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Avtomatika i Telemekhanika, 2017, Issue 2, Pages 82–98 (Mi at14685)  
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
Full-text PDF (1949 kB) Citations (2)
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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.
Funding agency Grant number
Russian Foundation for Basic Research 15-07-03005
This work was supported by the Russian Foundation for Basic Research, project no. 15-07-03005.
Presented by the member of Editorial Board: P. S. Shcherbakov

Received: 27.09.2015
English version:
Automation and Remote Control, 2017, Volume 78, Issue 2, Pages 261–275
DOI: https://doi.org/10.1134/S0005117917020060
Bibliographic databases:
Document Type: Article
Language: Russian
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
Citation in format AMSBIB
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    • This publication is cited in the following 2 articles:
    Citing articles in Google Scholar: Russian citations, English citations
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