  We consider the minimization of composite objective functions composed of the
expectation of quadratic functions and an arbitrary convex function. We study
the stochastic dual averaging algorithm with a constant step-size, showing that
it leads to a convergence rate of O(1/n) without strong convexity assumptions.
This thus extends earlier results on least-squares regression with the
Euclidean geometry to (a) all convex regularizers and constraints, and (b) all
geome-tries represented by a Bregman divergence. This is achieved by a new
proof technique that relates stochastic and deterministic recursions.
