The norm of polynomials in large random and deterministic matrices

被引:41
|
作者
Male, Camille [1 ]
机构
[1] Ecole Normale Super Lyon, Unite Math Pures & Appl, UMR 5669, F-69364 Lyon 07, France
关键词
Random matrix; Free probability; Strong asymptotic; Freeness; C*-algebra; SAMPLE COVARIANCE-MATRIX; LIMITING SPECTRAL DISTRIBUTION; GAUSSIAN RANDOM MATRICES; LARGEST EIGENVALUE; ASYMPTOTIC FREENESS; SYMMETRIC-MATRICES; WISHART MATRICES; PRODUCT; MOMENTS; SUPPORT;
D O I
10.1007/s00440-011-0375-2
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Let be a family of N x N independent, normalized random matrices from the Gaussian Unitary Ensemble. We state sufficient conditions on matrices , possibly random but independent of X (N) , for which the operator norm of converges almost surely for all polynomials P. Limits are described by operator norms of objects from free probability theory. Taking advantage of the choice of the matrices Y (N) and of the polynomials P, we get for a large class of matrices the "no eigenvalues outside a neighborhood of the limiting spectrum" phenomena. We give examples of diagonal matrices Y (N) for which the convergence holds. Convergence of the operator norm is shown to hold for block matrices, even with rectangular Gaussian blocks, a situation including non-white Wishart matrices and some matrices encountered in MIMO systems.
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页码:477 / 532
页数:56
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