-# -*- coding: utf-8 -*-
-
from sage.matrix.constructor import matrix
from sage.modules.free_module import VectorSpace
from sage.modules.with_basis.indexed_element import IndexedFreeModuleElement
True
"""
- p = self.parent().characteristic_polynomial()
+ p = self.parent().characteristic_polynomial_of()
return p(*self.to_vector())
sage: while not x.is_invertible():
....: x = J.random_element()
sage: x_vec = x.to_vector()
- sage: x0 = x_vec[0]
+ sage: x0 = x_vec[:1]
sage: x_bar = x_vec[1:]
- 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: coeff = x0.inner_product(x0) - x_bar.inner_product(x_bar)
+ sage: x_inverse = x_vec.parent()(x0.list() + (-x_bar).list())
+ sage: if not coeff.is_zero(): x_inverse = x_inverse/coeff
sage: x.inverse() == J.from_vector(x_inverse)
True
whether or not the paren't algebra's zero element is a root
of this element's minimal polynomial.
+ That is... unless the coefficients of our algebra's
+ "characteristic polynomial of" function are already cached!
+ In that case, we just use the determinant (which will be fast
+ as a result).
+
Beware that we can't use the superclass method, because it
relies on the algebra being associative.
else:
return False
+ if self.parent()._charpoly_coefficients.is_in_cache():
+ # The determinant will be quicker than computing the minimal
+ # polynomial from scratch, most likely.
+ return (not self.det().is_zero())
+
# In fact, we only need to know if the constant term is non-zero,
# so we can pass in the field's zero element instead.
zero = self.base_ring().zero()
sage: set_random_seed()
sage: J = JordanSpinEJA.random_instance()
+ sage: n = J.dimension()
sage: x = J.random_element()
- sage: x == x.coefficient(0)*J.one() or x.degree() == 2
+ sage: x.degree() == min(n,2) or (x == x.coefficient(0)*J.one())
True
TESTS:
TESTS:
The minimal polynomial of the identity and zero elements are
- always the same::
+ always the same, except in trivial algebras where the minimal
+ polynomial of the unit/zero element is ``1``::
sage: set_random_seed()
- sage: J = random_eja(nontrivial=True)
- sage: J.one().minimal_polynomial()
+ sage: J = random_eja()
+ sage: mu = J.one().minimal_polynomial()
+ sage: t = mu.parent().gen()
+ sage: mu + int(J.is_trivial())*(t-2)
t - 1
- sage: J.zero().minimal_polynomial()
+ sage: mu = J.zero().minimal_polynomial()
+ sage: t = mu.parent().gen()
+ sage: mu + int(J.is_trivial())*(t-1)
t
The degree of an element is (by one definition) the degree
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: set_random_seed()
sage: x = JordanSpinEJA.random_instance().random_element()
sage: x_vec = x.to_vector()
+ sage: Q = matrix.identity(x.base_ring(), 0)
sage: n = x_vec.degree()
- sage: x0 = x_vec[0]
- sage: x_bar = x_vec[1:]
- sage: A = matrix(AA, 1, [x_vec.inner_product(x_vec)])
- sage: B = 2*x0*x_bar.row()
- sage: C = 2*x0*x_bar.column()
- sage: D = matrix.identity(AA, n-1)
- sage: D = (x0^2 - x_bar.inner_product(x_bar))*D
- sage: D = D + 2*x_bar.tensor_product(x_bar)
- sage: Q = matrix.block(2,2,[A,B,C,D])
+ sage: if n > 0:
+ ....: x0 = x_vec[0]
+ ....: x_bar = x_vec[1:]
+ ....: A = matrix(x.base_ring(), 1, [x_vec.inner_product(x_vec)])
+ ....: B = 2*x0*x_bar.row()
+ ....: C = 2*x0*x_bar.column()
+ ....: D = matrix.identity(x.base_ring(), n-1)
+ ....: D = (x0^2 - x_bar.inner_product(x_bar))*D
+ ....: D = D + 2*x_bar.tensor_product(x_bar)
+ ....: Q = matrix.block(2,2,[A,B,C,D])
sage: Q == x.quadratic_representation().matrix()
True
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)
projects / sage.d.git / blobdiff