A path integral approach to sparse non-Hermitian random matrices
The theory of large random matrices has proved an invaluable tool for the study of systems with disordered interactions in many quite disparate research areas. Widely applicable results, such as the celebrated elliptic law for dense random matrices, allow one to deduce the statistical properties of...
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Zusammenfassung: | The theory of large random matrices has proved an invaluable tool for the
study of systems with disordered interactions in many quite disparate research
areas. Widely applicable results, such as the celebrated elliptic law for dense
random matrices, allow one to deduce the statistical properties of the
interactions in a complex dynamical system that permit stability. However, such
simple and universal results have so far proved difficult to come by in the
case of sparse random matrices. Here, we present a new approach, which maps the
hermitized resolvent of a random matrix onto the response functions of a linear
dynamical system. The response functions are then evaluated using a path
integral formalism, enabling one to construct Feynman diagrams and perform a
perturbative analysis. This approach provides simple closed-form expressions
for the eigenvalue spectrum, allowing one to derive modified versions of the
classic elliptic and semi-circle laws that take into account the sparse
correction. Additionally, in order to demonstrate the broad utility of the path
integral framework, we derive a non-Hermitian generalization of the
Marchenko-Pastur law, and we also show how one can handle non-negligible
higher-order statistics (i.e. non-Gaussian statistics) in dense ensembles. |
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DOI: | 10.48550/arxiv.2308.13605 |