2014
DOI: 10.1007/s00365-014-9243-5
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Painlevé II in Random Matrix Theory and Related Fields

Abstract: We review some occurrences of Painlevé II transcendents in the study of two-dimensional Yang-Mills theory, fluctuation formulas for growth models, and as distribution functions within random matrix theory. We first discuss settings in which the parameter α in the Painlevé equation is zero, and the boundary condition is that of the HastingMacLeod solution. As well as expressions involving the Painlevé transcendent itself, one encounters the sigma form of the Painlevé II equation, and Lax pair equations in which… Show more

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Cited by 22 publications
(55 citation statements)
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References 65 publications
(72 reference statements)
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“…The pGUE arises naturally in the statistics of eigenvalues of GUE of random matrices. For αN and ω=1, the pGUE can be interpreted as the probability density function of the classical GUE under the condition that μ is an eigenvalue with multiplicity α . Moreover, the distribution of the largest eigenvalue of this conditioning GUE can be expressed as the ratio of two Hankel determinants with different parameters defined in Pro false(λmaxμ0.28emfalse|0.28emλ=μ4.ptis4.ptan4.pteigenvalue4.ptwith4.ptmultiplicity4.ptαfalse)=Hnfalse(μ;α,0false)Hnfalse(μ;α,1false).Generally, if we remove each eigenvalue of the conditioning GUE with probability ω[0,1], then the distribution of the remaining largest eigenvalue is described as Pro λmaxRμ|λ=μisaneigenvaluewithmultiplicityα=Hnfalse(μ;α,ωfalse)Hnfalse(μ;α,1false).The thinning and conditioning processes were introduced in random matrix theory in .…”
Section: Introduction and Statement Of Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…The pGUE arises naturally in the statistics of eigenvalues of GUE of random matrices. For αN and ω=1, the pGUE can be interpreted as the probability density function of the classical GUE under the condition that μ is an eigenvalue with multiplicity α . Moreover, the distribution of the largest eigenvalue of this conditioning GUE can be expressed as the ratio of two Hankel determinants with different parameters defined in Pro false(λmaxμ0.28emfalse|0.28emλ=μ4.ptis4.ptan4.pteigenvalue4.ptwith4.ptmultiplicity4.ptαfalse)=Hnfalse(μ;α,0false)Hnfalse(μ;α,1false).Generally, if we remove each eigenvalue of the conditioning GUE with probability ω[0,1], then the distribution of the remaining largest eigenvalue is described as Pro λmaxRμ|λ=μisaneigenvaluewithmultiplicityα=Hnfalse(μ;α,ωfalse)Hnfalse(μ;α,1false).The thinning and conditioning processes were introduced in random matrix theory in .…”
Section: Introduction and Statement Of Resultsmentioning
confidence: 99%
“…In , asymptotic formulas have been derived of the Hankel determinants associated with the Jacobi weight perturbed by a Fisher–Hartwig singularity close to the hard edge x=1, involving the Jimbo–Miwa–Okamoto σ‐form of the Painlevé III equation. The Painlevé equations play an important role in the asymptotic study of the Hankel determinants; see , , , and .…”
Section: Introduction and Statement Of Resultsmentioning
confidence: 99%
“…In the planar limit (large-N limit with µ 2 fixed), the theory has two phases -(I) SUSY phase for µ 2 > 2 and (II) SUSYbroken phase for µ 2 < 2. The phase (I) has infinitely degenerate minima parametrized have appeared repeatedly in the disguise of various combinatorial and statistical problems (see [15][16][17] for reviews), e.g. as a distribution of the length of the longest increasing subsequence in random permutations [18], as a distribution of particles in the asymmetric simple exclusion process [19,20], and as a one-dimensional surface growth process in the Karder-Parisi-Zhang universality class [21][22][23].…”
Section: )mentioning
confidence: 99%
“…(3.7) with the harmonic oscillator potential U (x) = N 2 x 2 , for which the orthogonal polynomials coincide with the Hermite polynomials: 16) and the orthonormal functions (3.11) become the wave functions of a particle under a onedimensional harmonic oscillator potential. In a simple large-N limit (planar limit), the eigenvalue density becomes…”
Section: Gue and Soft Edge Scaling Limitmentioning
confidence: 99%
“…We can view this as a gap probability in the soft edge GUE, conditioned to have an eigenvalue at −s. In such a setting, it is known [18,13] that the correlation kernel, to be denoted K soft s , can be written in terms of the usual soft edge scaled GUE kernel according to…”
Section: Conditioning With Fixed Eigenvaluesmentioning
confidence: 99%