Large-scale problems

Monday, January 25, 2016 - 10:15am - 11:05am
Aleksandr Aravkin (University of Washington)
We present a modeling framework for a wide range of large-scale optimization problems in data science, and show how conjugate representations can be exploited to design an interior point approach for this class. We then show several applications, with emphasis on modeling and problem structure, and discuss matrix-free extensions for large-scale problems.
Wednesday, October 15, 2014 - 10:15am - 11:05am
Stéphane Popinet (Université de Paris VI (Pierre et Marie Curie))
The Serre-Green-Naghdi (SGN) equations, also known as the fully nonlinear Boussinesq wave equations, are known to accurately describe a wide range of waves and in particular shoaling waves for which dispersive effects cannot be neglected. I will show how this model can be solved in a simple way using the combination of a robust, well-balanced existing Saint-Venant solver with the multigrid solution of the SGN equations.
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