Multi-index Importance Sampling for McKean-Vlasov Stochastic Differential Equation (2307.05149v1)

Abstract: This work introduces a novel approach that combines the multi-index Monte Carlo (MC) method with importance sampling (IS) to estimate rare event quantities expressed as an expectation of a smooth observable of solutions to a broad class of McKean-Vlasov stochastic differential equations. We extend the double loop Monte Carlo (DLMC) estimator, previously introduced in our works (Ben Rached et al., 2022a,b), to the multi-index setting. We formulate a new multi-index DLMC estimator and conduct a comprehensive cost-error analysis, leading to improved complexity results. To address rare events, an importance sampling scheme is applied using stochastic optimal control of the single level DLMC estimator. This combination of IS and multi-index DLMC not only reduces computational complexity by two orders but also significantly decreases the associated constant compared to vanilla MC. The effectiveness of the proposed multi-index DLMC estimator is demonstrated using the Kuramoto model from statistical physics. The results confirm a reduced complexity from $\mathcal{O}(\mathrm{TOL}{\mathrm{r}}^{-4})$ for the single level DLMC estimator (Ben Rached et al., 2022a) to $\mathcal{O}(\mathrm{TOL}{\mathrm{r}}^{-2} (\log \mathrm{TOL}{\mathrm{r}}^{-1})^2)$ for the considered example, while ensuring accurate estimation of rare event quantities within the prescribed relative error tolerance $\mathrm{TOL}\mathrm{r}$.