return self._multiplication_table[i][j]
@cached_method
- def characteristic_polynomial(self):
+ def characteristic_polynomial_of(self):
"""
- Return a characteristic polynomial that works for all elements
- of this algebra.
+ Return the algebra's "characteristic polynomial of" function,
+ which is itself a multivariate polynomial that, when evaluated
+ at the coordinates of some algebra element, returns that
+ element's characteristic polynomial.
The resulting polynomial has `n+1` variables, where `n` is the
dimension of this algebra. The first `n` variables correspond to
Alizadeh, Example 11.11::
sage: J = JordanSpinEJA(3)
- sage: p = J.characteristic_polynomial(); p
+ sage: p = J.characteristic_polynomial_of(); p
X1^2 - X2^2 - X3^2 + (-2*t)*X1 + t^2
sage: xvec = J.one().to_vector()
sage: p(*xvec)
any argument::
sage: J = TrivialEJA()
- sage: J.characteristic_polynomial()
+ sage: J.characteristic_polynomial_of()
1
"""
"""
Return the matrix space in which this algebra's natural basis
elements live.
+
+ Generally this will be an `n`-by-`1` column-vector space,
+ except when the algebra is trivial. There it's `n`-by-`n`
+ (where `n` is zero), to ensure that two elements of the
+ natural basis space (empty matrices) can be multiplied.
"""
- if self._natural_basis is None or len(self._natural_basis) == 0:
+ if self.is_trivial():
+ return MatrixSpace(self.base_ring(), 0)
+ elif self._natural_basis is None or len(self._natural_basis) == 0:
return MatrixSpace(self.base_ring(), self.dimension(), 1)
else:
return self._natural_basis[0].matrix_space()
Vector space of degree 6 and dimension 2...
sage: J1
Euclidean Jordan algebra of dimension 3...
+ sage: J0.one().natural_representation()
+ [0 0 0]
+ [0 0 0]
+ [0 0 1]
+ sage: orig_df = AA.options.display_format
+ sage: AA.options.display_format = 'radical'
+ sage: J.from_vector(J5.basis()[0]).natural_representation()
+ [ 0 0 1/2*sqrt(2)]
+ [ 0 0 0]
+ [1/2*sqrt(2) 0 0]
+ sage: J.from_vector(J5.basis()[1]).natural_representation()
+ [ 0 0 0]
+ [ 0 0 1/2*sqrt(2)]
+ [ 0 1/2*sqrt(2) 0]
+ sage: AA.options.display_format = orig_df
+ sage: J1.one().natural_representation()
+ [1 0 0]
+ [0 1 0]
+ [0 0 0]
TESTS:
sage: J1.superalgebra() == J and J1.dimension() == J.dimension()
True
- The identity elements in the two subalgebras are the
- projections onto their respective subspaces of the
- superalgebra's identity element::
+ The decomposition is into eigenspaces, and its components are
+ therefore necessarily orthogonal. Moreover, the identity
+ elements in the two subalgebras are the projections onto their
+ respective subspaces of the superalgebra's identity element::
sage: set_random_seed()
sage: J = random_eja()
....: x = J.random_element()
sage: c = x.subalgebra_idempotent()
sage: J0,J5,J1 = J.peirce_decomposition(c)
+ sage: ipsum = 0
+ sage: for (w,y,z) in zip(J0.basis(), J5.basis(), J1.basis()):
+ ....: w = w.superalgebra_element()
+ ....: y = J.from_vector(y)
+ ....: z = z.superalgebra_element()
+ ....: ipsum += w.inner_product(y).abs()
+ ....: ipsum += w.inner_product(z).abs()
+ ....: ipsum += y.inner_product(z).abs()
+ sage: ipsum
+ 0
sage: J1(c) == J1.one()
True
sage: J0(J.one() - c) == J0.one()
return (J0, J5, J1)
- def random_elements(self, count):
+ def random_element(self, thorough=False):
+ r"""
+ Return a random element of this algebra.
+
+ Our algebra superclass method only returns a linear
+ combination of at most two basis elements. We instead
+ want the vector space "random element" method that
+ returns a more diverse selection.
+
+ INPUT:
+
+ - ``thorough`` -- (boolean; default False) whether or not we
+ should generate irrational coefficients for the random
+ element when our base ring is irrational; this slows the
+ algebra operations to a crawl, but any truly random method
+ should include them
+
+ """
+ # For a general base ring... maybe we can trust this to do the
+ # right thing? Unlikely, but.
+ V = self.vector_space()
+ v = V.random_element()
+
+ if self.base_ring() is AA:
+ # The "random element" method of the algebraic reals is
+ # stupid at the moment, and only returns integers between
+ # -2 and 2, inclusive. Instead, we implement our own
+ # "random vector" method, and then coerce that into the
+ # algebra. We use the vector space degree here instead of
+ # the dimension because a subalgebra could (for example) be
+ # spanned by only two vectors, each with five coordinates.
+ # We need to generate all five coordinates.
