Base class for multivariate polynomial rings#

class sage.rings.polynomial.multi_polynomial_ring_base.MPolynomialRing_base#

Bases: CommutativeRing

Create a polynomial ring in several variables over a commutative ring.

EXAMPLES:

sage: R.<x,y> = ZZ['x,y']; R
Multivariate Polynomial Ring in x, y over Integer Ring
sage: class CR(CommutativeRing):
....:     def __init__(self):
....:         CommutativeRing.__init__(self,self)
....:     def __call__(self,x):
....:         return None
sage: cr = CR()
sage: cr.is_commutative()
True
sage: cr['x,y']
Multivariate Polynomial Ring in x, y over
<__main__.CR_with_category object at ...>
change_ring(base_ring=None, names=None, order=None)#

Return a new multivariate polynomial ring which isomorphic to self, but has a different ordering given by the parameter ‘order’ or names given by the parameter ‘names’.

INPUT:

  • base_ring – a base ring

  • names – variable names

  • order – a term order

EXAMPLES:

sage: P.<x,y,z> = PolynomialRing(GF(127),3,order='lex')
sage: x > y^2
True
sage: Q.<x,y,z> = P.change_ring(order='degrevlex')
sage: x > y^2
False
characteristic()#

Return the characteristic of this polynomial ring.

EXAMPLES:

sage: R = PolynomialRing(QQ, 'x', 3)
sage: R.characteristic()
0
sage: R = PolynomialRing(GF(7),'x', 20)
sage: R.characteristic()
7
completion(names, prec=20, extras={})#

Return the completion of self with respect to the ideal generated by the variable(s) names.

INPUT:

  • names – variable or list/tuple of variables (given either as elements of the polynomial ring or as strings)

  • prec – default precision of resulting power series ring

  • extras – passed as keywords to PowerSeriesRing

EXAMPLES:

sage: P.<x,y,z,w> = PolynomialRing(ZZ)
sage: P.completion('w')
Power Series Ring in w over Multivariate Polynomial Ring in
x, y, z over Integer Ring
sage: P.completion((w,x,y))
Multivariate Power Series Ring in w, x, y over Univariate
Polynomial Ring in z over Integer Ring
sage: Q.<w,x,y,z> = P.completion(); Q
Multivariate Power Series Ring in w, x, y, z over Integer Ring

sage: H = PolynomialRing(PolynomialRing(ZZ,3,'z'),4,'f'); H
Multivariate Polynomial Ring in f0, f1, f2, f3 over
Multivariate Polynomial Ring in z0, z1, z2 over Integer Ring

sage: H.completion(H.gens())
Multivariate Power Series Ring in f0, f1, f2, f3 over
Multivariate Polynomial Ring in z0, z1, z2 over Integer Ring

sage: H.completion(H.gens()[2])
Power Series Ring in f2 over
Multivariate Polynomial Ring in f0, f1, f3 over
Multivariate Polynomial Ring in z0, z1, z2 over Integer Ring
construction()#

Returns a functor F and base ring R such that F(R) == self.

EXAMPLES:

sage: S = ZZ['x,y']
sage: F, R = S.construction(); R
Integer Ring
sage: F
MPoly[x,y]
sage: F(R) == S
True
sage: F(R) == ZZ['x']['y']
False
flattening_morphism()#

Return the flattening morphism of this polynomial ring

EXAMPLES:

sage: QQ['a','b']['x','y'].flattening_morphism()
Flattening morphism:
  From: Multivariate Polynomial Ring in x, y over Multivariate
  Polynomial Ring in a, b over Rational Field
  To:   Multivariate Polynomial Ring in a, b, x, y over Rational
  Field

sage: QQ['x,y'].flattening_morphism()
Identity endomorphism of Multivariate Polynomial Ring in x, y
over Rational Field
gen(n=0)#
interpolation(bound, *args)#

Create a polynomial with specified evaluations.

CALL FORMATS:

This function can be called in two ways:

  1. interpolation(bound, points, values)

  2. interpolation(bound, function)

INPUT:

  • bound – either an integer bounding the total degree or a list/tuple of integers bounding the degree of the variables

  • points – list/tuple containing the evaluation points

  • values – list/tuple containing the desired values at points

  • function – evaluable function in \(n\) variables, where \(n\) is the number of variables of the polynomial ring

OUTPUT:

  1. A polynomial respecting the bounds and having values as values when evaluated at points.

  2. A polynomial respecting the bounds and having the same values as function at exactly so many points so that the polynomial is unique.

