Rapid Convergence of the Unadjusted Langevin Algorithm: Log-Sobolev Suffices
We prove a convergence guarantee on the unadjusted Langevin algorithm for sampling assuming only that the target distribution e^-f satisfies a log-Sobolev inequality and the Hessian of f is bounded. In particular, f is not required to be convex or have higher derivatives bounded.
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