Wednesday Afternoon, October 25

Theory and Applications of Two-Sided Orthogonal Decompositions

Rank-revealing two-sided orthogonal decompositions are important linear algebra "tools" in applications, such as signal processing, spectroscopy, and information retrieval, that depend on fast algorithms, updatable decompositions, and reliable detection of numerical rank. These decompositions emulate "partial" singular value decompositions (PSVD), and are faster and less computationally demanding than the full SVD. The speakers will discuss recent advances and applications of these decompositions in signal processing, low-rank sparse or structured least squares problems, and information retrieval.

Organizer: Ricardo D. Fierro
California State University, San Marcos

1:30 Modifying the ULV Decomposition for An Efficient Array Processing
Jesse L. Barlow, Leon H. Sibul, and Peter A. Yoon, The Pennsylvania State University

2:00 Partial Generalized SVD and its Application in Estimator-Correlator Array Signal Processing
Hongyuan Zha, The Pennsylvania State University

2:30 Fast Two-Sided Orthogonal Decompositions for Sparse or Structured Low-Rank Problems
Ricardo D. Fierro, Organizer

3:00 Low-Rank Orthogonal Decomposition for Information Retrieval Applications
Michael W. Berry, University of Tennessee and Ricardo D. Fierro, Organizer

Transportation | Registration | Hotel Information | Speaker Index | Program Overview