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Avtomat. i Telemekh., 2017, Issue 2, Pages 82–98 (Mi at14685)  

This article is cited in 1 scientific paper (total in 1 paper)

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.

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.


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English version:
Automation and Remote Control, 2017, 78:2, 261–275

Bibliographic databases:

Presented by the member of Editorial Board: П. С. Щербаков

Received: 27.09.2015

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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    Citing articles on Google Scholar: Russian citations, English citations
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    This publication is cited in the following articles:
    1. Feng M., “Heuristic Crossover Based on Biogeography-Based Optimization”, Proceedings of the 7Th International Conference on Education, Management, Information and Mechanical Engineering (Emim 2017), Acsr-Advances in Comptuer Science Research, 76, eds. Jing W., Ning X., Huiyu Z., Atlantis Press, 2017, 336–341  isi
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