  Mixed Integer Linear Programs (MILP) are well known to be NP-hard problems in
general, and therefore, tackling and using their solution in online
applications is a great challenge. Some Machine Learning techniques have been
proposed in the literature to alleviate their computational burden.
Unfortunately, these techniques report that a non-negligible percentage of the
resulting machine-learning-based solutions are infeasible.
  By linking Mathematical Optimization and Machine Learning, this paper
proposes an offline-online strategy to solve MILPs in a fast and reliable
manner. The offline step seeks to identify the so-called support constraints of
past instances of the target MILP and uses them to train a Machine Learning
model of our choice. This model is subsequently used online to generate a
reduced MILP that is significantly easier to solve.
  Through synthetic and real-life MILPs, we show that our approach dramatically
decreases the risk of obtaining solutions from the reduced MILP that are
infeasible in the original formulation without any extra cost in terms of
computational time. Hence, our methodology is appropriate for online
applications where feasibility is of particular importance.
