In this talk, we will consider the stochastic algorithms for finite-sum optimization problems with nonconvex loss models, which frequently arise in statistics and machine learning. For problems with simple convex regularizes, we introduce a stochastic semismooth Newton method. For problems with possibly nonconvex regularizes, we propose a stochastic first-order primal-dual method based on conjugate duality.

会议密码:115119