Stochastic Forward Douglas-Rachford Splitting Method for Monotone Inclusions

We propose a stochastic Forward-Douglas-Rachford Splitting framework for finding a zero point of the sum of three maximally monotone operators, one of which is cocoercive, in a real separable Hilbert space. We characterize the rate of convergence in expectation for strongly monotone operators. We further provide guidance on step-size sequence selection that achieve this rate, even when the strong convexity parameter is unknown.