[SciPy-dev] adding mpfit to scipy optimize (Please respond)

[Charles R Harris]

On Sat, May 9, 2009 at 10:07 AM, Charles R Harris <charlesr.harris at gmail.com
> wrote:

>
>
> On Sat, May 9, 2009 at 9:55 AM, Pauli Virtanen <pav at iki.fi> 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>
>> [clip]
>> > http://drop.io/mpfitdrop
>>
>> Nice!
>>
>> 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.
>

Not exactly though, the version quoted depends on the column ordering of A.
That doesn't look right.

Chuck
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