10:30 AM-12:30 PM

*Law School, Room 190*

Theory and Applications of Orthogonal Decompositions

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 = UTV^{T}, 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 **

**10:30 Recursive Algorithm for Approximate Rank-Revealing Subspace Projection Based Upon a One-Sided Decomposition***Mark J. Smith*and I. K. Proudler, Defence Research Agency, United Kingdom**11:00 Downdating Orthogonal Decompositions for Information Retrieval Models***Michael W. Berry*and Dian I. Witter, University of Tennessee, Knoxville**11:30 Modifying the ULV Decomposition and Recursive TLS Problems***Jesse L. Barlow*, Pennsylvania State University; and Zhenyue Zhang, Zheijiang University, People's Republic of China**12:00 Fast Low Rank-Revealing ULV Algorithms for Toeplitz Matrix Approximations with Applications in Magnetic Resonance Spectroscopy***Leentje Vanhamme*, Katholieke Universiteit Leuven, Belgium; Sabine Van Huffel and Ricardo D. Fierro, Organizers

AN97 Homepage | Program Updates|

Registration | Hotel and Dormitory Information | Transportation | Program-at-a-Glance | Program Overview