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arXiv 2101.08029cond-mat.stat-mech
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稀疏对称随机矩阵谱密度的腔和副本方法

Cavity and replica methods for the spectral density of sparse symmetric random matrices

Vito A R Susca, Pierpaolo Vivo, Reimer Kühn

摘要:

讨论了稀疏对称随机矩阵,即无向图的加权邻接矩阵的谱密度的计算问题。从爱德华-琼斯公式开始,我们说明了这条研究路线的里程碑,包括布雷和罗杰斯使用复制品的开创性工作。我们首先集中在腔的方法,表明它迅速提供了正确的递归方程,无论是单个实例,并在合奏水平。我们还描述了另一种副本的解决方案,证明是等效的腔的方法。无论是腔和副本的推导,让我们通过一个辅助概率密度函数的积分方程的解决方案,以获得的频谱密度。我们表明,这个方程可以解决使用随机种群动力学算法,我们提供了它的实现。在这种形式主义中,谱密度自然地被写在给定度的节点的局部贡献的叠加方面,其作用被彻底阐明。本文不包含原始材料,而是对该主题进行了教学概述。它确实是针对考虑进入该领域的学生和研究人员的。理论工具和数值算法进行了详细讨论,突出概念的微妙之处和实际方面。

英文摘要:

We review the problem of how to compute the spectral density of sparse symmetric random matrices, i.e. weighted adjacency matrices of undirected graphs. Starting from the Edwards-Jones formula, we illustrate the milestones of this line of research, including the pioneering work of Bray and Rodgers using replicas. We focus first on the cavity method, showing that it quickly provides the correct recursion equations both for single instances and at the ensemble level. We also describe an alternative replica solution that proves to be equivalent to the cavity method. Both the cavity and the replica derivations allow us to obtain the spectral density via the solution of an integral equation for an auxiliary probability density function. We show that this equation can be solved using a stochastic population dynamics algorithm, and we provide its implementation. In this formalism, the spectral density is naturally written in terms of a superposition of local contributions from nodes of given degree, whose role is thoroughly elucidated. This paper does not contain original material, but rather gives a pedagogical overview of the topic. It is indeed addressed to students and researchers who consider entering the field. Both the theoretical tools and the numerical algorithms are discussed in detail, highlighting conceptual subtleties and practical aspects.

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