-from sage.algebras.finite_dimensional_algebras.finite_dimensional_algebra_element import FiniteDimensionalAlgebraElement
from sage.matrix.constructor import matrix
+from sage.misc.cachefunc import cached_method
from sage.modules.free_module import VectorSpace
+from sage.modules.with_basis.indexed_element import IndexedFreeModuleElement
-# TODO: make this unnecessary somehow.
-from sage.misc.lazy_import import lazy_import
-lazy_import('mjo.eja.eja_algebra', 'FiniteDimensionalEuclideanJordanAlgebra')
-from mjo.eja.eja_operator import FiniteDimensionalEuclideanJordanAlgebraOperator
-from mjo.eja.eja_utils import _mat2vec
+from mjo.eja.eja_operator import FiniteDimensionalEJAOperator
+from mjo.eja.eja_utils import _scale
-class FiniteDimensionalEuclideanJordanAlgebraElement(FiniteDimensionalAlgebraElement):
+
+class FiniteDimensionalEJAElement(IndexedFreeModuleElement):
"""
An element of a Euclidean Jordan algebra.
"""
dir(self.__class__) )
- def __init__(self, A, elt=None):
- """
-
- SETUP::
-
- sage: from mjo.eja.eja_algebra import (RealSymmetricEJA,
- ....: random_eja)
-
- EXAMPLES:
-
- The identity in `S^n` is converted to the identity in the EJA::
-
- sage: J = RealSymmetricEJA(3)
- sage: I = matrix.identity(QQ,3)
- sage: J(I) == J.one()
- True
-
- This skew-symmetric matrix can't be represented in the EJA::
-
- sage: J = RealSymmetricEJA(3)
- sage: A = matrix(QQ,3, lambda i,j: i-j)
- sage: J(A)
- Traceback (most recent call last):
- ...
- ArithmeticError: vector is not in free module
-
- TESTS:
-
- Ensure that we can convert any element of the parent's
- underlying vector space back into an algebra element whose
- vector representation is what we started with::
- sage: set_random_seed()
- sage: J = random_eja()
- sage: v = J.vector_space().random_element()
- sage: J(v).vector() == v
- True
-
- """
- # Goal: if we're given a matrix, and if it lives in our
- # parent algebra's "natural ambient space," convert it
- # into an algebra element.
- #
- # The catch is, we make a recursive call after converting
- # the given matrix into a vector that lives in the algebra.
- # This we need to try the parent class initializer first,
- # to avoid recursing forever if we're given something that
- # already fits into the algebra, but also happens to live
- # in the parent's "natural ambient space" (this happens with
- # vectors in R^n).
- try:
- FiniteDimensionalAlgebraElement.__init__(self, A, elt)
- except ValueError:
- natural_basis = A.natural_basis()
- if elt in natural_basis[0].matrix_space():
- # Thanks for nothing! Matrix spaces aren't vector
- # spaces in Sage, so we have to figure out its
- # natural-basis coordinates ourselves.
- V = VectorSpace(elt.base_ring(), elt.nrows()**2)
- W = V.span( _mat2vec(s) for s in natural_basis )
- coords = W.coordinates(_mat2vec(elt))
- FiniteDimensionalAlgebraElement.__init__(self, A, coords)
def __pow__(self, n):
"""
Return ``self`` raised to the power ``n``.
Jordan algebras are always power-associative; see for
- example Faraut and Koranyi, Proposition II.1.2 (ii).
+ example Faraut and Korányi, Proposition II.1.2 (ii).
We have to override this because our superclass uses row
vectors instead of column vectors! We, on the other hand,
The definition of `x^2` is the unambiguous `x*x`::
- sage: set_random_seed()
sage: x = random_eja().random_element()
sage: x*x == (x^2)
True
A few examples of power-associativity::
- sage: set_random_seed()
sage: x = random_eja().random_element()
sage: x*(x*x)*(x*x) == x^5
True
We also know that powers operator-commute (Koecher, Chapter
III, Corollary 1)::
- sage: set_random_seed()
sage: x = random_eja().random_element()
sage: m = ZZ.random_element(0,10)
sage: n = ZZ.random_element(0,10)
elif n == 1:
return self
else:
- return (self.operator()**(n-1))(self)
+ return (self**(n-1))*self
def apply_univariate_polynomial(self, p):
SETUP::
- sage: from mjo.eja.eja_algebra import (RealCartesianProductEJA,
+ sage: from mjo.eja.eja_algebra import (HadamardEJA,
....: random_eja)
EXAMPLES::
sage: R = PolynomialRing(QQ, 't')
sage: t = R.gen(0)
sage: p = t^4 - t^3 + 5*t - 2
- sage: J = RealCartesianProductEJA(5)
+ sage: J = HadamardEJA(5)
sage: J.one().apply_univariate_polynomial(p) == 3*J.one()
True
We should always get back an element of the algebra::
- sage: set_random_seed()
- sage: p = PolynomialRing(QQ, 't').random_element()
+ sage: p = PolynomialRing(AA, 't').random_element()
sage: J = random_eja()
sage: x = J.random_element()
sage: x.apply_univariate_polynomial(p) in J
SETUP::
- sage: from mjo.eja.eja_algebra import RealCartesianProductEJA
+ sage: from mjo.eja.eja_algebra import (random_eja,
+ ....: HadamardEJA)
EXAMPLES:
the identity element is `(t-1)` from which it follows that
the characteristic polynomial should be `(t-1)^3`::
- sage: J = RealCartesianProductEJA(3)
+ sage: J = HadamardEJA(3)
sage: J.one().characteristic_polynomial()
t^3 - 3*t^2 + 3*t - 1
Likewise, the characteristic of the zero element in the
rank-three algebra `R^{n}` should be `t^{3}`::
- sage: J = RealCartesianProductEJA(3)
+ sage: J = HadamardEJA(3)
sage: J.zero().characteristic_polynomial()
t^3
The characteristic polynomial of an element should evaluate
to zero on that element::
- sage: set_random_seed()
- sage: x = RealCartesianProductEJA(3).random_element()
+ sage: x = random_eja().random_element()
sage: p = x.characteristic_polynomial()
- sage: x.apply_univariate_polynomial(p)
- 0
+ sage: x.apply_univariate_polynomial(p).is_zero()
+ True
