CBX: Python and Julia packages for consensus-based interacting particle methods
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Abstract (arXiv)
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'etre 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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Mar 2024.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 2.
5 papers.
- 2025Learning of Population Dynamics: Inverse Optimization Meets JKO Schemecited by 9
- 2026Quantifying Concentration Phenomena of Mean-Field Transformers in the Low-Temperature Regimecited by 5
- 2025Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategiescited by 2
- 2024Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimizationcited by 1
- 2026A Multiscale Kinetic Framework for Image Segmentation: From Particle Systems to Continuum Modelscited by 0
- 2026A Multiscale Kinetic Framework for Image Segmentation: From Particle Systems to Continuum Models27 May 2026
- 2026Quantifying Concentration Phenomena of Mean-Field Transformers in the Low-Temperature Regime11 May 2026
- 2025Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies30 Jun 2025
- 2025Learning of Population Dynamics: Inverse Optimization Meets JKO Scheme2 Jun 2025
- 2024Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimization3 Dec 2024