Email this article Optimization of Randomized Monte Carlo Algorithms for Solving Problems with Random Parameters
- Authors: Mikhailov G.A.1,2
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Affiliations:
- Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch
- Novosibirsk State University
- Issue: Vol 98, No 2 (2018)
- Pages: 448-451
- Section: Mathematics
- URL: https://journals.rcsi.science/1064-5624/article/view/225555
- DOI: https://doi.org/10.1134/S1064562418060157
- ID: 225555
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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.
About the authors
G. A. Mikhailov
Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch; Novosibirsk State University
Author for correspondence.
Email: gam@sscc.ru
Russian Federation, Novosibirsk, 630090; Novosibirsk, 630090
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