assume_associative=False,
category=None,
rank=None,
- natural_basis=None,
- inner_product=None):
+ natural_basis=None):
n = len(mult_table)
mult_table = [b.base_extend(field) for b in mult_table]
for b in mult_table:
names=names,
category=cat,
rank=rank,
- natural_basis=natural_basis,
- inner_product=inner_product)
+ natural_basis=natural_basis)
- def __init__(self, field,
+ def __init__(self,
+ field,
mult_table,
names='e',
assume_associative=False,
category=None,
rank=None,
- natural_basis=None,
- inner_product=None):
+ natural_basis=None):
"""
EXAMPLES:
"""
self._rank = rank
self._natural_basis = natural_basis
- self._inner_product = inner_product
+ self._multiplication_table = mult_table
fda = super(FiniteDimensionalEuclideanJordanAlgebra, self)
fda.__init__(field,
mult_table,
return fmt.format(self.degree(), self.base_ring())
+
+ @cached_method
+ def _charpoly_coeff(self, i):
+ """
+ Return the coefficient polynomial "a_{i}" of this algebra's
+ general characteristic polynomial.
+
+ Having this be a separate cached method lets us compute and
+ store the trace/determinant (a_{r-1} and a_{0} respectively)
+ separate from the entire characteristic polynomial.
+ """
+ (A_of_x, x) = self._charpoly_matrix()
+ R = A_of_x.base_ring()
+ A_cols = A_of_x.columns()
+ A_cols[i] = (x**self.rank()).vector()
+ numerator = column_matrix(A_of_x.base_ring(), A_cols).det()
+ denominator = A_of_x.det()
+
+ # We're relying on the theory here to ensure that each a_i is
+ # indeed back in R, and the added negative signs are to make
+ # the whole charpoly expression sum to zero.
+ return R(-numerator/denominator)
+
+
+ @cached_method
+ def _charpoly_matrix(self):
+ """
+ Compute the matrix whose entries A_ij are polynomials in
+ X1,...,XN. This same matrix is used in more than one method and
+ it's not so fast to construct.
+ """
+ r = self.rank()
+ n = self.dimension()
+
+ # Construct a new algebra over a multivariate polynomial ring...
+ names = ['X' + str(i) for i in range(1,n+1)]
+ R = PolynomialRing(self.base_ring(), names)
+ J = FiniteDimensionalEuclideanJordanAlgebra(R,
+ self._multiplication_table,
+ rank=r)
+
+ idmat = identity_matrix(J.base_ring(), n)
+
+ x = J(vector(R, R.gens()))
+ l1 = [column_matrix((x**k).vector()) for k in range(r)]
+ l2 = [idmat.column(k-1).column() for k in range(r+1, n+1)]
+ A_of_x = block_matrix(R, 1, n, (l1 + l2))
+ return (A_of_x, x)
+
+
+ @cached_method
+ def characteristic_polynomial(self):
+ """
+ EXAMPLES:
+
+ The characteristic polynomial in the spin algebra is given in
+ Alizadeh, Example 11.11::
+
+ sage: J = JordanSpinEJA(3)
+ sage: p = J.characteristic_polynomial(); p
+ X1^2 - X2^2 - X3^2 + (-2*t)*X1 + t^2
+ sage: xvec = J.one().vector()
+ sage: p(*xvec)
+ t^2 - 2*t + 1
+
+ """
+ r = self.rank()
+ n = self.dimension()
+
+ # The list of coefficient polynomials a_1, a_2, ..., a_n.
+ a = [ self._charpoly_coeff(i) for i in range(n) ]
+
+ # We go to a bit of trouble here to reorder the
+ # indeterminates, so that it's easier to evaluate the
+ # characteristic polynomial at x's coordinates and get back
+ # something in terms of t, which is what we want.
+ R = a[0].parent()
+ S = PolynomialRing(self.base_ring(),'t')
+ t = S.gen(0)
+ S = PolynomialRing(S, R.variable_names())
+ t = S(t)
+
+ # Note: all entries past the rth should be zero. The
+ # coefficient of the highest power (x^r) is 1, but it doesn't
+ # appear in the solution vector which contains coefficients
+ # for the other powers (to make them sum to x^r).
+ if (r < n):
+ a[r] = 1 # corresponds to x^r
+ else:
+ # When the rank is equal to the dimension, trying to
+ # assign a[r] goes out-of-bounds.
+ a.append(1) # corresponds to x^r
+
+ return sum( a[k]*(t**k) for k in range(len(a)) )
+
+
def inner_product(self, x, y):
"""
The inner product associated with this Euclidean Jordan algebra.
- Will default to the trace inner product if nothing else.
+ Defaults to the trace inner product, but can be overridden by
+ subclasses if they are sure that the necessary properties are
+ satisfied.
