An interacting particle consensus method for constrained global optimization
<|reference_start|>An interacting particle consensus method for constrained global optimization: This paper presents a particle-based optimization method designed for addressing minimization problems with equality constraints, particularly in cases where the loss function exhibits non-differentiability or non-convexity. The proposed method combines components from consensus-based optimization algorithm with a newly introduced forcing term directed at the constraint set. A rigorous mean-field limit of the particle system is derived, and the convergence of the mean-field limit to the constrained minimizer is established. Additionally, we introduce a stable discretized algorithm and conduct various numerical experiments to demonstrate the performance of the proposed method.<|reference_end|>
@article{carrillo2024an,
    title={An interacting particle consensus method for constrained global
  optimization},
    author={Jos'e A. Carrillo, Shi Jin, Haoyu Zhang, Yuhua Zhu},
    journal={arXiv preprint arXiv:2405.00891},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.00891},
    primaryClass={math.OC cs.NA math.AP math.NA}
}