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Inexact alternating direction methods of multipliers for separable convex optimization

  • William W. HagerEmail author
  • Hongchao Zhang
Article
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Abstract

Inexact alternating direction multiplier methods (ADMMs) are developed for solving general separable convex optimization problems with a linear constraint and with an objective that is the sum of smooth and nonsmooth terms. The approach involves linearized subproblems, a back substitution step, and either gradient or accelerated gradient techniques. Global convergence is established. The methods are particularly useful when the ADMM subproblems do not have closed form solution or when the solution of the subproblems is expensive. Numerical experiments based on image reconstruction problems show the effectiveness of the proposed methods.

Keywords

Separable convex optimization Alternating direction method of multipliers Multiple blocks Inexact ADMM Global convergence 

Mathematics Subject Classification

90C06 90C25 65Y20 

Notes

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of FloridaGainesvilleUSA
  2. 2.Department of MathematicsLouisiana State UniversityBaton RougeUSA

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