Editing Open Problems:71
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{{Header | {{Header | ||
| + | |title=Metric TSP Cost Approximation | ||
|source=baltimore16 | |source=baltimore16 | ||
|who=Krzysztof Onak | |who=Krzysztof Onak | ||
}} | }} | ||
| − | This problem appeared in the paper of Czumaj and Sohler {{cite|CzumajS-09}}, who gave an efficient algorithm for approximating the weight of the minimum spanning tree of a metric space in the following setting. The input is a metric space $\mathcal M$ on $n$ points. For any pair of points in $\mathcal M$, the algorithm is allowed to query their distance. The algorithm of Czumaj and Sohler computes a multiplicative $(1+\epsilon)$-approximation to the | + | This problem appeared in the paper of Czumaj and Sohler {{cite|CzumajS-09}}, who gave an efficient algorithm for approximating the weight of the minimum spanning tree of a metric space in the following setting. The input is a metric space $\mathcal M$ on $n$ points. For any pair $u$ and $v$ of points in $\mathcal M$, the algorithm is allowed to query their distance. The algorithm of Czumaj and Sohler computes a multiplicative $(1+\epsilon)$-approximation to the weight of the minimum spanning tree in time $\tilde O(n)\cdot\operatorname{poly}(1/\epsilon)$. This immediately yields a $(2+\epsilon)$-approximation to the length of the minimum travelling salesman tour. |
| − | '''Question:''' Can one compute a $(2-\delta)$-approximation to | + | '''Question:''' Can one compute a $(2-\delta)$-approximation to this problem with $o(n^2)$ queries (or even better, in $o(n^2)$ time) for a positive absolute constant $\delta$? |
'''Notes:''' | '''Notes:''' | ||
| − | * It is difficult to apply the approach known from the Christofides algorithm, which computes a $3/2$-approximation in the more standard setting. It requires computing a minimum spanning tree, while Czumaj and Sohler show that obtaining a constant-factor approximation to the minimum spanning tree | + | * It is difficult to apply the approach known from the Christofides algorithm, which computes a $3/2$-approximation in the more standard setting. It requires computing a minimum spanning tree, while Czumaj and Sohler show that obtaining a constant-factor approximation to the minimum spanning tree requires $\Omega(n^2)$ queries. |
| − | * For the specific case of $(1,2)$-metrics (i.e., when all distances between points are either 1 or 2), there is | + | * For the specific case of $(1,2)$-metrics (i.e., when all distances between points are either 1 or 2), there is a $\tilde O(n)\cdot\operatorname{poly}(1/\epsilon)$-time $(1.75+\epsilon)$-approximation algorithm. It uses the fact that if there is a short travelling salesman tour, then there must be a large matching in the graph obtained by connecting pairs of points at distance 1. Therefore, the cases of long and short shortest tours can be distinguished efficiently using a known graph matching algorithm {{cite|OnakRRR-12}}. (This is a joint observation with Anupam Gupta.) However, this approach does not seem to generalize to arbitrary metrics. |
