  An usual problem in statistics consists in estimating the minimizer of a
convex function. When we have to deal with large samples taking values in high
dimensional spaces, stochastic gradient algorithms and their averaged versions
are efficient candidates. Indeed, (1) they do not need too much computational
efforts, (2) they do not need to store all the data, which is crucial when we
deal with big data, (3) they allow to simply update the estimates, which is
important when data arrive sequentially. The aim of this work is to give
asymptotic and non asymptotic rates of convergence of stochastic gradient
estimates as well as of their averaged versions when the function we would like
to minimize is only locally strongly convex.
