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A dual split Bregman method for fast $ \ell^1$ minimization

Authors: Yi Yang, Michael Möller and Stanley Osher
Journal: Math. Comp. 82 (2013), 2061-2085
MSC (2010): Primary 49M29, 65K10, 90C25; Secondary 65F22
Published electronically: May 1, 2013
MathSciNet review: 3073192
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Abstract | References | Similar Articles | Additional Information

Abstract: In this paper we propose a new algorithm for fast $ \ell ^1$ minimization as frequently arising in compressed sensing. Our method is based on a split Bregman algorithm applied to the dual of the problem of minimizing $ \Vert u\Vert _1 + \frac {1}{2 \alpha } \Vert u\Vert^2$ such that $ u$ solves the under-determined linear system $ Au=f$, which was recently investigated in the context of linearized Bregman methods.

Furthermore, we provide a convergence analysis for split Bregman methods in general and show with our compressed sensing example that a split Bregman approach to the primal energy can lead to a different type of convergence than split Bregman applied to the dual, thus making the analysis of different ways to minimize the same energy interesting for a wide variety of optimization problems.

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Additional Information

Yi Yang
Affiliation: Department of Mathematics, University of California Los Angeles, Portola Plaza, Los Angeles, California 90095

Michael Möller
Affiliation: Westfälische Wilhelms-Universität Münster, Institut für Numerische und Angewandte Mathematik, Einsteinstr, 62, D 48149 Münster, Germany

Stanley Osher
Affiliation: Department of Mathematics, University of California Los Angeles, Portola Plaza, Los Angeles, California 90095

Keywords: $\ell^1$ minimization, optimization algorithms, sparsity, compressed sensing, calculus of variation
Received by editor(s): September 21, 2011
Received by editor(s) in revised form: March 15, 2012
Published electronically: May 1, 2013
Article copyright: © Copyright 2013 American Mathematical Society
The copyright for this article reverts to public domain 28 years after publication.

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