def dmp_rr_lcm(f, g, u, K):
"""
Computes polynomial LCM over a ring in `K[X]`.
Examples
========
>>> from sympy.polys.domains import ZZ
>>> from sympy.polys.euclidtools import dmp_rr_lcm
>>> f = ZZ.map([[1], [2, 0], [1, 0, 0]])
>>> g = ZZ.map([[1], [1, 0], []])
>>> dmp_rr_lcm(f, g, 1, ZZ)
[[1], [2, 0], [1, 0, 0], []]
"""
fc, f = dmp_ground_primitive(f, u, K)
gc, g = dmp_ground_primitive(g, u, K)
c = K.lcm(fc, gc)
h = dmp_quo(dmp_mul(f, g, u, K), dmp_gcd(f, g, u, K), u, K)
return dmp_mul_ground(h, c, u, K)
def dmp_rr_lcm(f, g, u, K):
"""
Computes polynomial LCM over a ring in ``K[X]``.
**Examples**
>>> from sympy.polys.domains import ZZ
>>> from sympy.polys.euclidtools import dmp_rr_lcm
>>> f = ZZ.map([[1], [2, 0], [1, 0, 0]])
>>> g = ZZ.map([[1], [1, 0], []])
>>> dmp_rr_lcm(f, g, 1, ZZ)
[[1], [2, 0], [1, 0, 0], []]
"""
fc, f = dmp_ground_primitive(f, u, K)
gc, g = dmp_ground_primitive(g, u, K)
c = K.lcm(fc, gc)
h = dmp_exquo(dmp_mul(f, g, u, K),
dmp_gcd(f, g, u, K), u, K)
return dmp_mul_ground(h, c, u, K)
def dmp_rr_lcm(f, g, u, K):
"""
Computes polynomial LCM over a ring in `K[X]`.
Examples
========
>>> from sympy.polys import ring, ZZ
>>> R, x,y, = ring("x,y", ZZ)
>>> f = x**2 + 2*x*y + y**2
>>> g = x**2 + x*y
>>> R.dmp_rr_lcm(f, g)
x**3 + 2*x**2*y + x*y**2
"""
fc, f = dmp_ground_primitive(f, u, K)
gc, g = dmp_ground_primitive(g, u, K)
c = K.lcm(fc, gc)
h = dmp_quo(dmp_mul(f, g, u, K),
dmp_gcd(f, g, u, K), u, K)
return dmp_mul_ground(h, c, u, K)
def dmp_rr_lcm(f, g, u, K):
"""
Computes polynomial LCM over a ring in `K[X]`.
Examples
========
>>> from sympy.polys import ring, ZZ
>>> R, x,y, = ring("x,y", ZZ)
>>> f = x**2 + 2*x*y + y**2
>>> g = x**2 + x*y
>>> R.dmp_rr_lcm(f, g)
x**3 + 2*x**2*y + x*y**2
"""
fc, f = dmp_ground_primitive(f, u, K)
gc, g = dmp_ground_primitive(g, u, K)
c = K.lcm(fc, gc)
h = dmp_quo(dmp_mul(f, g, u, K),
dmp_gcd(f, g, u, K), u, K)
return dmp_mul_ground(h, c, u, K)
def dmp_sqf_part(f, u, K):
"""
Returns square-free part of a polynomial in ``K[X]``.
Examples
========
>>> from sympy.polys.domains import ZZ
>>> from sympy.polys.sqfreetools import dmp_sqf_part
>>> f = ZZ.map([[1], [2, 0], [1, 0, 0], []])
>>> dmp_sqf_part(f, 1, ZZ)
[[1], [1, 0], []]
"""
if not u:
return dup_sqf_part(f, K)
if not K.has_CharacteristicZero:
return dmp_gf_sqf_part(f, u, K)
if dmp_zero_p(f, u):
return f
if K.is_negative(dmp_ground_LC(f, u, K)):
f = dmp_neg(f, u, K)
gcd = dmp_gcd(f, dmp_diff(f, 1, u, K), u, K)
sqf = dmp_quo(f, gcd, u, K)
if K.has_Field or not K.is_Exact:
return dmp_ground_monic(sqf, u, K)
else:
return dmp_ground_primitive(sqf, u, K)[1]
def dmp_sqf_part(f, u, K):
"""
Returns square-free part of a polynomial in ``K[X]``.
Examples
========
>>> from sympy.polys import ring, ZZ
>>> R, x,y = ring("x,y", ZZ)
>>> R.dmp_sqf_part(x**3 + 2*x**2*y + x*y**2)
x**2 + x*y
"""
if not u:
return dup_sqf_part(f, K)
if K.is_FiniteField:
return dmp_gf_sqf_part(f, u, K)
if dmp_zero_p(f, u):
return f
if K.is_negative(dmp_ground_LC(f, u, K)):
f = dmp_neg(f, u, K)
gcd = dmp_gcd(f, dmp_diff(f, 1, u, K), u, K)
sqf = dmp_quo(f, gcd, u, K)
if K.is_Field:
return dmp_ground_monic(sqf, u, K)
else:
return dmp_ground_primitive(sqf, u, K)[1]
def dmp_sqf_part(f, u, K):
"""
Returns square-free part of a polynomial in ``K[X]``.
Examples
========
>>> from sympy.polys import ring, ZZ
>>> R, x,y = ring("x,y", ZZ)
>>> R.dmp_sqf_part(x**3 + 2*x**2*y + x*y**2)
x**2 + x*y
"""
if not u:
return dup_sqf_part(f, K)
if K.is_FiniteField:
return dmp_gf_sqf_part(f, u, K)
if dmp_zero_p(f, u):
return f
if K.is_negative(dmp_ground_LC(f, u, K)):
f = dmp_neg(f, u, K)
gcd = dmp_gcd(f, dmp_diff(f, 1, u, K), u, K)
sqf = dmp_quo(f, gcd, u, K)
if K.has_Field:
return dmp_ground_monic(sqf, u, K)
else:
return dmp_ground_primitive(sqf, u, K)[1]
def dmp_sqf_part(f, u, K):
"""
Returns square-free part of a polynomial in ``K[X]``.
Examples
========
>>> from sympy.polys.domains import ZZ
>>> from sympy.polys.sqfreetools import dmp_sqf_part
>>> f = ZZ.map([[1], [2, 0], [1, 0, 0], []])
>>> dmp_sqf_part(f, 1, ZZ)
[[1], [1, 0], []]
"""
if not u:
return dup_sqf_part(f, K)
if not K.has_CharacteristicZero:
return dmp_gf_sqf_part(f, u, K)
if dmp_zero_p(f, u):
return f
if K.is_negative(dmp_ground_LC(f, u, K)):
f = dmp_neg(f, u, K)
gcd = dmp_gcd(f, dmp_diff(f, 1, u, K), u, K)
sqf = dmp_quo(f, gcd, u, K)
if K.has_Field or not K.is_Exact:
return dmp_ground_monic(sqf, u, K)
else:
return dmp_ground_primitive(sqf, u, K)[1]
def dmp_sqf_list(f, u, K, all=False):
"""
Return square-free decomposition of a polynomial in ``K[X]``.
