from sage.rings.all import (ZZ, QQ, AA, QQbar, RR, RLF, CLF,
PolynomialRing,
QuadraticField)
-from mjo.eja.eja_element import FiniteDimensionalEJAElement
+from mjo.eja.eja_element import (CartesianProductEJAElement,
+ FiniteDimensionalEJAElement)
from mjo.eja.eja_operator import FiniteDimensionalEJAOperator
-from mjo.eja.eja_utils import _all2list, _mat2vec
+from mjo.eja.eja_utils import _all2list
def EuclideanJordanAlgebras(field):
r"""
We should compute that an element subalgebra is associative even
if we circumvent the element method::
- sage: set_random_seed()
sage: J = random_eja(field=QQ,orthonormalize=False)
sage: x = J.random_element()
sage: A = x.subalgebra_generated_by(orthonormalize=False)
matrix_space=None,
orthonormalize=True,
associative=None,
- cartesian_product=False,
check_field=True,
check_axioms=True,
prefix="b"):
if associative:
# Element subalgebras can take advantage of this.
category = category.Associative()
- if cartesian_product:
- # Use join() here because otherwise we only get the
- # "Cartesian product of..." and not the things themselves.
- category = category.join([category,
- category.CartesianProducts()])
# Call the superclass constructor so that we can use its from_vector()
# method to build our multiplication table.
if orthonormalize:
# Now "self._matrix_span" is the vector space of our
- # algebra coordinates. The variables "X1", "X2",... refer
+ # algebra coordinates. The variables "X0", "X1",... refer
# to the entries of vectors in self._matrix_span. Thus to
# convert back and forth between the orthonormal
# coordinates and the given ones, we need to stick the
# Now we actually compute the multiplication and inner-product
# tables/matrices using the possibly-orthonormalized basis.
self._inner_product_matrix = matrix.identity(field, n)
- self._multiplication_table = [ [0 for j in range(i+1)]
+ zed = self.zero()
+ self._multiplication_table = [ [zed for j in range(i+1)]
for i in range(n) ]
# Note: the Jordan and inner-products are defined in terms
TESTS::
- sage: set_random_seed()
sage: J = random_eja()
sage: J(1)
Traceback (most recent call last):
TESTS::
- sage: set_random_seed()
sage: J = random_eja()
sage: n = J.dimension()
sage: bi = J.zero()
Our inner product is "associative," which means the following for
a symmetric bilinear form::
- sage: set_random_seed()
sage: J = random_eja()
sage: x,y,z = J.random_elements(3)
sage: (x*y).inner_product(z) == y.inner_product(x*z)
Ensure that this is the usual inner product for the algebras
over `R^n`::
- sage: set_random_seed()
sage: J = HadamardEJA.random_instance()
sage: x,y = J.random_elements(2)
sage: actual = x.inner_product(y)
one). This is in Faraut and Koranyi, and also my "On the
symmetry..." paper::
- sage: set_random_seed()
sage: J = BilinearFormEJA.random_instance()
sage: n = J.dimension()
sage: x = J.random_element()
The values we've presupplied to the constructors agree with
the computation::
- sage: set_random_seed()
sage: J = random_eja()
sage: J.is_associative() == J._jordan_product_is_associative()
True
Ensure that we can convert any element back and forth
faithfully between its matrix and algebra representations::
- sage: set_random_seed()
sage: J = random_eja()
sage: x = J.random_element()
sage: J(x.to_matrix()) == x
sage: J = JordanSpinEJA(3)
sage: p = J.characteristic_polynomial_of(); p
- X1^2 - X2^2 - X3^2 + (-2*t)*X1 + t^2
+ X0^2 - X1^2 - X2^2 + (-2*t)*X0 + t^2
sage: xvec = J.one().to_vector()
sage: p(*xvec)
t^2 - 2*t + 1
sage: J = HadamardEJA(2)
sage: J.coordinate_polynomial_ring()
- Multivariate Polynomial Ring in X1, X2...
+ Multivariate Polynomial Ring in X0, X1...
sage: J = RealSymmetricEJA(3,field=QQ,orthonormalize=False)
sage: J.coordinate_polynomial_ring()
- Multivariate Polynomial Ring in X1, X2, X3, X4, X5, X6...
+ Multivariate Polynomial Ring in X0, X1, X2, X3, X4, X5...
