Stochastic proximal gradient consensus over time varying networks
We consider solving a convex, nonsmooth and stochastic optimization problem over a multi agent network. Each agent has access to a local objective function and can communicate with its immediate neighbors only. We develop a dynamic stochastic proximal gradient consensus (DySPGC) algorithm, featuring: i) it works for both the static and randomly time varying networks; ii) it can deal with either the exact or the stochastic gradient information; iii) it has provable rate of convergence. Interestingly, the developed algorithm includes as special cases many existing (and seemingly unrelated) first order algorithms for distributed optimization over static networks, such as the EXTRA (Shi et al 2014) the PG EXTRA (Shi at 2015) the IC/IDC ADMM (Chang et al 2014) and the DLM (Ling et al 2015) It is also closely related to the classical distributed gradient method.
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)