Structure As Search: Unsupervised Permutation Learning for Combinatorial Optimization

• URL: http://arxiv.org/abs/2507.04164v1
• Date: Sat, 05 Jul 2025 21:17:38 GMT
• Title: Structure As Search: Unsupervised Permutation Learning for Combinatorial Optimization
• Authors: Yimeng Min, Carla P. Gomes,
• Abstract summary: We propose a non-autoregressive framework for the Travelling Salesman Problem.<n>By applying a similarity transformation to Hamiltonian cycles, the model learns to approximate permutation via continuous relaxations.
• Score: 30.652280460904375
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a non-autoregressive framework for the Travelling Salesman Problem where solutions emerge directly from learned permutations without explicit search. By applying a similarity transformation to Hamiltonian cycles, the model learns to approximate permutation matrices via continuous relaxations. Our unsupervised approach achieves competitive performance against classical heuristics, demonstrating that the inherent structure of the problem can effectively guide combinatorial optimization without sequential decision-making.

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