  Techniques for reducing the variance of gradient estimates used in stochastic
programming algorithms for convex finite-sum problems have received a great
deal of attention in recent years. By leveraging dissipativity theory from
control, we provide a new perspective on two important variance-reduction
algorithms: SVRG and its direct accelerated variant Katyusha. Our perspective
provides a physically intuitive understanding of the behavior of SVRG-like
methods via a principle of energy conservation. The tools discussed here allow
us to automate the convergence analysis of SVRG-like methods by capturing their
essential properties in small semidefinite programs amenable to standard
analysis and computational techniques. Our approach recovers existing
convergence results for SVRG and Katyusha and generalizes the theory to
alternative parameter choices. We also discuss how our approach complements the
linear coupling technique. Our combination of perspectives leads to a better
understanding of accelerated variance-reduced stochastic methods for finite-sum
problems.
