coords = W.coordinate_vector(_mat2vec(elt))
return self.from_vector(coords)
+ @staticmethod
+ def _max_test_case_size():
+ """
+ Return an integer "size" that is an upper bound on the size of
+ this algebra when it is used in a random test
+ case. Unfortunately, the term "size" is quite vague -- when
+ dealing with `R^n` under either the Hadamard or Jordan spin
+ product, the "size" refers to the dimension `n`. When dealing
+ with a matrix algebra (real symmetric or complex/quaternion
+ Hermitian), it refers to the size of the matrix, which is
+ far less than the dimension of the underlying vector space.
+
+ We default to five in this class, which is safe in `R^n`. The
+ matrix algebra subclasses (or any class where the "size" is
+ interpreted to be far less than the dimension) should override
+ with a smaller number.
+ """
+ return 5
def _repr_(self):
"""
"""
return tuple( self.random_element() for idx in range(count) )
+ @classmethod
+ def random_instance(cls, field=AA, **kwargs):
+ """
+ Return a random instance of this type of algebra.
+
+ Beware, this will crash for "most instances" because the
+ constructor below looks wrong.
+ """
+ if cls is TrivialEJA:
+ # The TrivialEJA class doesn't take an "n" argument because
+ # there's only one.
+ return cls(field)
+
+ n = ZZ.random_element(cls._max_test_case_size()) + 1
+ return cls(n, field, **kwargs)
+
@cached_method
- def rank(self):
+ def _charpoly_coefficients(self):
+ r"""
+ The `r` polynomial coefficients of the "characteristic polynomial
+ of" function.
"""
+ n = self.dimension()
+ var_names = [ "X" + str(z) for z in range(1,n+1) ]
+ R = PolynomialRing(self.base_ring(), var_names)
+ vars = R.gens()
+ F = R.fraction_field()
+
+ 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(F, n, n, L_x_i_j)
+ # Compute an extra power in case the rank is equal to
+ # the dimension (otherwise, we would stop at x^(r-1)).
+ x_powers = [ (L_x**k)*self.one().to_vector()
+ for k in range(n+1) ]
+ A = matrix.column(F, x_powers[:n])
+ AE = A.extended_echelon_form()
+ E = AE[:,n:]
+ A_rref = AE[:,:n]
+ r = A_rref.rank()
+ b = x_powers[r]
+
+ # The theory says that only the first "r" coefficients are
+ # nonzero, and they actually live in the original polynomial
+ # ring and not the fraction field. We negate them because
+ # in the actual characteristic polynomial, they get moved
+ # to the other side where x^r lives.
+ return -A_rref.solve_right(E*b).change_ring(R)[:r]
+
+ @cached_method
+ def rank(self):
+ r"""
Return the rank of this EJA.
ALGORITHM:
2
"""
- 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
+ return len(self._charpoly_coefficients())
def vector_space(self):
Element = FiniteDimensionalEuclideanJordanAlgebraElement
-class KnownRankEJA(object):
- """
- A class for algebras that we actually know we can construct. The
- main issue is that, for most of our methods to make sense, we need
- to know the rank of our algebra. Thus we can't simply generate a
- "random" algebra, or even check that a given basis and product
- satisfy the axioms; because even if everything looks OK, we wouldn't
- know the rank we need to actuallty build the thing.
-
- Not really a subclass of FDEJA because doing that causes method
- resolution errors, e.g.
-
- TypeError: Error when calling the metaclass bases
- Cannot create a consistent method resolution
- order (MRO) for bases FiniteDimensionalEuclideanJordanAlgebra,
- KnownRankEJA
-
- """
- @staticmethod
- def _max_test_case_size():
- """
- Return an integer "size" that is an upper bound on the size of
- this algebra when it is used in a random test
- case. Unfortunately, the term "size" is quite vague -- when
- dealing with `R^n` under either the Hadamard or Jordan spin
- product, the "size" refers to the dimension `n`. When dealing
- with a matrix algebra (real symmetric or complex/quaternion
- Hermitian), it refers to the size of the matrix, which is
- far less than the dimension of the underlying vector space.
-
- We default to five in this class, which is safe in `R^n`. The
- matrix algebra subclasses (or any class where the "size" is
- interpreted to be far less than the dimension) should override
- with a smaller number.
- """
- return 5
-
- @classmethod
- def random_instance(cls, field=AA, **kwargs):
- """
- Return a random instance of this type of algebra.
-
- Beware, this will crash for "most instances" because the
- constructor below looks wrong.
- """
- if cls is TrivialEJA:
- # The TrivialEJA class doesn't take an "n" argument because
- # there's only one.
- return cls(field)
-
- n = ZZ.random_element(cls._max_test_case_size()) + 1
- return cls(n, field, **kwargs)
-
-
-class HadamardEJA(FiniteDimensionalEuclideanJordanAlgebra, KnownRankEJA):
+class HadamardEJA(FiniteDimensionalEuclideanJordanAlgebra):
"""
Return the Euclidean Jordan Algebra corresponding to the set
`R^n` under the Hadamard product.
Euclidean Jordan algebra of dimension...
"""
- eja_classes = KnownRankEJA.__subclasses__()
- if nontrivial:
- eja_classes.remove(TrivialEJA)
+ eja_classes = [HadamardEJA,
+ JordanSpinEJA,
+ RealSymmetricEJA,
+ ComplexHermitianEJA,
+ QuaternionHermitianEJA]
+ if not nontrivial:
+ eja_classes.append(TrivialEJA)
classname = choice(eja_classes)
return classname.random_instance(field=field)
return M
-class RealSymmetricEJA(RealMatrixEuclideanJordanAlgebra, KnownRankEJA):
+class RealSymmetricEJA(RealMatrixEuclideanJordanAlgebra):
"""
The rank-n simple EJA consisting of real symmetric n-by-n
matrices, the usual symmetric Jordan product, and the trace inner
return RealMatrixEuclideanJordanAlgebra.natural_inner_product(X,Y)/2
-class ComplexHermitianEJA(ComplexMatrixEuclideanJordanAlgebra, KnownRankEJA):
+class ComplexHermitianEJA(ComplexMatrixEuclideanJordanAlgebra):
"""
The rank-n simple EJA consisting of complex Hermitian n-by-n
matrices over the real numbers, the usual symmetric Jordan product,
return RealMatrixEuclideanJordanAlgebra.natural_inner_product(X,Y)/4
-class QuaternionHermitianEJA(QuaternionMatrixEuclideanJordanAlgebra,
- KnownRankEJA):
+class QuaternionHermitianEJA(QuaternionMatrixEuclideanJordanAlgebra):
"""
The rank-n simple EJA consisting of self-adjoint n-by-n quaternion
matrices, the usual symmetric Jordan product, and the
self.rank.set_cache(n)
-class BilinearFormEJA(FiniteDimensionalEuclideanJordanAlgebra, KnownRankEJA):
+class BilinearFormEJA(FiniteDimensionalEuclideanJordanAlgebra):
r"""
The rank-2 simple EJA consisting of real vectors ``x=(x0, x_bar)``
with the half-trace inner product and jordan product ``x*y =
return super(JordanSpinEJA, self).__init__(n, field, **kwargs)
-class TrivialEJA(FiniteDimensionalEuclideanJordanAlgebra, KnownRankEJA):
+class TrivialEJA(FiniteDimensionalEuclideanJordanAlgebra):
"""
The trivial Euclidean Jordan algebra consisting of only a zero element.
projects / sage.d.git / blobdiff