Windowed thinning and query complexity for the bouncy particle and Zigzag samplers

Abstract:Let $\mu(d x)\propto e^{-U(x)} d x$ on $\R^d$, where $U$ is $m$-strongly convex and $L$-smooth, and denote by $\kappa=L/m$ the condition number. We consider windowed thinning, an exact simulation method for the bouncy particle sampler and the coordinate Zigzag process. The method divides a trajectory into deterministic windows and uses a gradient evaluation at the beginning of each window to construct a tractable local envelope for the event rate. Combining this construction with quantitative mixing estimates and finite-time bounds on the expected numbers of bounces and flips yields query complexity guarantees from a Gaussian cold start. For total-variation error $\varepsilon$, the expected query counts are $O(\kappa^{1/2}d\,(d\log\kappa+\log\frac1\varepsilon))$ gradient queries for the bouncy particle sampler and $O(\kappa d^{1/4}(d\log\kappa+\log\frac1\varepsilon))$ full-gradient equivalents for Zigzag, where $d$ coordinate-partial queries count as one equivalent.

Numerical Analysis (math.NA); Machine Learning (cs.LG); Probability (math.PR); Statistics Theory (math.ST)

arXiv:2607.28413 [math.NA]

(or arXiv:2607.28413v1 [math.NA] for this version)

https://doi.org/10.48550/arXiv.2607.28413 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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