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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 compare the ground state of the random-field Ising model with Gaussian distributed random fields, with its non-equilibrium hysteretic counterpart, the demagnetized state. This is a low energy state obtained by a sequence of slow magnetic…

Ground states and domain walls are investigated with exact combinatorial optimization in two-dimensional random field Ising magnets. The ground states break into domains above a length scale that depends exponentially on the random field…

Disordered Systems and Neural Networks · Physics 2009-10-31 E. T. Seppala , V. Petaja , M. J. Alava

We apply a stochastic method of minimizing the ground state energy in variational calculations of light nuclei using the Refined Resonating Group Model (RRGM). The method utilizes a bit representation of the width parameters to be varied.…

Nuclear Theory · Physics 2008-11-26 Christian Winkler , Hartmut M. Hofmann

We study the maximum and minimum occupancy fraction of the antiferromagnetic Ising model in regular graphs. The minimizing problem is known to determine a computational threshold in the complexity of approximately sampling from the Ising…

Combinatorics · Mathematics 2024-12-25 Ewan Davies , Olivia LeBlanc

We develop an approach for characterizing non-local quantum correlations in spin systems with exactly or nearly degenerate ground states. Starting with linearly independent degenerate eigenfunctions calculated with exact diagonalization we…

Quantum Physics · Physics 2025-04-08 V. S. Okatev , O. M. Sotnikov , V. V. Mazurenko

A method is presented for the calculation of all exact ground states of diluted antiferromagnets and random field systems in an arbitrary range of fields. It works by calculating all jump-fields B,\Delta where the system changes it's ground…

Disordered Systems and Neural Networks · Physics 2015-06-25 Alexander K. Hartmann

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 study numerically the number of single-spin-flip stable states in the T=0 Random Field Ising Model (RFIM) on random regular graphs of connectivity $z=2$ and $z=4$ and on the cubic lattice. The annealed and quenched complexities (i.e. the…

Disordered Systems and Neural Networks · Physics 2007-10-08 F. J. Perez-Reche , M. L. Rosinberg , G. Tarjus

We study the ground-state (T = 0) morphologies in the d = 3 random-field Ising model (RFIM) using a computationally efficient graph-cut method. We focus on paramagnetic states which arise for disorder strengths \Delta > \Delta c, where…

Statistical Mechanics · Physics 2015-05-30 Gaurav P. Shrivastav , Siddharth Krishnamoorthy , Varsha Banerjee , Sanjay Puri

Matroid theory provides a unifying framework for studying dependence across combinatorics, geometry, and applications ranging from rigidity to statistics. In this work, we study circuit varieties of matroids, defined by their minimal…

Combinatorics · Mathematics 2025-12-05 Emiliano Liwski , Fatemeh Mohammadi , Rémi Prébet

Based on Jaynes' maximum entropy principle, exponential random graphs provide a family of principled models that allow the prediction of network properties as constrained by empirical data (observables). However, their use is often hindered…

Statistical Mechanics · Physics 2020-12-03 Szabolcs Horvát , Éva Czabarka , Zoltán Toroczkai

We study the hysteretic evolution of the random field Ising model (RFIM) at T=0 when the magnetization M is controlled externally and the magnetic field H becomes the output variable. The dynamics is a simple modification of the…

Disordered Systems and Neural Networks · Physics 2009-11-11 Xavier Illa , Martin-Luc Rosinberg , Prabodh Shukla , Eduard Vives

We show that the nonlinear stochastic dynamics of a measurement-feedback-based coherent Ising machine (MFB-CIM) in the presence of quantum noise can be exploited to sample degenerate ground and low-energy spin configurations of the Ising…

The properties of the ground state of one of the simplest models of frustrated magnetic systems, a dilute Ising chain in a magnetic field, are considered for all values of the concentration of charged non-magnetic impurities. An analytical…

Statistical Mechanics · Physics 2024-01-23 Yury Panov

We develop an efficient stochastic variance reduced gradient descent algorithm to solve the affine rank minimization problem consists of finding a matrix of minimum rank from linear measurements. The proposed algorithm as a stochastic…

Optimization and Control · Mathematics 2022-11-08 Ningning Han , Juan Nie , Jian Lu , Michael K. Ng

We study the problem of approximating the partition function of the ferromagnetic Ising model in graphs and hypergraphs. Our first result is a deterministic approximation scheme (an FPTAS) for the partition function in bounded degree graphs…

Data Structures and Algorithms · Computer Science 2018-12-26 Jingcheng Liu , Alistair Sinclair , Piyush Srivastava

We investigate the critical behavior of three-dimensional random-field Ising systems with both Gauss and bimodal distribution of random fields and additional the three-dimensional diluted Ising antiferromagnet in an external field. These…

Statistical Mechanics · Physics 2009-10-31 A. K. Hartmann , U. Nowak

We study the network dismantling problem, which consists in determining a minimal set of vertices whose removal leaves the network broken into connected components of sub-extensive size. For a large class of random graphs, this problem is…

Physics and Society · Physics 2016-11-16 Alfredo Braunstein , Luca Dall'Asta , Guilhem Semerjian , Lenka Zdeborová

Large optimal transport problems can be approached via domain decomposition, i.e. by iteratively solving small partial problems independently and in parallel. Convergence to the global minimizers under suitable assumptions has been shown in…

Optimization and Control · Mathematics 2021-06-16 Mauro Bonafini , Ismael Medina , Bernhard Schmitzer