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Short recurrences for computing extended Krylov bases for Hermitian and unitary matrices

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Abstract

It is well known that the projection of a matrix \(A\) onto a Krylov subspace span \(\left\{ \mathbf {h}, A\mathbf {h}, A^2\mathbf {h}, \ldots , A^{k-1}\mathbf {h}\right\} \), with \(A \in \mathbb {C}^{n \times n}\) and \(\mathbf {h} \in \mathbb {C}^n\), results in a Hessenberg matrix. We show that the projection of the matrix \(A\) onto an extended Krylov subspace, which is of the form span \(\left\{ A^{-k_r}\mathbf {h}, \ldots , A^{-2}\mathbf {h},A^{-1}\mathbf {h}, \mathbf {h}, A\mathbf {h}, A^2 \mathbf {h}, \ldots , A^{k_\ell } \mathbf {h} \right\} \), is a matrix of so-called extended Hessenberg form which can be characterized uniquely by its \(QR\)-factorization. This \(QR\)-factorization will be presented by means of a pattern of \(2 \times 2\) unitary rotations. We will show how this rotation pattern leads to new insights and allows to elegantly predict the structure of the matrix. In case \(A\) is Hermitian or unitary, this extended Hessenberg matrix is banded and structured, allowing the design of short recurrence relations. For the unitary case, coupled two term recurrence relations are derived of which the coefficients capture all information necessary for a sparse factorization of the corresponding extended Hessenberg matrix.

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Notes

  1. These matrices were termed compressed matrices in [14], as they admit an equally expensive \(QR\)-factorization as a Hessenberg matrix.

  2. Given a rotation \(Q\) and an upper triangular matrix \(R\), the product \(QR\) can be rewritten as \({\tilde{R}}{\tilde{Q}}\), where \({\tilde{R}}\) is upper triangular. Hence the rotation has been passed from the left to the right of the upper triangular matrix (of course rotations can also be passed from the right to the left). This shows that one can pass an entire twisted shape of rotations through an upper triangular matrix without changing the shape.

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Correspondence to Clara Mertens.

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The research was partially supported by the Research Council KU Leuven, projects OT/11/055 (Spectral Properties of Perturbed Normal Matrices and their Applications), CREA/13/012 (Can Unconventional Eigenvalue Algorithms Supersede the State-Of-The-Art), PFV/10/002 (Optimization in Engineering, OPTEC) , by the Fund for Scientific Research–Flanders (Belgium) project G034212N (Reestablishing Smoothness for Matrix Manifold Optimization via Resolution of Singularities), and by the Interuniversity Attraction Poles Programme, initiated by the Belgian State, Science Policy Office, Belgian Network DYSCO (Dynamical Systems, Control, and Optimization).

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Mertens, C., Vandebril, R. Short recurrences for computing extended Krylov bases for Hermitian and unitary matrices. Numer. Math. 131, 303–328 (2015). https://doi.org/10.1007/s00211-014-0692-3

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  • DOI: https://doi.org/10.1007/s00211-014-0692-3

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