Mean width of regular polytopes and expected maxima of correlated Gaussian variables
Zakhar Kabluchko, Alexander E. Litvak, and Dmitry Zaporozhets

TL;DR
This paper investigates the geometric properties of regular polytopes and their probabilistic interpretations, deriving asymptotic results, moments, and distributional limits for the maxima of Gaussian variables and projections of polytopes.
Contribution
It establishes new bounds and asymptotic behaviors for the mean width of regular polytopes and the expected maxima of correlated Gaussian variables, including moments and limit distributions.
Findings
Asymptotic version of the maximal mean width conjecture.
Close comparison of mean widths of regular simplex and crosspolytope.
Distributional limit theorems for projections of high-dimensional polytopes.
Abstract
An old conjecture states that among all simplices inscribed in the unit sphere the regular one has the maximal mean width. An equivalent formulation is that for any centered Gaussian vector satisfying one has where are independent standard Gaussian variables. Using this probabilistic interpretation we derive an asymptotic version of the conjecture. We also show that the mean width of the regular simplex with vertices is remarkably close to the mean width of the regular crosspolytope with the same number of vertices. Interpreted probabilistically, our result states that $$ 1\leq\frac{\mathbb E\,\max\{|\eta_1|,\dots,|\eta_n|\}}{\mathbb E\,\max\{\eta_1,\dots,\eta_{2n}\}}…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
