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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2510.03932 |
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| _version_ | 1866916992659750912 |
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| author | Montoison, Alexis Caillau, Jean-Baptiste |
| author_facet | Montoison, Alexis Caillau, Jean-Baptiste |
| contents | 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 \texttt{OptimalControl.jl} into structured sparse nonlinear programs. These programs are compiled into GPU kernels using \texttt{ExaModels.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 \texttt{MadNLP.jl} and the GPU sparse linear solver cuDSS, yielding significant speed-ups over CPU-based approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03932 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling and Optimization of Control Problems on GPUs Montoison, Alexis Caillau, Jean-Baptiste Optimization and Control 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 \texttt{OptimalControl.jl} into structured sparse nonlinear programs. These programs are compiled into GPU kernels using \texttt{ExaModels.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 \texttt{MadNLP.jl} and the GPU sparse linear solver cuDSS, yielding significant speed-ups over CPU-based approaches. |
| title | Modeling and Optimization of Control Problems on GPUs |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2510.03932 |