def _representation_matrix(self):
r"""
Returns the matrix used to represents elements of the absolute field
as vectors in the basis of the relative field over the prime field.
EXAMPLES::
sage: from sage.coding.relative_finite_field_extension import *
sage: Fqm.<aa> = GF(16)
sage: Fq.<a> = GF(4)
sage: FE = RelativeFiniteFieldExtension(Fqm, Fq)
sage: FE._representation_matrix()
[1 0 0 0]
[0 0 1 1]
[0 1 1 1]
[0 0 0 1]
"""
s = self.relative_field_degree()
m = self.extension_degree()
betas = self.absolute_field_basis()
phi_alphas = [self._phi(self._alphas[i]) for i in range(s)]
A = column_matrix([
vector(betas[i] * phi_alphas[j]) for i in range(m)
for j in range(s)
])
return A.inverse()
def _gram_schmidt(m, fixed_vector_index, inner_product):
r"""
Orthogonalize a set of vectors, starting at a fixed vector, with respect to a given
inner product.
INPUT:
- ``m`` -- a square matrix whose columns represent vectors
- ``fixed_vector_index`` -- any vectors preceding the vector (i.e. to its left)
at this index are not changed.
- ``inner_product`` - a function that takes two vector arguments and returns a scalar,
representing an inner product.
OUTPUT:
- A matrix consisting of orthogonal columns with respect to the given inner product
EXAMPLES::
sage: from sage.quadratic_forms.quadratic_form__equivalence_testing import _gram_schmidt
sage: Q = QuadraticForm(QQ, 3, [1, 2, 2, 2, 1, 3]); Q
Quadratic form in 3 variables over Rational Field with coefficients:
[ 1 2 2 ]
[ * 2 1 ]
[ * * 3 ]
sage: QM = Q.Gram_matrix(); QM
[ 1 1 1]
[ 1 2 1/2]
[ 1 1/2 3]
sage: std_basis = matrix.identity(3)
sage: ortho_basis = _gram_schmidt(std_basis, 0, Q.bilinear_map); ortho_basis
[ 1 -1 -3/2]
[ 0 1 1/2]
[ 0 0 1]
sage: Q(ortho_basis).Gram_matrix_rational()
[ 1 0 0]
[ 0 1 0]
[ 0 0 7/4]
sage: v1 = ortho_basis.column(0); v2 = ortho_basis.column(1); v3 = ortho_basis.column(2);
sage: Q.bilinear_map(v1, v2) == 0
True
sage: Q.bilinear_map(v1, v3) == 0
True
sage: Q.bilinear_map(v2, v3) == 0
True
"""
from sage.matrix.constructor import column_matrix
n = m.dimensions()[0]
vectors = [0] * n
for i in range(n):
vectors[i] = m.column(i)
for i in range(fixed_vector_index, n):
for j in range(i + 1, n):
vectors[j] = vectors[j] - (
inner_product(vectors[j], vectors[i]) /
inner_product(vectors[i], vectors[i])) * vectors[i]
return column_matrix(vectors)
def _representation_matrix(self):
r"""
Returns the matrix used to represents elements of the absolute field
as vectors in the basis of the relative field over the prime field.
EXAMPLES::
sage: from sage.coding.relative_finite_field_extension import *
sage: Fqm.<aa> = GF(16)
sage: Fq.<a> = GF(4)
sage: FE = RelativeFiniteFieldExtension(Fqm, Fq)
sage: FE._representation_matrix()
[1 0 0 0]
[0 0 1 1]
[0 1 1 1]
[0 0 0 1]
"""
s = self.relative_field_degree()
m = self.extension_degree()
betas = self.absolute_field_basis()
phi_alphas = [ self._phi(self._alphas[i]) for i in range(s) ]
A = column_matrix([vector(betas[i] * phi_alphas[j])
for i in range(m) for j in range(s)])
return A.inverse()
def fan_2d_echelon_forms(fan):
"""
Return echelon forms of all cyclically ordered ray matrices.
Note that the echelon form of the ordered ray matrices are unique
up to different cyclic orderings.
INPUT:
- ``fan`` -- a fan.
OUTPUT:
A set of matrices. The set of all echelon forms for all different
cyclic orderings.
