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arXiv · 1611.02189

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

Virginia Smith·Simone Forte·Chenxin Ma·Martin Takac·Michael I. Jordan·Martin Jaggi
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Abstract

The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed computing environments, CoCoA, that has an efficient communication scheme and is applicable to a wide variety of problems in machine learning and signal processing. We extend the framework to cover general non-strongly-convex regularizers, including L1-regularized problems like lasso, sparse logistic regression, and elastic net regularization, and show how earlier work can be derived as a special case. We provide convergence guarantees for the class of convex regularized loss minimization objectives, leveraging a novel approach in handling non-strongly-convex regularizers and non-smooth loss functions. The resulting framework has markedly improved performance over state-of-the-art methods, as we illustrate with an extensive set of experiments on real distributed datasets.

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Virginia Smith, Simone Forte, Chenxin Ma, Martin Takac, Michael I. Jordan, Martin Jaggi. 2016-11-07. CoCoA: A General Framework for Communication-Efficient Distributed Optimization. https://arxiv.org/abs/1611.02189

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