TY - JOUR A1 - Berthold, Timo A1 - Francobaldi, Matteo A1 - Hendel, Gregor T1 - Learning to use local cuts T2 - Mathematical Programming Computation N2 - An essential component in modern solvers for mixed-integer (linear) programs (MIPs) is the separation of additional inequalities (cutting planes) to tighten the linear pro- gramming 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 overhead, 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 perfor- mance 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. Y1 - 2025 UR - https://opus4.kobv.de/opus4-mpc/frontdoor/index/index/docId/299 SN - 1867-2949 VL - 17 IS - 3 SP - 437 EP - 450 PB - Springer Science and Business Media LLC ER -