"""
- var_names = tuple( "X%d" % z for z in range(1, self.dimension()+1) )
+ var_names = tuple( "X%d" % z for z in range(self.dimension()) )
return PolynomialRing(self.base_ring(), var_names)
def inner_product(self, x, y):
Our inner product is "associative," which means the following for
a symmetric bilinear form::
- sage: set_random_seed()
sage: J = random_eja()
sage: x,y,z = J.random_elements(3)
sage: (x*y).inner_product(z) == y.inner_product(x*z)
Ensure that this is the usual inner product for the algebras
over `R^n`::
- sage: set_random_seed()
sage: J = HadamardEJA.random_instance()
sage: x,y = J.random_elements(2)
sage: actual = x.inner_product(y)
one). This is in Faraut and Koranyi, and also my "On the
symmetry..." paper::
- sage: set_random_seed()
sage: J = BilinearFormEJA.random_instance()
sage: n = J.dimension()
sage: x = J.random_element()
The identity element acts like the identity, regardless of
whether or not we orthonormalize::
- sage: set_random_seed()
sage: J = random_eja()
sage: x = J.random_element()
sage: J.one()*x == x and x*J.one() == x
::
- sage: set_random_seed()
sage: J = random_eja(field=QQ, orthonormalize=False)
sage: x = J.random_element()
sage: J.one()*x == x and x*J.one() == x
regardless of the base field and whether or not we
orthonormalize::
- sage: set_random_seed()
sage: J = random_eja()
sage: actual = J.one().operator().matrix()
sage: expected = matrix.identity(J.base_ring(), J.dimension())
::
- sage: set_random_seed()
sage: J = random_eja(field=QQ, orthonormalize=False)
sage: actual = J.one().operator().matrix()
sage: expected = matrix.identity(J.base_ring(), J.dimension())
Ensure that the cached unit element (often precomputed by
hand) agrees with the computed one::
- sage: set_random_seed()
sage: J = random_eja()
sage: cached = J.one()
sage: J.one.clear_cache()
::
- sage: set_random_seed()
sage: J = random_eja(field=QQ, orthonormalize=False)
sage: cached = J.one()
sage: J.one.clear_cache()
#
# Of course, matrices aren't vectors in sage, so we have to
# appeal to the "long vectors" isometry.
- oper_vecs = [ _mat2vec(g.operator().matrix()) for g in self.gens() ]
+
+ V = VectorSpace(self.base_ring(), self.dimension()**2)
+ oper_vecs = [ V(g.operator().matrix().list()) for g in self.gens() ]
# Now we use basic linear algebra to find the coefficients,
# of the matrices-as-vectors-linear-combination, which should
# We used the isometry on the left-hand side already, but we
# still need to do it for the right-hand side. Recall that we
# wanted something that summed to the identity matrix.
- b = _mat2vec( matrix.identity(self.base_ring(), self.dimension()) )
+ b = V( matrix.identity(self.base_ring(), self.dimension()).list() )
# Now if there's an identity element in the algebra, this
# should work. We solve on the left to avoid having to
Every algebra decomposes trivially with respect to its identity
element::
- sage: set_random_seed()
sage: J = random_eja()
sage: J0,J5,J1 = J.peirce_decomposition(J.one())
sage: J0.dimension() == 0 and J5.dimension() == 0
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()
sage: x = J.random_element()
sage: if not J.is_trivial():
for idx in range(count) )
+ def operator_polynomial_matrix(self):
+ r"""
+ Return the matrix of polynomials (over this algebra's
+ :meth:`coordinate_polynomial_ring`) that, when evaluated at
+ the basis coordinates of an element `x`, produces the basis
+ representation of `L_{x}`.
