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arXiv 0810.1554math-phcond-mat.stat-mechmath.MP
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一类随机矩阵模型的特征值分离性

Eigenvalue Separation in Some Random Matrix Models

Kevin E. Bassler, Peter J. Forrester, Norman E. Frankel

摘要:

高斯正交和酉系综的成员的本征值密度遵循维格纳半圆定律。如果高斯项都移动了一个常数c/Sqrt(2N),其中N是矩阵的大小,在大的N限制下,一个单一的本征值将与维格纳半圆的支撑分离,前提是c > 1。在这项研究中,使用的特征值条件的长期方程的渐近分析,我们比较这种效果发生在一般方差Wishart矩阵和矩阵的移动平均手征系综类似的效果。我们进行类似的比较研究时,矩阵的大小是固定的,c去无穷大,和更高的秩类似物的这种设置的特征值分离性能。这是使用广义超几何函数的特征值概率密度的精确表达式,并使用后者作为戴森布朗运动模型中的格林函数的解释。对于平移平均高斯酉系综及其类似物,另一种方法是使用经典正交多项式和相关多重推广方面的关联函数的精确表达式。通过使用这些精确的表达式来计算和绘制本征值密度,得到各种本征值分离效果的说明。

英文摘要:

The eigenvalue density for members of the Gaussian orthogonal and unitary ensembles follows the Wigner semi-circle law. If the Gaussian entries are all shifted by a constant amount c/Sqrt(2N), where N is the size of the matrix, in the large N limit a single eigenvalue will separate from the support of the Wigner semi-circle provided c > 1. In this study, using an asymptotic analysis of the secular equation for the eigenvalue condition, we compare this effect to analogous effects occurring in general variance Wishart matrices and matrices from the shifted mean chiral ensemble. We undertake an analogous comparative study of eigenvalue separation properties when the size of the matrices are fixed and c goes to infinity, and higher rank analogues of this setting. This is done using exact expressions for eigenvalue probability densities in terms of generalized hypergeometric functions, and using the interpretation of the latter as a Green function in the Dyson Brownian motion model. For the shifted mean Gaussian unitary ensemble and its analogues an alternative approach is to use exact expressions for the correlation functions in terms of classical orthogonal polynomials and associated multiple generalizations. By using these exact expressions to compute and plot the eigenvalue density, illustrations of the various eigenvalue separation effects are obtained.

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