On Closest Pair in Euclidean Metric: Monochromatic is as Hard as Bichromatic

Given a set of $n$ points in $\mathbb R^d$, the (monochromatic) Closest Pair problem asks to find a pair of distinct points in the set that are closest in the $\ellp$-metric. Closest Pair is a fundamental problem in Computational Geometry and understanding its fine-grained complexity in the Euclidean metric when $d=\omega(\log n)$ was raised as an open question in recent works (Abboud-Rubinstein-Williams [FOCS'17], Williams [SODA'18], David-Karthik-Laekhanukit [SoCG'18]). In this paper, we show that for every $p\in\mathbb R{\ge 1}\cup{0}$, under the Strong Exponential Time Hypothesis (SETH), for every $\varepsilon>0$, the following holds: $\bullet$ No algorithm running in time $O(n^{{2-\varepsilon})$} can solve the Closest Pair problem in $d=(\log n)^{{\Omega_{\varepsilon}(1)}$} dimensions in the $\ellp$-metric. $\bullet$ There exists $\delta = \delta(\varepsilon)>0$ and $c = c(\varepsilon)\ge 1$ such that no algorithm running in time $O(n^{{1.5-\varepsilon})$} can approximate Closest Pair problem to a factor of $(1+\delta)$ in $d\ge c\log n$ dimensions in the $\ellp$-metric. At the heart of all our proofs is the construction of a dense bipartite graph with low contact dimension, i.e., we construct a balanced bipartite graph on $n$ vertices with $n^{{2-\varepsilon}$} edges whose vertices can be realized as points in a $(\log n)^{{\Omega_\varepsilon(1)}$-dimensional} Euclidean space such that every pair of vertices which have an edge in the graph are at distance exactly 1 and every other pair of vertices are at distance greater than 1. This graph construction is inspired by the construction of locally dense codes introduced by Dumer-Miccancio-Sudan [IEEE Trans. Inf. Theory'03].