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Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized federated learning (DFL). The performance of decentralized SGD is jointly influenced by inter-node communications and local updates. In this paper, we…

Machine Learning · Computer Science 2022-02-14 Wei Liu , Li Chen , Wenyi Zhang

An interface description and numerical simulations of model A kinetics are used for the first time to investigate the intra-surface kinetics of phase ordering on corrugated surfaces. Geometrical dynamical equations are derived for the…

Statistical Mechanics · Physics 2015-06-25 Oliver Schoenborn , Rashmi C. Desai

The quantum antiferromagnetic spin-1/2 Ising model on a triangular lattice and analogous fully frustrated Ising model on a square lattice with quantum fluctuations induced by the application of the transverse magnetic field are studied at…

Strongly Correlated Electrons · Physics 2015-06-05 S. E. Korshunov

$L_0$-smoothness, which has been pivotal to advancing decentralized optimization theory, is often fairly restrictive for modern tasks like deep learning. The recent advent of relaxed $(L_0,L_1)$-smoothness condition enables improved…

Optimization and Control · Mathematics 2025-08-13 Zhanhong Jiang , Aditya Balu , Soumik Sarkar

We study the 2D Ising model on three different types of lattices that are topologically equivalent to spheres. The geometrical shapes are reminiscent of the surface of a pillow, a 3D cube and a sphere, respectively. Systems of volumes…

High Energy Physics - Lattice · Physics 2009-10-28 Ch. Hoelbling , C. B. Lang

Interface undercooling is one of the most significant parameters in the solidification of colloidal suspensions. However, quantitative measurement of interface undercooling of colloidal suspensions is still a challenge. Here, a new…

Materials Science · Physics 2015-08-13 Jiaxue You , Lilin Wang , Zhijun Wang , Junjie Li , Jincheng Wang , Xin Lin , Weidong Huang

The local structure of a solid-on-solid (SOS) interface in a two-dimensional kinetic Ising ferromagnet with single-spin-flip Glauber dynamics, which is driven far from equilibrium by an applied field, is studied by an analytic mean-field,…

Statistical Mechanics · Physics 2009-11-07 P. A. Rikvold , M. Kolesik

There is no an accepted exact partition function (PF) for the two-dimensional (2D) Ising model with a non-zero external magnetic field to our knowledge. Here we infer an empirical PF for such an Ising model. We compare the PFs for two…

Statistical Mechanics · Physics 2019-08-27 Rong Qiang Wei

We map the ground-state ensemble of antiferromagnetic Ising model of spin-S on a triangular lattice to an interface model whose entropic fluctuations are proposed to be described by an effective Gaussian free energy, which enables us to…

Condensed Matter · Physics 2009-10-28 C. Zeng , C. L. Henley

In this paper we study the inference of the kinetic Ising model on sparse graphs by the decimation method. The decimation method, which was first proposed in [Phys. Rev. Lett. 112, 070603] for the static inverse Ising problem, tries to…

Disordered Systems and Neural Networks · Physics 2016-06-30 Aurélien Decelle , Pan Zhang

We study the flux tube thickness of a generic Lattice Gauge Theory near the deconfining phase transition. It is well known that the effective string model predicts a logarithmic increase of the flux tube thickness as a function of the…

High Energy Physics - Lattice · Physics 2009-12-04 A. Allais , M. Caselle

Finite-size scaling at fixed renormalization-group invariant is a powerful and flexible technique to analyze Monte Carlo data at a critical point. It consists in fixing a given renormalization-group invariant quantity to a given value,…

Statistical Mechanics · Physics 2022-03-30 Francesco Parisen Toldin

We investigate dynamics of the supersymmetric fermion lattice model defined by Hermann Nicolai. We provide its local fermionic constants of motion that exist infinitely many. These generate hidden local supersymmetries that the Nicolai…

Mathematical Physics · Physics 2018-07-10 Hajime Moriya

We present and analyze a methodology for numerical homogenization of spatial networks, modelling e.g. diffusion processes and deformation of mechanical structures. The aim is to construct an accurate coarse model of the network. By solving…

Numerical Analysis · Mathematics 2022-09-14 Fredrik Edelvik , Morgan Görtz , Fredrik Hellman , Gustav Kettil , Axel Målqvist

We consider gradient models on the lattice $\mathbb{Z}^d$. These models serve as effective models for interfaces and are also known as continuous Ising models. The height of the interface is modelled by a random field with an energy which…

Mathematical Physics · Physics 2020-07-21 Susanne Hilger

Extensive dynamical simulations of Restricted Solid on Solid models in $D=2+1$ dimensions have been done using parallel multisurface algorithms implemented on graphics cards. Numerical evidence is presented that these models exhibit KPZ…

Statistical Mechanics · Physics 2016-08-08 Jeffrey Kelling , Géza Ódor , Sibylle Gemming

We consider the quantum cat map - a toy model of a quantized chaotic system. We show that its eigenstates are fully delocalized on $\mathbb{T}^2$ in the semiclassical limit (or equivalently that each semiclassical measure is fully supported…

Analysis of PDEs · Mathematics 2025-01-01 Nir Schwartz

We develop a model for sheared gouge layers that accounts for the local increase in temperature at the grain contacts during sliding. We use the shear transformation zone (STZ) theory, a statistical thermodynamic theory, to describe…

Materials Science · Physics 2015-06-18 Ahmed E. Elbanna , Jean M. Carlson

This article studies descent theory in the setting of Berkovich spaces. We give sufficient conditions for a given fibered category over the category of k-affinoid algebras to be a stack for the Berkovich analogue of the faithfully-flat…

Algebraic Geometry · Mathematics 2023-01-11 Mathieu Daylies

Stochastic gradient descent (SGD) is a widely adopted iterative method for optimizing differentiable objective functions. In this paper, we propose and discuss a novel approach to scale up SGD in applications involving non-convex functions…

Machine Learning · Statistics 2022-10-07 Saad Mohamad , Hamad Alamri , Abdelhamid Bouchachia
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