X-Git-Url: http://gitweb.michael.orlitzky.com/?a=blobdiff_plain;f=mjo%2Feja%2Feja_algebra.py;h=b9389da25e69722f04633c8324998d8d4834608a;hb=5f522a9ac121a1229c0a9c62244b18ca67e65b12;hp=26fe1929be872b393fe41926eeda801dbd0a9436;hpb=3e46389a46db107db3fe36ace6fe5f2c2b52f815;p=sage.d.git diff --git a/mjo/eja/eja_algebra.py b/mjo/eja/eja_algebra.py index 26fe192..b9389da 100644 --- a/mjo/eja/eja_algebra.py +++ b/mjo/eja/eja_algebra.py @@ -1007,81 +1007,6 @@ class FiniteDimensionalEuclideanJordanAlgebra(CombinatorialFreeModule): Element = FiniteDimensionalEuclideanJordanAlgebraElement -class HadamardEJA(FiniteDimensionalEuclideanJordanAlgebra): - """ - Return the Euclidean Jordan Algebra corresponding to the set - `R^n` under the Hadamard product. - - Note: this is nothing more than the Cartesian product of ``n`` - copies of the spin algebra. Once Cartesian product algebras - are implemented, this can go. - - SETUP:: - - sage: from mjo.eja.eja_algebra import HadamardEJA - - EXAMPLES: - - This multiplication table can be verified by hand:: - - sage: J = HadamardEJA(3) - sage: e0,e1,e2 = J.gens() - sage: e0*e0 - e0 - sage: e0*e1 - 0 - sage: e0*e2 - 0 - sage: e1*e1 - e1 - sage: e1*e2 - 0 - sage: e2*e2 - e2 - - TESTS: - - We can change the generator prefix:: - - sage: HadamardEJA(3, prefix='r').gens() - (r0, r1, r2) - - """ - def __init__(self, n, field=AA, **kwargs): - V = VectorSpace(field, n) - mult_table = [ [ V.gen(i)*(i == j) for j in range(n) ] - for i in range(n) ] - - super(HadamardEJA, self).__init__(field, - mult_table, - check_axioms=False, - **kwargs) - self.rank.set_cache(n) - - def inner_product(self, x, y): - """ - Faster to reimplement than to use natural representations. - - SETUP:: - - sage: from mjo.eja.eja_algebra import HadamardEJA - - TESTS: - - Ensure that this is the usual inner product for the algebras - over `R^n`:: - - sage: set_random_seed() - sage: J = HadamardEJA.random_instance() - sage: x,y = J.random_elements(2) - sage: X = x.natural_representation() - sage: Y = y.natural_representation() - sage: x.inner_product(y) == J.natural_inner_product(X,Y) - True - - """ - return x.to_vector().inner_product(y.to_vector()) - def random_eja(field=AA): """ @@ -1108,6 +1033,65 @@ def random_eja(field=AA): +class RationalBasisEuclideanJordanAlgebra(FiniteDimensionalEuclideanJordanAlgebra): + r""" + Algebras whose basis consists of vectors with rational + entries. Equivalently, algebras whose multiplication tables + contain only rational coefficients. + + When an EJA has a basis that can be made rational, we can speed up + the computation of its characteristic polynomial by doing it over + ``QQ``. All of the named EJA constructors that we provide fall + into this category. + """ + @cached_method + def _charpoly_coefficients(self): + r""" + Override the parent method with something that tries to compute + over a faster (non-extension) field. + + SETUP:: + + sage: from mjo.eja.eja_algebra import JordanSpinEJA + + EXAMPLES: + + The base ring of the resulting polynomial coefficients is what + it should be, and not the rationals (unless the algebra was + already over the rationals):: + + sage: J = JordanSpinEJA(3) + sage: J._charpoly_coefficients() + (X1^2 - X2^2 - X3^2, -2*X1) + sage: a0 = J._charpoly_coefficients()[0] + sage: J.base_ring() + Algebraic Real Field + sage: a0.base_ring() + Algebraic Real Field + + """ + if self.base_ring() is QQ: + # There's no need to construct *another* algebra over the + # rationals if this one is already over the rationals. + superclass = super(RationalBasisEuclideanJordanAlgebra, self) + return superclass._charpoly_coefficients() + + mult_table = tuple( + map(lambda x: x.to_vector(), ls) + for ls in self._multiplication_table + ) + + # Do the computation over the rationals. The answer will be + # the same, because our basis coordinates are (essentially) + # rational. + J = FiniteDimensionalEuclideanJordanAlgebra(QQ, + mult_table, + check_field=False, + check_axioms=False) + a = J._charpoly_coefficients() + return tuple(map(lambda x: x.change_ring(self.base_ring()), a)) + + class MatrixEuclideanJordanAlgebra(FiniteDimensionalEuclideanJordanAlgebra): @staticmethod def _max_test_case_size(): @@ -1156,44 +1140,44 @@ class MatrixEuclideanJordanAlgebra(FiniteDimensionalEuclideanJordanAlgebra): 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. + if self._basis_normalizers is None or self.base_ring() is QQ: + # We didn't normalize, or the basis we started with had + # entries in a nice field already. Just compute the thing. return super(MatrixEuclideanJordanAlgebra, self)._charpoly_coefficients() - 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. The argument ``check_axioms=False`` is required - # because the trace inner-product method for this - # class is a stub and can't actually be checked. - J = MatrixEuclideanJordanAlgebra(QQ, - basis, - normalize_basis=False, - check_field=False, - check_axioms=False) - a = J._charpoly_coefficients() - - # Unfortunately, changing the basis does change the - # coefficients of the characteristic polynomial, but since - # these are really the coefficients of the "characteristic - # polynomial of" function, everything is still nice and - # unevaluated. It's therefore "obvious" how scaling the - # basis affects the coordinate variables X1, X2, et - # cetera. Scaling the first basis vector up by "n" adds a - # factor of 1/n into every "X1" term, for example. So here - # we simply undo the basis_normalizer scaling that we - # performed earlier. - # - # The a[0] access here is safe because trivial algebras - # won't have any basis normalizers and therefore won't - # make it to this "else" branch. - XS = a[0].parent().gens() - subs_dict = { XS[i]: self._basis_normalizers[i]*XS[i] - for i in range(len(XS)) } - return tuple( a_i.subs(subs_dict) for a_i in a ) + + 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. The argument ``check_axioms=False`` is required + # because the trace inner-product method for this + # class is a stub and can't actually be checked. + J = MatrixEuclideanJordanAlgebra(QQ, + basis, + normalize_basis=False, + check_field=False, + check_axioms=False) + a = J._charpoly_coefficients() + + # Unfortunately, changing the basis does change the + # coefficients of the characteristic polynomial, but since + # these are really the coefficients of the "characteristic + # polynomial of" function, everything is still nice and + # unevaluated. It's therefore "obvious" how scaling the + # basis affects the coordinate variables X1, X2, et + # cetera. Scaling the first basis vector up by "n" adds a + # factor of 1/n into every "X1" term, for example. So here + # we simply undo the basis_normalizer scaling that we + # performed earlier. + # + # The a[0] access here is safe because trivial algebras + # won't have any basis normalizers and therefore won't + # make it to this "else" branch. + XS = a[0].parent().gens() + subs_dict = { XS[i]: self._basis_normalizers[i]*XS[i] + for i in range(len(XS)) } + return tuple( a_i.subs(subs_dict) for a_i in a ) @staticmethod @@ -2021,7 +2005,83 @@ class QuaternionHermitianEJA(QuaternionMatrixEuclideanJordanAlgebra): self.rank.set_cache(n) -class BilinearFormEJA(FiniteDimensionalEuclideanJordanAlgebra): +class HadamardEJA(RationalBasisEuclideanJordanAlgebra): + """ + Return the Euclidean Jordan Algebra corresponding to the set + `R^n` under the Hadamard product. + + Note: this is nothing more than the Cartesian product of ``n`` + copies of the spin algebra. Once Cartesian product algebras + are implemented, this can go. + + SETUP:: + + sage: from mjo.eja.eja_algebra import HadamardEJA + + EXAMPLES: + + This multiplication table can be verified by hand:: + + sage: J = HadamardEJA(3) + sage: e0,e1,e2 = J.gens() + sage: e0*e0 + e0 + sage: e0*e1 + 0 + sage: e0*e2 + 0 + sage: e1*e1 + e1 + sage: e1*e2 + 0 + sage: e2*e2 + e2 + + TESTS: + + We can change the generator prefix:: + + sage: HadamardEJA(3, prefix='r').gens() + (r0, r1, r2) + + """ + def __init__(self, n, field=AA, **kwargs): + V = VectorSpace(field, n) + mult_table = [ [ V.gen(i)*(i == j) for j in range(n) ] + for i in range(n) ] + + super(HadamardEJA, self).__init__(field, + mult_table, + check_axioms=False, + **kwargs) + self.rank.set_cache(n) + + def inner_product(self, x, y): + """ + Faster to reimplement than to use natural representations. + + SETUP:: + + sage: from mjo.eja.eja_algebra import HadamardEJA + + TESTS: + + Ensure that this is the usual inner product for the algebras + over `R^n`:: + + sage: set_random_seed() + sage: J = HadamardEJA.random_instance() + sage: x,y = J.random_elements(2) + sage: X = x.natural_representation() + sage: Y = y.natural_representation() + sage: x.inner_product(y) == J.natural_inner_product(X,Y) + True + + """ + return x.to_vector().inner_product(y.to_vector()) + + +class BilinearFormEJA(RationalBasisEuclideanJordanAlgebra): 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 =