Limiting Spectral Distribution of a Random Commutator Matrix
Javed Hazarika, Debashis Paul

TL;DR
This paper analyzes the spectral distribution of a class of random commutator matrices, establishing their limiting behavior as dimensions grow, and characterizing the distribution supported on the imaginary axis.
Contribution
It introduces the limiting spectral distribution for a new class of random commutator matrices and characterizes it via coupled Marčenko-Pastur-type equations.
Findings
LSD exists for the commutator matrices as dimensions grow
The LSD is supported on the imaginary axis
Special case: for identity covariance, the LSD is a mixture with a symmetric density
Abstract
We study the spectral properties of a class of random matrices of the form where , for , 's are independent complex-valued random matrices, and is a positive semi-definite matrix, independent of the 's. We assume that 's have independent entries with zero mean and unit variance. The skew-symmetric/skew-Hermitian matrix will be referred to as a random commutator matrix associated with the samples and . We show that, when the dimension and sample size increase simultaneously, so that , there exists a limiting spectral distribution (LSD) for , supported on the imaginary axis, under the assumptions that the spectral distribution of converges weakly and the entries of 's have moments of…
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Taxonomy
TopicsRandom Matrices and Applications · advanced mathematical theories · Matrix Theory and Algorithms
