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In this paper we consider the problem of sampling from the low-temperature exponential random graph model (ERGM). The usual approach is via Markov chain Monte Carlo, but Bhamidi et al. showed that any local Markov chain suffers from an…

Probability · Mathematics 2022-10-05 Guy Bresler , Dheeraj Nagaraj , Eshaan Nichani

We study metastability and mixing time for a non-reversible probabilistic cellular automaton. With a suitable choice of the parameters, we first show that the stationary distribution is close in total variation to a low temperature Ising…

Probability · Mathematics 2014-07-25 Paolo Dai Pra , Benedetto Scoppola , Elisabetta Scoppola

We study the metastability of the ferromagnetic Ising model on a random $r$-regular graph in the zero temperature limit. We prove that in the presence of a small positive external field the time that it takes to go from the all minus state…

Probability · Mathematics 2015-11-23 Sander Dommers

We show that a distribution related to Gaussian Boson Sampling (GBS) on graphs can be sampled classically in polynomial time. Graphical applications of GBS typically sample from this distribution, and thus quantum algorithms do not provide…

Random walks on expanders play a crucial role in Markov Chain Monte Carlo algorithms, derandomization, graph theory, and distributed computing. A desirable property is that they are rapidly mixing, which is equivalent to having a spectral…

Probability · Mathematics 2024-12-18 Sam Olesker-Taylor , Thomas Sauerwald , John Sylvester

We study the Glauber dynamics of Ising spin models with random bonds, on finitely connected random graphs. We generalize a recent dynamical replica theory with which to predict the evolution of the joint spin-field distribution, to include…

Disordered Systems and Neural Networks · Physics 2015-05-13 A. Mozeika , A. C. C. Coolen

The independence number of a sparse random graph G(n,m) of average degree d=2m/n is well-known to be \alpha(G(n,m))~2n ln(d)/d with high probability. Moreover, a trivial greedy algorithm w.h.p. finds an independent set of size (1+o(1)) n…

Discrete Mathematics · Computer Science 2017-11-29 Amin Coja-Oghlan , Charilaos Efthymiou

In this work, we study the maximum matching problem from the perspective of sensitivity. The sensitivity of an algorithm $A$ on a graph $G$ is defined as the maximum Wasserstein distance between the output distributions of $A$ on $G$ and on…

Data Structures and Algorithms · Computer Science 2025-11-24 Yuichi Yoshida , Zihan Zhang

Suppose in a graph $G$ vertices can be either red or blue. Let $k$ be odd. At each time step, each vertex $v$ in $G$ polls $k$ random neighbours and takes the majority colour. If it doesn't have $k$ neighbours, it simply polls all of them,…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-08-01 Mohammed Amin Abdullah , Moez Draief

We consider the ferromagnetic Ising model on a sequence of graphs $G_n$ converging locally weakly to a rooted random tree. Generalizing [Montanari, Mossel, Sly '11], under an appropriate "continuity" property, we show that the Ising…

Probability · Mathematics 2015-10-30 Anirban Basak , Amir Dembo

We study the limiting behavior of interacting particle systems indexed by large sparse graphs, which evolve either according to a discrete time Markov chain or a diffusion, in which particles interact directly only with their nearest…

Probability · Mathematics 2022-05-18 Daniel Lacker , Kavita Ramanan , Ruoyu Wu

We prove that the the mixing time of the Glauber dynamics for sampling independent sets on $n$-vertex $k$-uniform hypergraphs is $O(n\log n)$ when the maximum degree $\Delta$ satisfies $\Delta \leq c 2^{k/2}$, improving on the previous…

Probability · Mathematics 2019-12-25 Jonathan Hermon , Allan Sly , Yumeng Zhang

This paper considers the problem of distributed optimization over time-varying graphs. For the case of undirected graphs, we introduce a distributed algorithm, referred to as DIGing, based on a combination of a distributed inexact gradient…

Optimization and Control · Mathematics 2017-03-21 Angelia Nedich , Alex Olshevsky , Wei Shi

In the model of randomly perturbed graphs we consider the union of a deterministic graph $\mathcal{G}_\alpha$ with minimum degree $\alpha n$ and the binomial random graph $\mathbb{G}(n,p)$. This model was introduced by Bohman, Frieze, and…

Combinatorics · Mathematics 2020-04-10 Max Hahn-Klimroth , Giulia S. Maesaka , Yannick Mogge , Samuel Mohr , Olaf Parczyk

The usual setting for learning the structure and parameters of a graphical model assumes the availability of independent samples produced from the corresponding multivariate probability distribution. However, for many models the mixing time…

Machine Learning · Computer Science 2022-10-13 Arkopal Dutt , Andrey Y. Lokhov , Marc Vuffray , Sidhant Misra

Let $\gS=(V,E)$ be a finite, $d$-regular bipartite graph. For any $\lambda>0$ let $\pi_\lambda$ be the probability measure on the independent sets of $\gS$ in which the set $I$ is chosen with probability proportional to $\lambda^{|I|}$…

Combinatorics · Mathematics 2012-06-15 David Galvin , Prasad Tetali

Let $G = (V,E)$ be a graph on $n$ vertices and let $m^*(G)$ denote the size of a maximum matching in $G$. We show that for any $\delta > 0$ and for any $1 \leq k \leq (1-\delta)m^*(G)$, the down-up walk on matchings of size $k$ in $G$ mixes…

Data Structures and Algorithms · Computer Science 2024-08-08 Vishesh Jain , Clayton Mizgerd

We give a survey of the known results on mixing time of Glauber dynamics for the Ising model on the square lattice and present a technique that makes exact sampling of the Ising model at all temperatures possible in polynomial time. At high…

Probability · Mathematics 2014-04-23 Mario Ullrich

De Berg et al. in [SICOMP 2020] gave an algorithmic framework for subexponential algorithms on geometric graphs with tight (up to ETH) running times. This framework is based on dynamic programming on graphs of weighted treewidth resulting…

Data Structures and Algorithms · Computer Science 2021-07-15 Fedor V. Fomin , Petr A. Golovach , Tanmay Inamdar , Saket Saurabh

We present a simple combinatorial framework for establishing approximate tensorization of variance and entropy in the setting of spin systems (a.k.a. undirected graphical models) based on balanced separators of the underlying graph. Such…

Data Structures and Algorithms · Computer Science 2023-07-18 Zongchen Chen
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