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 a...

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Bibliographic Details
Published in:arXiv.org 2015-02
Main Author: Shalev-Shwartz, Shai
Format: Article
Language:English
Subjects:
Ascent
Online Access:Get full text
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