Gossip-based random projection algorithm for distributed optimization: Error bound

We consider a fully distributed constrained convex optimization problem over a multi-agent network. We discuss an asynchronous gossip-based random projection (GRP) algorithm that solves the distributed problem using only local communication and computation. We analyze its error bound for a constant stepsize and provide simulation results on a distributed robust model predictive control problem.