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Characterizing GSVD by singular value expansion of linear operators and its computation

31 Mar 2024arXiv:2404.00655links table onlyarchive 2025-07-28

Haibo Li

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The generalized singular value decomposition (GSVD) of a matrix pair {A, L} with A∈ℝ^(m×n) and L∈ℝ^(p×n) generalizes the singular value decomposition (SVD) of a single matrix. In this paper, we provide a new understanding of GSVD from the viewpoint of SVD, based on which we propose a new iterative method for computing nontrivial GSVD components of a large-scale matrix pair. By introducing two linear operators 𝒜 and ℒ induced by {A, L} between two finite-dimensional Hilbert spaces and applying the theory of singular value expansion (SVE) for linear compact operators, we show that the GSVD of {A, L} is nothing but the SVEs of 𝒜 and ℒ. This result characterizes completely the structure of GSVD for any matrix pair with the same number of columns. As a direct application of this result, we generalize the standard Golub-Kahan bidiagonalization (GKB) that is a basic routine for large-scale SVD computation such that the resulting generalized GKB (gGKB) process can be used to approximate nontrivial extreme GSVD components of {A, L}, which is named the gGKB\_GSVD algorithm. We use the GSVD of {A, L} to study several basic properties of gGKB and also provide preliminary results about convergence and accuracy of gGKB\_GSVD for GSVD computation. Numerical experiments are presented to demonstrate the effectiveness of this method.

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