论文标题
混合构成线性程序的有效原始启发式方法
Efficient primal heuristics for mixed-integer linear programs
论文作者
论文摘要
本文是关于我们完成组合优化神经2021竞赛机器学习中原始任务的工作的简短报告。对于我们在竞争中兴趣的每个数据集，我们提出了定制的原始启发式方法，以有效地识别高质量的可行解决方案。计算研究证明了我们提出的方法比竞争对手的优越性。
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'.
