X-Git-Url: http://gitweb.michael.orlitzky.com/?a=blobdiff_plain;f=mjo%2Feja%2Feuclidean_jordan_algebra.py;h=70a77701b342e36eec827d3ec151b87bf67fe902;hb=220688f4c9dcee4a4f980c955fc159e38514bbcb;hp=4713ff0859876b5832e6e5e9551f632444c6a138;hpb=8ee3c1ac3dd5a2b08f91cfd4c700d87f617196e6;p=sage.d.git diff --git a/mjo/eja/euclidean_jordan_algebra.py b/mjo/eja/euclidean_jordan_algebra.py index 4713ff0..70a7770 100644 --- a/mjo/eja/euclidean_jordan_algebra.py +++ b/mjo/eja/euclidean_jordan_algebra.py @@ -5,13 +5,369 @@ are used in optimization, and have some additional nice methods beyond what can be supported in a general Jordan Algebra. """ -from sage.categories.magmatic_algebras import MagmaticAlgebras +from sage.categories.finite_dimensional_algebras_with_basis import FiniteDimensionalAlgebrasWithBasis +from sage.categories.map import Map from sage.structure.element import is_Matrix from sage.structure.category_object import normalize_names from sage.algebras.finite_dimensional_algebras.finite_dimensional_algebra import FiniteDimensionalAlgebra from sage.algebras.finite_dimensional_algebras.finite_dimensional_algebra_element import FiniteDimensionalAlgebraElement + +class FiniteDimensionalEuclideanJordanAlgebraOperator(Map): + def __init__(self, domain_eja, codomain_eja, mat): + if not ( + isinstance(domain_eja, FiniteDimensionalEuclideanJordanAlgebra) and + isinstance(codomain_eja, FiniteDimensionalEuclideanJordanAlgebra) ): + raise ValueError('(co)domains must be finite-dimensional Euclidean ' + 'Jordan algebras') + + F = domain_eja.base_ring() + if not (F == codomain_eja.base_ring()): + raise ValueError("domain and codomain must have the same base ring") + + # We need to supply something here to avoid getting the + # default Homset of the parent FiniteDimensionalAlgebra class, + # which messes up e.g. equality testing. We use FreeModules(F) + # instead of VectorSpaces(F) because our characteristic polynomial + # algorithm will need to F to be a polynomial ring at some point. + # When F is a field, FreeModules(F) returns VectorSpaces(F) anyway. + parent = Hom(domain_eja, codomain_eja, FreeModules(F)) + + # The Map initializer will set our parent to a homset, which + # is explicitly NOT what we want, because these ain't algebra + # homomorphisms. + super(FiniteDimensionalEuclideanJordanAlgebraOperator,self).__init__(parent) + + # Keep a matrix around to do all of the real work. It would + # be nice if we could use a VectorSpaceMorphism instead, but + # those use row vectors that we don't want to accidentally + # expose to our users. + self._matrix = mat + + + def _call_(self, x): + """ + Allow this operator to be called only on elements of an EJA. + + EXAMPLES:: + + sage: J = JordanSpinEJA(3) + sage: x = J.linear_combination(zip(range(len(J.gens())), J.gens())) + sage: id = identity_matrix(J.base_ring(), J.dimension()) + sage: f = FiniteDimensionalEuclideanJordanAlgebraOperator(J,J,id) + sage: f(x) == x + True + + """ + return self.codomain()(self.matrix()*x.vector()) + + + def _add_(self, other): + """ + Add the ``other`` EJA operator to this one. + + EXAMPLES: + + When we add two EJA operators, we get another one back:: + + sage: J = RealSymmetricEJA(2) + sage: id = identity_matrix(J.base_ring(), J.dimension()) + sage: f = FiniteDimensionalEuclideanJordanAlgebraOperator(J,J,id) + sage: g = FiniteDimensionalEuclideanJordanAlgebraOperator(J,J,id) + sage: f + g + Linear operator between finite-dimensional Euclidean Jordan + algebras represented by the matrix: + [2 0 0] + [0 2 0] + [0 0 2] + Domain: Euclidean Jordan algebra of degree 3 over Rational Field + Codomain: Euclidean Jordan algebra of degree 3 over Rational Field + + If you try to add two identical vector space operators but on + different EJAs, that should blow up:: + + sage: J1 = RealSymmetricEJA(2) + sage: J2 = JordanSpinEJA(3) + sage: id = identity_matrix(QQ, 3) + sage: f = FiniteDimensionalEuclideanJordanAlgebraOperator(J1,J1,id) + sage: g = FiniteDimensionalEuclideanJordanAlgebraOperator(J2,J2,id) + sage: f + g + Traceback (most recent call last): + ... + TypeError: unsupported operand parent(s) for +: ... + + """ + return FiniteDimensionalEuclideanJordanAlgebraOperator( + self.domain(), + self.codomain(), + self.matrix() + other.matrix()) + + + def _composition_(self, other, homset): + """ + Compose two EJA operators to get another one (and NOT a formal + composite object) back. + + EXAMPLES:: + + sage: J1 = JordanSpinEJA(3) + sage: J2 = RealCartesianProductEJA(2) + sage: J3 = RealSymmetricEJA(1) + sage: mat1 = matrix(QQ, [[1,2,3], + ....: [4,5,6]]) + sage: mat2 = matrix(QQ, [[7,8]]) + sage: g = FiniteDimensionalEuclideanJordanAlgebraOperator(J1, + ....: J2, + ....: mat1) + sage: f = FiniteDimensionalEuclideanJordanAlgebraOperator(J2, + ....: J3, + ....: mat2) + sage: f*g + Linear operator between finite-dimensional Euclidean Jordan + algebras