return (J0, J5, J1)
- def a_jordan_frame(self):
- r"""
- Generate a Jordan frame for this algebra.
-
- This implementation is based on the so-called "central
- orthogonal idempotents" implemented for (semisimple) centers
- of SageMath ``FiniteDimensionalAlgebrasWithBasis``. Since all
- Euclidean Jordan algebas are commutative (and thus equal to
- their own centers) and semisimple, the method should work more
- or less as implemented, if it ever worked in the first place.
- (I don't know the justification for the original implementation.
- yet).
-
- How it works: we loop through the algebras generators, looking
- for their eigenspaces. If there's more than one eigenspace,
- and if they result in more than one subalgebra, then we split
- those subalgebras recursively until we get to subalgebras of
- dimension one (whose idempotent is the unit element). Why does
- some generator have to produce at least two subalgebras? I
- dunno. But it seems to work.
-
- Beware that Koecher defines the "center" of a Jordan algebra to
- be something else, because the usual definition is stupid in a
- (necessarily commutative) Jordan algebra.
-
- SETUP::
-
- sage: from mjo.eja.eja_algebra import (random_eja,
- ....: JordanSpinEJA,
- ....: TrivialEJA)
-
- EXAMPLES:
-
- A Jordan frame for the trivial algebra has to be empty
- (zero-length) since its rank is zero. More to the point, there
- are no non-zero idempotents in the trivial EJA. This does not
- cause any problems so long as we adopt the convention that the
- empty sum is zero, since then the sole element of the trivial
- EJA has an (empty) spectral decomposition::
-
- sage: J = TrivialEJA()
- sage: J.a_jordan_frame()
- ()
-
- A one-dimensional algebra has rank one (equal to its dimension),
- and only one primitive idempotent, namely the algebra's unit
- element::
-
- sage: J = JordanSpinEJA(1)
- sage: J.a_jordan_frame()
- (e0,)
-
- TESTS::
-
- sage: J = random_eja()
- sage: c = J.a_jordan_frame()
- sage: all( x^2 == x for x in c )
- True
- sage: r = len(c)
- sage: all( c[i]*c[j] == c[i]*(i==j) for i in range(r)
- ....: for j in range(r) )
- True
-
- """
- if self.dimension() == 0:
- return ()
- if self.dimension() == 1:
- return (self.one(),)
-
- for g in self.gens():
- eigenpairs = g.operator().matrix().right_eigenspaces()
- if len(eigenpairs) >= 2:
- subalgebras = []
- for eigval, eigspace in eigenpairs:
- # Make sub-EJAs from the matrix eigenspaces...
- sb = tuple( self.from_vector(b) for b in eigspace.basis() )
- try:
- # This will fail if e.g. the eigenspace basis
- # contains two elements and their product
- # isn't a linear combination of the two of
- # them (i.e. the generated EJA isn't actually
- # two dimensional).
- s = FiniteDimensionalEuclideanJordanSubalgebra(self, sb)
- subalgebras.append(s)
- except ArithmeticError as e:
- if str(e) == "vector is not in free module":
- # Ignore only the "not a sub-EJA" error
- pass
-
- if len(subalgebras) >= 2:
- # apply this method recursively.
- return tuple( c.superalgebra_element()
- for subalgebra in subalgebras
- for c in subalgebra.a_jordan_frame() )
-
- # If we got here, the algebra didn't decompose, at least not when we looked at
- # the eigenspaces corresponding only to basis elements of the algebra. The
- # implementation I stole says that this should work because of Schur's Lemma,
- # so I personally blame Schur's Lemma if it does not.
- raise Exception("Schur's Lemma didn't work!")
-
-
def random_elements(self, count):
"""
Return ``count`` random elements as a tuple.
True
"""
- return tuple( self.random_element() for idx in range(count) )
-
+ return tuple( self.random_element() for idx in range(count) )
+ @cached_method
def rank(self):
"""
Return the rank of this EJA.
ALGORITHM:
- The author knows of no algorithm to compute the rank of an EJA
- where only the multiplication table is known. In lieu of one, we
- require the rank to be specified when the algebra is created,
- and simply pass along that number here.
+ We first compute the polynomial "column matrices" `p_{k}` that
+ evaluate to `x^k` on the coordinates of `x`. Then, we begin
+ adding them to a matrix one at a time, and trying to solve the
+ system that makes `p_{0}`,`p_{1}`,..., `p_{s-1}` add up to
+ `p_{s}`. This will succeed only when `s` is the rank of the
+ algebra, as proven in a recent draft paper of mine.
SETUP::
sage: r > 0 or (r == 0 and J.is_trivial())
True
+ Ensure that computing the rank actually works, since the ranks
+ of all simple algebras are known and will be cached by default::
+
+ sage: J = HadamardEJA(4)
+ sage: J.rank.clear_cache()
+ sage: J.rank()
+ 4
+
+ ::
+
+ sage: J = JordanSpinEJA(4)
+ sage: J.rank.clear_cache()
+ sage: J.rank()
+ 2
+
+ ::
+
+ sage: J = RealSymmetricEJA(3)
+ sage: J.rank.clear_cache()
+ sage: J.rank()
+ 3
+
+ ::
+
+ sage: J = ComplexHermitianEJA(2)
+ sage: J.rank.clear_cache()
+ sage: J.rank()
+ 2
+
+ ::
+
+ sage: J = QuaternionHermitianEJA(2)
+ sage: J.rank.clear_cache()
+ sage: J.rank()
+ 2
+
"""
- return self._rank
+ n = self.dimension()
+ if n == 0:
+ return 0
+ elif n == 1:
+ return 1
+
+ var_names = [ "X" + str(z) for z in range(1,n+1) ]
+ R = PolynomialRing(self.base_ring(), var_names)
+ vars = R.gens()
+
+ def L_x_i_j(i,j):
+ # From a result in my book, these are the entries of the
+ # basis representation of L_x.
+ return sum( vars[k]*self.monomial(k).operator().matrix()[i,j]
+ for k in range(n) )
+
+ L_x = matrix(R, n, n, L_x_i_j)
+ x_powers = [ vars[k]*(L_x**k)*self.one().to_vector()
+ for k in range(n) ]
+
+ # Can assume n >= 2
+ M = matrix([x_powers[0]])
+ old_rank = 1
+
+ for d in range(1,n):
+ M = matrix(M.rows() + [x_powers[d]])
+ M.echelonize()
+ # TODO: we've basically solved the system here.
+ # We should save the echelonized matrix somehow
+ # so that it can be reused in the charpoly method.
+ new_rank = M.rank()
+ if new_rank == old_rank:
+ return new_rank
+ else:
+ old_rank = new_rank
+
+ return n
def vector_space(self):
**kwargs)
+ def _rank_computation(self):
+ r"""
+ Override the parent method with something that tries to compute
+ over a faster (non-extension) field.
+ """
+ if self._basis_normalizers is None:
+ # We didn't normalize, so assume that the basis we started
+ # with had entries in a nice field.
+ return super(MatrixEuclideanJordanAlgebra, self)._rank_computation()
+ else:
+ basis = ( (b/n) for (b,n) in zip(self.natural_basis(),
+ self._basis_normalizers) )
+
+ # Do this over the rationals and convert back at the end.
+ # Only works because we know the entries of the basis are
+ # integers.
+ J = MatrixEuclideanJordanAlgebra(QQ,
+ basis,
+ self.rank(),
+ normalize_basis=False)
+ return J._rank_computation()
+
@cached_method
def _charpoly_coeff(self, i):
"""
projects / sage.d.git / blobdiff