A method of stochastic subgradients with complete feedback stepsize rule for convex stochastic approximation problems

A stochastic subgradient algorithm for solving convex stochastic approximation problems is considered. In the algorithm, the stepsize coefficients are controlled on-line on the basis of information gathered in the course of computations according to a new, complete feedback rule derived from the concept of regularized improvement function. Convergence with probability 1 of the method is established.This work was supported by Project No. CPBP/02.15.