Examples
========
>>> from sympy.polys.domains import ZZ
>>> from sympy.polys.sqfreetools import dmp_sqf_list
>>> f = ZZ.map([[1], [2, 0], [1, 0, 0], [], [], []])
>>> dmp_sqf_list(f, 1, ZZ)
(1, [([[1], [1, 0]], 2), ([[1], []], 3)])
>>> dmp_sqf_list(f, 1, ZZ, all=True)
(1, [([[1]], 1), ([[1], [1, 0]], 2), ([[1], []], 3)])
"""
if not u:
return dup_sqf_list(f, K, all=all)
if not K.has_CharacteristicZero:
return dmp_gf_sqf_list(f, u, K, all=all)
if K.has_Field or not K.is_Exact:
coeff = dmp_ground_LC(f, u, K)
f = dmp_ground_monic(f, u, K)
else:
coeff, f = dmp_ground_primitive(f, u, K)
if K.is_negative(dmp_ground_LC(f, u, K)):
f = dmp_neg(f, u, K)
coeff = -coeff
if dmp_degree(f, u) <= 0:
return coeff, []
result, i = [], 1
h = dmp_diff(f, 1, u, K)
g, p, q = dmp_inner_gcd(f, h, u, K)
while True:
d = dmp_diff(p, 1, u, K)
h = dmp_sub(q, d, u, K)
if dmp_zero_p(h, u):
result.append((p, i))
break
g, p, q = dmp_inner_gcd(p, h, u, K)
if all or dmp_degree(g, u) > 0:
result.append((g, i))
i += 1
return coeff, result
def dmp_sqf_list(f, u, K, all=False):
"""
Return square-free decomposition of a polynomial in ``K[X]``.
Examples
========
>>> from sympy.polys.domains import ZZ
>>> from sympy.polys.sqfreetools import dmp_sqf_list
>>> f = ZZ.map([[1], [2, 0], [1, 0, 0], [], [], []])
>>> dmp_sqf_list(f, 1, ZZ)
(1, [([[1], [1, 0]], 2), ([[1], []], 3)])
>>> dmp_sqf_list(f, 1, ZZ, all=True)
(1, [([[1]], 1), ([[1], [1, 0]], 2), ([[1], []], 3)])
"""
if not u:
return dup_sqf_list(f, K, all=all)
if not K.has_CharacteristicZero:
return dmp_gf_sqf_list(f, u, K, all=all)
if K.has_Field or not K.is_Exact:
coeff = dmp_ground_LC(f, u, K)
f = dmp_ground_monic(f, u, K)
else:
coeff, f = dmp_ground_primitive(f, u, K)
if K.is_negative(dmp_ground_LC(f, u, K)):
f = dmp_neg(f, u, K)
coeff = -coeff
if dmp_degree(f, u) <= 0:
return coeff, []
result, i = [], 1
h = dmp_diff(f, 1, u, K)
g, p, q = dmp_inner_gcd(f, h, u, K)
while True:
d = dmp_diff(p, 1, u, K)
h = dmp_sub(q, d, u, K)
if dmp_zero_p(h, u):
result.append((p, i))
break
g, p, q = dmp_inner_gcd(p, h, u, K)
if all or dmp_degree(g, u) > 0:
result.append((g, i))
i += 1
return coeff, result
def dmp_sqf_list(f, u, K, all=False):
"""
Return square-free decomposition of a polynomial in ``K[X]``.
Examples
========
>>> from sympy.polys import ring, ZZ
>>> R, x,y = ring("x,y", ZZ)
>>> f = x**5 + 2*x**4*y + x**3*y**2
>>> R.dmp_sqf_list(f)
(1, [(x + y, 2), (x, 3)])
>>> R.dmp_sqf_list(f, all=True)
(1, [(1, 1), (x + y, 2), (x, 3)])
"""
if not u:
return dup_sqf_list(f, K, all=all)
if K.is_FiniteField:
return dmp_gf_sqf_list(f, u, K, all=all)
if K.has_Field:
coeff = dmp_ground_LC(f, u, K)
f = dmp_ground_monic(f, u, K)
else:
coeff, f = dmp_ground_primitive(f, u, K)
if K.is_negative(dmp_ground_LC(f, u, K)):
f = dmp_neg(f, u, K)
coeff = -coeff
if dmp_degree(f, u) <= 0:
return coeff, []
result, i = [], 1
h = dmp_diff(f, 1, u, K)
g, p, q = dmp_inner_gcd(f, h, u, K)
while True:
d = dmp_diff(p, 1, u, K)
h = dmp_sub(q, d, u, K)
if dmp_zero_p(h, u):
result.append((p, i))
break
g, p, q = dmp_inner_gcd(p, h, u, K)
if all or dmp_degree(g, u) > 0:
result.append((g, i))
i += 1
return coeff, result
def dmp_sqf_list(f, u, K, all=False):
"""
Return square-free decomposition of a polynomial in ``K[X]``.
Examples
========
>>> from sympy.polys import ring, ZZ
>>> R, x,y = ring("x,y", ZZ)
>>> f = x**5 + 2*x**4*y + x**3*y**2
>>> R.dmp_sqf_list(f)
(1, [(x + y, 2), (x, 3)])
>>> R.dmp_sqf_list(f, all=True)
(1, [(1, 1), (x + y, 2), (x, 3)])
"""
if not u:
return dup_sqf_list(f, K, all=all)
if K.is_FiniteField:
return dmp_gf_sqf_list(f, u, K, all=all)
if K.is_Field:
coeff = dmp_ground_LC(f, u, K)
f = dmp_ground_monic(f, u, K)
else:
coeff, f = dmp_ground_primitive(f, u, K)
if K.is_negative(dmp_ground_LC(f, u, K)):
f = dmp_neg(f, u, K)
coeff = -coeff
if dmp_degree(f, u) <= 0:
return coeff, []
result, i = [], 1
h = dmp_diff(f, 1, u, K)
g, p, q = dmp_inner_gcd(f, h, u, K)
while True:
d = dmp_diff(p, 1, u, K)
h = dmp_sub(q, d, u, K)
if dmp_zero_p(h, u):
result.append((p, i))
break
g, p, q = dmp_inner_gcd(p, h, u, K)
if all or dmp_degree(g, u) > 0:
result.append((g, i))
i += 1
return coeff, result
def dmp_zz_i_factor(f, u, K0):
"""Factor multivariate polynomials into irreducibles in `ZZ_I[X]`. """
# First factor in QQ_I
K1 = K0.get_field()
f = dmp_convert(f, u, K0, K1)
coeff, factors = dmp_qq_i_factor(f, u, K1)
new_factors = []
for fac, i in factors:
# Extract content
fac_denom, fac_num = dmp_clear_denoms(fac, u, K1)
fac_num_ZZ_I = dmp_convert(fac_num, u, K1, K0)
content, fac_prim = dmp_ground_primitive(fac_num_ZZ_I, u, K1)
coeff = (coeff * content**i) // fac_denom**i
new_factors.append((fac_prim, i))
factors = new_factors
coeff = K0.convert(coeff, K1)
return coeff, factors
def dmp_zz_factor(f, u, K):
"""
Factor (non square-free) polynomials in `Z[X]`.
