/

# Source code for sympy.solvers.polysys

"""Solvers of systems of polynomial equations. """

from __future__ import print_function, division

from sympy.core import S
from sympy.polys import Poly, groebner, roots
from sympy.polys.polytools import parallel_poly_from_expr
from sympy.polys.polyerrors import (ComputationFailed,
PolificationFailed, CoercionFailed)
from sympy.simplify import rcollect
from sympy.utilities import default_sort_key, postfixes

class SolveFailed(Exception):
"""Raised when solver's conditions weren't met. """

[docs]def solve_poly_system(seq, *gens, **args):
"""
Solve a system of polynomial equations.

Examples
========

>>> from sympy import solve_poly_system
>>> from sympy.abc import x, y

>>> solve_poly_system([x*y - 2*y, 2*y**2 - x**2], x, y)
[(0, 0), (2, -sqrt(2)), (2, sqrt(2))]

"""
try:
polys, opt = parallel_poly_from_expr(seq, *gens, **args)
except PolificationFailed as exc:
raise ComputationFailed('solve_poly_system', len(seq), exc)

if len(polys) == len(opt.gens) == 2:
f, g = polys

a, b = f.degree_list()
c, d = g.degree_list()

if a <= 2 and b <= 2 and c <= 2 and d <= 2:
try:
return solve_biquadratic(f, g, opt)
except SolveFailed:
pass

return solve_generic(polys, opt)

def solve_biquadratic(f, g, opt):
"""Solve a system of two bivariate quadratic polynomial equations.

Examples
========

>>> from sympy.polys import Options, Poly
>>> from sympy.abc import x, y
>>> from sympy.solvers.polysys import solve_biquadratic
>>> NewOption = Options((x, y), {'domain': 'ZZ'})

>>> a = Poly(y**2 - 4 + x, y, x, domain='ZZ')
>>> b = Poly(y*2 + 3*x - 7, y, x, domain='ZZ')
>>> solve_biquadratic(a, b, NewOption)
[(1/3, 3), (41/27, 11/9)]

>>> a = Poly(y + x**2 - 3, y, x, domain='ZZ')
>>> b = Poly(-y + x - 4, y, x, domain='ZZ')
>>> solve_biquadratic(a, b, NewOption)
[(-sqrt(29)/2 + 7/2, -sqrt(29)/2 - 1/2), (sqrt(29)/2 + 7/2, -1/2 + \
sqrt(29)/2)]
"""
G = groebner([f, g])

if len(G) == 1 and G.is_ground:
return None

if len(G) != 2:
raise SolveFailed

p, q = G
x, y = opt.gens

p = Poly(p, x, expand=False)
q = q.ltrim(-1)

p_roots = [ rcollect(expr, y) for expr in roots(p).keys() ]
q_roots = list(roots(q).keys())

solutions = []

for q_root in q_roots:
for p_root in p_roots:
solution = (p_root.subs(y, q_root), q_root)
solutions.append(solution)

return sorted(solutions, key=default_sort_key)

def solve_generic(polys, opt):
"""
Solve a generic system of polynomial equations.

Returns all possible solutions over C[x_1, x_2, ..., x_m] of a
set F = { f_1, f_2, ..., f_n } of polynomial equations,  using
Groebner basis approach. For now only zero-dimensional systems
are supported, which means F can have at most a finite number
of solutions.

The algorithm works by the fact that, supposing G is the basis
of F with respect to an elimination order  (here lexicographic
order is used), G and F generate the same ideal, they have the
same set of solutions. By the elimination property,  if G is a
reduced, zero-dimensional Groebner basis, then there exists an
univariate polynomial in G (in its last variable). This can be
solved by computing its roots. Substituting all computed roots
for the last (eliminated) variable in other elements of G, new
polynomial system is generated. Applying the above procedure
recursively, a finite number of solutions can be found.

The ability of finding all solutions by this procedure depends
on the root finding algorithms. If no solutions were found, it
means only that roots() failed, but the system is solvable. To
overcome this difficulty use numerical algorithms instead.

