def ldl(G):
"""
Compute the LDL decomposition of G.
Input:
G A self-adjoint matrix (i.e. G is equal to its conjugate transpose)
Output:
L, D The LDL decomposition of G, that is G = L * D * (L*), where:
- L is lower triangular with a diagonal of 1's
- D is diagonal
Format: Coefficient
"""
deg = len(G[0][0])
dim = len(G)
L = [[[0 for k in range(deg)] for j in range(dim)] for i in range(dim)]
D = [[[0 for k in range(deg)] for j in range(dim)] for i in range(dim)]
for i in range(dim):
L[i][i] = [1] + [0 for j in range(deg - 1)]
D[i][i] = G[i][i]
for j in range(i):
L[i][j] = G[i][j]
for k in range(j):
L[i][j] = sub(L[i][j], mul(mul(L[i][k], adj(L[j][k])),
D[k][k]))
L[i][j] = div(L[i][j], D[j][j])
D[i][i] = sub(D[i][i], mul(mul(L[i][j], adj(L[i][j])), D[j][j]))
return [L, D]
def ffldl(G):
"""Compute the ffLDL decomposition tree of G.
Args:
G: a Gram matrix
Format: coefficient
Corresponds to algorithm 9 (ffLDL) of Falcon's documentation,
except it's in polynomial representation.
"""
n = len(G[0][0])
L, D = ldl(G)
# Coefficients of L, D are elements of R[x]/(x^n - x^(n/2) + 1), in coefficient representation
if (n > 2):
# A bisection is done on elements of a 2*2 diagonal matrix.
d00, d01 = split(D[0][0])
d10, d11 = split(D[1][1])
G0 = [[d00, d01], [adj(d01), d00]]
G1 = [[d10, d11], [adj(d11), d10]]
return [L[1][0], ffldl(G0), ffldl(G1)]
elif (n == 2):
# Bottom of the recursion.
D[0][0][1] = 0
D[1][1][1] = 0
return [L[1][0], D[0][0], D[1][1]]
def ldl(G):
"""Compute the LDL decomposition of G.
Args:
G: a Gram matrix
Format: coefficient
Corresponds to algorithm 14 (LDL) of Falcon's documentation,
except it's in polynomial representation.
"""
deg = len(G[0][0])
dim = len(G)
L = [[[0 for k in range(deg)] for j in range(dim)] for i in range(dim)]
D = [[[0 for k in range(deg)] for j in range(dim)] for i in range(dim)]
for i in range(dim):
L[i][i] = [1] + [0 for j in range(deg - 1)]
D[i][i] = G[i][i]
for j in range(i):
L[i][j] = G[i][j]
for k in range(j):
L[i][j] = sub(L[i][j], mul(mul(L[i][k], adj(L[j][k])),
D[k][k]))
L[i][j] = div(L[i][j], D[j][j])
D[i][i] = sub(D[i][i], mul(mul(L[i][j], adj(L[i][j])), D[j][j]))
return [L, D]
def gs_norm(f, g, q):
"""Compute the squared Gram-Schmidt norm of the NTRU matrix generated by f, g.
This matrix is [[g, - f], [G, - F]].
This algorithm is equivalent to line 9 of algorithm 5 (NTRUGen).
"""
sqnorm_fg = sqnorm([f, g])
ffgg = add(mul(f, adj(f)), mul(g, adj(g)))
Ft = div(adj(g), ffgg)
Gt = div(adj(f), ffgg)
sqnorm_FG = (q**2) * sqnorm([Ft, Gt])
return max(sqnorm_fg, sqnorm_FG)
def ldl(G):
"""
Compute the LDL decomposition of G. Only works with 2 * 2 matrices.
Args:
G: a Gram matrix
Format: coefficient
Corresponds to algorithm 8 (LDL*) of Falcon's documentation,
except it's in polynomial representation.
"""
deg = len(G[0][0])
dim = len(G)
assert (dim == 2)
assert (dim == len(G[0]))
zero = [0] * deg
one = [1] + [0] * (deg - 1)
D00 = G[0][0][:]
L10 = div(G[1][0], G[0][0])
D11 = sub(G[1][1], mul(mul(L10, adj(L10)), G[0][0]))
L = [[one, zero], [L10, one]]
D = [[D00, zero], [zero, D11]]
return [L, D]
def ffldl(G):
"""
Compute the ffLDL decomposition tree of G.
Input:
G A Gram matrix
Output:
T The ffLDL decomposition tree of G
Format: Coefficient
Similar to algorithm ffLDL of Falcon's documentation,
except it's in polynomial representation.
"""
m = len(G) - 1
d = len(G[0][0])
# LDL decomposition
L, D = ldl(G)
# General case
if (d > 2):
rep = [L]
for i in range(m + 1):
# Split the output
d0, d1 = split(D[i][i])
Gi = [[d0, d1], [adj(d1), d0]]
# Recursive call on the split parts
rep += [ffldl(Gi)]
return rep
# Bottom case
elif (d == 2):
D[0][0][1] = 0
D[1][1][1] = 0
return [L, D[0][0], D[1][1]]
def gram(B):
"""Compute the Gram matrix of B.
Args:
B: a matrix
Format: coefficient
"""
rows = range(len(B))
ncols = len(B[0])
deg = len(B[0][0])
G = [[[0 for coef in range(deg)] for j in rows] for i in rows]
for i in rows:
for j in rows:
for k in range(ncols):
G[i][j] = add(G[i][j], mul(B[i][k], adj(B[j][k])))
return G
def gram(B):
"""
Compute the Gram matrix of B.
Input:
B A matrix
Output:
G The Gram matrix of B: G = B * (B*)
Format: Coefficient
"""
rows = range(len(B))
ncols = len(B[0])
deg = len(B[0][0])
G = [[[0 for coef in range(deg)] for j in rows] for i in rows]
for i in rows:
for j in rows:
for k in range(ncols):
G[i][j] = add(G[i][j], mul(B[i][k], adj(B[j][k])))
return G
