Machine Learning NeEDS Mathematical Optimization with Prof Andrea Lodi

Abstract: In this talk, we discuss how a careful use of Machine Learning concepts can have an impact in primal heuristics for Mixed-Integer Programming (MIP). More precisely, we consider two applications. First, we design a data-driven scheduler for running both diving and large-neighborhood search heuristics in SCIP, one of the most effective open-source MIP solvers. Second, we incorporate a major learning component into Local Branching, one of the most well-known primal heuristic paradigms. In both cases, computational results show solid improvements over the state of the art.