  An essential component in modern solvers for mixed-integer (linear) programs
(MIPs) is the separation of additional inequalities (cutting planes) to tighten
the linear programming relaxation. Various algorithmic decisions are necessary
when integrating cutting plane methods into a branch-and-bound (B&B) solver as
there is always the trade-off between the efficiency of the cuts and their
costs, given that they tend to slow down the solution time of the relaxation.
One of the most crucial questions is: Should cuts only be generated globally at
the root or also locally at nodes of the tree? We address this question by a
machine learning approach for which we train a regression forest to predict the
speed-up (or slow-down) provided by using local cuts. We demonstrate with an
open implementation that this helps to improve the performance of the FICO
Xpress MIP solver on a public test set of general MIP instances. We further
report on the impact of a practical implementation inside Xpress on a large,
diverse set of real-world industry MIPs.
