Strengthening Convex Relaxations of Mixed Integer Non Linear Programming Problems with Separable Non Convexities

In this work we focus on methods for solving mixed integer non linear programming problems with separable non convexities. In particular, we propose a strengthening of a convex mixed integer non linear programming relaxation based on perspective reformulations. The relaxation is a subproblem of an iterative global optimization algorithm and it is solved at each iteration. Computational results confirm that the perspective reformulation outperforms the standard solution approaches.