  We describe a novel optimization method for finite sums (such as empirical
risk minimization problems) building on the recently introduced SAGA method.
Our method achieves an accelerated convergence rate on strongly convex smooth
problems. Our method has only one parameter (a step size), and is radically
simpler than other accelerated methods for finite sums. Additionally it can be
applied when the terms are non-smooth, yielding a method applicable in many
areas where operator splitting methods would traditionally be applied.
