ADMM and Accelerated ADMM as Continuous Dynamical Systems
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Abstract (arXiv)
Recently, there has been an increasing interest in using tools from dynamical systems to analyze the behavior of simple optimization algorithms such as gradient descent and accelerated variants. This paper strengthens such connections by deriving the differential equations that model the continuous limit of the sequence of iterates generated by the alternating direction method of multipliers, as well as an accelerated variant. We employ the direct method of Lyapunov to analyze the stability of critical points of the dynamical systems and to obtain associated convergence rates.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 May 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 1; 2025: 1.
6 papers.
- 2021Continuous vs. Discrete Optimization of Deep Neural Networkscited by 34
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- 2023GA-HQS: MRI reconstruction via a generically accelerated unfolding approachcited by 3
- 2022NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizercited by 2
- 2025Learning by solving differential equationscited by 1
- 2024A General Continuous-Time Formulation of Stochastic ADMM and Its Variantscited by 0
- 2025Learning by solving differential equations19 May 2025
- 2024A General Continuous-Time Formulation of Stochastic ADMM and Its Variants22 Apr 2024
- 2023GA-HQS: MRI reconstruction via a generically accelerated unfolding approach6 Apr 2023
- 2022NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizer29 Sep 2022
- 2022Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents20 Mar 2022
- 2021Continuous vs. Discrete Optimization of Deep Neural Networks14 Jul 2021