represented by the matrix: + [39 54 69] + Domain: Euclidean Jordan algebra of degree 3 over Rational Field + Codomain: Euclidean Jordan algebra of degree 1 over Rational Field + + """ + return FiniteDimensionalEuclideanJordanAlgebraOperator( + other.domain(), + self.codomain(), + self.matrix()*other.matrix()) + + + def __eq__(self, other): + if self.domain() != other.domain(): + return False + if self.codomain() != other.codomain(): + return False + if self.matrix() != other.matrix(): + return False + return True + + + def __invert__(self): + """ + Invert this EJA operator. + + EXAMPLES:: + + sage: J = RealSymmetricEJA(2) + sage: id = identity_matrix(J.base_ring(), J.dimension()) + sage: f = FiniteDimensionalEuclideanJordanAlgebraOperator(J,J,id) + sage: ~f + Linear operator between finite-dimensional Euclidean Jordan + algebras represented by the matrix: + [1 0 0] + [0 1 0] + [0 0 1] + Domain: Euclidean Jordan algebra of degree 3 over Rational Field + Codomain: Euclidean Jordan algebra of degree 3 over Rational Field + + """ + return FiniteDimensionalEuclideanJordanAlgebraOperator( + self.codomain(), + self.domain(), + ~self.matrix()) + + + def __mul__(self, other): + """ + Compose two EJA operators, or scale myself by an element of the + ambient vector space. + + We need to override the real ``__mul__`` function to prevent the + coercion framework from throwing an error when it fails to convert + a base ring element into a morphism. + + EXAMPLES: + + We can scale an operator on a rational algebra by a rational number:: + + sage: J = RealSymmetricEJA(2) + sage: e0,e1,e2 = J.gens() + sage: x = 2*e0 + 4*e1 + 16*e2 + sage: x.operator() + Linear operator between finite-dimensional Euclidean Jordan algebras + represented by the matrix: + [ 2 4 0] + [ 2 9 2] + [ 0 4 16] + Domain: Euclidean Jordan algebra of degree 3 over Rational Field + Codomain: Euclidean Jordan algebra of degree 3 over Rational Field + sage: x.operator()*(1/2) + Linear operator between finite-dimensional Euclidean Jordan algebras + represented by the matrix: + [ 1 2 0] + [ 1 9/2 1] + [ 0 2 8] + Domain: Euclidean Jordan algebra of degree 3 over Rational Field + Codomain: Euclidean Jordan algebra of degree 3 over Rational Field + + """ + if other in self.codomain().base_ring(): + return FiniteDimensionalEuclideanJordanAlgebraOperator( + self.domain(), + self.codomain(), + self.matrix()*other) + + # This should eventually delegate to _composition_ after performing + # some sanity checks for us. + mor = super(FiniteDimensionalEuclideanJordanAlgebraOperator,self) + return mor.__mul__(other) + + + def _neg_(self): + """ + Negate this EJA operator. + + EXAMPLES:: + + sage: J = RealSymmetricEJA(2) + sage: id = identity_matrix(J.base_ring(), J.dimension()) + sage: f = FiniteDimensionalEuclideanJordanAlgebraOperator(J,J,id) + sage: -f + Linear operator between finite-dimensional Euclidean Jordan + algebras represented by the matrix: + [-1 0 0] + [ 0 -1 0] + [ 0 0 -1] + Domain: Euclidean Jordan algebra of degree 3 over Rational Field + Codomain: Euclidean Jordan algebra of degree 3 over Rational Field + + """ + return FiniteDimensionalEuclideanJordanAlgebraOperator( + self.domain(), + self.codomain(), + -self.matrix()) + + + def __pow__(self, n): + """ + Raise this EJA operator to the power ``n``. + + TESTS: + + Ensure that we get back another EJA operator that can be added, + subtracted, et cetera:: + + sage: J = RealSymmetricEJA(2) + sage: id = identity_matrix(J.base_ring(), J.dimension()) + sage: f = FiniteDimensionalEuclideanJordanAlgebraOperator(J,J,id) + sage: f^0 + f^1 + f^2 + Linear operator between finite-dimensional Euclidean Jordan + algebras represented by the matrix: + [3 0 0] + [0 3 0] + [0 0 3] + Domain: Euclidean Jordan algebra of degree 3 over Rational Field + Codomain: Euclidean Jordan algebra of degree 3 over Rational Field + + """ + if (n == 1): + return self + elif (n == 0): + # Raising a vector space morphism to the zero power gives + # you back a special IdentityMorphism that is useless to us. + rows = self.codomain().dimension() + cols = self.domain().dimension() + mat = matrix.identity(self.base_ring(), rows, cols) + else: + mat = self.matrix()**n + + return FiniteDimensionalEuclideanJordanAlgebraOperator( + self.domain(), + self.codomain(), + mat) + + + def _repr_(self): + r""" + + A text representation of this linear operator on a Euclidean + Jordan Algebra. + + EXAMPLES:: + + sage: J = JordanSpinEJA(2) + sage: id = identity_matrix(J.base_ring(), J.dimension()) + sage: FiniteDimensionalEuclideanJordanAlgebraOperator(J,J,id) + Linear operator between finite-dimensional Euclidean Jordan + algebras represented by the matrix: + [1 0] + [0 1] + Domain: Euclidean Jordan algebra of degree 2 over Rational Field + Codomain: Euclidean Jordan algebra of degree 2 over Rational Field + + """ + msg = ("Linear operator between