Metric dimension reduction modulus for superlogarithmic distortion
Abstract
The metric dimension reduction modulus is the smallest such that every --point metric space can be embedded into some -dimensional normed space, with bi--Lipschitz distortion at most . Determining sharp asymptotics for is a fundamental task in metric geometry, with bearing particular interest. A line of advances over the past decades has led to an upper bound on for , but a matching lower bound has remained open. We close this gap, establishing: for every fixed , k^{\alpha}_n(\ell_\infty) =\Theta\bigg(\frac{\log n}{\log(\frac{\alpha}{\log n}+1)}\bigg)\quad \mbox{for every $\alpha\geq \beta \log n$}. This resolves a question from Naor's 2018 ICM plenary lecture. Our result is obtained by characterizing the minimum dimension for which, with high probability, a random regular graph admits an --embedding into some --dimensional normed space.
Cite
@article{arxiv.2507.02785,
title = {Metric dimension reduction modulus for superlogarithmic distortion},
author = {Dylan J. Altschuler and Konstantin Tikhomirov},
journal= {arXiv preprint arXiv:2507.02785},
year = {2025}
}
Comments
Significant revision: our results now include the superlogarithmic distortion regime in addition to the logarithmic regime. The proof is also simplified