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arXiv 1601.00271cs.DScs.DM
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超越完整性缺口的树木灭火

Firefighting on Trees Beyond Integrality Gaps

David Adjiashvili, Andrea Baggio, Rico Zenklusen

摘要:

消防员问题和它的一个变体,称为火灾遏制资源最小化(RMFC),是最佳抑制有害传播过程的自然模型。尽管在几个方面取得了相当大的进展,这些问题的近似性仍然很难理解。即使底层图是树,也是如此,树是本文中研究最多的图结构之一,也是本文的重点。在他们最简单的版本中,火从一个固定的顶点逐步从燃烧到相邻的非燃烧顶点,并且在每个时间步,$B$许多非燃烧顶点可以被保护免于着火。对于给定的$B$,消防员问题要求最大化不会着火的顶点的数量,而RMFC(在树上)要求找到最小的$B$,允许保存树的所有叶子。在这项工作之前,最著名的近似比是$O(1)$ -近似的消防员问题和$O(\log^* n)$ -近似的RMFC,都是LP为基础的,基本上匹配的两个自然LP松弛的完整性差距。我们提出了一个PTAS的消防员问题和一个$O(1)$ -近似RMFC,都定性匹配已知的硬度结果,这两个近似改进。我们的研究结果是通过结合已知的LP与几种新的技术,它允许有效地枚举子集的超常数大小的一个好的解决方案,以获得更强的LP。

英文摘要:

The Firefighter problem and a variant of it, known as Resource Minimization for Fire Containment (RMFC), are natural models for optimal inhibition of harmful spreading processes. Despite considerable progress on several fronts, the approximability of these problems is still badly understood. This is the case even when the underlying graph is a tree, which is one of the most-studied graph structures in this context and the focus of this paper. In their simplest version, a fire spreads from one fixed vertex step by step from burning to adjacent non-burning vertices, and at each time step, $B$ many non-burning vertices can be protected from catching fire. The Firefighter problem asks, for a given $B$, to maximize the number of vertices that will not catch fire, whereas RMFC (on a tree) asks to find the smallest $B$ that allows for saving all leaves of the tree. Prior to this work, the best known approximation ratios were an $O(1)$-approximation for the Firefighter problem and an $O(\log^* n)$-approximation for RMFC, both being LP-based and essentially matching the integrality gaps of two natural LP relaxations. We improve on both approximations by presenting a PTAS for the Firefighter problem and an $O(1)$-approximation for RMFC, both qualitatively matching the known hardness results. Our results are obtained through a combination of the known LPs with several new techniques, which allow for efficiently enumerating subsets of super-constant size of a good solution to obtain stronger LPs.

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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