Please use this identifier to cite or link to this item: http://repository.vnu.edu.vn/handle/VNU_123/10986
Title: Dualization of Signal Recovery Problems
Authors: Patrick L. Combettes, Đinh Dũng, Bằng Công Vũ
Keywords: Convex, optimization, Denoising Dictionary, Dykstra-like, algorithm, Duality, Forward-backward, splitting image, reconstruction image, restoration Inverse, problem Signal, recovery Primal-dual, algorithm Proximity operator, Total variation
Issue Date: 2010
Publisher: Set-Valued and Variational Analysis
Abstract: In convex optimization, duality theory can sometimes lead to simpler solution methods than those resulting from direct primal analysis. In this paper, this principle is applied to a class of composite variational problems arising in particular in signal recovery. These problems are not easily amenable to solution by current methods but they feature Fenchel–Moreau–Rockafellar dual problems that can be solved by forward-backward splitting. The proposed algorithm produces simultaneously a sequence converging weakly to a dual solution, and a sequence converging strongly to the primal solution. Our framework is shown to capture and extend several existing duality-based signal recovery methods and to be applicable to a variety of new problems beyond their scope.
URI: http://repository.vnu.edu.vn/handle/VNU_123/10986
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