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On the Approximation Ratio of the $k$-Opt and Lin-Kernighan Algorithm for Metric and Graph TSP
arXiv - CS - Discrete Mathematics Pub Date : 2019-09-27 , DOI: arxiv-1909.12755
Xianghui Zhong

The $k$-Opt and Lin-Kernighan algorithm are two of the most important local search approaches for the Metric TSP. Both start with an arbitrary tour and make local improvements in each step to get a shorter tour. We show that, for any fixed $k\geq 3$, the approximation ratio of the $k$-Opt algorithm for Metric TSP is $O(\sqrt[k]{n})$. Assuming the Erd\H{o}s girth conjecture, we prove a matching lower bound of $\Omega(\sqrt[k]{n})$. Unconditionally, we obtain matching bounds for $k=3,4,6$ and a lower bound of $\Omega(n^{\frac{2}{3k-3}})$. Our most general bounds depend on the values of a function from extremal graph theory and are tight up to a factor logarithmic in the number of vertices unconditionally. Moreover, all the upper bounds also apply to a parameterized version of the Lin-Kernighan algorithm with appropriate parameter. In the end, we show that the approximation ratio of $k$-Opt for Graph TSP is $\Omega\left(\frac{\log(n)}{\log\log(n)}\right)$ and $O\left(\left(\frac{\log(n)}{\log\log(n)}\right)^{\log_2(9)+\epsilon}\right)$ for all $\epsilon>0$.
更新日期:2020-02-18

 

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