Learning Structural Hardness for Combinatorial Auctions: Instance-Dependent Algorithm Selection via Graph Neural Networks
arXiv:2602.14772v2 Announce Type: replace Abstract: The Winner Determination Problem (WDP) in combinatorial auctions is NP-hard, and no existing method reliably predicts which instances will defeat fast greedy heuristics. The ML-for-combinatorial-optimization community has focused on learning to \emph{replace} solvers, yet recent evidence shows that graph neural networks (GNNs) rarely outperform well-tuned classical methods on standard benchmarks. We pursue a different objective: learning to predict \emph{when} a given instance is hard for greedy allocation, enabling instance-dependent algorit
The paper leverages recent advancements in Graph Neural Networks to tackle the long-standing NP-hard Winner Determination Problem, specifically aiming to improve the efficiency of combinatorial auctions.
Improving the prediction of 'hard' instances in combinatorial optimization can significantly enhance the efficiency and fairness of complex resource allocation problems across various industries.
This research shifts the ML-for-combinatorial-optimization paradigm from attempting to replace solvers to intelligently selecting the right algorithm for a given problem instance, making existing solvers more effective.
- · Logistics and supply chain companies
- · E-commerce platforms
- · AI researchers in combinatorial optimization
- · Cloud resource allocation platforms
- · Inefficient auction systems
- · Purely classical heuristic developers
More efficient and faster resolution of complex allocation problems will become possible.
This could lead to optimized resource utilization and cost savings in sectors relying on auctioned resources.
Broader adoption of intelligent algorithm selection could drive demand for specialized AI/ML tools in operational research.
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Read at arXiv cs.LG