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Accelerated Sampling with the Inertial Langevin Algorithm
We consider the \emph{inertial Langevin algorithm} (ILA), a simple momentum-based method for sampling from Gibbs distributions of the form $π(x) \propto \exp(-U(x))$. ILA augments the unadjusted Langevin algorithm with an inertia term and a matching noise rescaling, yielding a sampling analogue of P
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