[Numpy-discussion] Numpy Jenkin's Traub?

[.]

There was a little discussion of putting Aberth's method into SciPy.  I'm
not sure how those methods compare.

https://github.com/scipy/scipy/issues/5999

On Thu, Jul 14, 2016 at 12:16 AM, Ryan J. Kinnear ryan-at-kinnear.ca |numpy
mailing list/Example Allow| <ob3rpk8ngt at sneakemail.com> wrote:

> Dear list,
>
> I'm working on implementing an ARMA(p, q) modeling method in Python.  The
> particular method is described in section 4.7.1 of Statistical Digital
> Signal Processing and Modeling by Hayes.
>
> The upshot is that I need to calculate the roots of a polynomial.
>
> I've learned that this is a numerically ill conditioned problem, and have
> found many cases where np.roots produces roots that are clearly spurious.
> This is causing me a lot of issues.
>
> I have done some further searching and learned of the "Jenkins-Traub"
> algorithm for root finding, which seems to be considered the most robust
> method.  I found a Python implementation of this method here (
> https://github.com/vrdabomb5717/jenkins_traub), and it is certainly much
> more robust than np.roots.
>
> Are there reasons for Jenkins-Traub not being implemented as part of
> Numpy?  It is built into Scilab (
> https://help.scilab.org/docs/6.0.0/en_US/roots.html)  Is anyone working
> on getting it into Numpy?
>
> -RJK
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