def __init__(self,
field,
mult_table,
- rank,
prefix='e',
category=None,
natural_basis=None,
# a real embedding.
raise ValueError('field is not real')
- self._rank = rank
self._natural_basis = natural_basis
if category is None:
True
"""
- return tuple( self.random_element() for idx in range(count) )
-
+ return tuple( self.random_element() for idx in range(count) )
+ @cached_method
def rank(self):
"""
Return the rank of this EJA.
ALGORITHM:
- The author knows of no algorithm to compute the rank of an EJA
- where only the multiplication table is known. In lieu of one, we
- require the rank to be specified when the algebra is created,
- and simply pass along that number here.
+ We first compute the polynomial "column matrices" `p_{k}` that
+ evaluate to `x^k` on the coordinates of `x`. Then, we begin
+ adding them to a matrix one at a time, and trying to solve the
+ system that makes `p_{0}`,`p_{1}`,..., `p_{s-1}` add up to
+ `p_{s}`. This will succeed only when `s` is the rank of the
+ algebra, as proven in a recent draft paper of mine.
SETUP::
- sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
+ sage: from mjo.eja.eja_algebra import (HadamardEJA,
+ ....: JordanSpinEJA,
....: RealSymmetricEJA,
....: ComplexHermitianEJA,
....: QuaternionHermitianEJA,
sage: r > 0 or (r == 0 and J.is_trivial())
True
+ Ensure that computing the rank actually works, since the ranks
+ of all simple algebras are known and will be cached by default::
+
+ sage: J = HadamardEJA(4)
+ sage: J.rank.clear_cache()
+ sage: J.rank()
+ 4
+
+ ::
+
+ sage: J = JordanSpinEJA(4)
+ sage: J.rank.clear_cache()
+ sage: J.rank()
+ 2
+
+ ::
+
+ sage: J = RealSymmetricEJA(3)
+ sage: J.rank.clear_cache()
+ sage: J.rank()
+ 3
+
+ ::
+
+ sage: J = ComplexHermitianEJA(2)
+ sage: J.rank.clear_cache()
+ sage: J.rank()
+ 2
+
+ ::
+
+ sage: J = QuaternionHermitianEJA(2)
+ sage: J.rank.clear_cache()
+ sage: J.rank()
+ 2
+
"""
- return self._rank
+ n = self.dimension()
+ if n == 0:
+ return 0
+ elif n == 1:
+ return 1
+
+ var_names = [ "X" + str(z) for z in range(1,n+1) ]
+ R = PolynomialRing(self.base_ring(), var_names)
+ vars = R.gens()
+
+ def L_x_i_j(i,j):
+ # From a result in my book, these are the entries of the
+ # basis representation of L_x.
+ return sum( vars[k]*self.monomial(k).operator().matrix()[i,j]
+ for k in range(n) )
+
+ L_x = matrix(R, n, n, L_x_i_j)
+ x_powers = [ vars[k]*(L_x**k)*self.one().to_vector()
+ for k in range(n) ]
+
+ # Can assume n >= 2
+ M = matrix([x_powers[0]])
+ old_rank = 1
+
+ for d in range(1,n):
+ M = matrix(M.rows() + [x_powers[d]])
+ M.echelonize()
+ # TODO: we've basically solved the system here.
+ # We should save the echelonized matrix somehow
+ # so that it can be reused in the charpoly method.
