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Jul 10 – 14, 2023
Heinz Nixdorf MuseumsForum (HNF)
Europe/Berlin timezone

On the Fine-Grained Complexity of Small-Size Geometric Set Cover and Discrete $k$-Center for Small $k$

Jul 12, 2023, 10:30 AM
20m
Seminar Room 4 (HNF)

Seminar Room 4

HNF

Speaker

Timothy M. Chan

Description

Timothy M. Chan, Qizheng He and Yuancheng Yu

Abstract: We study the time complexity of the discrete $k$-center problem and related (exact) geometric set cover problems when $k$ or the size of the cover is small. We obtain a plethora of new results:

  • We give the first subquadratic algorithm for \emph{rectilinear discrete 3-center} in 2D, running in $\tilde{O}(n^{3/2})$ time.
  • We prove a lower bound of $\Omega(n^{4/3-\delta})$ for rectilinear discrete 3-center in 4D, for any constant $\delta>0$, under a standard hypothesis about triangle detection in sparse graphs.
  • Given $n$ points and $n$ \emph{weighted} axis-aligned unit squares in 2D, we give the first subquadratic algorithm for finding a minimum-weight cover of the points by 3 unit squares, running in $\tilde{O}(n^{8/5})$ time. We also prove a lower bound of $\Omega(n^{3/2-\delta})$ for the same problem in 2D, under the well-known APSP Hypothesis. For arbitrary axis-aligned rectangles in 2D, our upper bound is $\tilde{O}(n^{7/4})$.
  • We prove a lower bound of $\Omega(n^{2-\delta})$ for Euclidean discrete 2-center in 13D, under the Hyperclique Hypothesis. This lower bound nearly matches the straightforward upper bound of $\tilde{O}(n^\omega)$, if the matrix multiplication exponent $\omega$ is equal to 2.
  • We similarly prove an $\Omega(n^{k-\delta})$ lower bound for Euclidean discrete $k$-center in $O(k)$ dimensions for any constant $k\ge 3$, under the Hyperclique Hypothesis. This lower bound again nearly matches known upper bounds if $\omega=2$.
  • We also prove an $\Omega(n^{2-\delta})$ lower bound for the problem of finding 2 boxes to cover the largest number of points, given $n$ points and $n$ boxes in 12D\@. This matches the straightforward near-quadratic upper bound.

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