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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/2505.12078 |
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| _version_ | 1866909614815051776 |
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| author | Moran, Ruairi Sopasakis, Pantelis |
| author_facet | Moran, Ruairi Sopasakis, Pantelis |
| contents | This paper presents a GPU-accelerated implementation of the SPOCK algorithm, a proximal method designed for solving scenario-based risk-averse optimal control problems. The proposed implementation leverages the massive parallelization of the SPOCK algorithm, and benchmarking against state-of-the-art interior-point solvers demonstrates GPU-accelerated SPOCK's competitive execution time and memory footprint for large-scale problems. We further investigate the effect of the scenario tree structure on parallelizability, and so on solve time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12078 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPU-Accelerated SPOCK for Scenario-Based Risk-Averse Optimal Control Problems Moran, Ruairi Sopasakis, Pantelis Optimization and Control This paper presents a GPU-accelerated implementation of the SPOCK algorithm, a proximal method designed for solving scenario-based risk-averse optimal control problems. The proposed implementation leverages the massive parallelization of the SPOCK algorithm, and benchmarking against state-of-the-art interior-point solvers demonstrates GPU-accelerated SPOCK's competitive execution time and memory footprint for large-scale problems. We further investigate the effect of the scenario tree structure on parallelizability, and so on solve time. |
| title | GPU-Accelerated SPOCK for Scenario-Based Risk-Averse Optimal Control Problems |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2505.12078 |