Normalized convergence in stochastic optimization

Ermoliev, Y. & Norkin, V.I. (1991). Normalized convergence in stochastic optimization. Annals of Operations Research 30 (1) 187-198. 10.1007/BF02204816.

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Abstract

A new concept of (normalized) convergence of random variables is introduced. This convergence is preserved under Lipschitz transformations, follows from convergence in mean and itself implies convergence in probability. If a sequence of random variables satisfies a limit theorem then it is a normalized convergent sequence. The introduced concept is applied to the convergence rate study of a statistical approach in stochastic optimization.

Item Type:	Article
Uncontrolled Keywords:	Probability theory; normalized convergence; stochastic optimization; statistical approach; rate of convergence
Research Programs:	Adaption and Optimization (ADO)
Bibliographic Reference:	Annals of Operations Research; 30(1):187-198 [1991]
Depositing User:	IIASA Import
Date Deposited:	15 Jan 2016 02:00
Last Modified:	27 Aug 2021 17:13
URI:	https://pure.iiasa.ac.at/3462

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