Random Polynomials in Several Complex Variables

TL;DR

This paper extends results on the behavior of random polynomials in several complex variables, establishing convergence properties of their logarithmic magnitudes under broad conditions on the random coefficients and basis functions.

Contribution

It generalizes previous models by allowing more flexible bases and array coefficients, and proves convergence of the scaled logarithm of the polynomials to extremal functions.

Findings

Convergence in probability of scaled log |H_n| to extremal functions.

Almost sure convergence under certain conditions.

Applicable to generalized basis polynomials including Fekete polynomials.

Abstract

We generalize some previous results on random polynomials in several complex variables. A standard setting is to consider random polynomials $H_n(z):=\sum_{j=1}^{m_n} a_jp_j(z)$ that are linear combinations of basis polynomials $\{p_j\}$ with i.i.d. complex random variable coefficients $\{a_j\}$ where $\{p_j\}$ form an orthonormal basis for a Bernstein-Markov measure on a compact set $K\subset {\bf C}^d$. Here $m_n$ is the dimension of $\mathcal P_n$, the holomorphic polynomials of degree at most $n$ in ${\bf C}^d$. We consider more general bases $\{p_j\}$, which include, e.g., higher-dimensional generalizations of Fekete polynomials. Moreover we allow $H_n(z):=\sum_{j=1}^{m_n} a_{nj}p_{nj}(z)$; i.e., we have an array of basis polynomials $\{p_{nj}\}$ and random coefficients $\{a_{nj}\}$. This always occurs in a weighted situation. We prove results on convergence in probability and on almost sure convergence of $\frac{1}{n}\log |H_n|$ in $L^1_{loc}({\bf C}^d)$ to the (weighted) extremal plurisubharmonic function for $K$. We aim for weakest possible sufficient conditions on the random coefficients to guarantee convergence.