Distributed nonconvex optimization over time-varying networks

Abstract

In this paper we introduce a novel algorithmic framework for non-convex distributed optimization in multi-agent networks with time-varying (nonsymmetric) topology. The proposed method hinges on successive convex approximation (SCA) techniques while leveraging dynamic consensus as a mechanism to diffuse information: each agent first solves (possibly inexactly) a local convex approximation of the nonconvex original problem, and then performs local averaging operations. Asymptotic convergence to (stationary) solutions of the nonconvex problem is established. Finally, the framework is applied to a distributed nonlinear regression problem.