Online Spanners in Metric Spaces
February 21, 2022 · Declared Dead · 🏛 Embedded Systems and Applications
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Sujoy Bhore, Arnold Filtser, Hadi Khodabandeh, Csaba D. Tóth
arXiv ID
2202.09991
Category
cs.CG: Computational Geometry
Cross-listed
cs.DS
Citations
5
Venue
Embedded Systems and Applications
Last Checked
2 months ago
Abstract
Given a metric space $\mathcal{M}=(X,δ)$, a weighted graph $G$ over $X$ is a metric $t$-spanner of $\mathcal{M}$ if for every $u,v \in X$, $δ(u,v)\le d_G(u,v)\le t\cdot δ(u,v)$, where $d_G$ is the shortest path metric in $G$. In this paper, we construct spanners for finite sets in metric spaces in the online setting. Here, we are given a sequence of points $(s_1, \ldots, s_n)$, where the points are presented one at a time (i.e., after $i$ steps, we saw $S_i = \{s_1, \ldots , s_i\}$). The algorithm is allowed to add edges to the spanner when a new point arrives, however, it is not allowed to remove any edge from the spanner. The goal is to maintain a $t$-spanner $G_i$ for $S_i$ for all $i$, while minimizing the number of edges, and their total weight. We construct online $(1+\varepsilon)$-spanners in Euclidean $d$-space, $(2k-1)(1+\varepsilon)$-spanners for general metrics, and $(2+\varepsilon)$-spanners for ultrametrics. Most notably, in Euclidean plane, we construct a $(1+\varepsilon)$-spanner with competitive ratio $O(\varepsilon^{-3/2}\log\varepsilon^{-1}\log n)$, bypassing the classic lower bound $Ω(\varepsilon^{-2})$ for lightness, which compares the weight of the spanner, to that of the MST.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
📜 Similar Papers
In the same crypt — Computational Geometry
R.I.P.
👻
Ghosted
R.I.P.
👻
Ghosted
Dynamic Planar Convex Hull
R.I.P.
👻
Ghosted
TEMPO: Feature-Endowed Teichmüller Extremal Mappings of Point Clouds
R.I.P.
👻
Ghosted
Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature Visualization
R.I.P.
👻
Ghosted
Coresets for Clustering in Euclidean Spaces: Importance Sampling is Nearly Optimal
R.I.P.
👻
Ghosted
Momen(e)t: Flavor the Moments in Learning to Classify Shapes
Died the same way — 👻 Ghosted
R.I.P.
👻
Ghosted
Language Models are Few-Shot Learners
R.I.P.
👻
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
👻
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
👻
Ghosted