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We investigate the computational efficiency of two stochastic based alternatives to the Sequential Propagator Method used in Lattice QCD calculations of heavy-light semileptonic form factors. In the first method, we replace the sequential…

High Energy Physics - Lattice · Physics 2010-11-11 Richard Evans , Gunnar Bali , Sara Collins

We explore a self-learning Markov chain Monte Carlo method based on the Adversarial Non-linear Independent Components Estimation Monte Carlo, which utilizes generative models and artificial neural networks. We apply this method to the…

Disordered Systems and Neural Networks · Physics 2021-01-06 Matija Medvidovic , Juan Carrasquilla , Lauren E. Hayward , Bohdan Kulchytskyy

We have implemented different algorithms for generating Poissonian and vectorizable random lattices. The random lattices fulfil the Voronoi/Delaunay construction. We measure the performance of our algorithms for the two types of random…

Condensed Matter · Physics 2015-06-25 K. B. Lauritsen , H. Puhl , H. -J. Tillemans

Driven by several successful applications such as in stochastic gradient descent or in Bayesian computation, control variates have become a major tool for Monte Carlo integration. However, standard methods do not allow the distribution of…

Machine Learning · Statistics 2022-10-06 Rémi Leluc , François Portier , Johan Segers , Aigerim Zhuman

In this paper, we extend our analysis of lattice systems using the wavelet transform to systems for which exact enumeration is impractical. For such systems, we illustrate a wavelet-accelerated Monte Carlo (WAMC) algorithm, which…

Chemical Physics · Physics 2009-11-07 Ahmed E. Ismail , George Stephanopoulos , Gregory C. Rutledge

We propose a randomized lattice algorithm for approximating multivariate periodic functions over the $d$-dimensional unit cube from the weighted Korobov space with mixed smoothness $\alpha > 1/2$ and product weights…

Numerical Analysis · Mathematics 2025-08-26 Mou Cai , Takashi Goda , Yoshihito Kazashi

In the analysis of using quasi-Monte Carlo (QMC) methods to approximate expectations of a linear functional of the solution of an elliptic PDE with random diffusion coefficient the sensitivity w.r.t. the parameters is often stated in terms…

Numerical Analysis · Mathematics 2019-03-01 Adrian Ebert , Peter Kritzer , Dirk Nuyens

In her recent paper [Negative dependence, scrambled nets, and variance bounds. Math. Oper. Res. 43 (2018), 228-251] Christiane Lemieux studied a framework to analyze the dependence structure of sampling schemes. The main goal of the…

Numerical Analysis · Mathematics 2021-09-21 Marcin Wnuk , Michael Gnewuch

In this project we initiate an investigation of the applicability of Quasi-Monte Carlo methods to lattice field theories in order to improve the asymptotic error behavior of observables for such theories. In most cases the error of an…

High Energy Physics - Lattice · Physics 2015-06-12 K. Jansen , H. Leovey , A. Nube , A. Griewank , M. Mueller-Preussker

A rigourous Monte Carlo method for protein folding simulation on lattice model is introduced. We show that a parameter which can be seen as the rigidity of the conformations has to be introduced in order to satisfy the detailed balance…

Soft Condensed Matter · Physics 2007-05-23 Olivier Collet

Quasi-Monte Carlo (QMC) integration of output functionals of solutions of the diffusion problem with a log-normal random coefficient is considered. The random coefficient is assumed to be given by an exponential of a Gaussian random field…

Numerical Analysis · Mathematics 2017-01-24 Yoshihito Kazashi

We present a general class of unbiased improved estimators for physical observables in lattice gauge theory computations which significantly reduces statistical errors at modest computational cost. The error reduction techniques, referred…

High Energy Physics - Lattice · Physics 2013-11-13 Thomas Blum , Taku Izubuchi , Eigo Shintani

In many financial applications Quasi Monte Carlo (QMC) based on Sobol low-discrepancy sequences (LDS) outperforms Monte Carlo showing faster and more stable convergence. However, unlike MC QMC lacks a practical error estimate. Randomized…

Computational Finance · Quantitative Finance 2023-10-17 J. Hok , S. Kucherenko

We present the results of Monte Carlo simulation for a Kondo lattice model in which itinerant electrons interact with Ising spins with spin-ice type easy-axis anisotropy on a pyrochlore lattice. We demonstrate the efficiency of the…

Strongly Correlated Electrons · Physics 2012-12-19 Hiroaki Ishizuka , Masafumi Udagawa , Yukitoshi Motome

In a polynomial regression model, the divisibility conditions implicit in polynomial hierarchy give way to a natural construction of constraints for the model parameters. We use this principle to derive versions of strong and weak hierarchy…

Computation · Statistics 2020-01-23 Hugo Maruri-Aguilar , Simon Lunagomez

In lattice quantum field theory studies, parameters defining the lattice theory must be tuned toward criticality to access continuum physics. Commonly used Markov chain Monte Carlo (MCMC) methods suffer from critical slowing down in this…

High Energy Physics - Lattice · Physics 2021-06-04 Gurtej Kanwar

We introduce a new method to approximate integrals $\int_{\mathbb{R}^d} f(\boldsymbol{x}) \, \mathrm{d} \boldsymbol{x}$ which simply scales lattice rules from the unit cube $[0,1]^d$ to properly sized boxes on $\mathbb{R}^d$, hereby…

Numerical Analysis · Mathematics 2023-08-25 Dirk Nuyens , Yuya Suzuki

An off-lattice Monte Carlo algorithm for solutions of equilibrium polymers (EP) is proposed. At low and moderate densities this is shown to reproduce faithfully the (static) properties found recently for flexible linear EP using a lattice…

Statistical Mechanics · Physics 2009-10-31 A. Milchev , J. P. Wittmer , D. P. Landau

The search for multivariate quadrature rules of minimal size with a specified polynomial accuracy has been the topic of many years of research. Finding such a rule allows accurate integration of moments, which play a central role in many…

Numerical Analysis · Mathematics 2021-05-04 John D. Jakeman , Akil Narayan

We present simple and practical strategies to reduce the variance of Monte Carlo estimators. Our focus is on variational Monte Carlo calculations of atomic forces and pressure in electronic systems, although we show that the underlying…

Strongly Correlated Electrons · Physics 2026-03-17 David Linteau , Saverio Moroni , Giuseppe Carleo , Markus Holzmann