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Probabilistic zero-forcing is a coloring process on a graph. In this process, an initial set of vertices is colored blue, and the remaining vertices are colored white. At each time step, blue vertices have a non-zero probability of forcing…

Combinatorics · Mathematics 2020-10-26 David Hu , Alec Sun

We consider a natural local dynamic on the set of all rooted planar maps with $n$ edges that is in some sense analogous to "edge flip" Markov chains, which have been considered before on a variety of combinatorial structures (triangulations…

Probability · Mathematics 2020-01-14 Alessandra Caraceni

We prove rigorously that the ferromagnetic Ising model on any nonamenable Cayley graph undergoes a continuous (second-order) phase transition in the sense that there is a unique Gibbs measure at the critical temperature. The proof of this…

Probability · Mathematics 2020-07-31 Tom Hutchcroft

We propose a new proof technique that aims to be applied to the same problems as the Lov\'asz Local Lemma or the entropy-compression method. We present this approach in the context of non-repetitive colorings and we use it to improve…

Combinatorics · Mathematics 2020-06-24 Matthieu Rosenfeld

We consider the finite-timestep Brownian dynamics of a single particle confined in one dimension, with a nonuniform temperature profile. In such an open-boundary scenario, one cannot observe any net probability current in the nonequilibrium…

Statistical Mechanics · Physics 2026-01-19 Chi-Lun Lee , Yu-Syuan Lin , Pik-Yin Lai

A class of integrable models, such as the one-dimensional transverse-field Ising model, respond nonmonotonically to a periodic drive with respect to the driving parameters and freezes almost absolutely for certain combinations of the…

Strongly Correlated Electrons · Physics 2023-07-14 Soumya Sasidharan , Naveen Surendran

The six-vertex model in statistical physics is a weighted generalization of the ice model on $\mathbb{Z}^2$ (i.e., Eulerian orientations) and the zero-temperature three-state Potts model (i.e., proper three-colorings). The phase diagram of…

Data Structures and Algorithms · Computer Science 2020-12-23 Matthew Fahrbach , Dana Randall

Mixing of finite time-homogeneous Markov chains is well understood nowadays, with a rich set of techniques to estimate their mixing time. In this paper, we study the mixing time of random walks in dynamic random environments. To that end,…

Probability · Mathematics 2023-09-27 Raphael Erb

Consider the interchange process on a connected graph $G=(V,E)$ on $n$ vertices. I.e.\ shuffle a deck of cards by first placing one card at each vertex of $G$ in a fixed order and then at each tick of the clock, picking an edge uniformly at…

Probability · Mathematics 2012-10-26 Johan Jonasson

Interaction graphs, such as those recording emails between individuals or transactions between institutions, tend to be sparse yet structured, and often grow in an unbounded manner. Such behavior can be well-captured by structured,…

Machine Learning · Computer Science 2019-10-15 Elahe Ghalebi , Hamidreza Mahyar , Radu Grosu , Graham W. Taylor , Sinead A. Williamson

We revisit classical bounds of M. E. Fisher on the ferromagnetic Ising model, and show how to efficiently use them on an arbitrary given graph to rigorously upper-bound the partition function, magnetizations, and correlations. The results…

Disordered Systems and Neural Networks · Physics 2017-05-24 Alaa Saade , Florent Krzakala , Lenka Zdeborová

We consider Ising model on edge-dual of uncorrelated random networks with arbitrary degree distribution. These networks have a finite clustering in the thermodynamic limit. High and low temperature expansions of Ising model on the edge-dual…

Disordered Systems and Neural Networks · Physics 2009-11-10 A. Ramezanpour

The Gonihedric 3D Ising model is a lattice spin model in which planar Peierls boundaries between + and - spins can be created at zero energy cost. Instead of weighting the area of Peierls boundaries as the case for the usual 3D Ising model…

Statistical Mechanics · Physics 2007-09-11 D. A. Johnston , A. Lipowski , Ranasinghe P. K. C. Malmini

There are not many kinetic models where it is possible to prove bifurcation phenomena for any value of the Knudsen number. Here we consider a binary mixture over a line with collisions and long range repulsive interaction between different…

Mathematical Physics · Physics 2015-05-13 R. Esposito , Y. Guo , R. Marra

We consider the ferromagnetic Ising model with Glauber spin flip dynamics in one dimension. The external magnetic field vanishes and the couplings are i.i.d. random variables. If their distribution has compact support, the disorder averaged…

Mathematical Physics · Physics 2007-05-23 H. Spohn , E. Zhizhina

We consider three extensions of the standard 2D Ising model with Glauber dynamics on a finite torus at low temperature. The first model is an anisotropic version, where the interaction energy takes different values on vertical and on…

Probability · Mathematics 2019-07-02 Kaveh Bashiri

In this note we consider the Glauber dynamics for the mean-field Ising model, when all couplings are equal and the external field is uniform. It is proved that the relaxation time of the dynamics is monotonically decreasing in temperature.

Probability · Mathematics 2012-03-26 Vladislav Kargin

In this paper we study homogeneous Gibbs measures on a Cayley tree, subjected to an infinite-temperature Glauber evolution, and consider their (non-)Gibbsian properties. We show that the intermediate Gibbs state (which in zero field is the…

Mathematical Physics · Physics 2016-11-25 Aernout van Enter , Victor Ermolaev , Giulio Iacobelli , Christof Kuelske

Restricted Boltzmann Machines are a class of undirected graphical models that play a key role in deep learning and unsupervised learning. In this study, we prove a phase transition phenomenon in the mixing time of the Gibbs sampler for a…

Machine Learning · Computer Science 2024-10-14 Youngwoo Kwon , Qian Qin , Guanyang Wang , Yuchen Wei

We study how to establish $\textit{spectral independence}$, a key concept in sampling, without relying on total influence bounds, by applying an $\textit{approximate inverse}$ of the influence matrix. Our method gives constant upper bounds…

Data Structures and Algorithms · Computer Science 2024-04-09 Xiaoyu Chen , Xiongxin Yang , Yitong Yin , Xinyuan Zhang