Given a multivariate polynomial `f` in `Z[x]` computes its complete
factorization `f_1, ..., f_n` into irreducibles over integers::
f = content(f) f_1**k_1 ... f_n**k_n
The factorization is computed by reducing the input polynomial
into a primitive square-free polynomial and factoring it using
Enhanced Extended Zassenhaus (EEZ) algorithm. Trial division
is used to recover the multiplicities of factors.
The result is returned as a tuple consisting of::
(content(f), [(f_1, k_1), ..., (f_n, k_n))
Consider polynomial `f = 2*(x**2 - y**2)`::
>>> from sympy.polys import ring, ZZ
>>> R, x,y = ring("x,y", ZZ)
>>> R.dmp_zz_factor(2*x**2 - 2*y**2)
(2, [(x - y, 1), (x + y, 1)])
In result we got the following factorization::
f = 2 (x - y) (x + y)
References
==========
1. [Gathen99]_
"""
if not u:
return dup_zz_factor(f, K)
if dmp_zero_p(f, u):
return K.zero, []
cont, g = dmp_ground_primitive(f, u, K)
if dmp_ground_LC(g, u, K) < 0:
cont, g = -cont, dmp_neg(g, u, K)
if all(d <= 0 for d in dmp_degree_list(g, u)):
return cont, []
G, g = dmp_primitive(g, u, K)
factors = []
if dmp_degree(g, u) > 0:
g = dmp_sqf_part(g, u, K)
H = dmp_zz_wang(g, u, K)
factors = dmp_trial_division(f, H, u, K)
for g, k in dmp_zz_factor(G, u - 1, K)[1]:
factors.insert(0, ([g], k))
return cont, _sort_factors(factors)
def primitive(f):
"""Returns content and a primitive form of `f`. """
cont, F = dmp_ground_primitive(f.rep, f.lev, f.dom)
return cont, f.per(F)
def dmp_zz_factor(f, u, K):
"""
Factor (non square-free) polynomials in `Z[X]`.
Given a multivariate polynomial `f` in `Z[x]` computes its complete
factorization `f_1, ..., f_n` into irreducibles over integers::
f = content(f) f_1**k_1 ... f_n**k_n
The factorization is computed by reducing the input polynomial
into a primitive square-free polynomial and factoring it using
Enhanced Extended Zassenhaus (EEZ) algorithm. Trial division
is used to recover the multiplicities of factors.
The result is returned as a tuple consisting of::
(content(f), [(f_1, k_1), ..., (f_n, k_n))
Consider polynomial `f = 2*(x**2 - y**2)`::
>>> from sympy.polys.factortools import dmp_zz_factor
>>> from sympy.polys.domains import ZZ
>>> dmp_zz_factor([[2], [], [-2, 0, 0]], 1, ZZ)
(2, [([[1], [-1, 0]], 1), ([[1], [1, 0]], 1)])
In result we got the following factorization::
f = 2 (x - y) (x + y)
**References**
1. [Gathen99]_
"""
if not u:
return dup_zz_factor(f, K)
if dmp_zero_p(f, u):
return K.zero, []
cont, g = dmp_ground_primitive(f, u, K)
if dmp_ground_LC(g, u, K) < 0:
cont, g = -cont, dmp_neg(g, u, K)
if all([ d <= 0 for d in dmp_degree_list(g, u) ]):
return cont, []
G, g = dmp_primitive(g, u, K)
factors = []
if dmp_degree(g, u) > 0:
g = dmp_sqf_part(g, u, K)
H = dmp_zz_wang(g, u, K)
for h in H:
k = 0
while True:
q, r = dmp_div(f, h, u, K)
if dmp_zero_p(r, u):
f, k = q, k+1
else:
break
factors.append((h, k))
for g, k in dmp_zz_factor(G, u-1, K)[1]:
factors.insert(0, ([g], k))
return cont, _sort_factors(factors)
def dmp_zz_wang(f, u, K, mod=None):
"""
Factor primitive square-free polynomials in `Z[X]`.
Given a multivariate polynomial `f` in `Z[x_1,...,x_n]`, which
is primitive and square-free in `x_1`, computes factorization
of `f` into irreducibles over integers.
The procedure is based on Wang's Enhanced Extended Zassenhaus
algorithm. The algorithm works by viewing `f` as a univariate
polynomial in `Z[x_2,...,x_n][x_1]`, for which an evaluation
mapping is computed::
x_2 -> a_2, ..., x_n -> a_n
where `a_i`, for `i = 2, ..., n`, are carefully chosen integers.
The mapping is used to transform `f` into a univariate polynomial
in `Z[x_1]`, which can be factored efficiently using Zassenhaus
algorithm. The last step is to lift univariate factors to obtain
true multivariate factors. For this purpose a parallel Hensel
lifting procedure is used.
