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GraphBU: MILP Instance Generation with Graph-Native Block Units

About

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%.

Xiaolei Guo, Chenyu Zhou, Jianghao Lin, Dongdong Ge• 2026

Related benchmarks

TaskDatasetResultRank
MILP Instance GenerationCA (Combinatorial Auction)
Similarity0.989
4
MILP Instance GenerationFA (Facilities Allocation)
Solution Similarity81.3
4
MILP Instance GenerationIP (Independet Set)
Similarity0.94
4
MILP Instance GenerationWA (Workload Assignment)
Similarity99.5
4
Predict-and-searchCA original (test)
Object Count9.75e+4
4
Predict-and-searchFA original (test)
Time3.61
4
Predict-and-searchIP original (test)
Obj. Score10.778
4
Predict-and-searchWA original (test)
Objective Score699.3
4
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