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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

The circular law: Proof of the replacement principle

Tang, ZHIWEI 13 July 2009 (has links)
It was conjectured in the early 1950¡¯s that the empirical spectral distribution (ESD) of an $n \times n$ matrix whose entries are independent and identically distributed with mean zero and variance one, normalized by a factor of $\frac{1}{\sqrt{n}}$, converges to the uniform distribution over the unit disk on the complex plane, which is called the circular law. The goal of this thesis is to prove the so called Replacement Principle introduced by Tao and Vu which is a crucial step in their recent proof of the circular law in full generality. It gives a general criterion for the difference of the ESDs of two normalised random matrices $\frac{1}{\sqrt{n}}A_n$, $\frac{1}{\sqrt{n}}B_n$ to converge to 0. / Thesis (Master, Mathematics & Statistics) -- Queen's University, 2009-07-11 14:57:44.225
2

[pt] MATRIZES ALEATÓRIAS E A LEI DO SEMICÍRCULO / [en] RANDOM MATRICES AND THE SEMICIRCLE LAW

DANIEL BYRON SOUZA P DE ANDRADE 14 June 2022 (has links)
[pt] Nessa dissertação vamos abordar a famosa lei do Semicírculo de Wigner, que dá uma descrição do comportamento do espectro de autovalores de matrizes aleatórias simétricas. A demonstração combina ideias e técnicas de Combinatória e Probabilidade, incluindo uma analise cautelosa dos momentos da distribuição de autovalores. / [en] In this dissertation we will approach the famous Wigner s Semicircle Law, which gives a description of the behavior of the eigenvalue spectrum of symmetric random matrices. The proof combines ideas and techniques from Combinatorics and Probability, including a careful analysis of the moments of the eigenvalue distribution.

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