Cutting planes (cuts) are important for solving mixed-integer linear programs (MILPs), which
formulate a wide range of important real-world applications. Cut selection--which aims to …

We survey recent work on machine learning (ML) techniques for selecting cutting planes (or
cuts) in mixed-integer linear programming (MILP). Despite the availability of various classes …

Cutting planes (cuts) play an important role in solving mixed-integer linear programs
(MILPs), as they significantly tighten the dual bounds and improve the solving performance …

Cutting plane selection is a subroutine used in all modern mixed-integer linear programming
solvers with the goal of selecting a subset of generated cuts that induce optimal solver …

Cutting planes are crucial in solving mixed integer linear programs (MILP) as they facilitate
bound improvements on the optimal solution. Modern MILP solvers rely on a variety of …

Cutting planes are essential for solving mixed-integer linear problems (MILPs), because
they facilitate bound improvements on the optimal solution value. For selecting cuts, modern …

Abstract Machine learning components commonly appear in larger decision-making
pipelines; however, the model training process typically focuses only on a loss that …

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 …

Mixed Integer Programming (MIP) solvers rely on an array of sophisticated heuristics
developed with decades of research to solve large-scale MIP instances encountered in …

Abstract Machine learning (ML) has been recently introduced to solving optimization
problems, especially for combinatorial optimization (CO) tasks. In this paper, we survey the …