class Element(FiniteDimensionalAlgebraElement):
"""
An element of a Euclidean Jordan algebra.
-
- Since EJAs are commutative, the "right multiplication" matrix is
- also the left multiplication matrix and must be symmetric::
-
- sage: set_random_seed()
- sage: n = ZZ.random_element(1,10).abs()
- sage: J = eja_rn(5)
- sage: J.random_element().matrix().is_symmetric()
- True
- sage: J = eja_ln(5)
- sage: J.random_element().matrix().is_symmetric()
- True
-
"""
def __pow__(self, n):
EXAMPLES:
sage: set_random_seed()
- sage: J = eja_ln(5)
- sage: x = J.random_element()
+ sage: x = random_eja().random_element()
sage: x.matrix()*x.vector() == (x**2).vector()
True
The identity element is never nilpotent::
sage: set_random_seed()
- sage: n = ZZ.random_element(2,10).abs()
- sage: J = eja_rn(n)
- sage: J.one().is_nilpotent()
- False
- sage: J = eja_ln(n)
- sage: J.one().is_nilpotent()
+ sage: random_eja().one().is_nilpotent()
False
The additive identity is always nilpotent::
sage: set_random_seed()
- sage: n = ZZ.random_element(2,10).abs()
- sage: J = eja_rn(n)
- sage: J.zero().is_nilpotent()
- True
- sage: J = eja_ln(n)
- sage: J.zero().is_nilpotent()
+ sage: random_eja().zero().is_nilpotent()
True
"""
EXAMPLES::
sage: set_random_seed()
- sage: n = ZZ.random_element(1,10).abs()
- sage: J = eja_rn(n)
- sage: x = J.random_element()
+ sage: x = random_eja().random_element()
sage: x.degree() == x.minimal_polynomial().degree()
True
::
sage: set_random_seed()
- sage: n = ZZ.random_element(1,10).abs()
- sage: J = eja_ln(n)
- sage: x = J.random_element()
+ sage: x = random_eja().random_element()
sage: x.degree() == x.minimal_polynomial().degree()
True
return elt.minimal_polynomial()
+ def quadratic_representation(self):
+ """
+ Return the quadratic representation of this element.
+
+ EXAMPLES:
+
+ The explicit form in the spin factor algebra is given by
+ Alizadeh's Example 11.12::
+
+ sage: n = ZZ.random_element(1,10).abs()
+ sage: J = eja_ln(n)
+ sage: x = J.random_element()
+ sage: x_vec = x.vector()
+ sage: x0 = x_vec[0]
+ sage: x_bar = x_vec[1:]
+ sage: A = matrix(QQ, 1, [x_vec.inner_product(x_vec)])
+ sage: B = 2*x0*x_bar.row()
+ sage: C = 2*x0*x_bar.column()
+ sage: D = identity_matrix(QQ, n-1)
+ sage: D = (x0^2 - x_bar.inner_product(x_bar))*D
+ sage: D = D + 2*x_bar.tensor_product(x_bar)
+ sage: Q = block_matrix(2,2,[A,B,C,D])
+ sage: Q == x.quadratic_representation()
+ True
+
+ """
+ return 2*(self.matrix()**2) - (self**2).matrix()
+
+
def span_of_powers(self):
"""
Return the vector space spanned by successive powers of
TESTS::
sage: set_random_seed()
- sage: n = ZZ.random_element(1,10).abs()
- sage: J = eja_rn(n)
- sage: x = J.random_element()
- sage: x.subalgebra_generated_by().is_associative()
- True
- sage: J = eja_ln(n)
- sage: x = J.random_element()
+ sage: x = random_eja().random_element()
sage: x.subalgebra_generated_by().is_associative()
True
squaring in the superalgebra::
sage: set_random_seed()
- sage: J = eja_ln(5)
- sage: x = J.random_element()
+ sage: x = random_eja().random_element()
sage: u = x.subalgebra_generated_by().random_element()
sage: u.matrix()*u.vector() == (u**2).vector()
True
raise ValueError('charpoly had fewer than 2 coefficients')
+ def trace_inner_product(self, other):
+ """
+ Return the trace inner product of myself and ``other``.