EXAMPLES::
sage: fan = toric_varieties.P2().fan()
sage: from sage.geometry.fan_isomorphism import fan_2d_echelon_forms
sage: fan_2d_echelon_forms(fan)
frozenset({[ 1 0 -1]
[ 0 1 -1]})
sage: fan = toric_varieties.dP7().fan()
sage: list(fan_2d_echelon_forms(fan))
[
[ 1 0 -1 0 1] [ 1 0 -1 -1 0] [ 1 0 -1 -1 1] [ 1 0 -1 -1 0]
[ 0 1 0 -1 -1], [ 0 1 1 0 -1], [ 0 1 1 0 -1], [ 0 1 0 -1 -1],
<BLANKLINE>
[ 1 0 -1 0 1]
[ 0 1 1 -1 -1]
]
TESTS::
sage: rays = [(1, 1), (-1, -1), (-1, 1), (1, -1)]
sage: cones = [(0,2), (2,1), (1,3), (3,0)]
sage: fan1 = Fan(cones, rays)
sage: from sage.geometry.fan_isomorphism import fan_2d_echelon_form, fan_2d_echelon_forms
sage: echelon_forms = fan_2d_echelon_forms(fan1)
sage: S4 = CyclicPermutationGroup(4)
sage: rays.reverse()
sage: cones = [(3,1), (1,2), (2,0), (0,3)]
sage: for i in range(100):
... m = random_matrix(ZZ,2,2)
... if abs(det(m)) != 1: continue
... perm = S4.random_element()
... perm_cones = [ (perm(c[0]+1)-1, perm(c[1]+1)-1) for c in cones ]
... perm_rays = [ rays[perm(i+1)-1] for i in range(len(rays)) ]
... fan2 = Fan(perm_cones, rays=[m*vector(r) for r in perm_rays])
... assert fan_2d_echelon_form(fan2) in echelon_forms
"""
if fan.nrays() == 0:
return frozenset()
rays = list(fan_2d_cyclically_ordered_rays(fan))
echelon_forms = []
for i in range(2):
for j in range(len(rays)):
echelon_forms.append(column_matrix(rays).echelon_form())
first = rays.pop(0)
rays.append(first)
rays.reverse()
return frozenset(echelon_forms)
def fan_2d_echelon_forms(fan):
"""
Return echelon forms of all cyclically ordered ray matrices.
Note that the echelon form of the ordered ray matrices are unique
up to different cyclic orderings.
INPUT:
- ``fan`` -- a fan.
OUTPUT:
A set of matrices. The set of all echelon forms for all different
cyclic orderings.
EXAMPLES::
sage: fan = toric_varieties.P2().fan()
sage: from sage.geometry.fan_isomorphism import fan_2d_echelon_forms
sage: fan_2d_echelon_forms(fan)
frozenset([[ 1 0 -1]
[ 0 1 -1]])
sage: fan = toric_varieties.dP7().fan()
sage: list(fan_2d_echelon_forms(fan))
[
[ 1 0 -1 0 1] [ 1 0 -1 -1 0] [ 1 0 -1 -1 1] [ 1 0 -1 -1 0]
[ 0 1 0 -1 -1], [ 0 1 1 0 -1], [ 0 1 1 0 -1], [ 0 1 0 -1 -1],
<BLANKLINE>
[ 1 0 -1 0 1]
[ 0 1 1 -1 -1]
]
TESTS::
sage: rays = [(1, 1), (-1, -1), (-1, 1), (1, -1)]
sage: cones = [(0,2), (2,1), (1,3), (3,0)]
sage: fan1 = Fan(cones, rays)
sage: from sage.geometry.fan_isomorphism import fan_2d_echelon_form, fan_2d_echelon_forms
sage: echelon_forms = fan_2d_echelon_forms(fan1)
sage: S4 = CyclicPermutationGroup(4)
sage: rays.reverse()
sage: cones = [(3,1), (1,2), (2,0), (0,3)]
sage: for i in range(100):
... m = random_matrix(ZZ,2,2)
... if abs(det(m)) != 1: continue
... perm = S4.random_element()
... perm_cones = [ (perm(c[0]+1)-1, perm(c[1]+1)-1) for c in cones ]
... perm_rays = [ rays[perm(i+1)-1] for i in range(len(rays)) ]
... fan2 = Fan(perm_cones, rays=[m*vector(r) for r in perm_rays])
... assert fan_2d_echelon_form(fan2) in echelon_forms
"""
if fan.nrays() == 0:
return frozenset()
rays = list(fan_2d_cyclically_ordered_rays(fan))
echelon_forms = []
for i in range(2):
for j in range(len(rays)):
echelon_forms.append(column_matrix(rays).echelon_form())
first = rays.pop(0)
rays.append(first)
rays.reverse()
return frozenset(echelon_forms)
def bdd_height(K, height_bound, tolerance=1e-2, precision=53):
r"""
Compute all elements in the number field `K` which have relative
multiplicative height at most ``height_bound``.
The function can only be called for number fields `K` with positive unit
rank. An error will occur if `K` is `QQ` or an imaginary quadratic field.