finite-dimensional Euclidean Jordan " + "algebras represented by the matrix:\n", + "{!r}\n", + "Domain: {}\n", + "Codomain: {}") + return ''.join(msg).format(self.matrix(), + self.domain(), + self.codomain()) + + + def _sub_(self, other): + """ + Subtract ``other`` from this EJA operator. + + EXAMPLES:: + + sage: J = RealSymmetricEJA(2) + sage: id = identity_matrix(J.base_ring(),J.dimension()) + sage: f = FiniteDimensionalEuclideanJordanAlgebraOperator(J,J,id) + sage: f - (f*2) + Linear operator between finite-dimensional Euclidean Jordan + algebras represented by the matrix: + [-1 0 0] + [ 0 -1 0] + [ 0 0 -1] + Domain: Euclidean Jordan algebra of degree 3 over Rational Field + Codomain: Euclidean Jordan algebra of degree 3 over Rational Field + + """ + return (self + (-other)) + + + def matrix(self): + """ + Return the matrix representation of this operator with respect + to the default bases of its (co)domain. + + EXAMPLES:: + + sage: J = RealSymmetricEJA(2) + sage: mat = matrix(J.base_ring(), J.dimension(), range(9)) + sage: f = FiniteDimensionalEuclideanJordanAlgebraOperator(J,J,mat) + sage: f.matrix() + [0 1 2] + [3 4 5] + [6 7 8] + + """ + return self._matrix + + + def minimal_polynomial(self): + """ + Return the minimal polynomial of this linear operator, + in the variable ``t``. + + EXAMPLES:: + + sage: J = RealSymmetricEJA(3) + sage: J.one().operator().minimal_polynomial() + t - 1 + + """ + # The matrix method returns a polynomial in 'x' but want one in 't'. + return self.matrix().minimal_polynomial().change_variable_name('t') + + class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): @staticmethod def __classcall_private__(cls, @@ -20,7 +376,8 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): names='e', assume_associative=False, category=None, - rank=None): + rank=None, + natural_basis=None): n = len(mult_table) mult_table = [b.base_extend(field) for b in mult_table] for b in mult_table: @@ -29,7 +386,7 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): raise ValueError("input is not a multiplication table") mult_table = tuple(mult_table) - cat = MagmaticAlgebras(field).FiniteDimensional().WithBasis() + cat = FiniteDimensionalAlgebrasWithBasis(field) cat.or_subcategory(category) if assume_associative: cat = cat.Associative() @@ -43,15 +400,18 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): assume_associative=assume_associative, names=names, category=cat, - rank=rank) + rank=rank, + natural_basis=natural_basis) - def __init__(self, field, + def __init__(self, + field, mult_table, names='e', assume_associative=False, category=None, - rank=None): + rank=None, + natural_basis=None): """ EXAMPLES: @@ -66,6 +426,8 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): """ self._rank = rank + self._natural_basis = natural_basis + self._multiplication_table = mult_table fda = super(FiniteDimensionalEuclideanJordanAlgebra, self) fda.__init__(field, mult_table, @@ -80,6 +442,245 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): fmt = "Euclidean Jordan algebra of degree {} over {}" return fmt.format(self.degree(), self.base_ring()) + + def _a_regular_element(self): + """ + Guess a regular element. Needed to compute the basis for our + characteristic polynomial coefficients. + """ + gs = self.gens() + z = self.sum( (i+1)*gs[i] for i in range(len(gs)) ) + if not z.is_regular(): + raise ValueError("don't know a regular element") + return z + + + @cached_method + def _charpoly_basis_space(self): + """ + Return the vector space spanned by the basis used in our + characteristic polynomial coefficients. This is used not only to + compute those coefficients, but also any time we need to + evaluate the coefficients (like when we compute the trace or + determinant). + """ + z = self._a_regular_element() + V = self.vector_space() + V1 = V.span_of_basis( (z**k).vector() for k in range(self.rank()) ) + b = (V1.basis() + V1.complement().basis()) + return V.span_of_basis(b) + + + @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, xr, detA) = self._charpoly_matrix_system() + R = A_of_x.base_ring() + if i >= self.rank(): + # Guaranteed by theory + return R.zero() + + # Danger: the in-place modification is done for performance + # reasons (reconstructing a matrix with huge polynomial + # entries is slow), but I don't know how cached_method works, + # so it's highly possible that we're modifying some global + # list variable by reference, here. In other words, you + # probably shouldn't call this method twice on the same + # algebra, at the same time, in two threads + Ai_orig = A_of_x.column(i) + A_of_x.set_column(i,xr) + numerator = A_of_x.det() + A_of_x.set_column(i,Ai_orig) + + # 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/detA) + + + @cached_method + def _charpoly_matrix_system(self): + """ + Compute the matrix whose entries A_ij are polynomials in + X1,...,XN, the vector ``x`` of variables X1,...,XN, the vector + corresponding to `x^r` and the determinent of the matrix A = + [A_ij]. In other words, all of the fixed (cachable) data needed + to compute the coefficients of the characteristic polynomial. + """ + 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) + + W = self._charpoly_basis_space() + W = W.change_ring(R.fraction_field()) + + # Starting with the standard coordinates x = (X1,X2,...,Xn) + # and then converting the entries to W-coordinates allows us + # to pass in the standard coordinates to the charpoly and get + # back the right answer. Specifically, with x = (X1,X2,...,Xn), + # we have + # + # W.coordinates(x^2) eval'd at (standard z-coords) + # = + # W-coords of (z^2) + # = + # W-coords of (standard coords of x^2 eval'd at std-coords of z) + # + # We want the middle equivalent thing in our matrix, but use + # the first equivalent thing instead so that we can pass in + # standard coordinates. + x = J(vector(R, R.gens())) + l1 = [column_matrix(W.coordinates((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)) + xr = W.coordinates((x**r).vector()) + return (A_of_x, x, xr, A_of_x.det()) + + + @cached_method + def characteristic_polynomial(self): + """ + + .. WARNING:: + + This implementation doesn't guarantee that the polynomial + denominator in the coefficients is not identically zero, so + theoretically it could crash. The way that this is handled + in e.g. Faraut and Koranyi is to use a basis that guarantees + the denominator is non-zero. But, doing so requires knowledge + of at least one regular element, and we don't even know how + to do that. The trade-off is that, if we use the standard basis, + the resulting polynomial will accept the "usual" coordinates. In + other words, we don't have to do a change of basis before e.g. + computing the trace or determinant. + + 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. + + Defaults to the trace inner product, but can be overridden by + subclasses if they are sure that the necessary properties are + satisfied. + + EXAMPLES: + + The inner product must satisfy its axiom for this algebra to truly + be 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).inner_product(z) == y.inner_product(x*z) + True + + """ + if (not x in self) or (not y in self): + raise TypeError("arguments must live in this algebra") + return x.trace_inner_product(y) + + + def natural_basis(self): + """ + Return a more-natural representation of this algebra's basis. + + 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" basis + for our underlying vector space. (Typically, the natural basis + is used to construct the multiplication table in the first place.) + + Note that this will always return a matrix. The standard basis + in `R^n` will be returned as `n`-by-`1` column matrices. + + EXAMPLES:: + + sage: J = RealSymmetricEJA(2) + sage: J.basis() + Family (e0, e1, e2) + sage: J.natural_basis() + ( + [1 0] [0 1] [0 0] + [0 0], [1 0], [0 1] + ) + + :: + + sage: J = JordanSpinEJA(2) + sage: J.basis() + Family (e0, e1) + sage: J.natural_basis() + ( + [1] [0] + [0], [1] + ) + + """ + if self._natural_basis is None: + return tuple( b.vector().column() for b in self.basis() ) + else: + return self._natural_basis + + def rank(self): """ Return the rank of this EJA. @@ -89,12 +690,80 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): else: return self._rank + def vector_space(self): + """ + Return the vector space that underlies this algebra. + + EXAMPLES:: + + sage: J = RealSymmetricEJA(2) + sage: J.vector_space() + Vector space of dimension 3 over Rational Field + + """ + return self.zero().vector().parent().ambient_vector_space() + class Element(FiniteDimensionalAlgebraElement): """ 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 __init__(self, A, elt=None): + """ + EXAMPLES: + + The identity in `S^n` is converted to the identity in the EJA:: + + 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 = RealSymmetricEJA(3) + sage: A = matrix(QQ,3, lambda i,j: i-j) + sage: J(A) + Traceback (most recent call last): + ... + ArithmeticError: vector is not in free module + + """ + # Goal: if we're given a matrix, and if it lives in our + # parent algebra's "natural ambient space," convert it + # into an algebra element. + # + # The catch is, we make a recursive call after converting + # the given matrix into a vector that lives in the algebra. + # This we need to try the parent class initializer first, + # to avoid recursing forever if we're given something that + # already fits into the algebra, but also happens to live + # in the parent's "natural ambient space" (this happens with + # vectors in R^n). + try: + FiniteDimensionalAlgebraElement.