+ """
+ if not other in self.parent():
+ raise ArgumentError("'other' must live in the same algebra")
+
+ return (self*other).trace()
+
+
def eja_rn(dimension, field=QQ):
"""
Return the Euclidean Jordan Algebra corresponding to the set
e2
"""
- Qs = []
+ S = _real_symmetric_basis(dimension, field=field)
+ Qs = _multiplication_table_from_matrix_basis(S)
+
+ return FiniteDimensionalEuclideanJordanAlgebra(field,Qs,rank=dimension)
- # In S^2, for example, we nominally have four coordinates even
- # though the space is of dimension three only. The vector space V
- # 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.
- V = VectorSpace(field, dimension**2)
+def random_eja():
+ """
+ Return a "random" finite-dimensional Euclidean Jordan Algebra.
+
+ ALGORITHM:
+
+ For now, we choose a random natural number ``n`` (greater than zero)
+ and then give you back one of the following:
+
+ * The cartesian product of the rational numbers ``n`` times; this is
+ ``QQ^n`` with the Hadamard product.
+
+ * The Jordan spin algebra on ``QQ^n``.
+
+ * The ``n``-by-``n`` rational symmetric matrices with the symmetric
+ product.
+
+ Later this might be extended to return Cartesian products of the
+ EJAs above.
+
+ TESTS::
+
+ sage: random_eja()
+ Euclidean Jordan algebra of degree...
+
+ """
+ n = ZZ.random_element(1,10).abs()
+ constructor = choice([eja_rn, eja_ln, eja_sn])
+ return constructor(dimension=n, field=QQ)
+
+
+
+def _real_symmetric_basis(n, field=QQ):
+ """
+ Return a basis for the space of real symmetric n-by-n matrices.
+ """
# The basis of symmetric matrices, as matrices, in their R^(n-by-n)
# coordinates.
S = []
-
- for i in xrange(dimension):
+ for i in xrange(n):
for j in xrange(i+1):
- Eij = matrix(field, dimension, lambda k,l: k==i and l==j)
+ Eij = matrix(field, n, lambda k,l: k==i and l==j)
if i == j:
Sij = Eij
else:
+ # Beware, orthogonal but not normalized!
Sij = Eij + Eij.transpose()
S.append(Sij)
+ return S
+
+
+def _multiplication_table_from_matrix_basis(basis):
+ """
+ At least three of the five simple Euclidean Jordan algebras have the
+ symmetric multiplication (A,B) |-> (AB + BA)/2, where the
+ multiplication on the right is matrix multiplication. Given a basis
+ for the underlying matrix space, this function returns a
+ multiplication table (obtained by looping through the basis
+ elements) for an algebra of those matrices.
+ """
+ # In S^2, for example, we nominally have four coordinates even
+ # though the space is of dimension three only. The vector space V
+ # 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.
+ field = basis[0].base_ring()
+ dimension = basis[0].nrows()
def mat2vec(m):
return vector(field, m.list())
- W = V.span( mat2vec(s) for s in S )
+ def vec2mat(v):
+ return matrix(field, dimension, v.list())
+
+ V = VectorSpace(field, dimension**2)
+ W = V.span( mat2vec(s) for s in basis )
+
+ # Taking the span above reorders our basis (thanks, jerk!) so we
+ # need to put our "matrix basis" in the same order as the
+ # (reordered) vector basis.
+ S = [ vec2mat(b) for b in W.basis() ]
- for s in S:
+ Qs = []
+ for s in basis:
# Brute force the multiplication-by-s matrix by looping
# through all elements of the basis and doing the computation
# to find out what the corresponding row should be. BEWARE:
# constructor uses ROW vectors and not COLUMN vectors. That's
# why we're computing rows here and not columns.
Q_rows = []
- for t in S:
+ for t in basis:
this_row = mat2vec((s*t + t*s)/2)
Q_rows.append(W.coordinates(this_row))
Q = matrix(field,Q_rows)
Qs.append(Q)
- return FiniteDimensionalEuclideanJordanAlgebra(field,Qs,rank=dimension)
+ return Qs
def random_eja():