EXAMPLES:

sage: def F(a,b,c):
....:     return a^3*b + b + c^2 + 25
....:
sage: R.<x,y,z> = PolynomialRing(QQ)
sage: R.interpolation(4, F)
x^3*y + z^2 + y + 25


sage: def F(a,b,c):
....:     return a^3*b + b + c^2 + 25
....:
sage: R.<x,y,z> = PolynomialRing(QQ)
sage: R.interpolation([3,1,2], F)
x^3*y + z^2 + y + 25


sage: def F(a,b,c):
....:     return a^3*b + b + c^2 + 25
....:
sage: R.<x,y,z> = PolynomialRing(QQ)
sage: points = [(5,1,1),(7,2,2),(8,5,-1),(2,5,3),(1,4,0),(5,9,0),
....: (2,7,0),(1,10,13),(0,0,1),(-1,1,0),(2,5,3),(1,1,1),(7,4,11),
....: (12,1,9),(1,1,3),(4,-1,2),(0,1,5),(5,1,3),(3,1,-2),(2,11,3),
....: (4,12,19),(3,1,1),(5,2,-3),(12,1,1),(2,3,4)]
sage: R.interpolation([3,1,2], points, [F(*x) for x in points])
x^3*y + z^2 + y + 25

ALGORITHM:

Solves a linear system of equations with the linear algebra module. If the points are not specified, it samples exactly as many points as needed for a unique solution.

Note

It will only run if the base ring is a field, even though it might work otherwise as well. If your base ring is an integral domain, let it run over the fraction field.

Also, if the solution is not unique, it spits out one solution, without any notice that there are more.

Lastly, the interpolation function for univariate polynomial rings is called lagrange_polynomial().

Warning

If you don’t provide point/value pairs but just a function, it will only use as many points as needed for a unique solution with the given bounds. In particular it will not notice or check whether the result yields the correct evaluation for other points as well. So if you give wrong bounds, you will get a wrong answer without any warning.

sage: def F(a,b,c): ….: return a^3*b + b + c^2 + 25 ….: sage: R.<x,y,z> = PolynomialRing(QQ) sage: R.interpolation(3,F) 1/2*x^3 + x*y + z^2 - 1/2*x + y + 25

See also

lagrange_polynomial

irrelevant_ideal()#

Return the irrelevant ideal of this multivariate polynomial ring.

This is the ideal generated by all of the indeterminate generators of this ring.

EXAMPLES:

sage: R.<x,y,z> = QQ[]
sage: R.irrelevant_ideal()
Ideal (x, y, z) of Multivariate Polynomial Ring in x, y, z over
Rational Field
is_exact()#

Test whether this multivariate polynomial ring is defined over an exact base ring.

EXAMPLES:

sage: PolynomialRing(QQ, 2, 'x').is_exact()
True
sage: PolynomialRing(RDF, 2, 'x').is_exact()
False
is_field(proof=True)#

Test whether this multivariate polynomial ring is a field.

A polynomial ring is a field when there are no variable and the base ring is a field.

EXAMPLES:

sage: PolynomialRing(QQ, 'x', 2).is_field()
False
sage: PolynomialRing(QQ, 'x', 0).is_field()
True
sage: PolynomialRing(ZZ, 'x', 0).is_field()
False
sage: PolynomialRing(Zmod(1), names=['x','y']).is_finite()
True
is_integral_domain(proof=True)#

EXAMPLES:

sage: ZZ['x,y'].is_integral_domain()
True
sage: Integers(8)['x,y'].is_integral_domain()
False
is_noetherian()#

EXAMPLES:

sage: ZZ['x,y'].is_noetherian()
True
sage: Integers(8)['x,y'].is_noetherian()
True
krull_dimension()#
macaulay_resultant(*args, **kwds)#

This is an implementation of the Macaulay Resultant. It computes the resultant of universal polynomials as well as polynomials with constant coefficients. This is a project done in sage days 55. It’s based on the implementation in Maple by Manfred Minimair, which in turn is based on the references listed below: It calculates the Macaulay resultant for a list of polynomials, up to sign!

