SVAG: Unified Convergence Results for SAG-SAGA Interpolation with Stochastic Variance Adjusted Gradient Descent

03/21/2019

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by   Martin Morin, et al.

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We analyze SVAG, a variance reduced stochastic gradient method with SAG and SAGA as special cases. Our convergence result for SVAG is the first to simultaneously capture both the biased low-variance method SAG and the unbiased high-variance method SAGA. In the case of SAGA, it matches previous upper bounds on the allowed step-size. The SVAG algorithm has a parameter that decides the bias-variance trade-off in the stochastic gradient estimate. We provide numerical examples demonstrating the intuition behind this bias-variance trade-off.