[sage.d.git] / eja_element.py

from itertools import izip

from sage.matrix.constructor import matrix
from sage.modules.free_module import VectorSpace
from sage.modules.with_basis.indexed_element import IndexedFreeModuleElement

# TODO: make this unnecessary somehow.
from sage.misc.lazy_import import lazy_import
lazy_import('mjo.eja.eja_algebra', 'FiniteDimensionalEuclideanJordanAlgebra')
lazy_import('mjo.eja.eja_subalgebra',
            'FiniteDimensionalEuclideanJordanElementSubalgebra')
from mjo.eja.eja_operator import FiniteDimensionalEuclideanJordanAlgebraOperator
from mjo.eja.eja_utils import _mat2vec

class FiniteDimensionalEuclideanJordanAlgebraElement(IndexedFreeModuleElement):
    """
    An element of a Euclidean Jordan algebra.
    """

    def __dir__(self):
        """
        Oh man, I should not be doing this. This hides the "disabled"
        methods ``left_matrix`` and ``matrix`` from introspection;
        in particular it removes them from tab-completion.
        """
        return filter(lambda s: s not in ['left_matrix', 'matrix'],
                      dir(self.__class__) )




    def __pow__(self, n):
        """
        Return ``self`` raised to the power ``n``.

        Jordan algebras are always power-associative; see for
        example Faraut and Koranyi, Proposition II.1.2 (ii).

        We have to override this because our superclass uses row
        vectors instead of column vectors! We, on the other hand,
        assume column vectors everywhere.

        SETUP::

            sage: from mjo.eja.eja_algebra import random_eja

        TESTS:

        The definition of `x^2` is the unambiguous `x*x`::

            sage: set_random_seed()
            sage: x = random_eja().random_element()
            sage: x*x == (x^2)
            True

        A few examples of power-associativity::

            sage: set_random_seed()
            sage: x = random_eja().random_element()
            sage: x*(x*x)*(x*x) == x^5
            True
            sage: (x*x)*(x*x*x) == x^5
            True

        We also know that powers operator-commute (Koecher, Chapter
        III, Corollary 1)::

            sage: set_random_seed()
            sage: x = random_eja().random_element()
            sage: m = ZZ.random_element(0,10)
            sage: n = ZZ.random_element(0,10)
            sage: Lxm = (x^m).operator()
            sage: Lxn = (x^n).operator()
            sage: Lxm*Lxn == Lxn*Lxm
            True

        """
        if n == 0:
            return self.parent().one()
        elif n == 1:
            return self
        else:
            return (self**(n-1))*self


    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.

        SETUP::

            sage: from mjo.eja.eja_algebra import (RealCartesianProductEJA,
            ....:                                  random_eja)

        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 the characteristic polynomial of this element.

        SETUP::

            sage: from mjo.eja.eja_algebra import RealCartesianProductEJA

        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

        TESTS:

        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

        The characteristic polynomials of the zero and unit elements
        should be what we think they are in a subalgebra, too::

            sage: J = RealCartesianProductEJA(3)
            sage: p1 = J.one().characteristic_polynomial()
            sage: q1 = J.zero().characteristic_polynomial()
            sage: e0,e1,e2 = J.gens()
            sage: A = (e0 + 2*e1 + 3*e2).subalgebra_generated_by() # dim 3
            sage: p2 = A.one().characteristic_polynomial()
            sage: q2 = A.zero().characteristic_polynomial()
            sage: p1 == p2
            True
            sage: q1 == q2
            True

        """
        p = self.parent().characteristic_polynomial()
        return p(*self.to_vector())


    def inner_product(self, other):
        """
        Return the parent algebra's inner product of myself and ``other``.

        SETUP::

            sage: from mjo.eja.eja_algebra import (
            ....:   ComplexHermitianEJA,
            ....:   JordanSpinEJA,
            ....:   QuaternionHermitianEJA,
            ....:   RealSymmetricEJA,
            ....:   random_eja)

        EXAMPLES:

        The inner product in the Jordan spin algebra is the usual
        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 = JordanSpinEJA(3)
            sage: x = vector(QQ,[1,2,3])
            sage: y = vector(QQ,[4,5,6])
            sage: x.inner_product(y)
            32
            sage: J.from_vector(x).inner_product(J.from_vector(y))
            32

        The inner product on `S^n` is `<X,Y> = trace(X*Y)`, where
        multiplication is the usual matrix multiplication in `S^n`,
        so the inner product of the identity matrix with itself
        should be the `n`::

            sage: J = RealSymmetricEJA(3)
            sage: J.one().inner_product(J.one())
            3

