# SuperADMM: Solving Quadratic Programs Faster with Dynamic Weighting ADMM

arXiv: https://arxiv.org/abs/2506.11608 · HTML: https://mlsanity.com/paper/2506.11608/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2025.

## Built on by (most cited first, 1 of 1)

- [Learning Over-Relaxation Policies for ADMM with Convergence Guarantees](https://mlsanity.com/paper/2604.26932/) (2026)

## Abstract (arXiv)

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.