BenLOC: A Benchmark for Learning to Configure MIP Optimizers
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918150244663296 |
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| author | Li, Hongpei He, Ziyan Wang, Yufei Tu, Wenting Pu, Shanwen Deng, Qi Ge, Dongdong |
| author_facet | Li, Hongpei He, Ziyan Wang, Yufei Tu, Wenting Pu, Shanwen Deng, Qi Ge, Dongdong |
| contents | The automatic configuration of Mixed-Integer Programming (MIP) optimizers has become increasingly critical as the large number of configurations can significantly affect solver performance. Yet the lack of standardized evaluation frameworks has led to data leakage and over-optimistic claims, as prior studies often rely on homogeneous datasets and inconsistent experimental setups. To promote a fair evaluation process, we present BenLOC, a comprehensive benchmark and open-source toolkit, which not only offers an end-to-end pipeline for learning instance-wise MIP optimizer configurations, but also standardizes dataset selection, train-test splits, feature engineering and baseline choice for unbiased and comprehensive evaluations. Leveraging this framework, we conduct an empirical analysis on five well-established MIP datasets and compare classical machine learning models with handcrafted features against state-of-the-art deep-learning techniques. The results demonstrate the importance of datasets, features and baseline criteria proposed by BenLOC and the effectiveness of BenLOC in providing unbiased and comprehensive evaluations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02752 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BenLOC: A Benchmark for Learning to Configure MIP Optimizers Li, Hongpei He, Ziyan Wang, Yufei Tu, Wenting Pu, Shanwen Deng, Qi Ge, Dongdong Optimization and Control Artificial Intelligence The automatic configuration of Mixed-Integer Programming (MIP) optimizers has become increasingly critical as the large number of configurations can significantly affect solver performance. Yet the lack of standardized evaluation frameworks has led to data leakage and over-optimistic claims, as prior studies often rely on homogeneous datasets and inconsistent experimental setups. To promote a fair evaluation process, we present BenLOC, a comprehensive benchmark and open-source toolkit, which not only offers an end-to-end pipeline for learning instance-wise MIP optimizer configurations, but also standardizes dataset selection, train-test splits, feature engineering and baseline choice for unbiased and comprehensive evaluations. Leveraging this framework, we conduct an empirical analysis on five well-established MIP datasets and compare classical machine learning models with handcrafted features against state-of-the-art deep-learning techniques. The results demonstrate the importance of datasets, features and baseline criteria proposed by BenLOC and the effectiveness of BenLOC in providing unbiased and comprehensive evaluations. |
| title | BenLOC: A Benchmark for Learning to Configure MIP Optimizers |
| topic | Optimization and Control Artificial Intelligence |
| url | https://arxiv.org/abs/2506.02752 |