Convergence of multi-block Bregman ADMM for nonconvex composite problems
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Abstract (arXiv)
The alternating direction method with multipliers (ADMM) has been one of most powerful and successful methods for solving various composite problems. The convergence of the conventional ADMM (i.e., 2-block) for convex objective functions has been justified for a long time, and its convergence for nonconvex objective functions has, however, been established very recently. The multi-block ADMM, a natural extension of ADMM, is a widely used scheme and has also been found very useful in solving various nonconvex optimization problems. It is thus expected to establish convergence theory of the multi-block ADMM under nonconvex frameworks. In this paper we present a Bregman modification of 3-block ADMM and establish its convergence for a large family of nonconvex functions. We further extend the convergence results to the N-block case (N ≥ 3), which underlines the feasibility of multi-block ADMM applications in nonconvex settings. Finally, we present a simulation study and a real-world application to support the correctness of the obtained theoretical assertions.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 May 2015.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 0; 2025: 1; 2026: 2.
8 papers.
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- 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
- 2025AugFL: Augmenting Federated Learning with Pretrained Modelscited by 6
- 2023Convergence of Nonconvex PnP-ADMM with MMSE Denoiserscited by 3
- 20263BASiL: An Algorithmic Framework for Sparse plus Low-Rank Compression of LLMscited by 1
- 2021Joint learning of multiple Granger causal networks via non-convex regularizations: Inference of group-level brain connectivitycited by 0
- 2026Second-Order KKT Guarantees for Bregman ADMM in Nonconvex and Non-Lipschitz Optimizationcited by 0
- 2026Second-Order KKT Guarantees for Bregman ADMM in Nonconvex and Non-Lipschitz Optimization26 Jun 2026
- 20263BASiL: An Algorithmic Framework for Sparse plus Low-Rank Compression of LLMs2 Mar 2026
- 2025AugFL: Augmenting Federated Learning with Pretrained Models4 Mar 2025
- 2023Convergence of Nonconvex PnP-ADMM with MMSE Denoisers30 Nov 2023
- 2023Fusion of Global and Local Knowledge for Personalized Federated Learning21 Feb 2023
- 2022Federated Learning and Meta Learning: Approaches, Applications, and Directions24 Oct 2022
- 2021Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model21 May 2021
- 2021Joint learning of multiple Granger causal networks via non-convex regularizations: Inference of group-level brain connectivity15 May 2021