We look for the maximum order of a square (0, 1)-matrix A with a fixed rank r, provided A has no repeated rows or columns. If A is the adjacency matrix of a graph, Kotlov and Lovász [J. Graph Theory 23, 1996] proved that the maximum order equals Θ(2r/2). In this note we show that this result remains correct if A is symmetric, but becomes false if symmetry is not required.(0;1)-matrix;rank;graph

Haemers, W.H.

Peeters, M.J.P.

English

Research Papers in Economics

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
THE MAXIMUM ORDER OF
 
 
 
 
 
 
 
 
 
 
 
 
 
No. 2011-113 
THE MAXIMUM ORDER OF REDUCED SQUARE
MATRICES WITH A GIVEN
 
By W.H. Haemers, M.J.P. Peeters
 
 
 
 
 
 
October, 2011 
 
 
 
 
 
ISSN 0924-7815 
REDUCED SQUARE (0, 1)-
N RANK 
W.H. Haemers, M.J.P. Peeters 
 The maximum order of reduced square
(0,1)-matrices with a given rank
W.H. Haemers∗ and M.J.P. Peeters
Department of Econometrics and Operations Research,
Tilburg University, Tilburg, The Netherlands
Abstract
We look for the maximum order of a square (0,1)-matrix A with a ﬁxed
rank r, provided A has no repeated rows or columns. If A is the adjacency
matrix of a graph, Kotlov and Lov´ asz [J. Graph Theory 23, 1996] proved
that the maximum order equals Θ(2
r/2). In this note we show that this
result remains correct if A is symmetric, but becomes false if symmetry
is not required.
Keywords: (0,1)-matrix, rank, graph.
AMS Subject Classiﬁcation: 05B20, 05E99, 15B36. JEL-code: C0.
1 Introduction
At the workshop ‘Directions in Matrix Theory 2011’ in Coimbra, Portugal, the
ﬁrst author gave a presentation about the results from [2] on the maximum
order of reduced adjacency matrices of graphs with a given rank. At the end
of the presentation there was some discussion on what can be said if the zero
diagonal, or the symmetry condition is dropped. Here we give some answers to
these more general situations.
Given a (0,1)-matrix, we can duplicate a row or column and add zero rows
and columns without changing the rank. This motivates the following deﬁnition.
Deﬁnition 1 A (0,1)-matrix is reduced if no two rows or columns are depen-
dent.
We deﬁne n(r) to be the maximum order of a reduced square (0,1)-matrix of
rank r. When we restrict to symmetric matrices, or adjacency matrices of graphs
we denote the maximum order by nsym(r) and ngr(r), respectively. Kotlov and
Lov´ asz [3] prove that ngr(r) = O(2r/2), and give a construction showing that
ngr(r) ≥ 2(r+2)/2 − 2 if r is even, and ngr(r) ≥ 5 · 2(r−3)/2 − 2 if r is odd.
In [1] the authors present a second construction giving the same number of
vertices. They conjecture that the mentioned values are equal to the maximum
∗corresponding author, e-mail: haemers@uvt.nl
1ngr(r), and that all graphs attaining this maximum can be obtained by the two
construction methods. In [2] we show that the conjecture is correct if the graph
contains r
2K2 or K3 + r−3
2 K2 as an induced subgraph.
Here we show that the same asymptotic upper bound holds for nsym(r) as
for ngr(r). For even r we ﬁnd no better lower bound for nsym(r) than for ngr(r),
but for odd r the lower bound for nsym(r) can be improved. In addition, we
prove that n(r) ≥ 2(r−3)/2√
r, which shows that the upper bound of Kotlov and
Lov´ asz doesn’t hold anymore for the nonsymmetric case.
2 Symmetric (0,1)-matrices
2.1 The upper Bound
A symmetric (0,1)-matrix can be interpreted as the adjacency matrix of a (sim-
ple) graph admitting loops. For the proof of the upper bound ngr(r) = O(2r/2),
Kotlov and Lov´ asz deﬁned twins in a graph as two vertices with the same set of
neighbors. For a loopless graph twins are necessarily nonadjacent, but if loops
are allowed twins can be adjacent vertices with loops. In other words, twins
correspond to a pair of repeated rows and columns in the adjacency matrix.
Keeping this in mind, we claim that the mentioned proof of Kotlov and Lov´ asz
is also valid for graphs admitting loops, since nowhere it is used that the matrix
has zero diagonal. Therefore:
Theorem 1 nsym(r) = O(2r/2).
2.2 Constructions
The following recursive construction was given in [3] (see also [1, 2]). Suppose
A be a reduced adjacency matrix of a graph on n vertices with rank r, then

