Searcharxiv⌕ Search

arXiv subjects

Amir Dembo

Publications and source records attributed to Amir Dembo.

At least 37 records · Page 2Linked to original sources

Dynamics for spherical spin glasses: disorder dependent initial conditions

We derive the thermodynamic limit of the empirical correlation and response functions in the Langevin dynamics for spherical mixed $p$-spin disordered mean-field models, starting uniformly within one of the spherical bands on which the Gibbs measure concentrates at low temperature for the pure $p$-spin models and mixed perturbations of them. We further relate the large time asymptotics of the resulting coupled non-linear integro-differential equations, to the geometric structure of the Gibbs measures (at low temperature), and derive their FDT solution (at high temperature).

math.PR↗

Empirical spectral distributions of sparse random graphs

We study the spectrum of a random multigraph with a degree sequence ${\bf D}_n=(D_i)_{i=1}^n$ and average degree $1 \ll ω_n \ll n$, generated by the configuration model, and also the spectrum of the analogous random simple graph. We show that, when the empirical spectral distribution (ESD) of $ω_n^{-1} {\bf D}_n $ converges weakly to a limit $ν$, under mild moment assumptions (e.g., $D_i/ω_n$ are i.i.d. with a finite second moment), the ESD of the normalized adjacency matrix converges in probability to $ν\boxtimes σ_{\rm sc}$, the free multiplicative convolution of $ν$ with the semicircle law. Relating this limit with a variant of the Marchenko--Pastur law yields the continuity of its density (away from zero), and an effective procedure for determining its support. Our proof of convergence is based on a coupling between the random simple graph and multigraph with the same degrees, which might be of independent interest. We further construct and rely on a coupling of the multigraph to an inhomogeneous Erdős-Rényi graph with the target ESD, using three intermediate random graphs, with a negligible fraction of edges modified in each step.

math.PR↗

Large deviations of subgraph counts for sparse Erdős--Rényi graphs

For any fixed simple graph $H=(V,E)$ and any fixed $u>0$, we establish the leading order of the exponential rate function for the probability that the number of copies of $H$ in the Erdős--Rényi graph $G(n,p)$ exceeds its expectation by a factor $1+u$, assuming $n^{-κ(H)}\ll p\ll1$, with $κ(H) = 1/(2Δ)$, where $Δ\ge 1$ is the maximum degree of $H$. This improves on a previous result of Chatterjee and the second author, who obtained $κ(H)=c/(Δ|E|)$ for a constant $c>0$. Moreover, for the case of cycle counts we can take $κ$ as large as $1/2$. We additionally obtain the sharp upper tail for Schatten norms of the adjacency matrix, as well as the sharp lower tail for counts of graphs for which Sidorenko's conjecture holds. As a key step, we establish quantitative versions of Szemerédi's regularity lemma and the counting lemma, suitable for the analysis of random graphs in the large deviations regime.

math.PR↗

Proof-of-Stake Longest Chain Protocols: Security vs Predictability

The Nakamoto longest chain protocol is remarkably simple and has been proven to provide security against any adversary with less than 50% of the total hashing power. Proof-of-stake (PoS) protocols are an energy efficient alternative; however existing protocols adopting Nakamoto's longest chain design achieve provable security only by allowing long-term predictability (which have serious security implications). In this paper, we prove that a natural longest chain PoS protocol with similar predictability as Nakamoto's PoW protocol can achieve security against any adversary with less than 1/(1+e) fraction of the total stake. Moreover we propose a new family of longest chain PoS protocols that achieve security against a 50% adversary, while only requiring short-term predictability. Our proofs present a new approach to analyzing the formal security of blockchains, based on a notion of adversary-proof convergence.

cs.CR↗

Universality for Langevin-like spin glass dynamics

We study dynamics for asymmetric spin glass models, proposed by Hertz et al. and Sompolinsky et al. in the 1980's in the context of neural networks: particles evolve via a modified Langevin dynamics for the Sherrington--Kirkpatrick model with soft spins, whereby the disorder is i.i.d. standard Gaussian rather than symmetric. Ben Arous and Guionnet (1995), followed by Guionnet (1997), proved for Gaussian interactions that as the number of particles grows, the short-term empirical law of this dynamics converges a.s. to a non-random law $μ_\star$ of a ``self-consistent single spin dynamics,'' as predicted by physicists. Here we obtain universality of this fact: For asymmetric disorder given by i.i.d. variables of zero mean, unit variance and exponential or better tail decay, at every temperature, the empirical law of sample paths of the Langevin-like dynamics in a fixed time interval has the same a.s. limit $μ_\star$.

