A lower bound on convergence rates of nonadaptive algorithms for univariate optimization with noise

Document Type

Article

Publication Date

9-1-2010

Abstract

This paper considers complexity bounds for the problem of approximating the global minimum of a univariate function when the function evaluations are corrupted by random noise. We take an average-case point of view, where the objective function is taken to be a sample function of a Wiener process and the noise is independent Gaussian. Previous papers have bounded the convergence rates of some nonadaptive algorithms. We establish a lower bound on the convergence rate of any nonadaptive algorithm. © 2010 Springer Science+Business Media, LLC.

Identifier

77955516177 (Scopus)

Publication Title

Journal of Global Optimization

External Full Text Location

https://doi.org/10.1007/s10898-010-9530-z

e-ISSN

15732916

ISSN

09255001

First Page

17

Last Page

27

Issue

1

Volume

48

Grant

CMMI-0825381

Fund Ref

National Science Foundation

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