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We introduce a modification of the well-known Metropolis importance sampling algorithm by using a methodology inspired on the consideration of the reparametrization invariance of the microcanonical ensemble. The most important feature of…

Statistical Mechanics · Physics 2007-05-23 L. Velazquez , J. C. Castro Palacio

We introduce a Metropolis-Hastings Markov chain for Boltzmann distributions of classical spin systems. It relies on approximate tensor network contractions to propose correlated collective updates at each step of the evolution. We present…

The Metropolis algorithm is arguably the most fundamental Markov chain Monte Carlo (MCMC) method. But the algorithm is not guaranteed to converge to the desired distribution in the case of multivariate binary distributions (e.g., Ising…

Machine Learning · Statistics 2020-06-29 Kai Brügge , Asja Fischer , Christian Igel

We propose an adaptive Metropolis-Hastings algorithm in which sampled data are used to update the proposal distribution. We use the samples found by the algorithm at a particular step to form the information-theoretically optimal mean-field…

Other Condensed Matter · Physics 2007-05-23 David H. Wolpert , Chiu Fan Lee

In a state-update protocol for a system of $L$ asynchronous parallel processes that communicate only with nearest neighbors, global desynchronization in operation times can be deduced from kinetic roughening of the corresponding…

Distributed, Parallel, and Cluster Computing · Computer Science 2007-05-23 A. Kolakowska , M. A. Novotny

In this work, we employed the Ising model to identify phase transitions in a magnetic system where the degree distribution of the network follows a power-law and the connections are assortatively mixed. In the Ising model, the spins assume…

Statistical Mechanics · Physics 2024-12-20 R. A. Dumer , M. Godoy

The quantum phase transition of the one-dimensional long-range transverse-field Ising model is explored by combining the quantum Monte Carlo method and stochastic parameter optimization, specifically achieved by tuning correlation ratios so…

Statistical Mechanics · Physics 2024-12-05 Sora Shiratani , Synge Todo

Timely status updating is the premise of emerging interaction-based applications in the Internet of Things (IoT). Using redundant devices to update the status of interest is a promising method to improve the timeliness of information.…

Information Theory · Computer Science 2024-05-28 Zhengchuan Chen , Kang Lang , Nikolaos Pappas , Howard H. Yang , Min Wang , Zhong Tian , Tony Q. S. Quek

We present a fast, hierarchical, and adaptive algorithm for Metropolis Monte Carlo simulations of systems with long-range interactions that reproduces the dynamics of a standard implementation exactly, i.e., the generated configurations and…

Computational Physics · Physics 2023-07-27 Fabio Müller , Henrik Christiansen , Stefan Schnabel , Wolfhard Janke

By constructing an exactly solvable spin model, we investigate the critical behaviors of transverse field Ising chains interpolated with cluster interactions, which exhibit various types of topologically distinct Ising critical points.…

Strongly Correlated Electrons · Physics 2024-07-12 Xue-Jia Yu , Wei-Lin Li

We study the non-equilibrium phase diagram of a fully-connected Ising $p$-spin model, for generic $p>2$, and investigate its robustness with respect to the inclusion of spin-wave fluctuations, resulting from a ferromagnetic, short-range…

Statistical Mechanics · Physics 2024-02-01 Lorenzo Correale , Alessandro Silva

Phase transitions appear all over science, and are familiar from everyday life, as water boiling, sugar melting into caramel or as nematic molecules turning smectic in liquid-crystal displays. The dynamics of phase transitions can be…

Statistical Mechanics · Physics 2026-03-18 Gabriele Tartero , Sora Shiratani , Werner Krauth

We study properties of Markov chain Monte Carlo simulations of classical spin models with local updates. We derive analytic expressions for the mean value of the acceptance rate of single-spin-flip algorithms for the one-dimensional Ising…

Statistical Mechanics · Physics 2019-12-18 Evgeni Burovski , Wolfhard Janke , Maria Guskova , Lev Shchur

We propose a new class of phenomenological models for dynamic glass transitions. The system consists of an ensemble of mesoscopic regions to which local energies are allocated. At each time step, a region is randomly chosen and a new local…

Disordered Systems and Neural Networks · Physics 2009-11-11 Ivan Junier

The Glauber model on a one-dimensional lattice with boundaries (for the ferromagnetic- and anti-ferromagnetic case) is considered. The large-time behaviour of the one-point function is studied. It is shown that, for any positive…

Statistical Mechanics · Physics 2009-11-07 Mohammad Khorrami , Amir Aghamohammadi

We use an efficient method that eases the daunting task of simulating dynamics in spin systems with long-range interaction. Our Monte Carlo simulations of the long-range Ising model for the nonequilibrium phase ordering dynamics in two…

Statistical Mechanics · Physics 2019-01-23 Henrik Christiansen , Suman Majumder , Wolfhard Janke

The two-dimensional Ising model with nearest-neighbor ferromagnetic and long-range dipolar interactions exhibits a rich phase diagram. The presence of the dipolar interaction changes the ferromagnetic ground state expected for the pure…

Statistical Mechanics · Physics 2010-02-18 Leandro G. Rizzi , Nelson A. Alves

We study the response of a thermal state of an Ising chain to a nonlocal non-Hermitian perturbation, which coalesces the topological Kramer-like degeneracy in the ferromagnetic phase. The dynamic responses for initial thermal states in…

Strongly Correlated Electrons · Physics 2021-03-24 K. L. Zhang , Z. Song

Given a target Gibbs distribution $\pi^0_{\beta} \propto e^{-\beta \mathcal{H}}$ to sample from in the low-temperature regime on $\Sigma_N := \{-1,+1\}^N$, in this paper we propose and analyze Metropolis dynamics that instead target an…

Probability · Mathematics 2022-08-23 Michael C. H. Choi

We analyze phase transitions in the conditional entropy of a sequence caused by a change in the conditional variables. Such transitions happen, for example, when training to learn the parameters of a system, since the transition from the…

Information Theory · Computer Science 2021-01-07 Kang Gao , Bertrand Hochwald