A completely positive representation of 0-1 linear programs with joint probabilistic constraints

In this paper, we study 0-1 linear programs with joint probabilistic constraints. The constraint matrix vector rows are assumed to be independent, and the coefficients to be normally distributed. Our main results show that this non-convex problem can be approximated by a convex completely positive problem. Moreover, we show that the optimal values of the latter converge to the optimal values of the original problem. Examples randomly generated highlight the efficiency of our approach.