Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"

A method for analyzing non-asymptotic guarantees of numerical discretizations of ergodic SDEs in Wasserstein-2 distance is presented by Sanz-Serna and Zygalakis in Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations. They analyze the UBU integrator which is strong order two and only requires one gradient evaluation per step, resulting in desirable non-asymptotic guarantees, in particular O ( d 1 / 4 E - 1 / 2 ) steps to reach a distance of E > 0 in Wasserstein-2 distance away from the target distribution. However, there is a mistake in the local error estimates in Sanz-Serna and Zygalakis (2021), in particular, a stronger assumption is needed to achieve these complexity estimates. This note reconciles the theory with the dimension dependence observed in practice in many applications of interest.

Publication Details

Journal
Repository for Publications and Research Data (ETH Zurich)
Published
2024-12-01
DOI
https://doi.org/10.3929/ethz-b-000714967
Primary Topic
Stochastic processes and financial applications
Type
article
Field-Weighted Citation Impact
0.00
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Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"

Repository for Publications and Research Data (ETH Zurich)
Stochastic processes and financial applications
article

Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"

article en

Abstract

A method for analyzing non-asymptotic guarantees of numerical discretizations of ergodic SDEs in Wasserstein-2 distance is presented by Sanz-Serna and Zygalakis in Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations. They analyze the UBU integrator which is strong order two and only requires one gradient evaluation per step, resulting in desirable non-asymptotic guarantees, in particular O ( d 1 / 4 E - 1 / 2 ) steps to reach a distance of E > 0 in Wasserstein-2 distance away from the target distribution. However, there is a mistake in the local error estimates in Sanz-Serna and Zygalakis (2021), in particular, a stronger assumption is needed to achieve these complexity estimates. This note reconciles the theory with the dimension dependence observed in practice in many applications of interest.

Repository for Publications and Research Data (ETH Zurich)
Openalex Percentile: Top 58%
Stochastic processes and financial applications
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Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations" · Repository for Publications and Research Data (ETH Zurich) (2024) | TGRS Research Map | TGRS