# HPIPM: a high-performance quadratic programming framework for model predictive control

arXiv: https://arxiv.org/abs/2003.02547 · HTML: https://mlsanity.com/paper/2003.02547/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 23 of 23)

- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification](https://mlsanity.com/paper/2502.02133/) (2025)
- [Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking](https://mlsanity.com/paper/2206.03305/) (2022)
- [Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control](https://mlsanity.com/paper/2210.12583/) (2022)
- [Differentiation Through Black-Box Quadratic Programming Solvers](https://mlsanity.com/paper/2410.06324/) (2024)
- [Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds](https://mlsanity.com/paper/2207.11136/) (2022)
- [Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU](https://mlsanity.com/paper/2604.07644/) (2026)
- [Differentiable Nonlinear Model Predictive Control](https://mlsanity.com/paper/2505.01353/) (2025)
- [Zero-Order Optimization for Gaussian Process-based Model Predictive Control](https://mlsanity.com/paper/2211.15522/) (2022)
- [Optimality Principles in Spacecraft Neural Guidance and Control](https://mlsanity.com/paper/2305.13078/) (2023)
- [Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots](https://mlsanity.com/paper/2212.02941/) (2022)
- [Introducing a Deep Neural Network-based Model Predictive Control Framework for Rapid Controller Implementation](https://mlsanity.com/paper/2310.08392/) (2023)
- [er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speeds](https://mlsanity.com/paper/2310.18112/) (2023)
- [MPC4RL -- A Software Package for Reinforcement Learning based on Model Predictive Control](https://mlsanity.com/paper/2501.15897/) (2025)
- [Context-Aware Deep Lagrangian Networks for Model Predictive Control](https://mlsanity.com/paper/2506.15249/) (2025)
- [TurboMPC: Fast, Scalable, and Differentiable Model Predictive Control on the GPU](https://mlsanity.com/paper/2606.24039/) (2026)
- [Modular Control Architecture for Safe Marine Navigation: Reinforcement Learning and Predictive Safety Filters](https://mlsanity.com/paper/2312.01855/) (2023)
- [ODYN: An All-Shifted Non-Interior-Point Method for Quadratic Programming in Robotics and AI](https://mlsanity.com/paper/2602.16005/) (2026)
- [Learning Residual Model of Model Predictive Control via Random Forests for Autonomous Driving](https://mlsanity.com/paper/2304.04366/) (2023)
- [Autonomous Wheel Loader Navigation Using Goal-Conditioned Actor-Critic MPC](https://mlsanity.com/paper/2409.15717/) (2024)
- [Zero-Shot Function Encoder-Based Differentiable Predictive Control](https://mlsanity.com/paper/2511.05757/) (2025)
- [Driving Context-guided Model Predictive Planning and Control for Autonomous Car Racing at the Limit and Beyond](https://mlsanity.com/paper/2609.12660/) (2026)
- [Language-Guided Terrain-Adaptive Neural MPC for Autonomous Traversal of Articulated Tracked Robots](https://mlsanity.com/paper/2609.13083/) (2026)

## Abstract (arXiv)

This paper introduces HPIPM, a high-performance framework for quadratic programming (QP), designed to provide building blocks to efficiently and reliably solve model predictive control problems. HPIPM currently supports three QP types, and provides interior point method (IPM) solvers as well (partial) condensing routines. In particular, the IPM for optimal control QPs is intended to supersede the HPMPC solver, and it largely improves robustness while keeping the focus on speed. Numerical experiments show that HPIPM reliably solves challenging QPs, and that it outperforms other state-of-the-art solvers in speed.