True
"""
- p = self.parent().characteristic_polynomial()
+ p = self.parent().characteristic_polynomial_of()
return p(*self.to_vector())
two here so that said elements actually exist::
sage: set_random_seed()
- sage: n_max = max(2, JordanSpinEJA._max_test_case_size())
+ sage: n_max = max(2, JordanSpinEJA._max_random_instance_size())
sage: n = ZZ.random_element(2, n_max)
sage: J = JordanSpinEJA(n)
sage: y = J.random_element()
and in particular, a re-scaling of the basis::
sage: set_random_seed()
- sage: n_max = RealSymmetricEJA._max_test_case_size()
+ sage: n_max = RealSymmetricEJA._max_random_instance_size()
sage: n = ZZ.random_element(1, n_max)
sage: J1 = RealSymmetricEJA(n)
sage: J2 = RealSymmetricEJA(n,normalize_basis=False)
# in the "normal" case without us having to think about it.
return self.operator().minimal_polynomial()
- A = self.subalgebra_generated_by()
+ A = self.subalgebra_generated_by(orthonormalize_basis=False)
return A(self).operator().minimal_polynomial()
"""
B = self.parent().natural_basis()
W = self.parent().natural_basis_space()
+
+ # This is just a manual "from_vector()", but of course
+ # matrix spaces aren't vector spaces in sage, so they
+ # don't have a from_vector() method.
return W.linear_combination(zip(B,self.to_vector()))
sage: (J0, J5, J1) = J.peirce_decomposition(c1)
sage: (f0, f1, f2) = J1.gens()
sage: f0.spectral_decomposition()
- [(0, 1.000000000000000?*f2), (1, 1.000000000000000?*f0)]
+ [(0, f2), (1, f0)]
"""
A = self.subalgebra_generated_by(orthonormalize_basis=True)