Adaptive step regulation for a stochastic optimization algorithm

The quasigradient algorithm of stochastic optimization is considered. The conditions to be imposed on the step multiplier, for Cesaro convergence of the algorithms with probability $1$, are studied. Adaptive step adjustment is proposed, and the convergence of the corresponding algorithm is proved. A numerical algorithm containing heuristic elements is described. The results of numerical experiments are quoted.