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Statistical estimation in global random search algorithms in case of large dimensions

Pepelyshev, Andrey ORCID: https://orcid.org/0000-0001-5634-5559, Kornikov, Vladimir and Zhigljavsky, Anatoly ORCID: https://orcid.org/0000-0003-0630-8279 2017. Statistical estimation in global random search algorithms in case of large dimensions. Lecture Notes in Computer Science 10556 , pp. 364-369. 10.1007/978-3-319-69404-7_32

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Abstract

We study asymptotic properties of optimal statistical esti- mators in global random search algorithms when the dimension of the feasible domain is large. The results obtained can be helpful in deciding what sample size is required for achieving a given accuracy of estimation.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Uncontrolled Keywords: Global optimization, extreme value, random search, esti- mation of end-point
Publisher: Springer Verlag
ISSN: 0302-9743
Date of First Compliant Deposit: 30 May 2017
Date of Acceptance: 26 February 2017
Last Modified: 08 Nov 2023 03:59
URI: https://orca.cardiff.ac.uk/id/eprint/100843

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