CBX: Python and Julia packages for consensus-based interacting particle methods
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917854982438912 |
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| author | Bailo, Rafael Barbaro, Alethea Gomes, Susana N. Riedl, Konstantin Roith, Tim Totzeck, Claudia Vaes, Urbain |
| author_facet | Bailo, Rafael Barbaro, Alethea Gomes, Susana N. Riedl, Konstantin Roith, Tim Totzeck, Claudia Vaes, Urbain |
| contents | We introduce CBXPy and ConsensusBasedX.jl, Python and Julia implementations of consensus-based interacting particle systems (CBX), which generalise consensus-based optimization methods (CBO) for global, derivative-free optimisation. The raison d'être of our libraries is twofold: on the one hand, to offer high-performance implementations of CBX methods that the community can use directly, while on the other, providing a general interface that can accommodate and be extended to further variations of the CBX family. Python and Julia were selected as the leading high-level languages in terms of usage and performance, as well as their popularity among the scientific computing community. Both libraries have been developed with a common ethos, ensuring a similar API and core functionality, while leveraging the strengths of each language and writing idiomatic code. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14470 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CBX: Python and Julia packages for consensus-based interacting particle methods Bailo, Rafael Barbaro, Alethea Gomes, Susana N. Riedl, Konstantin Roith, Tim Totzeck, Claudia Vaes, Urbain Optimization and Control 49-04 (Primary) 90C26, 90C56, 93A16 (Secondary) We introduce CBXPy and ConsensusBasedX.jl, Python and Julia implementations of consensus-based interacting particle systems (CBX), which generalise consensus-based optimization methods (CBO) for global, derivative-free optimisation. The raison d'être of our libraries is twofold: on the one hand, to offer high-performance implementations of CBX methods that the community can use directly, while on the other, providing a general interface that can accommodate and be extended to further variations of the CBX family. Python and Julia were selected as the leading high-level languages in terms of usage and performance, as well as their popularity among the scientific computing community. Both libraries have been developed with a common ethos, ensuring a similar API and core functionality, while leveraging the strengths of each language and writing idiomatic code. |
| title | CBX: Python and Julia packages for consensus-based interacting particle methods |
| topic | Optimization and Control 49-04 (Primary) 90C26, 90C56, 93A16 (Secondary) |
| url | https://arxiv.org/abs/2403.14470 |