X-Git-Url: http://gitweb.michael.orlitzky.com/?a=blobdiff_plain;f=mjo%2Feja%2Feuclidean_jordan_algebra.py;h=70a77701b342e36eec827d3ec151b87bf67fe902;hb=220688f4c9dcee4a4f980c955fc159e38514bbcb;hp=30881da2bf696d01b51b6d159cb005f3a9459728;hpb=581b25d4af5dd7a287c9c3c11d74c6157958c3e1;p=sage.d.git diff --git a/mjo/eja/euclidean_jordan_algebra.py b/mjo/eja/euclidean_jordan_algebra.py index 30881da..70a7770 100644 --- a/mjo/eja/euclidean_jordan_algebra.py +++ b/mjo/eja/euclidean_jordan_algebra.py @@ -28,8 +28,11 @@ class FiniteDimensionalEuclideanJordanAlgebraOperator(Map): # We need to supply something here to avoid getting the # default Homset of the parent FiniteDimensionalAlgebra class, - # which messes up e.g. equality testing. - parent = Hom(domain_eja, codomain_eja, VectorSpaces(F)) + # 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 @@ -143,6 +146,7 @@ class FiniteDimensionalEuclideanJordanAlgebraOperator(Map): return False return True + def __invert__(self): """ Invert this EJA operator. @@ -348,6 +352,22 @@ class FiniteDimensionalEuclideanJordanAlgebraOperator(Map): 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, @@ -761,7 +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:: @@ -780,19 +800,18 @@ 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): @@ -963,6 +982,57 @@ 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") @@ -1225,7 +1295,7 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): Our parent class defines ``left_matrix`` and ``matrix`` methods whose names are misleading. We don't want them. """ - raise NotImplementedError("use operator_matrix() instead") + raise NotImplementedError("use operator().matrix() instead") matrix = left_matrix @@ -1296,11 +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().minimal_polynomial() - # We get back a symbolic polynomial in 'x' but want a real - # polynomial in 't'. - p_of_x = elt.operator_matrix().minimal_polynomial() - return p_of_x.change_variable_name('t') def natural_representation(self): @@ -1369,85 +1436,11 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): """ P = self.parent() + fda_elt = FiniteDimensionalAlgebraElement(P, self) return FiniteDimensionalEuclideanJordanAlgebraOperator( - P,P, - self.operator_matrix() ) - - - - def operator_matrix(self): - """ - Return the matrix that represents left- (or right-) - multiplication by this element in the parent algebra. - - We implement this ourselves to work around the fact that - our parent class represents everything with row vectors. - - 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 - - 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_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) - 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) - True - - Ensure that our operator's ``matrix`` method agrees with - this implementation:: - - sage: set_random_seed() - sage: J = random_eja() - sage: x = J.random_element() - sage: x.operator().matrix() == x.operator_matrix() - True - - """ - fda_elt = FiniteDimensionalAlgebraElement(self.parent(), self) - return fda_elt.matrix().transpose() + P, + P, + fda_elt.matrix().transpose() ) def quadratic_representation(self, other=None): @@ -1592,13 +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 """ @@ -1669,7 +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 @@ -1686,7 +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