Global stochastic optimization with low-dispersion point sets

Global stochastic optimization with low-dispersion point sets

0.00 Avg rating0 Votes
Article ID: iaor20022044
Country: United States
Volume: 48
Issue: 6
Start Page Number: 939
End Page Number: 950
Publication Date: Nov 2000
Journal: Operations Research
Authors: , ,
Keywords: optimization
Abstract:

This study concerns a generic model-free stochastic optimization problem requiring the minimization of a risk function defined on a given bounded domain in a Euclidean space. Smoothness assumptions regarding the risk function are hypothesized, and members of the underlying space of probabilities are presumed subject to a large deviation principle; however, the risk function may well be nonconvex and multimodal. A general approach to finding the risk minimizer on the basis of decision/observation pairs is proposed. It consists of repeatedly observing pairs over a collection of design points. Principles are derived for choosing the number of these design points on the basis of an observation budget, and for allocating the observations between these points in both prescheduled and adaptive settings. On the basis of these principles, large-deviation type bounds of the minimizer in terms of sample size are established.

Reviews

Required fields are marked *. Your email address will not be published.