__init__(self, A, elt) + except ValueError: + natural_basis = A.natural_basis() + if elt in natural_basis[0].matrix_space(): + # Thanks for nothing! Matrix spaces aren't vector + # spaces in Sage, so we have to figure out its + # natural-basis coordinates ourselves. + V = VectorSpace(elt.base_ring(), elt.nrows()**2) + W = V.span( _mat2vec(s) for s in natural_basis ) + coords = W.coordinates(_mat2vec(elt)) + FiniteDimensionalAlgebraElement.__init__(self, A, coords) + def __pow__(self, n): """ Return ``self`` raised to the power ``n``. @@ -112,7 +781,7 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): sage: set_random_seed() sage: x = random_eja().random_element() - sage: x.matrix()*x.vector() == (x^2).vector() + sage: x.operator()(x) == (x^2) True A few examples of power-associativity:: @@ -131,34 +800,158 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): sage: x = random_eja().random_element() sage: m = ZZ.random_element(0,10) sage: n = ZZ.random_element(0,10) - sage: Lxm = (x^m).matrix() - sage: Lxn = (x^n).matrix() + sage: Lxm = (x^m).operator() + sage: Lxn = (x^n).operator() sage: Lxm*Lxn == Lxn*Lxm True """ - A = self.parent() if n == 0: - return A.one() + return self.parent().one() elif n == 1: return self else: - return A.element_class(A, (self.matrix()**(n-1))*self.vector()) + return (self.operator()**(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. + + 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): + """ + Return the parent algebra's inner product of myself and ``other``. + + 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(x).inner_product(J(y)) + 32 + + The inner product on `S^n` is ` = 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 ` = + 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 = J.random_element() + sage: y = J.random_element() + sage: x.inner_product(y) in RR + 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): @@ -189,12 +982,63 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): 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: J = random_eja() + sage: x = J.random_element() + sage: y = J.random_element() + 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: J = random_eja() + sage: x = J.random_element() + sage: y = J.random_element() + sage: z = J.random_element() + 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: J = random_eja() + sage: u = J.random_element() + sage: y = J.random_element() + sage: z = J.random_element() + 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 ArgumentError("'other' must live in the same algebra") + raise TypeError("'other' must live in the same algebra") - A = self.matrix() - B = other.matrix() + A = self.operator() + B = other.operator() return (A*B == B*A) @@ -204,32 +1048,49 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): 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): """ Return the Jordan-multiplicative inverse of this element. - We can't use the superclass method because it relies on the - algebra being associative. + ALGORITHM: + + We appeal to the quadratic representation as in Koecher's + Theorem 12 in Chapter III, Section 5. EXAMPLES: @@ -238,14 +1099,14 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): 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(): + sage: while not x.is_invertible(): ....: x = J.random_element() sage: x_vec = x.vector() sage: x0 = x_vec[0] sage: x_bar = x_vec[1:] - sage: coeff = 1/(x0^2 - x_bar.inner_product(x_bar)) + 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(x_inverse) @@ -260,48 +1121,35 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): 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 - """ - if self.parent().is_associative(): - elt = FiniteDimensionalAlgebraElement(self.parent(), self) - return elt.inverse() + The inverse of the inverse is what we started with:: - # TODO: we can do better once the call to is_invertible() - # doesn't crash on irregular elements. - #if not self.is_invertible(): - # raise ArgumentError('element is not invertible') + sage: set_random_seed() + sage: J = random_eja() + sage: x = J.random_element() + sage: (not x.is_invertible()) or (x.inverse().inverse() == x) + True - # We do this a little different than the usual recursive - # call to a finite-dimensional algebra element, because we - # wind up with an inverse that lives in the subalgebra and - # we need information about the parent to convert it back. - 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())) + 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 - # This will be in the subalgebra's coordinates... - fda_elt = FiniteDimensionalAlgebraElement(assoc_subalg, elt) - subalg_inverse = fda_elt.inverse() + """ + if not self.is_invertible(): + raise ValueError("element is not invertible") - # So we have to convert back... - basis = [ self.parent(v) for v in V.basis() ] - pairs = zip(subalg_inverse.vector(), basis) - return self.parent().linear_combination(pairs) + return (~self.quadratic_representation())(self) def is_invertible(self): @@ -310,8 +1158,36 