Longtime behaviors of $\theta$-Euler-Maruyama method for stochastic functional differential equations
<|reference_start|>Longtime behaviors of $\theta$-Euler-Maruyama method for stochastic functional differential equations: This paper investigates longtime behaviors of the $\theta$-Euler-Maruyama method for the stochastic functional differential equation with superlinearly growing coefficients. We focus on the longtime convergence analysis in mean-square sense and weak sense of the $\theta$-Euler-Maruyama method, the convergence of the numerical invariant measure, the existence and convergence of the numerical density function, and the Freidlin-Wentzell large deviation principle of the method. The main contributions are outlined as follows. First, we obtain the longtime mean-square convergence of the $\theta$-Euler-Maruyama method and show that the mean-square convergence rate is $\frac12$. A key step in the proof is to establish the time-independent boundedness of high-order moments of the numerical functional solution. Second, based on the technique of the Malliavin calculus, we present the longtime weak convergence of the $\theta$-Euler-Maruyama method, which implies that the invariant measure of the $\theta$-Euler-Maruyama functional solution converges to the exact one with rate $1.$ Third, by the analysis of the test-functional-independent weak convergence and negative moment estimates of the determinant of the corresponding Malliavin covariance matrix, we derive the existence, convergence, and the logarithmic estimate of the density function of the $\theta$-Euler-Maruyama solution. At last, utilizing the weak convergence method, we obtain the Freidlin-Wentzell large deviation principle for the $\theta$-Euler-Maruyama solution on the infinite time horizon.<|reference_end|>
@article{chen2024longtime,
    title={Longtime behaviors of $\theta$-Euler-Maruyama method for stochastic
  functional differential equations},
    author={Chuchu Chen, Tonghe Dang, Jialin Hong, Guoting Song},
    journal={arXiv preprint arXiv:2404.08891},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08891},
    primaryClass={math.NA cs.NA}
}