dynamics as follows: instead of minimizing the quadratic approximation of their objective function parameterized by the other player current decision (as a typical Jacobi-type strategy), we show that the Newton iterate follows this approach but with the objective function parameterized by a prediction of the other player action, considering that they are following the same Newtonian strategy. This interpretation allows us to present a new Newtonian algorithm where a backtracking procedure is introduced in order to guarantee that the computed Newtonian directions, for each player, are descent directions for their corresponding parameterized functions. Thus, besides favoring global convergence, our algorithm also favors true minimizers instead of maximizers or saddle points, differently from the standard Newton method, which does not consider the minimization structure of the problem in the non-convex case. Thus, our method is more robust in comparison with other Jacobi-type strategies or the pure Newtonian approach, which is corroborated by our illustrative numerical experiments. We also present a proof of the well-definiteness of the algorithm under some standard assumptions, together with a thorough analysis of its convergence properties taking into account the game dynamics.