• Go Mobile »
  • Access by Staats- und Universitaetsbibliothek Bremen

Cavity approach to the spectral density of sparse symmetric random matrices

Tim Rogers, Isaac Pérez Castillo, Reimer Kühn, and Koujin Takeda
Phys. Rev. E 78, 031116 – Published 10 September 2008
×

Abstract

The spectral density of various ensembles of sparse symmetric random matrices is analyzed using the cavity method. We consider two cases: matrices whose associated graphs are locally treelike, and sparse covariance matrices. We derive a closed set of equations from which the density of eigenvalues can be efficiently calculated. Within this approach, the Wigner semicircle law for Gaussian matrices and the Marčenko-Pastur law for covariance matrices are recovered easily. Our results are compared with numerical diagonalization, showing excellent agreement.

  • Figure
  • Figure
  • Figure
  • Figure
  • Figure
  • Figure
  • Received 13 March 2008

DOI:

Authors & Affiliations

Tim Rogers, Isaac Pérez Castillo, and Reimer Kühn

  • Department of Mathematics, King’s College London, Strand, London WC2R 2LS, United Kingdom

Koujin Takeda

  • Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama 226-8502, Japan and Department of Mathematics, King’s College London, Strand, London WC2R 2LS, United Kingdom

Article Text

Click to Expand

References

Click to Expand
Issue

Vol. 78, Iss. 3 — September 2008

Reuse & Permissions
International Year Of Light
The Physical Review Journals Celebrate the International Year of Light

The editors of the Physical Review journals revisit papers that represent important breakthroughs in the field of optics. The articles covered are free to read throughout 2015. Read more.

Authorization Required


×
×

Images

6 of 6
×

Log In

Cancel
×

Search


Article Lookup

Paste a citation or DOI

Enter a citation
×

Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 3.0 License. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

×