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In this work we introduce a new optimisation method called SAGA in the spirit SAG, SDCA, MISO and SVRG, a set of recently proposed incremental gradient with fast linear convergence rates. SAGA improves on the theory SAG and SVRG, with better theoretical convergence rates, and has support composite objectives where a proximal operator is used on the regulariser. SDCA, SAGA supports non-strongly convex problems directly, and is to any inherent strong convexity of the problem. We give experimental showing the effectiveness of our method.
Defazio et al. (Tue,) studied this question.