+
+ The characteristic polynomials of the zero and unit elements
+ should be what we think they are in a subalgebra, too::
+
+ sage: J = HadamardEJA(3)
+ sage: p1 = J.one().characteristic_polynomial()
+ sage: q1 = J.zero().characteristic_polynomial()
+ sage: b0,b1,b2 = J.gens()
+ sage: A = (b0 + 2*b1 + 3*b2).subalgebra_generated_by() # dim 3
+ sage: p2 = A.one().characteristic_polynomial()
+ sage: q2 = A.zero().characteristic_polynomial()
+ sage: p1 == p2
+ True
+ sage: q1 == q2
+ True
"""
- p = self.parent().characteristic_polynomial()
- return p(*self.vector())
+ p = self.parent().characteristic_polynomial_of()
+ return p(*self.to_vector())
def inner_product(self, other):
sage: y = vector(QQ,[4,5,6])
sage: x.inner_product(y)
32
- sage: J(x).inner_product(J(y))
+ sage: J.from_vector(x).inner_product(J.from_vector(y))
32
The inner product on `S^n` is `<X,Y> = trace(X*Y)`, where
Ditto for the quaternions::
- sage: J = QuaternionHermitianEJA(3)
+ sage: J = QuaternionHermitianEJA(2)
sage: J.one().inner_product(J.one())
- 3
+ 2
TESTS:
Ensure that we can always compute an inner product, and that
it gives us back a real number::
- sage: set_random_seed()
sage: J = random_eja()
- sage: x = J.random_element()
- sage: y = J.random_element()
- sage: x.inner_product(y) in RR
+ sage: x,y = J.random_elements(2)
+ sage: x.inner_product(y) in RLF
True
"""
The definition of a Jordan algebra says that any element
operator-commutes with its square::
- sage: set_random_seed()
sage: x = random_eja().random_element()
sage: x.operator_commutes_with(x^2)
True
Test Lemma 1 from Chapter III of Koecher::
- sage: set_random_seed()
- sage: J = random_eja()
- sage: u = J.random_element()
- sage: v = J.random_element()
+ sage: u,v = random_eja().random_elements(2)
sage: lhs = u.operator_commutes_with(u*v)
sage: rhs = v.operator_commutes_with(u^2)
sage: lhs == rhs
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: x,y = random_eja().random_elements(2)
sage: Lx = x.operator()
sage: Ly = y.operator()
sage: Lxx = (x*x).operator()
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)
+ sage: x,y,z = random_eja().random_elements(3) # long time
+ sage: Lx = x.operator() # long time
+ sage: Ly = y.operator() # long time
+ sage: Lz = z.operator() # long time
+ sage: Lzy = (z*y).operator() # long time
+ sage: Lxy = (x*y).operator() # long time
+ sage: Lxz = (x*z).operator() # long time
+ sage: lhs = Lx*Lzy + Lz*Lxy + Ly*Lxz # long time
+ sage: rhs = Lzy*Lx + Lxy*Lz + Lxz*Ly # long time
+ sage: bool(lhs == rhs) # long time
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)
+ sage: u,y,z = random_eja().random_elements(3) # long time
+ sage: Lu = u.operator() # long time
+ sage: Ly = y.operator() # long time
+ sage: Lz = z.operator() # long time
+ sage: Lzy = (z*y).operator() # long time
+ sage: Luy = (u*y).operator() # long time
+ sage: Luz = (u*z).operator() # long time
+ sage: Luyz = (u*(y*z)).operator() # long time
+ sage: lhs = Lu*Lzy + Lz*Luy + Ly*Luz # long time
+ sage: rhs = Luyz + Ly*Lu*Lz + Lz*Lu*Ly # long time
+ sage: bool(lhs == rhs) # long time
True
"""
SETUP::
sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
+ ....: TrivialEJA,
+ ....: RealSymmetricEJA,
+ ....: ComplexHermitianEJA,
....: random_eja)
EXAMPLES::
sage: J = JordanSpinEJA(2)
- sage: e0,e1 = J.gens()
sage: x = sum( J.gens() )
sage: x.det()
0
::
sage: J = JordanSpinEJA(3)
- sage: e0,e1,e2 = J.gens()
sage: x = sum( J.gens() )
sage: x.det()
-1
+ The determinant of the sole element in the rank-zero trivial
+ algebra is ``1``, by three paths of reasoning. First, its
+ characteristic polynomial is a constant ``1``, so the constant
+ term in that polynomial is ``1``. Second, the characteristic
+ polynomial evaluated at zero is again ``1``. And finally, the
+ (empty) product of its eigenvalues is likewise just unity::
+
+ sage: J = TrivialEJA()
+ sage: J.zero().det()
+ 1
+
TESTS:
An element is invertible if and only if its determinant is
non-zero::
- sage: set_random_seed()
sage: x = random_eja().random_element()
sage: x.is_invertible() == (x.det() != 0)
True
+ Ensure that the determinant is multiplicative on an associative
+ subalgebra as in Faraut and Korányi's Proposition II.2.2::
+
+ sage: J = random_eja().random_element().subalgebra_generated_by()
+ sage: x,y = J.random_elements(2)
+ sage: (x*y).det() == x.det()*y.det()
+ True
+
+ The determinant in real matrix algebras is the usual determinant::
+
+ sage: X = matrix.random(QQ,3)
+ sage: X = X + X.T
+ sage: J1 = RealSymmetricEJA(3)
+ sage: J2 = RealSymmetricEJA(3,field=QQ,orthonormalize=False)
+ sage: expected = X.det()
+ sage: actual1 = J1(X).det()
+ sage: actual2 = J2(X).det()
+ sage: actual1 == expected
+ True
+ sage: actual2 == expected
+ True
+
"""
P = self.parent()
r = P.rank()
- p = P._charpoly_coeff(0)
- # The _charpoly_coeff function already adds the factor of
- # -1 to ensure that _charpoly_coeff(0) is really what
- # appears in front of t^{0} in the charpoly. However,
- # we want (-1)^r times THAT for the determinant.
- return ((-1)**r)*p(*self.vector())
+ if r == 0:
+ # Special case, since we don't get the a0=1
+ # coefficient when the rank of the algebra
+ # is zero.
+ return P.base_ring().one()
+
+ p = P._charpoly_coefficients()[0]
+ # The _charpoly_coeff function already adds the factor of -1
+ # to ensure that _charpoly_coefficients()[0] is really what
+ # appears in front of t^{0} in the charpoly. However, we want
+ # (-1)^r times THAT for the determinant.
