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Found 2 papers, 1 papers with code
Optimal Scaling for the Proximal Langevin Algorithm in High Dimensions
In \cite{pere:16}, Pereyra introduced the proximal MALA algorithm where the gradient of the log target density is replaced by the proximal function (mainly aimed at implementing MALA non-differentiable target densities).
Does Hamiltonian Monte Carlo mix faster than a random walk on multimodal densities?
In this paper, we investigate a different scaling question: does HMC beat RWM for highly $\textit{multimodal}$ targets?
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