Metropolis-adjusted Subdifferential Langevin Algorithm
Abstract
The Metropolis-Adjusted Langevin Algorithm (MALA) is a widely used Markov Chain Monte Carlo (MCMC) method for sampling from high-dimensional distributions. However, MALA relies on differentiability assumptions that restrict its applicability. In this paper, we introduce the Metropolis-Adjusted Subdifferential Langevin Algorithm (MASLA), a generalization of MALA that extends its applicability to distributions whose log-densities are locally Lipschitz, generally non-differentiable, and non-convex. We evaluate the performance of MASLA by comparing it with other sampling algorithms in settings where they are applicable. Our results demonstrate the effectiveness of MASLA in handling a broader class of distributions while maintaining computational efficiency.
Keywords
Cite
@article{arxiv.2507.06950,
title = {Metropolis-adjusted Subdifferential Langevin Algorithm},
author = {Ning Ning},
journal= {arXiv preprint arXiv:2507.06950},
year = {2025}
}