Augmented Lagrangian Tracking for distributed optimization with equality and inequality coupling constraints

In this paper we propose a novel Augmented Lagrangian Tracking distributed optimization algorithm for solving multi-agent optimization problems where each agent has its own decision variables, cost function and constraint set, and the goal is to minimize the sum of the agents' cost functions subject to local constraints plus some additional coupling constraint involving the decision variables of all the agents. In contrast to alternative approaches available in the literature, the proposed algorithm jointly features a constant penalty parameter, the ability to cope with unbounded local constraint sets, and the ability to handle both affine equality and nonlinear inequality coupling constraints, while requiring convexity only. The effectiveness of the approach is shown first on an artificial example with complexity features that make other state-of-the-art algorithms not applicable and then on a realistic example involving the optimization of the charging schedule of a fleet of electric vehicles.