# Modeling and Optimization of Control Problems on GPUs

arXiv: https://arxiv.org/abs/2510.03932 · HTML: https://mlsanity.com/paper/2510.03932/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [Differentiable Model Predictive Control on the GPU](https://mlsanity.com/paper/2510.06179/) (2025)

## Abstract (arXiv)

We present a fully Julia-based, GPU-accelerated workflow for solving large-scale sparse nonlinear optimal control problems. Continuous-time dynamics are modeled and then discretized via direct transcription with textttOptimalControl.jl into structured sparse nonlinear programs. These programs are compiled into GPU kernels using textttExaModels.jl, leveraging SIMD parallelism for fast evaluation of objectives, constraints, gradients, Jacobians and Hessians. The resulting sparse problems are solved entirely on GPU using the interior-point solver textttMadNLP.jl and the GPU sparse linear solver cuDSS, yielding significant speed-ups over CPU-based approaches.