2011
DOI: 10.1088/1751-8113/44/6/065102
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Eigenvalue distributions of large Euclidean random matrices for waves in random media

Abstract: We study probability distributions of eigenvalues of Hermitian and non-Hermitian Euclidean random matrices that are typically encountered in the problems of wave propagation in random media.

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Cited by 66 publications
(118 citation statements)
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References 45 publications
(227 reference statements)
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“…Random matrices that incorporate this constraint are known as Euclidean random matrices [28], and have been used to model physical phenomena such as diffusion [29] and wave propagation [30]. Neurons in the neocortex reside in physical space, with connection probabilities modulated smoothly across the cortical surface [31][32][33].…”
Section: B Spatial and Functional Connectivity Constraintsmentioning
confidence: 99%
See 1 more Smart Citation
“…Random matrices that incorporate this constraint are known as Euclidean random matrices [28], and have been used to model physical phenomena such as diffusion [29] and wave propagation [30]. Neurons in the neocortex reside in physical space, with connection probabilities modulated smoothly across the cortical surface [31][32][33].…”
Section: B Spatial and Functional Connectivity Constraintsmentioning
confidence: 99%
“…Previous results have derived Eigenvalue spectra for Hermitian and non-Hermitian Euclidean random matrices by forming analytical decompositions of the spatial operator resolvent [30,45], but have not analyzed matrices with block-signed structure similar to those we discuss here. Our results for networks with smooth spatial and functional connectivity indicate that in Euclidean random matrices including a signed bipartition of positive and negative elements the bound of the eigenspectrum becomes increasingly sensitive to the local balance between positive and negative interactions as the spatial range of interactions decreases.…”
Section: Implications For Dynamics and Computation In Neural Networkmentioning
confidence: 99%
“…While tempting, it is not possible to interpolate between the 1-stable and universal Gaussian RMT by introducing a high-energy cutoff to the density (17), since the statistics of (infinitely) large matrices with a truncated Lévy distribution always lie in the basin of attraction of the GOE and therefore make exactly the same predictions as universal Gaussian RMT. However, it might be possible to treat the intermediate case within the general theory of Euclidean random matrices (ERMT) [63,65,[93][94][95][96][97][98] that addresses all those very large N × N matrices M the elements M ij of which depend on pairs R i , R j of N randomly chosen coordinates-precisely as for the Rydberg Hamiltonian, Eq. (4).…”
Section: Stable Random Matricesmentioning
confidence: 99%
“…1(b)]. In particular, the m = ±1 groups of eigenvalues develop "holes" that were previously associated with Anderson localization in the framework of the scalar model of wave scattering [32,33].…”
mentioning
confidence: 91%