Quantifying a convergence theorem of Gyöngy and Krylov

Fuente: arXiv
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Autori principali: Dareiotis, Konstantinos, Gerencsér, Máté, Lê, Khoa
Natura: Preprint
Pubblicazione: 2021
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author Dareiotis, Konstantinos
Gerencsér, Máté
Lê, Khoa
author_facet Dareiotis, Konstantinos
Gerencsér, Máté
Lê, Khoa
contents We derive sharp strong convergence rates for the Euler-Maruyama scheme approximating multidimensional SDEs with multiplicative noise without imposing any regularity condition on the drift coefficient. In case the noise is additive, we show that Sobolev regularity can be leveraged to obtain improved rate: drifts with regularity of order $α\in (0,1)$ lead to rate $(1+α)/2$.
format Preprint
id arxiv_https___arxiv_org_abs_2101_12185
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle Quantifying a convergence theorem of Gyöngy and Krylov
Dareiotis, Konstantinos
Gerencsér, Máté
Lê, Khoa
Probability
60H10, 60H50, 65C30
We derive sharp strong convergence rates for the Euler-Maruyama scheme approximating multidimensional SDEs with multiplicative noise without imposing any regularity condition on the drift coefficient. In case the noise is additive, we show that Sobolev regularity can be leveraged to obtain improved rate: drifts with regularity of order $α\in (0,1)$ lead to rate $(1+α)/2$.
title Quantifying a convergence theorem of Gyöngy and Krylov
topic Probability
60H10, 60H50, 65C30
url https://arxiv.org/abs/2101.12185