        Likewise, the inner product on `C^n` is `<X,Y> =
        Re(trace(X*Y))`, where we must necessarily take the real
        part because the product of Hermitian matrices may not be
        Hermitian::

            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

        TESTS:

        Ensure that we can always compute an inner product, and that
        it gives us back a real number::

            sage: set_random_seed()
            sage: J = random_eja()
            sage: x,y = J.random_elements(2)
            sage: x.inner_product(y) in RLF
            True

        """
        P = self.parent()
        if not other in P:
            raise TypeError("'other' must live in the same algebra")

        return P.inner_product(self, other)


    def operator_commutes_with(self, other):
        """
        Return whether or not this element operator-commutes
        with ``other``.

        SETUP::

            sage: from mjo.eja.eja_algebra import random_eja

        EXAMPLES:

        The definition of a Jordan algebra says that any element
        operator-commutes with its square::

            sage: set_random_seed()
            sage: x = random_eja().random_element()
            sage: x.operator_commutes_with(x^2)
            True

        TESTS:

        Test Lemma 1 from Chapter III of Koecher::

            sage: set_random_seed()
            sage: u,v = random_eja().random_elements(2)
            sage: lhs = u.operator_commutes_with(u*v)
            sage: rhs = v.operator_commutes_with(u^2)
            sage: lhs == rhs
            True

        Test the first polarization identity from my notes, Koecher
        Chapter III, or from Baes (2.3)::

            sage: set_random_seed()
            sage: x,y = random_eja().random_elements(2)
            sage: Lx = x.operator()
            sage: Ly = y.operator()
            sage: Lxx = (x*x).operator()
            sage: Lxy = (x*y).operator()
            sage: bool(2*Lx*Lxy + Ly*Lxx == 2*Lxy*Lx + Lxx*Ly)
            True

        Test the second polarization identity from my notes or from
        Baes (2.4)::

            sage: set_random_seed()
            sage: x,y,z = random_eja().random_elements(3)
            sage: Lx = x.operator()
            sage: Ly = y.operator()
            sage: Lz = z.operator()
            sage: Lzy = (z*y).operator()
            sage: Lxy = (x*y).operator()
            sage: Lxz = (x*z).operator()
            sage: bool(Lx*Lzy + Lz*Lxy + Ly*Lxz == Lzy*Lx + Lxy*Lz + Lxz*Ly)
            True

        Test the third polarization identity from my notes or from
        Baes (2.5)::

            sage: set_random_seed()
            sage: u,y,z = random_eja().random_elements(3)
            sage: Lu = u.operator()
            sage: Ly = y.operator()
            sage: Lz = z.operator()
            sage: Lzy = (z*y).operator()
            sage: Luy = (u*y).operator()
            sage: Luz = (u*z).operator()
            sage: Luyz = (u*(y*z)).operator()
            sage: lhs = Lu*Lzy + Lz*Luy + Ly*Luz
            sage: rhs = Luyz + Ly*Lu*Lz + Lz*Lu*Ly
            sage: bool(lhs == rhs)
            True

        """
        if not other in self.parent():
            raise TypeError("'other' must live in the same algebra")

        A = self.operator()
        B = other.operator()
        return (A*B == B*A)


    def det(self):
        """
        Return my determinant, the product of my eigenvalues.

        SETUP::

            sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
            ....:                                  random_eja)

        EXAMPLES::

            sage: J = JordanSpinEJA(2)
            sage: e0,e1 = J.gens()
            sage: x = sum( J.gens() )
            sage: x.det()
            0

        ::

            sage: J = JordanSpinEJA(3)
            sage: e0,e1,e2 = J.gens()
            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

        Ensure that the determinant is multiplicative on an associative
        subalgebra as in Faraut and Koranyi's Proposition II.2.2::

            sage: set_random_seed()
            sage: J = random_eja().random_element().subalgebra_generated_by()
            sage: x,y = J.random_elements(2)
            sage: (x*y).det() == x.det()*y.det()
            True

        """
        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.to_vector())


    def inverse(self):
        """
        Return the Jordan-multiplicative inverse of this element.

        ALGORITHM:

        We appeal to the quadratic representation as in Koecher's
        Theorem 12 in Chapter III, Section 5.

