I\'d like to take the modular inverse of a matrix like [[1,2],[3,4]] mod 7 in Python. I\'ve looked at numpy (which does matrix inversion but not modular matrix inversion) an
A hackish trick which works when rounding errors aren't an issue:
A less hackish way is to actually implement gaussian elimination. Here's my code using Gaussian elimination, which I wrote for my own purposes (rounding errors were an issue for me). q is the modulus, which is not necessarily prime.
def generalizedEuclidianAlgorithm(a, b):
if b > a:
return generalizedEuclidianAlgorithm(b,a);
elif b == 0:
return (1, 0);
else:
(x, y) = generalizedEuclidianAlgorithm(b, a % b);
return (y, x - (a / b) * y)
def inversemodp(a, p):
a = a % p
if (a == 0):
print "a is 0 mod p"
return None
if a > 1 and p % a == 0:
return None
(x,y) = generalizedEuclidianAlgorithm(p, a % p);
inv = y % p
assert (inv * a) % p == 1
return inv
def identitymatrix(n):
return [[long(x == y) for x in range(0, n)] for y in range(0, n)]
def inversematrix(matrix, q):
n = len(matrix)
A = np.matrix([[ matrix[j, i] for i in range(0,n)] for j in range(0, n)], dtype = long)
Ainv = np.matrix(identitymatrix(n), dtype = long)
for i in range(0, n):
factor = inversemodp(A[i,i], q)
if factor is None:
raise ValueError("TODO: deal with this case")
A[i] = A[i] * factor % q
Ainv[i] = Ainv[i] * factor % q
for j in range(0, n):
if (i != j):
factor = A[j, i]
A[j] = (A[j] - factor * A[i]) % q
Ainv[j] = (Ainv[j] - factor * Ainv[i]) % q
return Ainv
EDIT: as commenters point out, there are some cases this algorithm fails. It's slightly nontrivial to fix, and I don't have time nowadays. Back then it worked for random matrices in my case (the moduli were products of large primes). Basically, the first non-zero entry might not be relatively prime to the modulus. The prime case is easy since you can search for a different row and swap. In the non-prime case, I think it could be that all leading entries aren't relatively prime so you have to combine them