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A Randomised Non-descent Method for Global Optimisation

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Advances in Optimization and Applications (OPTIMA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1913))

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Abstract

This paper proposes novel algorithm for non-convex multimodal constrained optimisation problems. It is based on sequential solving restrictions of problem to sections of feasible set by random subspaces (in general, manifolds) of low dimensionality. This approach varies in a way to draw subspaces, dimensionality of subspaces, and method to solve restricted problems. We provide empirical study of algorithm on convex, unimodal and multimodal optimisation problems and compare it with efficient algorithms intended for each class of problems.

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Notes

  1. 1.

    https://github.com/dmivilensky/Solar-method-non-convex-optimisation.

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Author information

Authors and Affiliations

  1. Moscow Institute of Physics and Technology, Dolgoprudny, Russia

    Dmitry A. Pasechnyuk

  2. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

    Dmitry A. Pasechnyuk

  3. Institute for Information Transmission Problems RAS, Moscow, Russia

    Dmitry A. Pasechnyuk

  4. Matrosov Institute for System Dynamics and Control Theory, Irkutsk, Russia

    Alexander Gornov

Authors
  1. Dmitry A. Pasechnyuk
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  2. Alexander Gornov
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Corresponding author

Correspondence to Dmitry A. Pasechnyuk .

Editor information

Editors and Affiliations

  1. FRC CSC RAS, Moscow, Russia

    Nicholas Olenev

  2. FRC CSC RAS, Moscow, Russia

    Yuri Evtushenko

  3. University of Montenegro, Podgorica, Montenegro

    Milojica Jaćimović

  4. Krasovsky Institute of Mathematics and Mechanics, Ekaterinburg, Russia

    Michael Khachay

  5. FRC CSC RAS, Moscow, Russia

    Vlasta Malkova

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Pasechnyuk, D.A., Gornov, A. (2024). A Randomised Non-descent Method for Global Optimisation. In: Olenev, N., Evtushenko, Y., Jaćimović, M., Khachay, M., Malkova, V. (eds) Advances in Optimization and Applications. OPTIMA 2023. Communications in Computer and Information Science, vol 1913. Springer, Cham. https://doi.org/10.1007/978-3-031-48751-4_1

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  • DOI: https://doi.org/10.1007/978-3-031-48751-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48750-7

  • Online ISBN: 978-3-031-48751-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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