Learning to Select Cuts for Efficient Mixed-Integer Programming

Understand

Cutting plane methods play a significant role in modern solvers for tackling mixed-integer programming (MIP) problems.

• Proper selection of cuts would remove infeasible solutions in the early stage, thus largely reducing the computational burden without hurting the solution accuracy.
• However, the major cut selection approaches heavily rely on heuristics, which strongly depend on the specific problem at hand and thus limit their generalization capability.
• In this paper, we propose a data-driven and generalizable cut selection approach, named Cut Ranking, in the settings of multiple instance learning.