Universal hypotrochoidic law for random matrices with cyclic correlations
arXiv:1812.07055 · doi:10.1103/PhysRevE.100.010302
Abstract
The celebrated elliptic law describes the distribution of eigenvalues of random matrices with correlations between off-diagonal pairs of elements, having applications to a wide range of physical and biological systems. Here, we investigate the generalization of this law to random matrices exhibiting higher-order cyclic correlations between -tuples of matrix entries. We show that the eigenvalue spectrum in this ensemble is bounded by a hypotrochoid curve with -fold rotational symmetry. This hypotrochoid law applies to full matrices as well as sparse ones, and thereby holds with remarkable universality. We further extend our analysis to matrices and graphs with competing cycle motifs, which are described more generally by polytrochoid spectral boundaries.
arXiv comment7 pages, 4 figures
References in corpus (19)
- Random Matrix Theories in Quantum Physics: Common Concepts
- Stability Criteria for Complex Ecosystems
- Spectra of complex networks
- Eigenvector statistics in non-Hermitian random matrix ensembles
- Sparse random matrices: the eigenvalue spectrum revisited
- Non-Gaussian Non-Hermitean Random Matrix Theory: phase transitions and addition formalism
- A single defect approximation for localized states on random lattices
- Quantum limit of the laser linewidth in chaotic cavities and statistics of residues of scattering matrix poles
- Cavity approach to the spectral density of non-Hermitian sparse matrices
- Secular determinants of random unitary matrices
- Correlations of Eigenvectors for Non-Hermitian Random-Matrix Models
- Spectral Theory of Sparse Non-Hermitian Random Matrices
- Density of eigenvalues of random normal matrices
- Spectra of sparse regular graphs with loops
- Spectral density of random graphs with topological constraints
- Universal sum and product rules for random matrices
- Spectral Theory as Influenced by Fritz Gesztesy
- Exactly Solvable Random Graph Ensemble with Extensively Many Short Cycles
- Tailoring Artificial Neural Networks for Optimal Learning
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- A path integral approach to sparse non-Hermitian random matrices
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