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We study the critical behavior of frustrated systems by means of Pade-Borel resummed three-loop renormalization-group expansions and numerical Monte Carlo simulations. Amazingly, for six-component spins where the transition is second order,…

Statistical Mechanics · Physics 2009-10-31 D. Loison , A. I. Sokolov , B. Delamotte , S. A. Antonenko , K. D. Schotte , H. T. Diep

We investigate the isotropic-anisotropic phase transition of the two-dimensional XY model with six-fold anisotropy, using Monte Carlo renormalization group method. The result indicates difficulty of observing asymptotic critical behavior in…

Statistical Mechanics · Physics 2009-11-07 M. Itakura

Monte Carlo methods play an important role in scientific computation, especially when problems have a vast phase space. In this lecture an introduction to the Monte Carlo method is given. Concepts such as Markov chains, detailed balance,…

Statistical Mechanics · Physics 2011-05-05 Helmut G. Katzgraber

We develop a novel real-space renormalization group (RG) scheme which accurately estimates correlation length exponent $\nu$ near criticality of higher-dimensional quantum Ising and Potts models in a transverse field. Our method is…

Statistical Mechanics · Physics 2014-02-05 Aleksander Kubica , Beni Yoshida

We introduce a numerical method to study critical properties near classical and quantum phase transitions. Our method applies ideas of the Tensor Renormalization Group to obtain an improved action which is used to extract critical…

Statistical Mechanics · Physics 2024-08-14 Guy Segall , Snir Gazit , Daniel Podolsky

Monte Carlo simulations are an essential tool in particle physics data analysis. Events are typically generated alongside weights that redistribute the cross section of the simulated process across the phase space. These weights can be…

High Energy Physics - Phenomenology · Physics 2026-05-13 Benjamin Nachman , Dennis Noll

We present a Monte Carlo method to compute efficiently susceptibilites or covariances of two physical variables. The method relies on a generalization of the exchange cluster algorithm to any model of interacting particles with any $2$-body…

Computational Physics · Physics 2025-02-11 Assaraf Roland , Chevreau Hilaire

Adaptive importance sampling is a widely spread Monte Carlo technique that uses a re-weighting strategy to iteratively estimate the so-called target distribution. A major drawback of adaptive importance sampling is the large variance of the…

Statistics Theory · Mathematics 2021-11-01 Anna Korba , François Portier

It is shown by the method of renormalized field theory that in contrast to a statement based on a mathematically ill-defined invariance transformation and found in most of the recent publications on growth models with surface diffusion, the…

Statistical Mechanics · Physics 2009-10-28 H. K. Janssen

We develop renormalization group methods for solving partial and stochastic differential equations on coarse meshes. Renormalization group transformations are used to calculate the precise effect of small scale dynamics on the dynamics at…

Statistical Mechanics · Physics 2009-10-31 Qing Hou , Nigel Goldenfeld , Alan McKane

Renormalization group method is one of the most powerful tool to obtain approximate solutions to differential equations. We apply the renormalization group method to Hamiltonian systems whose integrable parts linearly depend on action…

chao-dyn · Physics 2007-05-23 Yoshiyuki Y. Yamaguchi , Yasusada Nambu

Monte Carlo event generators are an essential tool for data analysis in collider physics. To include subleading quantum corrections, these generators often need to produce negative weight events, which leads to statistical dilution of the…

High Energy Physics - Phenomenology · Physics 2020-10-21 Benjamin Nachman , Jesse Thaler

In this note we study the numerical stability problem that may take place when calculating the cumulative distribution function of the {\it Hypoexponential} random variable. This computation is extensively used during the execution of Monte…

Applications · Statistics 2013-06-26 Ilya Gertsbakh , Eyal Neuman , Radislav Vaisman

We discuss different approaches for studying the influence of disorder in the three-dimensional Ising model. From the theoretical point of view, renormalisation group calculations provide quite accurate results. Experiments carried out on…

Statistical Mechanics · Physics 2007-05-23 Bertrand Berche , Pierre-Emmanuel Berche , Christophe Chatelain , Wolfhard Janke

Markov chain Monte Carlo (MCMC) methods provide consistent of integrals as the number of iterations goes to infinity. MCMC estimators are generally biased after any fixed number of iterations. We propose to remove this bias by using…

Methodology · Statistics 2019-07-18 Pierre E. Jacob , John O'Leary , Yves F. Atchadé

In this paper we propose a novel method to study critical systems numerically by a combined collective-mode algorithm and Renormalization Group on the lattice. This method is an improved version of MCRG in the sense that it has all the…

Statistical Mechanics · Physics 2009-12-03 G. Palma , D. Zambrano

We discuss possible sources of systematic errors in the computation of critical exponents by renormalization-group methods, extrapolations from exact enumerations and Monte Carlo simulations. A careful Monte Carlo determination of the…

High Energy Physics - Lattice · Physics 2009-10-30 Sergio Caracciolo , Maria Serena Causo , Andrea Pelissetto

We study the effective theory of the conformal factor near its infrared stable fixed point.The renormalization group equations for the effective coupling constants are found and their solutions near the critical point are obtained,…

High Energy Physics - Theory · Physics 2009-09-17 I. Antoniadis , S. D. Odintsov

Inspired by recent conflicting views on the order of the phase transition from an antiferromagnetic Neel state to a valence bond solid, we use the functional renormalization group to study the underlying quantum critical field theory which…

Strongly Correlated Electrons · Physics 2013-11-26 Lorenz Bartosch

We present a general sample reweighting scheme and its underlying theory for the integration of an unknown function with low dimensionality. Our method produces better results than standard weighting schemes for common sampling strategies,…

Graphics · Computer Science 2019-08-07 Jerry Jinfeng Guo , Elmar Eisemann