        SETUP::

            sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
            ....:                                  random_eja)

        EXAMPLES:

        The inverse in the spin factor algebra is given in Alizadeh's
        Example 11.11::

            sage: set_random_seed()
            sage: J = JordanSpinEJA.random_instance()
            sage: x = J.random_element()
            sage: while not x.is_invertible():
            ....:     x = J.random_element()
            sage: x_vec = x.to_vector()
            sage: x0 = x_vec[0]
            sage: x_bar = x_vec[1:]
            sage: coeff = ~(x0^2 - x_bar.inner_product(x_bar))
            sage: inv_vec = x_vec.parent()([x0] + (-x_bar).list())
            sage: x_inverse = coeff*inv_vec
            sage: x.inverse() == J.from_vector(x_inverse)
            True

        TESTS:

        The identity element is its own inverse::

            sage: set_random_seed()
            sage: J = random_eja()
            sage: J.one().inverse() == J.one()
            True

        If an element has an inverse, it acts like one::

            sage: set_random_seed()
            sage: J = random_eja()
            sage: x = J.random_element()
            sage: (not x.is_invertible()) or (x.inverse()*x == J.one())
            True

        The inverse of the inverse is what we started with::

            sage: set_random_seed()
            sage: J = random_eja()
            sage: x = J.random_element()
            sage: (not x.is_invertible()) or (x.inverse().inverse() == x)
            True

        The zero element is never invertible::

            sage: set_random_seed()
            sage: J = random_eja().zero().inverse()
            Traceback (most recent call last):
            ...
            ValueError: element is not invertible

        """
        if not self.is_invertible():
            raise ValueError("element is not invertible")

        return (~self.quadratic_representation())(self)


    def is_invertible(self):
        """
        Return whether or not this element is invertible.

        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.

        Beware that we can't use the superclass method, because it
        relies on the algebra being associative.

        SETUP::

            sage: from mjo.eja.eja_algebra import random_eja

        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 in a non-trivial algebra::

            sage: set_random_seed()
            sage: J = random_eja()
            sage: (not J.is_trivial()) and J.zero().is_invertible()
            False

        """
        if self.is_zero():
            if self.parent().is_trivial():
                return True
            else:
                return False

        # In fact, we only need to know if the constant term is non-zero,
        # so we can pass in the field's zero element instead.
        zero = self.base_ring().zero()
        p = self.minimal_polynomial()
        return not (p(zero) == zero)


    def is_nilpotent(self):
        """
        Return whether or not some power of this element is zero.

        ALGORITHM:

        We use Theorem 5 in Chapter III of Koecher, which says that
        an element ``x`` is nilpotent if and only if ``x.operator()``
        is nilpotent. And it is a basic fact of linear algebra that
        an operator on an `n`-dimensional space is nilpotent if and
        only if, when raised to the `n`th power, it equals the zero
        operator (for example, see Axler Corollary 8.8).

        SETUP::

            sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
            ....:                                  random_eja)

        EXAMPLES::

            sage: J = JordanSpinEJA(3)
            sage: x = sum(J.gens())
            sage: x.is_nilpotent()
            False

        TESTS:

        The identity element is never nilpotent::

            sage: set_random_seed()
            sage: random_eja().one().is_nilpotent()
            False

        The additive identity is always nilpotent::

            sage: set_random_seed()
            sage: random_eja().zero().is_nilpotent()
            True

        """
        P = self.parent()
        zero_operator = P.zero().operator()
        return self.operator()**P.dimension() == zero_operator


    def is_regular(self):
        """
        Return whether or not this is a regular element.

        SETUP::

            sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
            ....:                                  random_eja)

        EXAMPLES:

        The identity element always has degree one, but any element
        linearly-independent from it is regular::

            sage: J = JordanSpinEJA(5)
            sage: J.one().is_regular()
            False
            sage: e0, e1, e2, e3, e4 = J.gens() # e0 is the identity
            sage: for x in J.gens():
            ....:     (J.one() + x).is_regular()
            False
            True
            True
            True
            True

        TESTS:

        The zero element should never be regular, unless the parent
        algebra has dimension one::

            sage: set_random_seed()
            sage: J = random_eja()
            sage: J.dimension() == 1 or not J.zero().is_regular()
            True

        The unit element isn't regular unless the algebra happens to
        consist of only its scalar multiples::

            sage: set_random_seed()
            sage: J = random_eja()
            sage: J.dimension() == 1 or not J.one().is_regular()
            True

        """
        return self.degree() == self.parent().rank()


    def degree(self):
        """
        Return the degree of this element, which is defined to be
        the degree of its minimal polynomial.

