We analyze a tour-uncrossing heuristic for the Travelling Salesperson Problem, showing that its worst-case approximation ratio is $\Omega(n)$ and its average-case approximation ratio is $\Omega(\sqrt{n})$ in expectation. We furthermore evaluate the approximation performance of this heuristic numerically on average-case instances, and find that it performs far better than the average-case lower bound suggests. This indicates a shortcoming in the approach we use for our analysis, which is a rather common approach in the analysis of local search heuristics.
翻译:我们分析了旅行销售商问题游览式的横跨逻辑,表明其最坏的近似比率为$\Omega(n),其平均近似比率预期为$\Omega(sqrt{n}) 。我们进一步从数字上评估了平均案例的这个粗略近似表现,发现它的表现远优于平均案例下限的表示。这表明了我们用于分析的方法存在缺陷,这是分析本地搜索超自然学的一种相当常见的方法。