Light Spanners for High Dimensional Norms via Stochastic Decompositions
Arnold Filtser, Ofer Neiman

TL;DR
This paper develops new light and sparse spanners for high-dimensional normed spaces, especially for subsets of lp spaces, using stochastic decompositions to improve upon previous bounds and handle various metric families.
Contribution
It introduces a method to construct light, sparse spanners in high-dimensional normed spaces based on stochastic decompositions, extending results to broader metric families.
Findings
Constructed lp space spanners with near-optimal size and lightness.
Established a tradeoff between decomposability parameters and spanner lightness.
Reduced exponential dependency on dimension for certain metric families.
Abstract
Spanners for low dimensional spaces (e.g. Euclidean space of constant dimension, or doubling metrics) are well understood. This lies in contrast to the situation in high dimensional spaces, where except for the work of Har-Peled, Indyk and Sidiropoulos (SODA 2013), who showed that any -point Euclidean metric has an -spanner with edges, little is known. In this paper we study several aspects of spanners in high dimensional normed spaces. First, we build spanners for finite subsets of with . Second, our construction yields a spanner which is both sparse and also {\em light}, i.e., its total weight is not much larger than that of the minimum spanning tree. In particular, we show that any -point subset of for has an -spanner with edges and lightness . In fact,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
