Covering large-dimensional Euclidean spaces by random translates of a given convex body
Boris Bukh, Jun Gao, Xizhi Liu, Oleg Pikhurko, Shumin Sun

TL;DR
This paper constructs high-density Euclidean space coverings with controlled multiplicity, improving bounds on covering density and multiplicity, and analyzes the limitations of existing random covering methods.
Contribution
It demonstrates the existence of near-optimal coverings with bounded multiplicity and shows the failure of current random methods below a certain density threshold.
Findings
Achieves coverings with density (1/2+o(1))n log n and multiplicity at most (1.79556+o(1))n log n.
Proves existing random covering methods cannot reach densities below (1/2+o(1))n log n.
Identifies cubes as the worst-case convex bodies for random covering densities.
Abstract
Determining the minimum density of a covering of by Euclidean unit balls as is a major open problem, with the best known results being the lower bound of by Coxeter, Few and Rogers [Mathematika 6, 1959] and the upper bound of by Dumer [Discrete Comput. Geom. 38, 2007]. We prove that there are ball coverings of attaining the asymptotically best known density such that, additionally, every point of is covered at most times. This strengthens the result of Erd\H{o}s and Rogers [Acta Arith. 7, 1961/62] who had the maximum multiplicity at most . On the other hand, we show that the method that was used for the best known ball coverings (when one…
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