optipoly: A Python package for boxed-constrained multi-variable polynomial cost functions optimization
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Accesso online: | |
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| _version_ | 1866913758683594752 |
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| author | Alamir, Mazen |
| author_facet | Alamir, Mazen |
| contents | In this paper, a new python package (optipoly) is described that solves box-constrained optimization problem over multivariate polynomial cost functions. The principle of the algorithm is described before its performance is compared to three general purpose NLP solvers implemented in the state-of-the-art Gekko and scipy packages. The comparison show statistically better best solution provided by the algorithm with significantly less computation times. The package will be shortly made freely and easily available through the simple (pip install) process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05689 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | optipoly: A Python package for boxed-constrained multi-variable polynomial cost functions optimization Alamir, Mazen Computational Engineering, Finance, and Science In this paper, a new python package (optipoly) is described that solves box-constrained optimization problem over multivariate polynomial cost functions. The principle of the algorithm is described before its performance is compared to three general purpose NLP solvers implemented in the state-of-the-art Gekko and scipy packages. The comparison show statistically better best solution provided by the algorithm with significantly less computation times. The package will be shortly made freely and easily available through the simple (pip install) process. |
| title | optipoly: A Python package for boxed-constrained multi-variable polynomial cost functions optimization |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2411.05689 |