不精确信息下随机化欧拉算法的研究
On the randomized Euler algorithm under inexact information
AI总结：
本文研究在系数和维纳过程信息含噪情况下随机化欧拉算法的误差，分析两类扰动过程及误差对扰动函数正则性的依赖，并用数值实验验证理论结果。
摘要：
本文重点分析当仅能获得底层随机微分方程（SDE）系数及驱动维纳过程的含噪信息时，随机化欧拉算法的误差。考虑了两种受扰动的维纳过程类别，并研究了算法误差对扰动函数正则性的依赖关系。本文还给出了数值实验结果以支持理论发现。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
