Asynchronous Gossip-Based Random Projection Algorithms Over Networks

By Soomin Lee, Angelia Nedić. 2013

88 citations recorded by the source corpus.

We consider a distributed constrained convex optimization problem over a multi-agent (no central coordinator) network. We propose a completely decentralized and 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 a diminishing and a constant stepsize which are uncoordinated among agents. 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.

Subfields: Computer Networks and Communications, Public Health, Environmental and Occupational Health

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Citations in the corpus

A Unified Theory of Decentralized SGD with Changing Topology and Local Updates (2020, methodology): “…such as alternating direction method of multipliers (ADMM) (Wei & Ozdaglar, 2012; Iutzeler et al., 2013), dual averaging (Duchi et al., 2012; Nedi´c et al., 2015; Rabbat, 2015), primal-dual methods (Alghunaim & Sayed, 2020), or block-coordinate methods for generalized linear models (He et…”
Push–Pull Gradient Methods for Distributed Optimization in Networks (2018, background): “Gossip-based communication protocols are popular choices for distributed computation due to their low communication costs [18], [19], [20], [21].”