Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userTransition from Tracy-Widom to Gaussian fluctuations of extremal eigenvalues of sparse ErdH{o}s-Renyi graphs
126
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We consider the statistics of the extreme eigenvalues of sparse random matrices, a class of random matrices that includes the normalized adjacency matrices of the ErdH{o}s-Renyi graph $G(N,p)$. Tracy-Widom fluctuations of the extreme eigenvalues for $pgg N^{-2/3}$ was proved in [17,46]. We prove that there is a crossover in the behavior of the extreme eigenvalues at $psim N^{-2/3}$. In the case that $N^{-7/9}ll pll N^{-2/3}$, we prove that the extreme eigenvalues have asymptotically Gaussian fluctuations. Under a mean zero condition and when $p=CN^{-2/3}$, we find that the fluctuations of the extreme eigenvalues are given by a combination of the Gaussian and the Tracy-Widom distribution. These results show that the eigenvalues at the edge of the spectrum of sparse ErdH{o}s-Renyi graphs are less rigid than those of random $d$-regular graphs [4] of the same average degree.
rate research
Read More
We consider an inhomogeneous ErdH{o}s-Renyi random graph $G_N$ with vertex set $[N] = {1,dots,N}$ for which the pair of vertices $i,j in [N]$, $i eq j$, is connected by an edge with probability $r(tfrac{i}{N},tfrac{j}{N})$, independently of other pairs of vertices. Here, $rcolon,[0,1]^2 to (0,1)$ is a symmetric function that plays the role of a reference graphon. Let $lambda_N$ be the maximal eigenvalue of the adjacency matrix of $G_N$. It is known that $lambda_N/N$ satisfies a large deviation principle as $N to infty$. The associated rate function $psi_r$ is given by a variational formula that involves the rate function $I_r$ of a large deviation principle on graphon space. We analyse this variational formula in order to identify the properties of $psi_r$, specially when the reference graphon is of rank 1.
We consider a dynamic ErdH{o}s-Renyi random graph (ERRG) on $n$ vertices in which each edge switches on at rate $lambda$ and switches off at rate $mu$, independently of other edges. The focus is on the analysis of the evolution of the associated empirical graphon in the limit as $ntoinfty$. Our main result is a large deviation principle (LDP) for the sample path of the empirical graphon observed until a fixed time horizon. The rate is $binom{n}{2}$, the rate function is a specific action integral on the space of graphon trajectories. We apply the LDP to identify (i) the most likely path that starting from a constant graphon creates a graphon with an atypically large density of $d$-regular subgraphs, and (ii) the mostly likely path between two given graphons. It turns out that bifurcations may occur in the solutions of associated variational problems.
We develop a quantitative large deviations theory for random Bernoulli tensors. The large deviation principles rest on a decomposition theorem for arbitrary tensors outside a set of tiny measure, in terms of a novel family of norms generalizing the cut norm. Combined with associated counting lemmas, these yield sharp asymptotics for upper tails of homomorphism counts in the $r$-uniform ErdH{o}s--Renyi hypergraph for any fixed $rge 2$, generalizing and improving on previous results for the ErdH{o}s--Renyi graph ($r=2$). The theory is sufficiently quantitative to allow the density of the hypergraph to vanish at a polynomial rate, and additionally yields (joint) upper and lower tail asymptotics for other nonlinear functionals of interest.
Let $bY =bR+bX$ be an $Mtimes N$ matrix, where $bR$ is a rectangular diagonal matrix and $bX$ consists of $i.i.d.$ entries. This is a signal-plus-noise type model. Its signal matrix could be full rank, which is rarely studied in literature compared with the low rank cases. This paper is to study the extreme eigenvalues of $bYbY^*$. We show that under the high dimensional setting ($M/Nrightarrow cin(0,1]$) and some regularity conditions on $bR$ the rescaled extreme eigenvalue converges in distribution to Tracy-Widom distribution ($TW_1$).
We establish a quantitative version of the Tracy--Widom law for the largest eigenvalue of high dimensional sample covariance matrices. To be precise, we show that the fluctuations of the largest eigenvalue of a sample covariance matrix $X^*X$ converge to its Tracy--Widom limit at a rate nearly $N^{-1/3}$, where $X$ is an $M times N$ random matrix whose entries are independent real or complex random variables, assuming that both $M$ and $N$ tend to infinity at a constant rate. This result improves the previous estimate $N^{-2/9}$ obtained by Wang [73]. Our proof relies on a Green function comparison method [27] using iterative cumulant expansions, the local laws for the Green function and asymptotic properties of the correlation kernel of the white Wishart ensemble.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
