Random matrix

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In probability theory and mathematical physics, a random matrix is a matrix-valued random variable. Many important properties of physical systems can be represented mathematically as matrix problems. For example, the thermal conductivity of a lattice can be computed from the dynamical matrix of the particle-particle interactions within the lattice.

Motivation[edit]

Physics[edit]

In nuclear physics, random matrices were introduced by Eugene Wigner[1] to model the spectra of heavy atoms. He postulated that the spacings between the lines in the spectrum of a heavy atom should resemble the spacings between the eigenvalues of a random matrix, and should depend only on the symmetry class of the underlying evolution.[2] In solid-state physics, random matrices model the behaviour of large disordered Hamiltonians in the mean field approximation.

In quantum chaos, the Bohigas–Giannoni–Schmit (BGS) conjecture[3] asserts that the spectral statistics of quantum systems whose classical counterparts exhibit chaotic behaviour are described by random matrix theory.

Random matrix theory has also found applications to the chiral Dirac operator in quantum chromodynamics,[4] quantum gravity in two dimensions,[5] mesoscopic physics,[6] and more.[7][8][9][10][11]

Mathematical statistics and numerical analysis[edit]

In multivariate statistics, random matrices were introduced by John Wishart for statistical analysis of large samples;[12] see estimation of covariance matrices.

Significant results have been shown that extend the classical scalar Chernoff, Bernstein, and Hoeffding inequalities to the largest eigenvalues of finite sums of random Hermitian matrices.[13] Corollary results are derived for the maximum singular values of rectangular matrices.

In numerical analysis, random matrices have been used since the work of John von Neumann and Herman Goldstine[14] to describe computation errors in operations such as matrix multiplication. See also[15] for more recent results.

Number theory[edit]

In number theory, the distribution of zeros of the Riemann zeta function (and other L-functions) is modelled by the distribution of eigenvalues of certain random matrices.[16] The connection was first discovered by Hugh Montgomery and Freeman J. Dyson. It is connected to the Hilbert–Pólya conjecture.

Theoretical neuroscience[edit]

In the field of theoretical neuroscience, random matrices are increasingly used to model the network of synaptic connections between neurons in the brain. Dynamical models of neuronal networks with random connectivity matrix were shown to exhibit a phase transition to chaos [17] when the variance of the synaptic weights crosses a critical value, at the limit of infinite system size. Relating the statistical properties of the spectrum of biologically inspired random matrix models to the dynamical behavior of randomly connected neural networks is an intensive research topic.[18][19][20][21]

Gaussian ensembles[edit]

The most studied random matrix ensembles are the Gaussian ensembles.

The Gaussian unitary ensemble GUE(n) is described by the Gaussian measure with density

 \frac{1}{Z_{\text{GUE}(n)}} e^{- \frac{n}{2} \mathrm{tr} H^2}

on the space of n × n Hermitian matrices H = (Hij)n
i,j=1
. Here ZGUE(n) = 2n/2 πn2/2 is a normalization constant, chosen so that the integral of the density is equal to one. The term unitary refers to the fact that the distribution is invariant under unitary conjugation. The Gaussian unitary ensemble models Hamiltonians lacking time-reversal symmetry.

The Gaussian orthogonal ensemble GOE(n) is described by the Gaussian measure with density

 \frac{1}{Z_{\text{GOE}(n)}} e^{- \frac{n}{4} \mathrm{tr} H^2}

on the space of n × n real symmetric matrices H = (Hij)n
i,j=1
. Its distribution is invariant under orthogonal conjugation, and it models Hamiltonians with time-reversal symmetry.

The Gaussian symplectic ensemble GSE(n) is described by the Gaussian measure with density

 \frac{1}{Z_{\text{GSE}(n)}} e^{- n \mathrm{tr} H^2} \,

on the space of n × n quaternionic Hermitian matrices H = (Hij)n
i,j=1
. Its distribution is invariant under conjugation by the symplectic group, and it models Hamiltonians with time-reversal symmetry but no rotational symmetry.

