Hyperbolic Metric Spaces and Stochastic Embeddings
Chris Gartland

TL;DR
This paper develops the theory of stochastic embeddings of metric spaces into trees, exploring their implications for Lipschitz free spaces and hyperbolic spaces, with applications to geometric group theory and functional analysis.
Contribution
It extends stochastic embedding theory to infinite metric spaces, linking embeddings into trees with properties of Lipschitz free spaces and hyperbolic groups.
Findings
Proper metric spaces embedding into $\\mathbb{R}$-trees have Lipschitz free spaces isomorphic to $L^1$.
Snowflakes of finite Nagata-dimensional spaces embed into ultrametrics with Lipschitz free spaces isomorphic to $\ell^1$.
Hyperbolic $n$-spaces have Lipschitz free spaces isomorphic to Euclidean $n$-spaces.
Abstract
Stochastic embeddings of finite metric spaces into graph-theoretic trees have proven to be a vital tool for constructing approximation algorithms in theoretical computer science. In the present work, we build out some of the basic theory of stochastic embeddings in the infinite setting with an aim towards applications to Lipschitz free space theory. We prove that proper metric spaces stochastically embedding into -trees have Lipschitz free spaces isomorphic to -spaces. We then undergo a systematic study of stochastic embeddability of Gromov hyperbolic metric spaces into -trees by way of stochastic embeddability of their boundaries into ultrametric spaces. The following are obtained as our main results: (1) Every snowflake of a compact, finite Nagata-dimensional metric space stochastically embeds into an ultrametric space and has Lipschitz free space…
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Taxonomy
Topicsadvanced mathematical theories · Computational Geometry and Mesh Generation · Mathematical Dynamics and Fractals
