[SciPy-dev] Proposal for more generic optimizers (posted before on scipy-user)
Alan G Isaac
Sun Mar 11 15:07:11 CDT 2007
> 4. I understand that you want an object that provides
>> the function, gradient, and hessian. But when you
>> make a class for these, it is full of (effectively)
>> class functions, which suggests just using a module.
On Sun, 11 Mar 2007, Matthieu Brucher apparently wrote:
> It's not only a module, it is a real class, with a state.
> For instance, an approximation function can need a set of
> points that will be stored in the class, and a module is
> not enough to describe it - a simple linear approximation
> with a robust cost function for instance -
This seems to be a different question?
One question is the question of optimizer design:
should it take as an argument an object that provides
a certain mix of services, or should it take instead
as arguments the functions proiding those services.
I am not sure, just exploring it.
I am used to optimizers that take a function,
gradient procedure, and hessian procedure as arguments.
I am just asking whether *requiring* these to be bundled
is the right thing to do.
This design would not mean that I cannot pass as arguments
methods of some object. (I think I am responding to your
Note that requiring bundling imposes an interface
requirement on the bundling object. This is not true
if I just provide the functions/methods as arguments.
> Perhaps a more precise example of the usefullness is needed ?
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