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Solving connectivity problems parameterized by treedepth in single-exponential time and polynomial space
arXiv - CS - Data Structures and Algorithms Pub Date : 2020-01-15 , DOI: arxiv-2001.05364
Falko Hegerfeld and Stefan Kratsch

A breakthrough result of Cygan et al. (FOCS 2011) showed that connectivity problems parameterized by treewidth can be solved much faster than the previously best known time $\mathcal{O}^*(2^{\mathcal{O}(tw \log(tw))})$. Using their inspired Cut\&Count technique, they obtained $\mathcal{O}^*(\alpha^{tw})$ time algorithms for many such problems. Moreover, they proved these running times to be optimal assuming the Strong Exponential-Time Hypothesis. Unfortunately, like other dynamic programming algorithms on tree decompositions, these algorithms also require exponential space, and this is widely believed to be unavoidable. In contrast, for the slightly larger parameter called treedepth, there are already several examples of matching the time bounds obtained for treewidth, but using only polynomial space. Nevertheless, this has remained open for connectivity problems. In the present work, we close this knowledge gap by applying the Cut\&Count technique to graphs of small treedepth. While the general idea is unchanged, we have to design novel procedures for counting consistently cut solution candidates using only polynomial space. Concretely, we obtain time $\mathcal{O}^*(3^d)$ and polynomial space for Connected Vertex Cover, Feedback Vertex Set, and Steiner Tree on graphs of treedepth $d$. Similarly, we obtain time $\mathcal{O}^*(4^d)$ and polynomial space for Connected Dominating Set and Connected Odd Cycle Transversal.

中文翻译:

解决单指数时间和多项式空间中由树深度参数化的连通性问题

Cygan 等人的一项突破性成果。(FOCS 2011) 表明,由树宽参数化的连通性问题可以比以前最知名的时间更快地解决 $\mathcal{O}^*(2^{\mathcal{O}(tw \log(tw))})$ . 使用他们启发的 Cut\&Count 技术,他们为许多此类问题获得了 $\mathcal{O}^*(\alpha^{tw})$ 时间算法。此外,假设强指数时间假设,他们证明了这些运行时间是最佳的。不幸的是,与其他关于树分解的动态规划算法一样,这些算法也需要指数空间,这被广泛认为是不可避免的。相比之下,对于称为 treedepth 的稍大的参数,已经有几个匹配为 treewidth 获得的时间界限的示例,但仅使用多项式空间。尽管如此,这对连接问题仍然开放。在目前的工作中,我们通过将 Cut\&Count 技术应用于小树深度图来缩小这一知识差距。虽然总体思路没有改变,但我们必须设计新颖的程序来仅使用多项式空间来计算一致切割的候选解。具体来说,我们在树深度$d$的图上获得了连接顶点覆盖、反馈顶点集和斯坦纳树的时间$\mathcal{O}^*(3^d)$和多项式空间。类似地,我们获得了时间 $\mathcal{O}^*(4^d)$ 和连通支配集和连通奇数循环横向的多项式空间。我们必须设计新颖的程序来仅使用多项式空间计算一致切割的候选解决方案。具体来说,我们在树深度$d$的图上获得了连接顶点覆盖、反馈顶点集和斯坦纳树的时间$\mathcal{O}^*(3^d)$和多项式空间。类似地,我们获得了时间 $\mathcal{O}^*(4^d)$ 和连通支配集和连通奇数循环横向的多项式空间。我们必须设计新颖的程序来仅使用多项式空间计算一致切割的候选解决方案。具体地,我们在树深度$d$的图上获得了连接顶点覆盖、反馈顶点集和斯坦纳树的时间$\mathcal{O}^*(3^d)$和多项式空间。类似地,我们获得了时间 $\mathcal{O}^*(4^d)$ 和连通支配集和连通奇数循环横向的多项式空间。
更新日期:2020-01-16
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