X-Git-Url: http://gitweb.michael.orlitzky.com/?a=blobdiff_plain;f=mjo%2Feja%2Feja_algebra.py;h=04929467d114dbb23cc4fcc15f11c80df58addf0;hb=0d2f419ecd30a2d7899a37ab38ae6b4ff0e3245e;hp=fc64510dde701f7f820456d9972691fe07f71178;hpb=884b2015690a50a12f99852ea82c399309164f22;p=sage.d.git diff --git a/mjo/eja/eja_algebra.py b/mjo/eja/eja_algebra.py index fc64510..0492946 100644 --- a/mjo/eja/eja_algebra.py +++ b/mjo/eja/eja_algebra.py @@ -688,6 +688,14 @@ class FiniteDimensionalEuclideanJordanAlgebra(CombinatorialFreeModule): for k in range(n) ) L_x = matrix(F, n, n, L_x_i_j) + + r = None + if self.rank.is_in_cache(): + r = self.rank() + # There's no need to pad the system with redundant + # columns if we *know* they'll be redundant. + n = r + # Compute an extra power in case the rank is equal to # the dimension (otherwise, we would stop at x^(r-1)). x_powers = [ (L_x**k)*self.one().to_vector() @@ -696,7 +704,8 @@ class FiniteDimensionalEuclideanJordanAlgebra(CombinatorialFreeModule): AE = A.extended_echelon_form() E = AE[:,n:] A_rref = AE[:,:n] - r = A_rref.rank() + if r is None: + r = A_rref.rank() b = x_powers[r] # The theory says that only the first "r" coefficients are @@ -984,7 +993,24 @@ class MatrixEuclideanJordanAlgebra(FiniteDimensionalEuclideanJordanAlgebra): J = MatrixEuclideanJordanAlgebra(QQ, basis, normalize_basis=False) - return J._charpoly_coefficients() + a = J._charpoly_coefficients() + + # Unfortunately, changing the basis does change the + # coefficients of the characteristic polynomial, but since + # these are really the coefficients of the "characteristic + # polynomial of" function, everything is still nice and + # unevaluated. It's therefore "obvious" how scaling the + # basis affects the coordinate variables X1, X2, et + # cetera. Scaling the first basis vector up by "n" adds a + # factor of 1/n into every "X1" term, for example. So here + # we simply undo the basis_normalizer scaling that we + # performed earlier. + # + # TODO: make this access safe. + XS = a[0].variables() + subs_dict = { XS[i]: self._basis_normalizers[i]*XS[i] + for i in range(len(XS)) } + return tuple( a_i.subs(subs_dict) for a_i in a ) @staticmethod @@ -1002,6 +1028,9 @@ class MatrixEuclideanJordanAlgebra(FiniteDimensionalEuclideanJordanAlgebra): # is supposed to hold the entire long vector, and the subspace W # of V will be spanned by the vectors that arise from symmetric # matrices. Thus for S^2, dim(V) == 4 and dim(W) == 3. + if len(basis) == 0: + return [] + field = basis[0].base_ring() dimension = basis[0].nrows() @@ -1159,6 +1188,11 @@ class RealSymmetricEJA(RealMatrixEuclideanJordanAlgebra): sage: x.operator().matrix().is_symmetric() True + We can construct the (trivial) algebra of rank zero:: + + sage: RealSymmetricEJA(0) + Euclidean Jordan algebra of dimension 0 over Algebraic Real Field + """ @classmethod def _denormalized_basis(cls, n, field): @@ -1432,6 +1466,11 @@ class ComplexHermitianEJA(ComplexMatrixEuclideanJordanAlgebra): sage: x.operator().matrix().is_symmetric() True + We can construct the (trivial) algebra of rank zero:: + + sage: ComplexHermitianEJA(0) + Euclidean Jordan algebra of dimension 0 over Algebraic Real Field + """ @classmethod @@ -1727,6 +1766,11 @@ class QuaternionHermitianEJA(QuaternionMatrixEuclideanJordanAlgebra): sage: x.operator().matrix().is_symmetric() True + We can construct the (trivial) algebra of rank zero:: + + sage: QuaternionHermitianEJA(0) + Euclidean Jordan algebra of dimension 0 over Algebraic Real Field + """ @classmethod def _denormalized_basis(cls, n, field):