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Some recent results showed that renormalization group can be considered as a promising framework to address open issues in data analysis. In this work, we focus on one of these aspects, closely related to principal component analysis for…

High Energy Physics - Theory · Physics 2022-03-04 Vincent Lahoche , Dine Ousmane Samary , Mohamed Tamaazousti

Renormalization group analysis for multi-band many-electron systems at half-filling at positive temperature is presented. The analysis includes the Matsubara ultra-violet integration and the infrared integration around the zero set of the…

Mathematical Physics · Physics 2015-09-01 Yohei Kashima

We study the critical behavior of the $O(n)$ model under steady shear flow using a dynamical renormalization group (RG) method. Incorporating the strong anisotropy in scaling ansatz, which has been neglected in earlier RG analyses, we…

Statistical Mechanics · Physics 2026-05-20 Harukuni Ikeda , Hiroyoshi Nakano

Two replicas of a 2D Ising model are coupled by frustrated spin-spin interactions. It is known that this inter-layer coupling is marginal and that the bulk critical behavior belongs to the Ashkin-Teller (AT) universality class, as the…

Statistical Mechanics · Physics 2026-05-06 Christophe Chatelain

The N-vector cubic model relevant, among others, to the physics of the randomly dilute Ising model is analyzed in arbitrary dimension by means of an exact renormalization-group equation. This study provides a unified picture of its critical…

Statistical Mechanics · Physics 2007-05-23 M. Tissier , D. Mouhanna , J. Vidal , B. Delamotte

The infinite disorder fixed point of the random transverse-field Ising model is expected to control the critical behavior of a large class of random quantum and stochastic systems having an order parameter with discrete symmetry. Here we…

Disordered Systems and Neural Networks · Physics 2015-05-19 Istvan A. Kovacs , Ferenc Igloi

The variational tensor network renormalization approach to two-dimensional (2D) quantum systems at finite temperature is applied for the first time to a model suffering the notorious quantum Monte Carlo sign problem --- the orbital $e_g$…

Strongly Correlated Electrons · Physics 2017-07-26 Piotr Czarnik , Jacek Dziarmaga , Andrzej M. Oleś

We present a new methodology for studying non-Hamiltonian nonlinear systems based on an information theoretic extension of a renormalization group technique using a modified maximum entropy principle. We obtain a rigorous dimensionally…

Computational Physics · Physics 2013-06-28 M. Schmuck , M. Pradas , S. Kalliadasis , G. A. Pavliotis

The one-dimensional extended Hubbard model with both the on-site $U$ and the nearest neighbor $V$ interactions at quarter filling is studied by using a novel finite size scaling. We diagonalize finite size systems numerically and calculate…

Strongly Correlated Electrons · Physics 2009-11-10 Kazuhiro Sano , Yoshiaki Ono

We consider a general class of spin systems with potentially unbounded real-valued spins, defined via a single-site potential with super-Gaussian tails on general graphs, allowing for both short- and long-range interactions. This class…

Probability · Mathematics 2026-03-30 Christoforos Panagiotis , William Veitch

We analyze the possibility of nonperturbative renormalizability of gauge theories in D > 4 dimensions. We develop a scenario, based on Weinberg's idea of asymptotic safety, that allows for renormalizability in extra dimensions owing to a…

High Energy Physics - Theory · Physics 2009-11-10 Holger Gies

In the problem of structured signal recovery from high-dimensional linear observations, it is commonly assumed that full-precision measurements are available. Under this assumption, the recovery performance of the popular Generalized Lasso…

Information Theory · Computer Science 2018-07-19 Christos Thrampoulidis , Ankit Singh Rawat

We have developed a very efficient numerical algorithm of the strong disorder renormalization group method to study the critical behaviour of the random transverse-field Ising model, which is a prototype of random quantum magnets. With this…

Disordered Systems and Neural Networks · Physics 2011-09-21 István A. Kovács , Ferenc Iglói

We investigate the critical behavior that d-dimensional systems with short-range forces and a n-component order parameter exhibit at Lifshitz points whose wave-vector instability occurs in a m-dimensional isotropic subspace of ${\mathbb…

Statistical Mechanics · Physics 2009-11-07 M. Shpot , H. W. Diehl

In this paper, we investigate the Gibbs measures associated with the focusing nonlinear Schr\"odinger equation with an anharmonic potential. We establish a dichotomy for normalizability and non-normalizability of the Gibbs measures in one…

Analysis of PDEs · Mathematics 2026-03-25 Van Duong Dinh , Nicolas Rougerie , Leonardo Tolomeo , Yuzhao Wang

We study Gibbs measures with log-correlated base Gaussian fields on the $d$-dimensional torus. In the defocusing case, the construction of such Gibbs measures follows from Nelson's argument. In this paper, we consider the focusing case with…

Probability · Mathematics 2024-04-29 Tadahiro Oh , Kihoon Seong , Leonardo Tolomeo

The Dobrushin comparison theorem is a powerful tool to bound the difference between the marginals of high-dimensional probability distributions in terms of their local specifications. Originally introduced to prove uniqueness and decay of…

Probability · Mathematics 2015-02-04 Patrick Rebeschini , Ramon van Handel

We investigate a two-dimensional Ising model with long-range interactions that emerge from a generalization of the magnetic dipolar interaction in spin systems with in-plane spin orientation. This interaction is, in general, anisotropic…

Statistical Mechanics · Physics 2009-11-10 Daniel Grüneberg , Alfred Hucht

The aim of these lectures is to describe a construction, as self-contained as possible, of renormalized gauge theories. Following a suggestion of Polchinski, we base our analysis on the Wilson renormalization group method. After a…

High Energy Physics - Theory · Physics 2007-05-23 Carlo Becchi

Statistical learning theory has largely focused on learning and generalization given independent and identically distributed (i.i.d.) samples. Motivated by applications involving time-series data, there has been a growing literature on…

Machine Learning · Computer Science 2019-06-24 Yuval Dagan , Constantinos Daskalakis , Nishanth Dikkala , Siddhartha Jayanti