2021
DOI: 10.1007/s11075-021-01130-9
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A matrix-less method to approximate the spectrum and the spectral function of Toeplitz matrices with real eigenvalues

Abstract: It is known that the generating function f of a sequence of Toeplitz matrices {Tn(f)}n may not describe the asymptotic distribution of the eigenvalues of Tn(f) if f is not real. In this paper, we assume as a working hypothesis that, if the eigenvalues of Tn(f) are real for all n, then they admit an asymptotic expansion of the same type as considered in previous works, where the first function, called the eigenvalue symbol $\mathfrak {f}$ f , appearing in this expansion is real and… Show more

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Cited by 4 publications
(18 citation statements)
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“…The time-covariance matrix T , eq , is a real symmetric Toeplitz matrixeach descending diagonal of T from left to right is constant, thereby it is fully defined by its first row. Toeplitz matrices arise often in theory of weakly stationary processes, theory of signal processing, and information theory applications in statistical physics.…”
Section: Computational Detailsmentioning
confidence: 99%
“…The time-covariance matrix T , eq , is a real symmetric Toeplitz matrixeach descending diagonal of T from left to right is constant, thereby it is fully defined by its first row. Toeplitz matrices arise often in theory of weakly stationary processes, theory of signal processing, and information theory applications in statistical physics.…”
Section: Computational Detailsmentioning
confidence: 99%
“…( 10), is a real symmetric Toeplitz matrix -each descending diagonal of from left to right is constant, thereby it is fully defined by its first row. Toeplitz matrices arise often [64][65][66][67][68] in theory of weakly stationary processes, theory of signal processing and information theory applications in statistical physics.…”
Section: ˆ( ) Ln ( )mentioning
confidence: 99%
“…In a recent work 16 the cases of interest were those in which false{Tnfalse(ffalse)false}nλf$$ {\left\{{T}_n(f)\right\}}_n{\nsim}_{\lambda }f $$ and the eigenvalues of Tnfalse(ffalse)$$ {T}_n(f) $$ are real for all n$$ n $$. In such a setting, often the matrix sequence false{Tnfalse(ffalse)false} n$$ {\left\{{T}_n(f)\right\}}_n $$ is such that there exists a real‐valued function frakturf$$ \mathfrak{f} $$ satisfying false{Tnfalse(ffalse)false}nλfrakturf$$ {\left\{{T}_n(f)\right\}}_n{\sim}_{\lambda}\mathfrak{f} $$, with the eigenvalues of Tnfalse(ffalse)$$ {T}_n(f) $$ admitting an asymptotic expansion of the same type as considered in previous theoretical 17,18 and numerical 19–21 works.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, Böttcher et al 15 proposed a novel numerical algorithm to approximate normalΛfalse(afalse)$$ \Lambda (a) $$ with high accuracy and moderate computational cost. Nevertheless, in this article, we will rely on the algorithm proposed by Ekström and Vassalos, 16 because it produces an expression for frakturf$$ \mathfrak{f} $$ instead of its range. With this connection we introduce the class 𝒯 as the collection of all generating functions fL1false(false[prefix−π,πfalse]false)$$ f\in {L}^1\left(\left[-\pi, \pi \right]\right) $$ satisfying one of the following conditions, f$$ f $$ belongs to the Tilli class 𝒯𝒞 and the essential range false(ffalse)$$ \mathit{\mathcal{ER}}(f) $$ is connected, the limiting set normalΛfalse(ffalse)$$ \Lambda (f) $$ has one nonclosed analytic arc only. …”
Section: Introductionmentioning
confidence: 99%
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