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International Conference on Learning and Intelligent Optimization

LION 2017: Learning and Intelligent Optimization pp 320–325Cite as

Global Optimization Using Numerical Approximations of Derivatives

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

Abstract

This paper presents an efficient method for solving global optimization problems. The new method unlike previous methods, was developed, based on numerical estimations of derivative values. The effect of using numerical estimations of derivative values was studied and the results of computational experiments prove the potential of such approach.

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References

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Acknowledgments

This research was supported by the Russian Science Foundation, project No 16-11-10150” Novel efficient methods and software tools for time-consuming decision making problems using supercomputers of superior performance.”

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Authors and Affiliations

  1. Lobachevsky State University of Nizhni Novgorod, Gagarin Prospect, 23, Nizhni Novgorod, 603600, Russian Federation

    Victor Gergel & Alexey Goryachih

Authors
  1. Victor Gergel
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  2. Alexey Goryachih
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Corresponding author

Correspondence to Alexey Goryachih .

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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Gergel, V., Goryachih, A. (2017). Global Optimization Using Numerical Approximations of Derivatives. 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_25

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  • DOI: https://doi.org/10.1007/978-3-319-69404-7_25

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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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