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arXiv 1702.08207cs.DS
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加权树中广义二叉搜索的逼近策略

Approximation Strategies for Generalized Binary Search in Weighted Trees

Dariusz Dereniowski, Adrian Kosowski, Przemyslaw Uznanski, Mengchuan Zou

摘要:

我们考虑以下二叉搜索问题的推广。需要搜索策略来定位给定树$T$中的未知目标节点$t$。在查询树的节点$v$时,该策略接收包含目标$t$的$T\setminus\{v\}$的连接分量的指示作为应答。查询每个节点的成本是由一个已知的非负权重函数,所考虑的目标是最小化的总查询成本的最坏情况下的选择的目标。设计一个加权树搜索实例的最优策略是已知的强NP-难的,相反,可以在线性时间内最优解决的问题的未加权的变体。在这里,我们表明,加权树搜索承认一个准多项式时间的近似方案:对于任何$0 \textless{} \varepsilon \textless{} 1$,存在一个$(1+\varepsilon)$ -近似策略的计算时间为$n^{O(\log n / \varepsilon^2)}$。因此,除非是$NP \subseteq DTIME(n^{O(\log n)})$,否则问题不是APX困难的。通过应用一般的减少,我们得到的推论,所研究的问题承认多项式时间$O(\sqrt{\log n})$ -近似。这改进了以前的$\hat O(\log n)$近似方法,其中$\hat O$表示法忽略了$O(\mathrm{poly}\log\log n)$因子.

英文摘要:

We consider the following generalization of the binary search problem. A search strategy is required to locate an unknown target node $t$ in a given tree $T$. Upon querying a node $v$ of the tree, the strategy receives as a reply an indication of the connected component of $T\setminus\{v\}$ containing the target $t$. The cost of querying each node is given by a known non-negative weight function, and the considered objective is to minimize the total query cost for a worst-case choice of the target. Designing an optimal strategy for a weighted tree search instance is known to be strongly NP-hard, in contrast to the unweighted variant of the problem which can be solved optimally in linear time. Here, we show that weighted tree search admits a quasi-polynomial time approximation scheme: for any $0 \textless{} \varepsilon \textless{} 1$, there exists a $(1+\varepsilon)$-approximation strategy with a computation time of $n^{O(\log n / \varepsilon^2)}$. Thus, the problem is not APX-hard, unless $NP \subseteq DTIME(n^{O(\log n)})$. By applying a generic reduction, we obtain as a corollary that the studied problem admits a polynomial-time $O(\sqrt{\log n})$-approximation. This improves previous $\hat O(\log n)$-approximation approaches, where the $\hat O$-notation disregards $O(\mathrm{poly}\log\log n)$-factors.

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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