Thanks Tim
Davide
On Thursday, September 25, 2014 2:35:43 PM UTC+1, Tim Holy wrote:
>
> Just FYI: you can easily find out the answer for yourself like this,
>
> julia> A = rand(5,4)
> 5x4 Array{Float64,2}:
> 0.248302 0.330028 0.893083 0.390297
> 0.0306052 0.298042 0.343798 0.569406
That is right but it can be slightly difficult to find the actual
algorithm/LAPACK function due to the initial promotion and allocation steps.
Med venlig hilsen
Andreas Noack
2014-09-25 9:35 GMT-04:00 Tim Holy :
> Just FYI: you can easily find out the answer for yourself like this,
>
> julia> A
Just FYI: you can easily find out the answer for yourself like this,
julia> A = rand(5,4)
5x4 Array{Float64,2}:
0.248302 0.330028 0.893083 0.390297
0.0306052 0.298042 0.343798 0.569406
0.935467 0.384105 0.972919 0.716717
0.455494 0.351314 0.443435 0.848758
0.752286 0.827971
Thank you Andreas.
Sooner or later one needs to have a precise idea of what is going on behing the
scenes. Having a reference to the relevant lapack function is fine.
Davide
Hi Davide
Unfortunately the documentation is not correct. A\b for least squares
problems uses a pivoted qr factorization algorithm identical to the
algorithm you can in LAPACK's xgelsy. I'll update the documentation.
Med venlig hilsen
Andreas Noack
2014-09-25 4:05 GMT-04:00 Davide Lasagna :
>
Hi,
Is there a reference to the algorithm used for solution of least-squares
problems like A\b, with A \in R^{m \times n} and b \in R^m ? Documentation says
it uses a decomposition to bidiagonal form, but it would be nice to have a more
precise reference to that.
Thanks,
Davide
