[SciPy-dev] Updated generic optimizers proposal

Matthieu Brucher matthieu.brucher@gmail....
Wed Apr 18 03:35:09 CDT 2007


Hi,

I'm lauching a new thread, the last was pretty big, and as I almost put
every advice in this proposal, I thought it would be better.
First, I used scipy coding standard, I hope I didn't forget something.

I do not know where it would be put at the moment on my scipy tree, and the
tests are visual for the moment, I have to make them automatic, but I do not
know the framework used by scipy, I have to check it first.

So, the proposal :
- combining several objects to make an optimizer
- a function should be an object defining the __call__ method and graient,
hessian, ... if needed. It can be passed as several separate functions as
Alan suggested it, a new object is then created
- an optimizer is a combination of a function, a step_kind, a line_search, a
criterion and a starting point x0.
- the result of the optimization is return after a call to the optimize()
method
- every object (step or line_search) saves its modification in a state
variable in the optimizer. This variable can be accessed if needed after the
optimization.
- after each iteration, a record function is called with this state variable
- it is a dict, BTW -, if you want to save the whole dict, don't forget to
copy it, as it is modified during the optimization

For the moment are implemented :
- a standard algorithm, only calls step_kind then line_search for a new
candidate - the next optimizer would be one that calls a modifying function
on the computed result, that can be useful in some cases -
- criteria :
 - monotony criterion : the cost is decreasing - a factor can be used to
allow an error -
 - relative value criterion : the relative value error is higher than a
fixed error
 - absolute value criterion : the same with the absolute error
- step :
 - gradient step
 - Newton step
 - Fletcher-Reeves conjugate gradient step - other conjugate gradient will
be available -
- line search :
 - no line search, just take the step
 - damped search, it's an inexact line search, that searches in the step
direction a set of parameters than decreases the cost by dividing by two the
step size while the cost is not decreasing
 - Golden section search
 - Fibonacci search

I'm not pulling other criterion, step or line search, as my time is finite
when doing a structural change.

There are 3 classic optimization test functions in the package, Rosenbrock,
Powell and a quadratic function, feel free to try them. Sometimes, the
optimizer converges to the true minimum, sometimes it does not, I tried to
propose several solutions to show that every combinaison does not manage to
find the minimum.

Matthieu
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