Fast Approximation Algorithms for Piercing Boxes by Points

\newcommand{\popt}{{\mathcal{p}}} \newcommand{\Re}{\mathbb{R}}\newcommand{\N}{{\mathcal{N}}} \newcommand{\BX}{\mathcal{B}} \newcommand{\bb}{\mathsf{b}} \newcommand{\eps}{\varepsilon} \newcommand{\polylog}{\mathrm{polylog}} Let $\mathcal{B}=\{\mathsf{b}_1, \ldots ,\mathsf{b}_n\}$ be a set of $n$ axis-aligned boxes in $\Re^d$ where $d\geq2$ is a constant. The \emph{piercing problem} is to compute a smallest set of points $\N \subset \Re^d$ that hits every box in $\mathcal{B}$, i.e., $\N\cap \mathsf{b}_i\neq \emptyset$, for $i=1,\ldots, n$. Let $\popt=\popt(\mathcal{B})$, the \emph{piercing number} be the minimum size of a piercing set of $\mathcal{B}$. We present a randomized $O(d^2\log\log \popt)$-approximation algorithm with expected running time $O(n^{d/2}\polylog n)$. Next, we present a faster $O(n^{\log d+1})$-time algorithm but with a slightly inferior approximation factor of $O(2^{4d}\log\log\popt)$. The running time of both algorithms can be improved to near-linear using a sampling-based technique, if $\popt = O(n^{1/d})$. For the dynamic version of the problem in the plane, we obtain a randomized $O(\log\log\popt)$-approximation algorithm with $O(n^{1/2}\polylog n )$ amortized expected update time for insertion or deletion of boxes. For squares in $\Re^2$, the update time can be improved to $O(n^{1/3}\polylog n )$.