EXAMPLES:
"""
if (not x in self) or (not y in self):
raise TypeError("arguments must live in this algebra")
- if self._inner_product is None:
- return x.trace_inner_product(y)
- else:
- return self._inner_product(x,y)
+ return x.trace_inner_product(y)
def natural_basis(self):
EXAMPLES::
- sage: J = RealSymmetricSimpleEJA(2)
+ sage: J = RealSymmetricEJA(2)
sage: J.basis()
Family (e0, e1, e2)
sage: J.natural_basis()
::
- sage: J = JordanSpinSimpleEJA(2)
+ sage: J = JordanSpinEJA(2)
sage: J.basis()
Family (e0, e1)
sage: J.natural_basis()
The identity in `S^n` is converted to the identity in the EJA::
- sage: J = RealSymmetricSimpleEJA(3)
+ sage: J = RealSymmetricEJA(3)
sage: I = identity_matrix(QQ,3)
sage: J(I) == J.one()
True
This skew-symmetric matrix can't be represented in the EJA::
- sage: J = RealSymmetricSimpleEJA(3)
+ sage: J = RealSymmetricEJA(3)
sage: A = matrix(QQ,3, lambda i,j: i-j)
sage: J(A)
Traceback (most recent call last):
return A( (self.operator_matrix()**(n-1))*self.vector() )
+ def apply_univariate_polynomial(self, p):
+ """
+ Apply the univariate polynomial ``p`` to this element.
+
+ A priori, SageMath won't allow us to apply a univariate
+ polynomial to an element of an EJA, because we don't know
+ that EJAs are rings (they are usually not associative). Of
+ course, we know that EJAs are power-associative, so the
+ operation is ultimately kosher. This function sidesteps
+ the CAS to get the answer we want and expect.
+
+ EXAMPLES::
+
+ sage: R = PolynomialRing(QQ, 't')
+ sage: t = R.gen(0)
+ sage: p = t^4 - t^3 + 5*t - 2
+ sage: J = RealCartesianProductEJA(5)
+ sage: J.one().apply_univariate_polynomial(p) == 3*J.one()
+ True
+
+ TESTS:
+
+ We should always get back an element of the algebra::
+
+ sage: set_random_seed()
+ sage: p = PolynomialRing(QQ, 't').random_element()
+ sage: J = random_eja()
+ sage: x = J.random_element()
+ sage: x.apply_univariate_polynomial(p) in J
+ True
+
+ """
+ if len(p.variables()) > 1:
+ raise ValueError("not a univariate polynomial")
+ P = self.parent()
+ R = P.base_ring()
+ # Convert the coeficcients to the parent's base ring,
+ # because a priori they might live in an (unnecessarily)
+ # larger ring for which P.sum() would fail below.
+ cs = [ R(c) for c in p.coefficients(sparse=False) ]
+ return P.sum( cs[k]*(self**k) for k in range(len(cs)) )
+
+
def characteristic_polynomial(self):
"""
- Return my characteristic polynomial (if I'm a regular
- element).
+ Return the characteristic polynomial of this element.
+
+ EXAMPLES:
+
+ The rank of `R^3` is three, and the minimal polynomial of
+ the identity element is `(t-1)` from which it follows that
+ the characteristic polynomial should be `(t-1)^3`::
+
+ sage: J = RealCartesianProductEJA(3)
+ sage: J.one().characteristic_polynomial()
+ t^3 - 3*t^2 + 3*t - 1
+
+ Likewise, the characteristic of the zero element in the
+ rank-three algebra `R^{n}` should be `t^{3}`::
+
+ sage: J = RealCartesianProductEJA(3)
+ sage: J.zero().characteristic_polynomial()
+ t^3
+
+ The characteristic polynomial of an element should evaluate
+ to zero on that element::
+
+ sage: set_random_seed()
+ sage: x = RealCartesianProductEJA(3).random_element()
+ sage: p = x.characteristic_polynomial()
+ sage: x.apply_univariate_polynomial(p)
+ 0
- Eventually this should be implemented in terms of the parent
- algebra's characteristic polynomial that works for ALL
- elements.
"""
- if self.is_regular():
- return self.minimal_polynomial()
- else:
- raise NotImplementedError('irregular element')
+ p = self.parent().characteristic_polynomial()
+ return p(*self.vector())
def inner_product(self, other):
inner product on `R^n` (this example only works because the
basis for the Jordan algebra is the standard basis in `R^n`)::
- sage: J = JordanSpinSimpleEJA(3)
+ sage: J = JordanSpinEJA(3)
sage: x = vector(QQ,[1,2,3])
sage: y = vector(QQ,[4,5,6])
sage: x.inner_product(y)
so the inner product of the identity matrix with itself
should be the `n`::
- sage: J = RealSymmetricSimpleEJA(3)
+ sage: J = RealSymmetricEJA(3)
sage: J.one().inner_product(J.one())
3
part because the product of Hermitian matrices may not be
Hermitian::
- sage: J = ComplexHermitianSimpleEJA(3)
+ sage: J = ComplexHermitianEJA(3)
+ sage: J.one().inner_product(J.one())
+ 3
+
+ Ditto for the quaternions::
+
+ sage: J = QuaternionHermitianEJA(3)
sage: J.one().inner_product(J.one())
3
EXAMPLES::
- sage: J = JordanSpinSimpleEJA(2)
+ sage: J = JordanSpinEJA(2)
sage: e0,e1 = J.gens()
- sage: x = e0 + e1
+ sage: x = sum( J.gens() )
sage: x.det()
0
- sage: J = JordanSpinSimpleEJA(3)
+
+ ::
+
+ sage: J = JordanSpinEJA(3)
sage: e0,e1,e2 = J.gens()
- sage: x = e0 + e1 + e2
+ sage: x = sum( J.gens() )
sage: x.det()
-1
+ TESTS:
+
+ An element is invertible if and only if its determinant is
+ non-zero::
+
+ sage: set_random_seed()
+ sage: x = random_eja().random_element()
+ sage: x.is_invertible() == (x.det() != 0)
+ True
+
"""
- cs = self.characteristic_polynomial().coefficients(sparse=False)
- r = len(cs) - 1
- if r >= 0:
- return cs[0] * (-1)**r
- else:
- raise ValueError('charpoly had no coefficients')
+ P = self.parent()
+ r = P.rank()
+ p = P._charpoly_coeff(0)
+ # The _charpoly_coeff function already adds the factor of
+ # -1 to ensure that _charpoly_coeff(0) is really what
+ # appears in front of t^{0} in the charpoly. However,
+ # we want (-1)^r times THAT for the determinant.
