One-dimensional global optimization for observations with noise

J. M. Calvin, A. Žilinskas

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

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 2005

All Science Journal Classification (ASJC) codes

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

Keywords

  • Optimization
  • Statistical models

Fingerprint Dive into the research topics of 'One-dimensional global optimization for observations with noise'. Together they form a unique fingerprint.

Cite this