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Bristol: A Python Package for Random Matrix Ensembles

Suezen, Mehmet

Application of Random Matrix Theory (RMT) appears in different fields of research. Generation of random matrices numerically is an essential part of this practice. While matrices should be generated in a numerically stable way and should represent correct matrix ensemble. Bristol implements techniques developed by Mezzadri that addresses these concerns in a Python module with parallel processing capabilities and a data model for further processing.  The circular module provides methods for generating matrices from Circular Unitary Ensemble (CUE), Circular Ortogonal Ensemble (COE) and Circular Symplectic Ensemble (CSE). Additional spectral analysis utilities are also implemented, such as computation of spectral density and spectral ergodicity.

Code review performed by C.Garbers in kind. LATEST version available at
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  • Spectral Ergodicity in Deep Learning Architectures via Surrogate Random Matrices, Mehmet Süzen, Cornelius Weber, Joan J. Cerdà, [arXiv:1704.08693](
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