Optimal quantization with branched optimal transport distances

TL;DR

This paper introduces a new branched optimal transport approach to optimal quantization, analyzing asymptotic behavior, limit distributions, and regularity properties of optimal measures for large N.

Contribution

It develops a branched transport version of optimal quantization, including asymptotic analysis, limit distribution results, and regularity bounds, extending classical optimal transport theory.

Findings

Derived the limit distribution of point clouds in branched transport quantization

Established a branched transport analog of Zador's theorem

Proved uniform bounds on separation and covering radius for optimal quantizers

Abstract

We consider the problem of optimal approximation of a target measure by an atomic measure with $N$ atoms, in branched optimal transport distance. This is a new branched transport version of optimal quantization problems. New difficulties arise, since in classical semi-discrete optimal transport with Wasserstein distance, the interfaces between cells associated with neighboring atoms have Voronoi structure and satisfy an explicit description. This description is missing for our problem, in which the cell interfaces are thought to have fractal boundary. We study the asymptotic behaviour of optimal quantizers for absolutely continuous measures as the number $N$ of atoms grows to infinity. We compute the limit distribution of the corresponding point clouds and show in particular a branched transport version of Zador's theorem. Moreover, we establish uniformity bounds of optimal quantizers in terms of separation distance and covering radius of the atoms, when the measure is $d$-Ahlfors regular. A crucial technical tool is the uniform in $N$ H\"older regularity of the landscape function, a branched transport analog to Kantorovich potentials in classical optimal transport.