This algorithm computes 2 lists: L containing elements x in `K` such that
H_k(x) <= B, and a list L' containing elements x in `K` that, due to
floating point issues,
may be slightly larger then the bound. This can be controlled
by lowering the tolerance.
In current implementation both lists (L,L') are merged and returned in
form of iterator.
ALGORITHM:
This is an implementation of the revised algorithm (Algorithm 4) in
[DK2013]_.
INPUT:
- ``height_bound`` -- real number
- ``tolerance`` -- (default: 0.01) a rational number in (0,1]
- ``precision`` -- (default: 53) positive integer
OUTPUT:
- an iterator of number field elements
EXAMPLES:
There are no elements of negative height::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = NumberField(x^5 - x + 7)
sage: list(bdd_height(K,-3))
[]
The only nonzero elements of height 1 are the roots of unity::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = QuadraticField(3)
sage: list(bdd_height(K,1))
[0, -1, 1]
::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = QuadraticField(36865)
sage: len(list(bdd_height(K,101))) # long time (4 s)
131
::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = NumberField(x^6 + 2)
sage: len(list(bdd_height(K,60))) # long time (5 s)
1899
::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = NumberField(x^4 - x^3 - 3*x^2 + x + 1)
sage: len(list(bdd_height(K,10)))
99
TESTS:
Check that :trac:`22771` is fixed::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<v> = NumberField(x^3 + x + 1)
sage: len(list(bdd_height(K,3)))
23
"""
# global values, used in internal function
B = height_bound
theta = tolerance
if B < 1:
return
embeddings = K.places(prec=precision)
O_K = K.ring_of_integers()
r1, r2 = K.signature()
r = r1 + r2 - 1
RF = RealField(precision)
lambda_gens_approx = {}
class_group_rep_norm_log_approx = []
unit_log_dict = {}
def rational_in(x, y):
r"""
Compute a rational number q, such that x<q<y using Archimedes' axiom
"""
z = y - x
if z == 0:
n = 1
else:
n = RR(1/z).ceil() + 1
if RR(n*y).ceil() is n*y: # WHAT !?
m = n*y - 1
else:
m = RR(n*y).floor()
return m / n
def delta_approximation(x, delta):
r"""
Compute a rational number in range (x-delta, x+delta)
"""
return rational_in(x - delta, x + delta)
def vector_delta_approximation(v, delta):
r"""
Compute a rational vector w=(w1, ..., wn)
such that |vi-wi|<delta for all i in [1, n]
"""
return [delta_approximation(vi, delta) for vi in v]
def log_map(number):
r"""
Compute the image of an element of `K` under the logarithmic map.
"""
x = number
x_logs = []
for i in range(r1):
sigma = embeddings[i] # real embeddings
x_logs.append(sigma(x).abs().log())
for i in range(r1, r + 1):
tau = embeddings[i] # Complex embeddings
x_logs.append(2 * tau(x).abs().log())
return vector(x_logs)
def log_height_for_generators_approx(alpha, beta, Lambda):
r"""
Compute the rational approximation of logarithmic height function.
Return a lambda approximation h_K(alpha/beta)
"""
delta = Lambda / (r + 2)
norm_log = delta_approximation(RR(O_K.ideal(alpha, beta).norm()).log(), delta)
log_ga = vector_delta_approximation(log_map(alpha), delta)
log_gb = vector_delta_approximation(log_map(beta), delta)
arch_sum = sum([max(log_ga[k], log_gb[k]) for k in range(r + 1)])
return (arch_sum - norm_log)
def packet_height(n, pair, u):
r"""
Compute the height of the element of `K` encoded by a given packet.
