English

Light, Reliable Spanners

Data Structures and Algorithms 2023-08-01 v1 Computational Geometry

Abstract

A \emph{ν\nu-reliable spanner} of a metric space (X,d)(X,d), is a (dominating) graph HH, such that for any possible failure set BXB\subseteq X, there is a set B+B^+ just slightly larger B+(1+ν)B|B^+|\le(1+\nu)\cdot|B|, and all distances between pairs in XB+X\setminus B^+ are (approximately) preserved in HBH\setminus B. Recently, there have been several works on sparse reliable spanners in various settings, but so far, the weight of such spanners has not been analyzed at all. In this work, we initiate the study of \emph{light} reliable spanners, whose weight is proportional to that of the Minimum Spanning Tree (MST) of XX. We first observe that unlike sparsity, the lightness of any deterministic reliable spanner is huge, even for the metric of the simple path graph. Therefore, randomness must be used: an \emph{oblivious} reliable spanner is a distribution over spanners, and the bound on B+|B^+| holds in expectation. We devise an oblivious ν\nu-reliable (2+2k1)(2+\frac{2}{k-1})-spanner for any kk-HST, whose lightness is ν2\approx \nu^{-2}. We demonstrate a matching Ω(ν2)\Omega(\nu^{-2}) lower bound on the lightness (for any finite stretch). We also note that any stretch below 2 must incur linear lightness. For general metrics, doubling metrics, and metrics arising from minor-free graphs, we construct {\em light} tree covers, in which every tree is a kk-HST of low weight. Combining these covers with our results for kk-HSTs, we obtain oblivious reliable light spanners for these metric spaces, with nearly optimal parameters. In particular, for doubling metrics we get an oblivious ν\nu-reliable (1+ε)(1+\varepsilon)-spanner with lightness εO(ddim)O~(ν2logn)\varepsilon^{-O({\rm ddim})}\cdot\tilde{O}(\nu^{-2}\cdot\log n), which is best possible (up to lower order terms).

Keywords

Cite

@article{arxiv.2307.16612,
  title  = {Light, Reliable Spanners},
  author = {Arnold Filtser and Yuval Gitlitz and Ofer Neiman},
  journal= {arXiv preprint arXiv:2307.16612},
  year   = {2023}
}
R2 v1 2026-06-28T11:44:20.963Z