On Fri, Jun 5, 2020 at 9:48 PM rondall jones <rejones7@msn.com> wrote:

Hello! I have supported constrained solvers for linear matrix problems for about 10 years in C++, but have now switched to Python. I am going to submit a couple of new routines for linalg called autoreg(A,b) and autoregnn(A,b). They work just like lstsq(A,b) normally, but when they detect that the problem is dominated by noise they revert to an automatic regularization scheme that returns a better behaved result than one gets from lstsq. In addition, autoregnn enforces a nonnegativity constraint on the solution. I have put on my web site a slightly fuller featured version of these same two algorithms, using a Class implementation to facilitate retuning several diagnostic or other artifacts. The web site contains tutorials on these methods and a number of examples of their use. See http://www.rejones7.net/autorej/ . I hope this community can take a look at these routines and see whether they are appropriate for linalg or should be in another location.

Hi Ron, thanks for proposing this. It seems out of scope for NumPy; scipy.linalg or scipy.optimize seem like the most obvious candidates.

If you propose inclusion into SciPy, it would be good to discuss whether the algorithm is based on a publication showing usage via citation stats or some other way. There's more details at http://scipy.github.io/devdocs/dev/core-dev/index.html#deciding-on-new-features

Cheers,

Ralf

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