[SciPy-User] leastsq interface and features

[bowie_22]

Hello,

at the moment am I am evaluating scipy as a subsitute for Matlab.
One important use case for me is to fit a model to measured data.

In Matlab I use lsqnonlin from the Optimization Toolbox.
In Scipy I would use leastsq.

By comparing the 2 approaches with a "daily use" point of view I see the
following improvements for the scipy module

1) setting the options for the algorithm:
   ML uses a structure together with optimset optimget 
   --> lsqnonlin has a quite short signature
IMPROVEMENT: introduce a common options structure for all optimization algos

2) there is the possibilty to set an output function that is called in each
iteration step in ML. That can be used for displaying the current status of the
optimization. For me a quite important point as my customers want to "see" what
happens (not just throwing measured data to an algorithm and get back a set of
numbers)
  IMPROVEMENT: introduce a output function that can be called each iteration

3) give lower and upper bounds for the optimization variables. Also quite
important as in my uses cases you have normally an idea in which range your
parameter should be (mass of a car 1200 - 1800 kg). In ML you can provide this
knowledge as lower bounds and upper bounds to lsqnonlin. 
IMPROVEMENT: introduce lower and upper bounds

My problem:
How can I help to get this improvements to scipy? Is this the correct address to
ask?

Regs 

Marcus