REFERENCES:

AUTHORS:

  • Hao Chen, Solomon Vishkautsan (7-2014)

INPUT:

  • args – a list of \(n\) homogeneous polynomials in \(n\) variables.

    works when args[0] is the list of polynomials, or args is itself the list of polynomials

kwds:

  • sparse – boolean (optional - default: False)

    if True function creates sparse matrices.

OUTPUT:

  • the macaulay resultant, an element of the base ring of self

Todo

Working with sparse matrices should usually give faster results, but with the current implementation it actually works slower. There should be a way to improve performance with regards to this.

EXAMPLES:

The number of polynomials has to match the number of variables:

sage: R.<x,y,z> = PolynomialRing(QQ,3)
sage: R.macaulay_resultant([y,x+z])
Traceback (most recent call last):
...
TypeError: number of polynomials(= 2) must equal number of
variables (= 3)

The polynomials need to be all homogeneous:

sage: R.<x,y,z> = PolynomialRing(QQ,3)
sage: R.macaulay_resultant([y, x+z, z+x^3])
Traceback (most recent call last):
...
TypeError: resultant for non-homogeneous polynomials is
not supported

All polynomials must be in the same ring:

sage: S.<x,y> = PolynomialRing(QQ, 2)
sage: R.<x,y,z> = PolynomialRing(QQ,3)
sage: S.macaulay_resultant([y, z+x])
Traceback (most recent call last):
...
TypeError: not all inputs are polynomials in the calling ring

The following example recreates Proposition 2.10 in Ch.3 in [CLO2005]:

sage: K.<x,y> = PolynomialRing(ZZ, 2)
sage: flist,R = K._macaulay_resultant_universal_polynomials([1,1,2])
sage: R.macaulay_resultant(flist)
u2^2*u4^2*u6 - 2*u1*u2*u4*u5*u6 + u1^2*u5^2*u6 - u2^2*u3*u4*u7 +
u1*u2*u3*u5*u7 + u0*u2*u4*u5*u7 - u0*u1*u5^2*u7 + u1*u2*u3*u4*u8 -
u0*u2*u4^2*u8 - u1^2*u3*u5*u8 + u0*u1*u4*u5*u8 + u2^2*u3^2*u9 -
2*u0*u2*u3*u5*u9 + u0^2*u5^2*u9 - u1*u2*u3^2*u10 +
u0*u2*u3*u4*u10 + u0*u1*u3*u5*u10 - u0^2*u4*u5*u10 +
u1^2*u3^2*u11 - 2*u0*u1*u3*u4*u11 + u0^2*u4^2*u11

The following example degenerates into the determinant of a \(3*3\) matrix:

sage: K.<x,y> = PolynomialRing(ZZ, 2)
sage: flist,R = K._macaulay_resultant_universal_polynomials([1,1,1])
sage: R.macaulay_resultant(flist)
-u2*u4*u6 + u1*u5*u6 + u2*u3*u7 - u0*u5*u7 - u1*u3*u8 + u0*u4*u8

The following example is by Patrick Ingram (arXiv 1310.4114):

sage: U = PolynomialRing(ZZ,'y',2); y0,y1 = U.gens()
sage: R = PolynomialRing(U,'x',3); x0,x1,x2 = R.gens()
sage: f0 = y0*x2^2 - x0^2 + 2*x1*x2
sage: f1 = y1*x2^2 - x1^2 + 2*x0*x2
sage: f2 = x0*x1 - x2^2
sage: flist = [f0,f1,f2]
sage: R.macaulay_resultant([f0,f1,f2])
y0^2*y1^2 - 4*y0^3 - 4*y1^3 + 18*y0*y1 - 27

A simple example with constant rational coefficients:

sage: R.<x,y,z,w> = PolynomialRing(QQ,4)
sage: R.macaulay_resultant([w,z,y,x])
1

An example where the resultant vanishes:

sage: R.<x,y,z> = PolynomialRing(QQ,3)
sage: R.macaulay_resultant([x+y,y^2,x])
0

An example of bad reduction at a prime \(p = 5\):

sage: R.<x,y,z> = PolynomialRing(QQ,3)
sage: R.macaulay_resultant([y,x^3+25*y^2*x,5*z])
125