+ return ((-1)**r)*p(*self.to_vector())
+
+ @cached_method
def inverse(self):
"""
Return the Jordan-multiplicative inverse of this element.
ALGORITHM:
- We appeal to the quadratic representation as in Koecher's
- Theorem 12 in Chapter III, Section 5.
+ In general we appeal to the quadratic representation as in
+ Koecher's Theorem 12 in Chapter III, Section 5. But if the
+ parent algebra's "characteristic polynomial of" coefficients
+ happen to be cached, then we use Proposition II.2.4 in Faraut
+ and Korányi which gives a formula for the inverse based on the
+ characteristic polynomial and the Cayley-Hamilton theorem for
+ Euclidean Jordan algebras::
SETUP::
- sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
+ sage: from mjo.eja.eja_algebra import (ComplexHermitianEJA,
+ ....: JordanSpinEJA,
....: random_eja)
EXAMPLES:
The inverse in the spin factor algebra is given in Alizadeh's
Example 11.11::
- sage: set_random_seed()
- sage: n = ZZ.random_element(1,10)
- sage: J = JordanSpinEJA(n)
+ sage: J = JordanSpinEJA.random_instance()
sage: x = J.random_element()
sage: while not x.is_invertible():
....: x = J.random_element()
- sage: x_vec = x.vector()
- sage: x0 = x_vec[0]
+ sage: x_vec = x.to_vector()
+ 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: x.inverse() == J(x_inverse)
+ 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
+ Trying to invert a non-invertible element throws an error:
+
+ sage: JordanSpinEJA(3).zero().inverse()
+ Traceback (most recent call last):
+ ...
+ ZeroDivisionError: element is not invertible
+
TESTS:
The identity element is its own inverse::
- sage: set_random_seed()
sage: J = random_eja()
sage: J.one().inverse() == J.one()
True
If an element has an inverse, it acts like one::
- sage: set_random_seed()
sage: J = random_eja()
sage: x = J.random_element()
sage: (not x.is_invertible()) or (x.inverse()*x == J.one())
The inverse of the inverse is what we started with::
- sage: set_random_seed()
sage: J = random_eja()
sage: x = J.random_element()
sage: (not x.is_invertible()) or (x.inverse().inverse() == x)
True
- The zero element is never invertible::
+ Proposition II.2.3 in Faraut and Korányi says that the inverse
+ of an element is the inverse of its left-multiplication operator
+ applied to the algebra's identity, when that inverse exists::
- sage: set_random_seed()
- sage: J = random_eja().zero().inverse()
- Traceback (most recent call last):
- ...
- ValueError: element is not invertible
+ sage: J = random_eja() # long time
+ sage: x = J.random_element() # long time
+ sage: (not x.operator().is_invertible()) or ( # long time
+ ....: x.operator().inverse()(J.one()) # long time
+ ....: == # long time
+ ....: x.inverse() ) # long time
+ True
+ Check that the fast (cached) and slow algorithms give the same
+ answer::
+
+ sage: J = random_eja(field=QQ, orthonormalize=False) # long time
+ sage: x = J.random_element() # long time
+ sage: while not x.is_invertible(): # long time
+ ....: x = J.random_element() # long time
+ sage: slow = x.inverse() # long time
+ sage: _ = J._charpoly_coefficients() # long time
+ sage: fast = x.inverse() # long time
+ sage: slow == fast # long time
+ True
"""
- if not self.is_invertible():
- raise ValueError("element is not invertible")
+ not_invertible_msg = "element is not invertible"
+ if self.parent()._charpoly_coefficients.is_in_cache():
+ # We can invert using our charpoly if it will be fast to
+ # compute. If the coefficients are cached, our rank had
+ # better be too!
+ if self.det().is_zero():
+ raise ZeroDivisionError(not_invertible_msg)
+ r = self.parent().rank()
+ a = self.characteristic_polynomial().coefficients(sparse=False)
+ return (-1)**(r+1)*sum(a[i+1]*self**i for i in range(r))/self.det()
- return (~self.quadratic_representation())(self)
+ try:
+ inv = (~self.quadratic_representation())(self)
+ self.is_invertible.set_cache(True)
+ return inv
+ except ZeroDivisionError:
+ self.is_invertible.set_cache(False)
+ raise ZeroDivisionError(not_invertible_msg)
+ @cached_method
def is_invertible(self):
"""
Return whether or not this element is invertible.
ALGORITHM:
- The usual way to do this is to check if the determinant is
- zero, but we need the characteristic polynomial for the
- determinant. The minimal polynomial is a lot easier to get,
- so we use Corollary 2 in Chapter V of Koecher to check
- whether or not the paren't algebra's zero element is a root
- of this element's minimal polynomial.
-
- Beware that we can't use the superclass method, because it
- relies on the algebra being associative.
+ If computing my determinant will be fast, we do so and compare
+ with zero (Proposition II.2.4 in Faraut and
+ Koranyi). Otherwise, Proposition II.3.2 in Faraut and Koranyi
+ reduces the problem to the invertibility of my quadratic
+ representation.
SETUP::
The identity element is always invertible::
- sage: set_random_seed()
sage: J = random_eja()
sage: J.one().is_invertible()
True
- The zero element is never invertible::
+ The zero element is never invertible in a non-trivial algebra::
- sage: set_random_seed()
sage: J = random_eja()
- sage: J.zero().is_invertible()
+ sage: (not J.is_trivial()) and J.zero().is_invertible()
False
+ Test that the fast (cached) and slow algorithms give the same
+ answer::
+
+ sage: J = random_eja(field=QQ, orthonormalize=False) # long time
+ sage: x = J.random_element() # long time
+ sage: slow = x.is_invertible() # long time
+ sage: _ = J._charpoly_coefficients() # long time
+ sage: fast = x.is_invertible() # long time
+ sage: slow == fast # long time
+ True
+ """
+ if self.is_zero():
+ if self.parent().is_trivial():
+ return True
+ else:
+ return False
+
+ if self.parent()._charpoly_coefficients.is_in_cache():
+ # The determinant will be quicker than inverting the
+ # quadratic representation, most likely.
+ return (not self.det().is_zero())
+
+ # The easiest way to determine if I'm invertible is to try.
