Subgradient averaging for multi-agent optimisation with different constraint sets

Licio Romao, Kostas Margellos, Giuseppe Notarstefano, Antonis Papachristodoulou

Abstract: We consider a multi-agent setting with agents exchanging information over a possibly time-varying network, aiming at minimising a separable objective function subject to constraints. To achieve this objective we propose a novel subgradient averaging algorithm that allows for non-differentiable objective functions and different constraint sets per agent. Allowing different constraints per agent simultaneously with a time-varying communication network constitutes a distinctive feature of our approach, extending existing results on distributed subgradient methods. To highlight the necessity of dealing with a different constraint set within a distributed optimisation context, we analyse a problem instance where an existing algorithm does not exhibit a convergent behaviour if adapted to account for different constraint sets. For our proposed iterative scheme we show asymptotic convergence of the iterates to a minimum of the underlying optimisation problem for step sizes of the form ηk+1, η>0. We also analyse this scheme under a step size choice of ηk+1, η>0, and establish a convergence rate of O(lnkk) in objective value. To demonstrate the efficacy of the proposed method, we investigate a robust regression problem and an ℓ2 regression problem with regularisation.