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Allan Sly

Publications and source records attributed to Allan Sly.

At least 73 records · Page 4Linked to original sources
arXiv2017

Fast initial conditions for Glauber dynamics

In the study of Markov chain mixing times, analysis has centered on the performance from a worst-case starting state. Here, in the context of Glauber dynamics for the one-dimensional Ising model, we show how new ideas from information percolation can be used to establish mixing times from other starting states. At high temperatures we show that the alternating initial condition is asymptotically the fastest one, and, surprisingly, its mixing time is faster than at infinite temperature, accelerating as the inverse-temperature $β$ ranges from 0 to $β_0=\frac12\mathrm{arctanh}(\frac13)$. Moreover, the dominant test function depends on the temperature: at $β<β_0$ it is autocorrelation, whereas at $β>β_0$ it is the Hamiltonian.

math.PR↗
arXiv2016

Random walks on the random graph

We study random walks on the giant component of the Erdős-Rényi random graph ${\cal G}(n,p)$ where $p=λ/n$ for $λ>1$ fixed. The mixing time from a worst starting point was shown by Fountoulakis and Reed, and independently by Benjamini, Kozma and Wormald, to have order $\log^2 n$. We prove that starting from a uniform vertex (equivalently, from a fixed vertex conditioned to belong to the giant) both accelerates mixing to $O(\log n)$ and concentrates it (the cutoff phenomenon occurs): the typical mixing is at $(ν{\bf d})^{-1}\log n \pm (\log n)^{1/2+o(1)}$, where $ν$ and ${\bf d}$ are the speed of random walk and dimension of harmonic measure on a ${\rm Poisson}(λ)$-Galton-Watson tree. Analogous results are given for graphs with prescribed degree sequences, where cutoff is shown both for the simple and for the non-backtracking random walk.

math.PR↗
arXiv2016

Belief propagation, robust reconstruction and optimal recovery of block models

We consider the problem of reconstructing sparse symmetric block models with two blocks and connection probabilities $a/n$ and $b/n$ for inter- and intra-block edge probabilities, respectively. It was recently shown that one can do better than a random guess if and only if $(a-b)^2>2(a+b)$. Using a variant of belief propagation, we give a reconstruction algorithm that is optimal in the sense that if $(a-b)^2>C(a+b)$ for some constant $C$ then our algorithm maximizes the fraction of the nodes labeled correctly. Ours is the only algorithm proven to achieve the optimal fraction of nodes labeled correctly. Along the way, we prove some results of independent interest regarding robust reconstruction for the Ising model on regular and Poisson trees.

math.PR↗
arXiv2016

On One-dimensional Multi-Particle Diffusion Limited Aggregation

We prove that the one dimensional Multi-Particle Diffusion Limited Aggregation model has linear growth whenever the particle density exceeds 1 answering a question of Kesten and Sidoravicius. As a corollary we prove linear growth in all dimensions d when the particle density is at least 1.

math.PR↗
arXiv2016

Lipschitz Embeddings of Random Fields

We consider the problem of embedding one i.i.d.\ collection of Bernoulli random variables indexed by $\mathbb{Z}^d$ into an independent copy in an injective $M$-Lipschitz manner. For the case $d=1$, it was shown by Basu and Sly (PTRF, 2014) to be possible almost surely for sufficiently large $M$. In this paper we provide a multi-scale argument extending this result to higher dimensions.

math.PR↗
arXiv2016

Last Passage Percolation with a Defect Line and the Solution of the Slow Bond Problem

We address the question of how a localized microscopic defect, especially if it is small with respect to certain dynamic parameters, affects the macroscopic behavior of a system. In particular we consider two classical exactly solvable models: Ulam's problem of the maximal increasing sequence and the totally asymmetric simple exclusion process. For the first model, using its representation as a Poissonian version of directed last passage percolation on $\mathbb R^2$, we introduce the defect by placing a positive density of extra points along the diagonal line. For the latter, the defect is produced by decreasing the jump rate of each particle when it crosses the origin. The powerful algebraic tools for studying these processes break down in the perturbed versions of the models. Taking a more geometric approach we show that in both cases the presence of an arbitrarily small defect affects the macroscopic behavior of the system: in Ulam's problem the time constant increases, and for the exclusion process the flux of particles decreases. This, in particular, settles the longstanding Slow Bond Problem.

