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Related papers: Macros and Multiscale Dynamics in Spin Glasses

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In this paper we present a dynamical Monte Carlo algorithm which is applicable to systems satisfying a clustering condition: during the dynamical evolution the system is mostly trapped in deep local minima (as happens in glasses, pinning…

Condensed Matter · Physics 2009-10-22 Werner Krauth , Olivier Pluchery

In simulations of some infinite range spin glass systems with finite connectivity, it is found that for any resonable computational time, the saturatedenergy per spin that is achieved by a cluster algorithm is lowered in comparison to that…

Condensed Matter · Physics 2016-08-31 N. Persky , I. Kanter , S. Solomon

Spin glasses are paradigmatic models that deliver concepts relevant for a variety of systems. However, rigorous analytical results are difficult to obtain for spin-glass models, in particular for realistic short-range models. Therefore…

Disordered Systems and Neural Networks · Physics 2008-02-12 Helmut G. Katzgraber

Finding the ground state of Ising spin glasses is notoriously difficult due to disorder and frustration. Often, this challenge is framed as a combinatorial optimization problem, for which a common strategy employs simulated annealing, a…

Many spin systems affected by critical slowing down can be efficiently simulated using cluster algorithms. Where such systems have long-range interactions, suitable formulations can additionally bring down the computational effort for each…

Statistical Mechanics · Physics 2017-04-07 Emilio Flores-Sola , Martin Weigel , Ralph Kenna , Bertrand Berche

Standard Monte Carlo cluster algorithms have proven to be very effective for many different spin models, however they fail for frustrated spin systems. Recently a generalized cluster algorithm was introduced that works extremely well for…

Condensed Matter · Physics 2009-10-22 P. D. Coddington , L. Han

Spin glasses are notoriously difficult to study both analytically and numerically due to the presence of frustration and metastability. Their highly non-convex landscapes require collective updates to explore efficiently. Currently, most…

Disordered Systems and Neural Networks · Physics 2022-01-03 Yan Ru Pei , Massimiliano Di Ventra

Spin systems with frustration and disorder are notoriously difficult to study both analytically and numerically. While the simulation of ferromagnetic statistical mechanical models benefits greatly from cluster algorithms, these accelerated…

Disordered Systems and Neural Networks · Physics 2015-08-18 Zheng Zhu , Andrew J. Ochoa , Helmut G. Katzgraber

We present results from Monte Carlo simulations to test for ultrametricity and clustering properties in spin-glass models. By using a one-dimensional Ising spin glass with random power-law interactions where the universality class of the…

Disordered Systems and Neural Networks · Physics 2009-01-26 Helmut G. Katzgraber , Alexander K. Hartmann

Developments in dynamical systems theory provides new support for the macroscale modelling of pdes and other microscale systems such as Lattice Boltzmann, Monte Carlo or Molecular Dynamics simulators. By systematically resolving subgrid…

Numerical Analysis · Mathematics 2012-01-18 A. J. Roberts , Tony MacKenzie , J. E. Bunder

We present computer simulations of concentrated solutions of unknotted nonconcatenated semiflexible ring polymers. Unlike in their flexible counterparts, shrinking involves a strong energetic penalty, favoring interpenetration and…

We present molecular dynamics (MD) simulations results for dense fluids of ultrasoft, fully-penetrable particles. These are a binary mixture and a polydisperse system of particles interacting via the generalized exponential model, which is…

Soft Condensed Matter · Physics 2012-12-24 Daniele Coslovich , Marco Bernabei , Angel J. Moreno

The ultraslow dynamics of glass-formers has been explained by two views considered as mutually exclusive: one invokes locally hindered mobility, the other rests on the complexity of the configuration space. Here we demonstrate that the…

Disordered Systems and Neural Networks · Physics 2026-02-02 Federico Ghimenti , Ludovic Berthier , Jorge Kurchan , Frédéric van Wijland

Understanding how frustration and disorder shape relaxation in complex systems is a central problem in statistical physics and quantum annealing. Spin-glass models provide a natural framework to explore this connection, as their energy…

Statistical Mechanics · Physics 2025-10-30 Viviana Gómez , Gabriel Téllez , Fernando J. Gómez-Ruiz

Various physical models can be expressed in terms of matrices. A valuable tool for analysing matrix models is numerical simulations, often the Metropolis algorithm with various improvements. The downside of this approach is that the…

High Energy Physics - Lattice · Physics 2026-05-29 Samuel Kováčik , Matej Hrmo

A disordered spin glass model where both static and dynamical properties depend on macroscopic magnetizations is presented. These magnetizations interact via random couplings and, therefore, the typical quenched realization of the system…

Disordered Systems and Neural Networks · Physics 2009-10-31 M. Pasquini , M. Serva

In dynamic Monte Carlo simulations, using for example the Metropolis dynamic, it is often required to simulate for long times and to simulate large systems. We present an overview of advanced algorithms to simulate for larger times and to…

Statistical Mechanics · Physics 2007-05-23 M. A. Novotny , Alice K. Kolakowska , G. Korniss

We discuss the problem of ultrametricity in mean field spin glasses by means of a hierarchical clustering algorithm. We complement the clustering approach with quantitative testing: we discuss both in some detail. We show that the…

Statistical Mechanics · Physics 2009-11-10 Stefano Ciliberti , Enzo Marinari

A new Monte Carlo algorithm for 2-dimensional spin glasses is presented. The use of clusters makes possible global updates and leads to a gain in speed of several orders of magnitude. As an example, we study the 2-dimensional +/-J…

Disordered Systems and Neural Networks · Physics 2009-11-07 J. Houdayer

Recently a cluster Monte Carlo algorithm has been used very successfully in the two-dimensional Edwards-Anderson (EA) model. We show that this algorithm and a variant thereof can also be used successfully in models with a non-zero spin…

Disordered Systems and Neural Networks · Physics 2015-06-24 Thomas Jorg
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