10:30 AM-12:30 PM
Law School, Room 190
Many numerical algorithms in applications require good approximations to the largest or smallest singular values and corresponding singular subspaces associated with a nearly rank-deficient m X n matrix A. Some of these applications also involve updating and downdating. The singular value decomposition is not practical for some problems due to real-time or storage constraints. However, the needed information can often be obtained by a more general orthogonal decomposition of the matrix A = UTVT, which is a product of three matrices: an orthogonal matrix U, a middle matrix T, and another orthogonal matrix V. We consider fast and reliable methods based on orthogonal decompositions to solve problems arising in signal processing, spectroscopy, and information retrieval applications.
Organizers: Ricardo D. Fierro, California State University, San Marcos; and Sabine Van Huffel, Katholieke Universiteit Leuven, Belgium
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