An algorithm given by Golub and Kahan [2] for reducing a general matrix to bidiagonal form is shown to be very important for large sparse matrices. The singular values of the matrix are those of the bidiagonal form, and these can be easily computed. The bidiagonalization algorithm is shown to be the basis of important methods for solving the linear least squares problem for large sparse matrices. Eigenvalues of certain 2-cyclic matrices can also be efficiently computed using this bidiagonalizati...
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M. Baroni, and R. Zamparelli. Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, page 1183--1193. Stroudsburg, PA, USA, Association for Computational Linguistics, (2010)
G. Tzagkarakis, B. Beferull-Lozano, and P. Tsakalides. Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth
Asilomar Conference on, 1, page 397--401. IEEE Computer Society, (2004)