Distributional MIPLIB: a Multi-Domain Library for Advancing ML-Guided MILP Methods
This provides a standardized dataset for researchers in ML-guided MILP solving, facilitating more comprehensive evaluations, though it is incremental as it builds on existing work by aggregating and organizing data.
The paper tackles the lack of a common repository for MILP instances by introducing Distributional MIPLIB, a multi-domain library with curated distributions and hardness levels, and demonstrates its utility by evaluating ML-guided variable branching and showing that mixed distributions improve performance and generalization.
Mixed Integer Linear Programming (MILP) is a fundamental tool for modeling combinatorial optimization problems. Recently, a growing body of research has used machine learning to accelerate MILP solving. Despite the increasing popularity of this approach, there is a lack of a common repository that provides distributions of similar MILP instances across different domains, at different hardness levels, with standardized test sets. In this paper, we introduce Distributional MIPLIB, a multi-domain library of problem distributions for advancing ML-guided MILP methods. We curate MILP distributions from existing work in this area as well as real-world problems that have not been used, and classify them into different hardness levels. It will facilitate research in this area by enabling comprehensive evaluation on diverse and realistic domains. We empirically illustrate the benefits of using Distributional MIPLIB as a research vehicle in two ways. We evaluate the performance of ML-guided variable branching on previously unused distributions to identify potential areas for improvement. Moreover, we propose to learn branching policies from a mix of distributions, demonstrating that mixed distributions achieve better performance compared to homogeneous distributions when there is limited data and generalize well to larger instances. The dataset is publicly available at https://sites.google.com/usc.edu/distributional-miplib/home.