SDCA without Duality

Published 22 Feb 2015 in cs.LG | (1502.06177v1)

Abstract: 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.

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Authors (1)

Shai Shalev-Shwartz

Citations (47)

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