Accelerated Alternating Descent Methods for Dykstra-Like Problems


DOI: 10.1007/s10851-017-0724-6

Cite this article as:
Chambolle, A., Tan, P. & Vaiter, S. J Math Imaging Vis (2017). doi:10.1007/s10851-017-0724-6


This paper extends recent results by the first author and T. Pock (ICG, TU Graz, Austria) on the acceleration of alternating minimization techniques for quadratic plus nonsmooth objectives depending on two variables. We discuss here the strongly convex situation, and how ‘fast’ methods can be derived by adapting the overrelaxation strategy of Nesterov for projected gradient descent. We also investigate slightly more general alternating descent methods, where several descent steps in each variable are alternatively performed.


Alternating minimizations Block descent algorithms Accelerated methods Total variation minimization 

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.CMAP, CNRSEcole PolytechniquePalaiseauFrance
  2. 2.IMB, CNRSUniversité de BourgogneDijonFrance

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