[SciPy-dev] Updated generic optimizers proposal

Matthieu Brucher matthieu.brucher@gmail....
Wed Apr 25 04:17:50 CDT 2007


Since nobody answered to this mail, I submitted the last developments to the
TRAC : http://projects.scipy.org/scipy/scipy/ticket/405
I added some conjugate grasient steps : PRP, CW, D and DY, and a special
optimizer that can modify the set of parameters before ans after an
iteration - this is useful when a set of parameters has some invariants, or
to add noise at each iteration, ... -

Matthieu

2007/4/18, Matthieu Brucher <matthieu.brucher@gmail.com>:
>
> 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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