Monday, 16:15 - 16:40 h, Room: H 0110


Figen Oztoprak
Two-phase active set methods with applications to inverse covariance estimation


We present a semi-smooth Newton framework that gives rise to a family of second order methods for structured convex optimization. The generality of our approach allows us to analyze their convergence properties in a unified setting, and to contrast their algorithmic components. These methods are well suited for a variety of machine learning applications, and in this talk we give particular attention to an inverse covariance matrix estimation problem arising in speech recognition. We compare our method to state-of-the-art techniques, both in terms of computational efficiency and theoretical properties.


Talk 3 of the invited session Mon.3.H 0110
"Nonlinear optimization III" [...]
Cluster 16
"Nonlinear programming" [...]


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