The Maximum Number of Bases in a Family of Vectors

TL;DR

This paper establishes an asymptotically sharp upper bound on the proportion of linearly independent subsets within large families of vectors over finite fields, showing the maximum is achieved by the entire space minus zero.

Contribution

It provides a negative answer to whether large subsets can surpass the base proportion and derives an exact probability bound for linear independence in finite vector spaces.

Findings

Maximum proportion of independent r-element subsets matches that of the entire space.

Probability of independence is maximized when sampling from the whole space minus zero.

Results extend to finite fields of any prime power q.

Abstract

The proportion of $d$-element subsets of $\mathbb{F}_2^d$ that are bases is asymptotic to $\prod_{j=1}^{\infty}(1-2^{-j}) \approx 0.29$ as $d \to \infty$. It is natural to ask whether there exists a (large) subset $\mathcal{F}$ of $\mathbb{F}_2^d$ such that the proportion of $d$-element subsets of $\mathcal{F}$ that are bases is (asymptotically) greater than this number. As well as being a natural question in its own right, this would imply better lower bounds on the Tur\'an densities of certain hypercubes and `daisy' hypergraphs. We give a negative answer to the above question. More generally, we obtain an asymptotically sharp upper bound on the proportion of linearly independent $r$-element subsets of a (large) family of vectors in $\mathbb{F}_2^d$, for $r \leq d$. This bound follows from an exact result concerning the probability of obtaining a linearly independent sequence when we randomly sample $r$ elements with replacement from our family of vectors: we show that this probability, for any family of vectors, is at most what it is when the family is the whole space $\mathbb{F}_2^d \setminus \{0\}$. Our results also go through when $\mathbb{F}_2$ is replaced by $\mathbb{F}_q$ for any prime power $q$.