# $\delta$-Greedy $t$-spanner

## TL;DR

The paper introduces the $
abla$-Greedy geometric spanner, a flexible and efficient construction that generalizes existing spanners, achieving near-optimal properties with improved construction time, especially for random point sets.

## Contribution

It presents the $
abla$-Greedy spanner, a new algorithm that generalizes Path-Greedy and Gap-Greedy spanners, offering improved construction efficiency and comparable properties.

## Key findings

- Constructs $
abla$-Greedy spanner in $O(n^2 ext{log} n)$ time for general sets.
- Expected construction time is $O(n ext{log} n)$ for random point sets.
- $
abla$-Greedy spanner matches Path-Greedy properties with fewer shortest path queries.

## Abstract

We introduce a new geometric spanner, $\delta$-Greedy, whose construction is based on a generalization of the known Path-Greedy and Gap-Greedy spanners. The $\delta$-Greedy spanner combines the most desirable properties of geometric spanners both in theory and in practice. More specifically, it has the same theoretical and practical properties as the Path-Greedy spanner: a natural definition, small degree, linear number of edges, low weight, and strong $(1+\varepsilon)$-spanner for every $\varepsilon>0$. The $\delta$-Greedy algorithm is an improvement over the Path-Greedy algorithm with respect to the number of shortest path queries and hence with respect to its construction time. We show how to construct such a spanner for a set of $n$ points in the plane in $O(n^2 \log n)$ time. The $\delta$-Greedy spanner has an additional parameter, $\delta$, which indicates how close it is to the Path-Greedy spanner on the account of the number of shortest path queries. For $\delta = t$ the output spanner is identical to the Path-Greedy spanner, while the number of shortest path queries is, in practice, linear. Finally, we show that for a set of $n$ points placed independently at random in a unit square the expected construction time of the $\delta$-Greedy algorithm is $O(n \log n)$. Our analysis indicates that the $\delta$-Greedy spanner gives the best results among the known spanners of expected $O(n \log n)$ time for random point sets. Moreover, the analysis implies that by setting $\delta = t$, the $\delta$-Greedy algorithm provides a spanner identical to the Path-Greedy spanner in expected $O(n \log n)$ time.