+ new_rank = M.rank()
+ if new_rank == old_rank:
+ return new_rank
+ else:
+ old_rank = new_rank
+
+ return n
def vector_space(self):
for i in range(n) ]
fdeja = super(HadamardEJA, self)
- return fdeja.__init__(field, mult_table, rank=n, **kwargs)
+ fdeja.__init__(field, mult_table, **kwargs)
+ self.rank.set_cache(n)
def inner_product(self, x, y):
"""
# field can have dimension 4 (quaternions) too.
return 2
- def __init__(self, field, basis, rank, normalize_basis=True, **kwargs):
+ def __init__(self, field, basis, normalize_basis=True, **kwargs):
"""
Compared to the superclass constructor, we take a basis instead of
a multiplication table because the latter can be computed in terms
# time to ensure that it isn't a generator expression.
basis = tuple(basis)
- if rank > 1 and normalize_basis:
+ if len(basis) > 1 and normalize_basis:
# We'll need sqrt(2) to normalize the basis, and this
# winds up in the multiplication table, so the whole
# algebra needs to be over the field extension.
Qs = self.multiplication_table_from_matrix_basis(basis)
fdeja = super(MatrixEuclideanJordanAlgebra, self)
- return fdeja.__init__(field,
- Qs,
- rank=rank,
- natural_basis=basis,
- **kwargs)
+ fdeja.__init__(field, Qs, natural_basis=basis, **kwargs)
+ return
+
+ @cached_method
+ def rank(self):
+ r"""
+ Override the parent method with something that tries to compute
+ over a faster (non-extension) field.
+ """
+ if self._basis_normalizers is None:
+ # We didn't normalize, so assume that the basis we started
+ # with had entries in a nice field.
+ return super(MatrixEuclideanJordanAlgebra, self).rank()
+ else:
+ basis = ( (b/n) for (b,n) in zip(self.natural_basis(),
+ self._basis_normalizers) )
+
+ # Do this over the rationals and convert back at the end.
+ # Only works because we know the entries of the basis are
+ # integers.
+ J = MatrixEuclideanJordanAlgebra(QQ,
+ basis,
+ normalize_basis=False)
+ return J.rank()
@cached_method
def _charpoly_coeff(self, i):
# Do this over the rationals and convert back at the end.
J = MatrixEuclideanJordanAlgebra(QQ,
basis,
- self.rank(),
normalize_basis=False)
(_,x,_,_) = J._charpoly_matrix_system()
p = J._charpoly_coeff(i)
def __init__(self, n, field=AA, **kwargs):
basis = self._denormalized_basis(n, field)
- super(RealSymmetricEJA, self).__init__(field, basis, n, **kwargs)
+ super(RealSymmetricEJA, self).__init__(field, basis, **kwargs)
+ self.rank.set_cache(n)
class ComplexMatrixEuclideanJordanAlgebra(MatrixEuclideanJordanAlgebra):
def __init__(self, n, field=AA, **kwargs):
basis = self._denormalized_basis(n,field)
- super(ComplexHermitianEJA,self).__init__(field, basis, n, **kwargs)
+ super(ComplexHermitianEJA,self).__init__(field, basis, **kwargs)
+ self.rank.set_cache(n)
class QuaternionMatrixEuclideanJordanAlgebra(MatrixEuclideanJordanAlgebra):
def __init__(self, n, field=AA, **kwargs):
basis = self._denormalized_basis(n,field)
- super(QuaternionHermitianEJA,self).__init__(field, basis, n, **kwargs)
+ super(QuaternionHermitianEJA,self).__init__(field, basis, **kwargs)
+ self.rank.set_cache(n)
class BilinearFormEJA(FiniteDimensionalEuclideanJordanAlgebra, KnownRankEJA):
# one-dimensional ambient space (because the rank is bounded
# by the ambient dimension).
fdeja = super(BilinearFormEJA, self)
- return fdeja.__init__(field, mult_table, rank=min(n,2), **kwargs)
+ fdeja.__init__(field, mult_table, **kwargs)
+ self.rank.set_cache(min(n,2))
def inner_product(self, x, y):
r"""
fdeja = super(TrivialEJA, self)
# The rank is zero using my definition, namely the dimension of the
# largest subalgebra generated by any element.
- return fdeja.__init__(field, mult_table, rank=0, **kwargs)
+ fdeja.__init__(field, mult_table, **kwargs)
+ self.rank.set_cache(0)
projects / sage.d.git / blobdiff