Set-Valued and Variational Analysis

, Volume 18, Issue 3–4, pp 373–404 | Cite as

Dualization of Signal Recovery Problems

  • Patrick L. Combettes
  • Đinh Dũng
  • Bằng Công Vũ


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.


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 

Mathematics Subject Classifications (2010)

90C25 49N15 94A12 94A08 


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Patrick L. Combettes
    • 1
  • Đinh Dũng
    • 2
  • Bằng Công Vũ
    • 1
  1. 1.Laboratoire Jacques-Louis Lions–UMR 7598UPMC Université Paris 06ParisFrance
  2. 2.Information Technology InstituteVietnam National UniversityHanoiVietnam

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