Random matrices close to Hermitian or unitary: overview of methods and results

By Yan V. Fyodorov, H. Sommers. 2002

157 citations recorded by the source corpus.

The paper discusses recent progress in understanding statistical properties of eigenvalues of (weakly) non-Hermitian and non-unitary random matrices. The first type of ensembles is of the form Ĵ = Ĥ − i, with Ĥ being a large random N × N Hermitian matrix with independent entries 'deformed' by a certain anti-Hermitian N × N matrix i satisfying in the limit of large dimension N the condition Tr Ĥ2 ∝ N Tr 2. Here can be either a random or just a fixed given Hermitian matrix. Ensembles of such a type with ≥ 0 emerge naturally when describing quantum scattering in systems with chaotic dynamics and serve to describe resonance statistics. Related models are used to mimic complex spectra of the Dirac operator with chemical potential in the context of quantum chromodynamics. Ensembles of the second type, arising naturally in scattering theory of discrete-time systems, are formed by N × N matrices  with complex entries such that † = I − . For = 0 this coincides with the circular unitary ensemble, and 0 ≤ ≤ I describes deviation from unitarity. Our result amounts to answering statistically the following old question: given the singular values of a matrix  describe the locus of its eigenvalues. We systematically show that the obtained expressions for the correlation functions of complex eigenvalues describe a non-trivial crossover from Wigner–Dyson statistics of real/unimodular eigenvalues typical of Hermitian/unitary matrices to Ginibre statistics in the complex plane typical of ensembles with strong non-Hermiticity: Tr Ĥ2 ∝ Tr 2 when N → ∞. Finally, we discuss (scarce) results available on eigenvector statistics for weakly non-Hermitian random matrices.

Subfields: Mathematical Physics, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2bae4cd35e2b340ea142004128d97697c45749ac

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In Mathematical Physics

  1. Roman Vershynin
  2. Mark Rudelson
  3. Terence Tao
  4. Konstantin E. Tikhomirov
  5. Brian Rider
  6. Van H. Vu
  7. J. Silverstein
  8. Nicholas A. Cook

In Statistical and Nonlinear Physics

  1. Brian Rider
  2. Mark Rudelson
  3. Roman Vershynin
  4. Arun K. Kuchibhotla
  5. Nathan Kallus
  6. Angela Zhou
  7. Abhishek Chakrabortty

In Statistics and Probability

  1. Roman Vershynin
  2. Mark Rudelson
  3. Van H. Vu
  4. Terence Tao
  5. Konstantin E. Tikhomirov
  6. Brian Rider
  7. Hoi H. Nguyen
  8. J. Silverstein

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