arXiv subjects

Jonathan Hermon

Publications and source records attributed to Jonathan Hermon.

50 records · Page 3Linked to original sources

arXiv2018

A version of Aldous' spectral-gap conjecture for the zero range process

We show that the spectral-gap of a general zero range process can be controlled in terms of the spectral-gap of a single particle. This is in the spirit of Aldous' famous spectral-gap conjecture for the interchange process. Our main inequality decouples the role of the geometry (defined by the jump matrix) from that of the kinetics (specified by the exit rates). Among other consequences, the various spectral-gap estimates that were so far only available on the complete graph or the $d$-dimensional torus now extend effortlessly to arbitrary geometries. As an illustration, we determine the exact order of magnitude of the spectral-gap of the rate-one $\textbf{ZRP}$ on any regular graph and, more generally, for any doubly stochastic jump matrix.

math.PR↗

arXiv2018

Cutoff for the mean-field zero-range process with bounded monotone rates

We consider the zero-range process with arbitrary bounded monotone rates on the complete graph, in the regime where the number of sites diverges while the density of particles per site converges. We determine the asymptotics of the mixing time from any initial configuration, and establish the cutoff phenomenon. The intuitive picture is that the system separates into a slowly evolving solid phase and a quickly relaxing liquid phase: as time passes, the solid phase dissolves into the liquid phase, and the mixing time is essentially the time at which the system becomes completely liquid. Our proof uses the path coupling technique of Bubley and Dyer, and the analysis of a suitable hydrodynamic limit. To the best of our knowledge, even the order of magnitude of the mixing time was unknown, except in the special case of constant rates.

math.PR↗

arXiv2018

Reversibility of the non-backtracking random walk

Let $G$ be a connected graph of uniformly bounded degree. A $k$ non-backtracking random walk ($k$-NBRW) $(X_n)_{n =0}^{\infty}$ on $G$ evolves according to the following rule: Given $ (X_n)_{n =0}^{s}$, at time $s+1$ the walk picks at random some edge which is incident to $X_s$ that was not crossed in the last $k$ steps and moves to its other end-point. If no such edge exists then it makes a simple random walk step. Assume that for some $R>0$ every ball of radius $R$ in $G$ contains a simple cycle of length at least $k$. We show that under some "nice" random time change the $k$-NBRW becomes reversible. This is used to prove that it is recurrent iff the simple random walk is.

math.PR↗

arXiv2018

Frogs on trees?

We study a system of simple random walks on $\mathcal{T}_{d,n} = \mathcal{V}_{d,n}, \mathcal{E}_{d,n})$, the $d$-ary tree of depth $n$, known as the frog model. Initially there are Pois($λ$) particles at each site, independently, with one additional particle planted at some vertex $\mathbf{o}$. Initially all particles are inactive, except for the ones which are placed at $\mathbf{o}$. Active particles perform (independent) $ t \in \mathbb{N} \cup \{\infty \} $ steps of simple random walk on the tree. When an active particle hits an inactive particle, the latter becomes active. The model is often interpreted as a model for a spread of an epidemic. As such, it is natural to investigate whether the entire population is eventually infected, and if so, how quickly does this happen. Let $\mathcal{R}_t$ be the set of vertices which are visited by the process. Let $\mathcal{S}(\mathcal{T}_{d,n}) := \inf \{t:\mathcal{R}_t = \mathcal{V}_{d,n} \} $. Let the cover time $\mathrm{CT}(\mathcal{T}_{d,n})$ be the first time by which every vertex was visited at least once, when we take $t=\infty$. We show that there exist absolute constants, $c,C>0$ such that for all $d \ge 2$ and all $λ= λ_n $ which does not diverge nor vanish too rapidly, with high probability $c \le λ\mathcal{S}(\mathcal{T}_{d,n}) /n \log (n/λ) \le C$ and $\mathrm{CT}(\mathcal{T}_{d,n}) \le 3^{4 \sqrt{ \log |\mathcal{V}_{d,n}| }}$.

math.PR↗

arXiv2018

A technical report on hitting times, mixing and cutoff

Consider a sequence of continuous-time irreducible reversible Markov chains and a sequence of initial distributions, $μ_n$. The sequence is said to exhibit $μ_n$-cutoff if the convergence to stationarity in total variation distance is abrupt, w.r.t. this sequence of initial distributions. In this work we give a characterization of $μ_n$-cutoff for an arbitrary sequence of initial distributions $μ_n$ (in the above setup). Our characterization is expressed in terms of hitting times of sets which are "worst" w.r.t. $μ_n$. Consider a Markov chain on $Ω$ whose stationary distribution in $π$. Let $t_{\mathrm{H}}(α) :=\max_{x \in Ω,A \subset Ω:\,π(A) \ge α}\mathbb{E}_{x}[T_{A}]$ be the expected hitting time of the worst set of size at least $α$. It was recently proved by Peres and Sousi and independently by Oliveira that $t_{\mathrm{H}}(1/4) $ captures the order of the mixing time. In this work we further refine this connection and show that $μ_n$-cutoff can be characterized in terms of concentration of hitting times (starting from $μ_n$) of sets which are worst in expectation w.r.t. $μ_n$. Conversely, we construct a counter-example which demonstrates that in general cutoff (as opposed to cutoff w.r.t. a certain sequence of initial distributions) cannot be characterized in this manner. Finally, we also prove that there exists an absolute constant $C$ such that for every Markov chain $ε( t_{\mathrm{H}}(ε)-t_{\mathrm{H}}(1-ε)) \le Ct_{\mathrm{rel}} |\log ε|$, for all $0< ε< 1/2$, where $t_{\mathrm{rel}} $ is the inverse of the spectral gap of the chain.

