arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2011.04324cs.DS
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几何 Steiner 森林的流式算法

Streaming Algorithms for Geometric Steiner Forest

Artur Czumaj, Shaofeng H. -C. Jiang, Robert Krauthgamer, Pavel Veselý

AI总结:

针对动态几何点流中的 Steiner 森林问题,提出结合采样、线性素描与 Arora 动态规划的单遍算法,达到接近欧几里得 Steiner 比的近似,并给出 $Ω(k)$ 空间下界。

摘要:

我们研究 Steiner 树问题的一个重要推广,即欧几里得平面中的 Steiner 森林问题:输入是一个多重集合 $X \subseteq \mathbb{R}^2$,被划分为 $k$ 个颜色类 $C_1, C_2, \ldots, C_k \subseteq X$。目标是找到一个最小代价的欧几里得图 $G$,使每个颜色类 $C_i$ 都在 $G$ 中连通。我们在流式设置下研究该 Steiner 森林问题,其中流由对 $X$ 中点的插入和删除组成。每个输入点 $x\in X$ 到达时带有其颜色 $\textsf{color}(x) \in [k]$;并且正如动态几何流中的通常设定,输入点被限制在离散网格 $\{0, \ldots, Δ\}^2$ 上。我们设计了一种单遍流式算法,使用 $\mathrm{poly}(k \cdot \logΔ)$ 的空间和时间,并将最优 Steiner 森林解的代价估计到任意接近著名的欧几里得 Steiner 比 $α_2$ 的比率(目前 $1.1547 \le α_2 \le 1.214$)。这一近似保证与流式 Steiner 树(即 $k=1$ 时)的最先进界相匹配;即使对这一特殊情形,把比率改进到 $1+ε$ 仍是一个重大开放问题。我们的方法依赖于将采样和线性素描等流式技术,与用于几何优化问题的经典 Arora 风格动态规划框架进行新颖结合;该框架通常需要大量内存,且迄今尚未应用于流式设置。我们还以简单论证补充了该 Steiner 森林流式算法,证明任何有限近似都需要 $Ω(k)$ 比特空间。

英文摘要:

We consider an important generalization of the Steiner tree problem, the \emph{Steiner forest problem}, in the Euclidean plane: the input is a multiset $X \subseteq \mathbb{R}^2$, partitioned into $k$ color classes $C_1, C_2, \ldots, C_k \subseteq X$. The goal is to find a minimum-cost Euclidean graph $G$ such that every color class $C_i$ is connected in $G$. We study this Steiner forest problem in the streaming setting, where the stream consists of insertions and deletions of points to $X$. Each input point $x\in X$ arrives with its color $\textsf{color}(x) \in [k]$, and as usual for dynamic geometric streams, the input points are restricted to the discrete grid $\{0, \ldots, Δ\}^2$. We design a single-pass streaming algorithm that uses $\mathrm{poly}(k \cdot \logΔ)$ space and time, and estimates the cost of an optimal Steiner forest solution within ratio arbitrarily close to the famous Euclidean Steiner ratio $α_2$ (currently $1.1547 \le α_2 \le 1.214$). This approximation guarantee matches the state-of-the-art bound for streaming Steiner tree, i.e., when $k=1$, and it is a major open question to improve the ratio to $1 + ε$ even for this special case. Our approach relies on a novel combination of streaming techniques, like sampling and linear sketching, with the classical Arora-style dynamic-programming framework for geometric optimization problems, which usually requires large memory and has so far not been applied in the streaming setting. We complement our streaming algorithm for the Steiner forest problem with simple arguments showing that any finite approximation requires $Ω(k)$ bits of space.

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