Machine Learning-based Two-Stage Graph Sparsification for the Travelling Salesman Problem
High-performance TSP solvers such as Lin-Kernighan-Helsgaun (LKH) search within a \emph{candidate graph} -- a small subset of edges pre-selected for the solver -- rather than over the complete graph. The two leading sparsification heuristics, $α$-Nearest and POPMUSIC, each fall short of the density-coverage balance: $α$-Nearest is dense with stable recall, while POPMUSIC is sparser but its recall degrades with scale. Their union closes the recall gap while remaining far below the complete graph in density, leaving room for further reduction. Existing learning-based sparsifiers score edges on the complete graph, an approach that is expensive and largely limited to Euclidean instances. We p...
| Authors | Bo-Cheng Lin, Yi Mei, Mengjie Zhang |
|---|---|
| Primary category | cs.LG |
| Published | 22 April 2026 |
| Last updated at source | 11 June 2026 |
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