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Avtomatika i Telemekhanika, 2017, Issue 2, Pages 82–98
(Mi at14685)
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This article is cited in 2 scientific papers (total in 2 papers)
System Analysis and Operations Research
Iterative MC-algorithm to solve the global optimization problems
A. Yu. Popkovab, B. S. Darkhovskyabc, Yu. S. Popkovabc a Institute for Systems Analysis, Russian Academy of Sciences, Moscow, Russia
b Moscow Institute of Physics and Technology, Dolgoprudnyi, Russia
c National Research University "Higher School of Economics", Moscow, Russia
Abstract:
A new method was proposed to solve the global minimization problems of the Hölder functions on compact sets obeying continuous functions. The method relies on the Monte Carlo batch processing intended for constructing the sequences of values of the “quasi-global” minima and their decrements. A numerical procedure was proposed to generate a probabilistic stopping rule whose operability was corroborated by numerous tests and benchmarks with algorithmically defined functions.
Keywords:
global optimization, batch Monte Carlo iterations, Hölder constants.
Citation:
A. Yu. Popkov, B. S. Darkhovsky, Yu. S. Popkov, “Iterative MC-algorithm to solve the global optimization problems”, Avtomat. i Telemekh., 2017, no. 2, 82–98; Autom. Remote Control, 78:2 (2017), 261–275
Linking options:
https://www.mathnet.ru/eng/at14685 https://www.mathnet.ru/eng/at/y2017/i2/p82
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Statistics & downloads: |
Abstract page: | 285 | Full-text PDF : | 47 | References: | 36 | First page: | 24 |
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