A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets

author: Nicolas Le Roux, Criteo
published: Jan. 22, 2013,   recorded: December 2012,   views: 759
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Description

We propose a new stochastic gradient method for optimizing the sum of a finite set of smooth functions, where the sum is strongly convex. While standard stochastic gradient methods converge at sublinear rates for this problem, the proposed method incorporates a memory of previous gradient values in order to achieve a linear convergence rate. In a machine learning context, numerical experiments indicate that the new algorithm can dramatically outperform standard algorithms, both in terms of optimizing the training error and reducing the test error quickly.

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