Convergence of kinetic Langevin samplers for non-convex potentials
<|reference_start|>Convergence of kinetic Langevin samplers for non-convex potentials: We study three kinetic Langevin samplers including the Euler discretization, the BU and the UBU splitting scheme. We provide contraction results in $L^1$-Wasserstein distance for non-convex potentials. These results are based on a carefully tailored distance function and an appropriate coupling construction. Additionally, the error in the $L^1$-Wasserstein distance between the true target measure and the invariant measure of the discretization scheme is bounded. To get an $\varepsilon$-accuracy in $L^1$-Wasserstein distance, we show complexity guarantees of order $\mathcal{O}(\sqrt{d}/\varepsilon)$ for the Euler scheme and $\mathcal{O}(d^{1/4}/\sqrt{\varepsilon})$ for the UBU scheme under appropriate regularity assumptions on the target measure. The results are applicable to interacting particle systems and provide bounds for sampling probability measures of mean-field type.<|reference_end|>
@article{schuh2024convergence,
    title={Convergence of kinetic Langevin samplers for non-convex potentials},
    author={Katharina Schuh and Peter A. Whalley},
    journal={arXiv preprint arXiv:2405.09992},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.09992},
    primaryClass={math.PR cs.NA math.NA}
}