Proves dimension-free long-time reverse transportation inequality for non-globally-dissipative Langevin dynamics with non-convex potentials controlling Rényi divergence.
Rapid convergence of the unadjusted Langevin algorithm: Isoperimetry suffices.Advances in neural information processing systems, 32
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New RSLMC sampling algorithms achieve uniform-in-time W2 error bounds of order O(sqrt(d) h) under gradient Lipschitz and log-Sobolev assumptions, with modified versions for superlinear gradient growth and supporting numerical examples.
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Long-time reverse transportation inequalities for non-globally-dissipative Langevin dynamics
Proves dimension-free long-time reverse transportation inequality for non-globally-dissipative Langevin dynamics with non-convex potentials controlling Rényi divergence.
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When Langevin Monte Carlo Meets Randomization: New Sampling Algorithms with Non-asymptotic Error Bounds beyond Log-Concavity and Gradient Lipschitzness
New RSLMC sampling algorithms achieve uniform-in-time W2 error bounds of order O(sqrt(d) h) under gradient Lipschitz and log-Sobolev assumptions, with modified versions for superlinear gradient growth and supporting numerical examples.