An Improved Upper Bound for the Euclidean TSP Constant Using Band Crossovers

2026-02-11

AI Summary

This work addresses the unknown asymptotic constant β in the Euclidean traveling salesman problem (TSP) by proposing an improved strip-based heuristic that overcomes the conventional limitation of treating strips independently. The method permits path crossings between adjacent strips and leverages local geometric structure to optimize inter-strip connections. By integrating strip partitioning, cross-strip path construction, Monte Carlo simulation, and concentration analysis, the approach theoretically reduces the upper bound on β from 0.90380 to 0.90367. Numerical experiments further suggest that the bound may be tightened to approximately 0.85, substantially enhancing the precision of current upper-bound estimates for β.

Technology Category

• Search and Optimization: Heuristic Search
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction
• Planning, Routing, and Scheduling: Planning under Uncertainty

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Consider $n$ points generated uniformly at random in the unit square, and let $L_n$ be the length of their optimal traveling salesman tour. Beardwood, Halton, and Hammersley (1959) showed $L_n / \sqrt n \to \beta$ almost surely as $n\to \infty$ for some constant $\beta$. The exact value of $\beta$ is unknown but estimated to be approximately $0.71$ (Applegate, Bixby, Chv\'atal, Cook 2011). Beardwood et al. further showed that $0.625 \leq \beta \leq 0.92116.$ Currently, the best known bounds are $0.6277 \leq \beta \leq 0.90380$, due to Gaudio and Jaillet (2019) and Carlsson and Yu (2023), respectively. The upper bound was derived using a computer-aided approach that is amenable to lower bounds with improved computation speed. In this paper, we show via simulation and concentration analysis that future improvement of the $0.90380$ is limited to $\sim0.88$. Moreover, we provide an alternative tour-constructing heuristic that, via simulation, could potentially improve the upper bound to $\sim0.85$. Our approach builds on a prior \emph{band-traversal} strategy, initially proposed by Beardwood et al. (1959) and subsequently refined by Carlsson and Yu (2023): divide the unit square into bands of height $\Theta(1/\sqrt{n})$, construct paths within each band, and then connect the paths to create a TSP tour. Our approach allows paths to cross bands, and takes advantage of pairs of points in adjacent bands which are close to each other. A rigorous numerical analysis improves the upper bound to $0.90367$.

Problem

• Euclidean TSP
• TSP constant
• upper bound
• random points
• asymptotic analysis

Innovation

• Euclidean TSP
• upper bound
• band crossovers
• traveling salesman problem
• heuristic algorithm