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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)448-451
Number of pages4
JournalDoklady Mathematics
Volume98
Issue number2
DOIs
Publication statusPublished - 1 Sep 2018

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