scipy.optimize.leastsq uses a trust-region Levenberg-Marquardt solver from MINPACK. I think one that uses LAPACK subroutine DGELS could be made more efficient. MINPACK has an unoptimized QR factorization and is also not re-entrant (global variables). But the numerical quality and stability of the MINPACK version in undisputed. Sturla Nico Del Piano <ndel314@gmail.com> wrote:
Hi all,
I am Nicolas Del Piano, and currently studying the last year of computer science career. I was searching for a GSoC project that involves mathematical concepts, and I found one very interesting. I have experience on python programming and I studied calculus, so I have a background of the problem context.
I am interested on the implementation and testing of Levenberg-Marquardt / trust region nonlinear least squares algorithm.
Here are some useful links, that I have searched to having a reference about the problem:
<a href="http://ananth.in/docs/lmtut.pdf">http://ananth.in/docs/lmtut.pdf</a> (Introduction) www.cs.nyu.edu/~roweis/notes/lm.pdf (Optimization) <a href="http://scribblethink.org/Computer/Javanumeric/index.html">http://scribblethink.org/Computer/Javanumeric/index.html</a> (java implementation)
I would be glad if there is an interested mentor to discuss the issues, talk about the problem and its possible implementations, and provide me some support and guide.
Thanks!
Regards.
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