On the randomized Euler algorithm under inexact information

07/10/2023
by   Marcin Baranek, et al.
0

This paper focuses on analyzing the error of the randomized Euler algorithm when only noisy information about the coefficients of the underlying stochastic differential equation (SDE) and the driving Wiener process is available. Two classes of disturbed Wiener process are considered, and the dependence of the algorithm's error on the regularity of the disturbing functions is investigated. The paper also presents results from numerical experiments to support the theoretical findings.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset