学术报告（徐礼虎 10.29）

In this paper, we develop a stochastic algorithm based on Euler-Maruyama scheme to approximate the invariant measure of the limiting multidimensional diffusion of the  queue. Specifically, we prove a non-asymptotic error bound between the invariant measures of the approximate model from the algorithm and the limiting diffusion of the queueing model. Our result also provides an approximation to the steady-state of the prelimit diffusion-scaled queueing processes in the Halfin-Whitt regime given the well established interchange of limits property. To establish the error bound, we employ the recently developed Stein's equation and Malliavian calculus for multi-dimensional diffusions. The main difficulty lies in the non--differentiability of the drift in the limiting diffusion, so that the standard approaches in Euler type of schemes for diffusions and Stein's method do not work. We first propose a mollified diffusion which has a sufficiently smooth drift to circumvent the nondifferentiability difficulty. We then provide some insights on the limiting diffusion and approximate diffusion from the algorithm by investigating some useful occupation times, as well as the associated Harnack inequalities, the support of the invariant measures and ergodicity properties. These results are used in analyzing the Stein's equation, which provide useful estimates to bound the differences between the corresponding invariant measures. This talk is based on https://arxiv.org/abs/2109.03623.