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arXiv · 2503.03642

Parameterized Approximation Algorithms for TSP on Non-Metric Graphs

Jingyang Zhao·Zimo Sheng·Mingyu Xiao
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Abstract

The Traveling Salesman Problem (TSP) is a classic and extensively studied problem with numerous real-world applications in artificial intelligence and operations research. It is well-known that TSP admits a constant approximation ratio on metric graphs but becomes NP-hard to approximate within any computable function $f(n)$ on general graphs. This disparity highlights a significant gap between the results on metric graphs and general graphs. Recent research has introduced some parameters to measure the ``distance'' of general graphs from being metric and explored Fixed-Parameter Tractable (FPT) approximation algorithms parameterized by these parameters. Two commonly studied parameters are $p$, the number of vertices in triangles violating the triangle inequality, and $q$, the minimum number of vertices whose removal results in a metric graph. In this paper, we present improved FPT approximation algorithms with respect to these two parameters. For $p$, we propose an FPT algorithm with a 1.5-approximation ratio, improving upon the previous ratio of 2.5. For $q$, we significantly enhance the approximation ratio from 11 to 3, advancing the state of the art in both cases. In addition, when $p$ (or $q$) is a constant, we obtain a better approximation ratio.

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Jingyang Zhao, Zimo Sheng, Mingyu Xiao. 2025-12-01. Parameterized Approximation Algorithms for TSP on Non-Metric Graphs. https://arxiv.org/abs/2503.03642

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