Streaming Algorithms for Geometric Steiner Forest

Fuente: arXiv
Guardado en:
Detalles Bibliográficos
Autores principales: Czumaj, Artur, Jiang, Shaofeng H. -C., Krauthgamer, Robert, Veselý, Pavel
Formato: Preprint
Publicado: 2020
Materias:
Acceso en línea:
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
_version_ 1866929339483815936
author Czumaj, Artur
Jiang, Shaofeng H. -C.
Krauthgamer, Robert
Veselý, Pavel
author_facet Czumaj, Artur
Jiang, Shaofeng H. -C.
Krauthgamer, Robert
Veselý, Pavel
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2011_04324
institution arXiv
publishDate 2020
record_format arxiv
spellingShingle Streaming Algorithms for Geometric Steiner Forest
Czumaj, Artur
Jiang, Shaofeng H. -C.
Krauthgamer, Robert
Veselý, Pavel
Data Structures and Algorithms
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.
title Streaming Algorithms for Geometric Steiner Forest
topic Data Structures and Algorithms
url https://arxiv.org/abs/2011.04324