The input can given as an unpacked list of polynomials:

sage: R.<x,y,z> = PolynomialRing(QQ,3)
sage: R.macaulay_resultant(y,x^3+25*y^2*x,5*z)
125

An example when the coefficients live in a finite field:

sage: F = FiniteField(11)
sage: R.<x,y,z,w> = PolynomialRing(F,4)
sage: R.macaulay_resultant([z,x^3,5*y,w])
4

Example when the denominator in the algorithm vanishes(in this case the resultant is the constant term of the quotient of char polynomials of numerator/denominator):

sage: R.<x,y,z> = PolynomialRing(QQ,3)
sage: R.macaulay_resultant([y, x+z, z^2])
-1

When there are only 2 polynomials, macaulay resultant degenerates to the traditional resultant:

sage: R.<x> = PolynomialRing(QQ,1)
sage: f =  x^2+1; g = x^5+1
sage: fh = f.homogenize()
sage: gh = g.homogenize()
sage: RH = fh.parent()
sage: f.resultant(g) == RH.macaulay_resultant([fh,gh])
True
monomial(*exponents)#

Return the monomial with given exponents.

EXAMPLES:

sage: R.<x,y,z> = PolynomialRing(ZZ, 3)
sage: R.monomial(1,1,1)
x*y*z
sage: e=(1,2,3)
sage: R.monomial(*e)
x*y^2*z^3
sage: m = R.monomial(1,2,3)
sage: R.monomial(*m.degrees()) == m
True
monomials_of_degree(degree)#

Return a list of all monomials of the given total degree in this multivariate polynomial ring.

EXAMPLES:

sage: R.<x,y,z> = ZZ[]
sage: mons = R.monomials_of_degree(2)
sage: mons
[x^2, x*y, x*z, y^2, y*z, z^2]

The number of such monomials equals \(\binom{n+k-1}{k}\) where \(n\) is the number of variables and \(k\) the degree:

sage: len(mons) == binomial(3+2-1,2)
True
ngens()#
random_element(degree=2, terms=None, choose_degree=False, *args, **kwargs)#

Return a random polynomial of at most degree \(d\) and at most \(t\) terms.

First monomials are chosen uniformly random from the set of all possible monomials of degree up to \(d\) (inclusive). This means that it is more likely that a monomial of degree \(d\) appears than a monomial of degree \(d-1\) because the former class is bigger.

Exactly \(t\) distinct monomials are chosen this way and each one gets a random coefficient (possibly zero) from the base ring assigned.

The returned polynomial is the sum of this list of terms.

INPUT:

  • degree – maximal degree (likely to be reached) (default: 2)

  • terms – number of terms requested (default: 5). If more terms are requested than exist, then this parameter is silently reduced to the maximum number of available terms.

  • choose_degree – choose degrees of monomials randomly first rather than monomials uniformly random.

  • **kwargs – passed to the random element generator of the base ring

EXAMPLES:

sage: P.<x,y,z> = PolynomialRing(QQ)
sage: f = P.random_element(2, 5)
sage: f.degree() <= 2
True
sage: f.parent() is P
True
sage: len(list(f)) <= 5
True

sage: f = P.random_element(2, 5, choose_degree=True)
sage: f.degree() <= 2
True
sage: f.parent() is P
True
sage: len(list(f)) <= 5
True

Stacked rings:

sage: R = QQ['x,y']
sage: S = R['t,u']
sage: f = S._random_nonzero_element(degree=2, terms=1)
sage: len(list(f))
1
sage: f.degree() <= 2
True
sage: f.parent() is S
True

Default values apply if no degree and/or number of terms is provided:

sage: M = random_matrix(QQ['x,y,z'], 2, 2)
sage: all(a.degree() <= 2 for a in M.list())
True
sage: all(len(list(a)) <= 5 for a in M.list())
True

sage: M = random_matrix(QQ['x,y,z'], 2, 2, terms=1, degree=2)
sage: all(a.degree() <= 2 for a in M.list())
True
sage: all(len(list(a)) <= 1 for a in M.list())
True

sage: P.random_element(0, 1) in QQ
True

sage: P.random_element(2, 0)
0

sage: R.<x> = PolynomialRing(Integers(3), 1)
sage: f = R.random_element()
sage: f.degree() <= 2
True
sage: len(list(f)) <= 3
True