math.PR↗

arXiv2017

On sensitivity of uniform mixing times

We show that the order of the $L_{\infty}$-mixing time of simple random walks on a sequence of uniformly bounded degree graphs of size $n$ may increase by an optimal factor of $Θ( \log \log n)$ as a result of a bounded perturbation of the edge weights. This answers a question and a conjecture of Kozma.

math.PR↗

arXiv2017

On sensitivity of mixing times and cutoff

A sequence of chains exhibits (total-variation) cutoff (resp., pre-cutoff) if for all $0<ε< 1/2$, the ratio $t_{\mathrm{mix}}^{(n)}(ε)/t_{\mathrm{mix}}^{(n)}(1-ε)$ tends to 1 as $n \to \infty $ (resp., the $\limsup$ of this ratio is bounded uniformly in $ε$), where $t_{\mathrm{mix}}^{(n)}(ε)$ is the $ε$-total-variation mixing-time of the $n$th chain in the sequence. We construct a sequence of bounded degree graphs $G_n$, such that the lazy simple random walks (LSRW) on $G_n$ satisfy the "product condition" $\mathrm{gap}(G_n) t_{\mathrm{mix}}^{(n)}(ε) \to \infty $ as $n \to \infty$, where $\mathrm{gap}(G_n)$ is the spectral gap of the LSRW on $G_n$ (a known necessary condition for pre-cutoff that is often sufficient for cutoff), yet this sequence does not exhibit pre-cutoff. Recently, Chen and Saloff-Coste showed that total-variation cutoff is equivalent for the sequences of continuous-time and lazy versions of some given sequence of chains. Surprisingly, we show that this is false when considering separation cutoff. We also construct a sequence of bounded degree graphs $G_n=(V_{n},E_{n})$ that does not exhibit cutoff, for which a certain bounded perturbation of the edge weights leads to cutoff and increases the order of the mixing-time by an optimal factor of $Θ(\log |V_n|)$. Similarly, we also show that "lumping" states together may increase the order of the mixing-time by an optimal factor of $Θ(\log |V_n|)$. This gives a negative answer to a question asked by Aldous and Fill.

math.PR↗

arXiv2017

Cutoff for Ramanujan graphs via degree inflation

Recently Lubetzky and Peres showed that simple random walks on a sequence of $d$-regular Ramanujan graphs $G_n=(V_n,E_n)$ of increasing sizes exhibit cutoff in total variation around the diameter lower bound $\frac{d}{d-2}\log_{d-1}|V_n| $. We provide a different argument under the assumption that for some $r(n) \gg 1$ the maximal number of simple cycles in a ball of radius $r(n)$ in $G_n$ is uniformly bounded in $n$.

math.PR↗

arXiv2017

Infinite and Giant Components in the Layers Percolation Model

In this work we continue the investigation launched in [FHR16] of the structural properties of the structural properties of the Layers model, a dependent percolation model. Given an undirected graph $G=(V,E)$ and an integer $k$, let $T_k(G)$ denote the random vertex-induced subgraph of $G$, generated by ordering $V$ according to Uniform$[0,1]$ $\mathrm{i.i.d.}$ clocks and including in $T_k(G)$ those vertices with at most $k-1$ of their neighbors having a faster clock. The distribution of subgraphs sampled in this manner is called the layers model with parameter $k$. The layers model has found applications in the study of $\ell$-degenerate subgraphs, the design of algorithms for the maximum independent set problem and in the study of bootstrap percolation. We prove that every infinite locally finite tree $T$ with no leaves, satisfying that the degree of the vertices grow sub-exponentially in their distance from the root, $T_3(T)$ $\mathrm{a.s.}$ has an infinite connected component. In contrast, we show that for any locally finite graph $G$, $\mathrm{a.s.}$ every connected component of $T_2(G)$ is finite. We also consider random graphs with a given degree sequence and show that if the minimal degree is at least 3 and the maximal degree is bounded, then $\mathrm{w.h.p.}$ $T_3$ has a giant component. Finally, we also consider ${\mathbb{Z}}^{d}$ and show that if $d$ is sufficiently large, then $\mathrm{a.s.}$ $T_4(\mathbb{Z}^d)$ contains an infinite cluster.

math.PR↗

arXiv2017

A characterization of $L_{2}$ mixing and hypercontractivity via hitting times and maximal inequalities

