Asymptotic normality and optimality in nonsmooth stochastic approximation
该文证明非光滑随机逼近算法（如随机非线性规划、随机变分不等式）同样具有中心极限定理，且其渐近协方差在局部极小极大意义下最优。
In their seminal work, Polyak and Juditsky showed that stochastic approximation algorithms for solving smooth equations enjoy a central limit theorem. Moreover, it has since been argued that the asymptotic covariance of the method is best possible among any estimation procedure in a local minimax sense of Hájek and Le Cam. A long-standing open question in this line of work is whether similar guarantees hold for important nonsmooth problems, such as stochastic nonlinear programming or stochastic variational inequalities. In this work, we show that this is indeed the case.