# ADMM Penalty Parameter Selection by Residual Balancing

arXiv: https://arxiv.org/abs/1704.06209 · HTML: https://mlsanity.com/paper/1704.06209/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2017.

## Built on by (most cited first, 8 of 8)

- [A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches](https://mlsanity.com/paper/2404.02817/) (2024)
- [Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel](https://mlsanity.com/paper/2309.08201/) (2023)
- [Learning Multi-Agent Coordination via Sheaf-ADMM](https://mlsanity.com/paper/2605.31005/) (2026)
- [Motion Planning with Model-Based Diffusion via Constraint Optimization and Adaptive Scheduling](https://mlsanity.com/paper/2607.14455/) (2026)
- [Optimizing ADMM and Over-Relaxed ADMM Parameters for Linear Quadratic Problems](https://mlsanity.com/paper/2401.00657/) (2024)
- [Two-Timescale Optimization Framework for Sparse-Feedback Linear-Quadratic Optimal Control](https://mlsanity.com/paper/2406.11168/) (2024)
- [Alternating minimization for square root principal component pursuit](https://mlsanity.com/paper/2501.00471/) (2024)
- [Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows](https://mlsanity.com/paper/2505.04354/) (2025)

## 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.