Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration
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Abstract (arXiv)
The alternating direction method of multipliers (ADMM) is one of the most widely used first-order optimisation methods in the literature owing to its simplicity, flexibility and efficiency. Over the years, numerous efforts are made to improve the performance of the method, such as the inertial technique. By studying the geometric properties of ADMM, we discuss the limitations of current inertial accelerated ADMM and then present and analyze an adaptive acceleration scheme for the method. Numerical experiments on problems arising from image processing, statistics and machine learning demonstrate the advantages of the proposed acceleration approach.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Jun 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 2; 2025: 1.
6 papers.
- 2022Beyond L1: Faster and Better Sparse Models with skglmcited by 16
- 2023Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUscited by 3
- 2021Training Generative Adversarial Networks with Adaptive Composite Gradientcited by 1
- 2024Anderson acceleration for iteratively reweighted ℓ1 algorithmcited by 0
- 2024A General Continuous-Time Formulation of Stochastic ADMM and Its Variantscited by 0
- 2025A Double Inertial Forward-Backward Splitting Algorithm With Applications to Regression and Classification Problemscited by 0
- 2025A Double Inertial Forward-Backward Splitting Algorithm With Applications to Regression and Classification Problems1 May 2025
- 2024A General Continuous-Time Formulation of Stochastic ADMM and Its Variants22 Apr 2024
- 2024Anderson acceleration for iteratively reweighted ℓ1 algorithm12 Mar 2024
- 2023Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUs29 May 2023
- 2022Beyond L1: Faster and Better Sparse Models with skglm16 Apr 2022
- 2021Training Generative Adversarial Networks with Adaptive Composite Gradient10 Nov 2021