**References**
1. [Wang78]_
2. [Geddes92]_
"""
ct, T = dmp_zz_factor(dmp_LC(f, K), u-1, K)
b = dmp_zz_mignotte_bound(f, u, K)
p = K(nextprime(b))
if mod is None:
if u == 1:
mod = 2
else:
mod = 1
history, configs, A, r = set([]), [], [K.zero]*u, None
try:
cs, s, E = dmp_zz_wang_test_points(f, T, ct, A, u, K)
_, H = dup_zz_factor_sqf(s, K)
r = len(H)
if r == 1:
return [f]
bad_points = set([tuple(A)])
configs = [(s, cs, E, H, A)]
except EvaluationFailed:
pass
eez_num_configs = query('EEZ_NUMBER_OF_CONFIGS')
eez_num_tries = query('EEZ_NUMBER_OF_TRIES')
eez_mod_step = query('EEZ_MODULUS_STEP')
while len(configs) < eez_num_configs:
for _ in xrange(eez_num_tries):
A = [ K(randint(-mod, mod)) for _ in xrange(u) ]
if tuple(A) not in history:
history.add(tuple(A))
else:
continue
try:
cs, s, E = dmp_zz_wang_test_points(f, T, ct, A, u, K)
except EvaluationFailed:
continue
_, H = dup_zz_factor_sqf(s, K)
rr = len(H)
if r is not None:
if rr != r: # pragma: no cover
if rr < r:
configs, r = [], rr
else:
continue
else:
r = rr
if r == 1:
return [f]
configs.append((s, cs, E, H, A))
if len(configs) == eez_num_configs:
break
else:
mod += eez_mod_step
s_norm, s_arg, i = None, 0, 0
for s, _, _, _, _ in configs:
_s_norm = dup_max_norm(s, K)
if s_norm is not None:
if _s_norm < s_norm:
s_norm = _s_norm
s_arg = i
else:
s_norm = _s_norm
i += 1
_, cs, E, H, A = configs[s_arg]
try:
f, H, LC = dmp_zz_wang_lead_coeffs(f, T, cs, E, H, A, u, K)
factors = dmp_zz_wang_hensel_lifting(f, H, LC, A, p, u, K)
except ExtraneousFactors: # pragma: no cover
if query('EEZ_RESTART_IF_NEEDED'):
return dmp_zz_wang(f, u, K, mod+1)
else:
raise ExtraneousFactors("we need to restart algorithm with better parameters")
negative, result = 0, []
for f in factors:
_, f = dmp_ground_primitive(f, u, K)
if K.is_negative(dmp_ground_LC(f, u, K)):
f = dmp_neg(f, u, K)
result.append(f)
return result
def dmp_zz_factor(f, u, K):
"""
Factor (non square-free) polynomials in `Z[X]`.
Given a multivariate polynomial `f` in `Z[x]` computes its complete
factorization `f_1, ..., f_n` into irreducibles over integers::
f = content(f) f_1**k_1 ... f_n**k_n
The factorization is computed by reducing the input polynomial
into a primitive square-free polynomial and factoring it using
Enhanced Extended Zassenhaus (EEZ) algorithm. Trial division
is used to recover the multiplicities of factors.
The result is returned as a tuple consisting of::
(content(f), [(f_1, k_1), ..., (f_n, k_n))
Consider polynomial `f = 2*(x**2 - y**2)`::
>>> from sympy.polys import ring, ZZ
>>> R, x,y = ring("x,y", ZZ)
>>> R.dmp_zz_factor(2*x**2 - 2*y**2)
(2, [(x - y, 1), (x + y, 1)])
In result we got the following factorization::
f = 2 (x - y) (x + y)
References
==========
.. [1] [Gathen99]_
"""
if not u:
return dup_zz_factor(f, K)
if dmp_zero_p(f, u):
return K.zero, []
cont, g = dmp_ground_primitive(f, u, K)
if dmp_ground_LC(g, u, K) < 0:
cont, g = -cont, dmp_neg(g, u, K)
if all(d <= 0 for d in dmp_degree_list(g, u)):
return cont, []
G, g = dmp_primitive(g, u, K)
factors = []
if dmp_degree(g, u) > 0:
g = dmp_sqf_part(g, u, K)
H = dmp_zz_wang(g, u, K)
factors = dmp_trial_division(f, H, u, K)
for g, k in dmp_zz_factor(G, u - 1, K)[1]:
factors.insert(0, ([g], k))
return cont, _sort_factors(factors)
def dmp_zz_heu_gcd(f, g, u, K):
"""
Heuristic polynomial GCD in ``Z[X]``.
Given univariate polynomials ``f`` and ``g`` in ``Z[X]``, returns
their GCD and cofactors, i.e. polynomials ``h``, ``cff`` and ``cfg``
such that::
h = gcd(f, g), cff = quo(f, h) and cfg = quo(g, h)
The algorithm is purely heuristic which means it may fail to compute
the GCD. This will be signaled by raising an exception. In this case
you will need to switch to another GCD method.
The algorithm computes the polynomial GCD by evaluating polynomials
f and g at certain points and computing (fast) integer GCD of those
evaluations. The polynomial GCD is recovered from the integer image
by interpolation. The evaluation proces reduces f and g variable by
variable into a large integer. The final step is to verify if the
interpolated polynomial is the correct GCD. This gives cofactors of
the input polynomials as a side effect.
**Examples**
>>> from sympy.polys.domains import ZZ
>>> from sympy.polys.euclidtools import dmp_zz_heu_gcd
>>> f = ZZ.map([[1], [2, 0], [1, 0, 0]])
>>> g = ZZ.map([[1], [1, 0], []])
>>> dmp_zz_heu_gcd(f, g, 1, ZZ)
([[1], [1, 0]], [[1], [1, 0]], [[1], []])
**References**
1. [Liao95]_
"""
if not u:
return dup_zz_heu_gcd(f, g, K)
result = _dmp_rr_trivial_gcd(f, g, u, K)
if result is not None:
return result
df = dmp_degree(f, u)
dg = dmp_degree(g, u)
gcd, f, g = dmp_ground_extract(f, g, u, K)
f_norm = dmp_max_norm(f, u, K)
g_norm = dmp_max_norm(g, u, K)
B = 2*min(f_norm, g_norm) + 29
x = max(min(B, 99*K.sqrt(B)),
2*min(f_norm // abs(dmp_ground_LC(f, u, K)),
g_norm // abs(dmp_ground_LC(g, u, K))) + 2)
for i in xrange(0, HEU_GCD_MAX):
ff = dmp_eval(f, x, u, K)
gg = dmp_eval(g, x, u, K)
v = u - 1
if not (dmp_zero_p(ff, v) or dmp_zero_p(gg, v)):
h, cff, cfg = dmp_zz_heu_gcd(ff, gg, v, K)
h = _dmp_zz_gcd_interpolate(h, x, v, K)
h = dmp_ground_primitive(h, u, K)[1]