"""
gens = generator_lists[n]
i = pair[0]
j = pair[1]
Log_gi = lambda_gens_approx[gens[i]]
Log_gj = lambda_gens_approx[gens[j]]
Log_u_gi = vector(Log_gi) + unit_log_dict[u]
arch_sum = sum([max(Log_u_gi[k], Log_gj[k]) for k in range(r + 1)])
return (arch_sum - class_group_rep_norm_log_approx[n])
# Step 1
# Computes ideal class representative and their rational approx norm
t = theta / (3*B)
delta_1 = t / (6*r+12)
class_group_reps = []
class_group_rep_norms = []
for c in K.class_group():
a = c.ideal()
a_norm = a.norm()
log_norm = RF(a_norm).log()
log_norm_approx = delta_approximation(log_norm, delta_1)
class_group_reps.append(a)
class_group_rep_norms.append(a_norm)
class_group_rep_norm_log_approx.append(log_norm_approx)
class_number = len(class_group_reps)
# Step 2
# Find generators for principal ideals of bounded norm
possible_norm_set = set([])
for n in range(class_number):
for m in range(1, (B + 1).ceil()):
possible_norm_set.add(m * class_group_rep_norms[n])
bdd_ideals = bdd_norm_pr_ideal_gens(K, possible_norm_set)
# Stores it in form of an dictionary and gives lambda(g)_approx for key g
for norm in possible_norm_set:
gens = bdd_ideals[norm]
for g in gens:
lambda_g_approx = vector_delta_approximation(log_map(g), delta_1)
lambda_gens_approx[g] = lambda_g_approx
# Step 3
# Find a list of all generators corresponding to each ideal a_l
generator_lists = []
for l in range(class_number):
this_ideal = class_group_reps[l]
this_ideal_norm = class_group_rep_norms[l]
gens = []
for i in range(1, (B + 1).ceil()):
for g in bdd_ideals[i * this_ideal_norm]:
if g in this_ideal:
gens.append(g)
generator_lists.append(gens)
# Step 4
# Finds all relevant pair and their height
gen_height_approx_dictionary = {}
relevant_pair_lists = []
for n in range(class_number):
relevant_pairs = []
gens = generator_lists[n]
l = len(gens)
for i in range(l):
for j in range(i+1, l):
if K.ideal(gens[i], gens[j]) == class_group_reps[n]:
relevant_pairs.append([i, j])
gen_height_approx_dictionary[(n, i, j)] = log_height_for_generators_approx(gens[i], gens[j], t/6)
relevant_pair_lists.append(relevant_pairs)
# Step 5
b = rational_in(t/12 + RR(B).log(), t/4 + RR(B).log())
maximum = 0
for n in range(class_number):
for p in relevant_pair_lists[n]:
maximum = max(maximum, gen_height_approx_dictionary[(n, p[0], p[1])])
d_tilde = b + t/6 + maximum
# Step 6
# computes fundamental units and their value under log map
fund_units = UnitGroup(K).fundamental_units()
fund_unit_logs = [log_map(fund_units[i]) for i in range(r)]
S = column_matrix(fund_unit_logs).delete_rows([r])
S_inverse = S.inverse()
S_norm = S.norm(Infinity)
S_inverse_norm = S_inverse.norm(Infinity)
upper_bound = (r**2) * max(S_norm, S_inverse_norm)
m = RR(upper_bound).ceil() + 1
# Step 7
# Variables needed for rational approximation
lambda_tilde = (t/12) / (d_tilde*r*(1+m))
delta_tilde = min(lambda_tilde/((r**2)*((m**2)+m*lambda_tilde)), 1/(r**2))
M = d_tilde * (upper_bound+lambda_tilde*RR(r).sqrt())
M = RR(M).ceil()
d_tilde = RR(d_tilde)
delta_2 = min(delta_tilde, (t/6)/(r*(r+1)*M))
# Step 8, 9
# Computes relevant points in polytope
fund_unit_log_approx = [vector_delta_approximation(fund_unit_logs[i], delta_2) for i in range(r)]
S_tilde = column_matrix(fund_unit_log_approx).delete_rows([r])
S_tilde_inverse = S_tilde.inverse()
U = integer_points_in_polytope(S_tilde_inverse, d_tilde)
# Step 10
# tilde suffixed list are used for computing second list (L_primed)
yield K(0)
U0 = []
U0_tilde = []
L0 = []
L0_tilde = []
# Step 11
# Computes unit height
unit_height_dict = {}
U_copy = copy(U)
inter_bound = b - (5*t)/12
for u in U:
u_log = sum([u[j]*vector(fund_unit_log_approx[j]) for j in range(r)])
unit_log_dict[u] = u_log
u_height = sum([max(u_log[k], 0) for k in range(r + 1)])
unit_height_dict[u] = u_height
if u_height < inter_bound:
U0.append(u)
if inter_bound <= u_height and u_height < b - (t/12):
U0_tilde.append(u)
if u_height > t/12 + d_tilde:
U_copy.remove(u)
U = U_copy
relevant_tuples = set(U0 + U0_tilde)
# Step 12
# check for relevant packets
for n in range(class_number):
for pair in relevant_pair_lists[n]:
i = pair[0]
j = pair[1]
u_height_bound = b + gen_height_approx_dictionary[(n, i, j)] + t/4
for u in U:
if unit_height_dict[u] < u_height_bound:
candidate_height = packet_height(n, pair, u)
if candidate_height <= b - 7*t/12:
L0.append([n, pair, u])
relevant_tuples.add(u)
elif candidate_height < b + t/4:
L0_tilde.append([n, pair, u])
relevant_tuples.add(u)
# Step 13
# forms a dictionary of all_unit_tuples and their value
tuple_to_unit_dict = {}
for u in relevant_tuples:
unit = K.one()
for k in range(r):
unit *= fund_units[k]**u[k]
tuple_to_unit_dict[u] = unit
# Step 14
# Build all output numbers
roots_of_unity = K.roots_of_unity()
for u in U0 + U0_tilde:
for zeta in roots_of_unity:
yield zeta * tuple_to_unit_dict[u]
# Step 15
for p in L0 + L0_tilde:
gens = generator_lists[p[0]]
i = p[1][0]
j = p[1][1]
u = p[2]
c_p = tuple_to_unit_dict[u] * (gens[i] / gens[j])
for zeta in roots_of_unity:
yield zeta * c_p
yield zeta / c_p
def bdd_height(K, height_bound, precision=53, LLL=False):
r"""
Computes all elements in the number number field `K` which have relative
multiplicative height at most ``height_bound``.
