Leveraging Structural Constraints for Diffusion-based Neural TSP Solvers
Neural combinatorial optimization has recently achieved strong results on the Euclidean Traveling Salesman Problem (TSP) using generative models such as diffusion and consistency models. State-ofthe-art approaches like FT2T combine fast consistency-based prediction with gradient-based inference time refinement. However, gradient search often incurs significant computational overhead and may not align with the discrete structure of feasible solutions. We introduce Projected Consistency Inference (PCI), a plug-and-play, retraining-free alternative that replaces gradient refinement with structure-aware projections: PCI decodes valid Hamiltonian tours from the consistency model output and app...
| Authors | Mickaël Basson, Philippe Preux |
|---|---|
| Primary category | cs.AI |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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