Optimistic Gradient Algorithms with Randomized Coordinate and Variance Reduction Strategies for Composite Inclusions

In this paper, we consider a class of possibly nonmonotone finite-sum composite inclusions in which evaluating all components or updating all coordinates at every iteration can be computationally expensive. We develop two optimistic gradient frameworks that combine variance reduction with randomized