def _initialize_fat_horizontal(self, s, L):
"""
Initialise reduction matrices for horizontal reductions for blocks from `s` to `L`.
TESTS::
sage: p = 4999
sage: x = PolynomialRing(GF(p),"x").gen()
sage: C = CyclicCover(3, x^4 + 4*x^3 + 9*x^2 + 3*x + 1)
sage: C._init_frob()
sage: C._initialize_fat_horizontal(p, 3)
sage: len(C._horizontal_fat_s[p])
3
"""
assert self._sqrtp
if s not in self._horizontal_fat_s:
N = self._N - 1 # padic precision of self._Zq0
d = self._d
L0 = min(L, N)
targets = [0] * (2 * L0)
for l in range(L0):
targets[2 * l] = self._p * l
targets[2 * l + 1] = self._p * (l + 1) - d - 1
(m0, m1), (M0, M1) = self._horizontal_matrix_reduction(s)
M0, M1 = [elt.change_ring(self._Zq0) for elt in [M0, M1]]
D0, D1 = [matrix(self._Zq0, [elt]) for elt in [m0, m1]]
MH = interval_products(M0, M1, targets)
DH = [elt[0, 0] for elt in interval_products(D0, D1, targets)]
if L > N: # Vandermonde interpolation
# f^{(r)}(0) p^r / r! for r = 0, ..., N-1,
MT = [None] * N
DT = [0] * N
for r in range(N):
MT[r] = matrix(self._Zq0, d, d)
for h in range(N):
v = self._vandermonde[r, h]
for i in range(d):
for j in range(d):
MT[r][i, j] += v * MH[h][i, j]
DT[r] += v * DH[h]
for k in range(N, L):
M = matrix(self._Zq0, d, d)
D = 0
k1pow = self._Zq0(1) # power of k + 1
for h in range(N):
for i in range(d):
for j in range(d):
M[i, j] += k1pow * MT[h][i, j]
D += k1pow * DT[h]
k1pow *= k + 1
MH.append(M)
DH.append(D)
iDH = [1 / elt for elt in DH]
self._horizontal_fat_s[s] = [(iDH[i], MH[i]) for i in range(L)]
assert len(self._horizontal_fat_s[s]) >= L
def _initialize_fat_vertical(self, s0, max_upper_target):
"""
Initialise reduction matrices for vertical reductions for blocks from `s0` to `s0 + max_upper_target`.
TESTS::
sage: p = 4999
sage: x = PolynomialRing(GF(p),"x").gen()
sage: C = CyclicCover(3, x^4 + 4*x^3 + 9*x^2 + 3*x + 1)
sage: C._init_frob()
sage: C._initialize_fat_vertical(1, p + p // 3)
sage: len(C._vertical_fat_s[1])
2
"""
L = floor((max_upper_target - self._epsilon) / self._p) + 1
if s0 not in self._vertical_fat_s:
(m0, m1), (M0, M1) = self._vertical_matrix_reduction(s0)
D0, D1 = map(lambda y: matrix(self._Zq, [y]), [m0, m1])
targets = [0] * (2 * L)
for l in reversed(range(L)):
targets[2 * l] = max_upper_target - self._p * (L - l)
targets[2 * l + 1] = max_upper_target - self._p * (L - 1 - l)
if targets[0] < 0:
targets[0] = self._epsilon
MV = interval_products(M0, M1, targets)
DV = interval_products(D0, D1, targets)
for l in range(L):
D = DV[l][0, 0]
if D % self._p == 0:
iD = 1 / self._Zq(D.lift() / self._p)
MV[l] = matrix(
self._Zq,
[
[iD * ZZ(elt.lift() / self._p) for elt in row]
for row in MV[l].rows()
],
)
else:
MV[l] *= 1 / D
self._vertical_fat_s[s0] = MV
assert len(self._vertical_fat_s[s0]) >= L