        ALGORITHM:

        For now, we skip the messy minimal polynomial computation
        and instead return the dimension of the vector space spanned
        by the powers of this element. The latter is a bit more
        straightforward to compute.

        SETUP::

            sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
            ....:                                  random_eja)

        EXAMPLES::

            sage: J = JordanSpinEJA(4)
            sage: J.one().degree()
            1
            sage: e0,e1,e2,e3 = J.gens()
            sage: (e0 - e1).degree()
            2

        In the spin factor algebra (of rank two), all elements that
        aren't multiples of the identity are regular::

            sage: set_random_seed()
            sage: J = JordanSpinEJA.random_instance()
            sage: x = J.random_element()
            sage: x == x.coefficient(0)*J.one() or x.degree() == 2
            True

        TESTS:

        The zero and unit elements are both of degree one::

            sage: set_random_seed()
            sage: J = random_eja()
            sage: J.zero().degree()
            1
            sage: J.one().degree()
            1

        Our implementation agrees with the definition::

            sage: set_random_seed()
            sage: x = random_eja().random_element()
            sage: x.degree() == x.minimal_polynomial().degree()
            True

        """
        if self.is_zero() and not self.parent().is_trivial():
            # The minimal polynomial of zero in a nontrivial algebra
            # is "t"; in a trivial algebra it's "1" by convention
            # (it's an empty product).
            return 1
        return self.subalgebra_generated_by().dimension()


    def left_matrix(self):
        """
        Our parent class defines ``left_matrix`` and ``matrix``
        methods whose names are misleading. We don't want them.
        """
        raise NotImplementedError("use operator().matrix() instead")

    matrix = left_matrix


    def minimal_polynomial(self):
        """
        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.

        SETUP::

            sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
            ....:                                  RealSymmetricEJA,
            ....:                                  random_eja)

        TESTS:

        The minimal polynomial of the identity and zero elements are
        always the same::

            sage: set_random_seed()
            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: x.degree() == x.minimal_polynomial().degree()
            True

        The minimal polynomial and the characteristic polynomial coincide
        and are known (see Alizadeh, Example 11.11) for all elements of
        the spin factor algebra that aren't scalar multiples of the
        identity. We require the dimension of the algebra to be at least
        two here so that said elements actually exist::

            sage: set_random_seed()
            sage: n_max = max(2, JordanSpinEJA._max_test_case_size())
            sage: n = ZZ.random_element(2, n_max)
            sage: J = JordanSpinEJA(n)
            sage: y = J.random_element()
            sage: while y == y.coefficient(0)*J.one():
            ....:     y = J.random_element()
            sage: y0 = y.to_vector()[0]
            sage: y_bar = y.to_vector()[1:]
            sage: actual = y.minimal_polynomial()
            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 minimal polynomial should always kill its element::

            sage: set_random_seed()
            sage: x = random_eja().random_element()
            sage: p = x.minimal_polynomial()
            sage: x.apply_univariate_polynomial(p)
            0

        The minimal polynomial is invariant under a change of basis,
        and in particular, a re-scaling of the basis::

            sage: set_random_seed()
            sage: n_max = RealSymmetricEJA._max_test_case_size()
            sage: n = ZZ.random_element(1, n_max)
            sage: J1 = RealSymmetricEJA(n,QQ)
            sage: J2 = RealSymmetricEJA(n,QQ,normalize_basis=False)
            sage: X = random_matrix(QQ,n)
            sage: X = X*X.transpose()
            sage: x1 = J1(X)
            sage: x2 = J2(X)
            sage: x1.minimal_polynomial() == x2.minimal_polynomial()
            True

        """
        if self.is_zero():
            # We would generate a zero-dimensional subalgebra
            # where the minimal polynomial would be constant.
            # That might be correct, but only if *this* algebra
            # is trivial too.
            if not self.parent().is_trivial():
                # Pretty sure we know what the minimal polynomial of
                # the zero operator is going to be. This ensures
                # consistency of e.g. the polynomial variable returned
                # in the "normal" case without us having to think about it.
                return self.operator().minimal_polynomial()

        A = self.subalgebra_generated_by()
        return A(self).operator().minimal_polynomial()



    def natural_representation(self):
        """
        Return a more-natural representation of this element.

        Every finite-dimensional Euclidean Jordan Algebra is a
        direct sum of five simple algebras, four of which comprise
        Hermitian matrices. This method returns the original
        "natural" representation of this element as a Hermitian
        matrix, if it has one. If not, you get the usual representation.

