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Free probability for purely discrete eigenvalues of random matrices

机译:随机矩阵纯离散特征值的自由概率

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摘要

In this paper, we study random matrix models which are obtained as a non-commutative polynomial in random matrix variables of two kinds: (a) a first kind which have a discrete spectrum in the limit, (b) a second kind which have a joint limiting distribution in Voiculescu's sense and are globally rotationally invariant. We assume that each monomial constituting this polynomial contains at least one variable of type (a), and show that this random matrix model has a set of eigenvalues that almost surely converges to a deterministic set of numbers that is either finite or accumulating to only zero in the large dimension limit. For this purpose we define a framework (cyclic monotone independence) for analyzing discrete spectra and develop the moment method for the eigenvalues of compact (and in particular Schatten class) operators. We give several explicit calculations of discrete eigenvalues of our model.
机译:在本文中,我们研究了随机矩阵模型,这些模型是在以下两种随机矩阵变量中作为非交换多项式获得的:(a)第一种在极限范围内具有离散频谱,(b)第二种具有a的极限谱。在Voiculescu的意义上,关节极限分布是全局旋转不变的。我们假设构成该多项式的每个单项式至少包含一个(a)类型的变量,并表明该随机矩阵模型具有一组特征值,几乎可以肯定地收敛到确定性数集,该确定性数集有限或仅累加到零在大尺寸限制中。为此,我们定义了一个框架(循环单调独立性)来分析离散频谱,并为紧凑型(尤其是Schatten类)算子的特征值开发了矩量法。我们给出了模型离散特征值的几个显式计算。

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