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This is a Quantitative Study study.

September 20, 2025

Scalable Primal Heuristics Using Graph Neural Networks for Combinatorial Optimization (Abstract Reprint)

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The GNN-based pipeline offers an incremental performance improvement compared to existing methods.
Our models achieve improvements ranging from 6-75% in optimality gap values for larger instances.
The study introduces a two-stage primal heuristic that uses uncertainty-quantification to enhance solution search.
Results indicate that models can generalize on instances up to 16 times larger than those used in training.

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This work demonstrates improved scalability and performance in combinatorial optimization models using graph neural networks and primal heuristics.

Cantürk et al. (2024) studied this question.

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This work demonstrates improved scalability and performance in combinatorial optimization models using graph neural networks and primal heuristics.