+ try:
+ inv = (~self.quadratic_representation())(self)
+ self.inverse.set_cache(inv)
+ return True
+ except ZeroDivisionError:
+ return False
+
+
+ def is_primitive_idempotent(self):
"""
- zero = self.parent().zero()
- p = self.minimal_polynomial()
- return not (p(zero) == zero)
+ Return whether or not this element is a primitive (or minimal)
+ idempotent.
+
+ A primitive idempotent is a non-zero idempotent that is not
+ the sum of two other non-zero idempotents. Remark 2.7.15 in
+ Baes shows that this is what he refers to as a "minimal
+ idempotent."
+
+ An element of a Euclidean Jordan algebra is a minimal idempotent
+ if it :meth:`is_idempotent` and if its Peirce subalgebra
+ corresponding to the eigenvalue ``1`` has dimension ``1`` (Baes,
+ Proposition 2.7.17).
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
+ ....: RealSymmetricEJA,
+ ....: TrivialEJA,
+ ....: random_eja)
+
+ WARNING::
+
+ This method is sloooooow.
+
+ EXAMPLES:
+
+ The spectral decomposition of a non-regular element should always
+ contain at least one non-minimal idempotent::
+
+ sage: J = RealSymmetricEJA(3)
+ sage: x = sum(J.gens())
+ sage: x.is_regular()
+ False
+ sage: [ c.is_primitive_idempotent()
+ ....: for (l,c) in x.spectral_decomposition() ]
+ [False, True]
+
+ On the other hand, the spectral decomposition of a regular
+ element should always be in terms of minimal idempotents::
+
+ sage: J = JordanSpinEJA(4)
+ sage: x = sum( i*J.monomial(i) for i in range(len(J.gens())) )
+ sage: x.is_regular()
+ True
+ sage: [ c.is_primitive_idempotent()
+ ....: for (l,c) in x.spectral_decomposition() ]
+ [True, True]
+
+ TESTS:
+
+ The identity element is minimal only in an EJA of rank one::
+
+ sage: J = random_eja()
+ sage: J.rank() == 1 or not J.one().is_primitive_idempotent()
+ True
+
+ A non-idempotent cannot be a minimal idempotent::
+
+ sage: J = JordanSpinEJA(4)
+ sage: x = J.random_element()
+ sage: (not x.is_idempotent()) and x.is_primitive_idempotent()
+ False
+
+ Proposition 2.7.19 in Baes says that an element is a minimal
+ idempotent if and only if it's idempotent with trace equal to
+ unity::
+
+ sage: J = JordanSpinEJA(4)
+ sage: x = J.random_element()
+ sage: expected = (x.is_idempotent() and x.trace() == 1)
+ sage: actual = x.is_primitive_idempotent()
+ sage: actual == expected
+ True
+
+ Primitive idempotents must be non-zero::
+
+ sage: J = random_eja()
+ sage: J.zero().is_idempotent()
+ True
+ sage: J.zero().is_primitive_idempotent()
+ False
+
+ As a consequence of the fact that primitive idempotents must
+ be non-zero, there are no primitive idempotents in a trivial
+ Euclidean Jordan algebra::
+
+ sage: J = TrivialEJA()
+ sage: J.one().is_idempotent()
+ True
+ sage: J.one().is_primitive_idempotent()
+ False
+
+ """
+ if not self.is_idempotent():
+ return False
+
+ if self.is_zero():
+ return False
+
+ (_,_,J1) = self.parent().peirce_decomposition(self)
+ return (J1.dimension() == 1)
def is_nilpotent(self):
TESTS:
- The identity element is never nilpotent::
+ The identity element is never nilpotent, except in a trivial EJA::
- sage: set_random_seed()
- sage: random_eja().one().is_nilpotent()
+ sage: J = random_eja()
+ sage: J.one().is_nilpotent() and not J.is_trivial()
False
The additive identity is always nilpotent::
- sage: set_random_seed()
sage: random_eja().zero().is_nilpotent()
True
sage: J = JordanSpinEJA(5)
sage: J.one().is_regular()
False
- sage: e0, e1, e2, e3, e4 = J.gens() # e0 is the identity
+ sage: b0, b1, b2, b3, b4 = J.gens()
+ sage: b0 == J.one()
+ True
sage: for x in J.gens():
....: (J.one() + x).is_regular()
False
TESTS:
The zero element should never be regular, unless the parent
- algebra has dimension one::
+ algebra has dimension less than or equal to one::
- sage: set_random_seed()
sage: J = random_eja()
- sage: J.dimension() == 1 or not J.zero().is_regular()
+ sage: J.dimension() <= 1 or not J.zero().is_regular()
True
The unit element isn't regular unless the algebra happens to
consist of only its scalar multiples::
- sage: set_random_seed()
sage: J = random_eja()
- sage: J.dimension() == 1 or not J.one().is_regular()
+ sage: J.dimension() <= 1 or not J.one().is_regular()
True
"""
ALGORITHM:
- For now, we skip the messy minimal polynomial computation
- and instead return the dimension of the vector space spanned
- by the powers of this element. The latter is a bit more
- straightforward to compute.
+ .........
SETUP::
sage: J = JordanSpinEJA(4)
sage: J.one().degree()
1
- sage: e0,e1,e2,e3 = J.gens()
- sage: (e0 - e1).degree()
+ sage: b0,b1,b2,b3 = J.gens()
+ sage: (b0 - b1).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)
- sage: J = JordanSpinEJA(n)
+ 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:
- The zero and unit elements are both of degree one::
+ The zero and unit elements are both of degree one in nontrivial
+ algebras::
- sage: set_random_seed()
sage: J = random_eja()
- sage: J.zero().degree()
- 1
- sage: J.one().degree()
- 1
+ sage: d = J.zero().degree()
+ sage: (J.is_trivial() and d == 0) or d == 1
+ True
+ sage: d = J.one().degree()
+ sage: (J.is_trivial() and d == 0) or d == 1
+ True
Our implementation agrees with the definition::
- sage: set_random_seed()
sage: x = random_eja().random_element()
sage: x.degree() == x.minimal_polynomial().degree()
True
"""
- return self.span_of_powers().dimension()
+ n = self.parent().dimension()
+
+ if n == 0:
+ # The minimal polynomial is an empty product, i.e. the
+ # constant polynomial "1" having degree zero.
