Asymptotics of the partition function for random matrices via Riemann-Hilbert techniques, and applications to graphical enumeration

dc.creatorErcolani, N. M.
dc.creatorMcLaughlin, K. D. T-R
dc.date2002-11-13
dc.date.accessioned2026-07-07T04:29:34Z
dc.date.available2026-07-07T04:29:34Z
dc.descriptionWe study the partition function from random matrix theory using a well known connection to orthogonal polynomials, and a recently developed Riemann-Hilbert approach to the computation of detailed asymptotics for these orthogonal polynomials. We obtain the first proof of a complete large N expansion for the partition function, for a general class of probability measures on matrices, originally conjectured by Bessis, Itzykson, and Zuber. We prove that the coefficients in the asymptotic expansion are analytic functions of parameters in the original probability measure, and that they are generating functions for the enumeration of labelled maps according to genus and valence. Central to the analysis is a large N expansion for the mean density of eigenvalues, uniformly valid on the entire real axis.
dc.description44 pages, 4 figures. To appear, International Mathematics Research Notices
dc.identifierhttps://arxiv.org/abs/math-ph/0211022
dc.identifierhttp://arxiv.org/abs/math-ph/0211022
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/57203
dc.subjectMathematical Physics
dc.subjectClassical Analysis and ODEs
dc.subject82D30
dc.titleAsymptotics of the partition function for random matrices via Riemann-Hilbert techniques, and applications to graphical enumeration
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