Polynomial convolutions and (finite) free probability
Adam W. Marcus
TL;DR
The paper develops finite free probability by embedding polynomial convolutions into a finite-dimensional framework and introducing the U transform to connect finite-root data with classical independence. It defines finite freeness and shows how both additive and multiplicative finite convolutions approximate Voiculescu’s free convolutions in the large-d limit, while remaining computable at finite d. The work derives finite counterparts to constant, Gaussian, Poisson, and compound Poisson laws and proves associated limit theorems (law of large numbers, central limit theorem, Poisson limit) within this finite setting, along with majorization results and applications to restricted invertibility. By uniting hyperbolic-polynomial techniques with determinant-based polynomial convolutions, the paper provides a practical bridge between combinatorics, random matrix theory, and free probability with potential impact on graph theory and quantum information.
Abstract
We introduce a finite version of free probability and show the link between recent results using polynomial convolutions and the traditional theory of free probability. One tool for accomplishing this is a seemingly new transformation that allows one to reduce computations in our new theory to computations using classically independent random variables. We then explore the idea of finite freeness and its implications. Lastly, we show applications of the new theory by deriving the finite versions of some well-known free distributions and then proving their associated limit laws directly. In the process, we gain a number of insights into the behavior of convolutions in traditional free probability that seem to get lost when the operators being convolved are no longer finite. This version contains the original preprint from 2016 as well as an extra section (Section 5.2) where we use finite freeness to prove majorization relations on certain convolution.