10:30 AM-12:30 PM
Law School, Room 190
For many important applications in signal processing, parameter estimation, and system identification, it is necessary to compute a good approximate solution to an overdetermined system A(alpha)x ~ b, where the parameter vector alpha and the amplitude vector x are to be estimated. The data vector b may be in error, due to noise in the measured signal. For most applications the matrix A(alpha) has a structure (such as Toeplitz or Vandermonde) which should be preserved. The methods Separable Nonlinear Least Squares, Constrained Total Least Squares, Structured Total Least Squares, and Structured Nonlinear Least Norm have all been developed to solve problems of this type. These methods, and their relationship, will be described, and their use in specific applications will be summarized in this minisymposium.
Organizers: Haesun Park, University of Minnesota, Minneapolis; and J. B. Rosen, University of California, San Diego
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