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Computational Optimization and Applications

, Volume 56, Issue 3, pp 507–530 | Cite as

An efficient augmented Lagrangian method with applications to total variation minimization

  • Chengbo Li
  • Wotao Yin
  • Hong Jiang
  • Yin Zhang
Article

Abstract

Based on the classic augmented Lagrangian multiplier method, we propose, analyze and test an algorithm for solving a class of equality-constrained non-smooth optimization problems (chiefly but not necessarily convex programs) with a particular structure. The algorithm effectively combines an alternating direction technique with a nonmonotone line search to minimize the augmented Lagrangian function at each iteration. We establish convergence for this algorithm, and apply it to solving problems in image reconstruction with total variation regularization. We present numerical results showing that the resulting solver, called TVAL3, is competitive with, and often outperforms, other state-of-the-art solvers in the field.

Keywords

Compressive sensing Non-smooth optimization Augmented Lagrangian method Nonmonotone line search Barzilai-Borwein method Single-pixel camera 

Notes

Acknowledgements

The work of the first author was supported in part by NSF Grant DMS-0811188. The work of the second author was supported in part by NSF grants DMS-07-48839 and ECCS-1028790, as well as ONR Grant N00014-08-1-1101. The work of the fourth author was supported in part by NSF Grant DMS-0811188, ONR Grant N00014-08-1-1101, and NSF Grant DMS-1115950. The first and the fourth authors also appreciate a gift fund from Bell Labs, Alcatel-Lucent to Rice University that partially supported their travels to international conferences. Last but not least, we thank the two anonymous referees for their constructive criticism and their helpful suggestions.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Computational and Applied MathematicsRice UniversityHoustonUSA
  2. 2.Bell LaboratoriesAlcatel-LucentMurray HillUSA

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