math.PR↗

The infinite Atlas process: Convergence to equilibrium

The semi-infinite Atlas process is a one-dimensional system of Brownian particles, where only the leftmost particle gets a unit drift to the right. Its particle spacing process has infinitely many stationary measures, with one distinguished translation invariant reversible measure. We show that the latter is attractive for a large class of initial configurations of slowly growing (or bounded) particle densities. Key to our proof is a new estimate on the rate of convergence to equilibrium for the particle spacing in a triangular array of finite, large size systems.

math.PR↗

Limit law for the cover time of a random walk on a binary tree

Let $T_n$ denote the binary tree of depth $n$ augmented by an extra edge connected to its root. Let $C_n$ denote the cover time of $T_n$ by simple random walk. We prove that $\sqrt{ \mathcal{C}_{n} 2^{-(n+1) } } - m_n$ converges in distribution as $n\to \infty$, where $m_n$ is an explicit constant, and identify the limit.

math.PR↗

Persistence versus stability for auto-regressive processes

The stability of an Auto-Regressive (AR) time sequence of finite order $L$, is determined by the maximal modulus $r^\star$ among all zeros of its generating polynomial. If $r^\star<1$ then the effect of input and initial conditions decays rapidly in time, whereas for $r^\star>1$ it is exponentially magnified (with constant or polynomially growing oscillations when $r^\star=1$). Persistence of such AR sequence (namely staying non-negative throughout $[0,N]$) with decent probability, requires the largest positive zero of the generating polynomial to have the largest multiplicity among all zeros of modulus $r^\star$. These objects are behind the rich spectrum of persistence probability decay for AR$_L$ with zero initial conditions and i.i.d. Gaussian input, all the way from bounded below to exponential decay in $N$, with intermediate regimes of polynomial and stretched exponential decay. In particular, for AR$_3$ the persistence decay power is expressed via the tail probability for Brownian motion to stay in a cone, exhibiting the discontinuity of such power decay between the AR$_3$ whose generating polynomial has complex zeros of rational versus irrational angles.

math.PR↗

Criticality of a randomly-driven front

Consider an advancing `front' $ R(t) \in \mathbb{Z}_{\geq 0} $ and particles performing independent continuous time random walks on $ (R(t),\infty)\cap\mathbb{Z} $. Starting at $R(0)=0$, whenever a particle attempts to jump into $R(t)$ the latter instantaneously moves $k \ge 1$ steps to the right, absorbing all particles along its path. We take $ k $ to be the minimal random integer such that exactly $ k $ particles are absorbed by the move of $ R $, and view the particle system as a discrete version of the Stefan problem \begin{align*} &\partial_t u_*(t,ξ) = \tfrac12 \partial^2_ξ u_*(t,ξ), \quad ξ>r(t), &u_*(t,r(t))=0, &\tfrac{d~}{dt}r(t) = \tfrac12 \partial_ξu_*(t,r(t)), &t\mapsto r(t) \text{ non-decreasing }, \quad r(0):=0. \end{align*} For a constant initial particles density $u_*(0,ξ)=ρ{\bf 1}_{\{ξ>0\}}$, at $ρ<1$ the particle system and the PDE exhibit the same diffusive behavior at large time, whereasat $ρ\ge 1$ the PDE explodes instantaneously. Focusing on the critical density $ ρ=1 $, we analyze the large time behavior of the front $ R(t) $ for the particle system, and obtain both the scaling exponent of $R(t)$ and an explicit description of its random scaling limit. Our result unveils a rarely seen phenomenon where the macroscopic scaling exponent is sensitive to the amount of initial local fluctuations. Further, the scaling limit demonstrates an interesting oscillation between instantaneous super- and sub-critical phases. Our method is based on a novel monotonicity as well as PDE-type estimates.

math.PR↗

Component sizes for large quantum erdos renyi graph near criticality

The $N$ vertices of a quantum random graph are each a circle independently punctured at Poisson points of arrivals, with parallel connections derived through for each pair of these punctured circles by yet another independent Poisson process. Considering these graphs at their critical parameters, we show that the joint law of the re-scaled by $N^{2/3}$ and ordered sizes of their connected components, converges to that of the ordered lengths of excursions above zero for a reflected Brownian motion with drift. Thereby, this work forms the first example of an inhomogeneous random graph, beyond the case of effectively rank-1 models, which is rigorously shown to be in the Erdős-Rényi graphs universality class in terms of Aldous's results.

