Multistart with early termination of descents

Antanas Žilinskas (), Jonathan Gillard (), Megan Scammell () and Anatoly Zhigljavsky ()
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Antanas Žilinskas: Vilnius University
Jonathan Gillard: Cardiff University
Megan Scammell: Cardiff University
Anatoly Zhigljavsky: Cardiff University

Journal of Global Optimization, 2021, vol. 79, issue 2, No 10, 447-462

Abstract: 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.

Keywords: Global optimization; Random search; Multistart; Statistical inference (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10898-019-00814-w

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