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arXiv · 2401.02878

Numerical approximation to the invariant measure of McKean-Vlasov stochastic differential equations

Yuanping Cui·Xiaoyue Li·Yi Liu·Fengyu Wang
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Abstract

Inspired by the stochastic particle method, this paper develops an easily implementable explicit scheme for McKean-Vlasov stochastic differential equations (MV-SDEs) with superlinear growth coefficients. We prove that the numerical solution of the interacting particle system (IPS) attains the optimal uniform-in-time strong convergence rate of order 1/2, and that it faithfully captures the long-term dynamics of MV-SDEs, including moment boundedness, stability, and ergodicity. In particular, the existence and uniqueness of an exchangeable numerical invariant probability measure for the IPS are established via an appropriately constructed operator semigroup. Concerning the approximation of the invariant measure, we derive a non-asymptotic error bound between the distribution of the one-particle numerical solution and the marginal distribution of the IPS's invariant measure; By the uniform-in-time propagation of chaos, we further obtain an asymptotic error bound between the one-particle marginal of the IPS's numerical invariant measure and the exact invariant measure of the MV-SDE. Numerical experiments are provided to validate the theoretical results.

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Yuanping Cui, Xiaoyue Li, Yi Liu, Fengyu Wang. 2026-09-05. Numerical approximation to the invariant measure of McKean-Vlasov stochastic differential equations. https://arxiv.org/abs/2401.02878

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