+ return 0
+ elif self.is_zero():
+ # The minimal polynomial of zero in a nontrivial algebra
+ # is "t", and is of degree one.
+ return 1
+ elif n == 1:
+ # If this is a nonzero element of a nontrivial algebra, it
+ # has degree at least one. It follows that, in an algebra
+ # of dimension one, the degree must be actually one.
+ return 1
+
+ # BEWARE: The subalgebra_generated_by() method uses the result
+ # of this method to construct a basis for the subalgebra. That
+ # means, in particular, that we cannot implement this method
+ # as ``self.subalgebra_generated_by().dimension()``.
+
+ # Algorithm: keep appending (vector representations of) powers
+ # self as rows to a matrix and echelonizing it. When its rank
+ # stops increasing, we've reached a redundancy.
+
+ # Given the special cases above, we can assume that "self" is
+ # nonzero, the algebra is nontrivial, and that its dimension
+ # is at least two.
+ M = matrix([(self.parent().one()).to_vector()])
+ old_rank = 1
+
+ # Specifying the row-reduction algorithm can e.g. help over
+ # AA because it avoids the RecursionError that gets thrown
+ # when we have to look too hard for a root.
+ #
+ # Beware: QQ supports an entirely different set of "algorithm"
+ # keywords than do AA and RR.
+ algo = None
+ from sage.rings.all import QQ
+ if self.parent().base_ring() is not QQ:
+ algo = "scaled_partial_pivoting"
+
+ for d in range(1,n):
+ M = matrix(M.rows() + [(self**d).to_vector()])
+ M.echelonize(algo)
+ new_rank = M.rank()
+ if new_rank == old_rank:
+ return new_rank
+ else:
+ old_rank = new_rank
+
+ return n
+
def left_matrix(self):
SETUP::
sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
+ ....: RealSymmetricEJA,
+ ....: TrivialEJA,
....: random_eja)
+ EXAMPLES:
+
+ Keeping in mind that the polynomial ``1`` evaluates the identity
+ element (also the zero element) of the trivial algebra, it is clear
+ that the polynomial ``1`` is the minimal polynomial of the only
+ element in a trivial algebra::
+
+ sage: J = TrivialEJA()
+ sage: J.one().minimal_polynomial()
+ 1
+ sage: J.zero().minimal_polynomial()
+ 1
+
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()
- sage: J.one().minimal_polynomial()
+ 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
of its minimal polynomial::
- sage: set_random_seed()
sage: x = random_eja().random_element()
sage: x.degree() == x.minimal_polynomial().degree()
True
The minimal polynomial and the characteristic polynomial coincide
and are known (see Alizadeh, Example 11.11) for all elements of
the spin factor algebra that aren't scalar multiples of the
- identity::
+ identity. We require the dimension of the algebra to be at least
+ two here so that said elements actually exist::
- sage: set_random_seed()
- sage: n = ZZ.random_element(2,10)
+ sage: d_max = JordanSpinEJA._max_random_instance_dimension()
+ sage: n = ZZ.random_element(2, max(2,d_max))
sage: J = JordanSpinEJA(n)
sage: y = J.random_element()
sage: while y == y.coefficient(0)*J.one():
....: y = J.random_element()
- sage: y0 = y.vector()[0]
- sage: y_bar = y.vector()[1:]
+ sage: y0 = y.to_vector()[0]
+ sage: y_bar = y.to_vector()[1:]
sage: actual = y.minimal_polynomial()
sage: t = PolynomialRing(J.base_ring(),'t').gen(0)
sage: expected = t^2 - 2*y0*t + (y0^2 - norm(y_bar)^2)
The minimal polynomial should always kill its element::
- sage: set_random_seed()
- sage: x = random_eja().random_element()
- sage: p = x.minimal_polynomial()
- sage: x.apply_univariate_polynomial(p)
+ sage: x = random_eja().random_element() # long time
+ sage: p = x.minimal_polynomial() # long time
+ sage: x.apply_univariate_polynomial(p) # long time
0
+ The minimal polynomial is invariant under a change of basis,
+ and in particular, a re-scaling of the basis::
+
+ sage: d_max = RealSymmetricEJA._max_random_instance_dimension()
+ sage: d = ZZ.random_element(1, d_max)
+ sage: n = RealSymmetricEJA._max_random_instance_size(d)
+ sage: J1 = RealSymmetricEJA(n)
+ sage: J2 = RealSymmetricEJA(n,orthonormalize=False)
+ sage: X = random_matrix(AA,n)
+ sage: X = X*X.transpose()
+ sage: x1 = J1(X)
+ sage: x2 = J2(X)
+ sage: x1.minimal_polynomial() == x2.minimal_polynomial()
+ True
+
"""
- V = self.span_of_powers()
- assoc_subalg = self.subalgebra_generated_by()
- # Mis-design warning: the basis used for span_of_powers()
- # 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()
+ if self.is_zero():
+ # Pretty sure we know what the minimal polynomial of
+ # the zero operator is going to be. This ensures
+ # consistency of e.g. the polynomial variable returned
+ # in the "normal" case without us having to think about it.
+ return self.operator().minimal_polynomial()
+
+ # If we don't orthonormalize the subalgebra's basis, then the
+ # first two monomials in the subalgebra will be self^0 and
+ # self^1... assuming that self^1 is not a scalar multiple of
+ # self^0 (the unit element). We special case these to avoid
+ # having to solve a system to coerce self into the subalgebra.
+ A = self.subalgebra_generated_by(orthonormalize=False)
+
+ if A.dimension() == 1:
+ # Does a solve to find the scalar multiple alpha such that
+ # alpha*unit = self. We have to do this because the basis
+ # for the subalgebra will be [ self^0 ], and not [ self^1 ]!
+ unit = self.parent().one()
+ alpha = self.to_vector() / unit.to_vector()
+ return (unit.operator()*alpha).minimal_polynomial()
+ else:
+ # If the dimension of the subalgebra is >= 2, then we just
+ # use the second basis element.
+ return A.monomial(1).operator().minimal_polynomial()
- def natural_representation(self):
+ def to_matrix(self):
"""
- Return a more-natural representation of this element.
+ Return an (often more natural) representation of this element as a
+ matrix.
- Every finite-dimensional Euclidean Jordan Algebra is a
- direct sum of five simple algebras, four of which comprise
- Hermitian matrices. This method returns the original
- "natural" representation of this element as a Hermitian
- matrix, if it has one. If not, you get the usual representation.
