电邮这篇文章 Optimization of Randomized Monte Carlo Algorithms for Solving Problems with Random Parameters
- 作者: Mikhailov G.A.1,2
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隶属关系:
- Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch
- Novosibirsk State University
- 期: 卷 98, 编号 2 (2018)
- 页面: 448-451
- 栏目: Mathematics
- URL: https://journals.rcsi.science/1064-5624/article/view/225555
- DOI: https://doi.org/10.1134/S1064562418060157
- ID: 225555
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详细
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.
作者简介
G. Mikhailov
Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch; Novosibirsk State University
编辑信件的主要联系方式.
Email: gam@sscc.ru
俄罗斯联邦, Novosibirsk, 630090; Novosibirsk, 630090
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