VOPy: A Framework for Black-box Vector Optimization
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866916514681061376 |
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| author | Yıldırım, Yaşar Cahit Karagözlü, Efe Mert Korkmaz, İlter Onat Ararat, Çağın Tekin, Cem |
| author_facet | Yıldırım, Yaşar Cahit Karagözlü, Efe Mert Korkmaz, İlter Onat Ararat, Çağın Tekin, Cem |
| contents | We introduce VOPy, an open-source Python library designed to address black-box vector optimization, where multiple objectives must be optimized simultaneously with respect to a partial order induced by a convex cone. VOPy extends beyond traditional multi-objective optimization (MOO) tools by enabling flexible, cone-based ordering of solutions; with an application scope that includes environments with observation noise, discrete or continuous design spaces, limited budgets, and batch observations. VOPy provides a modular architecture, facilitating the integration of existing methods and the development of novel algorithms. We detail VOPy's architecture, usage, and potential to advance research and application in the field of vector optimization. The source code for VOPy is available at https://github.com/Bilkent-CYBORG/VOPy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06604 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VOPy: A Framework for Black-box Vector Optimization Yıldırım, Yaşar Cahit Karagözlü, Efe Mert Korkmaz, İlter Onat Ararat, Çağın Tekin, Cem Machine Learning Applications We introduce VOPy, an open-source Python library designed to address black-box vector optimization, where multiple objectives must be optimized simultaneously with respect to a partial order induced by a convex cone. VOPy extends beyond traditional multi-objective optimization (MOO) tools by enabling flexible, cone-based ordering of solutions; with an application scope that includes environments with observation noise, discrete or continuous design spaces, limited budgets, and batch observations. VOPy provides a modular architecture, facilitating the integration of existing methods and the development of novel algorithms. We detail VOPy's architecture, usage, and potential to advance research and application in the field of vector optimization. The source code for VOPy is available at https://github.com/Bilkent-CYBORG/VOPy. |
| title | VOPy: A Framework for Black-box Vector Optimization |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2412.06604 |