References
==========

.. [Buchberger01] B. Buchberger, Groebner Bases: A Short
Introduction for Systems Theorists, In: R. Moreno-Diaz,
B. Buchberger, J.L. Freire, Proceedings of EUROCAST'01,
February, 2001

.. [Cox97] D. Cox, J. Little, D. O'Shea, Ideals, Varieties
and Algorithms, Springer, Second Edition, 1997, pp. 112

Examples
========

>>> from sympy.polys import Poly, Options
>>> from sympy.solvers.polysys import solve_generic
>>> from sympy.abc import x, y
>>> NewOption = Options((x, y), {'domain': 'ZZ'})

>>> a = Poly(x - y + 5, x, y, domain='ZZ')
>>> b = Poly(x + y - 3, x, y, domain='ZZ')
>>> solve_generic([a, b], NewOption)
[(-1, 4)]

>>> a = Poly(x - 2*y + 5, x, y, domain='ZZ')
>>> b = Poly(2*x - y - 3, x, y, domain='ZZ')
>>> solve_generic([a, b], NewOption)
[(11/3, 13/3)]

>>> a = Poly(x**2 + y, x, y, domain='ZZ')
>>> b = Poly(x + y*4, x, y, domain='ZZ')
>>> solve_generic([a, b], NewOption)
[(0, 0), (1/4, -1/16)]
"""
def _is_univariate(f):
"""Returns True if 'f' is univariate in its last variable. """
for monom in f.monoms():
if any(m > 0 for m in monom[:-1]):
return False

return True

def _subs_root(f, gen, zero):
"""Replace generator with a root so that the result is nice. """
p = f.as_expr({gen: zero})

if f.degree(gen) >= 2:
p = p.expand(deep=False)

return p

def _solve_reduced_system(system, gens, entry=False):
"""Recursively solves reduced polynomial systems. """
if len(system) == len(gens) == 1:
zeros = list(roots(system, gens[-1]).keys())
return [ (zero,) for zero in zeros ]

basis = groebner(system, gens, polys=True)

if len(basis) == 1 and basis.is_ground:
if not entry:
return []
else:
return None

univariate = list(filter(_is_univariate, basis))

if len(univariate) == 1:
f = univariate.pop()
else:
raise NotImplementedError("only zero-dimensional systems supported (finite number of solutions)")

gens = f.gens
gen = gens[-1]

zeros = list(roots(f.ltrim(gen)).keys())

if not zeros:
return []

if len(basis) == 1:
return [ (zero,) for zero in zeros ]

solutions = []

for zero in zeros:
new_system = []
new_gens = gens[:-1]

for b in basis[:-1]:
eq = _subs_root(b, gen, zero)

if eq is not S.Zero:
new_system.append(eq)

for solution in _solve_reduced_system(new_system, new_gens):
solutions.append(solution + (zero,))

return solutions

try:
result = _solve_reduced_system(polys, opt.gens, entry=True)
except CoercionFailed:
raise NotImplementedError

if result is not None:
return sorted(result, key=default_sort_key)
else:
return None

[docs]def solve_triangulated(polys, *gens, **args):
"""
Solve a polynomial system using Gianni-Kalkbrenner algorithm.

The algorithm proceeds by computing one Groebner basis in the ground
domain and then by iteratively computing polynomial factorizations in
appropriately constructed algebraic extensions of the ground domain.

Examples
========

>>> from sympy.solvers.polysys import solve_triangulated
>>> from sympy.abc import x, y, z

>>> F = [x**2 + y + z - 1, x + y**2 + z - 1, x + y + z**2 - 1]

>>> solve_triangulated(F, x, y, z)
[(0, 0, 1), (0, 1, 0), (1, 0, 0)]

References
==========

1. Patrizia Gianni, Teo Mora, Algebraic Solution of System of
Polynomial Equations using Groebner Bases, AAECC-5 on Applied Algebra,
Algebraic Algorithms and Error-Correcting Codes, LNCS 356 247--257, 1989

"""
G = groebner(polys, gens, polys=True)
G = list(reversed(G))

domain = args.get('domain')

if domain is not None:
for i, g in enumerate(G):
G[i] = g.set_domain(domain)

f, G = G.ltrim(-1), G[1:]
dom = f.get_domain()

zeros = f.ground_roots()
solutions = set([])

for zero in zeros:
solutions.add(((zero,), dom))

var_seq = reversed(gens[:-1])
vars_seq = postfixes(gens[1:])

for var, vars in zip(var_seq, vars_seq):
_solutions = set([])

for values, dom in solutions:
H, mapping = [], list(zip(vars, values))

for g in G:
_vars = (var,) + vars

if g.has_only_gens(*_vars) and g.degree(var) != 0:
h = g.ltrim(var).eval(dict(mapping))

if g.degree(var) == h.degree():
H.append(h)

p = min(H, key=lambda h: h.degree())
zeros = p.ground_roots()

for zero in zeros:
if not zero.is_Rational:
dom_zero = dom.algebraic_field(zero)
else:
dom_zero = dom

_solutions.add(((zero,) + values, dom_zero))

solutions = _solutions

solutions = list(solutions)

for i, (solution, _) in enumerate(solutions):
solutions[i] = solution

return sorted(solutions, key=default_sort_key)