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A Generalized Central Limit Conjecture for Convex Bodies

Functional Analysis 2019-10-01 v1 Metric Geometry Probability

Abstract

The central limit theorem for convex bodies says that with high probability the marginal of an isotropic log-concave distribution along a random direction is close to a Gaussian, with the quantitative difference determined asymptotically by the Cheeger/Poincare/KLS constant. Here we propose a generalized CLT for marginals along random directions drawn from any isotropic log-concave distribution; namely, for x,yx,y drawn independently from isotropic log-concave densities p,qp,q, the random variable x,y\langle x,y\rangle is close to Gaussian. Our main result is that this generalized CLT is quantitatively equivalent (up to a small factor) to the KLS conjecture. Any polynomial improvement in the current KLS bound of n1/4n^{1/4} in Rn\mathbb{R}^n implies the generalized CLT, and vice versa. This tight connection suggests that the generalized CLT might provide insight into basic open questions in asymptotic convex geometry.

Keywords

Cite

@article{arxiv.1909.13127,
  title  = {A Generalized Central Limit Conjecture for Convex Bodies},
  author = {Haotian Jiang and Yin Tat Lee and Santosh S. Vempala},
  journal= {arXiv preprint arXiv:1909.13127},
  year   = {2019}
}
R2 v1 2026-06-23T11:29:05.773Z