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arXiv · 2507.06950

Metropolis-adjusted Subdifferential Langevin Algorithm

Ning Ning
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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.

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Ning Ning. 2025-07-09. Metropolis-adjusted Subdifferential Langevin Algorithm. https://arxiv.org/abs/2507.06950

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