Distributed Proximal Splitting Algorithms with Rates and Acceleration

Published in The 12th Annual Workshop on Optimization for Machine Learning (NeurIPS Workshop OPT2020), Spotlight, 2020

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We propose new generic distributed proximal splitting algorithms, well suited for large-scale convex nonsmooth optimization. We derive sublinear and linear convergence results with new nonergodic rates, as well as new accelerated versions of the algorithms, using varying stepsizes.