The algorithm requires arithmetic with floating point numbers;
``precision`` gives the user the option to set the precision for such
computations.
It might be helpful to work with an LLL-reduced system of fundamental
units, so the user has the option to perform an LLL reduction for the
fundamental units by setting ``LLL`` to True.
Certain computations may be faster assuming GRH, which may be done
globally by using the number_field(True/False) switch.
The function will only be called for number fields `K` with positive unit
rank. An error will occur if `K` is `QQ` or an imaginary quadratic field.
ALGORITHM:
This is an implementation of the main algorithm (Algorithm 3) in
[Doyle-Krumm].
INPUT:
- ``height_bound`` - real number
- ``precision`` - (default: 53) positive integer
- ``LLL`` - (default: False) boolean value
OUTPUT:
- an iterator of number field elements
.. WARNING::
In the current implementation, the output of the algorithm cannot be
guaranteed to be correct due to the necessity of floating point
computations. In some cases, the default 53-bit precision is
considerably lower than would be required for the algorithm to
generate correct output.
.. TODO::
Should implement a version of the algorithm that guarantees correct
output. See Algorithm 4 in [Doyle-Krumm] for details of an
implementation that takes precision issues into account.
EXAMPLES:
There are no elements of negative height::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = NumberField(x^5 - x + 7)
sage: list(bdd_height(K,-3))
[]
The only nonzero elements of height 1 are the roots of unity::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = QuadraticField(3)
sage: list(bdd_height(K,1))
[0, -1, 1]
::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = QuadraticField(36865)
sage: len(list(bdd_height(K,101))) # long time (4 s)
131
::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = NumberField(x^3 - 197*x + 39)
sage: len(list(bdd_height(K, 200))) # long time (5 s)
451
::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = NumberField(x^6 + 2)
sage: len(list(bdd_height(K,60,precision=100))) # long time (5 s)
1899
::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = NumberField(x^4 - x^3 - 3*x^2 + x + 1)
sage: len(list(bdd_height(K,10,LLL=true)))
99
"""
B = height_bound
r1, r2 = K.signature(); r = r1 + r2 -1
if B < 1:
return
yield K(0)
roots_of_unity = K.roots_of_unity()
if B == 1:
for zeta in roots_of_unity:
yield zeta
return
RF = RealField(precision)
embeddings = K.places(prec=precision)
logB = RF(B).log()
def log_map(number):
r"""
Computes the image of an element of `K` under the logarithmic map.
"""
x = number
x_logs = []
for i in range(r1):
sigma = embeddings[i]
x_logs.append(abs(sigma(x)).log())
for i in range(r1, r + 1):
tau = embeddings[i]
x_logs.append(2*abs(tau(x)).log())
return vector(x_logs)
def log_height_for_generators(n, i, j):
r"""
Computes the logarithmic height of elements of the form `g_i/g_j`.
"""
gen_logs = generator_logs[n]
Log_gi = gen_logs[i]; Log_gj = gen_logs[j]
arch_sum = sum([max(Log_gi[k], Log_gj[k]) for k in range(r + 1)])
return (arch_sum - class_group_rep_norm_logs[n])
def packet_height(n, pair, u):
r"""
Computes the height of the element of `K` encoded by a given packet.