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import (HadamardEJA,
+ ....: JordanSpinEJA)
+
+ EXAMPLES::
+
+ sage: J = HadamardEJA(4)
+ sage: L_x = J.operator_polynomial_matrix()
+ sage: L_x
+ [X0 0 0 0]
+ [ 0 X1 0 0]
+ [ 0 0 X2 0]
+ [ 0 0 0 X3]
+ sage: x = J.one()
+ sage: d = zip(J.coordinate_polynomial_ring().gens(), x.to_vector())
+ sage: L_x.subs(dict(d))
+ [1 0 0 0]
+ [0 1 0 0]
+ [0 0 1 0]
+ [0 0 0 1]
+
+ ::
+
+ sage: J = JordanSpinEJA(4)
+ sage: L_x = J.operator_polynomial_matrix()
+ sage: L_x
+ [X0 X1 X2 X3]
+ [X1 X0 0 0]
+ [X2 0 X0 0]
+ [X3 0 0 X0]
+ sage: x = J.one()
+ sage: d = zip(J.coordinate_polynomial_ring().gens(), x.to_vector())
+ sage: L_x.subs(dict(d))
+ [1 0 0 0]
+ [0 1 0 0]
+ [0 0 1 0]
+ [0 0 0 1]
+
+ """
+ R = self.coordinate_polynomial_ring()
+
+ 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( v*self.monomial(k).operator().matrix()[i,j]
+ for (k,v) in enumerate(R.gens()) )
+
+ n = self.dimension()
+ return matrix(R, n, n, L_x_i_j)
+
@cached_method
def _charpoly_coefficients(self):
r"""
The theory shows that these are all homogeneous polynomials of
a known degree::
- sage: set_random_seed()
sage: J = random_eja()
sage: all(p.is_homogeneous() for p in J._charpoly_coefficients())
True
"""
n = self.dimension()
R = self.coordinate_polynomial_ring()
- vars = R.gens()
F = R.fraction_field()
- 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(F, n, n, L_x_i_j)
+ L_x = self.operator_polynomial_matrix()
r = None
if self.rank.is_in_cache():
positive integer rank, unless the algebra is trivial in
which case its rank will be zero::
- sage: set_random_seed()
sage: J = random_eja()
sage: r = J.rank()
sage: r in ZZ
Ensure that computing the rank actually works, since the ranks
of all simple algebras are known and will be cached by default::
- sage: set_random_seed() # long time
sage: J = random_eja() # long time
sage: cached = J.rank() # long time
sage: J.rank.clear_cache() # long time
check_field=False,
check_axioms=False)
+ def rational_algebra(self):
+ # Using None as a flag here (rather than just assigning "self"
+ # to self._rational_algebra by default) feels a little bit
+ # more sane to me in a garbage-collected environment.
+ if self._rational_algebra is None:
+ return self
+ else:
+ return self._rational_algebra
+
@cached_method
def _charpoly_coefficients(self):
r"""
sage: J = JordanSpinEJA(3)
sage: J._charpoly_coefficients()
- (X1^2 - X2^2 - X3^2, -2*X1)
+ (X0^2 - X1^2 - X2^2, -2*X0)
sage: a0 = J._charpoly_coefficients()[0]
sage: J.base_ring()
Algebraic Real Field
Algebraic Real Field
"""
- if self._rational_algebra is None:
- # There's no need to construct *another* algebra over the
- # rationals if this one is already over the
- # rationals. Likewise, if we never orthonormalized our
- # basis, we might as well just use the given one.
+ if self.rational_algebra() is self:
+ # Bypass the hijinks if they won't benefit us.
return super()._charpoly_coefficients()
# Do the computation over the rationals. The answer will be
# the same, because all we've done is a change of basis.
# Then, change back from QQ to our real base ring
a = ( a_i.change_ring(self.base_ring())
- for a_i in self._rational_algebra._charpoly_coefficients() )
+ for a_i in self.rational_algebra()._charpoly_coefficients() )
# Otherwise, convert the coordinate variables back to the
# deorthonormalized ones.
Our basis is normalized with respect to the algebra's inner
product, unless we specify otherwise::
- sage: set_random_seed()
sage: J = ConcreteEJA.random_instance()
sage: all( b.norm() == 1 for b in J.gens() )
True
natural->EJA basis representation is an isometry and within the
EJA the operator is self-adjoint by the Jordan axiom::
- sage: set_random_seed()
sage: J = ConcreteEJA.random_instance()
sage: x = J.random_element()
sage: x.operator().is_self_adjoint()
return eja_class.random_instance(max_dimension, *args, **kwargs)
-class MatrixEJA(FiniteDimensionalEJA):
+class HermitianMatrixEJA(FiniteDimensionalEJA):
@staticmethod
def _denormalized_basis(A):
"""
- Returns a basis for the space of complex Hermitian n-by-n matrices.
+ Returns a basis for the given Hermitian matrix space.
