Finite Free Point Processes

TL;DR

This paper introduces a new matrix process analyzed using finite free probability, revealing more general behavior than standard random matrix models and demonstrating the effectiveness of expected characteristic polynomials in understanding eigenvalue-related properties.

Contribution

The paper develops a novel matrix process framework using finite free probability, extending analysis beyond classical models like Hermite, Laguerre, and Jacobi, and demonstrates its potential through simulations.

Findings

The new process exhibits behaviors not seen in standard models.

Expected characteristic polynomials effectively analyze the process.

Simulation results support the theoretical findings.

Abstract

We use techniques from finite free probability to analyze matrix processes related to eigenvalues, singular values, and generalized singular values of random matrices. The models we use are quite basic and the analysis consists entirely of expected characteristic polynomials. A number of our results match known results in random matrix theory, however our main result (regarding generalized singular values) seems to be more general than any of the standard random matrix processes (Hermite/Laguerre/Jacobi) in the field. To test this, we perform a series of simulations of this new process that, on the one hand, confirms that this process can exhibit behavior not seen in the standard random matrix processes, but on the other hand provides evidence that the true behavior is captured quite well by our techniques. This, coupled with the fact that we are able to compute the same statistics for this new model that we are for the standard models, suggests that further investigation could be both interesting and fruitful.