A Hessian-free stochastic Runge-Kutta LMC algorithm achieves strong order 1.5 with two gradient evaluations per step and uniform-in-time convergence O(d^{3/2} h^{3/2}) in non-log-concave settings.
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Accelerating Langevin Monte Carlo via Efficient Stochastic Runge--Kutta Methods beyond Log-Concavity
A Hessian-free stochastic Runge-Kutta LMC algorithm achieves strong order 1.5 with two gradient evaluations per step and uniform-in-time convergence O(d^{3/2} h^{3/2}) in non-log-concave settings.