On 05/01/2012 08:56 AM, Russ P. wrote:
On Apr 29, 5:17 pm, someone<newsbo...@gmail.com> wrote:
On 04/30/2012 12:39 AM, Kiuhnm wrote:
You should try to avoid matrix inversion altogether if that's the case.
For instance you shouldn't invert a matrix just to solve a linear system.
What then?
Cramer's rule?
If you really want to know just about everything there is to know
about a matrix, take a look at its Singular Value Decomposition (SVD).
I know a bit about SVD - I used it for a short period of time in Matlab,
though I'm definately not an expert in it and I don't understand the
whole theory with orthogality behind making it work so elegant as it
is/does work out.
I've never used numpy, but I assume it can compute an SVD.
I'm making my first steps now with numpy, so there's a lot I don't know
and haven't tried with numpy...
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