One-dimensional global optimization for observations with noise

James Calvin, A. Žilinskas

Research output: Contribution to journalArticle

22 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)157-169
Number of pages13
JournalComputers and Mathematics with Applications
Volume50
Issue number1-2
DOIs
StatePublished - Jul 1 2005

Fingerprint

Global optimization
Global Optimization
Wiener Process
Gaussian Noise
Adaptive algorithms
Adaptive Algorithm
Objective function
Grid
Experimental Results
Modeling
Strategy
Observation
Standards

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Computational Theory and Mathematics
  • Modeling and Simulation

Cite this

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One-dimensional global optimization for observations with noise. / Calvin, James; Žilinskas, A.

In: Computers and Mathematics with Applications, Vol. 50, No. 1-2, 01.07.2005, p. 157-169.

Research output: Contribution to journalArticle

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