Why do we embed these? Basically, because all of numerical linear
algebra assumes that you're working with vectors consisting of `n`
sage: from mjo.hurwitz import (ComplexMatrixAlgebra,
....: QuaternionMatrixAlgebra,
....: OctonionMatrixAlgebra)
- sage: from mjo.eja.eja_algebra import MatrixEJA
+ sage: from mjo.eja.eja_algebra import HermitianMatrixEJA
TESTS::
- sage: set_random_seed()
sage: n = ZZ.random_element(1,5)
sage: A = MatrixSpace(QQ, n)
- sage: B = MatrixEJA._denormalized_basis(A)
+ sage: B = HermitianMatrixEJA._denormalized_basis(A)
sage: all( M.is_hermitian() for M in B)
True
::
- sage: set_random_seed()
sage: n = ZZ.random_element(1,5)
sage: A = ComplexMatrixAlgebra(n, scalars=QQ)
- sage: B = MatrixEJA._denormalized_basis(A)
+ sage: B = HermitianMatrixEJA._denormalized_basis(A)
sage: all( M.is_hermitian() for M in B)
True
::
- sage: set_random_seed()
sage: n = ZZ.random_element(1,5)
sage: A = QuaternionMatrixAlgebra(n, scalars=QQ)
- sage: B = MatrixEJA._denormalized_basis(A)
+ sage: B = HermitianMatrixEJA._denormalized_basis(A)
sage: all( M.is_hermitian() for M in B )
True
::
- sage: set_random_seed()
sage: n = ZZ.random_element(1,5)
sage: A = OctonionMatrixAlgebra(n, scalars=QQ)
- sage: B = MatrixEJA._denormalized_basis(A)
+ sage: B = HermitianMatrixEJA._denormalized_basis(A)
sage: all( M.is_hermitian() for M in B )
True
# if the user passes check_axioms=True.
if "check_axioms" not in kwargs: kwargs["check_axioms"] = False
-
super().__init__(self._denormalized_basis(matrix_space),
self.jordan_product,
self.trace_inner_product,
self.rank.set_cache(matrix_space.nrows())
self.one.set_cache( self(matrix_space.one()) )
-class RealSymmetricEJA(MatrixEJA, RationalBasisEJA, ConcreteEJA):
+class RealSymmetricEJA(HermitianMatrixEJA, RationalBasisEJA, ConcreteEJA):
"""
The rank-n simple EJA consisting of real symmetric n-by-n
matrices, the usual symmetric Jordan product, and the trace inner
The dimension of this algebra is `(n^2 + n) / 2`::
- sage: set_random_seed()
sage: d = RealSymmetricEJA._max_random_instance_dimension()
sage: n = RealSymmetricEJA._max_random_instance_size(d)
sage: J = RealSymmetricEJA(n)
The Jordan multiplication is what we think it is::
- sage: set_random_seed()
sage: J = RealSymmetricEJA.random_instance()
sage: x,y = J.random_elements(2)
sage: actual = (x*y).to_matrix()
return cls(n, **kwargs)
def __init__(self, n, field=AA, **kwargs):
- # We know this is a valid EJA, but will double-check
- # if the user passes check_axioms=True.
- if "check_axioms" not in kwargs: kwargs["check_axioms"] = False
-
A = MatrixSpace(field, n)
super().__init__(A, **kwargs)
from mjo.eja.eja_cache import real_symmetric_eja_coeffs
a = real_symmetric_eja_coeffs(self)
if a is not None:
- if self._rational_algebra is None:
- self._charpoly_coefficients.set_cache(a)
- else:
- self._rational_algebra._charpoly_coefficients.set_cache(a)
+ self.rational_algebra()._charpoly_coefficients.set_cache(a)
-class ComplexHermitianEJA(MatrixEJA, RationalBasisEJA, ConcreteEJA):
+class ComplexHermitianEJA(HermitianMatrixEJA, RationalBasisEJA, ConcreteEJA):
"""
The rank-n simple EJA consisting of complex Hermitian n-by-n
matrices over the real numbers, the usual symmetric Jordan product,
The dimension of this algebra is `n^2`::
- sage: set_random_seed()
sage: d = ComplexHermitianEJA._max_random_instance_dimension()
sage: n = ComplexHermitianEJA._max_random_instance_size(d)
sage: J = ComplexHermitianEJA(n)
The Jordan multiplication is what we think it is::
- sage: set_random_seed()
sage: J = ComplexHermitianEJA.random_instance()
sage: x,y = J.random_elements(2)
sage: actual = (x*y).to_matrix()
"""
def __init__(self, n, field=AA, **kwargs):
- # We know this is a valid EJA, but will double-check
- # if the user passes check_axioms=True.