cff_, r = dmp_div(f, h, u, K)
if dmp_zero_p(r, u):
cfg_, r = dmp_div(g, h, u, K)
if dmp_zero_p(r, u):
h = dmp_mul_ground(h, gcd, u, K)
return h, cff_, cfg_
cff = _dmp_zz_gcd_interpolate(cff, x, v, K)
h, r = dmp_div(f, cff, u, K)
if dmp_zero_p(r, u):
cfg_, r = dmp_div(g, h, u, K)
if dmp_zero_p(r, u):
h = dmp_mul_ground(h, gcd, u, K)
return h, cff, cfg_
cfg = _dmp_zz_gcd_interpolate(cfg, x, v, K)
h, r = dmp_div(g, cfg, u, K)
if dmp_zero_p(r, u):
cff_, r = dmp_div(f, h, u, K)
if dmp_zero_p(r, u):
h = dmp_mul_ground(h, gcd, u, K)
return h, cff_, cfg
x = 73794*x * K.sqrt(K.sqrt(x)) // 27011
raise HeuristicGCDFailed('no luck')
def primitive(f):
"""Returns content and a primitive form of `f`. """
cont, F = dmp_ground_primitive(f.rep, f.lev, f.dom)
return cont, f.per(F)
def test_dmp_ground_primitive():
assert dmp_ground_primitive([[]], 1, ZZ) == (ZZ(0), [[]])
assert dmp_ground_primitive(f_0, 2, ZZ) == (ZZ(1), f_0)
assert dmp_ground_primitive(dmp_mul_ground(f_0, ZZ(2), 2, ZZ), 2,
ZZ) == (ZZ(2), f_0)
assert dmp_ground_primitive(f_1, 2, ZZ) == (ZZ(1), f_1)
assert dmp_ground_primitive(dmp_mul_ground(f_1, ZZ(3), 2, ZZ), 2,
ZZ) == (ZZ(3), f_1)
assert dmp_ground_primitive(f_2, 2, ZZ) == (ZZ(1), f_2)
assert dmp_ground_primitive(dmp_mul_ground(f_2, ZZ(4), 2, ZZ), 2,
ZZ) == (ZZ(4), f_2)
assert dmp_ground_primitive(f_3, 2, ZZ) == (ZZ(1), f_3)
assert dmp_ground_primitive(dmp_mul_ground(f_3, ZZ(5), 2, ZZ), 2,
ZZ) == (ZZ(5), f_3)
assert dmp_ground_primitive(f_4, 2, ZZ) == (ZZ(1), f_4)
assert dmp_ground_primitive(dmp_mul_ground(f_4, ZZ(6), 2, ZZ), 2,
ZZ) == (ZZ(6), f_4)
assert dmp_ground_primitive(f_5, 2, ZZ) == (ZZ(1), f_5)
assert dmp_ground_primitive(dmp_mul_ground(f_5, ZZ(7), 2, ZZ), 2,
ZZ) == (ZZ(7), f_5)
assert dmp_ground_primitive(f_6, 3, ZZ) == (ZZ(1), f_6)
assert dmp_ground_primitive(dmp_mul_ground(f_6, ZZ(8), 3, ZZ), 3,
ZZ) == (ZZ(8), f_6)
assert dmp_ground_primitive([[ZZ(2)]], 1, ZZ) == (ZZ(2), [[ZZ(1)]])
assert dmp_ground_primitive([[QQ(2)]], 1, QQ) == (QQ(2), [[QQ(1)]])
assert dmp_ground_primitive([[QQ(2, 3)], [QQ(4, 9)]], 1,
QQ) == (QQ(2, 9), [[QQ(3)], [QQ(2)]])
assert dmp_ground_primitive([[QQ(2, 3)], [QQ(4, 5)]], 1,
QQ) == (QQ(2, 15), [[QQ(5)], [QQ(6)]])
def dmp_zz_wang(f, u, K, **args):
"""Factor primitive square-free polynomials in `Z[X]`.
Given a multivariate polynomial `f` in `Z[x_1,...,x_n]`, which
is primitive and square-free in `x_1`, computes factorization
of `f` into irreducibles over integers.
The procedure is based on Wang's Enhanced Extended Zassenhaus
algorithm. The algorithm works by viewing `f` as a univariate
polynomial in `Z[x_2,...,x_n][x_1]`, for which an evaluation
mapping is computed::
x_2 -> a_2, ..., x_n -> a_n
where `a_i`, for `i = 2, ..., n`, are carefully chosen integers.
The mapping is used to transform `f` into a univariate polynomial
in `Z[x_1]`, which can be factored efficiently using Zassenhaus
algorithm. The last step is to lift univariate factors to obtain
true multivariate factors. For this purpose a parallel Hensel
lifting procedure is used.
References
==========
.. [Wang78] P. S. Wang, An Improved Multivariate Polynomial Factoring
Algorithm, Math. of Computation 32, 1978, pp. 1215--1231
.. [Geddes92] K. Geddes, S. R. Czapor, G. Labahn, Algorithms for
Computer Algebra, Springer, 1992, pp. 264--272
"""
ct, T = dmp_zz_factor(dmp_LC(f, K), u - 1, K)
b = dmp_zz_mignotte_bound(f, u, K)
p = K(nextprime(b))
eez_mod = args.get('mod', None)
if eez_mod is None:
if u == 1:
eez_mod = 2
else:
eez_mod = 1
history, configs, A, r = set([]), [], [K.zero] * u, None
try:
cs, s, E = dmp_zz_wang_test_points(f, T, ct, A, u, K)
_, H = dup_zz_factor_sqf(s, K)
r = len(H)
if r == 1:
return [f]
bad_points = set([tuple(A)])
configs = [(s, cs, E, H, A)]
except EvaluationFailed:
pass
while len(configs) < EEZ_NUM_OK:
for _ in xrange(EEZ_NUM_TRY):
A = [K(randint(-eez_mod, eez_mod)) for _ in xrange(u)]
if tuple(A) not in history:
history.add(tuple(A))
else:
continue
try:
cs, s, E = dmp_zz_wang_test_points(f, T, ct, A, u, K)
except EvaluationFailed:
continue
_, H = dup_zz_factor_sqf(s, K)
rr = len(H)
if r is not None:
if rr != r: # pragma: no cover
if rr < r:
configs, r = [], rr
else:
continue
else:
r = rr
if r == 1:
return [f]
configs.append((s, cs, E, H, A))
if len(configs) == EEZ_NUM_OK:
break
else:
eez_mod += EEZ_MOD_STEP
s_norm, s_arg, i = None, 0, 0
for s, _, _, _, _ in configs:
_s_norm = dup_max_norm(s, K)
if s_norm is not None:
if _s_norm < s_norm:
s_norm = _s_norm
s_arg = i
else:
s_norm = _s_norm
i += 1
_, cs, E, H, A = configs[s_arg]
try:
f, H, LC = dmp_zz_wang_lead_coeffs(f, T, cs, E, H, A, u, K)
factors = dmp_zz_wang_hensel_lifting(f, H, LC, A, p, u, K)
except ExtraneousFactors: # pragma: no cover
if args.get('restart', True):
return dmp_zz_wang(f, u, K, mod=eez_mod + 1)
else:
raise ExtraneousFactors(
"we need to restart algorithm with better parameters")
negative, result = 0, []
for f in factors:
_, f = dmp_ground_primitive(f, u, K)
if K.is_negative(dmp_ground_LC(f, u, K)):
f = dmp_neg(f, u, K)
result.append(f)
return result
def dmp_zz_wang(f, u, K, mod=None):
"""
Factor primitive square-free polynomials in `Z[X]`.
