New Lagrangian function for nonconvex primal-dual decomposition

In this short paper, a new Lagrangian function is reported which is particularly suited for large-scale nonconvex optimization problems with separable structure Our modification convexifies the standard Lagrangian function without destroying its separable structure so that the primal-dual decomposition technique can be applied even to nonconvex optimization problems. Furthermore, the proposed Lagrangian results in two levels of iterative optimization as compared with the three levels needed for techniques recently proposed for nonconvex primal-dual decomposition.