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Symmetric Inertial Bregman ADMM and Its Application in Regularized Linear Regression Problems

Xue Zhonghui et al.

Pacific Journal of Optimization2026https://doi.org/10.61208/pjo-2026-003article
AJG 1
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0.50

What the paper says

(Communicated by Jie Sun) In addressing the challenges of nonconvex, nonsmooth, and nonseparable optimization, this paper introduces a novel symmetric inertial Bregman Alternating Direction Method of Multipliers (SIBADMM). This algorithm extends the symmetric ADMM by incorporating an inertial mechanism and Bregman divergence within the x-subproblem, aiming to accelerate convergence. The asymptotic convergence is established under the assumption of boundedness in the sequence generated by the algorithm, utilizing the Kurdyka-Łojasiewicz property. The practical utility of SIBADMM is demonstrated through its application to various linear regression problems with different regularization terms, thereby showcasing its effectiveness.

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https://doi.org/https://doi.org/10.61208/pjo-2026-003

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@article{xue2026,
  title        = {{Symmetric Inertial Bregman ADMM and Its Application in Regularized Linear Regression Problems}},
  author       = {Xue Zhonghui et al.},
  journal      = {Pacific Journal of Optimization},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.61208/pjo-2026-003},
}

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0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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