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Langevin Monte Carlo (LMC) is a popular Markov chain Monte Carlo sampling method. One drawback is that it requires the computation of the full gradient at each iteration, an expensive operation if the dimension of the problem is high. We…

Machine Learning · Statistics 2020-10-06 Zhiyan Ding , Qin Li , Jianfeng Lu , Stephen J. Wright

In this paper, we provide non-asymptotic upper bounds on the error of sampling from a target density using three schemes of discretized Langevin diffusions. The first scheme is the Langevin Monte Carlo (LMC) algorithm, the Euler…

Statistics Theory · Mathematics 2021-12-07 Arnak S. Dalalyan , Avetik Karagulyan , Lionel Riou-Durand

Fundamental understanding of complex dynamics in many-particle systems on the atomistic level is of utmost importance. Often the systems of interest are of macroscopic size but can be partitioned into few important degrees of freedom which…

Chemical Physics · Physics 2015-07-09 Fabian Gottwald , Sven Karsten , Sergei D. Ivanov , Oliver Kühn

We revisit the problem of sampling from a target distribution that has a smooth strongly log-concave density everywhere in $\mathbb R^p$. In this context, if no additional density information is available, the randomized midpoint…

Statistics Theory · Mathematics 2023-06-19 Lu Yu , Avetik Karagulyan , Arnak Dalalyan

Langevin Monte Carlo (LMC) is a popular Bayesian sampling method. For the log-concave distribution function, the method converges exponentially fast, up to a controllable discretization error. However, the method requires the evaluation of…

Machine Learning · Statistics 2025-03-07 Zhiyan Ding , Qin Li

Motivated by the increasingly popular Score-based Generative Modeling (SGM), we study the Inexact Langevin Dynamics (ILD) and Inexact Langevin Algorithm (ILA) where a score function estimate is used in place of the exact score. We establish…

Machine Learning · Computer Science 2026-03-31 Kaylee Yingxi Yang , Andre Wibisono

Modeling non-Markovian time series is a recent topic of research in many fields such as climate modeling, biophysics, molecular dynamics, or finance. The generalized Langevin equation (GLE), given naturally by the Mori-Zwanzig projection…

Data Analysis, Statistics and Probability · Physics 2022-07-25 Clemens Willers , Oliver Kamps

We introduce a novel and efficient algorithm called the stochastic approximate gradient descent (SAGD), as an alternative to the stochastic gradient descent for cases where unbiased stochastic gradients cannot be trivially obtained.…

Machine Learning · Computer Science 2020-02-14 Yixuan Qiu , Xiao Wang

In the complex Langevin approach to lattice simulations at nonzero density, zeroes of the fermion determinant lead to a meromorphic drift and hence a need to revisit the theoretical derivation. We discuss how poles in the drift affect the…

High Energy Physics - Lattice · Physics 2016-11-10 Gert Aarts , Erhard Seiler , Denes Sexty , Ion-Olimpiu Stamatescu

Path integrals with complex actions are encountered for many physical systems ranging from spin- or mass-imbalanced atomic gases and graphene to quantum chromo-dynamics at finite density to the non-equilibrium evolution of quantum systems.…

High Energy Physics - Lattice · Physics 2022-09-01 Lukas Kades , Martin Gärttner , Thomas Gasenzer , Jan M. Pawlowski

Recent progress of the complex Langevin method and the Lefschetz thimble in connection with the sign problem is reviewed. These methods rely on the complexification of the original field manifold and they allow direct simulations of…

High Energy Physics - Lattice · Physics 2014-12-01 Denes Sexty

In hot non-Abelian gauge theories, processes characterized by the momentum scale $g^2 T$ (such as electroweak baryon number violation in the very early universe) are non-perturbative. An effective theory for the soft ($|\vec{p}|\sim g^2 T$)…

High Energy Physics - Phenomenology · Physics 2008-11-26 Dietrich Bodeker

We show that complex Langevin simulation converges to a wrong result, by relating it to the Lefschetz-thimble path integral, when the path-integral weight has different phases among dominant complex saddle points. Equilibrium solution of…

High Energy Physics - Lattice · Physics 2017-02-07 Tomoya Hayata , Yoshimasa Hidaka , Yuya Tanizaki

We analyze the pressure and density equations of state of unpolarized non-relativistic fermions at finite temperature in one spatial dimension. For attractively interacting regimes, we perform a third-order lattice perturbation theory…

High Energy Physics - Lattice · Physics 2017-05-17 Andrew C. Loheac , Joaquin E. Drut

Discretization of continuous-time diffusion processes is a widely recognized method for sampling. However, the canonical Euler-Maruyama discretization of the Langevin diffusion process, also named as Langevin Monte Carlo (LMC), studied…

Computation · Statistics 2020-10-06 Anh Duc Doan , Xin Dang , Dao Nguyen

This paper is about well-posedness and realizability of the kinetic equation for gas-particle flows and its relationship to the Generalized Langevin Model (GLM) PDF equation. Previous analyses claim that this kinetic equation is ill-posed,…

Fluid Dynamics · Physics 2018-02-21 M. W. Reeks , D. C. Swailes , A. D. Bragg

The complex Langevin method is one hopeful candidate to tackle the sign problem. This method is applicable not only to QCD but also to nonrelativistic field theory, such as condensed matter physics. We present the simulation results of a…

High Energy Physics - Lattice · Physics 2015-08-04 Arata Yamamoto , Tomoya Hayata

Rotating systems in thermal equilibrium are ubiquitous in our world. In the context of high energy physics, rotations would affect the phase structure of QCD. However, the standard Monte-Carlo methods in rotating systems are problematic…

High Energy Physics - Theory · Physics 2023-08-23 Takehiro Azuma , Takeshi Morita , Hiroki Yoshida

Along with the recent advances in scalable Markov Chain Monte Carlo methods, sampling techniques that are based on Langevin diffusions have started receiving increasing attention. These so called Langevin Monte Carlo (LMC) methods are based…

Computation · Statistics 2017-06-14 Umut Şimşekli

Heavy-tailed distributions naturally occur in many real life problems. Unfortunately, it is typically not possible to compute inference in closed-form in graphical models which involve such heavy-tailed distributions. In this work, we…

Machine Learning · Computer Science 2011-03-22 Danny Bickson , Carlos Guestrin
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