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The push-relabel algorithm can be used to calculate rapidly the exact ground states for a given sample with a random-field Ising model (RFIM) Hamiltonian. Although the algorithm is guaranteed to terminate after a time polynomial in the…

Disordered Systems and Neural Networks · Physics 2007-05-23 D. Clay Hambrick , Jan H. Meinke , A. Alan Middleton

We investigate the application of graph-cut methods for the study of the critical behaviour of the two-dimensional random-field Ising model. We focus on exact ground-state calculations, crossing the phase boundary of the model at zero…

Disordered Systems and Neural Networks · Physics 2022-04-04 Argyro Mainou , Nikolaos G. Fytas , Martin Weigel

For many systems with quenched disorder the study of ground states can crucially contribute to a thorough understanding of the physics at play, be it for the critical behavior if that is governed by a zero-temperature fixed point or for…

Disordered Systems and Neural Networks · Physics 2020-06-12 Manoj Kumar , Martin Weigel

We simulate single and multiple Ising models coupled to 2-d gravity using both the Swendsen-Wang and Wolff algorithms to update the spins. We study the integrated autocorrelation time and find that there is considerable critical slowing…

High Energy Physics - Lattice · Physics 2009-10-22 M. Bowick , M. Falcioni , G. Harris , E. Marinari

We present an algorithm for finding ground states of two dimensional spin glass systems based on ideas from matrix product states in quantum information theory. The algorithm works directly at zero temperature and defines an approximate…

Disordered Systems and Neural Networks · Physics 2009-11-13 M. B. Hastings

The energy landscape for the random-field Ising model (RFIM) is complex, yet algorithms such as the push-relabel algorithm exist for computing the exact ground state of an RFIM sample in time polynomial in the sample volume. Simulations…

Disordered Systems and Neural Networks · Physics 2007-05-23 Jan H. Meinke , A. Alan Middleton

We consider the equilibrium dynamics of Ising spin models with multi-spin interactions on sparse random graphs (Bethe lattices). Such models undergo a mean field glass transition upon increasing the graph connectivity or lowering the…

Statistical Mechanics · Physics 2009-11-10 Andrea Montanari , Guilhem Semerjian

A new approach to combinatorial optimization based on systematic move-class deflation is proposed. The algorithm combines heuristics of genetic algorithms and simulated annealing, and is mainly entropy-driven. It is tested on two problems…

Statistical Mechanics · Physics 2007-05-23 Reimer Kuehn , Yu-Cheng Lin , Gerhard Poeppel

The renormalization group has proven to be a very powerful tool in physics for treating systems with many length scales. Here we show how it can be adapted to provide a new class of algorithms for discrete optimization. The heart of our…

Disordered Systems and Neural Networks · Physics 2009-10-31 J. Houdayer , O. C. Martin

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…

We analyze the zero-temperature behavior of the XY Edwards-Anderson spin glass model on a square lattice. A newly developed algorithm combining exact ground-state computations for Ising variables embedded into the planar spins with a…

Disordered Systems and Neural Networks · Physics 2007-05-23 Martin Weigel , Michel J. P. Gingras

Global changes of states are of crucial importance in optimization algorithms. We review some heuristic algorithms in which global updates are realized by a sort of real-space renormalization group transformation. Emphasis is on the…

Disordered Systems and Neural Networks · Physics 2009-10-31 Naoki Kawashima

The use of combinatorial optimization algorithms has contributed substantially to the major progress that has occurred in recent years in the understanding of the physics of disordered systems, such as the random-field Ising model. While…

Disordered Systems and Neural Networks · Physics 2023-02-22 Manoj Kumar , Martin Weigel

The chapter starts with a historical summary of first attempts to optimize the spin glass Hamiltonian, comparing it to recent results on searching largest cliques in random graphs. Exact algorithms to find ground states in generic spin…

Disordered Systems and Neural Networks · Physics 2023-01-03 Sergio Caracciolo , Alexander K. Hartmann , Scott Kirkpatrick , Martin Weigel

True ground states are evaluated for a 2d Ising model with random near neighbor interactions and ferromagnetic second neighbor interactions (the Randomly Coupled Ferromagnet). The spin glass stiffness exponent is positive when the absolute…

Disordered Systems and Neural Networks · Physics 2009-10-31 A. K. Hartmann , I. A. Campbell

Wang-Landau simulations offer the possibility to integrate explicitly over a collective coordinate and stochastically over the remainder of configuration space. We propose to choose the so-called "slow mode", which is responsible for large…

Statistical Mechanics · Physics 2022-11-30 Kurt Langfeld , Pavel Buividovich , P. E. L Rakow , James Roscoe

The so-called chaotic states that emerge on the model of $XY$ interacting on regular critical range networks are analyzed. Typical time scales are extracted from the time series analysis of the global magnetization. The large spectrum…

Statistical Mechanics · Physics 2017-02-10 Martin Belger , Sarah De Nigris , Xavier Leoncini

Critical slowing down dynamics of supercooled glass-forming liquids is usually understood at the mean-field level in the framework of Mode Coupling Theory, providing a two-time relaxation scenario and power-law behaviors of the time…

Disordered Systems and Neural Networks · Physics 2013-01-30 Ulisse Ferrari , Luca Leuzzi , Giorgio Parisi , Tommaso Rizzo

The critical behavior of the disordered ferromagnetic Ising model is studied numerically by the Monte Carlo method in a wide range of variation of concentration of nonmagnetic impurity atoms. The temperature dependences of correlation…

Disordered Systems and Neural Networks · Physics 2007-09-11 V. Prudnikov , P. Prudnikov , A. Vakilov , A. Krinitsyn

Ising machines are hardware solvers which aim to find the absolute or approximate ground states of the Ising model. The Ising model is of fundamental computational interest because it is possible to formulate any problem in the complexity…

Quantum Physics · Physics 2022-04-04 Naeimeh Mohseni , Peter L. McMahon , Tim Byrnes
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