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Composite Self-concordant Minimization

Quoc Tran Dinh(quoc.trandinh***at***epfl.ch)
Anastasios Kyrillidis(anastasios.kyrillidis***at***epfl.ch)
Volkan Cevher(volkan.cevher***at***epfl.ch)

Abstract: 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 and demonstrate them numerically on both synthetic and real data.

Keywords: Proximal-gradient/Newton method, composite minimization, self-concordance, sparse convex optimization, graph learning.

Category 1: Convex and Nonsmooth Optimization

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )

Category 3: Applications -- Science and Engineering (Statistics )

Citation: Technical Report 2013d, Laboratory for Information and Inference Systems, EPFL, Lausanne, Switzerland

Download: [PDF]

Entry Submitted: 08/15/2013
Entry Accepted: 08/15/2013
Entry Last Modified: 08/15/2013

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