+ return ((-1)**r)*p(*self.vector())
def inverse(self):
sage: set_random_seed()
sage: n = ZZ.random_element(1,10)
- sage: J = JordanSpinSimpleEJA(n)
+ sage: J = JordanSpinEJA(n)
sage: x = J.random_element()
sage: while x.is_zero():
....: x = J.random_element()
sage: J.one().inverse() == J.one()
True
- If an element has an inverse, it acts like one. TODO: this
- can be a lot less ugly once ``is_invertible`` doesn't crash
- on irregular elements::
+ If an element has an inverse, it acts like one::
sage: set_random_seed()
sage: J = random_eja()
sage: x = J.random_element()
- sage: try:
- ....: x.inverse()*x == J.one()
- ....: except:
- ....: True
+ sage: (not x.is_invertible()) or (x.inverse()*x == J.one())
True
"""
We can't use the superclass method because it relies on
the algebra being associative.
+
+ ALGORITHM:
+
+ The usual way to do this is to check if the determinant is
+ zero, but we need the characteristic polynomial for the
+ determinant. The minimal polynomial is a lot easier to get,
+ so we use Corollary 2 in Chapter V of Koecher to check
+ whether or not the paren't algebra's zero element is a root
+ of this element's minimal polynomial.
+
+ TESTS:
+
+ The identity element is always invertible::
+
+ sage: set_random_seed()
+ sage: J = random_eja()
+ sage: J.one().is_invertible()
+ True
+
+ The zero element is never invertible::
+
+ sage: set_random_seed()
+ sage: J = random_eja()
+ sage: J.zero().is_invertible()
+ False
+
"""
- return not self.det().is_zero()
+ zero = self.parent().zero()
+ p = self.minimal_polynomial()
+ return not (p(zero) == zero)
def is_nilpotent(self):
The identity element always has degree one, but any element
linearly-independent from it is regular::
- sage: J = JordanSpinSimpleEJA(5)
+ sage: J = JordanSpinEJA(5)
sage: J.one().is_regular()
False
sage: e0, e1, e2, e3, e4 = J.gens() # e0 is the identity
EXAMPLES::
- sage: J = JordanSpinSimpleEJA(4)
+ sage: J = JordanSpinEJA(4)
sage: J.one().degree()
1
sage: e0,e1,e2,e3 = J.gens()
sage: set_random_seed()
sage: n = ZZ.random_element(1,10)
- sage: J = JordanSpinSimpleEJA(n)
+ sage: J = JordanSpinEJA(n)
sage: x = J.random_element()
sage: x == x.coefficient(0)*J.one() or x.degree() == 2
True
def minimal_polynomial(self):
"""
- EXAMPLES::
+ Return the minimal polynomial of this element,
+ as a function of the variable `t`.
+
+ ALGORITHM:
+
+ We restrict ourselves to the associative subalgebra
+ generated by this element, and then return the minimal
+ polynomial of this element's operator matrix (in that
+ subalgebra). This works by Baes Proposition 2.3.16.
+
+ TESTS:
+
+ The minimal polynomial of the identity and zero elements are
+ always the same::
sage: set_random_seed()
- sage: x = random_eja().random_element()
- sage: x.degree() == x.minimal_polynomial().degree()
- True
+ sage: J = random_eja()
+ sage: J.one().minimal_polynomial()
+ t - 1
+ sage: J.zero().minimal_polynomial()
+ t
- ::
+ The degree of an element is (by one definition) the degree
+ of its minimal polynomial::
sage: set_random_seed()
sage: x = random_eja().random_element()
sage: set_random_seed()
sage: n = ZZ.random_element(2,10)
- sage: J = JordanSpinSimpleEJA(n)
+ sage: J = JordanSpinEJA(n)
sage: y = J.random_element()
sage: while y == y.coefficient(0)*J.one():
....: y = J.random_element()
sage: y0 = y.vector()[0]
sage: y_bar = y.vector()[1:]
sage: actual = y.minimal_polynomial()
- sage: x = SR.symbol('x', domain='real')
- sage: expected = x^2 - 2*y0*x + (y0^2 - norm(y_bar)^2)
+ sage: t = PolynomialRing(J.base_ring(),'t').gen(0)
+ sage: expected = t^2 - 2*y0*t + (y0^2 - norm(y_bar)^2)
sage: bool(actual == expected)
True
- """
- # The element we're going to call "minimal_polynomial()" on.
- # Either myself, interpreted as an element of a finite-
- # dimensional algebra, or an element of an associative
- # subalgebra.
- elt = None
+ The minimal polynomial should always kill its element::
- if self.parent().is_associative():
- elt = FiniteDimensionalAlgebraElement(self.parent(), self)
- else:
- V = self.span_of_powers()
- assoc_subalg = self.subalgebra_generated_by()
- # Mis-design warning: the basis used for span_of_powers()
- # and subalgebra_generated_by() must be the same, and in
- # the same order!
