Large-parameter asymptotics of generalized Hastings–McLeod functions

Abstract The generalized Hastings–McLeod solutions to the inhomogeneous Painlevé-II equation arise in multi-critical unitary random matrix ensembles, the chiral two-matrix model for rectangular matrices, non-intersecting squared Bessel paths, and non-intersecting Brownian bridges on the circle. We establish the leading-order asymptotic behavior of the generalized Hastings–McLeod functions as the inhomogeneous parameter approaches infinity using the Deift–Zhou nonlinear steepest-descent method for Riemann–Hilbert problems. This analysis is done in both the pole-free region and pole region. The asymptotic formulae show excellent agreement with numerically computed solutions in both regions.

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Publication Details

Journal
Nonlinearity
Published
2025-09-18
DOI
https://doi.org/10.1088/1361-6544/ae0230
Primary Topic
Random Matrices and Applications
Type
article
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article

Large-parameter asymptotics of generalized Hastings–McLeod functions

Robert Buckingham, Kurt Schmidt
Nonlinearity
Random Matrices and Applications
article

Large-parameter asymptotics of generalized Hastings–McLeod functions

Robert Buckingham, Kurt Schmidt
article en

Abstract

Abstract The generalized Hastings–McLeod solutions to the inhomogeneous Painlevé-II equation arise in multi-critical unitary random matrix ensembles, the chiral two-matrix model for rectangular matrices, non-intersecting squared Bessel paths, and non-intersecting Brownian bridges on the circle. We establish the leading-order asymptotic behavior of the generalized Hastings–McLeod functions as the inhomogeneous parameter approaches infinity using the Deift–Zhou nonlinear steepest-descent method for Riemann–Hilbert problems. This analysis is done in both the pole-free region and pole region. The asymptotic formulae show excellent agreement with numerically computed solutions in both regions.

NonlinearityVol. 38(9)
University of Central Florida (US), University of Cincinnati (US)
Openalex Percentile: Top 76%
Random Matrices and Applications
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