Stochastic approximation method with gradient averaging for unconstrained problems

A stochastic approximation algorithm is studied, in which random gradient estimates are averaged by an auxiliary, filter. Convergence of the algorithm is proved under a rather general noise condition. A practical version of the method is described and numerical results are given.