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We consider a high-dimensional random constrained optimization problem in which a set of binary variables is subjected to a linear system of equations. The cost function is a simple linear cost, measuring the Hamming distance with respect…

Disordered Systems and Neural Networks · Physics 2022-11-23 Alfredo Braunstein , Louise Budzynski , Stefano Crotti , Federico Ricci-Tersenghi

We present herein a scheme by which to accurately evaluate the error exponents of a lossy data compression problem, which characterize average probabilities over a code ensemble of compression failure and success above or below a critical…

Statistical Mechanics · Physics 2007-05-23 Tadaaki Hosaka , Yoshiyuki Kabashima

The set of solutions of random constraint satisfaction problems (zero energy groundstates of mean-field diluted spin glasses) undergoes several structural phase transitions as the amount of constraints is increased. This set first breaks…

Statistical Mechanics · Physics 2007-12-19 Guilhem Semerjian

We study the Ising spin glass model on scale-free networks generated by the static model using the replica method. Based on the replica-symmetric solution, we derive the phase diagram consisting of the paramagnetic (P), ferromagnetic (F),…

Statistical Mechanics · Physics 2009-11-11 D. -H. Kim , G. J. Rodgers , B. Kahng , D. Kim

In high-dimensional generalized linear models, it is crucial to identify a sparse model that adequately accounts for response variation. Although the best subset section has been widely regarded as the Holy Grail of problems of this type,…

Machine Learning · Statistics 2023-08-02 Junxian Zhu , Jin Zhu , Borui Tang , Xuanyu Chen , Hongmei Lin , Xueqin Wang

Color codes are topological stabilizer codes with unusual transversality properties. Here I show that their group of transversal gates is optimal and only depends on the spatial dimension, not the local geometry. I also introduce a…

Quantum Physics · Physics 2015-08-07 H. Bombin

We give a broad generalisation of the mapping, originally due to Dennis, Kitaev, Landahl and Preskill, from quantum error correcting codes to statistical mechanical models. We show how the mapping can be extended to arbitrary stabiliser or…

Quantum Physics · Physics 2021-06-03 Christopher T. Chubb , Steven T. Flammia

We study the numerical approximation by space-time finite element methods of a multi-physics system coupling hyperbolic elastodynamics with parabolic transport and modeling poro- and thermoelasticity. The equations are rewritten as a…

Numerical Analysis · Mathematics 2023-02-14 Markus Bause , Mathias Anselmann , Uwe Köcher , Florin A. Radu

We have performed comprehensive numerical simulations of the Random Phase Sine Gordon Model, studying both statics and dynamics for various values of the coupling. The glass transition can be seen both in static and dynamic signals at a…

Condensed Matter · Physics 2009-10-28 David J. Lancaster , Juan J. Ruiz Lorenzo

We show that the algebra of Parisi ultrametric matrices is recovered by the real-time, replica-free, Dyson-Keldysh equations of infinite-range quantum spin glasses in the late time glassy limit. This connects to earlier results on classical…

Disordered Systems and Neural Networks · Physics 2024-12-10 Johannes Lang , Subir Sachdev , Sebastian Diehl

Gaussian graphical models are of great interest in statistical learning. Because the conditional independencies between different nodes correspond to zero entries in the inverse covariance matrix of the Gaussian distribution, one can learn…

Machine Learning · Computer Science 2010-11-02 Katya Scheinberg , Shiqian Ma , Donald Goldfarb

Stochastic gradient methods for machine learning and optimization problems are usually analyzed assuming data points are sampled \emph{with} replacement. In practice, however, sampling \emph{without} replacement is very common, easier to…

Machine Learning · Computer Science 2016-10-18 Ohad Shamir

A spin glass is a diluted magnetic material in which the magnetic moments are randomly interacting, with a huge number of metastable states which prevent reaching equilibrium. Spin-glass models are conceptually simple, but require very…

Disordered Systems and Neural Networks · Physics 2023-03-03 Eric Vincent

A formalism capable of handling the first step of hierarchical replica symmetry breaking in finite-connectivity models is introduced. The emerging order parameter is claimed to be a probability distribution over the space of field…

Disordered Systems and Neural Networks · Physics 2009-10-30 Remi Monasson

We establish the connection between a recent new construction technique for quantum error correcting codes, based on graphs, and the so-called stabilizer codes: Each stabilizer code can be realized as a graph code and vice versa.

Quantum Physics · Physics 2007-05-23 D. Schlingemann

We investigate a novel class of quantum error correcting codes to correct errors on both qubits and higher-state quantum systems represented as qudits. These codes arise from an original graph-theoretic representation of sets of quantum…

Quantum Physics · Physics 2022-04-13 Robert Vandermolen , Duncan Wright

Turbo codes are a very efficient method for communicating reliably through a noisy channel. There is no theoretical understanding of their effectiveness. In [1] they are mapped onto a class of disordered spin models. The analytical…

Disordered Systems and Neural Networks · Physics 2009-10-31 Andrea Montanari

We study the graph alignment problem over two independent Erd\H{o}s-R\'enyi graphs on $n$ vertices, with edge density $p$ falling into two regimes separated by the critical window around $p_c=\sqrt{\log n/n}$. Our result reveals an…

Probability · Mathematics 2025-03-27 Hang Du , Shuyang Gong , Rundong Huang

In this thesis, we review and examine the replica method from several viewpoints. The replica method is a mathematical technique to calculate general moments of stochastic variables. This method provides a systematic way to evaluate…

Statistical Mechanics · Physics 2015-08-24 Tomoyuki Obuchi

This paper studies iteration convergence of Kronecker graphical lasso (KGLasso) algorithms for estimating the covariance of an i.i.d. Gaussian random sample under a sparse Kronecker-product covariance model and MSE convergence rates. The…

Methodology · Statistics 2013-11-04 Theodoros Tsiligkaridis , Alfred O. Hero , Shuheng Zhou