Random necklaces require fewer cuts

TL;DR

This paper studies the minimum cuts needed to partition random necklaces into balanced collections, revealing probabilistic bounds and asymptotic behaviors for various parameters.

Contribution

It provides probabilistic bounds and asymptotic analysis for the number of cuts in random necklaces, extending known results to stochastic settings.

Findings

Minimum cuts for random necklaces are at least (k-1)(t+1)/2 with high probability.

Probability distribution of cuts for k=2 and large m is characterized, showing polynomial decay and bounds.

For large t, the number of cuts is at most approximately 0.4t with high probability.

Abstract

It is known that any open necklace with beads of $t$ types in which the number of beads of each type is divisible by $k$, can be partitioned by at most $(k-1)t$ cuts into intervals that can be distributed into $k$ collections, each containing the same number of beads of each type. This is tight for all values of $k$ and $t$. Here, we consider the case of random necklaces, where the number of beads of each type is $km$. Then the minimum number of cuts required for a ``fair'' partition with the above property is a random variable $X(k,t,m)$. We prove that for fixed $k,t,$ and large $m$, this random variable is at least $(k-1)(t+1)/2$ with high probability. For $k=2$, fixed $t$, and large $m$, we determine the asymptotic behavior of the probability that $X(2,t,m)=s$ for all values of $s\le t $. We show that this probability is polynomially small when $s<(t+1)/2$, it is bounded away from zero when $s>(t+1)/2$, and decays like $\Theta ( 1/\log m)$ when $s=(t+1)/2$. We also show that for large $t$, $X(2,t,1)$ is at most $(0.4+o(1))t$ with high probability and that for large $t$ and large ratio $k/\log t$, $X(k,t,1)$ is $o(kt)$ with high probability.