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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Geometric tempering yields exponential convergence bounds for both Wasserstein and Fisher-Rao flows but produces no speedup in the Fisher-Rao metric, with new adaptive schedules derived from the tempered dynamics.
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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.
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Properties and limitations of geometric tempering for gradient flow dynamics
Geometric tempering yields exponential convergence bounds for both Wasserstein and Fisher-Rao flows but produces no speedup in the Fisher-Rao metric, with new adaptive schedules derived from the tempered dynamics.