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arXiv 2407.08392cs.DS
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非度量TSP的改进FPT近似算法

Improved FPT Approximation for Non-metric TSP

Evripidis Bampis, Bruno Escoffier, Michalis Xefteris

AI总结:

针对非度量旅行商问题,本文提出一种以违反三角不等式的三角形数量k为参数的FPT 2.5-近似算法,改进了此前ISAAC '22工作中的3-近似结果。

摘要:

在旅行商问题(TSP)中,给定一组地点及每对地点间的距离,目标是找到恰好访问每个地点一次并返回起点的最短路径。受一般TSP无法在多项式时间内获得任意常数因子近似、而度量TSP可在多项式时间内得到(略优于)1.5-近似这一事实的启发,Zhou、Li和Guo[Zhou等人,ISAAC '22]引入了一个衡量给定TSP实例与度量情形差距的参数。他们提出了一个以k为参数的FPT 3-近似算法,其中k是边代价违反三角不等式的三角形数量。本文设计了一个运行时间为FPT的2.5-近似算法,改进了[Zhou等人,ISAAC '22]的结果。

英文摘要:

In the Traveling Salesperson Problem (TSP) we are given a list of locations and the distances between each pair of them. The goal is to find the shortest possible tour that visits each location exactly once and returns to the starting location. Inspired by the fact that general TSP cannot be approximated in polynomial time within any constant factor, while metric TSP admits a (slightly better than) $1.5$-approximation in polynomial time, Zhou, Li and Guo [Zhou et al., ISAAC '22] introduced a parameter that measures the distance of a given TSP instance from the metric case. They gave an FPT $3$-approximation algorithm parameterized by $k$, where $k$ is the number of triangles in which the edge costs violate the triangle inequality. In this paper, we design a $2.5$-approximation algorithm that runs in FPT time, improving the result of [Zhou et al., ISAAC '22].

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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