- elt = assoc_subalg(V.coordinates(self.vector()))
+ sage: set_random_seed()
+ sage: x = random_eja().random_element()
+ sage: p = x.minimal_polynomial()
+ sage: x.apply_univariate_polynomial(p)
+ 0
- # Recursive call, but should work since elt lives in an
- # associative algebra.
- return elt.minimal_polynomial()
+ """
+ V = self.span_of_powers()
+ assoc_subalg = self.subalgebra_generated_by()
+ # Mis-design warning: the basis used for span_of_powers()
+ # and subalgebra_generated_by() must be the same, and in
+ # the same order!
+ elt = assoc_subalg(V.coordinates(self.vector()))
+
+ # We get back a symbolic polynomial in 'x' but want a real
+ # polynomial in 't'.
+ p_of_x = elt.operator_matrix().minimal_polynomial()
+ return p_of_x.change_variable_name('t')
def natural_representation(self):
EXAMPLES::
- sage: J = ComplexHermitianSimpleEJA(3)
+ sage: J = ComplexHermitianEJA(3)
sage: J.one()
e0 + e5 + e8
sage: J.one().natural_representation()
[0 0 0 0 1 0]
[0 0 0 0 0 1]
+ ::
+
+ sage: J = QuaternionHermitianEJA(3)
+ sage: J.one()
+ e0 + e9 + e14
+ sage: J.one().natural_representation()
+ [1 0 0 0 0 0 0 0 0 0 0 0]
+ [0 1 0 0 0 0 0 0 0 0 0 0]
+ [0 0 1 0 0 0 0 0 0 0 0 0]
+ [0 0 0 1 0 0 0 0 0 0 0 0]
+ [0 0 0 0 1 0 0 0 0 0 0 0]
+ [0 0 0 0 0 1 0 0 0 0 0 0]
+ [0 0 0 0 0 0 1 0 0 0 0 0]
+ [0 0 0 0 0 0 0 1 0 0 0 0]
+ [0 0 0 0 0 0 0 0 1 0 0 0]
+ [0 0 0 0 0 0 0 0 0 1 0 0]
+ [0 0 0 0 0 0 0 0 0 0 1 0]
+ [0 0 0 0 0 0 0 0 0 0 0 1]
+
"""
B = self.parent().natural_basis()
W = B[0].matrix_space()
sage: set_random_seed()
sage: n = ZZ.random_element(1,10)
- sage: J = JordanSpinSimpleEJA(n)
+ sage: J = JordanSpinEJA(n)
sage: x = J.random_element()
sage: x_vec = x.vector()
sage: x0 = x_vec[0]
# The dimension of the subalgebra can't be greater than
# the big algebra, so just put everything into a list
# and let span() get rid of the excess.
- V = self.vector().parent()
+ #
+ # We do the extra ambient_vector_space() in case we're messing
+ # with polynomials and the direct parent is a module.
+ V = self.vector().parent().ambient_vector_space()
return V.span( (self**d).vector() for d in xrange(V.dimension()) )
TESTS::
sage: set_random_seed()
- sage: J = eja_rn(5)
+ sage: J = RealCartesianProductEJA(5)
sage: c = J.random_element().subalgebra_idempotent()
sage: c^2 == c
True
- sage: J = JordanSpinSimpleEJA(5)
+ sage: J = JordanSpinEJA(5)
sage: c = J.random_element().subalgebra_idempotent()
sage: c^2 == c
True
EXAMPLES::
- sage: J = JordanSpinSimpleEJA(3)
- sage: e0,e1,e2 = J.gens()
- sage: x = e0 + e1 + e2
+ sage: J = JordanSpinEJA(3)
+ sage: x = sum(J.gens())
sage: x.trace()
2
+ ::
+
+ sage: J = RealCartesianProductEJA(5)
+ sage: J.one().trace()
+ 5
+
+ TESTS:
+
+ The trace of an element is a real number::
+
+ sage: set_random_seed()
+ sage: J = random_eja()
+ sage: J.random_element().trace() in J.base_ring()
+ True
+
"""
- cs = self.characteristic_polynomial().coefficients(sparse=False)
- if len(cs) >= 2:
- return -1*cs[-2]
- else:
- raise ValueError('charpoly had fewer than 2 coefficients')
+ P = self.parent()
+ r = P.rank()
+ p = P._charpoly_coeff(r-1)
+ # The _charpoly_coeff function already adds the factor of
+ # -1 to ensure that _charpoly_coeff(r-1) is really what
+ # appears in front of t^{r-1} in the charpoly. However,
+ # we want the negative of THAT for the trace.
+ return -p(*self.vector())
def trace_inner_product(self, other):
"""
Return the trace inner product of myself and ``other``.