- if "check_axioms" not in kwargs: kwargs["check_axioms"] = False
-
from mjo.hurwitz import ComplexMatrixAlgebra
A = ComplexMatrixAlgebra(n, scalars=field)
super().__init__(A, **kwargs)
from mjo.eja.eja_cache import complex_hermitian_eja_coeffs
a = complex_hermitian_eja_coeffs(self)
if a is not None:
- if self._rational_algebra is None:
- self._charpoly_coefficients.set_cache(a)
- else:
- self._rational_algebra._charpoly_coefficients.set_cache(a)
+ self.rational_algebra()._charpoly_coefficients.set_cache(a)
@staticmethod
def _max_random_instance_size(max_dimension):
return cls(n, **kwargs)
-class QuaternionHermitianEJA(MatrixEJA, RationalBasisEJA, ConcreteEJA):
+class QuaternionHermitianEJA(HermitianMatrixEJA, RationalBasisEJA, ConcreteEJA):
r"""
The rank-n simple EJA consisting of self-adjoint n-by-n quaternion
matrices, the usual symmetric Jordan product, and the
The dimension of this algebra is `2*n^2 - n`::
- sage: set_random_seed()
sage: d = QuaternionHermitianEJA._max_random_instance_dimension()
sage: n = QuaternionHermitianEJA._max_random_instance_size(d)
sage: J = QuaternionHermitianEJA(n)
The Jordan multiplication is what we think it is::
- sage: set_random_seed()
sage: J = QuaternionHermitianEJA.random_instance()
sage: x,y = J.random_elements(2)
sage: actual = (x*y).to_matrix()
"""
def __init__(self, n, field=AA, **kwargs):
- # We know this is a valid EJA, but will double-check
- # if the user passes check_axioms=True.
- if "check_axioms" not in kwargs: kwargs["check_axioms"] = False
-
from mjo.hurwitz import QuaternionMatrixAlgebra
A = QuaternionMatrixAlgebra(n, scalars=field)
super().__init__(A, **kwargs)
from mjo.eja.eja_cache import quaternion_hermitian_eja_coeffs
a = quaternion_hermitian_eja_coeffs(self)
if a is not None:
- if self._rational_algebra is None:
- self._charpoly_coefficients.set_cache(a)
- else:
- self._rational_algebra._charpoly_coefficients.set_cache(a)
+ self.rational_algebra()._charpoly_coefficients.set_cache(a)
n = ZZ.random_element(max_size + 1)
return cls(n, **kwargs)
-class OctonionHermitianEJA(MatrixEJA, RationalBasisEJA, ConcreteEJA):
+class OctonionHermitianEJA(HermitianMatrixEJA, RationalBasisEJA, ConcreteEJA):
r"""
SETUP::
from mjo.eja.eja_cache import octonion_hermitian_eja_coeffs
a = octonion_hermitian_eja_coeffs(self)
if a is not None:
- if self._rational_algebra is None:
- self._charpoly_coefficients.set_cache(a)
- else:
- self._rational_algebra._charpoly_coefficients.set_cache(a)
+ self.rational_algebra()._charpoly_coefficients.set_cache(a)
class AlbertEJA(OctonionHermitianEJA):
matrix. We opt not to orthonormalize the basis, because if we
did, we would have to normalize the `s_{i}` in a similar manner::
- sage: set_random_seed()
sage: n = ZZ.random_element(5)
sage: M = matrix.random(QQ, max(0,n-1), algorithm='unimodular')
sage: B11 = matrix.identity(QQ,1)
Ensure that we have the usual inner product on `R^n`::
- sage: set_random_seed()
sage: J = JordanSpinEJA.random_instance()
sage: x,y = J.random_elements(2)
sage: actual = x.inner_product(y)
sage: from mjo.eja.eja_algebra import (random_eja,
....: CartesianProductEJA,
+ ....: ComplexHermitianEJA,
....: HadamardEJA,
....: JordanSpinEJA,
....: RealSymmetricEJA)
The Jordan product is inherited from our factors and implemented by
our CombinatorialFreeModule Cartesian product superclass::
- sage: set_random_seed()
sage: J1 = HadamardEJA(2)
sage: J2 = RealSymmetricEJA(2)
sage: J = cartesian_product([J1,J2])
| b2 || 0 | 0 | b2 |
+----++----+----+----+
+ The "matrix space" of a Cartesian product always consists of
+ ordered pairs (or triples, or...) whose components are the
+ matrix spaces of its factors::
+
