3:15 PM-5:15 PM
Building 160, Room 161j
The minisymposium will highlight current research on the development of recursive algorithms that exhibit improved performance in terms of accuracy, robustness to unmodelled dynamics, and robustness to insufficient prior information. It is becoming increasingly relevant to design recursive algorithms that are more tolerant to modeling and numerical errors. This is especially true with the increasing complexity of the problems arising in modern applications. The talks in this minisymposium will describe several recent methods developed for this purpose. The talks in this minisymposium will touch upon issues that arise in numerical linear algebra, recursive filtering and estimation, and structured computations.
Organizers: Ming Gu and Ali H. Sayed, University of California, Los Angeles; and S. Chandrasekaran, University of California, Santa Barbara
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