TY - JOUR
T1 - The limit empirical spectral distribution of complex matrix polynomials
AU - Barbarino, Giovanni
AU - Noferini, Vanni
N1 - Publisher Copyright:
© 2022 World Scientific Publishing Company.
PY - 2021
Y1 - 2021
N2 - We study the empirical spectral distribution (ESD) for complex n × n matrix polynomials of degree k under relatively mild assumptions on the underlying distributions, thus highlighting universality phenomena. In particular, we assume that the entries of each matrix coefficient of the matrix polynomial have mean zero and finite variance, potentially allowing for distinct distributions for entries of distinct coefficients. We derive the almost sure limit of the ESD in two distinct scenarios: (1) n →∞ with k constant and (2) k →∞ with n bounded by O(kP) for some P > 0; the second result additionally requires that the underlying distributions are continuous and uniformly bounded. Our results are universal in the sense that they depend on the choice of the variances and possibly on k (if it is kept constant), but not on the underlying distributions. The results can be specialized to specific models by fixing the variances, thus obtaining matrix polynomial analogues of results known for special classes of scalar polynomials, such as Kac, Weyl, elliptic and hyperbolic polynomials.
AB - We study the empirical spectral distribution (ESD) for complex n × n matrix polynomials of degree k under relatively mild assumptions on the underlying distributions, thus highlighting universality phenomena. In particular, we assume that the entries of each matrix coefficient of the matrix polynomial have mean zero and finite variance, potentially allowing for distinct distributions for entries of distinct coefficients. We derive the almost sure limit of the ESD in two distinct scenarios: (1) n →∞ with k constant and (2) k →∞ with n bounded by O(kP) for some P > 0; the second result additionally requires that the underlying distributions are continuous and uniformly bounded. Our results are universal in the sense that they depend on the choice of the variances and possibly on k (if it is kept constant), but not on the underlying distributions. The results can be specialized to specific models by fixing the variances, thus obtaining matrix polynomial analogues of results known for special classes of scalar polynomials, such as Kac, Weyl, elliptic and hyperbolic polynomials.
KW - Companion matrix
KW - Empirical spectral distribution
KW - Logarithmic potential
KW - Polynomial eigenvalue problem
KW - Random matrix polynomial
KW - Universality
UR - http://www.scopus.com/inward/record.url?scp=85116912242&partnerID=8YFLogxK
U2 - 10.1142/S201032632250023X
DO - 10.1142/S201032632250023X
M3 - Article
AN - SCOPUS:85116912242
JO - Random Matrices: Theory and Application
JF - Random Matrices: Theory and Application
SN - 2010-3263
M1 - 2250023
ER -