X-Git-Url: http://gitweb.michael.orlitzky.com/?a=blobdiff_plain;f=mjo%2Feja%2Feja_element_subalgebra.py;h=34a63afdc0be38fb34ab95bb211df6d926d9be57;hb=95ae8e7b0ddca840da9631603a2f37cca888468b;hp=dceb3b405a4c5a663c61a966993ce890ed516b49;hpb=4c8f9aac69d1cb4097b60b10e5b198b6372ec55e;p=sage.d.git diff --git a/mjo/eja/eja_element_subalgebra.py b/mjo/eja/eja_element_subalgebra.py index dceb3b4..34a63af 100644 --- a/mjo/eja/eja_element_subalgebra.py +++ b/mjo/eja/eja_element_subalgebra.py @@ -6,43 +6,18 @@ from mjo.eja.eja_subalgebra import FiniteDimensionalEJASubalgebra class FiniteDimensionalEJAElementSubalgebra(FiniteDimensionalEJASubalgebra): - def __init__(self, elt, orthonormalize=True, **kwargs): + def __init__(self, elt, **kwargs): superalgebra = elt.parent() - powers = tuple( elt**k for k in range(superalgebra.dimension()) ) - power_vectors = ( p.to_vector() for p in powers ) - P = matrix(superalgebra.base_ring(), power_vectors) - - if orthonormalize: - basis = powers # let god sort 'em out - else: - # Echelonize the matrix ourselves, because otherwise the - # call to P.pivot_rows() below can choose a non-optimal - # row-reduction algorithm. In particular, scaling can - # 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 - if superalgebra.base_ring() is not QQ: - algo = "scaled_partial_pivoting" - P.echelonize(algorithm=algo) - - # In this case, we just need to figure out which elements - # of the "powers" list are redundant... First compute the - # vector subspace spanned by the powers of the given - # element. - - # Figure out which powers form a linearly-independent set. - ind_rows = P.pivot_rows() - - # Pick those out of the list of all powers. - basis = tuple(map(powers.__getitem__, ind_rows)) - + # TODO: going up to the superalgebra dimension here is + # overkill. We should append p vectors as rows to a matrix + # and continually rref() it until the rank stops going + # up. When n=10 but the dimension of the algebra is 1, that + # can save a shitload of time (especially over AA). + powers = tuple( elt**k for k in range(elt.degree()) ) super().__init__(superalgebra, - basis, + powers, associative=True, **kwargs) @@ -77,7 +52,7 @@ class FiniteDimensionalEJAElementSubalgebra(FiniteDimensionalEJASubalgebra): sage: J.one() e0 + e1 + e2 + e3 + e4 sage: x = sum(J.gens()) - sage: A = x.subalgebra_generated_by() + sage: A = x.subalgebra_generated_by(orthonormalize=False) sage: A.one() f0 sage: A.one().superalgebra_element() @@ -99,7 +74,7 @@ class FiniteDimensionalEJAElementSubalgebra(FiniteDimensionalEJASubalgebra): sage: set_random_seed() sage: x = random_eja().random_element() - sage: A = x.subalgebra_generated_by(orthonormalize_basis=True) + sage: A = x.subalgebra_generated_by() sage: x = A.random_element() sage: A.one()*x == x and x*A.one() == x True @@ -109,7 +84,7 @@ class FiniteDimensionalEJAElementSubalgebra(FiniteDimensionalEJASubalgebra): sage: set_random_seed() sage: x = random_eja(field=QQ,orthonormalize=False).random_element() - sage: A = x.subalgebra_generated_by() + sage: A = x.subalgebra_generated_by(orthonormalize=False) sage: actual = A.one().operator().matrix() sage: expected = matrix.identity(A.base_ring(), A.dimension()) sage: actual == expected @@ -120,7 +95,7 @@ class FiniteDimensionalEJAElementSubalgebra(FiniteDimensionalEJASubalgebra): sage: set_random_seed() sage: x = random_eja().random_element() - sage: A = x.subalgebra_generated_by(orthonormalize_basis=True) + sage: A = x.subalgebra_generated_by() sage: actual = A.one().operator().matrix() sage: expected = matrix.identity(A.base_ring(), A.dimension()) sage: actual == expected