math.PR↗
arXiv2015

Distances in critical long range percolation

We study the long range percolation model on $\mathbb{Z}$ where sites $i$ and $j$ are connected with probability $β|i-j|^{-s}$. Graph distances are now well understood for all exponents $s$ except in the case $s=2$ where the model exhibits non-trivial self-similar scaling. Establishing a conjecture of Benjamini and Berger \cite{BenBer:01}, we prove that the typical distance from site 0 to $n$ grows as a power law $n^{θ(β)}$ up to a multiplicative constant for some exponent $0<θ(β)<1$ as does the diameter of the graph on a box of length $n$.

math.PR↗
arXiv2015

A Proof Of The Block Model Threshold Conjecture

We study a random graph model named the "block model" in statistics and the "planted partition model" in theoretical computer science. In its simplest form, this is a random graph with two equal-sized clusters, with a between-class edge probability of $q$ and a within-class edge probability of $p$. A striking conjecture of Decelle, Krzkala, Moore and Zdeborová based on deep, non-rigorous ideas from statistical physics, gave a precise prediction for the algorithmic threshold of clustering in the sparse planted partition model. In particular, if $p = a/n$ and $q = b/n$, $s=(a-b)/2$ and $p=(a+b)/2$ then Decelle et al.\ conjectured that it is possible to efficiently cluster in a way correlated with the true partition if $s^2 > p$ and impossible if $s^2 < p$. By comparison, the best-known rigorous result is that of Coja-Oghlan, who showed that clustering is possible if $s^2 > C p \ln p$ for some sufficiently large $C$. In a previous work, we proved that indeed it is information theoretically impossible to to cluster if $s^2 < p$ and furthermore it is information theoretically impossible to even estimate the model parameters from the graph when $s^2 < p$. Here we complete the proof of the conjecture by providing an efficient algorithm for clustering in a way that is correlated with the true partition when $s^2 > p$. A different independent proof of the same result was recently obtained by Laurent Massoulie.

math.PR↗
arXiv2015

Strategic Learning and the Topology of Social Networks

We consider a group of strategic agents who must each repeatedly take one of two possible actions. They learn which of the two actions is preferable from initial private signals, and by observing the actions of their neighbors in a social network. We show that the question of whether or not the agents learn efficiently depends on the topology of the social network. In particular, we identify a geometric "egalitarianism" condition on the social network that guarantees learning in infinite networks, or learning with high probability in large finite networks, in any equilibrium. We also give examples of non-egalitarian networks with equilibria in which learning fails.

cs.GT↗
arXiv2015

Information percolation and cutoff for the stochastic Ising model

We introduce a new framework for analyzing Glauber dynamics for the Ising model. The traditional approach for obtaining sharp mixing results has been to appeal to estimates on spatial properties of the stationary measure from within a multi-scale analysis of the dynamics. Here we propose to study these simultaneously by examining "information percolation" clusters in the space-time slab. Using this framework, we obtain new results for the Ising model on $(\mathbb{Z}/n\mathbb{Z})^d$ throughout the high temperature regime: total-variation mixing exhibits cutoff with an $O(1)$-window around the time at which the magnetization is the square-root of the volume. (Previously, cutoff in the full high temperature regime was only known for $d\leq 2$, and only with an $O(\log\log n)$-window.) Furthermore, the new framework opens the door to understanding the effect of the initial state on the mixing time. We demonstrate this on the 1D Ising model, showing that starting from the uniform ("disordered") initial distribution asymptotically halves the mixing time, whereas almost every deterministic starting state is asymptotically as bad as starting from the ("ordered") all-plus state.

math.PR↗
arXiv2015

Evolving Voter Model on Dense Random Graphs

In this paper we examine a variant of the voter model on a dynamically changing network where agents have the option of changing their friends rather than changing their opinions. We analyse, in the context of dense random graphs, two models considered in Durrett et. al.(Proc. Natl. Acad. Sci. 109: 3682-3687, 2012). When an edge with two agents holding different opinion is updated, with probability $\fracβ{n}$, one agent performs a voter model step and changes its opinion to copy the other, and with probability $1-\fracβ{n}$, the edge between them is broken and reconnected to a new agent chosen randomly from (i) the whole network (rewire-to-random model) or, (ii) the agents having the same opinion (rewire-to-same model). We rigorously establish in both the models, the time for this dynamics to terminate exhibits a phase transition in the model parameter $β$. For $β$ sufficiently small, with high probability the network rapidly splits into two disconnected communities with opposing opinions, whereas for $β$ large enough the dynamics runs for longer and the density of opinion changes significantly before the process stops. In the rewire-to-random model, we show that a positive fraction of both opinions survive with high probability.

