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:	22 Nov 2024 07:30
URI:	https://orca.cardiff.ac.uk/id/eprint/100843

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