Machine learning techniques for Branch-and-Cut methods: the selection of cutting planes

The selection of cuts to be added to the current LP relaxation is one of the most critical task in Branch-and-Cut methods, since it strongly affects the performances of the algorithm. Recently, machine learning techniques have become popular to define effective cut selection strategies. In this paper we explore the possibility of selecting cuts by ranking them via support vector regression.