Ranking Constraint Relaxations for Mixed Integer Programs Using a Machine Learning Approach

TL;DR

This paper investigates the use of machine learning ranking functions to predict the quality of MIP decompositions, demonstrating state-of-the-art performance and potential for guiding decomposition creation in large-scale optimization.

Contribution

It introduces a new dataset of over 40,000 decompositions, benchmarks ML ranking against heuristics, and shows ML's effectiveness with minimal features for guiding MIP decomposition.

Findings

ML ranking achieves state-of-the-art prediction accuracy.

ML methods are competitive with heuristic techniques using few features.

The dataset enables comprehensive analysis of decomposition quality prediction.

Abstract

Solving large-scale Mixed Integer Programs (MIP) can be difficult without advanced algorithms such as decomposition based techniques. Even if a decomposition technique might be appropriate, there are still many possible decompositions for any large MIP and it may not be obvious which will be the most effective. This paper presents a comprehensive analysis of the predictive capabilities of a Machine Learning ranking (ML) function for predicting the quality of Mixed Integer Programming (MIP) decompositions created via constraint relaxation. In this analysis, the role of instance similarity and ML prediction quality is explored, as well as the benchmarking of a ML ranking function against existing heuristic functions. For this analysis, a new dataset consisting of over 40000 unique decompositions sampled from across 24 instances from the MIPLIB2017 library has been established. These decompostions have been created by both a greedy relaxation algorithm as well as a population based multi-objective algorithm, which has previously been shown to produce high quality decompositions. In this paper, we demonstrate that a ML ranking function is able to provide state-of-the-art predictions when benchmarked against existing heuristic ranking functions. Additionally, we demonstrate that by only considering a small set of features related to the relaxed constraints in each decomposition, a ML ranking function is still able to be competitive with heuristic techniques. Such a finding is promising for future constraint relaxation approaches, as these features can be used to guide decomposition creation. Finally, we highlight where a ML ranking function would be beneficial in a decomposition creation framework.