+ Every finite-dimensional Euclidean Jordan Algebra is a direct
+ sum of five simple algebras, four of which comprise Hermitian
+ matrices. This method returns a "natural" matrix
+ representation of this element as either a Hermitian matrix or
+ column vector.
SETUP::
sage: from mjo.eja.eja_algebra import (ComplexHermitianEJA,
- ....: QuaternionHermitianEJA)
+ ....: HadamardEJA,
+ ....: QuaternionHermitianEJA,
+ ....: RealSymmetricEJA)
EXAMPLES::
sage: J = ComplexHermitianEJA(3)
sage: J.one()
- e0 + e5 + e8
- sage: J.one().natural_representation()
- [1 0 0 0 0 0]
- [0 1 0 0 0 0]
- [0 0 1 0 0 0]
- [0 0 0 1 0 0]
- [0 0 0 0 1 0]
- [0 0 0 0 0 1]
+ b0 + b3 + b8
+ sage: J.one().to_matrix()
+ +---+---+---+
+ | 1 | 0 | 0 |
+ +---+---+---+
+ | 0 | 1 | 0 |
+ +---+---+---+
+ | 0 | 0 | 1 |
+ +---+---+---+
::
- sage: J = QuaternionHermitianEJA(3)
+ sage: J = QuaternionHermitianEJA(2)
sage: J.one()
- e0 + e9 + e14
- sage: J.one().natural_representation()
- [1 0 0 0 0 0 0 0 0 0 0 0]
- [0 1 0 0 0 0 0 0 0 0 0 0]
- [0 0 1 0 0 0 0 0 0 0 0 0]
- [0 0 0 1 0 0 0 0 0 0 0 0]
- [0 0 0 0 1 0 0 0 0 0 0 0]
- [0 0 0 0 0 1 0 0 0 0 0 0]
- [0 0 0 0 0 0 1 0 0 0 0 0]
- [0 0 0 0 0 0 0 1 0 0 0 0]
- [0 0 0 0 0 0 0 0 1 0 0 0]
- [0 0 0 0 0 0 0 0 0 1 0 0]
- [0 0 0 0 0 0 0 0 0 0 1 0]
- [0 0 0 0 0 0 0 0 0 0 0 1]
+ b0 + b5
+ sage: J.one().to_matrix()
+ +---+---+
+ | 1 | 0 |
+ +---+---+
+ | 0 | 1 |
+ +---+---+
+
+ This also works in Cartesian product algebras::
+
+ sage: J1 = HadamardEJA(1)
+ sage: J2 = RealSymmetricEJA(2)
+ sage: J = cartesian_product([J1,J2])
+ sage: x = sum(J.gens())
+ sage: x.to_matrix()[0]
+ [1]
+ sage: x.to_matrix()[1]
+ [ 1 0.7071067811865475?]
+ [0.7071067811865475? 1]
+
+ """
+ B = self.parent().matrix_basis()
+ W = self.parent().matrix_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()) )
+
+
+
+ def norm(self):
+ """
+ The norm of this element with respect to :meth:`inner_product`.
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
+ ....: HadamardEJA)
+
+ EXAMPLES::
+
+ sage: J = HadamardEJA(2)
+ sage: x = sum(J.gens())
+ sage: x.norm()
+ 1.414213562373095?
+
+ ::
+
+ sage: J = JordanSpinEJA(4)
+ sage: x = sum(J.gens())
+ sage: x.norm()
+ 2
"""
- B = self.parent().natural_basis()
- W = B[0].matrix_space()
- return W.linear_combination(zip(self.vector(), B))
+ return self.inner_product(self).sqrt()
def operator(self):
TESTS::
- sage: set_random_seed()
sage: J = random_eja()
- sage: x = J.random_element()
- sage: y = J.random_element()
+ sage: x,y = J.random_elements(2)
sage: x.operator()(y) == x*y
True
sage: y.operator()(x) == x*y
"""
P = self.parent()
- fda_elt = FiniteDimensionalAlgebraElement(P, self)
- return FiniteDimensionalEuclideanJordanAlgebraOperator(
- P,
- P,
- fda_elt.matrix().transpose() )
+ left_mult_by_self = lambda y: self*y
+ L = P.module_morphism(function=left_mult_by_self, codomain=P)
+ return FiniteDimensionalEJAOperator(P, P, L.matrix() )
def quadratic_representation(self, other=None):
The explicit form in the spin factor algebra is given by
Alizadeh's Example 11.12::
- sage: set_random_seed()
- sage: n = ZZ.random_element(1,10)
- sage: J = JordanSpinEJA(n)
- sage: x = J.random_element()
- sage: x_vec = x.vector()
- sage: x0 = x_vec[0]
- sage: x_bar = x_vec[1:]
- sage: A = matrix(QQ, 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(QQ, 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: 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: 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
Test all of the properties from Theorem 11.2 in Alizadeh::
- sage: set_random_seed()
sage: J = random_eja()
- sage: x = J.random_element()
- sage: y = J.random_element()
+ sage: x,y = J.random_elements(2)
sage: Lx = x.operator()
sage: Lxx = (x*x).operator()
sage: Qx = x.quadratic_representation()
Property 2 (multiply on the right for :trac:`28272`):
- sage: alpha = QQ.random_element()
+ sage: alpha = J.base_ring().random_element()
sage: (alpha*x).quadratic_representation() == Qx*(alpha^2)
True
sage: not x.is_invertible() or (
....: x.quadratic_representation(x.inverse())*Qx
....: ==
- ....: 2*x.operator()*Qex - Qx )
+ ....: 2*Lx*Qex - Qx )
True
- sage: 2*x.operator()*Qex - Qx == Lxx
+ sage: 2*Lx*Qex - Qx == Lxx
True
Property 5:
return ( L*M + M*L - (self*other).operator() )
- def span_of_powers(self):
- """
- Return the vector space spanned by successive powers of
- this element.
+
+ def spectral_decomposition(self):
"""
- # The dimension of the subalgebra can't be greater than
- # the big algebra, so just put everything into a list
- # and let span() get rid of the excess.
- #
- # We do the extra ambient_vector_space() in case we're messing
- # with polynomials and the direct parent is a module.
- V = self.parent().vector_space()
- return V.span( (self**d).vector() for d in xrange(V.dimension()) )
+ Return the unique spectral decomposition of this element.
