Tuesday, March 15, 2016 - 11:00am - 11:30am
Barbara Kaltenbacher (Universität Klagenfurt)
Parameter identification problems typically consist of a model equation, e.g. a (system of) ordinary or partial differential equation(s), and the observation equation. In the conventional reduced setting, the model equation is eliminated via the parameter-to-state map. Alternatively, one might consider both sets of equations (model and observations) as one large system, to which some regularization method is applied.
Thursday, September 8, 2011 - 2:00pm - 3:00pm
Joel Tropp (California Institute of Technology)
The purpose of this tutorial is to describe the intellectual apparatus that supports some modern techniques in statistics, machine learning, signal processing, and related areas. The main ingredient is the observation that many types of data admit parsimonious representations, i.e., there are far fewer degrees of freedom in the data than the ambient dimension would suggest.
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