math.PR↗

Random walks among time increasing conductances: heat kernel estimates

For any graph having a suitable uniform Poincare inequality and volume growth regularity, we establish two-sided Gaussian transition density estimates and parabolic Harnack inequality, for constant speed continuous time random walks evolving via time varying, uniformly elliptic conductances, provided the vertex conductances (i.e. reversing measures), increase in time. Such transition density upper bounds apply for discrete time uniformly lazy walks, with the matching lower bounds holding once the parabolic Harnack inequality is proved.

math.PR↗

Cut-off for lamplighter chains on tori: dimension interpolation and phase transition

Given a finite, connected graph $G$, the lamplighter chain on $G$ is the lazy random walk $X^\diamond$ on the associated lamplighter graph $G^\diamond={\mathbf Z}_2 \wr G$. The mixing time of the lamplighter chain on the torus ${\mathbf Z}_n^d$ is known to have a cutoff at a time asymptotic to the cover time of ${\mathbf Z}_n^d$ if $d=2$, and to half the cover time if $d \ge 3$. We show that the mixing time of the lamplighter chain on $G_n(a)={\mathbf Z}_n^2 \times {\mathbf Z}_{a \log n}$ has a cutoff at $ψ(a)$ times the cover time of $G_n(a)$ as $n \to \infty$, where $ψ$ is an explicit weakly decreasing map from $(0,\infty)$ onto $[1/2,1)$. In particular, as $a > 0$ varies, the threshold continuously interpolates between the known thresholds for ${\mathbf Z}_n^2$ and ${\mathbf Z}_n^3$. Perhaps surprisingly, we find a phase transition (non-smoothness of $ψ$) at the point $a_*=πr_3 (1+\sqrt{2})$, where high dimensional behavior ($ψ(a)=1/2$ for all $a \ge a_*$) commences. Here $r_3$ is the effective resistance from $0$ to $\infty$ in ${\mathbf Z}^3$.

math.PR↗

Cutoff for lamplighter chains on fractals

We show that the total-variation mixing time of the lamplighter random walk on fractal graphs exhibit sharp cutoff when the underlying graph is transient (namely of spectral dimension greater than two). In contrast, we show that such cutoff can not occur for strongly recurrent underlying graphs (i.e. of spectral dimension less than two).

math.PR↗

A large deviation principle for the Erdős-Rényi uniform random graph

Starting with the large deviation principle (LDP) for the Erdős-Rényi binomial random graph $\mathcal{G}(n,p)$ (edge indicators are i.i.d.), due to Chatterjee and Varadhan (2011), we derive the LDP for the uniform random graph $\mathcal{G}(n,m)$ (the uniform distribution over graphs with $n$ vertices and $m$ edges), at suitable $m=m_n$. Applying the latter LDP we find that tail decays for subgraph counts in $\mathcal{G}(n,m_n)$ are controlled by variational problems, which up to a constant shift, coincide with those studied by Kenyon et al. and Radin et al. in the context of constrained random graphs, e.g., the edge/triangle model.

math.PR↗

On the geometry of chemical reaction networks: Lyapunov function and large deviations

In an earlier paper, we proved the validity of large deviations theory for the particle approximation of quite general chemical reaction networks (CRNs). In this paper, we extend its scope and present a more geometric insight into the mechanism of that proof, exploiting the notion of spherical image of the reaction polytope. This allows to view the asymptotic behavior of the vector field describing the mass-action dynamics of chemical reactions as the result of an interaction between the faces of this polytope in different dimensions. We also illustrate some local aspects of the problem in a discussion of Wentzell-Freidlin (WF) theory, together with some examples.

math.DS↗

Large deviations theory for Markov jump models of chemical reaction networks

We prove a sample path Large Deviation Principle (LDP) for a class of jump processes whose rates are not uniformly Lipschitz continuous in phase space. Building on it we further establish the corresponding Wentzell-Freidlin (W-F) (infinite time horizon) asymptotic theory. These results apply to jump Markov processes that model the dynamics of chemical reaction networks under mass action kinetics, on a microscopic scale. We provide natural sufficient topological conditions for the applicability of our LDP and W-F results. This then justifies the computation of non-equilibrium potential and exponential transition time estimates between different attractors in the large volume limit, for systems that are beyond the reach of standard chemical reaction network theory.

math.PR↗

Exponential concentration for zeroes of stationary Gaussian processes

We show that for any centered stationary Gaussian process of integrable covariance, whose spectral measure has compact support, or finite exponential moments (and some additional regularity), the number of zeroes of the process in $[0,T]$ is within $ηT$ of its mean value, up to an exponentially small in $T$ probability.

math.PR↗