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arXiv 1004.0846math.CAmath.PR
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随机矩阵理论中的多重正交多项式

Multiple orthogonal polynomials in random matrix theory

Arno B. J. Kuijlaars

摘要:

多个正交多项式是正交多项式的推广,其中正交性分布在多个正交性权重之间。它们以特殊的行列式点过程的形式出现在随机矩阵理论中,称为多重正交多项式(MOP)系综。在这样的系综中的相关核用黎曼-希尔伯特问题的解表示,即在r个权重的情况下大小为(r+1)x(r+1)。一些模型产生MOP系综,我们讨论了最近的结果,非相交的布朗运动,埃尔米特随机矩阵与外部源的模型,和两个矩阵模型。后两种模型的渐近分析中的一个新特征是两个或多个措施的向量平衡问题,描述了极限平均特征值密度。矢量平衡问题既涉及外场又涉及上约束。

英文摘要:

Multiple orthogonal polynomials are a generalization of orthogonal polynomials in which the orthogonality is distributed among a number of orthogonality weights. They appear in random matrix theory in the form of special determinantal point processes that are called multiple orthogonal polynomial (MOP) ensembles. The correlation kernel in such an ensemble is expressed in terms of the solution of a Riemann-Hilbert problem, that is of size (r+1) x (r+1) in the case of r weights. A number of models give rise to a MOP ensemble, and we discuss recent results on models of non-intersecting Brownian motions, Hermitian random matrices with external source, and the two matrix model. A novel feature in the asymptotic analysis of the latter two models is a vector equilibrium problem for two or more measures, that describes the limiting mean eigenvalue density. The vector equilibrium problems involve both an external field and an upper constraint.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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