 

A A 0 0
A A 1 0
0> 1> 0 1
0> 0> 1 0

 

is the reduced adjacency of a graph on 2n+2 vertices with rank r+2. If we start
with K2 (which has r = n = 2) and K3 (which has r = n = 3), then the recursive
construction gives graphs with 2(r+2)/2−2 vertices if r is even, and 5·2(r−3)/2−2
vertices if r is odd. Note that there exist no graph whose adjacency matrix has
rank 1. Obviously the mentioned numbers are lower bounds for nsym(r), but
for odd r we can do better since we can start the recursive construction with
A = [1] and n = r = 1, which leads to reduced symmetric (0,1)-matrices of
order 3 · 2(r−1)/2 − 2 and rank r. Thus we have:
Proposition 1 nsym(r) ≥
(
2(r+2)/2 − 2 if r is even,
3 · 2(r−1)/2 − 2 if r is odd.
2There exist more recursive constructions that make a reduced adjacency matrix
of rank r+2 and order 2n+2 from one of rank r and order n (see [1, 2]), and if
we are not forced to have a zero diagonal we can ﬁnd even more. For example

 

A A 0 0
A A 1 0
0> 1> 1 1
0> 0> 1 0

 

also works. So there will be many symmetric (0,1)-matrices that meet the
bound given in the above lemma.
3 Square (0,1)-matrices
In order to construct large (0,1)-matrices of rank r, we consider two sets X and
Y of vectors in V = {0,1}r. Each vector v ∈ V , is partitioned into two parts:
v =

v1
v2

,
where v1 ∈ {0,1}k and v2 ∈ {0,1}r−k for some k ∈ {0,...,r}. The set X
consists of all nonzero vectors in V for which v2 = 0, together with the vectors
for which v1 = 0 and the weight1 of v2 equals 1. The set Y consists of all
nonzero vectors in V for which the weight of v1 is at most 1. Thus we have
|X| = 2k + r − k − 1 and |Y | = 2r−k(k + 1) − 1. Let M and N be the matrices
whose columns are the vectors from X and Y , respectively. Deﬁne A = M>N,
then clearly A is a (0,1)-matrix. Note that both M and N contain r unit
columns, so we can write
M = [I M1] , N = [I N1] , A =

I N1
M>
1 M>
1 N1

.
Therefore A is a reduced matrix with rank r, and A contains a reduced square
submatrix of order n = min{|X|,|Y |} with rank r. If we choose k such that
n is as large as possible, then we obtain the following values of k and n for
r = 1,...,10.
r 1 2 3 4 5 6 7 8 9 10
k 1 1 2 3 3 4 5 5 6 6
n 1 2 4 7 9 17 23 34 55 67
Note that for r = 5, the above construction gives n = 9, whilst in the previous
section we found a symmetric example with n = 10. So we can conclude that
n(5) ≥ 10. For general r, the above construction leads to the following bound.
1the weight of a vector is the number of nonzero coordinates
3Theorem 2 The maximum order n(r) of a reduced square (0,1)-matrix with
rank r is at least
2
r−3
2 √
r.
Proof. Assume r ≥ 2, and take k such that k ≥ 22k−r and k + 1 < 22(k+1)−r.
Then the ﬁrst inequality gives |X| ≤ |Y | and k < r, therefore n = |X| =
2k +r−k−1 ≥ 2k. The second inequality gives k > 1
2r+ 1
2 log2(k+1)−1, and
k + 1 > 1
2r. This implies
n ≥ 2k > 2
r
2+ 1
2 log2(k+1)−1 > 2
r−3
2 √
r. 
References
[1] S. Akbari, P.J. Cameron and G.B. Khosrovshahi, Ranks and signatures of adja-
cency matrices, http://www.maths.qmw.ac.uk/∼pjc/preprints/ranksign.pdf.
[2] W.H. Haemers and M.J.P. Peeters, The maximum order of adjacency matrices of
graphs with a given rank, Designs, Codes and Cryptography (in press).
[3] A. Kotlov and L. Lov´ asz, The rank and size of graphs, J. Graph Theory 23 (1996),
185-189.
4

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