Asynchronous Gossip-Based Random Projection Algorithms Over Networks
Abstract
We consider a fully distributed constrained convex optimization problem over a multi-agent (no central coordinator) network. We propose an asynchronous gossip-based random projection (GRP) algorithm that solves the distributed problem using only local communications and computations. We analyze the convergence properties of the algorithm for an uncoordinated diminishing stepsize and a constant stepsize. For a diminishing stepsize, we prove that the iterates of all agents converge to the same optimal point with probability 1. For a constant stepsize, we establish an error bound on the expected distance from the iterates of the algorithm to the optimal point. We also provide simulation results on a distributed robust model predictive control problem.
Cite
@article{arxiv.1304.1757,
title = {Asynchronous Gossip-Based Random Projection Algorithms Over Networks},
author = {Soomin Lee and Angelia Nedich},
journal= {arXiv preprint arXiv:1304.1757},
year = {2013}
}