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An accelerated augmented Lagrangian method for linearly constrained convex programming with the rate of convergence O(1/k 2)

  • Yi-fen Ke
  • Chang-feng Ma
Article

Abstract

In this paper, we propose and analyze an accelerated augmented Lagrangian method (denoted by AALM) for solving the linearly constrained convex programming. We show that the convergence rate of AALM is O(1/k 2) while the convergence rate of the classical augmented Lagrangian method (ALM) is O(1/k). Numerical experiments on the linearly constrained l 1l 2 minimization problem are presented to demonstrate the effectiveness of AALM.

Keywords

augmented Lagrangian method linearly constrained convex programming accelerated technique 

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Notes

Acknowledgments

The authors would like to express their great thankfulness to the referees for the comments and constructive suggestions, which are valuable in improving the quality of this manuscript.

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Copyright information

© Editorial Committee of Applied Mathematics-A Journal of Chinese Universities and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.School of Mathematics and Computer Science & FJKLMAAFujian Normal UniversityFuzhouChina

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