In this paper, we study the online Euclidean spanners problem for points in $mathbb{R}^d$. Suppose we are given a sequence of $n$ points $(s_1,s_2,ldots, s_n)$ in $mathbb{R}^d$, where point $s_i$ is presented in step~$i$ for $i=1,ldots, n$. The objec
tive of an online algorithm is to maintain a geometric $t$-spanner on $S_i={s_1,ldots, s_i}$ for each step~$i$. First, we establish a lower bound of $Omega(varepsilon^{-1}log n / log varepsilon^{-1})$ for the competitive ratio of any online $(1+varepsilon)$-spanner algorithm, for a sequence of $n$ points in 1-dimension. We show that this bound is tight, and there is an online algorithm that can maintain a $(1+varepsilon)$-spanner with competitive ratio $O(varepsilon^{-1}log n / log varepsilon^{-1})$. Next, we design online algorithms for sequences of points in $mathbb{R}^d$, for any constant $dge 2$, under the $L_2$ norm. We show that previously known incremental algorithms achieve a competitive ratio $O(varepsilon^{-(d+1)}log n)$. However, if the algorithm is allowed to use additional points (Steiner points), then it is possible to substantially improve the competitive ratio in terms of $varepsilon$. We describe an online Steiner $(1+varepsilon)$-spanner algorithm with competitive ratio $O(varepsilon^{(1-d)/2} log n)$. As a counterpart, we show that the dependence on $n$ cannot be eliminated in dimensions $d ge 2$. In particular, we prove that any online spanner algorithm for a sequence of $n$ points in $mathbb{R}^d$ under the $L_2$ norm has competitive ratio $Omega(f(n))$, where $lim_{nrightarrow infty}f(n)=infty$. Finally, we provide improved lower bounds under the $L_1$ norm: $Omega(varepsilon^{-2}/log varepsilon^{-1})$ in the plane and $Omega(varepsilon^{-d})$ in $mathbb{R}^d$ for $dgeq 3$.
Lightness is a fundamental parameter for Euclidean spanners; it is the ratio of the spanner weight to the weight of the minimum spanning tree of a finite set of points in $mathbb{R}^d$. In a recent breakthrough, Le and Solomon (2019) established the
precise dependencies on $varepsilon>0$ and $din mathbb{N}$ of the minimum lightness of $(1+varepsilon)$-spanners, and observed that additional Steiner points can substantially improve the lightness. Le and Solomon (2020) constructed Steiner $(1+varepsilon)$-spanners of lightness $O(varepsilon^{-1}logDelta)$ in the plane, where $Deltageq Omega(sqrt{n})$ is the emph{spread} of the point set, defined as the ratio between the maximum and minimum distance between a pair of points. They also constructed spanners of lightness $tilde{O}(varepsilon^{-(d+1)/2})$ in dimensions $dgeq 3$. Recently, Bhore and T{o}th (2020) established a lower bound of $Omega(varepsilon^{-d/2})$ for the lightness of Steiner $(1+varepsilon)$-spanners in $mathbb{R}^d$, for $dge 2$. The central open problem in this area is to close the gap between the lower and upper bounds in all dimensions $dgeq 2$. In this work, we show that for every finite set of points in the plane and every $varepsilon>0$, there exists a Euclidean Steiner $(1+varepsilon)$-spanner of lightness $O(varepsilon^{-1})$; this matches the lower bound for $d=2$. We generalize the notion of shallow light trees, which may be of independent interest, and use directional spanners and a modified window partitioning scheme to achieve a tight weight analysis.
Lightness and sparsity are two natural parameters for Euclidean $(1+varepsilon)$-spanners. Classical results show that, when the dimension $din mathbb{N}$ and $varepsilon>0$ are constant, every set $S$ of $n$ points in $d$-space admits an $(1+varepsi
lon)$-spanners with $O(n)$ edges and weight proportional to that of the Euclidean MST of $S$. Tight bounds on the dependence on $varepsilon>0$ for constant $din mathbb{N}$ have been established only recently. Le and Solomon (FOCS 2019) showed that Steiner points can substantially improve the lightness and sparsity of a $(1+varepsilon)$-spanner. They gave upper bounds of $tilde{O}(varepsilon^{-(d+1)/2})$ for the minimum lightness in dimensions $dgeq 3$, and $tilde{O}(varepsilon^{-(d-1))/2})$ for the minimum sparsity in $d$-space for all $dgeq 1$. They obtained lower bounds only in the plane ($d=2$). Le and Solomon (ESA 2020) also constructed Steiner $(1+varepsilon)$-spanners of lightness $O(varepsilon^{-1}logDelta)$ in the plane, where $Deltain Omega(sqrt{n})$ is the emph{spread} of $S$, defined as the ratio between the maximum and minimum distance between a pair of points. In this work, we improve several bounds on the lightness and sparsity of Euclidean Steiner $(1+varepsilon)$-spanners. Using a new geometric analysis, we establish lower bounds of $Omega(varepsilon^{-d/2})$ for the lightness and $Omega(varepsilon^{-(d-1)/2})$ for the sparsity of such spanners in Euclidean $d$-space for all $dgeq 2$. We use the geometric insight from our lower bound analysis to construct Steiner $(1+varepsilon)$-spanners of lightness $O(varepsilon^{-1}log n)$ for $n$ points in Euclidean plane.
This is the arXiv index for the electronic proceedings of GD 2019, which contains the peer-reviewed and revised accepted papers with an optional appendix. Proceedings (without appendices) are also to be published by Springer in the Lecture Notes in Computer Science series.
The classical measure of similarity between two polygonal chains in Euclidean space is the Frechet distance, which corresponds to the coordinated motion of two mobile agents along the chains while minimizing their maximum distance. As computing the F
rechet distance takes near-quadratic time under the Strong Exponential Time Hypothesis (SETH), we explore two new distance measures, called rock climber distance and $k$-station distance, in which the agents move alternately in their coordinated motion that traverses the polygonal chains. We show that the new variants are equivalent to the Frechet or the Hausdorff distance if the number of moves is unlimited. When the number of moves is limited to a given parameter $k$, we show that it is NP-hard to determine the distance between two curves. We also describe a 2-approximation algorithm to find the minimum $k$ for which the distance drops below a given threshold.
