[SciPy-dev] Proposal for more generic optimizers (posted before on scipy-user)

[Alan G Isaac]

I don't really have time to look at this for the
next week, but a couple quick comments.

1.  Instead of::

            if 'stepSize'  in kwargs:
              self.stepSize = kwargs['stepSize']
            else:
              self.stepSize = 1.

    I prefer this idiom::

             self.stepSize = kwargs.get('stepSize',1)

2. All optimizers should have a maxiter attribute,
   even if you wish to set a large default.  This needs
   corresponding changes in ``optimize``.

3. It seems like ``AppendList`` is an odd and specific
   object.  I'd stick in in the example file.

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.
   I suspect there is a design issue to think about here.
   This might (??) go so far as to raise questions about
   the usefulness of the bundling.

Cheers,
Alan Isaac