Efficient Nonsmooth Nonconvex Finite-sum Optimization via Normal Map-based Proximal Random Reshuffling

A new normal map-based proximal random reshuffling (norm-PRR) method is proposed for solving nonsmooth nonconvex finite-sum optimization problems. Norm-PRR achieves improved iteration complexity bounds compared to existing proximal-type random reshuffling methods, and also exhibits strong asymptotic convergence guarantees.