Passivity-Based Gradient-Play Dynamics for Distributed Generalized Nash Equilibrium Seeking

We consider seeking generalized Nash equilibria (GNE) for noncooperative games with coupled nonlinear constraints over networks. We first revisit a well-known gradient-play dynamics from a passivity-based perspective, and address that the strict monotonicity of the pseudo-gradient is a critical assumption to ensure the exact convergence of the dynamics. Then we propose two novel passivity-based gradient-play dynamics by introducing parallel feedforward compensators (PFCs) and output feedback compensators (OFCs). We show that the proposed dynamics can reach exact GNEs in merely monotone regimes if the PFCs are strictly passive or if the OFCs are output strictly passive. Following that, resorting to passivity, we develop a unifying framework to generalize the gradient-play dynamics, and moreover, design a class of explicit dynamics with convergence guarantees. In addition, we explore the relation between the proposed dynamics and some existing methods, and extend our results to partial-decision information settings.