+ sage: J1 = HadamardEJA(2)
+ sage: J2 = ComplexHermitianEJA(2)
+ sage: J = cartesian_product([J1,J2])
+ sage: J.matrix_space()
+ The Cartesian product of (Full MatrixSpace of 2 by 1 dense
+ matrices over Algebraic Real Field, Module of 2 by 2 matrices
+ with entries in Algebraic Field over the scalar ring Algebraic
+ Real Field)
+ sage: J.one().to_matrix()[0]
+ [1]
+ [1]
+ sage: J.one().to_matrix()[1]
+ +---+---+
+ | 1 | 0 |
+ +---+---+
+ | 0 | 1 |
+ +---+---+
+
TESTS:
All factors must share the same base field::
The cached unit element is the same one that would be computed::
- sage: set_random_seed() # long time
sage: J1 = random_eja() # long time
sage: J2 = random_eja() # long time
sage: J = cartesian_product([J1,J2]) # long time
sage: expected = J.one() # long time
sage: actual == expected # long time
True
-
"""
- Element = FiniteDimensionalEJAElement
-
-
+ Element = CartesianProductEJAElement
def __init__(self, factors, **kwargs):
m = len(factors)
if m == 0:
if not all( J.base_ring() == field for J in factors ):
raise ValueError("all factors must share the same base field")
+ # Figure out the category to use.
associative = all( f.is_associative() for f in factors )
-
- # Compute my matrix space. This category isn't perfect, but
- # is good enough for what we need to do.
+ category = EuclideanJordanAlgebras(field)
+ if associative: category = category.Associative()
+ category = category.join([category, category.CartesianProducts()])
+
+ # Compute my matrix space. We don't simply use the
+ # ``cartesian_product()`` functor here because it acts
+ # differently on SageMath MatrixSpaces and our custom
+ # MatrixAlgebras, which are CombinatorialFreeModules. We
+ # always want the result to be represented (and indexed) as an
+ # ordered tuple. This category isn't perfect, but is good
+ # enough for what we need to do.
MS_cat = MagmaticAlgebras(field).FiniteDimensional().WithBasis()
MS_cat = MS_cat.Unital().CartesianProducts()
MS_factors = tuple( J.matrix_space() for J in factors )
from sage.sets.cartesian_product import CartesianProduct
- MS = CartesianProduct(MS_factors, MS_cat)
+ self._matrix_space = CartesianProduct(MS_factors, MS_cat)
- basis = []
- zero = MS.zero()
+ self._matrix_basis = []
+ zero = self._matrix_space.zero()
for i in range(m):
for b in factors[i].matrix_basis():
z = list(zero)
z[i] = b
- basis.append(z)
+ self._matrix_basis.append(z)
- basis = tuple( MS(b) for b in basis )
+ self._matrix_basis = tuple( self._matrix_space(b)
+ for b in self._matrix_basis )
+ n = len(self._matrix_basis)
- # Define jordan/inner products that operate on that matrix_basis.
- def jordan_product(x,y):
- return MS(tuple(
- (factors[i](x[i])*factors[i](y[i])).to_matrix()
- for i in range(m)
- ))
-
- def inner_product(x, y):
- return sum(
- factors[i](x[i]).inner_product(factors[i](y[i]))
- for i in range(m)
- )
+ # We already have what we need for the super-superclass constructor.
+ CombinatorialFreeModule.__init__(self,
+ field,
+ range(n),
+ prefix="b",
+ category=category,
+ bracket=False)
- # There's no need to check the field since it already came
- # from an EJA. Likewise the axioms are guaranteed to be
- # satisfied, unless the guy writing this class sucks.
- #
- # If you want the basis to be orthonormalized, orthonormalize
- # the factors.
- FiniteDimensionalEJA.__init__(self,
- basis,
- jordan_product,
- inner_product,
- field=field,
- matrix_space=MS,
- orthonormalize=False,
- associative=associative,
- cartesian_product=True,
- check_field=False,
- check_axioms=False)
+ # Now create the vector space for the algebra, which will have
+ # its own set of non-ambient coordinates (in terms of the
+ # supplied basis).
