# A Dual Active-Set Solver for Embedded Quadratic Programming Using Recursive LDL' Updates

arXiv: https://arxiv.org/abs/2103.16236 · HTML: https://mlsanity.com/paper/2103.16236/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2021.

## Built on by (most cited first, 4 of 4)

- [Differentiation Through Black-Box Quadratic Programming Solvers](https://mlsanity.com/paper/2410.06324/) (2024)
- [Differentiable Nonlinear Model Predictive Control](https://mlsanity.com/paper/2505.01353/) (2025)
- [Warm-starting active-set solvers using graph neural networks](https://mlsanity.com/paper/2511.13174/) (2025)
- [Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping](https://mlsanity.com/paper/2607.23930/) (2026)

## Abstract (arXiv)

In this paper we present a dual active-set solver for quadratic programming which has properties suitable for use in embedded model predictive control applications. In particular, the solver is efficient, can easily be warm-started, and is simple to code. Moreover, the exact worst-case computational complexity of the solver can be determined offline and, by using outer proximal-point iterations, ill-conditioned problems can be handled in a robust manner.