On Fri, Nov 30, 2012 at 3:57 PM, Eviatar <eviatarb...@gmail.com> wrote:
> I think echelonize is a bit misleading, because it's not really doing > Gaussian elimination. Besides, I think it would have to be rref to be > consistent with matrices (it's reduced row echelon form, not simply row > echelon form): > > sage: matrix([0,3,4]).echelon_form() > [0 3 4] > sage: matrix([0,3,4]).rref() > [ 0 1 4/3] > > Even if "normal form" is used in some specific contexts, I don't think > many people would associate it with that function, and it might create > confusion with normalize(). > > How about a vote? > > 1. reduce() (kind of ambiguous and might be mistaken for the Python > function reduce) > 2. echelonize() (probably echelon_form() is more appropriate, since > it's not mutating the vector) > 3. rref() > 4. normal_form() > 5. monicize() > 6. divide_by_leading() > > I vote (6). > > The name 'normal_form' refers to the property of the returned vector. From that point of view, how about 7. monic() I vote (7). -- Benjamin Jones -- You received this message because you are subscribed to the Google Groups "sage-devel" group. To post to this group, send email to sage-devel@googlegroups.com. To unsubscribe from this group, send email to sage-devel+unsubscr...@googlegroups.com. Visit this group at http://groups.google.com/group/sage-devel?hl=en.