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We investigate dynamic scaling properties of the two-dimensional gauge glass model for the vortex glass phase in superconductors with quenched disorder. From extensive Monte Carlo simulations we obtain static and dynamic finite size scaling…

Disordered Systems and Neural Networks · Physics 2007-05-23 Marios Nikolaou , Mats Wallin

The nature of the ordering of the one-dimensional Heisenberg spin-glass model with a long-range power-law interaction is studied by extensive Monte Carlo simulations, with particular attention to the issue of the spin-chirality…

Disordered Systems and Neural Networks · Physics 2010-10-13 Dao Xuan Viet , Hikaru Kawamura

Monte Carlo simulations have boosted the numerical study of several different physical systems and in particular, the canonical ensemble has been especially useful because of the existence of easy and efficient relaxation algorithms…

Statistical Mechanics · Physics 2019-10-29 G. Palma , A. Riveros

We present a simple model that recalls two different patterns depending on the temperature. To realize a change in recall pattern due to temperature change, we embed two patterns to different graphs: the first pattern into a fully connected…

Disordered Systems and Neural Networks · Physics 2026-02-25 Munetaka Sasaki

We introduce a constrained Monte Carlo method which allows us to traverse the phase space of a classical spin system while fixing the magnetization direction. Subsequently we show the method's capability to model the temperature dependence…

Materials Science · Physics 2013-10-22 P. Asselin , R. F. L. Evans , J. Barker , R. W. Chantrell , R. Yanes , O. Chubykalo-Fesenko , D. Hinzke , U. Nowak

In replica exchange Monte Carlo (REM), tuning of the temperature set and the exchange scheduling are crucial in improving the accuracy and reducing calculation time. In multi-dimensional simulated tempering, the first order phase transition…

Statistical Mechanics · Physics 2016-12-28 Kenji Kimura , Saburo Higuchi

We present a Monte Carlo method that efficiently computes the density of states for spin models having any number of interaction per spin. By combining a random-walk in the energy space with collective updates controlled by the…

Statistical Mechanics · Physics 2009-11-11 Sylvain Reynal , Hung-The Diep

Population Monte Carlo simulations in the form commonly referred to as population annealing can serve as a useful meta-algorithm for simulating systems with complex free-energy landscapes. In the present paper we provide an easily…

Statistical Mechanics · Physics 2024-01-17 P. L. Ebert , D. Gessert , W. Janke , M. Weigel

Machine learning algorithms thrive on large data sets of good quality. Here we show that they can also excel in a typical research setting with little data of limited quality, through an interplay of insights coming from machine, and human…

Strongly Correlated Electrons · Physics 2025-07-18 Nicolas Sadoune , Ke Liu , Han Yan , Ludovic D. C. Jaubert , Nic Shannon , Lode Pollet

An extensive equilibrium Monte Carlo simulation is performed on the 3D isotropic Heisenberg SG model with the random nearest-neighbor Gaussian coupling, with particular interest in its chiral-glass (CG) and spin-glass (SG) orderings. For…

Disordered Systems and Neural Networks · Physics 2020-01-29 Takumi Ogawa , Kazuki Uematsu , Hikaru Kawamura

The dynamics of a polydisperse model glassformer are investigated by augmenting molecular dynamics (MD) simulation with swap Monte Carlo (SMC). Three variants of the SMC algorithm are analyzed with regard to convergence and performance. We…

Soft Condensed Matter · Physics 2023-05-09 Niklas Küchler , Jürgen Horbach

The quantum dynamics of a two-level system coupled to an Ohmic spin- bath is studied by means of the perturbation approach based on a unitary transformation. A scattering function $\xi_k$ is introduced in the transformation to take into…

Quantum Physics · Physics 2015-05-27 Zhiguo Lü , H. Zheng

Developing efficient MCMC algorithms is indispensable in Bayesian inference. In parallel tempering, multiple interacting MCMC chains run to more efficiently explore the state space and improve performance. The multiple chains advance…

Computation · Statistics 2021-09-15 A. Marie d'Avigneau , S. S. Singh , L. M. Murray

We propose a minimal generalization of the celebrated Markov-Chain Monte Carlo algorithm which allows for an arbitrary number of configurations to be visited at every Monte Carlo step. This is advantageous when a parallel computing machine…

Computational Physics · Physics 2021-02-11 Fedor Šimkovic , Riccardo Rossi

Multimodal structures in the sampling density (e.g. two competing phases) can be a serious problem for traditional Markov Chain Monte Carlo (MCMC), because correct sampling of the different structures can only be guaranteed for infinite…

Data Analysis, Statistics and Probability · Physics 2009-11-11 M. Daghofer , M. Konegger , H. G. Evertz , W. von der Linden

Finding a ground state of a given Hamiltonian of an Ising model on a graph $G=(V,E)$ is an important but hard problem. The standard approach for this kind of problem is the application of algorithms that rely on single-spin-flip Markov…

A spin-glass transition has been investigated for a long time but we have not yet reached a conclusion due to difficulties in the simulations. They are slow dynamics, strong finite-size effects, and sample-to-sample dependences. We…

Disordered Systems and Neural Networks · Physics 2019-02-11 Tota Nakamura

The magnetic properties and phase diagrams of the mixed spin-1 and spin-1/2 Ising model on a checkerboard square structure have been studied using the Monte Carlo simulations based on the Metropolis update protocol. The system consists of…

Statistical Mechanics · Physics 2021-09-15 Maen Gharaibeh , Mohammad H. A. Badarneh , Samah Alqaiem , Abdalla Obeidat , Mohammad-Khair Qaseer

We explore how warm-starting strategies can be integrated into scalarization-based approaches for multi-objective optimization in (mixed) integer linear programming. Scalarization methods remain widely used classical techniques to compute…

Optimization and Control · Mathematics 2025-07-30 Stephanie Riedmüller , Janina Zittel , Thorsten Koch

Variational inference (VI) combined with data subsampling enables approximate posterior inference over large data sets, but suffers from poor local optima. We first formulate a deterministic annealing approach for the generic class of…

Machine Learning · Statistics 2016-05-31 Stephan Mandt , James McInerney , Farhan Abrol , Rajesh Ranganath , David Blei