Abstract
In this paper we develop an accelerated Alternating Direction Method of Multipliers (ADMM) algorithm for solving quadratic programs called superADMM. Unlike standard ADMM QP solvers, superADMM uses a novel dynamic weighting method that penalizes each constraint individually and performs weight updates at every ADMM iteration. We provide a numerical stability analysis, methods for parameter selection and infeasibility detection. The algorithm is implemented in c with efficient linear algebra packages to provide a short execution time and allows calling superADMM from popular languages such as MATLAB and Python. A comparison of superADMM with state-of-the-art ADMM solvers and widely used commercial solvers showcases the efficiency and accuracy of the developed solver.
| Original language | English |
|---|---|
| Title of host publication | 2025 29th International Conference on System Theory, Control and Computing, ICSTCC 2025 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 569-575 |
| Number of pages | 7 |
| ISBN (Electronic) | 979-8-3315-9621-7 |
| DOIs | |
| Publication status | Published - 20 Nov 2025 |
| Event | 2025 29th International Conference on System Theory, Control and Computing, ICSTCC 2025 - Cluj-Napoca, Romania Duration: 9 Oct 2025 → 11 Oct 2025 |
Conference
| Conference | 2025 29th International Conference on System Theory, Control and Computing, ICSTCC 2025 |
|---|---|
| Country/Territory | Romania |
| City | Cluj-Napoca |
| Period | 9/10/25 → 11/10/25 |
Bibliographical note
Publisher Copyright:© 2025 IEEE.
Keywords
- Alternating direction method of multipliers
- Dynamic weighting
- Model predictive control
- Quadratic programming
- Superlinear convergence
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