We propose a new method to accelerate online Mixed Integer Optimization with
Pre-trained machine learning models (PreMIO). The key component of PreMIO is a
multi-variable cardinality branching procedure that splits the feasible region
with data-driven hyperplanes, which can be easily integrated into any MIP
solver with two lines of code. Moreover, we incorporate learning theory and
concentration inequalities to develop a straightforward and interpretable
hyper-parameter selection strategy for our method. We test the performance of
PreMIO by applying it to state-of-the-art MIP solvers and running numerical
experiments on both classical OR benchmark datasets and real-life instances.
The results validate the effectiveness of our proposed method.

本文提出了一种新的方法，使用预训练的机器学习模型（PreMIO）来加速在线混合整数优化，并通过基于数据驱动的超平面分割可行域的多变量基数分支程序以及结合学习理论和浓度不等式来开发一个直观易懂的超参数选择策略来加入任何 MIP 求解器中。作者通过在经典 OR 基准数据集和实际实例上运行数值实验并将其应用于最先进的 MIP 求解器，证实了所提出方法的有效性。