Szczegóły publikacji
Opis bibliograficzny
On the randomized Euler scheme for stochastic differential equations with integral-form drift / Paweł PRZYBYŁOWICZ, Michał SOBIERAJ // Journal of Computational and Applied Mathematics ; ISSN 0377-0427 . — 2026 — vol. 483 art. no. 117367, s. 1–21. — Bibliogr. s. 20–21, Abstr. — Publikacja dostępna online od: 2026-01-18
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 165854 |
|---|---|
| Data dodania do BaDAP | 2026-03-06 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.cam.2026.117367 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Journal of Computational and Applied Mathematics |
Abstract
In this paper, we investigate the problem of strong approximation of the solutions of stochastic differential equations (SDEs) when the drift coefficient is given in integral form. We investigate its upper error bounds, in terms of the discretization parameter n and the size M of the random sample drawn at each step of the algorithm, in different subclasses of coefficients of the underlying SDE presenting various rates of convergence. Integral-form drift often appears when analyzing stochastic dynamics of optimization procedures in machine learning (ML) problems. Hence, we additionally discuss connections of the defined randomized Euler approximation scheme with the perturbed version of the stochastic gradient descent (SGD) algorithm. Finally, the results of numerical experiments performed using GPU architecture are also reported, including a comparison with other popular optimizers used in ML.
