Generic Cuspidal Points and their Localization

In this work we consider generic coalescing of eigenvalues of smooth complex valued matrix functions depending on 2 parameters. We call generic cuspidal points the parameter values where eigenvalues coalesce and we discuss the relation between cuspidal points and the closely related exceptional points studied in the literature. By considering loops in parameter space enclosing the cuspidal points, we rigorously prove when there is a phase accumulation for the eigenvectors and further detail how, by looking at the periodicity of the eigenvalues along the loop, and/or by looking at the aforementioned phase accumulation, one may be able to localize generic cuspidal points.

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Publication Details

Journal
SIAM Journal on Matrix Analysis and Applications
Published
2026-04-22
DOI
https://doi.org/10.1137/25m1780857
Primary Topic
Matrix Theory and Algorithms
Type
article
Field-Weighted Citation Impact
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article

Generic Cuspidal Points and their Localization

Alessandro Pugliese, Luca Dieci
SIAM Journal on Matrix Analysis and Applications
Matrix Theory and Algorithms
article

Generic Cuspidal Points and their Localization

Alessandro Pugliese, Luca Dieci
article en

Abstract

In this work we consider generic coalescing of eigenvalues of smooth complex valued matrix functions depending on 2 parameters. We call generic cuspidal points the parameter values where eigenvalues coalesce and we discuss the relation between cuspidal points and the closely related exceptional points studied in the literature. By considering loops in parameter space enclosing the cuspidal points, we rigorously prove when there is a phase accumulation for the eigenvectors and further detail how, by looking at the periodicity of the eigenvalues along the loop, and/or by looking at the aforementioned phase accumulation, one may be able to localize generic cuspidal points.

SIAM Journal on Matrix Analysis and ApplicationsVol. 47(2)
Georgia Institute of Technology (US), University of Bari Aldo Moro (IT)
Openalex Percentile: Top 97%
Matrix Theory and Algorithms
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