A method of stochastic subgradients with complete feedback stepsize rule for convex stochastic approximation problems
type: Item
instance of: scholarly article
title: A method of stochastic subgradients with complete feedback stepsize rule for convex stochastic approximation problems
zbMATH Open document ID: 0628.90057
DOI: 10.1007/BF00940311
author: Wojciech Syski
published in: Journal of Optimization Theory and Applications
publication date: 1988-01-01T00:00:00Z
review text: 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.
Mathematics Subject Classification ID: 90C25
Mathematics Subject Classification ID: 65K05
zbMATH DE Number: 4023023
zbMATH Keywords: stochastic subgradient algorithm
zbMATH Keywords: convex stochastic approximation
zbMATH Keywords: feedback rule
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 4023023