"""
gen_logs = generator_logs[n]
i = pair[0] ; j = pair[1]
Log_gi = gen_logs[i]; Log_gj = gen_logs[j]
Log_u_gi = Log_gi + unit_log_dictionary[u]
arch_sum = sum([max(Log_u_gi[k], Log_gj[k]) for k in range(r + 1)])
return (arch_sum - class_group_rep_norm_logs[n])
class_group_reps = []
class_group_rep_norms = []
class_group_rep_norm_logs = []
for c in K.class_group():
a = c.ideal()
a_norm = a.norm()
class_group_reps.append(a)
class_group_rep_norms.append(a_norm)
class_group_rep_norm_logs.append(RF(a_norm).log())
class_number = len(class_group_reps)
# Get fundamental units and their images under the log map
fund_units = UnitGroup(K).fundamental_units()
fund_unit_logs = [log_map(fund_units[i]) for i in range(r)]
unit_prec_test = fund_unit_logs
try:
[l.change_ring(QQ) for l in unit_prec_test]
except ValueError:
raise ValueError('Precision too low.') # QQ(log(0)) may occur if precision too low
# If LLL is set to True, find an LLL-reduced system of fundamental units
if LLL:
cut_fund_unit_logs = column_matrix(fund_unit_logs).delete_rows([r])
lll_fund_units = []
for c in pari(cut_fund_unit_logs).qflll().sage().columns():
new_unit = 1
for i in range(r):
new_unit *= fund_units[i]**c[i]
lll_fund_units.append(new_unit)
fund_units = lll_fund_units
fund_unit_logs = [log_map(_) for _ in fund_units]
unit_prec_test = fund_unit_logs
try:
[l.change_ring(QQ) for l in unit_prec_test]
except ValueError:
raise ValueError('Precision too low.') # QQ(log(0)) may occur if precision too low
# Find generators for principal ideals of bounded norm
possible_norm_set = set([])
for n in range(class_number):
for m in range(1, B + 1):
possible_norm_set.add(m*class_group_rep_norms[n])
bdd_ideals = bdd_norm_pr_ideal_gens(K, possible_norm_set)
# Distribute the principal ideal generators
generator_lists = []
generator_logs = []
for n in range(class_number):
this_ideal = class_group_reps[n]
this_ideal_norm = class_group_rep_norms[n]
gens = []
gen_logs = []
for i in range(1, B + 1):
for g in bdd_ideals[i*this_ideal_norm]:
if g in this_ideal:
gens.append(g)
gen_logs.append(log_map(g))
generator_lists.append(gens)
generator_logs.append(gen_logs)
# Compute the lists of relevant pairs and corresponding heights
gen_height_dictionary = dict()
relevant_pair_lists = []
for n in range(class_number):
relevant_pairs = []
gens = generator_lists[n]
s = len(gens)
for i in range(s):
for j in range(i + 1, s):
if K.ideal(gens[i], gens[j]) == class_group_reps[n]:
relevant_pairs.append([i, j])
gen_height_dictionary[(n, i, j)] = log_height_for_generators(n, i, j)
relevant_pair_lists.append(relevant_pairs)
# Find the bound for units to be computed
gen_height_list = [gen_height_dictionary[x] for x in gen_height_dictionary.keys()]
if len(gen_height_list) == 0:
d = logB
else:
d = logB + max(gen_height_list)
# Create the matrix whose columns are the logs of the fundamental units
S = column_matrix(fund_unit_logs).delete_rows([r])
try:
T = S.inverse()
except ZeroDivisionError:
raise ValueError('Precision too low.')
# Find all integer lattice points in the unit polytope
U = integer_points_in_polytope(T, ceil(d))
U0 = []; L0 = []
# Compute unit heights
unit_height_dictionary = dict()
unit_log_dictionary = dict()
Ucopy = copy(U)
for u in U:
u_log = sum([u[j]*fund_unit_logs[j] for j in range(r)])
unit_log_dictionary[u] = u_log
u_height = sum([max(u_log[k], 0) for k in range(r + 1)])
unit_height_dictionary[u] = u_height
if u_height <= logB:
U0.append(u)
if u_height > d:
Ucopy.remove(u)
U = Ucopy
# Sort U by height
U = sorted(U, key=lambda u: unit_height_dictionary[u])
U_length = len(U)
all_unit_tuples = set(copy(U0))
# Check candidate heights
for n in range(class_number):
relevant_pairs = relevant_pair_lists[n]
for pair in relevant_pairs:
i = pair[0] ; j = pair[1]
gen_height = gen_height_dictionary[(n, i, j)]
u_height_bound = logB + gen_height
for k in range(U_length):
u = U[k]
u_height = unit_height_dictionary[u]
if u_height <= u_height_bound:
candidate_height = packet_height(n, pair, u)
if candidate_height <= logB:
L0.append([n, pair, u])
all_unit_tuples.add(u)
else:
break
# Use previous data to build all necessary units
units_dictionary = dict()
for u in all_unit_tuples:
unit = K(1)
for j in range(r):
unit *= (fund_units[j])**(u[j])
units_dictionary[u] = unit
# Build all the output numbers
for u in U0:
unit = units_dictionary[u]
for zeta in roots_of_unity:
yield zeta*unit
for packet in L0:
n = packet[0] ; pair = packet[1] ; u = packet[2]
i = pair[0] ; j = pair[1]
relevant_pairs = relevant_pair_lists[n]
gens = generator_lists[n]
unit = units_dictionary[u]
c = unit*gens[i]/gens[j]
for zeta in roots_of_unity:
yield zeta*c
yield zeta/c
def bdd_height(K, height_bound, precision=53, LLL=False):
r"""
Computes all elements in the number number field `K` which have relative
multiplicative height at most ``height_bound``.
