  Online averaged stochastic gradient algorithms are more and more studied
since (i) they can deal quickly with large sample taking values in high
dimensional spaces, (ii) they enable to treat data sequentially, (iii) they are
known to be asymptotically efficient. In this paper, we focus on giving
explicit bounds of the quadratic mean error of the estimates, and this, with
very weak assumptions, i.e without supposing that the function we would like to
minimize is strongly convex or admits a bounded gradient.