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): 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): @@ -368,7 +1244,7 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): 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 @@ -393,7 +1269,7 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): EXAMPLES:: - sage: J = JordanSpinSimpleEJA(4) + sage: J = JordanSpinEJA(4) sage: J.one().degree() 1 sage: e0,e1,e2,e3 = J.gens() @@ -405,7 +1281,7 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): 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 @@ -414,83 +1290,42 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): return self.span_of_powers().dimension() - def matrix(self): + def left_matrix(self): """ - Return the matrix that represents left- (or right-) - multiplication by this element in the parent algebra. - - We have to override this because the superclass method - returns a matrix that acts on row vectors (that is, on - the right). - - EXAMPLES: - - Test the first polarization identity from my notes, Koecher Chapter - III, or from Baes (2.3):: - - sage: set_random_seed() - sage: J = random_eja() - sage: x = J.random_element() - sage: y = J.random_element() - sage: Lx = x.matrix() - sage: Ly = y.matrix() - sage: Lxx = (x*x).matrix() - sage: Lxy = (x*y).matrix() - 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):: + Our parent class defines ``left_matrix`` and ``matrix`` + methods whose names are misleading. We don't want them. + """ + raise NotImplementedError("use operator().matrix() instead") - sage: set_random_seed() - sage: J = random_eja() - sage: x = J.random_element() - sage: y = J.random_element() - sage: z = J.random_element() - sage: Lx = x.matrix() - sage: Ly = y.matrix() - sage: Lz = z.matrix() - sage: Lzy = (z*y).matrix() - sage: Lxy = (x*y).matrix() - sage: Lxz = (x*z).matrix() - sage: bool(Lx*Lzy + Lz*Lxy + Ly*Lxz == Lzy*Lx + Lxy*Lz + Lxz*Ly) - True + matrix = left_matrix - Test the third polarization identity from my notes or from - Baes (2.5):: - - sage: set_random_seed() - sage: J = random_eja() - sage: u = J.random_element() - sage: y = J.random_element() - sage: z = J.random_element() - sage: Lu = u.matrix() - sage: Ly = y.matrix() - sage: Lz = z.matrix() - sage: Lzy = (z*y).matrix() - sage: Luy = (u*y).matrix() - sage: Luz = (u*z).matrix() - sage: Luyz = (u*(y*z)).matrix() - sage: lhs = Lu*Lzy + Lz*Luy + Ly*Luz - sage: rhs = Luyz + Ly*Lu*Lz + Lz*Lu*Ly - sage: bool(lhs == rhs) - True + def minimal_polynomial(self): """ - fda_elt = FiniteDimensionalAlgebraElement(self.parent(), self) - return fda_elt.matrix().transpose() + Return the minimal polynomial of this element, + as a function of the variable `t`. + ALGORITHM: - def minimal_polynomial(self): - """ - EXAMPLES:: + 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. - sage: set_random_seed() - sage: x = random_eja().random_element() - sage: x.degree() == x.minimal_polynomial().degree() - True + 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() @@ -504,38 +1339,108 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): 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 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 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 + 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())) + return elt.operator().minimal_polynomial() - 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())) - # Recursive call, but should work since elt lives in an - # associative algebra. - return elt.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. + + EXAMPLES:: + + sage: J = ComplexHermitianEJA(3) + sage: J.one() + e0 + e5 + 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 + 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() + return W.linear_combination(zip(self.vector(), B)) + + + def operator(self): + """ + Return the left-multiplication-by-this-element + operator on the ambient algebra. + + TESTS:: + + sage: set_random_seed() + sage: J = random_eja() + sage: x = J.random_element() + sage: y = J.random_element() + sage: x.operator()(y) == x*y + True + sage: y.operator()(x) == x*y + True + + """ + P = self.parent() + fda_elt = FiniteDimensionalAlgebraElement(P, self) + return FiniteDimensionalEuclideanJordanAlgebraOperator( + P, + P, + fda_elt.matrix().transpose() ) def quadratic_representation(self, other=None): @@ -549,7 +1454,7 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): 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] @@ -561,7 +1466,7 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): 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() + sage: Q == x.quadratic_representation().matrix() True Test all of the properties from Theorem 11.2 in Alizadeh:: @@ -570,49 +1475,87 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): sage: J = random_eja() sage: x = J.random_element() sage: y = J.random_element() + 