Unimodular matrices with additional restrictions

Daniel L
137 ●4 ●9 ●19

I'd like to generate some unimodular matrices over ZZ with some restrictions:

the entries are between -2 and 2
the matrix is symmetric: $A = A^t$

Item (1) above can be addressed with the option upper_bound. Simply searching for matrices satisfying (1) and (2),

for j in [1..1000]:
A = random_matrix(ZZ,3,3, algorithm = 'unimodular', upper_bound = 3, max_tries = 1000)
    if A == A.transpose():
        A

becomes extremely inefficient, especially as the dimension grows. Is there a way to enforce items (1),(2) within the unimodular algorithm? Or another workaround?

Edit: I originally also wanted the diagonal entries fixed at 2, but this is a bit too restrictive.

Comments

Are you sure that your restrictions are correct? For example, let $A=\begin{pmatrix} 2 & a & b \\ a & 2 & c \\ b & c & 2 \end{pmatrix},$ with $a,b,c\in\mathbb{Z}$ . Since $\det(A)= 8+2(abc-a^2-b^2-c^2),$ $A$ will be unimodular if and only if $a^2+b^2+c^2-abc=\frac{7}{2},$ which is impossible because $a^2+b^2+c^2-abc\in\mathbb{Z}$ .

Juanjo ( 5 years ago )

Good point. This certainly works if the dimension is 8 e.g. the $E_8$ root lattice gram matrix: E8 lattice wiki. I edited the post. Thank you

Daniel L ( 5 years ago )

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answered 5 years ago

Juanjo

3213 ●5 ●41 ●86

updated 5 years ago

I have adapted an idea I have found in Stackexchange. It is based on the following facts:

Let $J_k$ be the identity matrix of order $n$ with the null elements in the $k$ -th row replaced by random integers. Then $J_k$ is unimodular, i.e. $\lvert J_k\rvert=1$ .
If $A$ is an unimodular symmetric matrix, so it is $J_k^T A J_k$ .

Thus, one can start with $A=I_n$ , choose a row $k$ at random, construct $J_k$ , compute $J_k^T A J_k$ and replace $A$ by that matrix. This process can be repeated as many times as wanted. The result is an unimodular symmetric matrix. The following code implements this idea:

def unimod_sym_matrix(n, niter=1, min_int=-1, max_int=1):
    A = identity_matrix(n)
    rows = random_vector(niter, x=0, y=n)
    for k in rows:
        J = identity_matrix(n)
        J[k,:] = random_vector(n, x=min_int, y=max_int+1)
        J[k,k] = 1
        A = J.transpose()*A*J
    return A

Observe that, to get $J_k$ , random integers are taken from min_int to max_int, both included. Likewise, niter is the number of matrices $J_k$ that are constructed.

For example, the following code

set_random_seed(1000)
A = unimod_sym_matrix(6,3); show(A)
A = unimod_sym_matrix(6); show(A)

yields

$\begin{pmatrix} 2 & -2 & -1 & 0 & 1 & 0 \\ -2 & 9 & 4 & 1 & -3 & 3 \\ -1 & 4 & 2 & 0 & -1 & 2 \\ 0 & 1 & 0 & 2 & -1 & -1 \\ 1 & -3 & -1 & -1 & 2 & 1 \\ 0 & 3 & 2 & -1 & 1 & 6 \end{pmatrix}$

$\begin{pmatrix} 2 & 0 & 1 & -1 & -1 & -1 \\ 0 & 1 & 0 & 0 & 0 & 0 \\ 1 & 0 & 2 & -1 & -1 & -1 \\ -1 & 0 & -1 & 1 & 1 & 1 \\ -1 & 0 & -1 & 1 & 2 & 1 \\ -1 & 0 & -1 & 1 & 1 & 2 \end{pmatrix}$

I have included set_random_seed just for the sake of reproducibility of the above output.

Since you want matrix entries between $-2$ and $2$ , you had better use the default values for niter, min_int and max_int.

link

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