Given a multivariate polynomial `f` in `Z[x_1,...,x_n]`, which
is primitive and square-free in `x_1`, computes factorization
of `f` into irreducibles over integers.
The procedure is based on Wang's Enhanced Extended Zassenhaus
algorithm. The algorithm works by viewing `f` as a univariate
polynomial in `Z[x_2,...,x_n][x_1]`, for which an evaluation
mapping is computed::
x_2 -> a_2, ..., x_n -> a_n
where `a_i`, for `i = 2, ..., n`, are carefully chosen integers.
The mapping is used to transform `f` into a univariate polynomial
in `Z[x_1]`, which can be factored efficiently using Zassenhaus
algorithm. The last step is to lift univariate factors to obtain
true multivariate factors. For this purpose a parallel Hensel
lifting procedure is used.
References
==========
1. [Wang78]_
2. [Geddes92]_
"""
ct, T = dmp_zz_factor(dmp_LC(f, K), u - 1, K)
b = dmp_zz_mignotte_bound(f, u, K)
p = K(nextprime(b))
if mod is None:
if u == 1:
mod = 2
else:
mod = 1
history, configs, A, r = set([]), [], [K.zero] * u, None
try:
cs, s, E = dmp_zz_wang_test_points(f, T, ct, A, u, K)
_, H = dup_zz_factor_sqf(s, K)
r = len(H)
if r == 1:
return [f]
configs = [(s, cs, E, H, A)]
except EvaluationFailed:
pass
eez_num_configs = query('EEZ_NUMBER_OF_CONFIGS')
eez_num_tries = query('EEZ_NUMBER_OF_TRIES')
eez_mod_step = query('EEZ_MODULUS_STEP')
while len(configs) < eez_num_configs:
for _ in xrange(eez_num_tries):
A = [K(randint(-mod, mod)) for _ in xrange(u)]
if tuple(A) not in history:
history.add(tuple(A))
else:
continue
try:
cs, s, E = dmp_zz_wang_test_points(f, T, ct, A, u, K)
except EvaluationFailed:
continue
_, H = dup_zz_factor_sqf(s, K)
rr = len(H)
if r is not None:
if rr != r: # pragma: no cover
if rr < r:
configs, r = [], rr
else:
continue
else:
r = rr
if r == 1:
return [f]
configs.append((s, cs, E, H, A))
if len(configs) == eez_num_configs:
break
else:
mod += eez_mod_step
s_norm, s_arg, i = None, 0, 0
for s, _, _, _, _ in configs:
_s_norm = dup_max_norm(s, K)
if s_norm is not None:
if _s_norm < s_norm:
s_norm = _s_norm
s_arg = i
else:
s_norm = _s_norm
i += 1
_, cs, E, H, A = configs[s_arg]
try:
f, H, LC = dmp_zz_wang_lead_coeffs(f, T, cs, E, H, A, u, K)
factors = dmp_zz_wang_hensel_lifting(f, H, LC, A, p, u, K)
except ExtraneousFactors: # pragma: no cover
if query('EEZ_RESTART_IF_NEEDED'):
return dmp_zz_wang(f, u, K, mod + 1)
else:
raise ExtraneousFactors(
"we need to restart algorithm with better parameters")
negative, result = 0, []
for f in factors:
_, f = dmp_ground_primitive(f, u, K)
if K.is_negative(dmp_ground_LC(f, u, K)):
f = dmp_neg(f, u, K)
result.append(f)
return result
def test_dmp_ground_primitive():
assert dmp_ground_primitive([[]], 1, ZZ) == (ZZ(0), [[]])
assert dmp_ground_primitive(f_0, 2, ZZ) == (ZZ(1), f_0)
assert dmp_ground_primitive(
dmp_mul_ground(f_0, ZZ(2), 2, ZZ), 2, ZZ) == (ZZ(2), f_0)
assert dmp_ground_primitive(f_1, 2, ZZ) == (ZZ(1), f_1)
assert dmp_ground_primitive(
dmp_mul_ground(f_1, ZZ(3), 2, ZZ), 2, ZZ) == (ZZ(3), f_1)
assert dmp_ground_primitive(f_2, 2, ZZ) == (ZZ(1), f_2)
assert dmp_ground_primitive(
dmp_mul_ground(f_2, ZZ(4), 2, ZZ), 2, ZZ) == (ZZ(4), f_2)
assert dmp_ground_primitive(f_3, 2, ZZ) == (ZZ(1), f_3)
assert dmp_ground_primitive(
dmp_mul_ground(f_3, ZZ(5), 2, ZZ), 2, ZZ) == (ZZ(5), f_3)
assert dmp_ground_primitive(f_4, 2, ZZ) == (ZZ(1), f_4)
assert dmp_ground_primitive(
dmp_mul_ground(f_4, ZZ(6), 2, ZZ), 2, ZZ) == (ZZ(6), f_4)
assert dmp_ground_primitive(f_5, 2, ZZ) == (ZZ(1), f_5)
assert dmp_ground_primitive(
dmp_mul_ground(f_5, ZZ(7), 2, ZZ), 2, ZZ) == (ZZ(7), f_5)
assert dmp_ground_primitive(f_6, 3, ZZ) == (ZZ(1), f_6)
assert dmp_ground_primitive(
dmp_mul_ground(f_6, ZZ(8), 3, ZZ), 3, ZZ) == (ZZ(8), f_6)
assert dmp_ground_primitive([[ZZ(2)]], 1, ZZ) == (ZZ(2), [[ZZ(1)]])
assert dmp_ground_primitive([[QQ(2)]], 1, QQ) == (QQ(2), [[QQ(1)]])
assert dmp_ground_primitive(
[[QQ(2, 3)], [QQ(4, 9)]], 1, QQ) == (QQ(2, 9), [[QQ(3)], [QQ(2)]])
assert dmp_ground_primitive(
[[QQ(2, 3)], [QQ(4, 5)]], 1, QQ) == (QQ(2, 15), [[QQ(5)], [QQ(6)]])
def dmp_zz_heu_gcd(f, g, u, K):
"""
Heuristic polynomial GCD in `Z[X]`.