+
+ TESTS:
+
+ The trace inner product is commutative::
+
+ sage: set_random_seed()
+ sage: J = random_eja()
+ sage: x = J.random_element(); y = J.random_element()
+ sage: x.trace_inner_product(y) == y.trace_inner_product(x)
+ True
+
+ The trace inner product is bilinear::
+
+ sage: set_random_seed()
+ sage: J = random_eja()
+ sage: x = J.random_element()
+ sage: y = J.random_element()
+ sage: z = J.random_element()
+ sage: a = QQ.random_element();
+ sage: actual = (a*(x+z)).trace_inner_product(y)
+ sage: expected = ( a*x.trace_inner_product(y) +
+ ....: a*z.trace_inner_product(y) )
+ sage: actual == expected
+ True
+ sage: actual = x.trace_inner_product(a*(y+z))
+ sage: expected = ( a*x.trace_inner_product(y) +
+ ....: a*x.trace_inner_product(z) )
+ sage: actual == expected
+ True
+
+ The trace inner product satisfies the compatibility
+ condition in the definition of a Euclidean Jordan algebra::
+
+ sage: set_random_seed()
+ sage: J = random_eja()
+ sage: x = J.random_element()
+ sage: y = J.random_element()
+ sage: z = J.random_element()
+ sage: (x*y).trace_inner_product(z) == y.trace_inner_product(x*z)
+ True
+
"""
if not other in self.parent():
raise TypeError("'other' must live in the same algebra")
return (self*other).trace()
-def eja_rn(dimension, field=QQ):
+class RealCartesianProductEJA(FiniteDimensionalEuclideanJordanAlgebra):
"""
Return the Euclidean Jordan Algebra corresponding to the set
`R^n` under the Hadamard product.
+ Note: this is nothing more than the Cartesian product of ``n``
+ copies of the spin algebra. Once Cartesian product algebras
+ are implemented, this can go.
+
EXAMPLES:
This multiplication table can be verified by hand::
- sage: J = eja_rn(3)
+ sage: J = RealCartesianProductEJA(3)
sage: e0,e1,e2 = J.gens()
sage: e0*e0
e0
e2
"""
- # The FiniteDimensionalAlgebra constructor takes a list of
- # matrices, the ith representing right multiplication by the ith
- # basis element in the vector space. So if e_1 = (1,0,0), then
- # right (Hadamard) multiplication of x by e_1 picks out the first
- # component of x; and likewise for the ith basis element e_i.
- Qs = [ matrix(field, dimension, dimension, lambda k,j: 1*(k == j == i))
- for i in xrange(dimension) ]
-
- return FiniteDimensionalEuclideanJordanAlgebra(field,
- Qs,
- rank=dimension,
- inner_product=_usual_ip)
+ @staticmethod
+ def __classcall_private__(cls, n, field=QQ):
+ # The FiniteDimensionalAlgebra constructor takes a list of
+ # matrices, the ith representing right multiplication by the ith
+ # basis element in the vector space. So if e_1 = (1,0,0), then
+ # right (Hadamard) multiplication of x by e_1 picks out the first
+ # component of x; and likewise for the ith basis element e_i.
+ Qs = [ matrix(field, n, n, lambda k,j: 1*(k == j == i))
+ for i in xrange(n) ]
+
+ fdeja = super(RealCartesianProductEJA, cls)
+ return fdeja.__classcall_private__(cls, field, Qs, rank=n)
+ def inner_product(self, x, y):
+ return _usual_ip(x,y)
def random_eja():
* The ``n``-by-``n`` rational symmetric matrices with the symmetric
product.
+ * The ``n``-by-``n`` complex-rational Hermitian matrices embedded
+ in the space of ``2n``-by-``2n`` real symmetric matrices.
+
+ * The ``n``-by-``n`` quaternion-rational Hermitian matrices embedded
+ in the space of ``4n``-by-``4n`` real symmetric matrices.
+
Later this might be extended to return Cartesian products of the
EJAs above.
Euclidean Jordan algebra of degree...
"""
- n = ZZ.random_element(1,5)
- constructor = choice([eja_rn,
- JordanSpinSimpleEJA,
- RealSymmetricSimpleEJA,
- ComplexHermitianSimpleEJA])
+
+ # The max_n component lets us choose different upper bounds on the
+ # value "n" that gets passed to the constructor. This is needed
+ # because e.g. R^{10} is reasonable to test, while the Hermitian
+ # 10-by-10 quaternion matrices are not.
+ (constructor, max_n) = choice([(RealCartesianProductEJA, 6),
+ (JordanSpinEJA, 6),
+ (RealSymmetricEJA, 5),
+ (ComplexHermitianEJA, 4),
+ (QuaternionHermitianEJA, 3)])
+ n = ZZ.random_element(1, max_n)
return constructor(n, field=QQ)
return tuple(S)
+def _quaternion_hermitian_basis(n, field=QQ):
+ """
+ Returns a basis for the space of quaternion Hermitian n-by-n matrices.
+
+ TESTS::
+
+ sage: set_random_seed()
+ sage: n = ZZ.random_element(1,5)
+ sage: all( M.is_symmetric() for M in _quaternion_hermitian_basis(n) )
+ True
+
+ """
+ Q = QuaternionAlgebra(QQ,-1,-1)
+ I,J,K = Q.gens()
+
+ # This is like the symmetric case, but we need to be careful:
+ #
+ # * We want conjugate-symmetry, not just symmetry.
+ # * The diagonal will (as a result) be real.
+ #
+ S = []
+ for i in xrange(n):
+ for j in xrange(i+1):
+ Eij = matrix(Q, n, lambda k,l: k==i and l==j)
+ if i == j:
+ Sij = _embed_quaternion_matrix(Eij)
+ S.append(Sij)
+ else:
+ # Beware, orthogonal but not normalized! The second,
+ # third, and fourth ones have a minus because they're
+ # conjugated.
