Locally-polynomial algorithms of passive stochastic approximation

The recursive algorithms of estimation of the minimum of a regression function and of the root of a regression equation in the passive stochastic approximation framework are proposed. The almost sure and mean square convergence of the algorithms is proved. It is shown that under a certain choice of the parameters these algorithms have the optimal rates of convergence in the minimax sense on the classes of smooth regression functions.