Diagrammatic techniques to compute perturbatively the spectral properties of Euclidean random matrices (ERM) in the high-density regime are introduced and discussed in detail. Such techniques are developed in two alternative and very different formulations of the mathematical problem and are shown to give identical results up to second order in the perturbative expansion. One method, based on writing the so-called resolvent function as a Taylor series, allows us to group the diagrams into a small number of topological classes, providing a simple way to determine the infrared (small momenta) behaviour of the theory up to third order, which is of interest for the comparison with experiments. The other method, which reformulates the problem as...
138 pages, based on lectures by Bertrand Eynard at IPhT, SaclayWe provide a self-contained introduct...
Akemann G, Baik J, Di Francesco P, eds. The Oxford Handbook of Random Matrix Theory. Oxford: Oxford ...
The past decade saw an intensification of the deluge of data available to learning algorithms, which...
Diagrammatic techniques to compute perturbatively the spectral properties of Euclidean random matric...
Diagrammatic techniques to compute perturbatively the spectral properties of Euclidean random matric...
We study the spectrum of a random matrix, whose elements depend on the euclidean distance between po...
We review the state of the art of the theory of Euclidean random matrices, focusing on the density o...
We study the spectra and localization properties of Euclidean random matrices defined on a random gr...
International audienceImproved mean-eld technics are a central theme of statistical physics methods ...
International audienceImproved mean-eld technics are a central theme of statistical physics methods ...
International audienceImproved mean-eld technics are a central theme of statistical physics methods ...
International audienceImproved mean-eld technics are a central theme of statistical physics methods ...
We study the spectra and localization properties of Euclidean random matrices defined on a random gr...
We develop a theoretical approach to compute the conditioned spectral density of N × N noninvariant ...
We describe a random matrix approach that can provide generic and readily soluble mean-field descrip...
138 pages, based on lectures by Bertrand Eynard at IPhT, SaclayWe provide a self-contained introduct...
Akemann G, Baik J, Di Francesco P, eds. The Oxford Handbook of Random Matrix Theory. Oxford: Oxford ...
The past decade saw an intensification of the deluge of data available to learning algorithms, which...
Diagrammatic techniques to compute perturbatively the spectral properties of Euclidean random matric...
Diagrammatic techniques to compute perturbatively the spectral properties of Euclidean random matric...
We study the spectrum of a random matrix, whose elements depend on the euclidean distance between po...
We review the state of the art of the theory of Euclidean random matrices, focusing on the density o...
We study the spectra and localization properties of Euclidean random matrices defined on a random gr...
International audienceImproved mean-eld technics are a central theme of statistical physics methods ...
International audienceImproved mean-eld technics are a central theme of statistical physics methods ...
International audienceImproved mean-eld technics are a central theme of statistical physics methods ...
International audienceImproved mean-eld technics are a central theme of statistical physics methods ...
We study the spectra and localization properties of Euclidean random matrices defined on a random gr...
We develop a theoretical approach to compute the conditioned spectral density of N × N noninvariant ...
We describe a random matrix approach that can provide generic and readily soluble mean-field descrip...
138 pages, based on lectures by Bertrand Eynard at IPhT, SaclayWe provide a self-contained introduct...
Akemann G, Baik J, Di Francesco P, eds. The Oxford Handbook of Random Matrix Theory. Oxford: Oxford ...
The past decade saw an intensification of the deluge of data available to learning algorithms, which...