Strong law of large numbers for the $L^1$-Karcher mean

TL;DR

This paper proves a generalized strong law of large numbers for the $L^1$-Karcher mean in Banach-Finsler spaces, extending previous results to infinite-dimensional operators using a stochastic resolvent flow approach.

Contribution

It establishes the $L^1$-form of Sturm's strong law and Holbrook's

Findings

Proves the $L^1$-strong law of large numbers for the Karcher mean in Banach-Finsler spaces.

Develops a stochastic resolvent flow for the Karcher barycenter.

Extends convergence results to infinite-dimensional operator settings.

Abstract

Sturm's strong law of large numbers in $\mathrm{CAT}(0)$ spaces has been an influential tool to study the geometric mean or also called Karcher barycenter of positive definite matrices. It provides an easily computable stochastic approximation based on inductive means. Convergence of a deterministic version of this approximation has been proved by Holbrook, providing his "nodice" theorem for the Karcher mean of positive definite matrices. The Karcher mean has also been extended to the infinite dimensional case of positive operators on a Hilbert space by Lawson-Lim and then to probability measures with bounded support by the second author, however the $\mathrm{CAT}(0)$ property of the space is lost and one defines the mean as the unique solution of a nonlinear operator equation on a convex Banach-Finsler manifold. The formulations of Sturm's strong law of large numbers and Holbrook's "nodice" approximation are natural and both conjectured to converge, however all previous techniques of their proofs break down, due to the Banach-Finsler nature of the space. In this paper we prove both conjectures by establishing the most general $L^1$-form of Sturm's strong law of large numbers and Holbrook's "nodice" theorem in the operator norm by developing a stochastic discrete-time resolvent flow for the Karcher barycenter using its Wasserstein contraction property.