1,071 research outputs found
Implicit Langevin Algorithms for Sampling From Log-concave Densities
For sampling from a log-concave density, we study implicit integrators
resulting from -method discretization of the overdamped Langevin
diffusion stochastic differential equation. Theoretical and algorithmic
properties of the resulting sampling methods for and a
range of step sizes are established. Our results generalize and extend prior
works in several directions. In particular, for , we prove
geometric ergodicity and stability of the resulting methods for all step sizes.
We show that obtaining subsequent samples amounts to solving a strongly-convex
optimization problem, which is readily achievable using one of numerous
existing methods. Numerical examples supporting our theoretical analysis are
also presented
Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo
This paper presents a detailed theoretical analysis of the Langevin Monte
Carlo sampling algorithm recently introduced in Durmus et al. (Efficient
Bayesian computation by proximal Markov chain Monte Carlo: when Langevin meets
Moreau, 2016) when applied to log-concave probability distributions that are
restricted to a convex body . This method relies on a
regularisation procedure involving the Moreau-Yosida envelope of the indicator
function associated with . Explicit convergence bounds in total
variation norm and in Wasserstein distance of order are established. In
particular, we show that the complexity of this algorithm given a first order
oracle is polynomial in the dimension of the state space. Finally, some
numerical experiments are presented to compare our method with competing MCMC
approaches from the literature
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