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: actual = x.quadratic_representation(y) - sage: expected = ( (x+y).quadratic_representation() - ....: -x.quadratic_representation() - ....: -y.quadratic_representation() ) / 2 - sage: actual == expected + sage: 2*Qxy == (x+y).quadratic_representation() - Qx - Qy True - Property 2: + Property 2 (multiply on the right for :trac:`28272`): sage: alpha = QQ.random_element() - sage: actual = (alpha*x).quadratic_representation() - sage: expected = (alpha^2)*x.quadratic_representation() - sage: actual == expected + 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*x.operator()*Qex - Qx ) + True + + sage: 2*x.operator()*Qex - Qx == Lxx True Property 5: - sage: Qy = y.quadratic_representation() - sage: actual = J(Qy*x.vector()).quadratic_representation() - sage: expected = Qy*x.quadratic_representation()*Qy - sage: actual == expected + sage: Qy(x).quadratic_representation() == Qy*Qx*Qy True Property 6: - sage: k = ZZ.random_element(1,10) - sage: actual = (x^k).quadratic_representation() - sage: expected = (x.quadratic_representation())^k - sage: actual == expected + 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 ArgumentError("'other' must live in the same algebra") + raise TypeError("'other' must live in the same algebra") - return ( self.matrix()*other.matrix() - + other.matrix()*self.matrix() - - (self*other).matrix() ) + L = self.operator() + M = other.operator() + return ( L*M + M*L - (self*other).operator() ) def span_of_powers(self): @@ -623,7 +1566,10 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): # 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.parent().vector_space() return V.span( (self**d).vector() for d in xrange(V.dimension()) ) @@ -639,13 +1585,13 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): sage: x.subalgebra_generated_by().is_associative() True - Squaring in the subalgebra should be the same thing as - squaring in the superalgebra:: + Squaring in the subalgebra should work the same as in + the superalgebra:: sage: set_random_seed() sage: x = random_eja().random_element() sage: u = x.subalgebra_generated_by().random_element() - sage: u.matrix()*u.vector() == (u**2).vector() + sage: u.operator()(u) == u^2 True """ @@ -692,12 +1638,11 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): TESTS:: sage: set_random_seed() - sage: J = eja_rn(5) - sage: c = J.random_element().subalgebra_idempotent() - sage: c^2 == c - True - sage: J = JordanSpinSimpleEJA(5) - sage: c = J.random_element().subalgebra_idempotent() + 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 @@ -717,7 +1662,7 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): s = 0 minimal_dim = V.dimension() for i in xrange(1, V.dimension()): - this_dim = (u**i).matrix().image().dimension() + this_dim = (u**i).operator().matrix().image().dimension() if this_dim < minimal_dim: minimal_dim = this_dim s = i @@ -734,7 +1679,7 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): # Beware, solve_right() means that we're using COLUMN vectors. # Our FiniteDimensionalAlgebraElement superclass uses rows. u_next = u**(s+1) - A = u_next.matrix() + A = u_next.operator().matrix() c_coordinates = A.solve_right(u_next.vector()) # Now c_coordinates is the idempotent we want, but it's in @@ -752,40 +1697,102 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): 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 ArgumentError("'other' must live in the same algebra") + 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 @@ -801,16 +1808,21 @@ def eja_rn(dimension, field=QQ): 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) ] + @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) ] - return FiniteDimensionalEuclideanJordanAlgebra(field,Qs,rank=dimension) + 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(): @@ -830,6 +1842,12 @@ 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. @@ -839,11 +1857,17 @@ def random_eja(): 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) @@ -864,7 +1888,7 @@ def _real_symmetric_basis(n, field=QQ): # Beware, orthogonal but not normalized! Sij = Eij + Eij.transpose() S.append(Sij) - return S + return tuple(S) def _complex_hermitian_basis(n, field=QQ): @@ -901,8 +1925,56 @@ def _complex_hermitian_basis(n, field=QQ): S.append(Sij_real) Sij_imag = _embed_complex_matrix(I*Eij - I*Eij.transpose()) S.append(Sij_imag) - return S + 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()) + +def _vec2mat(v): + return matrix(v.base_ring(), sqrt(v.degree()), v.list()) def _multiplication_table_from_matrix_basis(basis): """ @@ -911,7 +1983,10 @@ def _multiplication_table_from_matrix_basis(basis): 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. + elements) for an algebra of those matrices. A reordered copy + of the basis is also returned to work around the fact that + the ``span()`` in this function will change the order