The Limiting Spectral Measure for Ensembles of Symmetric Block Circulant Matrices
Murat Kologlu, Gene S. Kopp, Steven J. Miller, Frederick W. Strauch,, Wentao Xiong

TL;DR
This paper investigates the spectral measures of symmetric m-block circulant matrices, demonstrating their convergence to the semi-circle law as m increases, and provides explicit formulas for their densities.
Contribution
It introduces explicit closed-form expressions for the limiting spectral densities of symmetric m-block circulant matrices and connects combinatorial topology with spectral analysis.
Findings
Limiting spectral densities converge to the semi-circle law as m increases.
Explicit formulas for densities as products of Gaussian and polynomial factors.
Connection between combinatorial topology and spectral measure calculations.
Abstract
Given an ensemble of NxN random matrices, a natural question to ask is whether or not the empirical spectral measures of typical matrices converge to a limiting spectral measure as N --> oo. While this has been proved for many thin patterned ensembles sitting inside all real symmetric matrices, frequently there is no nice closed form expression for the limiting measure. Further, current theorems provide few pictures of transitions between ensembles. We consider the ensemble of symmetric m-block circulant matrices with entries i.i.d.r.v. These matrices have toroidal diagonals periodic of period m. We view m as a "dial" we can "turn" from the thin ensemble of symmetric circulant matrices, whose limiting eigenvalue density is a Gaussian, to all real symmetric matrices, whose limiting eigenvalue density is a semi-circle. The limiting eigenvalue densities f_m show a visually stunning…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRandom Matrices and Applications · Stochastic processes and statistical mechanics · Advanced Combinatorial Mathematics