+ if thorough:
+ v *= QQbar.random_element().real()
+ else:
+ v *= QQ.random_element()
+
+ return self.from_vector(V.coordinate_vector(v))
+
+ def random_elements(self, count, thorough=False):
"""
Return ``count`` random elements as a tuple.
+ INPUT:
+
+ - ``thorough`` -- (boolean; default False) whether or not we
+ should generate irrational coefficients for the random
+ elements when our base ring is irrational; this slows the
+ algebra operations to a crawl, but any truly random method
+ should include them
+
SETUP::
sage: from mjo.eja.eja_algebra import JordanSpinEJA
True
"""
- return tuple( self.random_element() for idx in range(count) )
+ return tuple( self.random_element(thorough)
+ for idx in range(count) )
@classmethod
def random_instance(cls, field=AA, **kwargs):
# there's only one.
return cls(field)
- n = ZZ.random_element(cls._max_test_case_size()) + 1
+ n = ZZ.random_element(cls._max_test_case_size() + 1)
return cls(n, field, **kwargs)
@cached_method
for k in range(n) )
L_x = matrix(F, n, n, L_x_i_j)
+
+ r = None
+ if self.rank.is_in_cache():
+ r = self.rank()
+ # There's no need to pad the system with redundant
+ # columns if we *know* they'll be redundant.
+ n = r
+
# Compute an extra power in case the rank is equal to
# the dimension (otherwise, we would stop at x^(r-1)).
x_powers = [ (L_x**k)*self.one().to_vector()
AE = A.extended_echelon_form()
E = AE[:,n:]
A_rref = AE[:,:n]
- r = A_rref.rank()
+ if r is None:
+ r = A_rref.rank()
b = x_powers[r]
# The theory says that only the first "r" coefficients are
return x.to_vector().inner_product(y.to_vector())
-def random_eja(field=AA, nontrivial=False):
+def random_eja(field=AA):
"""
Return a "random" finite-dimensional Euclidean Jordan Algebra.
Euclidean Jordan algebra of dimension...
"""
- eja_classes = [HadamardEJA,
- JordanSpinEJA,
- RealSymmetricEJA,
- ComplexHermitianEJA,
- QuaternionHermitianEJA]
- if not nontrivial:
- eja_classes.append(TrivialEJA)
- classname = choice(eja_classes)
+ classname = choice([TrivialEJA,
+ HadamardEJA,
+ JordanSpinEJA,
+ RealSymmetricEJA,
+ ComplexHermitianEJA,
+ QuaternionHermitianEJA])
return classname.random_instance(field=field)
-
-
class MatrixEuclideanJordanAlgebra(FiniteDimensionalEuclideanJordanAlgebra):
@staticmethod
def _max_test_case_size():
J = MatrixEuclideanJordanAlgebra(QQ,
basis,
normalize_basis=False)
- return J._charpoly_coefficients()
+ a = J._charpoly_coefficients()
+
+ # Unfortunately, changing the basis does change the
+ # coefficients of the characteristic polynomial, but since
+ # these are really the coefficients of the "characteristic
+ # polynomial of" function, everything is still nice and
+ # unevaluated. It's therefore "obvious" how scaling the
+ # basis affects the coordinate variables X1, X2, et
+ # cetera. Scaling the first basis vector up by "n" adds a
+ # factor of 1/n into every "X1" term, for example. So here
+ # we simply undo the basis_normalizer scaling that we
+ # performed earlier.
+ #
+ # The a[0] access here is safe because trivial algebras
+ # won't have any basis normalizers and therefore won't
+ # make it to this "else" branch.
+ XS = a[0].parent().gens()
+ subs_dict = { XS[i]: self._basis_normalizers[i]*XS[i]
+ for i in range(len(XS)) }
+ return tuple( a_i.subs(subs_dict) for a_i in a )
@staticmethod
# is supposed to hold the entire long vector, and the subspace W
# of V will be spanned by the vectors that arise from symmetric
# matrices. Thus for S^2, dim(V) == 4 and dim(W) == 3.
+ if len(basis) == 0:
+ return []
+
field = basis[0].base_ring()
dimension = basis[0].nrows()
sage: x.operator().matrix().is_symmetric()
True
+ We can construct the (trivial) algebra of rank zero::
+
+ sage: RealSymmetricEJA(0)
+ Euclidean Jordan algebra of dimension 0 over Algebraic Real Field
+
"""
@classmethod
def _denormalized_basis(cls, n, field):
sage: x.operator().matrix().is_symmetric()
True
+ We can construct the (trivial) algebra of rank zero::
+
+ sage: ComplexHermitianEJA(0)
+ Euclidean Jordan algebra of dimension 0 over Algebraic Real Field
+
"""
@classmethod
sage: x.operator().matrix().is_symmetric()
True
+ We can construct the (trivial) algebra of rank zero::
+
+ sage: QuaternionHermitianEJA(0)
+ Euclidean Jordan algebra of dimension 0 over Algebraic Real Field
+
"""
@classmethod
def _denormalized_basis(cls, n, field):
projects / sage.d.git / blobdiff