+
+ ALGORITHM:
+
+ Following Faraut and Korányi's Theorem III.1.1, we restrict this
+ element's left-multiplication-by operator to the subalgebra it
+ generates. We then compute the spectral decomposition of that
+ operator, and the spectral projectors we get back must be the
+ left-multiplication-by operators for the idempotents we
+ seek. Thus applying them to the identity element gives us those
+ idempotents.
+
+ Since the eigenvalues are required to be distinct, we take
+ the spectral decomposition of the zero element to be zero
+ times the identity element of the algebra (which is idempotent,
+ obviously).
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import RealSymmetricEJA
+
+ EXAMPLES:
+
+ The spectral decomposition of the identity is ``1`` times itself,
+ and the spectral decomposition of zero is ``0`` times the identity::
+
+ sage: J = RealSymmetricEJA(3)
+ sage: J.one()
+ b0 + b2 + b5
+ sage: J.one().spectral_decomposition()
+ [(1, b0 + b2 + b5)]
+ sage: J.zero().spectral_decomposition()
+ [(0, b0 + b2 + b5)]
+
+ TESTS::
+
+ sage: J = RealSymmetricEJA(4)
+ sage: x = sum(J.gens())
+ sage: sd = x.spectral_decomposition()
+ sage: l0 = sd[0][0]
+ sage: l1 = sd[1][0]
+ sage: c0 = sd[0][1]
+ sage: c1 = sd[1][1]
+ sage: c0.inner_product(c1) == 0
+ True
+ sage: c0.is_idempotent()
+ True
+ sage: c1.is_idempotent()
+ True
+ sage: c0 + c1 == J.one()
+ True
+ sage: l0*c0 + l1*c1 == x
+ True
+
+ The spectral decomposition should work in subalgebras, too::
+ sage: J = RealSymmetricEJA(4)
+ sage: (b0, b1, b2, b3, b4, b5, b6, b7, b8, b9) = J.gens()
+ sage: A = 2*b5 - 2*b8
+ sage: (lambda1, c1) = A.spectral_decomposition()[1]
+ sage: (J0, J5, J1) = J.peirce_decomposition(c1)
+ sage: (f0, f1, f2) = J1.gens()
+ sage: f0.spectral_decomposition()
+ [(0, 1.000000000000000?*c2), (1, 1.000000000000000?*c0)]
- def subalgebra_generated_by(self):
+ """
+ A = self.subalgebra_generated_by(orthonormalize=True)
+ result = []
+ for (evalue, proj) in A(self).operator().spectral_decomposition():
+ result.append( (evalue, proj(A.one()).superalgebra_element()) )
+ return result
+
+ def subalgebra_generated_by(self, **kwargs):
"""
Return the associative subalgebra of the parent EJA generated
by this element.
+ Since our parent algebra is unital, we want "subalgebra" to mean
+ "unital subalgebra" as well; thus the subalgebra that an element
+ generates will itself be a Euclidean Jordan algebra after
+ restricting the algebra operations appropriately. This is the
+ subalgebra that Faraut and Korányi work with in section II.2, for
+ example.
+
SETUP::
- sage: from mjo.eja.eja_algebra import random_eja
+ sage: from mjo.eja.eja_algebra import (random_eja,
+ ....: HadamardEJA,
+ ....: RealSymmetricEJA)
- TESTS::
+ EXAMPLES:
- sage: set_random_seed()
- sage: x = random_eja().random_element()
- sage: x.subalgebra_generated_by().is_associative()
+ We can create subalgebras of Cartesian product EJAs that are not
+ themselves Cartesian product EJAs (they're just "regular" EJAs)::
+
+ sage: J1 = HadamardEJA(3)
+ sage: J2 = RealSymmetricEJA(2)
+ sage: J = cartesian_product([J1,J2])
+ sage: J.one().subalgebra_generated_by()
+ Euclidean Jordan algebra of dimension 1 over Algebraic Real Field
+
+ TESTS:
+
+ This subalgebra, being composed of only powers, is associative::
+
+ sage: x0 = random_eja().random_element()
+ sage: A = x0.subalgebra_generated_by()
+ sage: x,y,z = A.random_elements(3)
+ sage: (x*y)*z == x*(y*z)
True
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()(u) == u^2
+ sage: A = x.subalgebra_generated_by()
+ sage: A(x^2) == A(x)*A(x)
+ True
+
+ By definition, the subalgebra generated by the zero element is
+ the one-dimensional algebra generated by the identity
+ element... unless the original algebra was trivial, in which
+ case the subalgebra is trivial too::
+
+ sage: A = random_eja().zero().subalgebra_generated_by()
+ sage: (A.is_trivial() and A.dimension() == 0) or A.dimension() == 1
True
"""
- # 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...
- #
- # IMPORTANT: this assumes that all vectors are COLUMN
- # vectors, unlike our superclass (which uses row vectors).
- 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)
-
- # It's an algebra of polynomials in one element, and EJAs
- # are power-associative.
- #
- # TODO: choose generator names intelligently.
- #
- # The rank is the highest possible degree of a minimal polynomial,
- # and is bounded above by the dimension. We know in this case that
- # there's an element whose minimal polynomial has the same degree
- # as the space's dimension, so that must be its rank too.
- return FiniteDimensionalEuclideanJordanAlgebra(
- F,
- mats,
- V.dimension(),
- assume_associative=True,
- names='f')
+ powers = tuple( self**k for k in range(self.degree()) )
+ A = self.parent().subalgebra(powers,
+ associative=True,
+ check_field=False,
+ check_axioms=False,
+ **kwargs)
+ A.one.set_cache(A(self.parent().one()))
+ return A
def subalgebra_idempotent(self):
sage: from mjo.eja.eja_algebra import random_eja
- TESTS::
+ TESTS:
+
+ Ensure that we can find an idempotent in a non-trivial algebra
+ where there are non-nilpotent elements, or that we get the dumb
+ solution in the trivial algebra::
- sage: set_random_seed()
sage: J = random_eja()
sage: x = J.random_element()
- sage: while x.is_nilpotent():
+ sage: while x.is_nilpotent() and not J.is_trivial():
....: x = J.random_element()
sage: c = x.subalgebra_idempotent()
sage: c^2 == c
True
"""
+ if self.parent().is_trivial():
+ return self
+
if self.is_nilpotent():
raise ValueError("this only works with non-nilpotent elements!")
