We consider composite minimax optimization problems where the goal is to find a saddle-point of large sum non-bilinear objective functions augmented by simple regularizers for primal and dual variables. For such problems, under average-smoothness assumption, we propose accelerated stochastic variance-reduced algorithms with optimal up logarithmic factors complexity bounds. In particular, strong...
