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We prove that the empirical density of states of quantum spin glasses on arbitrary graphs converges to a normal distribution as long as the maximal degree is negligible compared with the total number of edges. This extends the recent…

Mathematical Physics · Physics 2016-10-25 László Erdős , Dominik Schröder

Glassy behavior is one of the main open problems in condensed matter physics. In this thesis, we approach the problem by studying spin-glasses and colloids, using several complementary strategies. From the point of view of model building,…

Disordered Systems and Neural Networks · Physics 2013-09-10 B. Seoane

We analyze the properties of the energy landscape of {\it finite-size} fully connected p-spin-like models whose high temperature phase is described, in the thermodynamic limit, by the schematic Mode Coupling Theory of super-cooled liquids.…

Disordered Systems and Neural Networks · Physics 2009-10-31 A. Crisanti , F. Ritort

We study the problem of chaos in temperature in some mean-field spin-glass models by means of a replica computation over a model of coupled systems. We propose a set of solutions of the saddle point equations which are intrinsically…

Disordered Systems and Neural Networks · Physics 2009-11-07 Tommaso Rizzo

We consider an invariant random matrix model where the standard Gaussian potential is distorted by an additional single pole of order $m$. We compute the average or macroscopic spectral density in the limit of large matrix size, solving the…

Mathematical Physics · Physics 2014-07-09 Gernot Akemann , Dario Villamaina , Pierpaolo Vivo

Numerical studies in random systems are plagued with strong finite-size effects and boundary effects. We introduce a window-measurement method as a practical solution to these difficulties. We observe physical quantities only within a…

Disordered Systems and Neural Networks · Physics 2015-06-19 Tota Nakamura , Takayuki Shirakura

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

We study a p-spin spin-glass model to understand if the finite-temperature glass transition found in the mean-field regime of p-spin models, and used to model the behavior of structural glasses, persists in the non-mean-field regime. By…

Disordered Systems and Neural Networks · Physics 2010-02-18 Derek Larson , Helmut G. Katzgraber , M. A. Moore , A. P. Young

To establish a unified framework for studying both discrete and continuous coupling distributions, we introduce the {\it binomial} spin glass, a class of models where the couplings are sums of $m$ identically distributed Bernoulli random…

Disordered Systems and Neural Networks · Physics 2018-08-29 Mohammad-Sadegh Vaezi , Gerardo Ortiz , Martin Weigel , Zohar Nussinov

It is a folklore belief in the theory of spin glasses and disordered systems that out-of-equilibrium dynamics fail to find stable local optima exhibiting e.g. local strict convexity on physical time-scales. In the context of the…

Disordered Systems and Neural Networks · Physics 2026-04-02 Brice Huang , Mark Sellke

High-dimensional random landscapes underlie phenomena as diverse as glassy physics and optimization in machine learning, and even their simplest toy models already display extraordinarily rich behavior. This thesis aims to deepen our…

Disordered Systems and Neural Networks · Physics 2025-10-28 Alessandro Pacco

The work of this thesis concerns the problem of linear low energy excitations of vector spin glass models. An analytical and numerical study is carried out, considering a fully connected random-field Heisenberg model at zero temperature, a…

Disordered Systems and Neural Networks · Physics 2023-06-16 Flavio Nicoletti

We study the fluctuation problems at high temperature in the general mixed $p$-spin glass models under the weak external field assumption: $h= \rho N^{-\alpha}, \rho>0, \alpha \in [1/4,\infty]$. By extending the cluster expansion approach…

Probability · Mathematics 2024-07-16 Partha S. Dey , Qiang Wu

The microscopic probability distribution function of the Sherrington-Kirkpatrick (SK) model of spin glasses is calculated explicitly as a function of time by a high-temperature expansion. The resulting formula to the third order of the…

Condensed Matter · Physics 2009-10-28 Hidetoshi Nishimori , Michiko Yamana

A comprehensive review will be given about the rich mathematical structure of mean field spin glass theory, mostly developed, until now, in the frame of the methods of theoretical physics, based on deep physical intuition and hints coming…

Disordered Systems and Neural Networks · Physics 2007-05-23 F. Guerra

The aim of this review paper is to give a panoramic of the impact of spin glass theory and statistical physics in the study of the K-sat problem. The introduction of spin glass theory in the study of the random K-sat problem has indeed left…

Computational Complexity · Computer Science 2014-05-15 Stefano Gogioso

We study numerically the Sherrington--Kirkpatrick model as function of the magnetic field h, with fixed temperature T=0.6 Tc. We investigate the finite size scaling behavior of several quantities, such as the spin glass susceptibility,…

Statistical Mechanics · Physics 2009-11-10 Alain Billoire , Barbara Coluzzi

In these lectures I will present an introduction to the modern way of studying the properties of glassy systems. I will start from soluble models of increasing complications, the Random Energy Model, the $p$-spins interacting model and I…

Disordered Systems and Neural Networks · Physics 2009-10-30 Giorgio Parisi

The richness of the mean-field solution of simple glasses leaves many of its features challenging to interpret. A minimal model that illuminates glass physics the same way the random energy model clarifies spin glass behavior would…

Disordered Systems and Neural Networks · Physics 2025-03-14 Gilles Bonnet , Patrick Charbonneau , Giampaolo Folena

Optimizing a high-dimensional non-convex function is, in general, computationally hard and many problems of this type are hard to solve even approximately. Complexity theory characterizes the optimal approximation ratios achievable in…

Statistical Mechanics · Physics 2020-09-25 Ahmed El Alaoui , Andrea Montanari