![[2602.16005] ODYN: An All-Shifted Non-Interior-Point Method for Quadratic Programming in Robotics and AI](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.16005] ODYN: An All-Shifted Non-Interior-Point Method for Quadratic Programming in Robotics and AI
Summary
The paper introduces ODYN, a novel non-interior-point method for quadratic programming, designed for efficiency in robotics and AI applications, showcasing superior performance in various optimization tasks.
Why It Matters
ODYN addresses significant challenges in solving quadratic programming problems, particularly in robotics and AI, where real-time performance and robustness are critical. Its open-source implementation and strong benchmark results position it as a valuable tool for researchers and practitioners in these fields.
Key Takeaways
- ODYN is an all-shifted primal-dual non-interior-point solver for quadratic programming.
- It effectively handles ill-conditioned and degenerate problems without requiring linear independence of constraints.
- The method demonstrates strong warm-start performance, crucial for real-time applications.
- ODYN has been benchmarked against the Maros-Mészáros test set, showing state-of-the-art convergence.
- It can be integrated into various applications, including predictive control and deep learning optimization.
Computer Science > Robotics arXiv:2602.16005 (cs) [Submitted on 17 Feb 2026] Title:ODYN: An All-Shifted Non-Interior-Point Method for Quadratic Programming in Robotics and AI Authors:Jose Rojas, Aristotelis Papatheodorou, Sergi Martinez, Ioannis Havoutis, Carlos Mastalli View a PDF of the paper titled ODYN: An All-Shifted Non-Interior-Point Method for Quadratic Programming in Robotics and AI, by Jose Rojas and 3 other authors View PDF HTML (experimental) Abstract:We introduce ODYN, a novel all-shifted primal-dual non-interior-point quadratic programming (QP) solver designed to efficiently handle challenging dense and sparse QPs. ODYN combines all-shifted nonlinear complementarity problem (NCP) functions with proximal method of multipliers to robustly address ill-conditioned and degenerate problems, without requiring linear independence of the constraints. It exhibits strong warm-start performance and is well suited to both general-purpose optimization, and robotics and AI applications, including model-based control, estimation, and kernel-based learning methods. We provide an open-source implementation and benchmark ODYN on the Maros-Mészáros test set, demonstrating state-of-the-art convergence performance in small-to-high-scale problems. The results highlight ODYN's superior warm-starting capabilities, which are critical in sequential and real-time settings common in robotics and AI. These advantages are further demonstrated by deploying ODYN as the backend of an SQP-base...