The joint probability density for the eigenvalues λ1,λ2,...,λn of GUE/GOE/GSE is given by

\frac{1}{Z_{\beta, n}} \prod_{k=1}^n e^{-\frac{\beta n}{4}\lambda_k^2}\prod_{i<j}\left|\lambda_j-\lambda_i\right|^\beta~, \quad (1)

where the Dyson index, β = 1 for GOE, β = 2 for GUE, and β = 4 for GSE, counts the number of real components per matrix element; Zβ,n is a normalisation constant which can be explicitly computed, see Selberg integral. In the case of GUE (β = 2), the formula (1) describes a determinantal point process. Eigenvalues repel as the joint probability density has a zero (of \betath order) for coinciding eigenvalues \lambda_j=\lambda_i.

Generalisations[edit]

Wigner matrices are random Hermitian matrices  \textstyle H_n = (H_n(i,j))_{i,j=1}^n such that the entries

 \left\{ H_n(i, j)~, \, 1 \leq i \leq j \leq n \right\}

above the main diagonal are independent random variables with zero mean, and

 \left\{ H_n(i, j)~, \, 1 \leq i < j \leq n \right\}

have identical second moments.

Invariant matrix ensembles are random Hermitian matrices with density on the space of real symmetric/ Hermitian/ quaternionic Hermitian matrices, which is of the form \textstyle \frac{1}{Z_n} e^{- n \mathrm{tr} V(H)}~, where the function V is called the potential.

The Gaussian ensembles are the only common special cases of these two classes of random matrices.

Spectral theory of random matrices[edit]

The spectral theory of random matrices studies the distribution of the eigenvalues as the size of the matrix goes to infinity.

Global regime[edit]

In the global regime, one is interested in the distribution of linear statistics of the form Nf, H = n-1 tr f(H).

Empirical spectral measure[edit]

The empirical spectral measure μH of H is defined by

 \mu_{H}(A) = \frac{1}{n} \, \# \left\{ \text{eigenvalues of }H\text{ in }A \right\} = N_{1_A, H}, \quad A \subset \mathbb{R}.

Usually, the limit of  \mu_{H} is a deterministic measure; this is a particular case of self-averaging. The cumulative distribution function of the limiting measure is called the integrated density of states and is denoted N(λ). If the integrated density of states is differentiable, its derivative is called the density of states and is denoted ρ(λ).

The limit of the empirical spectral measure for Wigner matrices was described by Eugene Wigner, see Wigner's law. As far as sample covariance matrices are concerned, a theory was developed by Marčenko and Pastur.[22][23]

The limit of the empirical spectral measure of invariant matrix ensembles is described by a certain integral equation which arises from potential theory.[24]

Fluctuations[edit]

For the linear statistics Nf,H = n−1 ∑ f(λj), one is also interested in the fluctuations about ∫ f(λdN(λ). For many classes of random matrices, a central limit theorem of the form

 \frac{N_{f,H} - \int f(\lambda) \, dN(\lambda)}{\sigma_{f, n}} \overset{D}{\longrightarrow} N(0, 1)

is known, see,[25][26] etc.

Local regime[edit]

In the local regime, one is interested in the spacings between eigenvalues, and, more generally, in the joint distribution of eigenvalues in an interval of length of order 1/n. One distinguishes between bulk statistics, pertaining to intervals inside the support of the limiting spectral measure, and edge statistics, pertaining to intervals near the boundary of the support.

Bulk statistics[edit]

Formally, fix \lambda_0 in the interior of the support of N(\lambda). Then consider the point process

 \Xi(\lambda_0) = \sum_j \delta\Big({\cdot} - n \rho(\lambda_0) (\lambda_j - \lambda_0) \Big)~,

where \lambda_j are the eigenvalues of the random matrix.

The point process \Xi(\lambda_0) captures the statistical properties of eigenvalues in the vicinity of \lambda_0. For the Gaussian ensembles, the limit of \Xi(\lambda_0) is known;[2] thus, for GUE it is a determinantal point process with the kernel

 K(x, y) = \frac{\sin \pi(x-y)}{\pi(x-y)}

(the sine kernel).

The universality principle postulates that the limit of \Xi(\lambda_0) as n \to \infty should depend only on the symmetry class of the random matrix (and neither on the specific model of random matrices nor on \lambda_0). This was rigorously proved for several models of random matrices: for invariant matrix ensembles,[27][28] for Wigner matrices,[29][30] et cet.

Edge statistics[edit]

See Tracy–Widom distribution.

