A proximal stochastic gradient method with variance reduction and adaptive steps is shown to converge strongly at rate O(sqrt(1/k)) for convex composite problems when the smooth term is Lipschitz continuous.
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A Proximal Stochastic Gradient Method with Adaptive Step Size and Variance Reduction for Convex Composite Optimization
A proximal stochastic gradient method with variance reduction and adaptive steps is shown to converge strongly at rate O(sqrt(1/k)) for convex composite problems when the smooth term is Lipschitz continuous.