+ Sij_real = _embed_quaternion_matrix(Eij + Eij.transpose())
+ S.append(Sij_real)
+ Sij_I = _embed_quaternion_matrix(I*Eij - I*Eij.transpose())
+ S.append(Sij_I)
+ Sij_J = _embed_quaternion_matrix(J*Eij - J*Eij.transpose())
+ S.append(Sij_J)
+ Sij_K = _embed_quaternion_matrix(K*Eij - K*Eij.transpose())
+ S.append(Sij_K)
+ return tuple(S)
+
+
def _mat2vec(m):
return vector(m.base_ring(), m.list())
[ 0 -1| 6 0]
[ 1 0| 0 6]
+ TESTS:
+
+ Embedding is a homomorphism (isomorphism, in fact)::
+
+ sage: set_random_seed()
+ sage: n = ZZ.random_element(5)
+ sage: F = QuadraticField(-1, 'i')
+ sage: X = random_matrix(F, n)
+ sage: Y = random_matrix(F, n)
+ sage: actual = _embed_complex_matrix(X) * _embed_complex_matrix(Y)
+ sage: expected = _embed_complex_matrix(X*Y)
+ sage: actual == expected
+ True
+
"""
n = M.nrows()
if M.ncols() != n:
....: [ 9, 10, 11, 12],
....: [-10, 9, -12, 11] ])
sage: _unembed_complex_matrix(A)
- [ -2*i + 1 -4*i + 3]
- [ -10*i + 9 -12*i + 11]
+ [ 2*i + 1 4*i + 3]
+ [ 10*i + 9 12*i + 11]
+
+ TESTS:
+
+ Unembedding is the inverse of embedding::
+
+ sage: set_random_seed()
+ sage: F = QuadraticField(-1, 'i')
+ sage: M = random_matrix(F, 3)
+ sage: _unembed_complex_matrix(_embed_complex_matrix(M)) == M
+ True
+
"""
n = ZZ(M.nrows())
if M.ncols() != n:
for j in xrange(n/2):
submat = M[2*k:2*k+2,2*j:2*j+2]
if submat[0,0] != submat[1,1]:
- raise ValueError('bad real submatrix')
+ raise ValueError('bad on-diagonal submatrix')
if submat[0,1] != -submat[1,0]:
- raise ValueError('bad imag submatrix')
- z = submat[0,0] + submat[1,0]*i
+ raise ValueError('bad off-diagonal submatrix')
+ z = submat[0,0] + submat[0,1]*i
elements.append(z)
return matrix(F, n/2, elements)
+
+def _embed_quaternion_matrix(M):
+ """
+ Embed the n-by-n quaternion matrix ``M`` into the space of real
+ matrices of size 4n-by-4n by first sending each quaternion entry
+ `z = a + bi + cj + dk` to the block-complex matrix
+ ``[[a + bi, c+di],[-c + di, a-bi]]`, and then embedding those into
+ a real matrix.
+
+ EXAMPLES::
+
+ sage: Q = QuaternionAlgebra(QQ,-1,-1)
+ sage: i,j,k = Q.gens()
+ sage: x = 1 + 2*i + 3*j + 4*k
+ sage: M = matrix(Q, 1, [[x]])
+ sage: _embed_quaternion_matrix(M)
+ [ 1 2 3 4]
+ [-2 1 -4 3]
+ [-3 4 1 -2]
+ [-4 -3 2 1]
+
+ Embedding is a homomorphism (isomorphism, in fact)::
+
+ sage: set_random_seed()
+ sage: n = ZZ.random_element(5)
+ sage: Q = QuaternionAlgebra(QQ,-1,-1)
+ sage: X = random_matrix(Q, n)
+ sage: Y = random_matrix(Q, n)
+ sage: actual = _embed_quaternion_matrix(X)*_embed_quaternion_matrix(Y)
+ sage: expected = _embed_quaternion_matrix(X*Y)
+ sage: actual == expected
+ True
+
+ """
+ quaternions = M.base_ring()
+ n = M.nrows()
+ if M.ncols() != n:
+ raise ValueError("the matrix 'M' must be square")
+
+ F = QuadraticField(-1, 'i')
+ i = F.gen()
+
+ blocks = []
+ for z in M.list():
+ t = z.coefficient_tuple()
+ a = t[0]
+ b = t[1]
+ c = t[2]
+ d = t[3]
+ cplx_matrix = matrix(F, 2, [[ a + b*i, c + d*i],
+ [-c + d*i, a - b*i]])
+ blocks.append(_embed_complex_matrix(cplx_matrix))
+
+ # We should have real entries by now, so use the realest field
+ # we've got for the return value.
+ return block_matrix(quaternions.base_ring(), n, blocks)
+
+
+def _unembed_quaternion_matrix(M):
+ """
+ The inverse of _embed_quaternion_matrix().