The algorithm requires arithmetic with floating point numbers;
``precision`` gives the user the option to set the precision for such
computations.
It might be helpful to work with an LLL-reduced system of fundamental
units, so the user has the option to perform an LLL reduction for the
fundamental units by setting ``LLL`` to True.
Certain computations may be faster assuming GRH, which may be done
globally by using the number_field(True/False) switch.
The function will only be called for number fields `K` with positive unit
rank. An error will occur if `K` is `QQ` or an imaginary quadratic field.
ALGORITHM:
This is an implementation of the main algorithm (Algorithm 3) in
[Doyle-Krumm].
INPUT:
- ``height_bound`` - real number
- ``precision`` - (default: 53) positive integer
- ``LLL`` - (default: False) boolean value
OUTPUT:
- an iterator of number field elements
.. WARNING::
In the current implementation, the output of the algorithm cannot be
guaranteed to be correct due to the necessity of floating point
computations. In some cases, the default 53-bit precision is
considerably lower than would be required for the algorithm to
generate correct output.
.. TODO::
Should implement a version of the algorithm that guarantees correct
output. See Algorithm 4 in [Doyle-Krumm] for details of an
implementation that takes precision issues into account.
EXAMPLES:
There are no elements of negative height::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = NumberField(x^5 - x + 7)
sage: list(bdd_height(K,-3))
[]
The only nonzero elements of height 1 are the roots of unity::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = QuadraticField(3)
sage: list(bdd_height(K,1))
[0, -1, 1]
::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = QuadraticField(36865)
sage: len(list(bdd_height(K,101))) # long time (4 s)
131
::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = NumberField(x^3 - 197*x + 39)
sage: len(list(bdd_height(K, 200))) # long time (5 s)
451
::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = NumberField(x^6 + 2)
sage: len(list(bdd_height(K,60,precision=100))) # long time (5 s)
1899
::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = NumberField(x^4 - x^3 - 3*x^2 + x + 1)
sage: len(list(bdd_height(K,10,LLL=true)))
99
"""
B = height_bound
r1, r2 = K.signature(); r = r1 + r2 -1
if B < 1:
return
yield K(0)
roots_of_unity = K.roots_of_unity()
if B == 1:
for zeta in roots_of_unity:
yield zeta
return
RF = RealField(precision)
embeddings = K.places(prec=precision)
logB = RF(B).log()
def log_map(number):
r"""
Computes the image of an element of `K` under the logarithmic map.
"""
x = number
x_logs = []
for i in xrange(r1):
sigma = embeddings[i]
x_logs.append(abs(sigma(x)).log())
for i in xrange(r1, r + 1):
tau = embeddings[i]
x_logs.append(2*abs(tau(x)).log())
return vector(x_logs)
def log_height_for_generators(n, i, j):
r"""
Computes the logarithmic height of elements of the form `g_i/g_j`.
"""
gen_logs = generator_logs[n]
Log_gi = gen_logs[i]; Log_gj = gen_logs[j]
arch_sum = sum([max(Log_gi[k], Log_gj[k]) for k in range(r + 1)])
return (arch_sum - class_group_rep_norm_logs[n])
def packet_height(n, pair, u):
r"""
Computes the height of the element of `K` encoded by a given packet.