Given univariate polynomials `f` and `g` in `Z[X]`, returns
their GCD and cofactors, i.e. polynomials ``h``, ``cff`` and ``cfg``
such that::
h = gcd(f, g), cff = quo(f, h) and cfg = quo(g, h)
The algorithm is purely heuristic which means it may fail to compute
the GCD. This will be signaled by raising an exception. In this case
you will need to switch to another GCD method.
The algorithm computes the polynomial GCD by evaluating polynomials
f and g at certain points and computing (fast) integer GCD of those
evaluations. The polynomial GCD is recovered from the integer image
by interpolation. The evaluation proces reduces f and g variable by
variable into a large integer. The final step is to verify if the
interpolated polynomial is the correct GCD. This gives cofactors of
the input polynomials as a side effect.
Examples
========
>>> from sympy.polys import ring, ZZ
>>> R, x,y, = ring("x,y", ZZ)
>>> f = x**2 + 2*x*y + y**2
>>> g = x**2 + x*y
>>> R.dmp_zz_heu_gcd(f, g)
(x + y, x + y, x)
References
==========
1. [Liao95]_
"""
if not u:
return dup_zz_heu_gcd(f, g, K)
result = _dmp_rr_trivial_gcd(f, g, u, K)
if result is not None:
return result
gcd, f, g = dmp_ground_extract(f, g, u, K)
f_norm = dmp_max_norm(f, u, K)
g_norm = dmp_max_norm(g, u, K)
B = K(2*min(f_norm, g_norm) + 29)
x = max(min(B, 99*K.sqrt(B)),
2*min(f_norm // abs(dmp_ground_LC(f, u, K)),
g_norm // abs(dmp_ground_LC(g, u, K))) + 2)
for i in xrange(0, HEU_GCD_MAX):
ff = dmp_eval(f, x, u, K)
gg = dmp_eval(g, x, u, K)
v = u - 1
if not (dmp_zero_p(ff, v) or dmp_zero_p(gg, v)):
h, cff, cfg = dmp_zz_heu_gcd(ff, gg, v, K)
h = _dmp_zz_gcd_interpolate(h, x, v, K)
h = dmp_ground_primitive(h, u, K)[1]
cff_, r = dmp_div(f, h, u, K)
if dmp_zero_p(r, u):
cfg_, r = dmp_div(g, h, u, K)
if dmp_zero_p(r, u):
h = dmp_mul_ground(h, gcd, u, K)
return h, cff_, cfg_
cff = _dmp_zz_gcd_interpolate(cff, x, v, K)
h, r = dmp_div(f, cff, u, K)
if dmp_zero_p(r, u):
cfg_, r = dmp_div(g, h, u, K)
if dmp_zero_p(r, u):
h = dmp_mul_ground(h, gcd, u, K)
return h, cff, cfg_
cfg = _dmp_zz_gcd_interpolate(cfg, x, v, K)
h, r = dmp_div(g, cfg, u, K)
if dmp_zero_p(r, u):
cff_, r = dmp_div(f, h, u, K)
if dmp_zero_p(r, u):
h = dmp_mul_ground(h, gcd, u, K)
return h, cff_, cfg
x = 73794*x * K.sqrt(K.sqrt(x)) // 27011
raise HeuristicGCDFailed('no luck')
def dmp_factor_list(f, u, K0):
"""Factor polynomials into irreducibles in `K[X]`. """
if not u:
return dup_factor_list(f, K0)
J, f = dmp_terms_gcd(f, u, K0)
cont, f = dmp_ground_primitive(f, u, K0)
if K0.is_FiniteField: # pragma: no cover
coeff, factors = dmp_gf_factor(f, u, K0)
elif K0.is_Algebraic:
coeff, factors = dmp_ext_factor(f, u, K0)
else:
if not K0.is_Exact:
K0_inexact, K0 = K0, K0.get_exact()
f = dmp_convert(f, u, K0_inexact, K0)
else:
K0_inexact = None
if K0.has_Field:
K = K0.get_ring()
denom, f = dmp_clear_denoms(f, u, K0, K)
f = dmp_convert(f, u, K0, K)
else:
K = K0
if K.is_ZZ:
levels, f, v = dmp_exclude(f, u, K)
coeff, factors = dmp_zz_factor(f, v, K)
for i, (f, k) in enumerate(factors):
factors[i] = (dmp_include(f, levels, v, K), k)
elif K.is_Poly:
f, v = dmp_inject(f, u, K)
coeff, factors = dmp_factor_list(f, v, K.dom)
for i, (f, k) in enumerate(factors):
factors[i] = (dmp_eject(f, v, K), k)
coeff = K.convert(coeff, K.dom)
else: # pragma: no cover
raise DomainError('factorization not supported over %s' % K0)
if K0.has_Field:
for i, (f, k) in enumerate(factors):
factors[i] = (dmp_convert(f, u, K, K0), k)
coeff = K0.convert(coeff, K)
if K0_inexact is None:
coeff = coeff / denom
else:
for i, (f, k) in enumerate(factors):
f = dmp_quo_ground(f, denom, u, K0)
f = dmp_convert(f, u, K0, K0_inexact)
factors[i] = (f, k)
coeff = K0_inexact.convert(coeff, K0)
K0 = K0_inexact
for i, j in enumerate(reversed(J)):
if not j:
continue
term = {(0, ) * (u - i) + (1, ) + (0, ) * i: K0.one}
factors.insert(0, (dmp_from_dict(term, u, K0), j))
return coeff * cont, _sort_factors(factors)
def dmp_factor_list(f, u, K0):
"""Factor polynomials into irreducibles in `K[X]`. """
if not u:
return dup_factor_list(f, K0)
J, f = dmp_terms_gcd(f, u, K0)
cont, f = dmp_ground_primitive(f, u, K0)
if K0.is_FiniteField: # pragma: no cover
coeff, factors = dmp_gf_factor(f, u, K0)
elif K0.is_Algebraic:
coeff, factors = dmp_ext_factor(f, u, K0)
else:
if not K0.is_Exact:
K0_inexact, K0 = K0, K0.get_exact()
f = dmp_convert(f, u, K0_inexact, K0)
else:
K0_inexact = None
if K0.is_Field:
K = K0.get_ring()
denom, f = dmp_clear_denoms(f, u, K0, K)
f = dmp_convert(f, u, K0, K)
else:
K = K0
if K.is_ZZ:
levels, f, v = dmp_exclude(f, u, K)
coeff, factors = dmp_zz_factor(f, v, K)
for i, (f, k) in enumerate(factors):
factors[i] = (dmp_include(f, levels, v, K), k)
elif K.is_Poly:
f, v = dmp_inject(f, u, K)
coeff, factors = dmp_factor_list(f, v, K.dom)
for i, (f, k) in enumerate(factors):
factors[i] = (dmp_eject(f, v, K), k)
coeff = K.convert(coeff, K.dom)
else: # pragma: no cover
raise DomainError('factorization not supported over %s' % K0)
if K0.is_Field:
for i, (f, k) in enumerate(factors):
factors[i] = (dmp_convert(f, u, K, K0), k)
coeff = K0.convert(coeff, K)
if K0_inexact is None:
coeff = coeff/denom
else:
for i, (f, k) in enumerate(factors):
f = dmp_quo_ground(f, denom, u, K0)
f = dmp_convert(f, u, K0, K0_inexact)
factors[i] = (f, k)
coeff = K0_inexact.convert(coeff, K0)
K0 = K0_inexact
for i, j in enumerate(reversed(J)):
if not j:
continue
term = {(0,)*(u - i) + (1,) + (0,)*i: K0.one}
factors.insert(0, (dmp_from_dict(term, u, K0), j))
return coeff*cont, _sort_factors(factors)
def dmp_zz_factor(f, u, K):
"""
Factor (non square-free) polynomials in `Z[X]`.
