A Stochastic Gradient Method with an Exponential Convergence Rate for   Finite Training Sets
  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. 
Nicolas Le Roux (INRIA Paris - Rocquencourt, LIENS), Mark Schmidt   (INRIA Paris - Rocquencourt, LIENS), Francis Bach (INRIA...
math.OC cs.LG