Average performance of a class of adaptive algorithms for global optimization

Document Type

Article

Publication Date

1-1-1997

Abstract

We describe a class of adaptive algorithms for approximating the global minimum of a continuous function on the unit interval. The limiting distribution of the error is derived under the assumption of Wiener measure on the objective functions. For any δ > 0, we construct an algorithm which has error converging to zero at rate n-(1 - δ) in the number of function evaluations n. This convergence rate contrasts with the n11/2 rate of previously studied nonadaptive methods.

Identifier

0031532941 (Scopus)

Publication Title

Annals of Applied Probability

External Full Text Location

https://doi.org/10.1214/aoap/1034801250

ISSN

10505164

First Page

711

Last Page

730

Issue

3

Volume

7

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