Efficient Primal Heuristics for Mixed-Integer Linear Programs
The aim of this work is to utilize machine learning techniques to enhance the efficiency and effectiveness of primal heuristics for solving challenging mixed-integer linear programming problems. Here, we use ML techniques to optimize the configuration parameters of the heuristics, thereby achieving better performance and results.
primal heuristics at the root node
Final Results of Primal Task
- item placement
- load balancing
Linxin Yang, Sha Lai, Akang Wang, Xiaodong Luo from SRIBD；
Xiang Zhou, Haohan Huang, Shengcheng Shao, Yuanming Zhu, Dong Zhang from Huawei.
We won the first place in the NeurIPS 2021 ML4CO(Machine Learning for Combinatorial Optimization) Competition.