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SUMMARY:Composite self-concordant minimization - Volkan Cevher (EPFL) 
DTSTART:20140313T150000Z
DTEND:20140313T160000Z
UID:TALK49344@talks.cam.ac.uk
CONTACT:Dr Hansen
DESCRIPTION:We propose a variable metric framework for minimizing the sum 
 of a self-concordant function and a possibly non-smooth convex function en
 dowed with a computable proximal operator. We theoretically establish the 
 convergence of our framework without relying on the usual Lipschitz gradie
 nt assumption on the smooth part. An important highlight of our work is a 
 new set of analytic step-size selection and correction procedures based on
  the structure of the problem. We describe concrete algorithmic instances 
 of our framework for several interesting large-scale applications\, such a
 s graph learning\, Poisson regression with total variation regularization\
 , and heteroscedastic LASSO.
LOCATION:MR 14\, CMS
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