  Stochastic variance reduced gradient (SVRG) is a popular variance reduction
technique for accelerating stochastic gradient descent (SGD). We provide a
first analysis of the method for solving a class of linear inverse problems in
the lens of the classical regularization theory. We prove that for a suitable
constant step size schedule, the method can achieve an optimal convergence rate
in terms of the noise level (under suitable regularity condition) and the
variance of the SVRG iterate error is smaller than that by SGD. These
theoretical findings are corroborated by a set of numerical experiments.
