  We propose and analyze the convergence of a novel stochastic algorithm for
solving monotone inclusions that are the sum of a maximal monotone operator and
a monotone, Lipschitzian operator. The propose algorithm requires only unbiased
estimations of the Lipschitzian operator. We obtain the rate
$\mathcal{O}(log(n)/n)$ in expectation for the strongly monotone case, as well
as almost sure convergence for the general case. Furthermore, in the context of
application to convex-concave saddle point problems, we derive the rate of the
primal-dual gap. In particular, we also obtain $\mathcal{O}(1/n)$ rate
convergence of the primal-dual gap in the deterministic setting.