Other classes of random matrices[edit]

Wishart matrices[edit]

Wishart matrices are n × n random matrices of the form H = X X*, where X is an n × n random matrix with independent entries, and X* is its conjugate matrix. In the important special case considered by Wishart, the entries of X are identically distributed Gaussian random variables (either real or complex).

The limit of the empirical spectral measure of Wishart matrices was found[22] by Vladimir Marchenko and Leonid Pastur, see Marchenko–Pastur distribution.

Random unitary matrices[edit]

See circular ensembles

Non-Hermitian random matrices[edit]

See circular law.

Guide to references[edit]

References[edit]

  1. ^ a b Wigner, E. (1955). "Characteristic vectors of bordered matrices with infinite dimensions". Annals of Mathematics 62 (3): 548–564. doi:10.2307/1970079. 
  2. ^ a b c Mehta, M.L. (2004). Random Matrices. Amsterdam: Elsevier/Academic Press. ISBN 0-12-088409-7. 
  3. ^ Bohigas, O.; Giannoni, M.J.; Schmit, Schmit (1984). "Characterization of Chaotic Quantum Spectra and Universality of Level Fluctuation Laws". Phys. Rev. Lett. 52: 1–4. Bibcode:1984PhRvL..52....1B. doi:10.1103/PhysRevLett.52.1. 
  4. ^ Verbaarschot JJM, Wettig T (2000). "Random Matrix Theory and Chiral Symmetry in QCD". Ann.Rev.Nucl.Part.Sci. 50: 343. arXiv:hep-ph/0003017. Bibcode:2000ARNPS..50..343V. doi:10.1146/annurev.nucl.50.1.343. 
  5. ^ Franchini F, Kravtsov VE (October 2009). "Horizon in random matrix theory, the Hawking radiation, and flow of cold atoms". Phys. Rev. Lett. 103 (16): 166401. arXiv:0905.3533. Bibcode:2009PhRvL.103p6401F. doi:10.1103/PhysRevLett.103.166401. PMID 19905710. 
  6. ^ Sánchez D, Büttiker M (September 2004). "Magnetic-field asymmetry of nonlinear mesoscopic transport". Phys. Rev. Lett. 93 (10): 106802. arXiv:cond-mat/0404387. Bibcode:2004PhRvL..93j6802S. doi:10.1103/PhysRevLett.93.106802. PMID 15447435. 
  7. ^ Rychkov VS, Borlenghi S, Jaffres H, Fert A, Waintal X (August 2009). "Spin torque and waviness in magnetic multilayers: a bridge between Valet-Fert theory and quantum approaches". Phys. Rev. Lett. 103 (6): 066602. arXiv:0902.4360. Bibcode:2009PhRvL.103f6602R. doi:10.1103/PhysRevLett.103.066602. PMID 19792592. 
  8. ^ Callaway DJE (April 1991). "Random matrices, fractional statistics, and the quantum Hall effect". Phys. Rev. B Condens. Matter 43 (10): 8641–8643. Bibcode:1991PhRvB..43.8641C. doi:10.1103/PhysRevB.43.8641. PMID 9996505. 
  9. ^ Janssen M, Pracz K (June 2000). "Correlated random band matrices: localization-delocalization transitions". Phys. Rev. E 61 (6 Pt A): 6278–86. arXiv:cond-mat/9911467. Bibcode:2000PhRvE..61.6278J. doi:10.1103/PhysRevE.61.6278. PMID 11088301. 
  10. ^ Zumbühl DM, Miller JB, Marcus CM, Campman K, Gossard AC (December 2002). "Spin-orbit coupling, antilocalization, and parallel magnetic fields in quantum dots". Phys. Rev. Lett. 89 (27): 276803. arXiv:cond-mat/0208436. Bibcode:2002PhRvL..89A6803Z. doi:10.1103/PhysRevLett.89.276803. PMID 12513231. 
  11. ^ Bahcall SR (December 1996). "Random Matrix Model for Superconductors in a Magnetic Field". Phys. Rev. Lett. 77 (26): 5276–5279. arXiv:cond-mat/9611136. Bibcode:1996PhRvL..77.5276B. doi:10.1103/PhysRevLett.77.5276. PMID 10062760. 
  12. ^ a b Wishart, J. (1928). "Generalized product moment distribution in samples". Biometrika 20A (1–2): 32–52. 