        SETUP::

            sage: from mjo.eja.eja_algebra import (ComplexHermitianEJA,
            ....:                                  QuaternionHermitianEJA)

        EXAMPLES::

            sage: J = ComplexHermitianEJA(3)
            sage: J.one()
            e0 + e3 + e8
            sage: J.one().natural_representation()
            [1 0 0 0 0 0]
            [0 1 0 0 0 0]
            [0 0 1 0 0 0]
            [0 0 0 1 0 0]
            [0 0 0 0 1 0]
            [0 0 0 0 0 1]

        ::

            sage: J = QuaternionHermitianEJA(3)
            sage: J.one()
            e0 + e5 + 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 = self.parent().natural_basis_space()
        return W.linear_combination(izip(B,self.to_vector()))


    def norm(self):
        """
        The norm of this element with respect to :meth:`inner_product`.

        SETUP::

            sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
            ....:                                  RealCartesianProductEJA)

        EXAMPLES::

            sage: J = RealCartesianProductEJA(2)
            sage: x = sum(J.gens())
            sage: x.norm()
            sqrt(2)

        ::

            sage: J = JordanSpinEJA(4)
            sage: x = sum(J.gens())
            sage: x.norm()
            2

        """
        return self.inner_product(self).sqrt()


    def operator(self):
        """
        Return the left-multiplication-by-this-element
        operator on the ambient algebra.

        SETUP::

            sage: from mjo.eja.eja_algebra import random_eja

        TESTS::

            sage: set_random_seed()
            sage: J = random_eja()
            sage: x,y = J.random_elements(2)
            sage: x.operator()(y) == x*y
            True
            sage: y.operator()(x) == x*y
            True

        """
        P = self.parent()
        left_mult_by_self = lambda y: self*y
        L = P.module_morphism(function=left_mult_by_self, codomain=P)
        return FiniteDimensionalEuclideanJordanAlgebraOperator(
                 P,
                 P,
                 L.matrix() )


    def quadratic_representation(self, other=None):
        """
        Return the quadratic representation of this element.

        SETUP::

            sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
            ....:                                  random_eja)

        EXAMPLES:

        The explicit form in the spin factor algebra is given by
        Alizadeh's Example 11.12::

            sage: set_random_seed()
            sage: x = JordanSpinEJA.random_instance().random_element()
            sage: x_vec = x.to_vector()
            sage: n = x_vec.degree()
            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 = matrix.identity(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 = matrix.block(2,2,[A,B,C,D])
            sage: Q == x.quadratic_representation().matrix()
            True

        Test all of the properties from Theorem 11.2 in Alizadeh::

            sage: set_random_seed()
            sage: J = random_eja()
            sage: x,y = J.random_elements(2)
            sage: Lx = x.operator()
            sage: Lxx = (x*x).operator()
            sage: Qx = x.quadratic_representation()
            sage: Qy = y.quadratic_representation()
            sage: Qxy = x.quadratic_representation(y)
            sage: Qex = J.one().quadratic_representation(x)
            sage: n = ZZ.random_element(10)
            sage: Qxn = (x^n).quadratic_representation()

        Property 1:

            sage: 2*Qxy == (x+y).quadratic_representation() - Qx - Qy
            True

        Property 2 (multiply on the right for :trac:`28272`):

            sage: alpha = J.base_ring().random_element()
            sage: (alpha*x).quadratic_representation() == Qx*(alpha^2)
            True

        Property 3:

            sage: not x.is_invertible() or ( Qx(x.inverse()) == x )
            True

            sage: not x.is_invertible() or (
            ....:   ~Qx
            ....:   ==
            ....:   x.inverse().quadratic_representation() )
            True

            sage: Qxy(J.one()) == x*y
            True

        Property 4:

            sage: not x.is_invertible() or (
            ....:   x.quadratic_representation(x.inverse())*Qx
            ....:   == Qx*x.quadratic_representation(x.inverse()) )
            True

            sage: not x.is_invertible() or (
            ....:   x.quadratic_representation(x.inverse())*Qx
            ....:   ==
            ....:   2*Lx*Qex - Qx )
            True

            sage: 2*Lx*Qex - Qx == Lxx
            True

        Property 5:

            sage: Qy(x).quadratic_representation() == Qy*Qx*Qy
            True

        Property 6:

            sage: Qxn == (Qx)^n
            True

        Property 7:

            sage: not x.is_invertible() or (
            ....:   Qx*x.inverse().operator() == Lx )
            True

        Property 8:

            sage: not x.operator_commutes_with(y) or (
            ....:   Qx(y)^n == Qxn(y^n) )
            True

        """
        if other is None:
            other=self
        elif not other in self.parent():
            raise TypeError("'other' must live in the same algebra")

        L = self.operator()
        M = other.operator()
        return ( L*M + M*L - (self*other).operator() )




    def subalgebra_generated_by(self, orthonormalize_basis=False):
        """
        Return the associative subalgebra of the parent EJA generated
        by this element.

