TY - JOUR

T1 - A Stirling-Type Formula for the Distribution of the Length of Longest Increasing Subsequences

AU - Bornemann, Folkmar

N1 - Publisher Copyright:
© 2023, The Author(s).

PY - 2023

Y1 - 2023

N2 - The discrete distribution of the length of longest increasing subsequences in random permutations of n integers is deeply related to random matrix theory. In a seminal work, Baik, Deift and Johansson provided an asymptotics in terms of the distribution of the scaled largest level of the large matrix limit of GUE. As a numerical approximation, however, this asymptotics is inaccurate for small n and has a slow convergence rate, conjectured to be just of order n- 1 / 3. Here, we suggest a different type of approximation, based on Hayman’s generalization of Stirling’s formula. Such a formula gives already a couple of correct digits of the length distribution for n as small as 20 but allows numerical evaluations, with a uniform error of apparent order n- 2 / 3, for n as large as 10 12, thus closing the gap between a table of exact values (compiled for up to n= 1000) and the random matrix limit. Being much more efficient and accurate than Monte Carlo simulations, the Stirling-type formula allows for a precise numerical understanding of the first few finite size correction terms to the random matrix limit. From this we derive expansions of the expected value and variance of the length, exhibiting several more terms than previously put forward.

AB - The discrete distribution of the length of longest increasing subsequences in random permutations of n integers is deeply related to random matrix theory. In a seminal work, Baik, Deift and Johansson provided an asymptotics in terms of the distribution of the scaled largest level of the large matrix limit of GUE. As a numerical approximation, however, this asymptotics is inaccurate for small n and has a slow convergence rate, conjectured to be just of order n- 1 / 3. Here, we suggest a different type of approximation, based on Hayman’s generalization of Stirling’s formula. Such a formula gives already a couple of correct digits of the length distribution for n as small as 20 but allows numerical evaluations, with a uniform error of apparent order n- 2 / 3, for n as large as 10 12, thus closing the gap between a table of exact values (compiled for up to n= 1000) and the random matrix limit. Being much more efficient and accurate than Monte Carlo simulations, the Stirling-type formula allows for a precise numerical understanding of the first few finite size correction terms to the random matrix limit. From this we derive expansions of the expected value and variance of the length, exhibiting several more terms than previously put forward.

KW - H-admissibility

KW - Random matrices

KW - Random permutations

KW - Stirling-type formula

UR - http://www.scopus.com/inward/record.url?scp=85146913014&partnerID=8YFLogxK

U2 - 10.1007/s10208-023-09604-z

DO - 10.1007/s10208-023-09604-z

M3 - Article

AN - SCOPUS:85146913014

SN - 1615-3375

JO - Foundations of Computational Mathematics

JF - Foundations of Computational Mathematics

ER -