+ degree = sum( f._matrix_span.ambient_vector_space().degree()
+ for f in factors )
+ V = VectorSpace(field, degree)
+ vector_basis = tuple( V(_all2list(b)) for b in self._matrix_basis )
+
+ # Save the span of our matrix basis (when written out as long
+ # vectors) because otherwise we'll have to reconstruct it
+ # every time we want to coerce a matrix into the algebra.
+ self._matrix_span = V.span_of_basis( vector_basis, check=False)
# Since we don't (re)orthonormalize the basis, the FDEJA
# constructor is going to set self._deortho_matrix to the
# identity matrix. Here we set it to the correct value using
# the deortho matrices from our factors.
- self._deortho_matrix = matrix.block_diagonal( [J._deortho_matrix
- for J in factors] )
+ self._deortho_matrix = matrix.block_diagonal(
+ [J._deortho_matrix for J in factors]
+ )
+
+ self._inner_product_matrix = matrix.block_diagonal(
+ [J._inner_product_matrix for J in factors]
+ )
+ self._inner_product_matrix._cache = {'hermitian': True}
+ self._inner_product_matrix.set_immutable()
+
+ # Building the multiplication table is a bit more tricky
+ # because we have to embed the entries of the factors'
+ # multiplication tables into the product EJA.
+ zed = self.zero()
+ self._multiplication_table = [ [zed for j in range(i+1)]
+ for i in range(n) ]
+
+ # Keep track of an offset that tallies the dimensions of all
+ # previous factors. If the second factor is dim=2 and if the
+ # first one is dim=3, then we want to skip the first 3x3 block
+ # when copying the multiplication table for the second factor.
+ offset = 0
+ for f in range(m):
+ phi_f = self.cartesian_embedding(f)
+ factor_dim = factors[f].dimension()
+ for i in range(factor_dim):
+ for j in range(i+1):
+ f_ij = factors[f]._multiplication_table[i][j]
+ e = phi_f(f_ij)
+ self._multiplication_table[offset+i][offset+j] = e
+ offset += factor_dim
self.rank.set_cache(sum(J.rank() for J in factors))
ones = tuple(J.one().to_matrix() for J in factors)
self.one.set_cache(self(ones))
+ def _sets_keys(self):
+ r"""
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import (ComplexHermitianEJA,
+ ....: RealSymmetricEJA)
+
+ TESTS:
+
+ The superclass uses ``_sets_keys()`` to implement its
+ ``cartesian_factors()`` method::
+
+ sage: J1 = RealSymmetricEJA(2,
+ ....: field=QQ,
+ ....: orthonormalize=False,
+ ....: prefix="a")
+ sage: J2 = ComplexHermitianEJA(2,field=QQ,orthonormalize=False)
+ sage: J = cartesian_product([J1,J2])
+ sage: x = sum(i*J.gens()[i] for i in range(len(J.gens())))
+ sage: x.cartesian_factors()
+ (a1 + 2*a2, 3*b0 + 4*b1 + 5*b2 + 6*b3)
+
+ """
+ # Copy/pasted from CombinatorialFreeModule_CartesianProduct,
+ # but returning a tuple instead of a list.
+ return tuple(range(len(self.cartesian_factors())))
+
def cartesian_factors(self):
# Copy/pasted from CombinatorialFreeModule_CartesianProduct.
return self._sets
return cartesian_product.symbol.join("%s" % factor
for factor in self._sets)
- def matrix_space(self):
- r"""
- Return the space that our matrix basis lives in as a Cartesian
- product.
-
- We don't simply use the ``cartesian_product()`` functor here
- because it acts differently on SageMath MatrixSpaces and our
- custom MatrixAlgebras, which are CombinatorialFreeModules. We
- always want the result to be represented (and indexed) as
- an ordered tuple.