        SETUP::

            sage: from mjo.eja.eja_algebra import random_eja

        TESTS:

        This subalgebra, being composed of only powers, is associative::

            sage: set_random_seed()
            sage: x0 = random_eja().random_element()
            sage: A = x0.subalgebra_generated_by()
            sage: x,y,z = A.random_elements(3)
            sage: (x*y)*z == x*(y*z)
            True

        Squaring in the subalgebra should work the same as in
        the superalgebra::

            sage: set_random_seed()
            sage: x = random_eja().random_element()
            sage: A = x.subalgebra_generated_by()
            sage: A(x^2) == A(x)*A(x)
            True

        The subalgebra generated by the zero element is trivial::

            sage: set_random_seed()
            sage: A = random_eja().zero().subalgebra_generated_by()
            sage: A
            Euclidean Jordan algebra of dimension 0 over...
            sage: A.one()
            0

        """
        return FiniteDimensionalEuclideanJordanElementSubalgebra(self, orthonormalize_basis)


    def subalgebra_idempotent(self):
        """
        Find an idempotent in the associative subalgebra I generate
        using Proposition 2.3.5 in Baes.

        SETUP::

            sage: from mjo.eja.eja_algebra import random_eja

        TESTS::

            sage: set_random_seed()
            sage: J = random_eja()
            sage: x = J.random_element()
            sage: while x.is_nilpotent():
            ....:     x = J.random_element()
            sage: c = x.subalgebra_idempotent()
            sage: c^2 == c
            True

        """
        if self.is_nilpotent():
            raise ValueError("this only works with non-nilpotent elements!")

        J = self.subalgebra_generated_by()
        u = J(self)

        # The image of the matrix of left-u^m-multiplication
        # will be minimal for some natural number s...
        s = 0
        minimal_dim = J.dimension()
        for i in xrange(1, minimal_dim):
            this_dim = (u**i).operator().matrix().image().dimension()
            if this_dim < minimal_dim:
                minimal_dim = this_dim
                s = i

        # Now minimal_matrix should correspond to the smallest
        # non-zero subspace in Baes's (or really, Koecher's)
        # proposition.
        #
        # However, we need to restrict the matrix to work on the
        # subspace... or do we? Can't we just solve, knowing that
        # A(c) = u^(s+1) should have a solution in the big space,
        # too?
        #
        # Beware, solve_right() means that we're using COLUMN vectors.
        # Our FiniteDimensionalAlgebraElement superclass uses rows.
        u_next = u**(s+1)
        A = u_next.operator().matrix()
        c = J.from_vector(A.solve_right(u_next.to_vector()))

        # Now c is the idempotent we want, but it still lives in the subalgebra.
        return c.superalgebra_element()


    def trace(self):
        """
        Return my trace, the sum of my eigenvalues.

        SETUP::

            sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
            ....:                                  RealCartesianProductEJA,
            ....:                                  random_eja)

        EXAMPLES::

            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 RLF
            True

        """
        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.to_vector())


    def trace_inner_product(self, other):
        """
        Return the trace inner product of myself and ``other``.

        SETUP::

            sage: from mjo.eja.eja_algebra import random_eja

        TESTS:

        The trace inner product is commutative, bilinear, and associative::

            sage: set_random_seed()
            sage: J = random_eja()
            sage: x,y,z = J.random_elements(3)
            sage: # commutative
            sage: x.trace_inner_product(y) == y.trace_inner_product(x)
            True
            sage: # bilinear
            sage: a = J.base_ring().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
            sage: # associative
            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 trace_norm(self):
        """
        The norm of this element with respect to :meth:`trace_inner_product`.

        SETUP::

            sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
            ....:                                  RealCartesianProductEJA)

        EXAMPLES::

            sage: J = RealCartesianProductEJA(2)
            sage: x = sum(J.gens())
            sage: x.trace_norm()
            sqrt(2)

        ::

            sage: J = JordanSpinEJA(4)
            sage: x = sum(J.gens())
            sage: x.trace_norm()
            2*sqrt(2)

        """
        return self.trace_inner_product(self).sqrt()