  13. ^ Tropp, J. (2011). "User-Friendly Tail Bounds for Sums of Random Matrices". Foundations of Computational Mathematics. doi:10.1007/s10208-011-9099-z. 
  14. ^ a b von Neumann, J.; Goldstine, H.H. (1947). "Numerical inverting of matrices of high order". Bull. Amer. Math. Soc. 53 (11): 1021–1099. doi:10.1090/S0002-9904-1947-08909-6. 
  15. ^ a b Edelman, A.; Rao, N.R (2005). "Random matrix theory". Acta Numer. 14: 233–297. doi:10.1017/S0962492904000236. 
  16. ^ Keating, Jon (1993). "The Riemann zeta-function and quantum chaology". Proc. Internat. School of Phys. Enrico Fermi CXIX: 145–185. 
  17. ^ Sompolinsky, H.; Crisanti, A.; Sommers, H. (July 1988). "Chaos in Random Neural Networks". Physical Review Letters 61 (3): 259–262. doi:10.1103/PhysRevLett.61.259. 
  18. ^ García del Molino, Luis Carlos; Pakdaman, Khashayar; Touboul, Jonathan; Wainrib, Gilles (October 2013). "Synchronization in random balanced networks". Physical Review E 88 (4). doi:10.1103/PhysRevE.88.042824. 
  19. ^ Rajan, Kanaka; Abbott, L. (November 2006). "Eigenvalue Spectra of Random Matrices for Neural Networks". Physical Review Letters 97 (18). doi:10.1103/PhysRevLett.97.188104. 
  20. ^ Wainrib, Gilles; Touboul, Jonathan (March 2013). "Topological and Dynamical Complexity of Random Neural Networks". Physical Review Letters 110 (11). doi:10.1103/PhysRevLett.110.118101. 
  21. ^ Timme, Marc; Wolf, Fred; Geisel, Theo (February 2004). "Topological Speed Limits to Network Synchronization". Physical Review Letters 92 (7). doi:10.1103/PhysRevLett.92.074101. 
  22. ^ a b .Marčenko, V A; Pastur, L A (1967). "Distribution of eigenvalues for some sets of random matrices". Mathematics of the USSR-Sbornik 1 (4): 457–483. Bibcode:1967SbMat...1..457M. doi:10.1070/SM1967v001n04ABEH001994. 
  23. ^ a b Pastur, L.A. (1973). "Spectra of random self-adjoint operators". Russ. Math. Surv. 28 (1): 1−67. Bibcode:1973RuMaS..28....1P. doi:10.1070/RM1973v028n01ABEH001396. 
  24. ^ Pastur, L.; Shcherbina, M.; Shcherbina, M. (1995). "On the Statistical Mechanics Approach in the Random Matrix Theory: Integrated Density of States". J. Stat. Phys. 79 (3–4): 585−611. Bibcode:1995JSP....79..585D. doi:10.1007/BF02184872. 
  25. ^ Johansson, K. (1998). "On fluctuations of eigenvalues of random Hermitian matrices". Duke Math. J. 91 (1): 151–204. doi:10.1215/S0012-7094-98-09108-6. 
  26. ^ Pastur, L.A. (2005). "A simple approach to the global regime of Gaussian ensembles of random matrices". Ukrainian Math. J. 57 (6): 936–966. doi:10.1007/s11253-005-0241-4. 
  27. ^ Pastur, L.; Shcherbina, M. (1997). "Universality of the local eigenvalue statistics for a class of unitary invariant random matrix ensembles". Journal of Statistical Physics 86 (1–2): 109−147. Bibcode:1997JSP....86..109P. doi:10.1007/BF02180200. 
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  29. ^ Erdős, L.; Péché, S.; Ramírez, J.A.; Schlein, B.; Yau, H.T. (2010). "Bulk universality for Wigner matrices". Communications on Pure and Applied Mathematics 63 (7): 895–925. 
  30. ^ Tao, Terence; Vu, Van H. (2010). "Random matrices: universality of local eigenvalue statistics up to the edge". Communications in Mathematical Physics 298 (2): 549−572. arXiv:0908.1982. Bibcode:2010CMaPh.298..549T. doi:10.1007/s00220-010-1044-5. 
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  34. ^ Diaconis, Persi (2005). "What is ... a random matrix?". Notices of the American Mathematical Society 52 (11): 1348–1349. ISSN 0002-9920. MR 2183871. 

External links[edit]