A smooth approximation approach for optimization with probabilistic constraints based on sigmoid function

Abstract Many practical problems, such as computer science, communications network, product design, system control, statistics and finance, etc.,can be formulated a probabilistic constrained optimization problem (PCOP) which is challenging to solve since it usually nonconvex nonsmooth. Effective methods for the mostly focus on approximation techniques, convex approximation, D.C. (difference of two functions) so on. This paper aims at studying smooth approach. A constraint function based sigmoid analyzed. Equivalence PCOP corresponding are shown under some appropriate assumptions. Sequential (SCA) algorithm implemented problem. Numerical results suggest that approach proposed effective problems with constraints.