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

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

в: Doklady Mathematics, Том 98, № 2, 01.09.2018, стр. 448-451.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

BibTeX

@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.

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