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arXiv:2202.02725 [math.OC]AbstractReferencesReviewsResources

Efficient primal heuristics for mixed-integer linear programs

Akang Wang, Linxin Yang, Sha Lai, Xiaodong Luo, Xiang Zhou, Haohan Huang, Shengcheng Shao, Yuanming Zhu, Dong Zhang, Tao Quan

Published 2022-02-06Version 1

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'.

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
Categories: math.OC
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