Polyhedral separation via difference of convex (DC) programming
Annabella Astorino, Massimo Di Francesco, Manlio Gaudioso, Enrico Gorgone, Benedetto Manca · Soft Computing · 2021
Abstract We consider polyhedral separation of sets as a possible tool in supervised classification. In particular, we focus on the optimization model introduced by Astorino and Gaudioso (J Optim Theory Appl 112(2):265–293, 2002) and adopt its reformulation in difference of convex (DC) form. We tackle the problem by adapting the algorithm for DC programming known as DCA. We present the results of the implementation of DCA on a number of benchmark classification datasets.