mlsanityData to 13 Oct 2026

Optimal parameter selection for the alternating direction method of multipliers (ADMM): quadratic problems

arXiv 1306.2454 ↗First posted 11 Jun 2013math.OC math.DScited by 19

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Abstract (arXiv)

The alternating direction method of multipliers (ADMM) has emerged as a powerful technique for large-scale structured optimization. Despite many recent results on the convergence properties of ADMM, a quantitative characterization of the impact of the algorithm parameters on the convergence times of the method is still lacking. In this paper we find the optimal algorithm parameters that minimize the convergence factor of the ADMM iterates in the context of l2-regularized minimization and constrained quadratic programming. Numerical examples show that our parameter selection rules significantly outperform existing alternatives in the literature.

Key facts. Cited by 19 papers in the map (data through 13 Oct 2026). First posted on arXiv 11 Jun 2013.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 3; 2024: 5; 2025: 3; 2026: 2.

19 papers.

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  1. 2023Chasing Fairness in Graphs: A GNN Architecture Perspectivecited by 12
  2. 20211-Bit Compressive Sensing for Efficient Federated Learning Over the Aircited by 11
  3. 2021Functional optimal transport: map estimation and domain adaptation for functional datacited by 7
  4. 2022Learning context-aware adaptive solvers to accelerate quadratic programmingcited by 7
  5. 2024Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programmingcited by 6
  6. 2023SCQPTH: an efficient differentiable splitting method for convex quadratic programmingcited by 5
  7. 2022Bi-Level Optimization Augmented with Conditional Variational Autoencoder for Autonomous Driving in Dense Trafficcited by 2
  8. 2025DiffCoord: Differentiable Coordination for Distributed Multi-Agent Trajectory Optimizationcited by 2
  9. 2025Bregman Douglas-Rachford Splitting Methodcited by 2
  10. 2026Learning Multi-Agent Coordination via Sheaf-ADMMcited by 2
  11. 2024Sparse outlier-robust PCA for multi-source datacited by 1
  12. 2021Generalized ADMM in Distributed Learning via Variational Inequalitycited by 0
7 more
  1. 2022Optimizing One-pixel Black-box Adversarial Attackscited by 0
  2. 2023A novel decomposed-ensemble time series forecasting framework: capturing underlying volatility informationcited by 0
  3. 2024Optimizing ADMM and Over-Relaxed ADMM Parameters for Linear Quadratic Problemscited by 0
  4. 2024Robust Multi-Modal Speech In-Painting: A Sequence-to-Sequence Approachcited by 0
  5. 2024Alternating minimization for square root principal component pursuitcited by 0
  6. 2025Safe and Nonconservative Contingency Planning for Autonomous Vehicles via Online Learning-Based Reachable Set Barrierscited by 0
  7. 2026AI-Assisted Discovery and Construction of a Counterexample to the Convergence of Three-Block ADMM with the Identity Matrix as its Third Constraint Blockcited by 0