Two-timescale recurrent neural networks for distributed minimax optimization

In this paper, we present two-timescale neurodynamic optimization approaches to distributed min-imax optimization. We propose four multilayer recurrent neural networks for solving four different types of generally nonlinear convex-concave minimax problems subject to linear equality and nonlin-ear inequality constraints. We derive sufficient conditions to guarantee the stability and optimality of the neural networks. We demonstrate the viability and efficiency of the proposed neural networks in two specific paradigms for Nash-equilibrium seeking in a zero-sum game and distributed constrained nonlinear optimization.& COPY; 2023 Elsevier Ltd. All rights reserved.