http://repository.vnu.edu.vn/handle/VNU_123/10986
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.
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.
| 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 |
| Appears in Collections: | ITI - Papers |
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