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

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Learning and Intelligent Optimization (LION 2017)

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

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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.

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References

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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’.

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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 .

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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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