X-Git-Url: http://gitweb.michael.orlitzky.com/?a=blobdiff_plain;f=mjo%2Feja%2Feuclidean_jordan_algebra.py;h=70a77701b342e36eec827d3ec151b87bf67fe902;hb=220688f4c9dcee4a4f980c955fc159e38514bbcb;hp=0ea9a4debe9c2ad0e33652f50b779fb8fc3838d3;hpb=9c34119f1c650df0a4c2c20ea4723152de80edae;p=sage.d.git diff --git a/mjo/eja/euclidean_jordan_algebra.py b/mjo/eja/euclidean_jordan_algebra.py index 0ea9a4d..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, @@ -30,7 +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() @@ -71,6 +427,7 @@ 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, @@ -86,6 +443,175 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): 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. @@ -164,12 +690,35 @@ 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: @@ -232,7 +781,7 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): sage: set_random_seed() sage: x = random_eja().random_element() - sage: x.operator_matrix()*x.vector() == (x^2).vector() + sage: x.operator()(x) == (x^2) True A few examples of power-associativity:: @@ -251,34 +800,96 @@ 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).operator_matrix() - sage: Lxn = (x^n).operator_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( (self.operator_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): @@ -371,12 +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 TypeError("'other' must live in the same algebra") - A = self.operator_matrix() - B = other.operator_matrix() + A = self.operator() + B = other.operator() return (A*B == B*A) @@ -388,30 +1050,47 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): sage: J = JordanSpinEJA(2) sage: e0,e1 = J.gens() - sage: x = e0 + e1 + sage: x = sum( J.gens() ) sage: x.det() 0 + + :: + 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: @@ -422,12 +1101,12 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): sage: n = ZZ.random_element(1,10) 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) @@ -442,48 +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 ValueError('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): @@ -624,8 +1290,21 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): return self.span_of_powers().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 @@ -633,14 +1312,20 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): polynomial of this element's operator matrix (in that subalgebra). This works by Baes Proposition 2.3.16. - EXAMPLES:: + TESTS: + + The minimal polynomial of the identity and zero elements are + always the same:: sage: set_random_seed() - sage: x = random_eja().random_element() - sage: x.degree() == x.minimal_polynomial().degree() - True + sage: J = random_eja() + sage: J.one().minimal_polynomial() + t - 1 + sage: J.zero().minimal_polynomial() + t - :: + The degree of an element is (by one definition) the degree + of its minimal polynomial:: sage: set_random_seed() sage: x = random_eja().random_element() @@ -661,11 +1346,19 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): 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 + """ V = self.span_of_powers() assoc_subalg = self.subalgebra_generated_by() @@ -673,7 +1366,8 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): # and subalgebra_generated_by() must be the same, and in # the same order! elt = assoc_subalg(V.coordinates(self.vector())) - return elt.operator_matrix().minimal_polynomial() + return elt.operator().minimal_polynomial() + def natural_representation(self): @@ -724,71 +1418,29 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): return W.linear_combination(zip(self.vector(), B)) - def operator_matrix(self): + def operator(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.operator_matrix() - sage: Ly = y.operator_matrix() - sage: Lxx = (x*x).operator_matrix() - sage: Lxy = (x*y).operator_matrix() - sage: bool(2*Lx*Lxy + Ly*Lxx == 2*Lxy*Lx + Lxx*Ly) - True + Return the left-multiplication-by-this-element + operator on the ambient algebra. - Test the second polarization identity from my notes or from - Baes (2.4):: + TESTS:: 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_matrix() - sage: Ly = y.operator_matrix() - sage: Lz = z.operator_matrix() - sage: Lzy = (z*y).operator_matrix() - sage: Lxy = (x*y).operator_matrix() - sage: Lxz = (x*z).operator_matrix() - sage: bool(Lx*Lzy + Lz*Lxy + Ly*Lxz == Lzy*Lx + Lxy*Lz + Lxz*Ly) + sage: x.operator()(y) == x*y 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_matrix() - sage: Ly = y.operator_matrix() - sage: Lz = z.operator_matrix() - sage: Lzy = (z*y).operator_matrix() - sage: Luy = (u*y).operator_matrix() - sage: Luz = (u*z).operator_matrix() - sage: Luyz = (u*(y*z)).operator_matrix() - sage: lhs = Lu*Lzy + Lz*Luy + Ly*Luz - sage: rhs = Luyz + Ly*Lu*Lz + Lz*Lu*Ly - sage: bool(lhs == rhs) + sage: y.operator()(x) == x*y True """ - fda_elt = FiniteDimensionalAlgebraElement(self.parent(), self) - return fda_elt.matrix().transpose() + P = self.parent() + fda_elt = FiniteDimensionalAlgebraElement(P, self) + return FiniteDimensionalEuclideanJordanAlgebraOperator( + P, + P, + fda_elt.matrix().transpose() ) def quadratic_representation(self, other=None): @@ -814,7 +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:: @@ -823,38 +1475,76 @@ 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 """ @@ -863,9 +1553,9 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): elif not other in self.parent(): raise TypeError("'other' must live in the same algebra") - L = self.operator_matrix() - M = other.operator_matrix() - return ( L*M + M*L - (self*other).operator_matrix() ) + L = self.operator() + M = other.operator() + return ( L*M + M*L - (self*other).operator() ) def span_of_powers(self): @@ -876,7 +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()) ) @@ -892,13 +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.operator_matrix()*u.vector() == (u**2).vector() + sage: u.operator()(u) == u^2 True """ @@ -945,12 +1638,11 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): TESTS:: sage: set_random_seed() - sage: J = RealCartesianProductEJA(5) - sage: c = J.random_element().subalgebra_idempotent() - sage: c^2 == c - True - sage: J = JordanSpinEJA(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 @@ -970,7 +1662,7 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): s = 0 minimal_dim = V.dimension() for i in xrange(1, V.dimension()): - this_dim = (u**i).operator_matrix().image().dimension() + this_dim = (u**i).operator().matrix().image().dimension() if this_dim < minimal_dim: minimal_dim = this_dim s = i @@ -987,7 +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.operator_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 @@ -1006,22 +1698,80 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): EXAMPLES:: sage: J = JordanSpinEJA(3) - sage: e0,e1,e2 = J.gens() - sage: x = e0 + e1 + e2 + sage: x = sum(J.gens()) sage: x.trace() 2 + :: + + sage: J = RealCartesianProductEJA(5) + sage: J.one().trace() + 5 + + TESTS: + + The trace of an element is a real number:: + + sage: set_random_seed() + sage: J = random_eja() + sage: J.random_element().trace() in J.base_ring() + True + """ - cs = self.characteristic_polynomial().coefficients(sparse=False) - if len(cs) >= 2: - return -1*cs[-2] - else: - raise ValueError('charpoly had fewer than 2 coefficients') + P = self.parent() + r = P.rank() + p = P._charpoly_coeff(r-1) + # The _charpoly_coeff function already adds the factor of + # -1 to ensure that _charpoly_coeff(r-1) is really what + # appears in front of t^{r-1} in the charpoly. However, + # we want the negative of THAT for the trace. + return -p(*self.vector()) def trace_inner_product(self, other): """ Return the trace inner product of myself and ``other``. + + TESTS: + + The trace inner product is commutative:: + + sage: set_random_seed() + sage: J = random_eja() + sage: x = J.random_element(); y = J.random_element() + sage: x.trace_inner_product(y) == y.trace_inner_product(x) + True + + The trace inner product is bilinear:: + + sage: set_random_seed() + sage: J = random_eja() + sage: x = J.random_element() + sage: y = J.random_element() + sage: z = J.random_element() + sage: a = QQ.random_element(); + sage: actual = (a*(x+z)).trace_inner_product(y) + sage: expected = ( a*x.trace_inner_product(y) + + ....: a*z.trace_inner_product(y) ) + sage: actual == expected + True + sage: actual = x.trace_inner_product(a*(y+z)) + sage: expected = ( a*x.trace_inner_product(y) + + ....: a*x.trace_inner_product(z) ) + sage: actual == expected + True + + The trace inner product satisfies the compatibility + condition in the definition of a Euclidean Jordan algebra:: + + sage: set_random_seed() + sage: J = random_eja() + sage: x = J.random_element() + sage: y = J.random_element() + sage: z = J.random_element() + sage: (x*y).trace_inner_product(z) == y.trace_inner_product(x*z) + True + """ if not other in self.parent(): raise TypeError("'other' must live in the same algebra") @@ -1107,12 +1857,17 @@ def random_eja(): Euclidean Jordan algebra of degree... """ - n = ZZ.random_element(1,5) - constructor = choice([RealCartesianProductEJA, - JordanSpinEJA, - RealSymmetricEJA, - ComplexHermitianEJA, - QuaternionHermitianEJA]) + + # 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)