"""
gen_logs = generator_logs[n]
i = pair[0] ; j = pair[1]
Log_gi = gen_logs[i]; Log_gj = gen_logs[j]
Log_u_gi = Log_gi + unit_log_dictionary[u]
arch_sum = sum([max(Log_u_gi[k], Log_gj[k]) for k in range(r + 1)])
return (arch_sum - class_group_rep_norm_logs[n])
class_group_reps = []
class_group_rep_norms = []
class_group_rep_norm_logs = []
for c in K.class_group():
a = c.ideal()
a_norm = a.norm()
class_group_reps.append(a)
class_group_rep_norms.append(a_norm)
class_group_rep_norm_logs.append(RF(a_norm).log())
class_number = len(class_group_reps)
# Get fundamental units and their images under the log map
fund_units = UnitGroup(K).fundamental_units()
fund_unit_logs = [log_map(fund_units[i]) for i in range(r)]
unit_prec_test = fund_unit_logs
try:
[l.change_ring(QQ) for l in unit_prec_test]
except ValueError:
raise ValueError('Precision too low.') # QQ(log(0)) may occur if precision too low
# If LLL is set to True, find an LLL-reduced system of fundamental units
if LLL:
cut_fund_unit_logs = column_matrix(fund_unit_logs).delete_rows([r])
lll_fund_units = []
for c in pari(cut_fund_unit_logs).qflll().python().columns():
new_unit = 1
for i in xrange(r):
new_unit *= fund_units[i]**c[i]
lll_fund_units.append(new_unit)
fund_units = lll_fund_units
fund_unit_logs = map(log_map, fund_units)
unit_prec_test = fund_unit_logs
try:
[l.change_ring(QQ) for l in unit_prec_test]
except ValueError:
raise ValueError('Precision too low.') # QQ(log(0)) may occur if precision too low
# Find generators for principal ideals of bounded norm
possible_norm_set = set([])
for n in xrange(class_number):
for m in xrange(1, B + 1):
possible_norm_set.add(m*class_group_rep_norms[n])
bdd_ideals = bdd_norm_pr_ideal_gens(K, possible_norm_set)
# Distribute the principal ideal generators
generator_lists = []
generator_logs = []
for n in xrange(class_number):
this_ideal = class_group_reps[n]
this_ideal_norm = class_group_rep_norms[n]
gens = []
gen_logs = []
for i in xrange(1, B + 1):
for g in bdd_ideals[i*this_ideal_norm]:
if g in this_ideal:
gens.append(g)
gen_logs.append(log_map(g))
generator_lists.append(gens)
generator_logs.append(gen_logs)
# Compute the lists of relevant pairs and corresponding heights
gen_height_dictionary = dict()
relevant_pair_lists = []
for n in xrange(class_number):
relevant_pairs = []
gens = generator_lists[n]
s = len(gens)
for i in xrange(s):
for j in xrange(i + 1, s):
if K.ideal(gens[i], gens[j]) == class_group_reps[n]:
relevant_pairs.append([i, j])
gen_height_dictionary[(n, i, j)] = log_height_for_generators(n, i, j)
relevant_pair_lists.append(relevant_pairs)
# Find the bound for units to be computed
gen_height_list = [gen_height_dictionary[x] for x in gen_height_dictionary.keys()]
if len(gen_height_list) == 0:
d = logB
else:
d = logB + max(gen_height_list)
# Create the matrix whose columns are the logs of the fundamental units
S = column_matrix(fund_unit_logs).delete_rows([r])
try:
T = S.inverse()
except ZeroDivisionError:
raise ValueError('Precision too low.')
# Find all integer lattice points in the unit polytope
U = integer_points_in_polytope(T, ceil(d))
U0 = []; L0 = []
# Compute unit heights
unit_height_dictionary = dict()
unit_log_dictionary = dict()
Ucopy = copy(U)
for u in U:
u_log = sum([u[j]*fund_unit_logs[j] for j in range(r)])
unit_log_dictionary[u] = u_log
u_height = sum([max(u_log[k], 0) for k in range(r + 1)])
unit_height_dictionary[u] = u_height
if u_height <= logB:
U0.append(u)
if u_height > d:
Ucopy.remove(u)
U = Ucopy
# Sort U by height
U = sorted(U, key=lambda u: unit_height_dictionary[u])
U_length = len(U)
all_unit_tuples = set(copy(U0))
# Check candidate heights
for n in xrange(class_number):
relevant_pairs = relevant_pair_lists[n]
for pair in relevant_pairs:
i = pair[0] ; j = pair[1]
gen_height = gen_height_dictionary[(n, i, j)]
u_height_bound = logB + gen_height
for k in xrange(U_length):
u = U[k]
u_height = unit_height_dictionary[u]
if u_height <= u_height_bound:
candidate_height = packet_height(n, pair, u)
if candidate_height <= logB:
L0.append([n, pair, u])
all_unit_tuples.add(u)
else:
break
# Use previous data to build all necessary units
units_dictionary = dict()
for u in all_unit_tuples:
unit = K(1)
for j in xrange(r):
unit *= (fund_units[j])**(u[j])
units_dictionary[u] = unit
# Build all the output numbers
for u in U0:
unit = units_dictionary[u]
for zeta in roots_of_unity:
yield zeta*unit
for packet in L0:
n = packet[0] ; pair = packet[1] ; u = packet[2]
i = pair[0] ; j = pair[1]
relevant_pairs = relevant_pair_lists[n]
gens = generator_lists[n]
unit = units_dictionary[u]
c = unit*gens[i]/gens[j]
for zeta in roots_of_unity:
yield zeta*c
yield zeta/c
