A Stirling-type formula for the distribution of the length of longest increasing subsequences

TL;DR

This paper introduces a Stirling-type formula for the distribution of the longest increasing subsequence length in random permutations, providing highly accurate approximations for small to very large n, bridging the gap to the random matrix theory limit.

Contribution

It proposes a novel Stirling-type approximation that improves accuracy and efficiency over existing methods, enabling precise numerical analysis of finite size corrections.

Findings

The formula achieves correct digits for n as small as 20.

It provides uniform error estimates of order n^{-2/3} for large n.

It allows detailed expansion of the expected value and variance of the length.

Abstract

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.