#20287: Implement the Moore–Penrose matrix pseudoinverse
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Reporter: jdemeyer | Owner:
Type: enhancement | Status: positive_review
Priority: major | Milestone: sage-7.2
Component: linear algebra | Resolution:
Keywords: | Merged in:
Authors: Jeroen Demeyer | Reviewers: Frédéric Chapoton
Report Upstream: N/A | Work issues:
Branch: | Commit:
u/jdemeyer/pseudoinverse | 3a3f5521eb8ced6c64a329918ac3544633286e89
Dependencies: #20286 | Stopgaps:
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Comment (by mjo):
Replying to [comment:12 jdemeyer]:
>
> I don't think that there are cases where the is a better choice than the
default one. However, there might be cases where there is no suitable
algorithm at all.
Then why offer the choice? I ask because, if you remove the `algorithm`
parameter, it looks like a matrix superclass method is trying to figure
out what to do by a "what subclass am I" heuristic. In that case, if the
matrix class hierarchy is good enough, it might make more sense to move
the subclass-specific behavior into subclass methods.
For example, the default `Matrix` would implement the "exact" or
"exactconj" algorithms. (Is there a situation where "exactconj" does the
wrong thing on a real matrix?) In `Matrix_real_double_dense` and
`Matrix_complex_double_dense`, you would call numpy. In
`Matrix_generic_dense`, you would do the precision check and either call
numpy or raise an error. Et cetera. On the other hand, if the matrix
hierarchy would make you copy/paste the same implementation around, forget
I said anything.
Later on if we get an SVD that works for RR/CC, we can just add it to
override the naive algorithm in those subclasses. With `algorithm` in the
function signature, we have to worry about breaking the user interface
with those changes.
--
Ticket URL: <http://trac.sagemath.org/ticket/20287#comment:15>
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