An Adaptive Working Set Algorithm
Messung, Modellierung und Bewertung von Rechensystemen, 2. GI/NTG-Fachtagung
A Jacobi-Davidson type method for the product eigenvalue problem
Journal of Computational and Applied Mathematics
Using semiseparable matrices to compute the SVD of a general matrix product/quotient
Journal of Computational and Applied Mathematics
Bilingual chunk alignment based on interactional matching and probabilistic latent semantic indexing
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Information Processing and Management: an International Journal
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In this paper we derive a new algorithm for constructing a unitary decomposition of a sequence of matrices in product or quotient form. The unitary decomposition requires only unitary left and right transformations on the individual matrices and amounts to computing the generalized singular value decomposition of the sequence. The proposed algorithm is related to the classical Golub--Kahan procedure for computing the singular value decomposition (SVD) of a single matrix in that it constructs a bidiagonal form of the sequence as an intermediate result. When applied to two matrices this new method is an alternative way of computing the quotient and product SVD and is more economical than current methods.