构建首个公平评估MIP优化器配置的基准工具，避免数据泄露与过度乐观结论。
BenLOC: A Benchmark for Learning to Configure MIP Optimizers
- 提出统一的数据集选择与实验流程，杜绝训练测试污染。
- 在5个主流MIP数据集上验证深度学习优于传统特征方法。
- 适合优化器开发者、运筹学研究者及机器学习应用者参考。
混合整数规划（MIP）求解器的自动配置日益重要，因配置数量庞大且显著影响求解性能。然而，缺乏标准化评估框架导致数据泄露和过度乐观的结论，以往研究多依赖同质数据集和不一致实验设置。为推动公平评估，我们提出BenLOC——一个全面的基准与开源工具包，提供端到端的实例级MIP优化器配置学习流程，同时标准化数据集选择、训练测试划分、特征工程与基线模型选择，实现无偏且全面的评估。基于此框架，我们在五个知名MIP数据集上对经典机器学习模型与手工特征方法，与前沿深度学习技术进行对比分析。结果表明，BenLOC所提出的数据集、特征与基线标准至关重要，其框架有效保障了评估的客观性与全面性。
原文摘要 · Abstract (English)
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.
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