Multipacking in Euclidean Metric Space

TL;DR

This paper investigates the multipacking problem in Euclidean space, establishing polynomial-time algorithms for 1-multipacking, proving NP-hardness for 2-multipacking, and offering approximation and parameterized solutions.

Contribution

It introduces the multipacking problem in geometric point sets, analyzes computational complexity, and provides algorithms for different multipacking variants.

Findings

Maximum 1-multipacking is computable in polynomial time.

Maximum 2-multipacking is NP-hard.

Approximation and parameterized algorithms are proposed for 2-multipacking.

Abstract

Here we study the multipacking problems for geometric point sets with respect to their Euclidean distances. We consider a set of $n$ points $P$ and define $N_s[v]$ as the subset of $P$ that includes the $s$ nearest points of $v \in P$ and the point $v$ itself. We assume that the \emph{$s$-th neighbor} of each point is unique, for every $s \in \{0, 1, 2, \dots , n-1\}$. For a natural number $r \leq n-1$, an $r$-multipacking is a set $ M \subseteq P $ such that for each point $ v \in P $ and for every integer $ 1\leq s \leq r $, $|N_s[v]\cap M|\leq (s+1)/2$. The $r$-multipacking number of $ P $ is the maximum cardinality of an $r$-multipacking of $ P $ and is denoted by $ \MP_{r}(P) $. For $r=n-1$, an $r$-multipacking is called a multipacking and $r$-multipacking number is called as multipacking number. For $r=1 \text{ and } 2$, we study the problem of computing a maximum $r$-multipacking of the point sets in $\mathbb{R}^2$. We show that a maximum $1$-multipacking can be computed in polynomial time but computing a maximum $2$-multipacking is \textsc{NP-hard}. Further, we provide approximation and parameterized solutions to the $2$-multipacking problem.