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Frustrated magnetism plays a central role in the phenomenology of exotic quantum states. However, because the magnetic structures of frustrated systems are aperiodic, there has always been the problem that they cannot be determined using…

Materials Science · Physics 2013-05-30 Joseph A. M. Paddison , Andrew L. Goodwin

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

We discuss ergodicity breaking in frustrated disordered systems with no apparent broken symmetry of the Hamiltonian and present a way how to amend it in the low-temperature phase. We demonstrate this phenomenon on mean-field models of spin…

Disordered Systems and Neural Networks · Physics 2015-01-09 V. Janis , A. Kauch , A. Klic

We review the basic ideas of the Tensor Renormalization Group method and show how they can be applied for lattice field theory models involving relativistic fermions and Grassmann variables in arbitrary dimensions. We discuss recent…

High Energy Physics - Lattice · Physics 2024-01-17 Shinichiro Akiyama , Yannick Meurice , Ryo Sakai

The numerical study of high-dimensional frustrated quantum magnets remains a challenging problem. Here we present an extension of the pseudo-Majorana functional renormalization group to spin-1/2 XXZ type Hamiltonians with field or…

Strongly Correlated Electrons · Physics 2025-02-18 Frederic Bippus , Benedikt Schneider , Björn Sbierski

We study the effect in geometrically frustrated antiferromagnets of weak, random variations in the strength of exchange interactions. Without disorder the simplest classical models for these systems have macroscopically degenerate ground…

Disordered Systems and Neural Networks · Physics 2010-05-05 A. Andreanov , J. T. Chalker , T. E. Saunders , D. Sherrington

A recently introduced Renormalization Group approach to frustrated spin models is applied in three dimensions through Monte Carlo computations. A class of spin glass models is analysed, with correlated disorder variables given by a Z_2…

Statistical Mechanics · Physics 2007-05-23 Leonardo Gnesi , Roberto Petronzio , Francesco Rosati

Numerical data on scaling of the normalized Binder cumulant and the normalized correlation length are shown for the Thermodynamic limit regime, first for canonical Ising ferromagnet models and then for a range of Ising spin glass models. A…

Disordered Systems and Neural Networks · Physics 2016-07-15 P. H. Lundow , I. A. Campbell

A real space Renormalization Group approach is presented for a non-mean field spin-glass. This approach has been conceived in the effort to develop an alternative method to the Renormalization Group approaches based on the replica method.…

Disordered Systems and Neural Networks · Physics 2014-09-09 Michele Castellana

Frustration is a ubiquitous phenomenon in many-body physics that influences the nature of the system in a profound way with exotic emergent behavior. Despite its long research history, the analytical or numerical investigations on…

Statistical Mechanics · Physics 2023-12-06 Feng-Feng Song , Tong-Yu Lin , Guang-Ming Zhang

We present a method to analyze magnetic properties of frustrated Ising spin models on specific hierarchical lattices with random dilution. Disorder is induced by dilution and geometrical frustration rather than randomness in the internal…

Disordered Systems and Neural Networks · Physics 2013-08-13 Jean-Yves Fortin

We review White's density matrix renormalisation group method, an increasingly popular method for the solution of low dimensional quantum Hamiltonians. We describe some applications to frustrated spin systems, quantum critical phenomena,…

Condensed Matter · Physics 2008-02-03 G. A. Gehring , R. J. Bursill , T. Xiang

We introduce a tensor renormalization group scheme for coarse-graining a two-dimensional tensor network that can be successfully applied to both classical and quantum systems on and off criticality. The key innovation in our scheme is to…

Strongly Correlated Electrons · Physics 2017-03-17 Shuo Yang , Zheng-Cheng Gu , Xiao-Gang Wen

We present a self consistent method based on cluster algorithms and Renormalization Group on the lattice to study critical systems numerically. We illustrate it by means of the 2D Ising model. We compute the critical exponents $\nu$ and…

Statistical Mechanics · Physics 2009-12-01 Guillermo Palma , David Zambrano

We review recent developments of machine learning algorithms pertinent to the inverse renormalization group, which was originally established as a generative numerical method by Ron-Swendsen-Brandt via the implementation of compatible Monte…

High Energy Physics - Lattice · Physics 2024-05-28 Dimitrios Bachtis

We consider an alternative to the usual spin glass paradigm for disordered magnetism, consisting of the previously unstudied combination of frustrated magnetic interactions and pseudo-dipolar disorder in spin positions. We argue that this…

Other Condensed Matter · Physics 2011-11-02 S. T. Banks , S. T. Bramwell

In this thesis we investigate the Renormalization Group (RG) approach in finite-dimensional glassy systems, whose critical features are still not well-established, or simply unknown. We focus on spin and structural-glass models built on…

Disordered Systems and Neural Networks · Physics 2015-04-02 Michele Castellana

The ferromagnetic Ising model on an $n\times n$ square lattice region $\Lambda$ with mixed boundary conditions can exhibit a phase transition as temperature varies. For this spin system, if we fix the spins on the top and bottom sides of…

Discrete Mathematics · Computer Science 2026-05-26 David Gillman , Dana Randall

In the paper [Angelini M C, Parisi G, and Ricci-Tersenghi F, Ensemble renormalization group for disordered systems, Phys. Rev. B 87 134201 (2013)] we introduced a real-space renormalization group called Ensemble Renormalization Group (ERG)…

Disordered Systems and Neural Networks · Physics 2020-09-04 Maria Chiara Angelini , Giorgio Parisi , Federico Ricci-Tersenghi

Obtaining the low-energy configurations of spin glasses that have rugged energy landscapes is of direct relevance to combinatorial optimization and fundamental science. Search-based heuristics have difficulty with this task due to the…

Disordered Systems and Neural Networks · Physics 2024-12-24 Adil A. Gangat , Johnnie Gray