On the mean-field limit for the consensus-based optimization
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Abstract (arXiv)
This paper is concerned with the large particle limit for the consensus-based optimization (CBO), which was postulated in the pioneering works [6,28]. In order to solve this open problem, we adapt a compactness argument by first proving the tightness of the empirical measures μNN≥ 2 associated to the particle system and then verifying that the limit measure μ is the unique weak solution to the mean-field CBO equation. Such results are extended to the model of particle swarm optimization (PSO).
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 May 2021.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 1; 2026: 1.
4 papers.
- 2023FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimizationcited by 7
- 2026MVNN: A Measure-Valued Neural Network for Learning McKean-Vlasov Dynamics from Particle Datacited by 1
- 2022Ensemble-based gradient inference for particle methods in optimization and samplingcited by 0
- 2025Mean-Field Model for Two-Layer Neural Networks Trained with Consensus-Based Optimizationcited by 0
- 2026MVNN: A Measure-Valued Neural Network for Learning McKean-Vlasov Dynamics from Particle Data1 Apr 2026
- 2025Mean-Field Model for Two-Layer Neural Networks Trained with Consensus-Based Optimization26 Nov 2025
- 2023FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization4 May 2023
- 2022Ensemble-based gradient inference for particle methods in optimization and sampling23 Sep 2022