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Performance of global random search algorithms for large dimensions

Pepelyshev, Andrey ORCID:, Zhigljavsky, Anatoly ORCID: and Zilinskas, Amtanas 2018. Performance of global random search algorithms for large dimensions. Journal of Global Optimization 71 , pp. 57-71. 10.1007/s10898-017-0535-8

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We investigate the rate of convergence of general global random search (GRS) algorithms. We show that if the dimension of the feasible domain is large then it is impossible to give any guarantee that the global minimizer is found by a general GRS algorithm with reasonable accuracy. We then study precision of statistical estimates of the global minimum in the case of large dimensions. We show that these estimates also suffer the curse of dimensionality. Finally, we demonstrate that the use of quasi-random points in place of the random ones does not give any visible advantage in large dimensions.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Uncontrolled Keywords: Global optimization, Statistical models, Extreme value statistics, Random search
Publisher: Springer Verlag (Germany)
ISSN: 0925-5001
Date of First Compliant Deposit: 24 May 2017
Date of Acceptance: 16 May 2017
Last Modified: 26 Oct 2022 05:53

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