GraphBU generates MILP instances via graph-native block units that pair local subproblems with explicit coupling interfaces, achieving high structural similarity and feasibility preservation across four MILP families.
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
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abstract
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning as a new approach for solving combinatorial problems, either directly as solvers or by enhancing exact solvers. Based on this context, the ML4CO aims at improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components. The competition featured three challenging tasks: finding the best feasible solution, producing the tightest optimality certificate, and giving an appropriate solver configuration. Three realistic datasets were considered: balanced item placement, workload apportionment, and maritime inventory routing. This last dataset was kept anonymous for the contestants.
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cs.LG 1years
2026 1verdicts
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GraphBU: MILP Instance Generation with Graph-Native Block Units
GraphBU generates MILP instances via graph-native block units that pair local subproblems with explicit coupling interfaces, achieving high structural similarity and feasibility preservation across four MILP families.