def test_dmp_div():
f, g, q, r = [5,4,3,2,1], [1,2,3], [5,-6,0], [20,1]
assert dmp_div(f, g, 0, ZZ) == (q, r)
assert dmp_quo(f, g, 0, ZZ) == q
assert dmp_rem(f, g, 0, ZZ) == r
raises(ExactQuotientFailed, lambda: dmp_exquo(f, g, 0, ZZ))
f, g, q, r = [[[1]]], [[[2]],[1]], [[[]]], [[[1]]]
assert dmp_div(f, g, 2, ZZ) == (q, r)
assert dmp_quo(f, g, 2, ZZ) == q
assert dmp_rem(f, g, 2, ZZ) == r
raises(ExactQuotientFailed, lambda: dmp_exquo(f, g, 2, ZZ))
def dmp_trial_division(f, factors, u, K):
"""Determine multiplicities of factors using trial division. """
result = []
for factor in factors:
k = 0
while True:
q, r = dmp_div(f, factor, u, K)
if dmp_zero_p(r, u):
f, k = q, k+1
else:
break
result.append((factor, k))
return _sort_factors(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.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_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 div(f, g):
"""Polynomial division with remainder of `f` and `g`. """
lev, dom, per, F, G = f.unify(g)
q, r = dmp_div(F, G, lev, dom)
return per(q), per(r)
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_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 div(f, g):
"""Polynomial division with remainder of `f` and `g`. """
lev, dom, per, F, G = f.unify(g)
q, r = dmp_div(F, G, lev, dom)
return per(q), per(r)
