An Inertial Langevin Algorithm
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912634836615168 |
|---|---|
| author | Falk, Alexander Habring, Andreas Griesbacher, Christoph Pock, Thomas |
| author_facet | Falk, Alexander Habring, Andreas Griesbacher, Christoph Pock, Thomas |
| contents | We present a novel method for drawing samples from Gibbs distributions with densities of the form $π(x) \propto \exp(-U(x))$. The method accelerates the unadjusted Langevin algorithm by introducing an inertia term similar to Polyak's heavy ball method, together with a corresponding noise rescaling. Interpreting the scheme as a discretization of \emph{kinetic} Langevin dynamics, we prove ergodicity (in continuous and discrete time) for twice continuously differentiable, strongly convex, and $L$-smooth potentials and bound the bias of the discretization to the target in Wasserstein-2 distance. In particular, the presented proofs allow for smaller friction parameters in the kinetic Langevin diffusion compared to existing literature. Moreover, we show the close ties of the proposed method to the over-relaxed Gibbs sampler. The scheme is tested in an extensive set of numerical experiments covering simple toy examples, total variation image denoising, and the complex task of maximum likelihood learning of an energy-based model for molecular structure generation. The experimental results confirm the acceleration provided by the proposed scheme even beyond the strongly convex and $L$-smooth setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06723 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Inertial Langevin Algorithm Falk, Alexander Habring, Andreas Griesbacher, Christoph Pock, Thomas Numerical Analysis 65C40, 65C05, 68U10, 65C60 G.3; I.4.5 We present a novel method for drawing samples from Gibbs distributions with densities of the form $π(x) \propto \exp(-U(x))$. The method accelerates the unadjusted Langevin algorithm by introducing an inertia term similar to Polyak's heavy ball method, together with a corresponding noise rescaling. Interpreting the scheme as a discretization of \emph{kinetic} Langevin dynamics, we prove ergodicity (in continuous and discrete time) for twice continuously differentiable, strongly convex, and $L$-smooth potentials and bound the bias of the discretization to the target in Wasserstein-2 distance. In particular, the presented proofs allow for smaller friction parameters in the kinetic Langevin diffusion compared to existing literature. Moreover, we show the close ties of the proposed method to the over-relaxed Gibbs sampler. The scheme is tested in an extensive set of numerical experiments covering simple toy examples, total variation image denoising, and the complex task of maximum likelihood learning of an energy-based model for molecular structure generation. The experimental results confirm the acceleration provided by the proposed scheme even beyond the strongly convex and $L$-smooth setting. |
| title | An Inertial Langevin Algorithm |
| topic | Numerical Analysis 65C40, 65C05, 68U10, 65C60 G.3; I.4.5 |
| url | https://arxiv.org/abs/2510.06723 |