  In this paper, we propose a new optimization algorithm for sparse logistic
regression based on a stochastic version of the Douglas-Rachford splitting
method. Our algorithm sweeps the training set by randomly selecting a
mini-batch of data at each iteration, and it allows us to update the variables
in a block coordinate manner. Our approach leverages the proximity operator of
the logistic loss, which is expressed with the generalized Lambert W function.
Experiments carried out on standard datasets demonstrate the efficiency of our
approach w.r.t. stochastic gradient-like methods.
