ADMM Penalty Parameter Selection by Residual Balancing
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Abstract (arXiv)
Appropriate selection of the penalty parameter is crucial to obtaining good performance from the Alternating Direction Method of Multipliers (ADMM). While analytic results for optimal selection of this parameter are very limited, there is a heuristic method that appears to be relatively successful in a number of different problems. The contribution of this paper is to demonstrate that their is a potentially serious flaw in this heuristic approach, and to propose a modification that at least partially addresses it.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Apr 2017.
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Citing papers per year: 2023: 1; 2024: 4; 2025: 1; 2026: 2.
8 papers.
- 2024A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approachescited by 19
- 2023Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernelcited by 2
- 2026Learning Multi-Agent Coordination via Sheaf-ADMMcited by 2
- 2026Motion Planning with Model-Based Diffusion via Constraint Optimization and Adaptive Schedulingcited by 1
- 2024Optimizing ADMM and Over-Relaxed ADMM Parameters for Linear Quadratic Problemscited by 0
- 2024Two-Timescale Optimization Framework for Sparse-Feedback Linear-Quadratic Optimal Controlcited by 0
- 2024Alternating minimization for square root principal component pursuitcited by 0
- 2025Optimization Problem Solving Can Transition to Evolutionary Agentic Workflowscited by 0
- 2026Motion Planning with Model-Based Diffusion via Constraint Optimization and Adaptive Scheduling16 Jul 2026
- 2026Learning Multi-Agent Coordination via Sheaf-ADMM29 May 2026
- 2025Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows7 May 2025
- 2024Alternating minimization for square root principal component pursuit31 Dec 2024
- 2024Two-Timescale Optimization Framework for Sparse-Feedback Linear-Quadratic Optimal Control17 Jun 2024
- 2024A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches3 Apr 2024
- 2024Optimizing ADMM and Over-Relaxed ADMM Parameters for Linear Quadratic Problems1 Jan 2024
- 2023Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel15 Sep 2023