This paper is a short report about our work for the primal task in the Machine Learning for Combinatorial Optimization NeurIPS 2021 Competition. For each dataset of our interest in the competition, we propose customized primal heuristic methods to efficiently identify high-quality feasible solutions. The computational studies demonstrate the superiority of our proposed approaches over the competitors'.
@article{arxiv.2202.02725,
title = {Efficient primal heuristics for mixed-integer linear programs},
author = {Akang Wang and Linxin Yang and Sha Lai and Xiaodong Luo and Xiang Zhou and Haohan Huang and Shengcheng Shao and Yuanming Zhu and Dong Zhang and Tao Quan},
journal= {arXiv preprint arXiv:2202.02725},
year = {2022}
}
Comments
This work will be published on the ML4CO NeurIPS 2021 Competition website (https://www.ecole.ai/2021/ml4co-competition/) in the proceedings section. A succinct version will appear in a special Proceedings of Machine Learning Research (PMLR) volume dedicated to the NeurIPS 2021 competitions