于腾腾 博士 （ ICMSEC, AMSS ）

Inexact proximal stochastic gradient method for nonconvex nonsmooth composite optimization

11 月 23 日（周三）下午 16:00-17:00 腾讯会议（ID：305-6933-7976）

Stochastic Hamiltonian partial differential equations, which possess the multi-symplectic conservation law, are an important and fairly large class of systems. 

The multi-symplectic methods inheriting the geometric features of stochastic Hamiltonian partial differential equations provide numerical approximations with better numerical stability, and are of vital significance for obtaining correct numerical results. We propose three novel multi-symplectic methods for stochastic Hamiltonian partial differential equations based on the local radial basis function collocation method, the splitting technique, and the partitioned Runge--Kutta method. Some numerical examples indicate the validity of the proposed methods.