There are several works characterizing the total-variation mixing time of a reversible Markov chain in term of natural probabilistic concepts such as stopping times and hitting times. In contrast, there is no known analog for the $L_{2}$ mixing time, $τ_{2}$ (while there are sophisticated analytic tools to bound $ τ_2$, in general they do not determine $τ_2$ up to a constant factor and they lack a probabilistic interpretation). In this work we show that $τ_2$ can be characterized up to a constant factor using hitting times distributions. We also derive a new extremal characterization of the Log-Sobolev constant, $c_{\mathrm{LS}}$, as a weighted version of the spectral gap. This characterization yields a probabilistic interpretation of $c_{\mathrm{LS}}$ in terms of a hitting time version of hypercontractivity. As applications of our results, we show that (1) for every reversible Markov chain, $τ_2$ is robust under addition of self-loops with bounded weights, and (2) for weighted nearest neighbor random walks on trees, $τ_2 $ is robust under bounded perturbations of the edge weights.

math.PR↗

arXiv2016

The power of averaging at two consecutive time steps: Proof of a mixing conjecture by Aldous and Fill

Let $(X_t)_{t = 0 }^{\infty}$ be an irreducible reversible discrete time Markov chain on a finite state space $Ω$. Denote its transition matrix by $P$. To avoid periodicity issues (and thus ensuring convergence to equilibrium) one often considers the continuous-time version of the chain $(X_t^{\mathrm{c}})_{t \ge 0} $ whose kernel is given by $H_t:=e^{-t}\sum_k (tP)^k/k! $. Another possibility is to consider the associated averaged chain $(X_t^{\mathrm{ave}})_{t = 0}^{\infty}$, whose distribution at time $t$ is obtained by replacing $P$ by $A_t:=(P^t+P^{t+1})/2$. A sequence of Markov chains is said to exhibit (total-variation) cutoff if the convergence to stationarity in total-variation distance is abrupt. Let $(X_t^{(n)})_{t = 0 }^{\infty}$ be a sequence of irreducible reversible discrete time Markov chains. In this work we prove that the sequence of associated continuous-time chains exhibits total-variation cutoff around time $t_n$ iff the sequence of the associated averaged chains exhibits total-variation cutoff around time $t_n$. Moreover, we show that the width of the cutoff window for the sequence of associated averaged chains is at most that of the sequence of associated continuous-time chains. In fact, we establish more precise quantitative relations between the mixing-times of the continuous-time and the averaged versions of a reversible Markov chain, which provide an affirmative answer to a problem raised by Aldous and Fill.

math.PR↗

arXiv2016

Total Variation and Separation Cutoffs are not equivalent and neither one implies the other

The cutoff phenomenon describes the case when an abrupt transition occurs in the convergence of a Markov chain to its equilibrium measure. There are various metrics which can be used to measure the distance to equilibrium, each of which corresponding to a different notion of cutoff. The most commonly used are the total-variation and the separation distances. In this note we prove that the cutoff for these two distances are not equivalent by constructing several counterexamples which display cutoff in total-variation but not in separation and with the opposite behavior, including lazy simple random walk on a sequence of uniformly bounded degree expander graphs. These examples give a negative answer to a question of Ding, Lubetzky and Peres.

math.PR↗

arXiv2015

Characterization of cutoff for reversible Markov chains

A sequence of Markov chains is said to exhibit (total variation) cutoff if the convergence to stationarity in total variation distance is abrupt. We consider reversible lazy chains. We prove a necessary and sufficient condition for the occurrence of the cutoff phenomena in terms of concentration of hitting time of "worst" (in some sense) sets of stationary measure at least $α$, for some $α\in (0,1)$. We also give general bounds on the total variation distance of a reversible chain at time $t$ in terms of the probability that some "worst" set of stationary measure at least $α$ was not hit by time $t$. As an application of our techniques we show that a sequence of lazy Markov chains on finite trees exhibits a cutoff iff the ratio of their relaxation-times and their (lazy) mixing-times tends to 0.

math.PR↗

arXiv2014

On giant components and treewidth in the layers model

Given an undirected $n$-vertex graph $G(V,E)$ and an integer $k$, let $T_k(G)$ denote the random vertex induced subgraph of $G$ generated by ordering $V$ according to a random permutation $π$ and including in $T_k(G)$ those vertices with at most $k-1$ of their neighbors preceding them in this order. The distribution of subgraphs sampled in this manner is called the \emph{layers model with parameter} $k$. The layers model has found applications in studying $\ell$-degenerate subgraphs, the design of algorithms for the maximum independent set problem, and in bootstrap percolation. In the current work we expand the study of structural properties of the layers model. We prove that there are $3$-regular graphs $G$ for which with high probability $T_3(G)$ has a connected component of size $Ω(n)$. Moreover, this connected component has treewidth $Ω(n)$. This lower bound on the treewidth extends to many other random graph models. In contrast, $T_2(G)$ is known to be a forest (hence of treewidth~1), and we establish that if $G$ is of bounded degree then with high probability the largest connected component in $T_2(G)$ is of size $O(\log n)$. We also consider the infinite two-dimensional grid, for which we prove that the first four layers contain a unique infinite connected component with probability $1$.

cs.DM↗