Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model
Authors: Zhihai Wang, Xijun Li, Jie Wang, Yufei Kuang, Mingxuan Yuan, Jia Zeng, Yongdong Zhang, Feng Wu
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that HEM significantly improves the efficiency of solving MILPs compared to human-designed and learning-based baselines on both synthetic and large-scale real-world MILPs, including MIPLIB 2017. Moreover, experiments demonstrate that HEM well generalizes to MILPs that are significantly larger than those seen during training. |
| Researcher Affiliation | Collaboration | Zhihai Wang 1 , Xijun Li 1,2, Jie Wang 1,3 , Yufei Kuang1, Mingxuan Yuan2, Jia Zeng2, Yongdong Zhang1,3, Feng Wu1,3 1 CAS Key Laboratory of Technology in GIPAS, University of Science and Technology of China 2 Noah s Ark Lab, Huawei Technologies 3 Institute of Artificial Intelligence, Hefei Comprehensive National Science Center |
| Pseudocode | Yes | Algorithm 2 Pseudo code for training the HEM . |
| Open Source Code | Yes | Code is available at https://github.com/MIRALab-USTC/L2O-HEM-Torch (Py Torch version), and https://gitee.com/mindspore/models/tree/ master/research/l2o/hem-learning-to-cut (Mind Spore version). |
| Open Datasets | Yes | For simplicity, we split each dataset into the train and test sets with 80% and 20% instances. |
| Dataset Splits | Yes | For simplicity, we split each dataset into the train and test sets with 80% and 20% instances. |
| Hardware Specification | Yes | Throughout all experiments, we use a single machine that contains eight GPU devices (NVidia Ge Force GTX 3090 Ti) and two Intel Gold 6246R CPUs. |
| Software Dependencies | Yes | Throughout all experiments, we use SCIP 8.0.0 (Bestuzheva et al., 2021) as the backend solver... We train HEM with ADAM (Kingma & Ba, 2014) using the Py Torch (Paszke et al., 2019). Additionally, we also provide another implementation using the Mind Spore (Chen, 2021). |
| Experiment Setup | Yes | Throughout all experiments, we set the solving time limit as 300 seconds... Throughout all experiments, we apply Adam optimizer with learning rate α1 = 1 10 4 to optimize the lower-level model, and learning rate α2 = 5 10 4 to optimize the higher-level model. For each epoch, we collect 32 samples for training, and we set the total epochs as 100. |