-
- SETUP::
-
- sage: from mjo.eja.eja_algebra import (ComplexHermitianEJA,
- ....: HadamardEJA,
- ....: OctonionHermitianEJA,
- ....: RealSymmetricEJA)
-
- EXAMPLES::
-
- sage: J1 = HadamardEJA(1)
- sage: J2 = RealSymmetricEJA(2)
- sage: J = cartesian_product([J1,J2])
- sage: J.matrix_space()
- The Cartesian product of (Full MatrixSpace of 1 by 1 dense
- matrices over Algebraic Real Field, Full MatrixSpace of 2
- by 2 dense matrices over Algebraic Real Field)
-
- ::
-
- sage: J1 = ComplexHermitianEJA(1)
- sage: J2 = ComplexHermitianEJA(1)
- sage: J = cartesian_product([J1,J2])
- sage: J.one().to_matrix()[0]
- +---+
- | 1 |
- +---+
- sage: J.one().to_matrix()[1]
- +---+
- | 1 |
- +---+
-
- ::
-
- sage: J1 = OctonionHermitianEJA(1)
- sage: J2 = OctonionHermitianEJA(1)
- sage: J = cartesian_product([J1,J2])
- sage: J.one().to_matrix()[0]
- +----+
- | e0 |
- +----+
- sage: J.one().to_matrix()[1]
- +----+
- | e0 |
- +----+
-
- """
- return super().matrix_space()
-
@cached_method
def cartesian_projection(self, i):
The answer never changes::
- sage: set_random_seed()
sage: J1 = random_eja()
sage: J2 = random_eja()
sage: J = cartesian_product([J1,J2])
The answer never changes::
- sage: set_random_seed()
sage: J1 = random_eja()
sage: J2 = random_eja()
sage: J = cartesian_product([J1,J2])
produce the identity map, and mismatching them should produce
the zero map::
- sage: set_random_seed()
sage: J1 = random_eja()
sage: J2 = random_eja()
sage: J = cartesian_product([J1,J2])
SETUP::
- sage: from mjo.eja.eja_algebra import (HadamardEJA,
+ sage: from mjo.eja.eja_algebra import (FiniteDimensionalEJA,
+ ....: HadamardEJA,
....: JordanSpinEJA,
- ....: OctonionHermitianEJA,
....: RealSymmetricEJA)
EXAMPLES:
The ``cartesian_product()`` function only uses the first factor to
decide where the result will live; thus we have to be careful to
- check that all factors do indeed have a `_rational_algebra` member
- before we try to access it::
-
- sage: J1 = OctonionHermitianEJA(1) # no rational basis
- sage: J2 = HadamardEJA(2)
- sage: cartesian_product([J1,J2])
- Euclidean Jordan algebra of dimension 1 over Algebraic Real Field
- (+) Euclidean Jordan algebra of dimension 2 over Algebraic Real Field
- sage: cartesian_product([J2,J1])
- Euclidean Jordan algebra of dimension 2 over Algebraic Real Field
- (+) Euclidean Jordan algebra of dimension 1 over Algebraic Real Field
+ check that all factors do indeed have a ``rational_algebra()`` method
+ before we construct an algebra that claims to have a rational basis::
+
+ sage: J1 = HadamardEJA(2)
+ sage: jp = lambda X,Y: X*Y
+ sage: ip = lambda X,Y: X[0,0]*Y[0,0]
+ sage: b1 = matrix(QQ, [[1]])
+ sage: J2 = FiniteDimensionalEJA((b1,), jp, ip)
+ sage: cartesian_product([J2,J1]) # factor one not RationalBasisEJA
+ Euclidean Jordan algebra of dimension 1 over Algebraic Real
+ Field (+) Euclidean Jordan algebra of dimension 2 over Algebraic
+ Real Field
+ sage: cartesian_product([J1,J2]) # factor one is RationalBasisEJA
+ Traceback (most recent call last):
+ ...
+ ValueError: factor not a RationalBasisEJA
"""
def __init__(self, algebras, **kwargs):
+ if not all( hasattr(r, "rational_algebra") for r in algebras ):
+ raise ValueError("factor not a RationalBasisEJA")
+
CartesianProductEJA.__init__(self, algebras, **kwargs)
- self._rational_algebra = None
- if self.vector_space().base_field() is not QQ:
- if all( hasattr(r, "_rational_algebra") for r in algebras ):
- self._rational_algebra = cartesian_product([
- r._rational_algebra for r in algebras
- ])
+ @cached_method
+ def rational_algebra(self):
+ if self.base_ring() is QQ:
+ return self
+
+ return cartesian_product([
+ r.rational_algebra() for r in self.cartesian_factors()
+ ])
RationalBasisEJA.CartesianProduct = RationalBasisCartesianProductEJA
TESTS::
- sage: set_random_seed()
sage: n = ZZ.random_element(1,5)
sage: J = random_eja(max_dimension=n, field=QQ, orthonormalize=False)
sage: J.dimension() <= n