English

Markov type and threshold embeddings

Metric Geometry 2013-09-24 v5 Functional Analysis Probability

Abstract

For two metric spaces X and Y, say that X {threshold-embeds} into Y if there exist a number K > 0 and a family of Lipschitz maps fτ:XY:τ>0}f_{\tau} : X \to Y : \tau > 0 \} such that for every x,yXx,y \in X, dX(x,y)τ=>dY(fτ(x),fτ(y))φτ\Lipτ/K d_X(x,y) \geq \tau => d_Y(f_{\tau}(x),f_{\tau}(y)) \geq \|\varphi_{\tau}\|_{\Lip} \tau/K where fτ\Lip\|f_{\tau}\|_{\Lip} denotes the Lipschitz constant of fτf_{\tau}. We show that if a metric space X threshold-embeds into a Hilbert space, then X has Markov type 2. As a consequence, planar graph metrics and doubling metrics have Markov type 2, answering questions of Naor, Peres, Schramm, and Sheffield. More generally, if a metric space X threshold-embeds into a p-uniformly smooth Banach space, then X has Markov type p. This suggests some non-linear analogs of Kwapien's theorem. For instance, a subset XL1X \subseteq L_1 threshold-embeds into Hilbert space if and only if X has Markov type 2.

Keywords

Cite

@article{arxiv.1208.6088,
  title  = {Markov type and threshold embeddings},
  author = {Jian Ding and James R. Lee and Yuval Peres},
  journal= {arXiv preprint arXiv:1208.6088},
  year   = {2013}
}
R2 v1 2026-06-21T21:57:10.678Z