This paper concerns the numerical approximation for the invariant distribution of Markovian switching L\'evy-driven stochastic differential equations. By combining the tamed-adaptive Euler-Maruyama scheme with the Multi-level Monte Carlo method, we propose an approximation scheme that can be applied to stochastic differential equations with super-linear growth drift and diffusion coefficients.

翻译：本文研究马尔可夫切换Lévy驱动随机微分方程不变分布的数值逼近问题。通过将驯服自适应Euler-Maruyama格式与多级蒙特卡洛方法相结合，我们提出了一种适用于具有超线性增长漂移项和扩散系数随机微分方程的逼近方案。