GraphBU: MILP Instance Generation with Graph-Native Block Units
Score
78首次提出显式记录耦合关系的图原生块单元，对MILP生成领域提供了新视角。
方法描述清晰，理论分析严谨，实验设计全面，结果量化可信。
为求解器开发和机器学习训练提供了实用的数据生成工具，但通用性仍需验证。
当前组合优化与机器学习交叉领域对高质量实例生成需求迫切，该工作及时。
来自arXiv预印本，作者单位可靠，方法有理论分析和复现潜力。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The method is novel with thorough experiments and strong results, though details and generalizability need further verification, resulting in a high overall score.
AI commentary
「This paper cleverly encodes local coupling relationships into generation units, addressing the long-standing data scarcity issue. It is a must-read for teams working on operations research, automatic algorithm configuration, and learning-based solver development.」
Quick overview
GraphBU is a novel graph-native generator for Mixed-Integer Linear Programming (MILP) instances, where the basic unit is a local subproblem plus its interface to the rest of the instance. By promoting coupling nodes into master constraints or boundary variables and performing compatibility-checked block replacements, it preserves the key graph structure and feasibility of source families. Experiments show that on multiple datasets, the average graph-statistical similarity reaches 0.934, average feasibility is about 96.7%, and the downstream Predict-and-Search training improves its main index by 8.0% on average. This addresses the challenge of obtaining private MILP instances while maintaining structural fidelity.
- Introduces graph-native block units where each unit is a local subproblem plus its interface, explicitly modeling coupling.
- Promotion operation turns coupling nodes into master constraints or boundary variables to preserve structure.
- Achieves average graph-statistical similarity of 0.934, feasibility of 96.7%, and 8.0% improvement in downstream Predict-and-Search training.
Full text
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