Conditions for Eigenvalue Configurations of Two Real Symmetric Matrices

In this extended abstract, we describe recent progress on finding conditions for eigenvalue configurations of two real symmetric matrices. To illustrate the problem, consider a simple example. Let F and G be 2 × 2 real symmetric matrices. Since the matrices are symmetric, their eigenvalues are all real. Thus we may consider the configuration (relative locations of) of the two eigenvalues of F and the two eigenvalues of G on the real line. The problem is, given a certain configuration of those eigenvalues, to find a simple condition on the entries of F and G so that the eigenvalues satisfy the given configuration. For a more precise statement of the problem, see Section 1.

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Publication Details

Journal
ACM communications in computer algebra
Published
2024-09-01
DOI
https://doi.org/10.1145/3717582.3717587
Primary Topic
Matrix Theory and Algorithms
Type
article
Field-Weighted Citation Impact
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article

Conditions for Eigenvalue Configurations of Two Real Symmetric Matrices

Daniel Profili, J. Rafael Sendra, Hoon Hong
ACM communications in computer algebra
Matrix Theory and Algorithms
article

Conditions for Eigenvalue Configurations of Two Real Symmetric Matrices

Daniel Profili, J. Rafael Sendra, Hoon Hong
article en

Abstract

In this extended abstract, we describe recent progress on finding conditions for eigenvalue configurations of two real symmetric matrices. To illustrate the problem, consider a simple example. Let F and G be 2 × 2 real symmetric matrices. Since the matrices are symmetric, their eigenvalues are all real. Thus we may consider the configuration (relative locations of) of the two eigenvalues of F and the two eigenvalues of G on the real line. The problem is, given a certain configuration of those eigenvalues, to find a simple condition on the entries of F and G so that the eigenvalues satisfy the given configuration. For a more precise statement of the problem, see Section 1.

ACM communications in computer algebraVol. 58(3)
North Carolina State University (US)
Openalex Percentile: Top 69%
Matrix Theory and Algorithms
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