Bondy's theorem

The theorem is as follows:

Let X be a set with n elements and let A₁, A₂, ..., A_n be distinct subsets of X. Then there exists a subset S of X with n − 1 elements such that the sets A_i ∩ S are all distinct.

In other words, if we have a 0-1 matrix with n rows and n columns such that each row is distinct, we can remove one column such that the rows of the resulting n × (n − 1) matrix are distinct.^[2][3]

Consider the 4 × 4 matrix

${\displaystyle {\begin{bmatrix}1&1&0&1\\0&1&0&1\\0&0&1&1\\0&1&1&0\end{bmatrix}}}$

where all rows are pairwise distinct. If we delete, for example, the first column, the resulting matrix

${\displaystyle {\begin{bmatrix}1&0&1\\1&0&1\\0&1&1\\1&1&0\end{bmatrix}}}$

no longer has this property: the first row is identical to the second row. Nevertheless, by Bondy's theorem we know that we can always find a column that can be deleted without introducing any identical rows. In this case, we can delete the third column: all rows of the 3 × 4 matrix

${\displaystyle {\begin{bmatrix}1&1&1\\0&1&1\\0&0&1\\0&1&0\end{bmatrix}}}$

are distinct. Another possibility would have been deleting the fourth column.

From the perspective of computational learning theory, Bondy's theorem can be rephrased as follows:^[4]

Let C be a concept class over a finite domain X. Then there exists a subset S of X with the size at most |C| − 1 such that S is a witness set for every concept in C.

This implies that every finite concept class C has its teaching dimension bounded by |C| − 1.