Solving two-parameter eigenvalue problems using an alternating method
Henrik Eisenmann, Yuji Nakatsukasa
Abstract
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Henrik Eisenmann, Yuji Nakatsukasa
Abstract
Open-access reader
We present a new approach to compute selected eigenvalues and eigenvectors of the two-parameter eigenvalue problem. Our method requires computing generalized eigenvalue problems of the same size as the matrices of the initial two-parameter eigenvalue problem. The method is applicable for right definite problems, possibly after performing an affine transformation. This includes a class of Helmholtz equations when separation of variables is applied. We provide a convergence proof for extremal eigenvalues and empirical evidence along with a local convergence proof for other eigenvalues.
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We present a new approach to compute selected eigenvalues and eigenvectors of the two-parameter eigenvalue problem. Our method requires computing generalized eigenvalue problems of the same size as the matrices of the initial two-parameter eigenvalue problem. The method is applicable for right definite problems, possibly after performing an affine transformation. This includes a class of Helmholtz equations when separation of variables is applied. We provide a convergence proof for extremal eigenvalues and empirical evidence along with a local convergence proof for other eigenvalues.
Key concepts: Eigenvalues and eigenvectors, Mathematics, Eigenvalue perturbation, Divide-and-conquer eigenvalue algorithm, Convergence (economics), Applied mathematics, Affine transformation, Mathematical analysis