On Fri, Jun 5, 2020 at 9:48 PM rondall jones <rejon...@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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