# Koopman-BoxQP: Solving Large-Scale NMPC at kHz Rates

arXiv: https://arxiv.org/abs/2602.18331 · HTML: https://mlsanity.com/paper/2602.18331/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2026.

## Built on by (most cited first, 2 of 2)

- [Parametric Nonconvex Optimization via Convex Surrogates](https://mlsanity.com/paper/2604.05640/) (2026)
- [Parallel Predictive World Models for Accurate and Efficient Long-Horizon Planning](https://mlsanity.com/paper/2610.08627/) (2026)

## Abstract (arXiv)

Solving large-scale nonlinear model predictive control (NMPC) problems at kilohertz (kHz) rates on standard processors remains a formidable challenge. This paper proposes a Koopman-BoxQP framework that i) learns a linear Koopman high-dimensional model, ii) eliminates the high-dimensional observables to construct a multi-step prediction model of the states and control inputs, iii) penalizes the multi-step prediction model into the objective, which results in a structured box-constrained quadratic program (BoxQP) whose decision variables include both the system states and control inputs, iv) develops a structure-exploited and warm-starting-supported variant of the feasible Mehrotra's interior-point algorithm for BoxQP. Numerical results demonstrate that Koopman-BoxQP can solve a large-scale NMPC problem with 1040 variables and 2080 inequalities at a kHz rate.