A Riemann--Hilbert approach to the perturbation theory for orthogonal polynomials: Applications to numerical linear algebra and random matrix theory

TL;DR

This paper introduces a novel Riemann--Hilbert based perturbation theory for orthogonal polynomials, enabling precise analysis of numerical algorithms and random matrix models with explicit formulas and stability as polynomial degree grows.

Contribution

It develops a new Riemann--Hilbert approach for perturbation analysis of orthogonal polynomials, with applications to numerical linear algebra and random matrix theory, including explicit expansion formulas and stability results.

Findings

Effective comparison of orthogonal polynomials via Stieltjes transforms.

Explicit and accurate expansion formulas for perturbed polynomials.

Precise estimates and a new mesoscopic CLT for algorithms on random matrices.

Abstract

We establish a new perturbation theory for orthogonal polynomials using a Riemann--Hilbert approach and consider applications in numerical linear algebra and random matrix theory. This new approach shows that the orthogonal polynomials with respect to two measures can be effectively compared using the difference of their Stieltjes transforms on a suitably chosen contour. Moreover, when two measures are close and satisfy some regularity conditions, we use the theta functions of a hyperelliptic Riemann surface to derive explicit and accurate expansion formulae for the perturbed orthogonal polynomials. In contrast to other approaches, a key strength of the methodology is that estimates can remain valid as the degree of the polynomial grows. The results are applied to analyze several numerical algorithms from linear algebra, including the Lanczos tridiagonalization procedure, the Cholesky factorization and the conjugate gradient algorithm. As a case study, we investigate these algorithms applied to a general spiked sample covariance matrix model by considering the eigenvector empirical spectral distribution and its limits. For the first time, we give precise estimates on the output of the algorithms, applied to this wide class of random matrices, as the number of iterations diverges. In this setting, beyond the first order expansion, we also derive a new mesoscopic central limit theorem for the associated orthogonal polynomials and other quantities relevant to numerical algorithms.