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SDCA without Duality

Stochastic Dual Coordinate Ascent is a popular method for solving regularized loss minimization for the case of convex losses. In this paper we show how a variant of SDCA can be applied for non-convex losses. We prove linear convergence rate even if individual loss functions are non-convex as long as the expected loss is convex.

3 nodes3 linksoverview mapSDCA without Duality
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AuthorshipWorks onTopic signalWSDCA without Dualitypreprint / 2015AShai Shalev-ShwartzResearcherTMachine Learning49008 works
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SDCA without Duality

preprint / 2015

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