  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.
