Distributed semi-stochastic optimization with quantization refinement

Abstract

We consider the problem of regularized regression in a network of communication-constrained devices. Each node has local data and objectives, and the goal is for the nodes to optimize a global objective. We develop a distributed optimization algorithm that is based on recent work on semi-stochastic proximal gradient methods. Our algorithm employs… (More)
DOI: 10.1109/ACC.2016.7526802

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Cite this paper

@article{McGlohon2016DistributedSO, title={Distributed semi-stochastic optimization with quantization refinement}, author={Neil McGlohon and Stacy Patterson}, journal={2016 American Control Conference (ACC)}, year={2016}, pages={7159-7164} }