This paper studies the numerical approximation for McKean-Vlasov stochastic differential equations driven by L\'evy processes. We propose a tamed-adaptive Euler-Maruyama scheme and consider its strong convergence in both finite and infinite time horizons when applying for some classes of L\'evy-driven McKean-Vlasov stochastic differential equations with non-globally Lipschitz continuous and super-linearly growth drift and diffusion coefficients.

翻译：本文研究由Lévy过程驱动的McKean-Vlasov随机微分方程的数值逼近问题。针对具有非全局Lipschitz连续且超线性增长漂移与扩散系数的一类Lévy驱动McKean-Vlasov随机微分方程，我们提出了一种驯服自适应Euler-Maruyama格式，并考察其在有限和无限时间区间上的强收敛性。