Smith, VirginiaForte, SimoneMa, ChenxinTakac, MartinJordan, Michael I.Jaggi, Martin2018-12-132018-12-132018-12-132018-07-01https://infoscience.epfl.ch/handle/20.500.14299/152602WOS:000440886000001The 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.Automation & Control SystemsComputer Science, Artificial IntelligenceAutomation & Control SystemsComputer Scienceconvex optimizationdistributed systemslarge-scale machine learningparallel and distributed algorithmsregularized loss minimizationcoordinate descentlinear classificationconvex-optimizationalgorithmstext::journal::journal article::research article