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Composite self-concordant minimization

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  • UserVolkan Cevher (EPFL)
  • ClockThursday 13 March 2014, 15:00-16:00
  • HouseMR 14, CMS.

If you have a question about this talk, please contact Dr Hansen.

We propose a variable metric framework for minimizing the sum of a self-concordant function and a possibly non-smooth convex function endowed with a computable proximal operator. We theoretically establish the convergence of our framework without relying on the usual Lipschitz gradient 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 as graph learning, Poisson regression with total variation regularization, and heteroscedastic LASSO .

This talk is part of the Applied and Computational Analysis series.

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