[SciPy-dev] adding mpfit to scipy optimize (Please respond)
Charles R Harris
Sat May 9 11:07:55 CDT 2009
On Sat, May 9, 2009 at 9:55 AM, Pauli Virtanen <email@example.com> wrote:
> Fri, 08 May 2009 16:09:35 -0400, william ratcliff wrote:
> > Hi! For a long time, there has been a standard package used by IDL
> > users for fitting functions to data called MPFIT:
> > http://cow.physics.wisc.edu/~craigm/idl/fitting.html<http://cow.physics.wisc.edu/%7Ecraigm/idl/fitting.html>
> > <http://cow.physics.wisc.edu/%7Ecraigm/idl/fitting.html>
> > http://drop.io/mpfitdrop
> However, I see some points where the code could be improved for better
> reusability and maintainability.
> If we lived in an ideal world with infinite time to polish everything,
> I'd like to see all of the points below addressed before landing this to
> Scipy. But since this would be lots of error-prone work, it's probably
> best to try to reach some compromise.
> Given these constraints, I'd still like to see at least the coding style
> and error handling fixed (which probably are not too difficult to
> change), in addition to having better test coverage. The rest could come
> later, even if we accrue yet more technical debt with this...
> First, broad maintenance concerns:
> - We already have `leastsq` from MINPACK. Having two MINPACK-derived
> least squares fitting routines is not good.
> So, I'd perhaps like to see the `leastsq` core part extracted out of
> MPFIT, and the MPFIT interface implemented on top of it as a thin
> wrapper, or the other way around.
> Maybe, if the modifications made on MINPACK are small, they can be
> backported to the Fortran code and MPFIT can be reimplemented on top
> of `leastsq`.
> Any thoughts on this?
> - What is the performance of the Python implementation as compared to the
> Fortran code? For small data sets, the Python code is probably much
> slower, but for large datasets is the performance is comparable?
> - Much of the code is Fortran written in Python: long routines,
> goto-equivalents, 6-letter variable names.
> Good commenting offset this, though.
> Then more specific points of refactoring:
> - The code implements QR factorization with column pivoting from scratch.
> Perhaps some of this could be replaced with suitable LAPACK routines,
> or with stuff from scipy.linalg. (Cf. DGEQPF, DGEQP3)
> I'm not sure whether there's something suitable for qrsolve in LAPACK;
> the triangular system solver could be replaced with DTRTRS.
> Anyway, it might be useful to refactor qrfac and qrsolve out of MPFIT;
> there may be other applications when it's useful to be able to solve
> ||(A + I lambda) x - b||_2 = min! efficiently for multiple different
> `lambda` in sequence.
This looks like Levenberg-Marquardt. There is a version already in MINPACK.
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