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Optimal upper bound for the infinity norm of eigenvectors of random matrices
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Wang, Ke. Optimal upper bound for the infinity norm of eigenvectors of random matrices. Retrieved from https://doi.org/doi:10.7282/T3GB22N0

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TitleOptimal upper bound for the infinity norm of eigenvectors of random matrices
NameWang, Ke (author); Vu, Van (chair); Kiessling, Michael (internal member); Kopparty, Swastik (internal member); Soshnikov, Alexander (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2013
Other Date2013-05 (degree)
SubjectMathematics, Random matrices, Eigenvectors
Extentvii, 74 p. : ill.
DescriptionLet $M_n$ be a random Hermitian (or symmetric) matrix whose upper diagonal and diagonal entries are independent random variables with mean zero and variance one. It is well known that the empirical spectral distribution (ESD) converges in probability to the semicircle law supported on $[-2,2]$. In this thesis we study the local convergence of ESD to the semicircle law. One main result is that if the entries of $M_n$ are bounded, then the semicircle law holds on intervals of scale $log n/n$. As a consequence, we obtain the delocalization result for the eigenvectors, i.e., the upper bound for the infinity norm of unit eigenvectors corresponding to eigenvalues in the bulk of spectrum, is $O(sqrt{log n/n})$. The bound is the same as the infinity norm of a vector chosen uniformly on the unit sphere in $R^n$. We also study the local version of Marchenko-Pastur law for random covariance matrices and obtain the optimal upper bound for the infinity norm of singular vectors. This is joint work with V. Vu. In the last chapter, we discuss the delocalization properties for the adjacency matrices of ErdH{o}s-R'{e}nyi random graph. This is part of some earlier results joint with L. Tran and V. Vu.
NotePh.D.
NoteIncludes bibliographical references
NoteIncludes vita
Noteby Ke Wang
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3GB22N0
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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