of the basis + from what we think it is, to... something else. """ # In S^2, for example, we nominally have four coordinates even # though the space is of dimension three only. The vector space V @@ -921,19 +1996,13 @@ def _multiplication_table_from_matrix_basis(basis): field = basis[0].base_ring() dimension = basis[0].nrows() - def mat2vec(m): - return vector(field, m.list()) - - def vec2mat(v): - return matrix(field, dimension, v.list()) - V = VectorSpace(field, dimension**2) - W = V.span( mat2vec(s) for s in basis ) + 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() ] + S = tuple( _vec2mat(b) for b in W.basis() ) Qs = [] for s in S: @@ -946,12 +2015,12 @@ def _multiplication_table_from_matrix_basis(basis): # why we're computing rows here and not columns. Q_rows = [] for t in S: - this_row = mat2vec((s*t + t*s)/2) + this_row = _mat2vec((s*t + t*s)/2) Q_rows.append(W.coordinates(this_row)) Q = matrix(field, W.dimension(), Q_rows) Qs.append(Q) - return Qs + return (Qs, S) def _embed_complex_matrix(M): @@ -967,24 +2036,38 @@ def _embed_complex_matrix(M): sage: x2 = F(1 + 2*i) sage: x3 = F(-i) sage: x4 = F(6) - sage: M = matrix(F,2,[x1,x2,x3,x4]) + sage: M = matrix(F,2,[[x1,x2],[x3,x4]]) sage: _embed_complex_matrix(M) - [ 4 2| 1 -2] - [-2 4| 2 1] + [ 4 -2| 1 2] + [ 2 4|-2 1] [-----+-----] - [ 0 1| 6 0] - [-1 0| 0 6] + [ 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: - raise ArgumentError("the matrix 'M' must be square") + raise ValueError("the matrix 'M' must be square") field = M.base_ring() blocks = [] for z in M.list(): a = z.real() b = z.imag() - blocks.append(matrix(field, 2, [[a,-b],[b,a]])) + blocks.append(matrix(field, 2, [[a,b],[-b,a]])) # We can drop the imaginaries here. return block_matrix(field.base_ring(), n, blocks) @@ -1001,14 +2084,25 @@ def _unembed_complex_matrix(M): ....: [ 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: - raise ArgumentError("the matrix 'M' must be square") + raise ValueError("the matrix 'M' must be square") if not n.mod(2).is_zero(): - raise ArgumentError("the matrix 'M' must be a complex embedding") + raise ValueError("the matrix 'M' must be a complex embedding") F = QuadraticField(-1, 'i') i = F.gen() @@ -1020,16 +2114,137 @@ def _unembed_complex_matrix(M): 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 ArgumentError('bad real submatrix') + raise ValueError('bad on-diagonal submatrix') if submat[0,1] != -submat[1,0]: - raise ArgumentError('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 RealSymmetricSimpleEJA(n, field=QQ): +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()) + +# The inner product used for the real symmetric simple EJA. +# We keep it as a separate function because e.g. the complex +# algebra uses the same inner product, except divided by 2. +def _matrix_ip(X,Y): + X_mat = X.natural_representation() + Y_mat = Y.natural_representation() + return (X_mat*Y_mat).trace() + + +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 @@ -1037,7 +2252,7 @@ def RealSymmetricSimpleEJA(n, field=QQ): EXAMPLES:: - sage: J = RealSymmetricSimpleEJA(2) + sage: J = RealSymmetricEJA(2) sage: e0, e1, e2 = J.gens() sage: e0*e0 e0 @@ -1052,18 +2267,44 @@ def RealSymmetricSimpleEJA(n, field=QQ): 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 = _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) + + fdeja = super(RealSymmetricEJA, cls) + return fdeja.__classcall_private__(cls, + field, + Qs, + rank=n, + natural_basis=T) - return FiniteDimensionalEuclideanJordanAlgebra(field,Qs,rank=n) + 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, @@ -1076,33 +2317,110 @@ def ComplexHermitianSimpleEJA(n, field=QQ): 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 - """ - S = _complex_hermitian_basis(n) - Qs = _multiplication_table_from_matrix_basis(S) - return FiniteDimensionalEuclideanJordanAlgebra(field, Qs, rank=n) + 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 -def QuaternionHermitianSimpleEJA(n): + """ + @staticmethod + def __classcall_private__(cls, n, field=QQ): + S = _complex_hermitian_basis(n) + (Qs, T) = _multiplication_table_from_matrix_basis(S) + + fdeja = super(ComplexHermitianEJA, cls) + return fdeja.__classcall_private__(cls, + field, + Qs, + rank=n, + natural_basis=T) + + 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 + + +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. - """ - n = 3 - pass + 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 -def JordanSpinSimpleEJA(n, field=QQ): + """ + @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 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 = @@ -1113,7 +2431,7 @@ def JordanSpinSimpleEJA(n, field=QQ): 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 @@ -1130,28 +2448,27 @@ def JordanSpinSimpleEJA(n, field=QQ): 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)) + @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)