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      Proximal Splitting Methods in Signal Processing

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          Abstract

          The proximity operator of a convex function is a natural extension of the notion of a projection operator onto a convex set. This tool, which plays a central role in the analysis and the numerical solution of convex optimization problems, has recently been introduced in the arena of signal processing, where it has become increasingly important. In this paper, we review the basic properties of proximity operators which are relevant to signal processing and present optimization methods based on these operators. These proximal splitting methods are shown to capture and extend several well-known algorithms in a unifying framework. Applications of proximal methods in signal recovery and synthesis are discussed.

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          An iterative thresholding algorithm for linear inverse problems with a sparsity constraint

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            Monotone Operators and the Proximal Point Algorithm

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              Smooth minimization of non-smooth functions

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                Author and article information

                Journal
                17 December 2009
                2010-05-18
                Article
                0912.3522
                757ae108-5b74-498a-81b6-d55a53ee9190

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                90C25, 65K05, 90C90, 94A08
                math.OC math.NA

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