Nonasymptotic convergence analysis for the unadjusted Langevin algorithm

DOI10.1214/16-AAP1238zbMATH Open1377.65007arXiv1507.05021OpenAlexW4299557478MaRDI QIDQ2403136

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Publication date: 15 September 2017

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Abstract: In this paper, we study a method to sample from a target distribution $pi$ over $mathbb{R}^d$ having a positive density with respect to the Lebesgue measure, known up to a normalisation factor. This method is based on the Euler discretization of the overdamped Langevin stochastic differential equation associated with $pi$. For both constant and decreasing step sizes in the Euler discretization, we obtain non-asymptotic bounds for the convergence to the target distribution $pi$ in total variation distance. A particular attention is paid to the dependency on the dimension $d$, to demonstrate the applicability of this method in the high dimensional setting. These bounds improve and extend the results of (Dalalyan 2014).

Full work available at URL: https://arxiv.org/abs/1507.05021

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