arXiv:2607.06532cs.LGmath.OC2026-07

GraphBU用图结构块生成保持耦合特性的混合整数规划实例。

GraphBU: MILP Instance Generation with Graph-Native Block Units

论文配图:GraphBU: MILP Instance Generation with Graph-Native Block Units
图 1 · 摘自论文原文
  • 以局部子问题及其接口为生成单元,显式保留耦合关系。
  • 生成实例与源数据图统计相似度达0.934,可行性超96.7%。
  • 适合需结构保真的优化求解器训练与学习策略研究者。

用于求解器开发的混合整数线性规划(MILP)实例在来自私有或特定应用流程时难以获取。生成器必须保留求解器和学习策略所依赖的结构特性。现有通用生成方法通常基于公式模板、统计特征、局部图修改或重组后发现的块作为生成单元,但这些单元未显式记录局部部分与整体实例的耦合方式。本文提出GraphBU,一种基于图结构的生成器,其基本单元为局部子问题及其接口。该方法将耦合节点提升为主约束或边界变量,并使用生成的块单位进行兼容性检查替换。分析聚焦于该构造所需性质:促进接口分离,替换可在接口松弛条件下保持可行性,且图构造对行列置换不变。在生成任务中,该单元使生成实例的图统计特性接近原始家族,多数数据集保持可行性,显著提升下游预测-搜索训练效果。生成实例平均图统计相似度约为0.934,平均可行性约为96.7%,下游PS主要指标平均提升约8.0%。

原文摘要 · Abstract (English)

Mixed-integer linear programming (MILP) instances used for solver development are hard to obtain when models come from private or application-specific pipelines. A generator must keep the structure that solvers and learned policies rely on. Existing general generators usually choose their generation unit from a formulation template, summary statistics, local graph edits, or blocks found after recombination. These units do not explicitly record how a local part of the MILP is coupled to the rest of the instance. We propose GraphBU, a graph-native generator whose basic unit is a local subproblem plus its interface. The method promotes coupling nodes into master constraints or boundary variables and uses the resulting block units for compatibility-checked replacement. The analysis focuses on the properties needed by this construction: promotion separates interfaces, replacement can preserve feasibility under an interface-slack condition, and the graph construction is invariant to row-column permutations. On MILP instances generation, this unit keeps graph statistics close to the source family, preserves feasibility on most datasets, and improves downstream Predict-and-Search training. Genrated by GraphBU, The average graph-statistical similarity was approximately 0.934, the average feasibility was approximately 96.7%, and the average increase in the main index of downstream PS was approximately 8.0%.

MILP生成图神经网络优化建模

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