A Proximal Stochastic Quasi-Newton Algorithm

Understand

In this paper, we discuss the problem of minimizing the sum of two convex functions: a smooth function plus a non-smooth function.

• Further, the smooth part can be expressed by the average of a large number of smooth component functions, and the non-smooth part is equipped with a simple proximal mapping.
• We propose a proximal stochastic second-order method, which is efficient and scalable.
• It incorporates the Hessian in the smooth part of the function and exploits multistage scheme to reduce the variance of the stochastic gradient.