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International Conference on Learning and Intelligent Optimization

LION 2017: Learning and Intelligent Optimization pp 364–369Cite as

Statistical Estimation in Global Random Search Algorithms in Case of Large Dimensions

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 10556)

Abstract

We study asymptotic properties of optimal statistical estimators 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.

Keywords

  • Global optimization
  • Extreme value
  • Random search
  • Estimation of end-point

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References

  1. Zhigljavsky, A.: Mathematical Theory of Global Random Search. Leningrad University Press (1985). in Russian

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Acknowledgements

The work of the first author was partially supported by the SPbSU project No. 6.38.435.2015 and the RFFI project No. 17-01-00161. The work of the third author was supported by the Russian Science Foundation, project No. 15-11-30022 ‘Global optimization, supercomputing computations, and applications’.

Author information

Authors and Affiliations

  1. School of Mathematics, Cardiff University, Cardiff, CF24 4AG, UK

    Andrey Pepelyshev & Anatoly Zhigljavsky

  2. Faculty of Applied Mathematics, St.Petersburg State University, Saint Petersburg, Russia

    Andrey Pepelyshev & Vladimir Kornikov

  3. Lobachevsky Nizhny Novgorod State University, 23 Prospekt Gagarina, 603950, Nizhny Novgorod, Russia

    Anatoly Zhigljavsky

Authors
  1. Andrey Pepelyshev
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  2. Vladimir Kornikov
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  3. Anatoly Zhigljavsky
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Corresponding author

Correspondence to Andrey Pepelyshev .

Editor information

Editors and Affiliations

  1. University of Trento, Trento, Italy

    Roberto Battiti

  2. University of Calabria, Rende, Italy

    Dmitri E. Kvasov

  3. University of Calabria, Rende, Italy

    Yaroslav D. Sergeyev

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Pepelyshev, A., Kornikov, V., Zhigljavsky, A. (2017). Statistical Estimation in Global Random Search Algorithms in Case of Large Dimensions. In: Battiti, R., Kvasov, D., Sergeyev, Y. (eds) Learning and Intelligent Optimization. LION 2017. Lecture Notes in Computer Science(), vol 10556. Springer, Cham. https://doi.org/10.1007/978-3-319-69404-7_32

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  • DOI: https://doi.org/10.1007/978-3-319-69404-7_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69403-0

  • Online ISBN: 978-3-319-69404-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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