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Observations on the Computation of Eigenvalue and Eigenvector Jacobians

1
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
2
Rensselaer Polytechnic Institute, Troy, NY 12180, USA
3
NASA Johnson Space Center, Houston, TX 77058, USA
*
Author to whom correspondence should be addressed.
A portion of this work appears in A.J. Liounis’s M.S. Thesis from West Virginia University.
Algorithms 2019, 12(12), 245; https://doi.org/10.3390/a12120245
Received: 24 September 2019 / Revised: 16 November 2019 / Accepted: 18 November 2019 / Published: 20 November 2019
Many scientific and engineering problems benefit from analytic expressions for eigenvalue and eigenvector derivatives with respect to the elements of the parent matrix. While there exists extensive literature on the calculation of these derivatives, which take the form of Jacobian matrices, there are a variety of deficiencies that have yet to be addressed—including the need for both left and right eigenvectors, limitations on the matrix structure, and issues with complex eigenvalues and eigenvectors. This work addresses these deficiencies by proposing a new analytic solution for the eigenvalue and eigenvector derivatives. The resulting analytic Jacobian matrices are numerically efficient to compute and are valid for the general complex case. It is further shown that this new general result collapses to previously known relations for the special cases of real symmetric matrices and real diagonal matrices. Finally, the new Jacobian expressions are validated using forward finite differencing and performance is compared with another technique. View Full-Text
Keywords: eigenvector; eigenvalue; Jacobian eigenvector; eigenvalue; Jacobian
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Liounis, A.J.; Christian, J.A.; Robinson, S.B. Observations on the Computation of Eigenvalue and Eigenvector Jacobians. Algorithms 2019, 12, 245.

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