Euclidean embedding, randomized clustering, and Lipschitz extension for finite and doubling subsets of $L_p$ when $p>2$
Assaf Naor, Kevin Ren

TL;DR
This paper establishes improved bounds on Euclidean distortion and separation modulus for finite subsets of $L_p$ spaces when $p>2$, leading to new Lipschitz extension results.
Contribution
It provides the first asymptotic bounds on Euclidean distortion and separation modulus for subsets of $L_p$ with $p>2$, and derives new Lipschitz extension theorems.
Findings
Euclidean distortion of $n$-point subsets of $L_p$ is $p^3(rac{1}{2}+o(1)) ext{log}^{1/2} n$
Separation modulus of $n$-point subsets of $L_p$ is $O(p^2 ext{log}^{1/2} n)$, sharp up to $p$ dependence
Existence of Lipschitz extensions with controlled Lipschitz constant for subsets of $L_p$ into arbitrary Banach spaces
Abstract
Fix . We prove that the Euclidean distortion of every -point subset of is , thus, in particular, demonstrating that all -point subsets of exhibit an asymptotic improvement over the Euclidean distortion guarantee that Bourgain's embedding theorem provides for arbitrary -point metric spaces. We also prove that the separation modulus of every -point subset of is , which is sharp up to the dependence on . We deduce from (a refinement of) this asymptotic evaluation of the finitary separation modulus of that for any -point subset of , any Banach space , and any -Lipschitz function , there exists a -Lipschitz function that extends . We obtain analogous separation and extension…
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.
Taxonomy
TopicsAdvanced Mathematical Modeling in Engineering
