# Coalescence under Preimage Constraints

## TL;DR

This paper investigates how preimage constraints affect the growth of iterated images of random functions, providing asymptotic results under certain conditions using singularity analysis.

## Contribution

It offers a comprehensive analysis of the asymptotic behavior of iterated images of functions with preimage constraints, extending existing theories.

## Key findings

- Asymptotic formulas for the size of iterated images under constraints
- Conditions under which the theory applies, including gcd and element restrictions
- Background framework for analyzing constrained random functions

## Abstract

The primary goal of this document is to record the asymptotic effects that preimage constraints impose upon the sizes of the iterated images of a random function. Specifically, given a subset $\mathcal{P}\subseteq \mathbb{Z}_{\geq 0}$ and a finite set $S$ of size $n$, choose a function uniformly from the set of functions $f:S\rightarrow S$ that satisfy the condition that $|f^{-1}(x)|\in\mathcal{P}$ for each $x\in S$, and ask what $|f^k(S)|$ looks like as $n$ goes to infinity. The robust theory of singularity analysis allows one to completely answer this question if one accepts that $0\in\mathcal{P}$, that $\mathcal{P}$ contains an element bigger than 1, and that $\gcd(\mathcal{P})=1$; only the third of these conditions is a meaningful restriction. The secondary goal of this paper is to record much of the background necessary to achieve the primary goal.