arbiter
JournalsAccount

Numerical approximation of McKean–Vlasov SDEs via stochastic gradient descent

Ankush Agarwal et al.

Advances in Applied Probability2026https://doi.org/10.1017/apr.2026.10057article
ABDC A
Weight
0.50

Abstract

We propose a novel approach to numerically approximate McKean–Vlasov stochastic differential equations (MV-SDEs) using stochastic gradient descent (SGD) while avoiding the use of interacting particle systems (IPSs) and the associated simulation costs required to achieve the ‘propagation of chaos’ limit. The SGD technique is deployed to solve a Euclidean minimization problem, obtained by first representing the MV-SDE as a minimization problem over the set of continuous functions of time, and then approximating the domain with a finite-dimensional sub-space. Convergence is established by proving certain intermediate stability and moment estimates of the relevant stochastic processes, including the tangent processes. Numerical experiments illustrate the competitive performance of our SGD-based method compared with the IPS benchmarks. This work offers a theoretical foundation for using the SGD method in the context of numerical approximation of MV-SDEs, and provides analytical tools to study its stability and convergence.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1017/apr.2026.10057

Or copy a formatted citation

@article{ankush2026,
  title        = {{Numerical approximation of McKean–Vlasov SDEs via stochastic gradient descent}},
  author       = {Ankush Agarwal et al.},
  journal      = {Advances in Applied Probability},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1017/apr.2026.10057},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Numerical approximation of McKean–Vlasov SDEs via stochastic gradient descent

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.