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On Acceleration of Derivative-Free Univariate Lipschitz Global Optimization Methods

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Numerical Computations: Theory and Algorithms (NUMTA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11974))

Abstract

Univariate box-constrained global optimization problems are considered, where the objective function is supposed to be Lipschitz continuous and multiextremal. It is assumed that its analytical representation is unknown (the function is given as a “black-box”) and even one its evaluation is a computationally expensive procedure. Geometric and information statistical frameworks for construction of global optimization algorithms are discussed. Several powerful acceleration techniques are described and a number of methods of both classes is constructed by mixing the introduced acceleration ideas. Numerical experiments executed on broad test classes taken from the literature show advantages of the presented techniques with respect to their direct competitors.

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Acknowledgements

The work of M.S. Mukhametzhanov was supported by the INdAM-GNCS funding “Giovani Ricercatori 2018–2019”.

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Authors and Affiliations

  1. University of Calabria, Rende, CS, Italy

    Dmitri E. Kvasov, Marat S. Mukhametzhanov, Maria Chiara Nasso & Yaroslav D. Sergeyev

  2. Lobachevsky State University, Nizhni Novgorod, Russia

    Dmitri E. Kvasov, Marat S. Mukhametzhanov & Yaroslav D. Sergeyev

Authors
  1. Dmitri E. Kvasov
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  2. Marat S. Mukhametzhanov
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  3. Maria Chiara Nasso
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  4. Yaroslav D. Sergeyev
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Corresponding author

Correspondence to Dmitri E. Kvasov .

Editor information

Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Yaroslav D. Sergeyev

  2. University of Calabria, Rende, Italy

    Dmitri E. Kvasov

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Kvasov, D.E., Mukhametzhanov, M.S., Nasso, M.C., Sergeyev, Y.D. (2020). On Acceleration of Derivative-Free Univariate Lipschitz Global Optimization Methods. In: Sergeyev, Y., Kvasov, D. (eds) Numerical Computations: Theory and Algorithms. NUMTA 2019. Lecture Notes in Computer Science(), vol 11974. Springer, Cham. https://doi.org/10.1007/978-3-030-40616-5_38

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  • DOI: https://doi.org/10.1007/978-3-030-40616-5_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-40615-8

  • Online ISBN: 978-3-030-40616-5

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