Large Neighborhood Search meets Iterative Neural Constraint Heuristics
Neural networks are being increasingly used as heuristics for constraint satisfaction. These neural methods are often recurrent, learning to iteratively refine candidate assignments. In this work, we make explicit the connection between such iterative neural heuristics and Large Neighborhood Search (LNS), and adapt an existing neural constraint satisfaction method-ConsFormer-into an LNS procedure. We decompose the resulting neural LNS into two standard components: the destroy and repair operators. On the destroy side, we instantiate several classical heuristics and introduce novel prediction-guided operators that exploit the model's internal scores to select neighborhoods. On the repair s...
| Authors | Yudong W. Xu, Wenhao Li, Scott Sanner et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 March 2026 |
| Last updated at source | 21 March 2026 |
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