math.PR↗
arXiv2014

An exposition to information percolation for the Ising model

Information percolation is a new method for analyzing stochastic spin systems through classifying and controlling the clusters of information-flow in the space-time slab. It yielded sharp mixing estimates (cutoff with an $O(1)$-window) for the Ising model on $Z^d$ up to the critical temperature, as well as results on the effect of initial conditions on mixing. In this expository note we demonstrate the method on lattices (more generally, on any locally-finite transitive graph) at very high temperatures.

math.PR↗
arXiv2014

Glauber Dynamics of colorings on trees

The mixing time of the Glauber dynamics for spin systems on trees is closely related to reconstruction problem. Martinelli, Sinclair and Weitz established this correspondence for a class of spin systems with soft constraints bounding the log-Sobolev constant by a comparison with the block dynamics. However, when there are hard constraints, the block dynamics may be reducible. We introduce a variant of the block dynamics extending these results to a wide class of spin systems with hard constraints. This applies for essentially any spin system that has non-reconstruction provided that on average the root is not locally frozen in a large neighborhood. In particular we prove that the mixing time of the Glauber dynamics for colorings on the regular tree is $O(n\log n)$ in the entire known non-reconstruction regime.

math.PR↗
arXiv2014

Bi-Lipschitz Expansion of Measurable Sets

We show that for $0<γ, γ' <1$ and for measurable subsets of the unit square with Lebesgue measure $γ$ there exist bi-Lipschitz maps with bounded Lipschitz constant (uniformly over all such sets) which are identity on the boundary and increases the Lebesgue measure of the set to at least $1-γ'$.

math.AP↗
arXiv2014

Scheduling of non-colliding random walks

On the complete graph ${\cal{K}}_M$ with $M \ge3$ vertices consider two independent discrete time random walks $\mathbb{X}$ and $\mathbb{Y}$, choosing their steps uniformly at random. A pair of trajectories $\mathbb{X} = \{ X_1, X_2, \dots \}$ and $\mathbb{Y} = \{Y_1, Y_2, \dots \}$ is called {\it{non-colliding}}, if by delaying their jump times one can keep both walks at distinct vertices forever. It was conjectured by P. Winkler that for large enough $M$ the set of pairs of non-colliding trajectories $\{\mathbb{X},\mathbb{Y} \} $ has positive measure. N. Alon translated this problem to the language of coordinate percolation, a class of dependent percolation models, which in most situations is not tractable by methods of Bernoulli percolation. In this representation Winkler's conjecture is equivalent to the existence of an infinite open cluster for large enough $M$. In this paper we establish the conjecture.

math.PR↗
arXiv2014

Rigidity and tolerance for perturbed lattices

A perturbed lattice is a point process $Π=\{x+Y_x:x\in \mathbb{Z}^d\}$ where the lattice points in $\mathbb{Z}^d$ are perturbed by i.i.d.\ random variables $\{Y_x\}_{x\in \mathbb{Z}^d}$. A random point process $Π$ is said to be rigid if $|Π\cap B_0(1)|$, the number of points in a ball, can be exactly determined given $Π\setminus B_0(1)$, the points outside the ball. The process $Π$ is called deletion tolerant if removing one point of $Π$ yields a process with distribution indistinguishable from that of $Π$. Suppose that $Y_x\sim N_d(0,σ^2 I)$ are Gaussian vectors with with $d$ independent components of variance $σ^2$. Holroyd and Soo showed that in dimensions $d=1,2$ the resulting Gaussian perturbed lattice $Π$ is rigid and deletion intolerant. We show that in dimension $d\geq 3$ there exists a critical parameter $σ_r(d)$ such that $Π$ is rigid if $σ<σ_r$ and deletion tolerant (hence non-rigid) if $σ>σ_r$.

math.PR↗