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 online 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.
scientific article; zbMATH DE number 4023023
scientific article