  Minibatch decomposition methods for empirical risk minimization are commonly
analysed in a stochastic approximation setting, also known as sampling with
replacement. On the other hands modern implementations of such techniques are
incremental: they rely on sampling without replacement, for which available
analysis are much scarcer. We provide convergence guaranties for the latter
variant by analysing a versatile incremental gradient scheme. For this scheme,
we consider constant, decreasing or adaptive step sizes. In the smooth setting
we obtain explicit complexity estimates in terms of epoch counter. In the
nonsmooth setting we prove that the sequence is attracted by solutions of
optimality conditions of the problem.
