Improving Primal Heuristics for Mixed Integer Programming Problems based on Problem Reduction: A Learning-based Approach
Abstract
In this paper, we propose a Bi-layer Predictionbased Reduction Branch (BP-RB) framework to speed up the process of finding a high-quality feasible solution for Mixed Integer Programming (MIP) problems. A graph convolutional network (GCN) is employed to predict binary variables' values. After that, a subset of binary variables is fixed to the predicted value by a greedy method conditioned on the predicted probabilities. By exploring the logical consequences, a learning-based problem reduction method is proposed, significantly reducing the variable and constraint sizes. With the reductive sub-MIP problem, the second layer GCN framework is employed to update the prediction for the remaining binary variables' values and to determine the selection of variables which are then used for branching to generate the Branch and Bound (B&B) tree. Numerical examples show that our BP-RB framework speeds up the primal heuristic and finds the feasible solution with high quality.
Cite
@article{arxiv.2209.13217,
title = {Improving Primal Heuristics for Mixed Integer Programming Problems based on Problem Reduction: A Learning-based Approach},
author = {Lingying Huang and Xiaomeng Chen and Wei Huo and Jiazheng Wang and Fan Zhang and Bo Bai and Ling Shi},
journal= {arXiv preprint arXiv:2209.13217},
year = {2022}
}
Comments
This paper has been accepted for the publication of ICARCV 2022