One-dimensional global optimization for observations with noise

Document Type

Article

Publication Date

7-1-2005

Abstract

A problem of one-dimensional global optimization in the presence of noise is considered. The approach is based on modeling the objective function as a standard Wiener process which is observed with independent Gaussian noise. An asymptotic bound for the average error is estimated for the nonadaptive strategy defined by a uniform grid. Experimental results consistent with the asymptotic results are presented. An adaptive algorithm is proposed and experimentally compared with the nonadaptive strategy with respect to the average error. © 2005 Elsevier Ltd. All rights reserved.

Identifier

27544449903 (Scopus)

Publication Title

Computers and Mathematics with Applications

External Full Text Location

https://doi.org/10.1016/j.camwa.2004.12.014

ISSN

08981221

First Page

157

Last Page

169

Issue

1-2

Volume

50

Grant

DMI-9900117

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