arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1507.03009cs.DS
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树扩充问题的$1.75$ LP近似

A $1.75$ LP approximation for the Tree Augmentation Problem

Guy Kortsarz, Zeev Nutov

摘要:

在树扩充问题(TAP)中,目标是通过来自给定边集$E$的最小尺寸边集$F$来扩充树$T$,使得$T \cup F$是$2$边连通的。已知TAP的最佳近似比是$1.5$。在更一般的加权TAP问题中,$F$应该具有最小权重。加权TAP允许几个$2$ -近似算法w.r.t.标准切割LP松弛,但对于所有这些性能比的$2$是紧的,即使对于TAP。这个问题等价于覆盖一个层集族的问题。在连通网络设计问题的近似算法设计中,层状集族起着重要的作用。事实上,加权TAP是最简单的连接网络设计问题,其比$2$更好的比率是未知的。提高这个“自然”比率是一个主要的开放问题,这可能对许多其他网络设计问题产生影响。似乎实现这一目标需要找到具有比$2$更好的完整性间隙的LP松弛,这即使对于TAP也是一个长期开放的问题。在本文中,我们引入了这样的LP松弛,并给出了一种算法,该算法计算大小最多为$1.75$乘以最佳LP值的TAP的可行解。这为打破加权情况下的比率$2$带来了一些希望。我们的算法计算一些初始的边集,通过解决一个部分系统的约束,形成完整的边覆盖多面体,然后应用本地搜索$3$ -叶子子树交换的一些边缘,并添加额外的边缘。因此,我们不需要求解LP,并且该算法的运行时间大致上是在一般图中找到最小权重边覆盖所需的时间。

英文摘要:

In the Tree Augmentation Problem (TAP) the goal is to augment a tree $T$ by a minimum size edge set $F$ from a given edge set $E$ such that $T \cup F$ is $2$-edge-connected. The best approximation ratio known for TAP is $1.5$. In the more general Weighted TAP problem, $F$ should be of minimum weight. Weighted TAP admits several $2$-approximation algorithms w.r.t. to the standard cut LP-relaxation, but for all of them the performance ratio of $2$ is tight even for TAP. The problem is equivalent to the problem of covering a laminar set family. Laminar set families play an important role in the design of approximation algorithms for connectivity network design problems. In fact, Weighted TAP is the simplest connectivity network design problem for which a ratio better than $2$ is not known. Improving this "natural" ratio is a major open problem, which may have implications on many other network design problems. It seems that achieving this goal requires finding an LP-relaxation with integrality gap better than $2$, which is a long time open problem even for TAP. In this paper we introduce such an LP-relaxation and give an algorithm that computes a feasible solution for TAP of size at most $1.75$ times the optimal LP value. This gives some hope to break the ratio $2$ for the weighted case. Our algorithm computes some initial edge set by solving a partial system of constraints that form the integral edge-cover polytope, and then applies local search on $3$-leaf subtrees to exchange some of the edges and to add additional edges. Thus we do not need to solve the LP, and the algorithm runs roughly in time required to find a minimum weight edge-cover in a general graph.

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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