[SciPy-dev] Python interface to COBYLA
js at jeannot.org
Tue Apr 13 11:57:38 CDT 2004
Following my previous mail, I did a preliminary Python interface to
M.J.D. Powell COBYLA optimization package.
I translated the Fortran package to C via f2c, did a few adjustments,
and added a Python interface so it could be easily integrated into SciPy.
As stated in a previous mail, M.J.D. Powell agreed for the inclusion of
COBYLA in SciPy. (Many thanks to him !)
COBYLA stands for Constrained Optimization BY Linear Approximation.
Two things make COBYLA an interessting algorithm to have in SciPy:
- It's derivative free (like Nelder-Mead) ;
- It can take non-linear constraints.
It has some drawbacks:
- It won't probably work if there are too many variables (say > 10) ;
- The constraint are not exactly enforced during the minimization ;
- It may be quite slow, most notably if there are no constraints.
In the last case, a higher performance unconstrained derivative free
method like Powell's UOBYQA or NEWUOA should probably be used.
(Conn-Scheinberg-Toint DFO is anoter option)
I've attached the preliminary Python interface. Suggestions are welcome!
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