interior point methods

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.
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