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Analysis of the Alternating Direction Method of Multipliers for Nonconvex Problems

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  • Stuart M. Harwood

    (ExxonMobil Research and Engineering)

Abstract

This work investigates the theoretical performance of the alternating-direction method of multipliers (ADMM) as it applies to nonconvex optimization problems, and in particular, problems with nonconvex constraint sets. The alternating direction method of multipliers is an optimization method that has largely been analyzed for convex problems. The ultimate goal is to assess what kind of theoretical convergence properties the method has in the nonconvex case, and to this end, theoretical contributions are twofold. First, this work analyzes the method with local optimal solution of the ADMM subproblems, which contrasts with much analysis that requires global solutions of the subproblems. Such a consideration is important to practical implementations. Second, it is established that the method still satisfies a local convergence result. The work concludes with some more detailed discussion of how the analysis relates to previous work.

Suggested Citation

  • Stuart M. Harwood, 2021. "Analysis of the Alternating Direction Method of Multipliers for Nonconvex Problems," SN Operations Research Forum, Springer, vol. 2(1), pages 1-29, March.
    • Handle: RePEc:spr:snopef:v:2:y:2021:i:1:d:10.1007_s43069-020-00043-y
    DOI: 10.1007/s43069-020-00043-y
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    References listed on IDEAS

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    1. Alexander Shapiro & Jie Sun, 2004. "Some Properties of the Augmented Lagrangian in Cone Constrained Optimization," Mathematics of Operations Research, INFORMS, vol. 29(3), pages 479-491, August.
    2. Frank E. Curtis & Arvind U. Raghunathan, 2017. "Solving nearly-separable quadratic optimization problems as nonsmooth equations," Computational Optimization and Applications, Springer, vol. 67(2), pages 317-360, June.
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