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Multistart with early termination of descents

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Abstract

Multistart is a celebrated global optimization technique frequently applied in practice. In its pure form, multistart has low efficiency. However, the simplicity of multistart and multitude of possibilities of its generalization make it very attractive especially in high-dimensional problems where e.g. Lipschitzian and Bayesian algorithms are not applicable. We propose a version of multistart where most of the local descents are terminated very early; we will call it METOD as an abbreviation for multistart with early termination of descents. The performance of the proposed algorithm is demonstrated on randomly generated test functions with 100 variables and a modest number of local minimizers.

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Acknowledgements

The work of A.Zilinskas was supported by the Research Council of Lithuania under Grant No. P-MIP-17-61. The work of A.Zhigljavsky was supported by a grant of Crimtan Holding Limited.

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Authors and Affiliations

  1. Institute of Data Science and Digital Technologies, Vilnius University, Akademijos 4, 08663, Vilnius, Lithuania

    Antanas Žilinskas

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

    Jonathan Gillard, Megan Scammell & Anatoly Zhigljavsky

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  1. Antanas Žilinskas
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  2. Jonathan Gillard
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  3. Megan Scammell
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  4. Anatoly Zhigljavsky
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Correspondence to Antanas Žilinskas.

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Žilinskas, A., Gillard, J., Scammell, M. et al. Multistart with early termination of descents. J Glob Optim 79, 447–462 (2021). https://doi.org/10.1007/s10898-019-00814-w

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  • DOI: https://doi.org/10.1007/s10898-019-00814-w

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