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arXiv 0802.2855cs.DS
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不确定条件下最小生成树的计算

Computing Minimum Spanning Trees with Uncertainty

Thomas Erlebach, Michael Hoffmann, Danny Krizanc, Matús Mihal'ák, Rajeev Raman

摘要:

我们考虑的最小生成树问题中的设置给定的图的边权重的信息是不确定的。最初,对于图的每个边$e$,仅已知包含实际边权重$w_e$的称为不确定区域的集合$A_e$。该算法可以“更新”$e$以获得边权重$w_e \in A_e$。任务是在最小更新次数后输出最小生成树的边集。如果一个算法使$k$的更新次数至多为最优值的$k$倍,则该算法是$k$ -更新竞争的。如果所有区域$A_e$都是开放的或平凡的,我们提出了一个2-更新的竞争算法,这是最好的确定性算法。区域$A_e$上的条件是排除退化输入,对于退化输入,不存在恒定更新竞争算法。接下来,我们考虑一个设置,其中图的顶点对应于欧几里得空间中的点,并且边的权重等于其端点的距离。每个点的位置最初作为不确定区域给出,更新后会显示该点的确切位置。我们给出了一个问题的边的不确定性和顶点的不确定性版本之间的一般关系,并使用它来获得一个4更新的竞争算法的最小生成树问题的顶点不确定性模型。我们再次证明,这是确定性算法中最好的可能性。

英文摘要:

We consider the minimum spanning tree problem in a setting where information about the edge weights of the given graph is uncertain. Initially, for each edge $e$ of the graph only a set $A_e$, called an uncertainty area, that contains the actual edge weight $w_e$ is known. The algorithm can `update' $e$ to obtain the edge weight $w_e \in A_e$. The task is to output the edge set of a minimum spanning tree after a minimum number of updates. An algorithm is $k$-update competitive if it makes at most $k$ times as many updates as the optimum. We present a 2-update competitive algorithm if all areas $A_e$ are open or trivial, which is the best possible among deterministic algorithms. The condition on the areas $A_e$ is to exclude degenerate inputs for which no constant update competitive algorithm can exist. Next, we consider a setting where the vertices of the graph correspond to points in Euclidean space and the weight of an edge is equal to the distance of its endpoints. The location of each point is initially given as an uncertainty area, and an update reveals the exact location of the point. We give a general relation between the edge uncertainty and the vertex uncertainty versions of a problem and use it to derive a 4-update competitive algorithm for the minimum spanning tree problem in the vertex uncertainty model. Again, we show that this is best possible among deterministic algorithms.

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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