Proximal bundle methods for nonsmooth DC programming

Abstract

We consider the problem of minimizing the difference of two nonsmooth convex functions over a simple convex set. To deal with this class of nonsmooth and nonconvex optimization problems, we propose new proximal bundle algorithms and show that the given approaches generate subsequences of iterates that converge to critical points. Trial points are obtained by solving strictly convex master programs defined by the sum of a convex cutting-plane model and a freely-chosen Bregman function. In the unconstrained case with the Bregman function being the Euclidean distance, new iterates are solutions of strictly convex quadratic programs of limited sizes. Stronger convergence results (d-stationarity) can be achieved depending on (a) further assumptions on the second DC component of the objective function and (b) solving possibly more than one master program at certain iterations. The given approaches are validated by encouraging numerical results on some academic DC programs.