Search arXivSearch

arXiv · 2102.02058

The limit empirical spectral distribution of complex matrix polynomials

Abstract

We study the empirical spectral distribution (ESD) for complex n x n matrix polynomials of degree k. We obtain exact formulae for the almost sure limit of the ESD in two distinct scenarios: (1) n -> \infty with k constant and (2) k -> \infty with n bounded by O(k^P) for some P>0. The main tools used are the logarithmic potential of some measure related to the matrix polynomial, and some classical estimates on the singular values of full random matrices with i.i.d. entries.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Giovanni Barbarino, Vanni Noferini. 2021-09-29. The limit empirical spectral distribution of complex matrix polynomials. https://doi.org/10.1142/s201032632250023x

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Distribution-uniform strong laws of large numbers

We revisit the question of whether the strong law of large numbers (SLLN) holds uniformly in a rich family of distributions, culminating in a distribution-uniform generalization of the Marcinkiewicz-Zygmund SLLN. These results can be viewed as extensions of Chung's distribution-uniform SLLN to random variables with uniformly integrable $q^\text{th}$ absolute central moments for $0 < q < 2$. Furthermore, we show that uniform integrability of the $q^\text{th}$ moment is both sufficient and necessary for the SLLN to hold uniformly at the Marcinkiewicz-Zygmund rate of $n^{1/q - 1}$. These proofs centrally rely on novel distribution-uniform analogues of some familiar almost sure convergence results including the Khintchine-Kolmogorov convergence theorem, Kolmogorov's three-series theorem, a stochastic generalization of Kronecker's lemma, and the Borel-Cantelli lemmas. We also consider the non-identically distributed case.

math.PR

Malliavin Calculus for rough stochastic differential equations

In this work we show that rough stochastic differential equations (RSDEs), as introduced by Friz, Hocquet, and Lê (2021), are Malliavin differentiable. We use this to prove existence of a density when the diffusion coefficients satisfies standard ellipticity assumptions. Moreover, when the coefficients are smooth and the diffusion coefficients satisfies a Hörmander condition, the density is shown to be smooth. The key ingredient is to develop a comprehensive theory of linear rough stochastic differential equations, which could be of independent interest.

math.PR

Upper tail bounds for irregular graphs

We consider the upper tail large deviations of subgraph counts for irregular graphs $\mathrm{H}$ in $\mathbb{G}(n,p)$, the sparse Erdős-Rényi graph on $n$ vertices with edge connectivity probability $p \in (0,1)$. For $n^{-1/Δ} \ll p \ll 1$, where $Δ$ is the maximum degree of $\mathrm{H}$, we derive the upper tail large deviations for any irregular graph $\mathrm{H}$. On the other hand, we show that for $p$ such that $1 \ll n^{v_{\mathrm{H}}} p^{e_{\mathrm{H}}} \ll (\log n)^{α^{*}_{\mathrm{H}}/\left(α^{*}_{\mathrm{H}}-1\right)}$, where $v_{\mathrm{H}}$ and $e_{\mathrm{H}}$ denote the number of vertices and edges of $\mathrm{H}$, and $α^*_{\mathrm{H}}$ denotes the fractional independence number, the upper tail large deviations of the number of unlabelled copies of $\mathrm{H}$ in $\mathbb{G}(n,p)$ is given by that of a sequence of Poisson random variables with diverging mean, for any strictly balanced graph $\mathrm{H}$. Restricting to the $r$-armed star graph we further prove a localized behavior in the intermediate range of $p$ (left open by the above two results) and show that the mean-field approximation is asymptotically tight for the logarithm of the upper tail probability. This work further identifies the typical structures of $\mathbb{G}(n,p)$ conditioned on upper tail rare events in the localized regime.

math.PR