Universality and Delocalization of Random Band Matrices

Speaker: 

Jun Yin

Institution: 

UCLA

Time: 

Tuesday, November 6, 2018 - 11:00am to 12:00pm

Location: 

RH 306

We consider N × N symmetric one-dimensional random band matrices with general distribution of the entries and band width $W$.   The localization - delocalization conjecture predicts that there is a phase transition on the behaviors of  eigenvectors and  eigenvalues of the band matrices. It occurs at $W=N^{1/2}$. For wider-band matrix, the eigenvalues satisfied the so called sine-kernal distribution, and the eigenvectors are delocalized. With Bourgade, Yau and Fan, we proved that it holds when $W\gg N^{3/4}$. The previous best work required $W=\Omega(N).$ 

 

On 1-factorizations of graphs

Speaker: 

Asaf Ferber

Institution: 

MIT

Time: 

Tuesday, October 30, 2018 - 11:00am to 12:00pm

Host: 

Location: 

RH 306

A 1-factorization of a graph G is a partitioning of its edges into perfect matchings. Clearly, if a graph G admits a 1-factorization then it must be regular, and the converse is easily verified to be false. In the special case where G is bipartite, it is an easy exercise to show that G has a 1-factorization, and observe that a 1-factorization corresponds to a partial Latin Square.  

In this talk we survey known results/conjectures regarding the existence and the number of 1-factorizations in graphs and the related problem about the existence of a proper edge coloring of a graph with exactly \Delta(G) colors.  Moreover, we prove that every `nice' d-regular pseudorandom graph has a 1-factorization. In particular, as a corollary, we obtain that for every d=\omega(1), a random d-regular graph typically has a 1-factorization.  This extends and completely solves a problem of Molloy, Robalewska, Robinson, and Wormald  (showed it for all constant d greater than or equal to 3).

 

Joint with: Vishesh Jain (PhD student in MIT).

Large deviations of subgraph counts for sparse random graphs

Speaker: 

Nicholas Cook

Institution: 

UCLA

Time: 

Tuesday, November 27, 2018 - 11:00am to 12:00pm

Location: 

RH 306

In their breakthrough 2011 paper, Chatterjee and Varadhan established a large deviations principle (LDP) for the Erdös-Rényi graph G(N,p), viewed as a measure on the space of graphons with the cut metric topology. This yields LDPs for subgraph counts, such as the number of triangles in G(N,p), as these are continuous functions of graphons. However, as with other applications of graphon theory, the LDP is only useful for dense graphs, with p ϵ (0,1) fixed independent of N. 

Since then, the effort to extend the LDP to sparse graphs with p ~ N^{-c} for some fixed c>0 has spurred rapid developments in the theory of "nonlinear large deviations". We will report on recent results increasing the sparsity range for the LDP, in particular allowing c as large as 1/2 for cycle counts, improving on previous results of Chatterjee-Dembo and Eldan. These come as applications of new quantitative versions of the classic regularity and counting lemmas from extremal graph theory, optimized for sparse random graphs. (Joint work with Amir Dembo.)

Lower-tail large deviations of the KPZ equation

Speaker: 

Li-Cheng Tsai

Institution: 

Columbia University

Time: 

Tuesday, October 23, 2018 - 11:00am to 12:00pm

Host: 

Location: 

306 RH

Regarding time as a scaling parameter, we prove the one-point, lower tail Large Deviation Principle (LDP) of the KPZ equation, with an explicit rate function. This result confirms existing physics predictions. We utilize a formula from [Borodin Gorin 16] to convert LDP of the KPZ equation to calculating an exponential moment of the Airy point process, and analyze the latter via stochastic Airy operator and Riccati transform.

A moment method for invariant ensembles

Speaker: 

Jonathan Novak

Institution: 

UCSD

Time: 

Tuesday, January 8, 2019 - 11:00am

Location: 

RH 306

Conjugation invariant ensembles of random matrices have long formed one of the basic paradigms in Random Matrix Theory. Apart from the Gaussian case, the matrix elements of a conjugation invariant random matrix are highly correlated, and this fact has traditionally been viewed as prohibiting the use of moment methods in the spectral analysis of invariant ensembles. However, it turns out that there is a very natural and appealing version of the moment method available for these ensembles which seems to have been overlooked. I will describe the rudiments of this method, and some of its applications. Based on joint work with Sho Matsumoto.

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