CBX: Python and Julia Packages for Consensus-Based Interacting Particle Methods

MCML Authors

Konstantin Riedl

Dr.

* Former Member

→ Group Massimo Fornasier
 Applied Numerical Analysis

Tim Roith

Dr.

→ Group Massimo Fornasier
 Applied Numerical Analysis

Abstract

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’ˆetre 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 for 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.

misc BBG+24

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Preprint

Mar. 2024

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Authors

R. Bailo • A. Barbaro • S. N. Gomes • K. Riedl • T. Roith • C. Totzeck • U. Vaes

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Links

arXiv

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Research Area

 A2 | Mathematical Foundations