+
+ EXAMPLES::
+
+ sage: M = matrix(QQ, [[ 1, 2, 3, 4],
+ ....: [-2, 1, -4, 3],
+ ....: [-3, 4, 1, -2],
+ ....: [-4, -3, 2, 1]])
+ sage: _unembed_quaternion_matrix(M)
+ [1 + 2*i + 3*j + 4*k]
+
+ TESTS:
+
+ Unembedding is the inverse of embedding::
+
+ sage: set_random_seed()
+ sage: Q = QuaternionAlgebra(QQ, -1, -1)
+ sage: M = random_matrix(Q, 3)
+ sage: _unembed_quaternion_matrix(_embed_quaternion_matrix(M)) == M
+ True
+
+ """
+ n = ZZ(M.nrows())
+ if M.ncols() != n:
+ raise ValueError("the matrix 'M' must be square")
+ if not n.mod(4).is_zero():
+ raise ValueError("the matrix 'M' must be a complex embedding")
+
+ Q = QuaternionAlgebra(QQ,-1,-1)
+ i,j,k = Q.gens()
+
+ # Go top-left to bottom-right (reading order), converting every
+ # 4-by-4 block we see to a 2-by-2 complex block, to a 1-by-1
+ # quaternion block.
+ elements = []
+ for l in xrange(n/4):
+ for m in xrange(n/4):
+ submat = _unembed_complex_matrix(M[4*l:4*l+4,4*m:4*m+4])
+ if submat[0,0] != submat[1,1].conjugate():
+ raise ValueError('bad on-diagonal submatrix')
+ if submat[0,1] != -submat[1,0].conjugate():
+ raise ValueError('bad off-diagonal submatrix')
+ z = submat[0,0].real() + submat[0,0].imag()*i
+ z += submat[0,1].real()*j + submat[0,1].imag()*k
+ elements.append(z)
+
+ return matrix(Q, n/4, elements)
+
+
# The usual inner product on R^n.
def _usual_ip(x,y):
return x.vector().inner_product(y.vector())
return (X_mat*Y_mat).trace()
-def RealSymmetricSimpleEJA(n, field=QQ):
+class RealSymmetricEJA(FiniteDimensionalEuclideanJordanAlgebra):
"""
The rank-n simple EJA consisting of real symmetric n-by-n
matrices, the usual symmetric Jordan product, and the trace inner
EXAMPLES::
- sage: J = RealSymmetricSimpleEJA(2)
+ sage: J = RealSymmetricEJA(2)
sage: e0, e1, e2 = J.gens()
sage: e0*e0
e0
sage: set_random_seed()
sage: n = ZZ.random_element(1,5)
- sage: J = RealSymmetricSimpleEJA(n)
+ sage: J = RealSymmetricEJA(n)
sage: J.degree() == (n^2 + n)/2
True
+ The Jordan multiplication is what we think it is::
+
+ sage: set_random_seed()
+ sage: n = ZZ.random_element(1,5)
+ sage: J = RealSymmetricEJA(n)
+ sage: x = J.random_element()
+ sage: y = J.random_element()
+ sage: actual = (x*y).natural_representation()
+ sage: X = x.natural_representation()
+ sage: Y = y.natural_representation()
+ sage: expected = (X*Y + Y*X)/2
+ sage: actual == expected
+ True
+ sage: J(expected) == x*y
+ True
+
"""
- S = _real_symmetric_basis(n, field=field)
- (Qs, T) = _multiplication_table_from_matrix_basis(S)
+ @staticmethod
+ def __classcall_private__(cls, n, field=QQ):
+ S = _real_symmetric_basis(n, field=field)
+ (Qs, T) = _multiplication_table_from_matrix_basis(S)
- return FiniteDimensionalEuclideanJordanAlgebra(field,
- Qs,
- rank=n,
- natural_basis=T,
- inner_product=_matrix_ip)
+ fdeja = super(RealSymmetricEJA, cls)
+ return fdeja.__classcall_private__(cls,
+ field,
+ Qs,
+ rank=n,
+ natural_basis=T)
+ def inner_product(self, x, y):
+ return _matrix_ip(x,y)
-def ComplexHermitianSimpleEJA(n, field=QQ):
+
+class ComplexHermitianEJA(FiniteDimensionalEuclideanJordanAlgebra):
"""
The rank-n simple EJA consisting of complex Hermitian n-by-n
matrices over the real numbers, the usual symmetric Jordan product,
sage: set_random_seed()
sage: n = ZZ.random_element(1,5)
- sage: J = ComplexHermitianSimpleEJA(n)
+ sage: J = ComplexHermitianEJA(n)
sage: J.degree() == n^2
True
+ The Jordan multiplication is what we think it is::
+
+ sage: set_random_seed()
+ sage: n = ZZ.random_element(1,5)
+ sage: J = ComplexHermitianEJA(n)
+ sage: x = J.random_element()
+ sage: y = J.random_element()
+ sage: actual = (x*y).natural_representation()
+ sage: X = x.natural_representation()
+ sage: Y = y.natural_representation()
+ sage: expected = (X*Y + Y*X)/2
+ sage: actual == expected
+ True
+ sage: J(expected) == x*y
+ True
+
"""
- S = _complex_hermitian_basis(n)
- (Qs, T) = _multiplication_table_from_matrix_basis(S)
+ @staticmethod
+ def __classcall_private__(cls, n, field=QQ):
+ S = _complex_hermitian_basis(n)
+ (Qs, T) = _multiplication_table_from_matrix_basis(S)
- # Since a+bi on the diagonal is represented as
- #
- # a + bi = [ a b ]
- # [ -b a ],
- #
- # we'll double-count the "a" entries if we take the trace of
- # the embedding.
