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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2501.17495 |
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| _version_ | 1866910804381532160 |
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| author | Zhang, Zixuan Song, Xiaowei Zeng, Yujiao Li, Jie Nie, Yaling Zhu, Min Chen, Jianhua Wang, Linmin Xiao, Xin |
| author_facet | Zhang, Zixuan Song, Xiaowei Zeng, Yujiao Li, Jie Nie, Yaling Zhu, Min Chen, Jianhua Wang, Linmin Xiao, Xin |
| contents | With the development of artificial intelligence, simulation-based optimization problems, which present a significant challenge in the process systems engineering community, are increasingly being addressed with the surrogate-based framework. In this work, we propose a deterministic algorithm framework based on feasible path sequential quadratic programming for optimizing differentiable machine learning models embedded problems. The proposed framework effectively addresses two key challenges: (i) achieving the computation of first- and second-order derivatives of machine learning models' outputs with respect to inputs; and (ii) by introducing the feasible path method, the massive intermediate variables resulting from the algebraic formulation of machine learning models eliminated. Surrogate models for six test functions and two process simulations were established and optimized. All six test functions were successfully optimized to the global optima, demonstrating the framework's effectiveness. The optimization time for all cases did not exceed 2s, highlighting the efficiency of the algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17495 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Feasible Path SQP Algorithm for Simulation-based Optimization Surrogated with Differentiable Machine Learning Models Zhang, Zixuan Song, Xiaowei Zeng, Yujiao Li, Jie Nie, Yaling Zhu, Min Chen, Jianhua Wang, Linmin Xiao, Xin Optimization and Control With the development of artificial intelligence, simulation-based optimization problems, which present a significant challenge in the process systems engineering community, are increasingly being addressed with the surrogate-based framework. In this work, we propose a deterministic algorithm framework based on feasible path sequential quadratic programming for optimizing differentiable machine learning models embedded problems. The proposed framework effectively addresses two key challenges: (i) achieving the computation of first- and second-order derivatives of machine learning models' outputs with respect to inputs; and (ii) by introducing the feasible path method, the massive intermediate variables resulting from the algebraic formulation of machine learning models eliminated. Surrogate models for six test functions and two process simulations were established and optimized. All six test functions were successfully optimized to the global optima, demonstrating the framework's effectiveness. The optimization time for all cases did not exceed 2s, highlighting the efficiency of the algorithm. |
| title | Feasible Path SQP Algorithm for Simulation-based Optimization Surrogated with Differentiable Machine Learning Models |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2501.17495 |