Given a multivariate polynomial `f` in `Z[x]` computes its complete
factorization `f_1, ..., f_n` into irreducibles over integers::
f = content(f) f_1**k_1 ... f_n**k_n
The factorization is computed by reducing the input polynomial
into a primitive square-free polynomial and factoring it using
Enhanced Extended Zassenhaus (EEZ) algorithm. Trial division
is used to recover the multiplicities of factors.
The result is returned as a tuple consisting of::
(content(f), [(f_1, k_1), ..., (f_n, k_n))
Consider polynomial `f = 2*(x**2 - y**2)`::
>>> from sympy.polys.factortools import dmp_zz_factor
>>> from sympy.polys.domains import ZZ
>>> dmp_zz_factor([[2], [], [-2, 0, 0]], 1, ZZ)
(2, [([[1], [-1, 0]], 1), ([[1], [1, 0]], 1)])
In result we got the following factorization::
f = 2 (x - y) (x + y)
References
==========
1. [Gathen99]_
"""
if not u:
return dup_zz_factor(f, K)
if dmp_zero_p(f, u):
return K.zero, []
cont, g = dmp_ground_primitive(f, u, K)
if dmp_ground_LC(g, u, K) < 0:
cont, g = -cont, dmp_neg(g, u, K)
if all(d <= 0 for d in dmp_degree_list(g, u)):
return cont, []
G, g = dmp_primitive(g, u, K)
factors = []
if dmp_degree(g, u) > 0:
g = dmp_sqf_part(g, u, K)
H = dmp_zz_wang(g, u, K)
for h in H:
k = 0
while True:
q, r = dmp_div(f, h, u, K)
if dmp_zero_p(r, u):
f, k = q, k + 1
else:
break
factors.append((h, k))
for g, k in dmp_zz_factor(G, u - 1, K)[1]:
factors.insert(0, ([g], k))
return cont, _sort_factors(factors)
def dmp_zz_wang(f, u, K, **args):
"""Factor primitive square-free polynomials in `Z[X]`.
Given a multivariate polynomial `f` in `Z[x_1,...,x_n]`, which
is primitive and square-free in `x_1`, computes factorization
of `f` into irreducibles over integers.
The procedure is based on Wang's Enhanced Extended Zassenhaus
algorithm. The algorithm works by viewing `f` as a univariate
polynomial in `Z[x_2,...,x_n][x_1]`, for which an evaluation
mapping is computed::
x_2 -> a_2, ..., x_n -> a_n
where `a_i`, for `i = 2, ..., n`, are carefully chosen integers.
The mapping is used to transform `f` into a univariate polynomial
in `Z[x_1]`, which can be factored efficiently using Zassenhaus
algorithm. The last step is to lift univariate factors to obtain
true multivariate factors. For this purpose a parallel Hensel
lifting procedure is used.
References
==========
.. [Wang78] P. S. Wang, An Improved Multivariate Polynomial Factoring
Algorithm, Math. of Computation 32, 1978, pp. 1215--1231
.. [Geddes92] K. Geddes, S. R. Czapor, G. Labahn, Algorithms for
Computer Algebra, Springer, 1992, pp. 264--272
"""
ct, T = dmp_zz_factor(dmp_LC(f, K), u-1, K)
b = dmp_zz_mignotte_bound(f, u, K)
p = K(nextprime(b))
eez_mod = args.get('mod', None)
if eez_mod is None:
if u == 1:
eez_mod = 2
else:
eez_mod = 1
history, configs, A, r = set([]), [], [K.zero]*u, None
try:
cs, s, E = dmp_zz_wang_test_points(f, T, ct, A, u, K)
_, H = dup_zz_factor_sqf(s, K)
r = len(H)
if r == 1:
return [f]
bad_points = set([tuple(A)])
configs = [(s, cs, E, H, A)]
except EvaluationFailed:
pass
while len(configs) < EEZ_NUM_OK:
for _ in xrange(EEZ_NUM_TRY):
A = [ K(randint(-eez_mod, eez_mod)) for _ in xrange(u) ]
if tuple(A) not in history:
history.add(tuple(A))
else:
continue
try:
cs, s, E = dmp_zz_wang_test_points(f, T, ct, A, u, K)
except EvaluationFailed:
continue
_, H = dup_zz_factor_sqf(s, K)
rr = len(H)
if r is not None:
if rr != r: # pragma: no cover
if rr < r:
configs, r = [], rr
else:
continue
else:
r = rr
if r == 1:
return [f]
configs.append((s, cs, E, H, A))
if len(configs) == EEZ_NUM_OK:
break
else:
eez_mod += EEZ_MOD_STEP
s_norm, s_arg, i = None, 0, 0
for s, _, _, _, _ in configs:
_s_norm = dup_max_norm(s, K)
if s_norm is not None:
if _s_norm < s_norm:
s_norm = _s_norm
s_arg = i
else:
s_norm = _s_norm
i += 1
_, cs, E, H, A = configs[s_arg]
try:
f, H, LC = dmp_zz_wang_lead_coeffs(f, T, cs, E, H, A, u, K)
factors = dmp_zz_wang_hensel_lifting(f, H, LC, A, p, u, K)
except ExtraneousFactors: # pragma: no cover
if args.get('restart', True):
return dmp_zz_wang(f, u, K, mod=eez_mod+1)
else:
raise ExtraneousFactors("we need to restart algorithm with better parameters")
negative, result = 0, []
for f in factors:
_, f = dmp_ground_primitive(f, u, K)
if K.is_negative(dmp_ground_LC(f, u, K)):
f = dmp_neg(f, u, K)
result.append(f)
return result
