X-Git-Url: http://gitweb.michael.orlitzky.com/?a=blobdiff_plain;f=mjo%2Feja%2Feuclidean_jordan_algebra.py;h=097233fdad86dea01e11483d7b7525f0ab816f2a;hb=c9f6a4e581371552f8e3340330caa07d76afacf7;hp=0b6b15da68fd4404f69ce094f4f96d07ad2c6adc;hpb=4b1bdeb8c097e6c162cb0a32c07ba2740d89b1e5;p=sage.d.git diff --git a/mjo/eja/euclidean_jordan_algebra.py b/mjo/eja/euclidean_jordan_algebra.py index 0b6b15d..097233f 100644 --- a/mjo/eja/euclidean_jordan_algebra.py +++ b/mjo/eja/euclidean_jordan_algebra.py @@ -94,10 +94,52 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): sage: (e0 - e1).degree() 2 + In the spin factor algebra (of rank two), all elements that + aren't multiples of the identity are regular:: + + sage: set_random_seed() + sage: n = ZZ.random_element(1,10).abs() + sage: J = eja_ln(n) + sage: x = J.random_element() + sage: x == x.coefficient(0)*J.one() or x.degree() == 2 + True + """ return self.span_of_powers().dimension() + def subalgebra_generated_by(self): + """ + Return the subalgebra of the parent EJA generated by this element. + """ + # First get the subspace spanned by the powers of myself... + V = self.span_of_powers() + F = self.base_ring() + + # Now figure out the entries of the right-multiplication + # matrix for the successive basis elements b0, b1,... of + # that subspace. + mats = [] + for b_right in V.basis(): + eja_b_right = self.parent()(b_right) + b_right_rows = [] + # The first row of the right-multiplication matrix by + # b1 is what we get if we apply that matrix to b1. The + # second row of the right multiplication matrix by b1 + # is what we get when we apply that matrix to b2... + for b_left in V.basis(): + eja_b_left = self.parent()(b_left) + # Multiply in the original EJA, but then get the + # coordinates from the subalgebra in terms of its + # basis. + this_row = V.coordinates((eja_b_left*eja_b_right).vector()) + b_right_rows.append(this_row) + b_right_matrix = matrix(F, b_right_rows) + mats.append(b_right_matrix) + + return FiniteDimensionalEuclideanJordanAlgebra(F, mats) + + def minimal_polynomial(self): return self.matrix().minimal_polynomial()