EVA-MILP: Towards Standardized Evaluation of MILP Instance Generation

• URL: http://arxiv.org/abs/2505.24779v2
• Date: Tue, 03 Jun 2025 07:58:54 GMT
• Title: EVA-MILP: Towards Standardized Evaluation of MILP Instance Generation
• Authors: Yidong Luo, Chenguang Wang, Jiahao Yang, Fanzeng Xia, Tianshu Yu,
• Abstract summary: Mixed-Integer Linear Programming (MILP) is fundamental to solving complex decision-making problems.<n>The proliferation of MILP instance generation methods, driven by machine learning's demand for diverse datasets, has significantly outpaced standardized evaluation techniques.<n>This paper introduces a comprehensive benchmark framework designed for the systematic and objective evaluation of MILP instance generation methods.
• Score: 13.49043811341421
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mixed-Integer Linear Programming (MILP) is fundamental to solving complex decision-making problems. The proliferation of MILP instance generation methods, driven by machine learning's demand for diverse optimization datasets and the limitations of static benchmarks, has significantly outpaced standardized evaluation techniques. Consequently, assessing the fidelity and utility of synthetic MILP instances remains a critical, multifaceted challenge. This paper introduces a comprehensive benchmark framework designed for the systematic and objective evaluation of MILP instance generation methods. Our framework provides a unified and extensible methodology, assessing instance quality across crucial dimensions: mathematical validity, structural similarity, computational hardness, and utility in downstream machine learning tasks. A key innovation is its in-depth analysis of solver-internal features -- particularly by comparing distributions of key solver outputs including root node gap, heuristic success rates, and cut plane usage -- leveraging the solver's dynamic solution behavior as an `expert assessment' to reveal nuanced computational resemblances. By offering a structured approach with clearly defined solver-independent and solver-dependent metrics, our benchmark aims to facilitate robust comparisons among diverse generation techniques, spur the development of higher-quality instance generators, and ultimately enhance the reliability of research reliant on synthetic MILP data. The framework's effectiveness in systematically comparing the fidelity of instance sets is demonstrated using contemporary generative models.

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