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arXiv 1504.06256math-phmath.MP
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非高斯随机矩阵及其乘积的实特征值

Real eigenvalues of non-Gaussian random matrices and their products

Sajna Hameed, Kavita Jain, Arul Lakshminarayan

摘要:

研究了实随机矩阵及其乘积的特征值的性质。已知当矩阵元素是高斯分布的独立随机变量时,随着乘积中矩阵个数的增加,实特征值的分数趋于1。在这里,我们提出的数值证据表明,这种现象是强大的矩阵元素的概率分布,因此是一个一般的属性,值得详细调查。由于乘积矩阵的元素不再像单个矩阵那样分布,也不再是独立的随机变量,因此我们详细研究了这两个因素的作用。我们数值研究了阿达玛的性质(或Schur)矩阵的乘积和矩阵的乘积,其元素是独立的,但具有与矩阵的正常乘积相同的边际分布,并发现在重复乘法下,所有特征值为实数的概率在两种情况下都增加,但是饱和到低于1的常数,这表明矩阵元素之间的相关性导致接近1。为了研究概率分布的非正态性质的作用,我们提出了一个彻底的分析处理的$2 \times 2$单一矩阵的几个标准分布。在零均值和有限方差的光滑分布类中,我们的结果表明,高斯分布具有最大概率的实特征值,但Cauchy分布的特点是无穷方差被发现有一个更大的概率比正常的实特征值。我们还发现,对于二维单矩阵,实特征值的概率在[5/8,7/8]范围内。

英文摘要:

We study the properties of the eigenvalues of real random matrices and their products. It is known that when the matrix elements are Gaussian-distributed independent random variables, the fraction of real eigenvalues tends to unity as the number of matrices in the product increases. Here we present numerical evidence that this phenomenon is robust with respect to the probability distribution of matrix elements, and is therefore a general property that merits detailed investigation. Since the elements of the product matrix are no longer distributed as those of the single matrix nor they remain independent random variables, we study the role of these two factors in detail. We study numerically the properties of the Hadamard (or Schur) product of matrices and also the product of matrices whose entries are independent but have the same marginal distribution as that of normal products of matrices, and find that under repeated multiplication, the probability of all eigenvalues to be real increases in both cases, but saturates to a constant below unity showing that the correlations amongst the matrix elements are responsible for the approach to one. To investigate the role of the non-normal nature of the probability distributions, we present a thorough analytical treatment of the $2 \times 2$ single matrix for several standard distributions. Within the class of smooth distributions with zero mean and finite variance, our results indicate that the Gaussian distribution has the maximum probability of real eigenvalues, but the Cauchy distribution characterised by infinite variance is found to have a larger probability of real eigenvalues than the normal. We also find that for the two-dimensional single matrices, the probability of real eigenvalues lies in the range [5/8,7/8].

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