A Dual Active-Set Solver for Embedded Quadratic Programming Using Recursive LDL' Updates
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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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Mar 2021.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 1.
4 papers.
- 2024Differentiation Through Black-Box Quadratic Programming Solverscited by 10
- 2025Differentiable Nonlinear Model Predictive Controlcited by 6
- 2025Warm-starting active-set solvers using graph neural networkscited by 2
- 2026Bridging Reinforcement Learning and Optimal Control via Feasible Action Mappingcited by 0
- 2026Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping27 Jul 2026
- 2025Warm-starting active-set solvers using graph neural networks17 Nov 2025
- 2025Differentiable Nonlinear Model Predictive Control2 May 2025
- 2024Differentiation Through Black-Box Quadratic Programming Solvers8 Oct 2024