Solution of Nonconvex Nonsmooth Stochastic Optimization Problems

Autor:	Vladimir I. Norkin, Yu. M. Ermoliev
Rok vydání:	2003
Předmět:	Class (set theory) Mathematical optimization General Computer Science Stochastic optimization Differentiable function Stochastic approximation Stochastic programming Mathematics
Zdroj:	Cybernetics and Systems Analysis. 39:701-715
ISSN:	1060-0396
DOI:	10.1023/b:casa.0000012091.84864.65
Popis:	Different classes of nonconvex nonsmooth stochastic optimization problems are analyzed, their generalized differentiability properties and necessary optimality conditions are studied, and a technique for calculating stochastic gradients is developed. For each class of the problems, corresponding solution methods are proposed, in particular, generalizations of the stochastic quasigradient method.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::7e783c267a48e57c389eb1a645278a52 https://doi.org/10.1023/b:casa.0000012091.84864.65 Zobrazit plný text záznamu Full text from SpringerLink