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Optimization of Randomized Monte Carlo Algorithms for Solving Problems with Random Parameters. / Mikhailov, G. A.

In: Doklady Mathematics, Vol. 98, No. 2, 01.09.2018, p. 448-451.

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Mikhailov GA. Optimization of Randomized Monte Carlo Algorithms for Solving Problems with Random Parameters. Doklady Mathematics. 2018 Sept 1;98(2):448-451. doi: 10.1134/S1064562418060157

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@article{998d611710044293960d6f6deea19185,
title = "Optimization of Randomized Monte Carlo Algorithms for Solving Problems with Random Parameters",
abstract = "Randomized Monte Carlo algorithms intended for statistical kernel estimation of the averaged solution to a problem with random baseline parameters are optimized. For this purpose, a criterion for the complexity of a functional Monte Carlo estimate is formulated. The algorithms involve a splitting method in which, for each realization of the parameters, a certain number of trajectories of the corresponding baseline process are constructed.",
author = "Mikhailov, {G. A.}",
note = "Publisher Copyright: {\textcopyright} 2018, Pleiades Publishing, Ltd.",
year = "2018",
month = sep,
day = "1",
doi = "10.1134/S1064562418060157",
language = "English",
volume = "98",
pages = "448--451",
journal = "Doklady Mathematics",
issn = "1064-5624",
publisher = "Maik Nauka-Interperiodica Publishing",
number = "2",

}

RIS

TY - JOUR

T1 - Optimization of Randomized Monte Carlo Algorithms for Solving Problems with Random Parameters

AU - Mikhailov, G. A.

N1 - Publisher Copyright: © 2018, Pleiades Publishing, Ltd.

PY - 2018/9/1

Y1 - 2018/9/1

N2 - Randomized Monte Carlo algorithms intended for statistical kernel estimation of the averaged solution to a problem with random baseline parameters are optimized. For this purpose, a criterion for the complexity of a functional Monte Carlo estimate is formulated. The algorithms involve a splitting method in which, for each realization of the parameters, a certain number of trajectories of the corresponding baseline process are constructed.

AB - Randomized Monte Carlo algorithms intended for statistical kernel estimation of the averaged solution to a problem with random baseline parameters are optimized. For this purpose, a criterion for the complexity of a functional Monte Carlo estimate is formulated. The algorithms involve a splitting method in which, for each realization of the parameters, a certain number of trajectories of the corresponding baseline process are constructed.

UR - http://www.scopus.com/inward/record.url?scp=85056333269&partnerID=8YFLogxK

U2 - 10.1134/S1064562418060157

DO - 10.1134/S1064562418060157

M3 - Article

AN - SCOPUS:85056333269

VL - 98

SP - 448

EP - 451

JO - Doklady Mathematics

JF - Doklady Mathematics

SN - 1064-5624

IS - 2

ER -

ID: 17415135