- V = self.span_of_powers()
J = self.subalgebra_generated_by()
- # Mis-design warning: the basis used for span_of_powers()
- # and subalgebra_generated_by() must be the same, and in
- # the same order!
- u = J(V.coordinates(self.vector()))
+ u = J(self)
# The image of the matrix of left-u^m-multiplication
# will be minimal for some natural number s...
s = 0
- minimal_dim = V.dimension()
- for i in xrange(1, V.dimension()):
+ minimal_dim = J.dimension()
+ for i in range(1, minimal_dim):
this_dim = (u**i).operator().matrix().image().dimension()
if this_dim < minimal_dim:
minimal_dim = this_dim
# Our FiniteDimensionalAlgebraElement superclass uses rows.
u_next = u**(s+1)
A = u_next.operator().matrix()
- c_coordinates = A.solve_right(u_next.vector())
+ c = J.from_vector(A.solve_right(u_next.to_vector()))
- # Now c_coordinates is the idempotent we want, but it's in
- # the coordinate system of the subalgebra.
- #
- # We need the basis for J, but as elements of the parent algebra.
- #
- basis = [self.parent(v) for v in V.basis()]
- return self.parent().linear_combination(zip(c_coordinates, basis))
+ # Now c is the idempotent we want, but it still lives in the subalgebra.
+ return c.superalgebra_element()
def trace(self):
"""
Return my trace, the sum of my eigenvalues.
+ In a trivial algebra, however you want to look at it, the trace is
+ an empty sum for which we declare the result to be zero.
+
SETUP::
sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
- ....: RealCartesianProductEJA,
+ ....: HadamardEJA,
+ ....: TrivialEJA,
....: random_eja)
EXAMPLES::
+ sage: J = TrivialEJA()
+ sage: J.zero().trace()
+ 0
+
+ ::
sage: J = JordanSpinEJA(3)
sage: x = sum(J.gens())
sage: x.trace()
::
- sage: J = RealCartesianProductEJA(5)
+ sage: J = HadamardEJA(5)
sage: J.one().trace()
5
The trace of an element is a real number::
- sage: set_random_seed()
sage: J = random_eja()
- sage: J.random_element().trace() in J.base_ring()
+ sage: J.random_element().trace() in RLF
+ True
+
+ The trace is linear::
+
+ sage: J = random_eja()
+ sage: x,y = J.random_elements(2)
+ sage: alpha = J.base_ring().random_element()
+ sage: (alpha*x + y).trace() == alpha*x.trace() + y.trace()
+ True
+
+ The trace of a square is nonnegative::
+
+ sage: x = random_eja().random_element()
+ sage: (x*x).trace() >= 0
True
"""
P = self.parent()
r = P.rank()
- p = P._charpoly_coeff(r-1)
+
+ if r == 0:
+ # Special case for the trivial algebra where
+ # the trace is an empty sum.
+ return P.base_ring().zero()
+
+ p = P._charpoly_coefficients()[r-1]
# The _charpoly_coeff function already adds the factor of
# -1 to ensure that _charpoly_coeff(r-1) is really what
# appears in front of t^{r-1} in the charpoly. However,
# we want the negative of THAT for the trace.
- return -p(*self.vector())
+ return -p(*self.to_vector())
def trace_inner_product(self, other):
TESTS:
- The trace inner product is commutative::
+ The trace inner product is commutative, bilinear, and associative::
- sage: set_random_seed()
sage: J = random_eja()
- sage: x = J.random_element(); y = J.random_element()
+ sage: x,y,z = J.random_elements(3)
+ sage: # commutative
sage: x.trace_inner_product(y) == y.trace_inner_product(x)
True
-
- The trace inner product is bilinear::
-
- sage: set_random_seed()
- sage: J = random_eja()
- sage: x = J.random_element()
- sage: y = J.random_element()
- sage: z = J.random_element()
- sage: a = QQ.random_element();
+ sage: # bilinear
+ sage: a = J.base_ring().random_element();
sage: actual = (a*(x+z)).trace_inner_product(y)
sage: expected = ( a*x.trace_inner_product(y) +
....: a*z.trace_inner_product(y) )
....: a*x.trace_inner_product(z) )
sage: actual == expected
True
-
- The trace inner product satisfies the compatibility
- condition in the definition of a Euclidean Jordan algebra::
-
- sage: set_random_seed()
- sage: J = random_eja()
- sage: x = J.random_element()
- sage: y = J.random_element()
- sage: z = J.random_element()
+ sage: # associative
sage: (x*y).trace_inner_product(z) == y.trace_inner_product(x*z)
True
raise TypeError("'other' must live in the same algebra")
return (self*other).trace()
+
+
+ def trace_norm(self):
+ """
+ The norm of this element with respect to :meth:`trace_inner_product`.
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
+ ....: HadamardEJA)
+
+ EXAMPLES::
+
+ sage: J = HadamardEJA(2)
+ sage: x = sum(J.gens())
+ sage: x.trace_norm()
+ 1.414213562373095?
+
+ ::
+
+ sage: J = JordanSpinEJA(4)
+ sage: x = sum(J.gens())
+ sage: x.trace_norm()
+ 2.828427124746190?
+
+ """
+ return self.trace_inner_product(self).sqrt()
+
+
+class CartesianProductEJAElement(FiniteDimensionalEJAElement):
+ def det(self):
+ r"""
+ Compute the determinant of this product-element using the
+ determianants of its factors.
+
+ This result Follows from the spectral decomposition of (say)
+ the pair `(x,y)` in terms of the Jordan frame `\left\{ (c_1,
+ 0),(c_2, 0),...,(0,d_1),(0,d_2),... \right\}.
+ """
+ from sage.misc.misc_c import prod
+ return prod( f.det() for f in self.cartesian_factors() )
+
+ def to_matrix(self):
+ # An override is necessary to call our custom _scale().
+ B = self.parent().matrix_basis()
+ W = self.parent().matrix_space()
+
+ # Aaaaand linear combinations don't work in Cartesian
+ # product spaces, even though they provide a method with
+ # that name. This is hidden behind an "if" because the
+ # _scale() function is slow.
+ pairs = zip(B, self.to_vector())
+ return W.sum( _scale(b, alpha) for (b,alpha) in pairs )