Random Polynomials in Several Complex Variables

Abstract

We generalize some previous results on random polynomials in several complex variables. A standard setting is to consider random polynomials $H_n(z):={\sum }_{j=1}^{m_n}{a}_j{p}_j(z)$ 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 $K\subset {\mathbf{C}}^d$. Here $m_n$ is the dimension of ${\mathcal{P}}_n$, the holomorphic polynomials of degree at most $n$ in ${\mathbf{C}}^d$. 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 $L_{loc}^1({\mathbf{C}}^d)$ to the (weighted) extremal plurisubharmonic function for $K$. We aim for weakest possible sufficient conditions on the random coefficients to guarantee convergence.

This replaces and improves a previous version which has a gap in the proof of the higher codimension case at the end

References in corpus (2)

• Asymptotic distribution of complex zeros of random analytic functions
• A necessary and sufficient condition for convergence of the zeros of random polynomials