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Approximate ADMM algorithms derived from Lagrangian splitting

Author

Listed:
  • Jonathan Eckstein

    (Rutgers University)

  • Wang Yao

    (Rutgers University)

Abstract

This paper presents two new approximate versions of the alternating direction method of multipliers (ADMM) derived by modifying of the original “Lagrangian splitting” convergence analysis of Fortin and Glowinski. They require neither strong convexity of the objective function nor any restrictions on the coupling matrix. The first method uses an absolutely summable error criterion and resembles methods that may readily be derived from earlier work on the relationship between the ADMM and the proximal point method, but without any need for restrictive assumptions to make it practically implementable. It permits both subproblems to be solved inexactly. The second method uses a relative error criterion and the same kind of auxiliary iterate sequence that has recently been proposed to enable relative-error approximate implementation of non-decomposition augmented Lagrangian algorithms. It also allows both subproblems to be solved inexactly, although ruling out “jamming” behavior requires a somewhat complicated implementation. The convergence analyses of the two methods share extensive underlying elements.

Suggested Citation

  • Jonathan Eckstein & Wang Yao, 2017. "Approximate ADMM algorithms derived from Lagrangian splitting," Computational Optimization and Applications, Springer, vol. 68(2), pages 363-405, November.
    • Handle: RePEc:spr:coopap:v:68:y:2017:i:2:d:10.1007_s10589-017-9911-z
    DOI: 10.1007/s10589-017-9911-z
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    References listed on IDEAS

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    1. Dettling, Marcel & Bühlmann, Peter, 2004. "Finding predictive gene groups from microarray data," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 106-131, July.
    2. M. V. Solodov & B. F. Svaiter, 2000. "An Inexact Hybrid Generalized Proximal Point Algorithm and Some New Results on the Theory of Bregman Functions," Mathematics of Operations Research, INFORMS, vol. 25(2), pages 214-230, May.
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    Cited by:

    1. Bingsheng He & Feng Ma & Xiaoming Yuan, 2020. "Optimally linearizing the alternating direction method of multipliers for convex programming," Computational Optimization and Applications, Springer, vol. 75(2), pages 361-388, March.
    2. Jiaxin Xie, 2018. "On inexact ADMMs with relative error criteria," Computational Optimization and Applications, Springer, vol. 71(3), pages 743-765, December.
    3. William W. Hager & Hongchao Zhang, 2019. "Inexact alternating direction methods of multipliers for separable convex optimization," Computational Optimization and Applications, Springer, vol. 73(1), pages 201-235, May.
    4. M. Marques Alves & Jonathan Eckstein & Marina Geremia & Jefferson G. Melo, 2020. "Relative-error inertial-relaxed inexact versions of Douglas-Rachford and ADMM splitting algorithms," Computational Optimization and Applications, Springer, vol. 75(2), pages 389-422, March.
    5. William W. Hager & Hongchao Zhang, 2020. "Convergence rates for an inexact ADMM applied to separable convex optimization," Computational Optimization and Applications, Springer, vol. 77(3), pages 729-754, December.
    6. Vando A. Adona & Max L. N. Gonçalves & Jefferson G. Melo, 2019. "A Partially Inexact Proximal Alternating Direction Method of Multipliers and Its Iteration-Complexity Analysis," Journal of Optimization Theory and Applications, Springer, vol. 182(2), pages 640-666, August.
    7. Yunier Bello-Cruz & Max L. N. Gonçalves & Nathan Krislock, 2023. "On FISTA with a relative error rule," Computational Optimization and Applications, Springer, vol. 84(2), pages 295-318, March.
    8. V. A. Adona & M. L. N. Gonçalves & J. G. Melo, 2020. "An inexact proximal generalized alternating direction method of multipliers," Computational Optimization and Applications, Springer, vol. 76(3), pages 621-647, July.

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