  A new stochastic primal--dual algorithm for solving a composite optimization
problem is proposed. It is assumed that all the functions/operators that enter
the optimization problem are given as statistical expectations. These
expectations are unknown but revealed across time through i.i.d. realizations.
The proposed algorithm is proven to converge to a saddle point of the
Lagrangian function. In the framework of the monotone operator theory, the
convergence proof relies on recent results on the stochastic Forward Backward
algorithm involving random monotone operators. An example of convex
optimization under stochastic linear constraints is considered.