To produce a dense polynomial, pick terms=Infinity:

sage: P.<x,y,z> = GF(127)[]
sage: f = P.random_element(degree=2, terms=Infinity)
sage: while len(list(f)) != 10:
....:     f = P.random_element(degree=2, terms=Infinity)
sage: f = P.random_element(degree=3, terms=Infinity)
sage: while len(list(f)) != 20:
....:     f = P.random_element(degree=3, terms=Infinity)
sage: f = P.random_element(degree=3, terms=Infinity, choose_degree=True)
sage: while len(list(f)) != 20:
....:     f = P.random_element(degree=3, terms=Infinity)

The number of terms is silently reduced to the maximum available if more terms are requested:

sage: P.<x,y,z> = GF(127)[]
sage: f = P.random_element(degree=2, terms=1000)
sage: len(list(f)) <= 10
True
remove_var(order=None, *var)#

Remove a variable or sequence of variables from self.

If order is not specified, then the subring inherits the term order of the original ring, if possible.

EXAMPLES:

sage: P.<x,y,z,w> = PolynomialRing(ZZ)
sage: P.remove_var(z)
Multivariate Polynomial Ring in x, y, w over Integer Ring
sage: P.remove_var(z,x)
Multivariate Polynomial Ring in y, w over Integer Ring
sage: P.remove_var(y,z,x)
Univariate Polynomial Ring in w over Integer Ring

Removing all variables results in the base ring:

sage: P.remove_var(y,z,x,w)
Integer Ring

If possible, the term order is kept:

sage: R.<x,y,z,w> = PolynomialRing(ZZ, order='deglex')
sage: R.remove_var(y).term_order()
Degree lexicographic term order

sage: R.<x,y,z,w> = PolynomialRing(ZZ, order='lex')
sage: R.remove_var(y).term_order()
Lexicographic term order

Be careful with block orders when removing variables:

sage: R.<x,y,z,u,v> = PolynomialRing(ZZ, order='deglex(2),lex(3)')
sage: R.remove_var(x,y,z)
Traceback (most recent call last):
...
ValueError: impossible to use the original term order (most
likely because it was a block order). Please specify the term
order for the subring
sage: R.remove_var(x,y,z, order='degrevlex')
Multivariate Polynomial Ring in u, v over Integer Ring
repr_long()#

Return structured string representation of self.

EXAMPLES:

sage: P.<x,y,z> = PolynomialRing(QQ,order=TermOrder('degrevlex',1)+TermOrder('lex',2))
sage: print(P.repr_long())
Polynomial Ring
 Base Ring : Rational Field
      Size : 3 Variables
  Block  0 : Ordering : degrevlex
             Names    : x
  Block  1 : Ordering : lex
             Names    : y, z
some_elements()#

Return a list of polynomials.

This is typically used for running generic tests.

EXAMPLES:

sage: R.<x,y> = QQ[]
sage: R.some_elements()
[x, y, x + y, x^2 + x*y, 0, 1]
term_order()#
univariate_ring(x)#

Return a univariate polynomial ring whose base ring comprises all but one variables of self.

INPUT:

  • x – a variable of self.

EXAMPLES:

sage: P.<x,y,z> = QQ[]
sage: P.univariate_ring(y)
Univariate Polynomial Ring in y over Multivariate Polynomial
Ring in x, z over Rational Field
variable_names_recursive(depth=None)#

Returns the list of variable names of this and its base rings, as if it were a single multi-variate polynomial.

EXAMPLES:

sage: R = QQ['x,y']['z,w']
sage: R.variable_names_recursive()
('x', 'y', 'z', 'w')
sage: R.variable_names_recursive(3)
('y', 'z', 'w')
weyl_algebra()#

Return the Weyl algebra generated from self.

EXAMPLES:

sage: R = QQ['x,y,z']
sage: W = R.weyl_algebra(); W
Differential Weyl algebra of polynomials in x, y, z over Rational Field
sage: W.polynomial_ring() == R
True
sage.rings.polynomial.multi_polynomial_ring_base.is_MPolynomialRing(x)#
sage.rings.polynomial.multi_polynomial_ring_base.unpickle_MPolynomialRing_generic(base_ring, n, names, order)#
sage.rings.polynomial.multi_polynomial_ring_base.unpickle_MPolynomialRing_generic_v1(base_ring, n, names, order)#