Stochastic Difference-of-Convex Algorithms for Solving nonconvex optimization problems

Understand

The paper deals with stochastic difference-of-convex functions (DC) programs, that is, optimization problems whose the cost function is a sum of a lower semicontinuous DC function and the expectation of a stochastic DC function with respect to a probability distribution.

• This class of nonsmooth and nonconvex stochastic optimization problems plays a central role in many practical applications.
• Although there are many contributions in the context of convex and/or smooth stochastic optimization, algorithms dealing with nonconvex and nonsmooth programs remain rare.
• In deterministic optimization literature, the DC Algorithm (DCA) is recognized to be one of the few algorithms to solve effectively nonconvex and nonsmooth optimization problems.