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Paper Detail

Paper:	SS-3.5
Session:	Convex Optimization Methods for Signal Processing and Communications
Time:	Tuesday, May 16, 17:50 - 18:10
Presentation:	Special Session Lecture
Topic:	Special Sessions: Convex optimization methods for signal processing and communications
Title:	A Decomposition Method for Nonsmooth Convex Variational Signal Recovery
Authors:	Heinz Bauschke, University of British Columbia Okanagan, Canada; Patrick Combettes, Université Pierre et Marie Curie - Paris 6, France; Jean-Christophe Pesquet, Université de Marne-la-Vallée, France
Abstract:	Under consideration is the large body of signal recovery problems that can be formulated as the problem of minimizing the sum of two (not necessarily smooth) proper lower semicontinuous convex functions in a real Hilbert space. This generic problem is analyzed and a decomposition method is proposed to solve it. The convergence of the method, which is based on an extension of the Douglas-Rachford algorithm for monotone operators splitting, is established under general conditions. Various signal recovery applications are discussed and numerical results are provided.