GraphBU: MILP Instance Generation with Graph-Native Block Units
Not on the map. Why?
Abstract (arXiv)
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%.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 12 references that are in the map. First posted on arXiv 7 Jul 2026.
Builds on
6 of 12 references.
References · 6
- 2018Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizoncited by 623
- 2020Solving Mixed Integer Programs Using Neural Networkscited by 182
- 2020Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solverscited by 43
- 2022The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insightscited by 39
- 2023A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availabilitycited by 21
- 2024MILP-StuDio: MILP Instance Generation via Block Structure Decompositioncited by 17
Built on by
Not cited yet.