Computing asymptotic eigenvectors and eigenvalues of perturbed symmetric matrices
<|reference_start|>Computing asymptotic eigenvectors and eigenvalues of perturbed symmetric matrices: Computing the eigenvectors and eigenvalues of a perturbed matrix can be remarkably difficult when the unperturbed matrix has repeated eigenvalues. In this work we show how the limiting eigenvectors and eigenvalues of a symmetric matrix $K(\varepsilon)$ as $\varepsilon \to 0$ can be obtained relatively easily from successive Schur complements, provided that the entries scale in different orders of $\varepsilon$. If the matrix does not directly exhibit this structure, we show that putting the matrix into a ``generalised kernel form'' can be very informative. The resulting formulas are much simpler than classical expressions obtained from complex integrals involving the resolvent. We apply our results to the problem of computing the eigenvalues and eigenvectors of kernel matrices in the ``flat limit'', a problem that appears in many applications in statistics and approximation theory. In particular, we prove a conjecture from [SIAM J. Matrix Anal. Appl., 2021, 42(1):17--57] which connects the eigenvectors of kernel matrices to multivariate orthogonal polynomials.<|reference_end|>
@article{usevich2024computing,
    title={Computing asymptotic eigenvectors and eigenvalues of perturbed symmetric
  matrices},
    author={Konstantin Usevich (CRAN), Simon Barthelme (GIPSA-GAIA)},
    journal={arXiv preprint arXiv:2407.17047},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.17047},
    primaryClass={math.NA cs.NA}
}