  In this work we propose a stochastic primal-dual preconditioned
three-operator splitting algorithm for solving a class of convex
three-composite optimization problems. Our proposed scheme is a direct
three-operator splitting extension of the SPDHG algorithm [Chambolle et al.
2018]. We provide theoretical convergence analysis showing ergodic O(1/K)
convergence rate, and demonstrate the effectiveness of our approach in imaging
inverse problems.
