X-Git-Url: http://gitweb.michael.orlitzky.com/?a=blobdiff_plain;f=mjo%2Feja%2Feja_element_subalgebra.py;h=ee8e0c67db2773537aefc04d82ee58b7a0b4cca0;hb=bbae1a93ab44c80896181ad8983393aa16a87868;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..ee8e0c6 100644
--- a/mjo/eja/eja_element_subalgebra.py
+++ b/mjo/eja/eja_element_subalgebra.py
@@ -9,40 +9,15 @@ class FiniteDimensionalEJAElementSubalgebra(FiniteDimensionalEJASubalgebra):
     def __init__(self, elt, orthonormalize=True, **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)