The Expanding Universe of the Geometric Mean

TL;DR

This paper reviews the recent development of the matrix geometric mean, extending from two-variable cases to multivariable, operator, and probabilistic settings, highlighting geometric and curvature-based insights.

Contribution

It provides an accessible overview of the expanding theory of the matrix geometric mean across various mathematical frameworks and introduces barycentric maps related to probability measures.

Findings

Development of multivariable matrix geometric mean

Extension to positive cones of unital C*-algebras

Connection with nonpositive curvature geometry

Abstract

In this paper the authors seek to trace in an accessible fashion the rapid recent development of the theory of the matrix geometric mean in the cone of positive definite matrices up through the closely related operator geometric mean in the positive cone of a unital $C^*$-algebra. The story begins with the two-variable matrix geometric mean, moves to the breakthrough developments in the multivariable matrix setting, the main focus of the paper, and then on to the extension to the positive cone of the $C^*$-algebra of operators on a Hilbert space, even to general unital $C^*$-algebras, and finally to the consideration of barycentric maps that grow out of the geometric mean on the space of integrable probability measures on the positive cone. Besides expected tools from linear algebra and operator theory, one observes a surprisingly substantial interplay with geometrical notions in metric spaces, particularly the notion of nonpositive curvature. Added features include a glance at the probabilistic theory of random variables with values in a metric space of nonpositive curvature, and the appearance of related means such as the inductive and power means.