On an inverse tridiagonal eigenvalue problem and its application to synchronization of network motion

In this work, motivated by the study of stability of the synchronous orbit of a network with tridiagonal Laplacian matrix, we first solve an inverse eigenvalue problem which builds a tridiagonal Laplacian matrix with eigenvalues λ 1 = 0 < λ 2 < ⋯ < λ N and null-vector . Then, we show how this result can be used to guarantee –if possible– that a synchronous orbit of a connected tridiagonal network associated to the matrix L above is asymptotically stable, in the sense of having an associated negative Master Stability Function (MSF). We further show that there are limitations when we also impose symmetry for L .

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Publication Details

Journal
Linear Algebra and its Applications
Published
2025-08-22
DOI
https://doi.org/10.1016/j.laa.2025.08.015
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
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article

On an inverse tridiagonal eigenvalue problem and its application to synchronization of network motion

Alessandro Pugliese, Cinzia Elia, Luca Dieci
Linear Algebra and its Applications
Neural Networks Stability and Synchronization
article

On an inverse tridiagonal eigenvalue problem and its application to synchronization of network motion

Alessandro Pugliese, Cinzia Elia, Luca Dieci
article en

Abstract

In this work, motivated by the study of stability of the synchronous orbit of a network with tridiagonal Laplacian matrix, we first solve an inverse eigenvalue problem which builds a tridiagonal Laplacian matrix with eigenvalues λ 1 = 0 < λ 2 < ⋯ < λ N and null-vector . Then, we show how this result can be used to guarantee –if possible– that a synchronous orbit of a connected tridiagonal network associated to the matrix L above is asymptotically stable, in the sense of having an associated negative Master Stability Function (MSF). We further show that there are limitations when we also impose symmetry for L .

Linear Algebra and its ApplicationsVol. 728
Georgia Institute of Technology (US), University of Bari Aldo Moro (IT)
Openalex Percentile: Top 76%
Neural Networks Stability and Synchronization
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