- ip = lambda X,Y: _matrix_ip(X,Y)/2
+ fdeja = super(ComplexHermitianEJA, cls)
+ return fdeja.__classcall_private__(cls,
+ field,
+ Qs,
+ rank=n,
+ natural_basis=T)
- return FiniteDimensionalEuclideanJordanAlgebra(field,
- Qs,
- rank=n,
- natural_basis=T,
- inner_product=ip)
+ def inner_product(self, x, y):
+ # Since a+bi on the diagonal is represented as
+ #
+ # a + bi = [ a b ]
+ # [ -b a ],
+ #
+ # we'll double-count the "a" entries if we take the trace of
+ # the embedding.
+ return _matrix_ip(x,y)/2
-def QuaternionHermitianSimpleEJA(n):
+class QuaternionHermitianEJA(FiniteDimensionalEuclideanJordanAlgebra):
"""
The rank-n simple EJA consisting of self-adjoint n-by-n quaternion
matrices, the usual symmetric Jordan product, and the
real-part-of-trace inner product. It has dimension `2n^2 - n` over
the reals.
- """
- pass
-def OctonionHermitianSimpleEJA(n):
- """
- This shit be crazy. It has dimension 27 over the reals.
+ TESTS:
+
+ The degree of this algebra is `n^2`::
+
+ sage: set_random_seed()
+ sage: n = ZZ.random_element(1,5)
+ sage: J = QuaternionHermitianEJA(n)
+ sage: J.degree() == 2*(n^2) - n
+ True
+
+ The Jordan multiplication is what we think it is::
+
+ sage: set_random_seed()
+ sage: n = ZZ.random_element(1,5)
+ sage: J = QuaternionHermitianEJA(n)
+ sage: x = J.random_element()
+ sage: y = J.random_element()
+ sage: actual = (x*y).natural_representation()
+ sage: X = x.natural_representation()
+ sage: Y = y.natural_representation()
+ sage: expected = (X*Y + Y*X)/2
+ sage: actual == expected
+ True
+ sage: J(expected) == x*y
+ True
+
"""
- n = 3
- pass
+ @staticmethod
+ def __classcall_private__(cls, n, field=QQ):
+ S = _quaternion_hermitian_basis(n)
+ (Qs, T) = _multiplication_table_from_matrix_basis(S)
+
+ fdeja = super(QuaternionHermitianEJA, cls)
+ return fdeja.__classcall_private__(cls,
+ field,
+ Qs,
+ rank=n,
+ natural_basis=T)
-def JordanSpinSimpleEJA(n, field=QQ):
+ def inner_product(self, x, y):
+ # Since a+bi+cj+dk on the diagonal is represented as
+ #
+ # a + bi +cj + dk = [ a b c d]
+ # [ -b a -d c]
+ # [ -c d a -b]
+ # [ -d -c b a],
+ #
+ # we'll quadruple-count the "a" entries if we take the trace of
+ # the embedding.
+ return _matrix_ip(x,y)/4
+
+
+class JordanSpinEJA(FiniteDimensionalEuclideanJordanAlgebra):
"""
The rank-2 simple EJA consisting of real vectors ``x=(x0, x_bar)``
with the usual inner product and jordan product ``x*y =
This multiplication table can be verified by hand::
- sage: J = JordanSpinSimpleEJA(4)
+ sage: J = JordanSpinEJA(4)
sage: e0,e1,e2,e3 = J.gens()
sage: e0*e0
e0
sage: e2*e3
0
- In one dimension, this is the reals under multiplication::
-
- sage: J1 = JordanSpinSimpleEJA(1)
- sage: J2 = eja_rn(1)
- sage: J1 == J2
- True
-
"""
- Qs = []
- id_matrix = identity_matrix(field, n)
- for i in xrange(n):
- ei = id_matrix.column(i)
- Qi = zero_matrix(field, n)
- Qi.set_row(0, ei)
- Qi.set_column(0, ei)
- Qi += diagonal_matrix(n, [ei[0]]*n)
- # The addition of the diagonal matrix adds an extra ei[0] in the
- # upper-left corner of the matrix.
- Qi[0,0] = Qi[0,0] * ~field(2)
- Qs.append(Qi)
-
- # The rank of the spin factor algebra is two, UNLESS we're in a
- # one-dimensional ambient space (the rank is bounded by the
- # ambient dimension).
- return FiniteDimensionalEuclideanJordanAlgebra(field,
- Qs,
- rank=min(n,2),
- inner_product=_usual_ip)
+ @staticmethod
+ def __classcall_private__(cls, n, field=QQ):
+ Qs = []
+ id_matrix = identity_matrix(field, n)
+ for i in xrange(n):
+ ei = id_matrix.column(i)
+ Qi = zero_matrix(field, n)
+ Qi.set_row(0, ei)
+ Qi.set_column(0, ei)
+ Qi += diagonal_matrix(n, [ei[0]]*n)
+ # The addition of the diagonal matrix adds an extra ei[0] in the
+ # upper-left corner of the matrix.
+ Qi[0,0] = Qi[0,0] * ~field(2)
+ Qs.append(Qi)
+
+ # The rank of the spin algebra is two, unless we're in a
+ # one-dimensional ambient space (because the rank is bounded by
+ # the ambient dimension).
+ fdeja = super(JordanSpinEJA, cls)
+ return fdeja.__classcall_private__(cls, field, Qs, rank=min(n,2))
+
+ def inner_product(self, x, y):
+ return _usual_ip(x,y)
projects / sage.d.git / blobdiff