Asymptotic relaxation enhancing flows are constructed to achieve arbitrarily fast convergence in Langevin sampling from Gibbs measures while preserving the invariant distribution.
Estimates of the numerical den- sity for stochastic differential equations with multiplicative noise
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Accelerating sampling via asymptotic relaxation enhancing flows
Asymptotic relaxation enhancing flows are constructed to achieve arbitrarily fast convergence in Langevin sampling from Gibbs measures while preserving the invariant distribution.