Fully distributed ADMM design for constrained nonconvex optimization

This paper investigates a distributed nonconvex optimization problem constrained by a global coupled linear equality and nonidentical local feasible sets. To solve this problem, we propose a fully distributed alternating direction method of multiplier (ADMM) with a distributed dynamic average consensus for dual variables. Moreover, we demonstrate that the proposed algorithm converges to an approximately stationary solution of the considered problem using Lyapunov theory. Finally, we illustrate the effectiveness of the proposed algorithm by two simulation examples.