Abstract
The usual way to compute a low-rank approximant of a matrix H is to take its singular value decomposition (SVD) and truncate it by setting the small singular values equal to 0. However, the SVD is computationally expensive. Using the Hankel-norm model reduction techniques in chapter 10, we can devise a much simpler generalized Schurtype algorithm to compute similar low-rank approximants. Since rank approximation plays an important role in many linear algebra applications, we devote an independent chapter to this topic, even though this leads to some overlap with previous chapters.
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© 1998 Springer Science+Business Media Dordrecht
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Dewilde, P., van der Veen, AJ. (1998). Low-Rank Matrix Approximation and Subspace Tracking. In: Time-Varying Systems and Computations. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-2817-0_11
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DOI: https://doi.org/10.1007/978-1-4757-2817-0_11
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-5045-1
Online ISBN: 978-1-4757-2817-0
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