Zeroth-Order Langevin Monte Carlo via SPSA under Noisy Function Measurements
In sampling problems, gradient-based schemes such as Langevin Monte Carlo (LMC) mix faster than non-gradient-based methods, but their applicability is limited by access to the gradient of the target log-density. In practice, gradients are often unavailable and function evaluations are noisy, e.g., stochastic simulators or black-box simulators, so we propose LMC-SPSA with noise, which approximates the gradient of the target log-density using two noisy function evaluations per iteration. We prove, under noisy gradient estimates, that LMC-SPSA converges in distribution by proving the convergence in Wasserstein distance. Furthermore, we construct a diminishing step-size schedule that still drives the Wasserstein error bound to convergence, extending convergence guarantees beyond the constant-step setting. Further, we sharpen the dominant dimension dependence of the Wasserstein error from $O(p^4)$ to $O(p^2)$ (with $p$ denoting the dimension), and support this analysis with numerical results. We show that LMC-SPSA achieves $W_2$-accuracy $\varepsilon$ with total noisy-oracle complexity of $O(p/\varepsilon^2+\delta^2p^3/\varepsilon^3)$, where $\delta$ is the paired-noise level. This improves the noise-dependent accuracy scaling relative to the ZO-LMC method of Roy et al. We further establish asymptotically vanishing Wasserstein error as the number of iterations $\to\infty$ under diminishing step-size and perturbation sequences and derive an explicit convergence rate for a balanced schedule under noisy zeroth-order feedback. Empirical experiments are conducted to verify the performance of LMC-SPSA with noise. We provide an oracle-budget-matched comparison with the ZO-LMC method, showing smaller empirical sampling errors under the same function-evaluation budget.