Stochastic optimization with averaging of trajectories

A recursive stochastic optimization procedure under dependent disturbances is studied. It is based on the Polyak-Ruppert algorithm with trajectory averaging. Almost sure convergence of the algorithm is proved as well as asymptotic normality of the delivered estimates. It is shown that the presented algorithm attains the highest possible asymptotic convergence rate for stochastic approximation algorithms