[SciPy-dev] GSoC schedule question (letter for my mentors & Matthieu Brucher)

dmitrey openopt@ukr....
Wed Aug 1 11:57:23 CDT 2007

Hi Matthieu,
thank you for the answer.
Please inform me, does your package provide a func that is equivalent to 
Can you supply an example of usage of the one?
here's a link to Wolfe conditions:
1) for lincher I need something like that (If you have a solver that 
uses better conditions than Wolfe ones - ok).
2) for ralg I need auxiliary line search solver that will surely provide 
(pk, grad f(xk+alpha*pk)) <= 0

1st task is much more important.
2nd one have a solver already, but not the best one, in future I intend 
to replace the one by something better.

Do you have any idea about 1st , or maybe 2nd?

Regards, D.

Matthieu Brucher wrote:
> Hi,
>     So, as you see, params are not described; especially I'm
>     interested in old_fval and old_old_fval.
>     So, there was a letter from NLPy developer about an alternative
>     (link to an article was provided), also, Matthieu proposed using
>     one of his own solvers.
>     I think the importance of the auxiliary solver is very high and
>     should be done in 1st order. My opinion about next thing to do is
>     using an appropriate solver from Matthieu's package. However,
>     Matthieu's syntax differs too much from openopt one. I think first
>     of all there should be an openopt binding to Matthieu's package,
>     that will allow for oo users to use same syntax:
>     prob = NLP(...)
>     r = prob.solve()
>     I would implement the binding by myself, but I miss well-described
>     API documentation of Matthieu's code. First of all I'm interested
>     1) what solvers does it contain?
> I put a small description here : 
> https://projects.scipy.org/scipy/scikits/wiki/Optimization (still in 
> progress).
>     2) how xtol, funtol, contol etc can be passed to the solvers?
> Each of these parameters are either step information, line search 
> information or criterion information. Each parameter must be given to 
> the corresponding object that will use it (I didn't want to centralize 
> everything as some modules need pre-computation before they can be 
> used in the optimizer, like the Fibonacci section search).
>     then, secondary (it can wait, maybe default parameters would be
>     enough for now)
>     3) which params can I modify (like type of step
>     (Armijo|Powell|etc), etc)
> You can modify everything. The goal is to provide bricks that you can 
> build together so that the optimizer makes what you need. If you want 
> to provide new modules, here are some "rules" :
> - the function should be provided as an object that defines the 
> correct methods, like __call__, gradient or hessian if needed (the 
> case of approximation of the gradient as a finite-element one should 
> be programmed with a class from which the function derives, but we can 
> speak of this in another mail if you want details on this one)
> - a criterion module takes only one argument which is the current 
> state of the optimizer
> - a step module takes three arguments : the function being optimized, 
> the point where to search for a step and the state of the optimizer
> - a line search module takes a four arguments : the point, the 
> computed step, the function and the state (I suppose this should be 
> refactored to be more consistent with the step module...)
> - the core optimizer that uses these mdoules and dispatches the data 
> accordingly
>     BTW ralg is also missing a good line-search optimizer. It requires
>     the one that finds solutions with slope angle > pi/2. But it can
>     wait, it has one quite good and problem with lincher is more actual. 
> If you have an algorithm that can do this, you only have to program it 
> and everyone will be able to use it with the other modules.
>     So I think the next GSoC schedule step should be connection of 1-2
>     Matthieu's solvers (that take into account slope angle, like
>     strong Wolfe conditions do) to native openopt syntax.
> If I understand this correctly, it is wrapping some usual combinations 
> together so that people use them without knowing, like for the brent 
> functiona nd the Brent class ? It should be easy by overriding the 
> optimizer constructor and by adding a solve method that just calls 
> optimize() (in this case, I'll probably modify optimize() so that it 
> does not return something, and the state of the optimizer would 
> provide the answer).
>     Are you agree?
>     if yes, my question to Matthieu: can you provide a description of
>     some appropriate solvers from your package? and/or an example of
>     usage? 
> If you need more details, ask specific questions, I'll gladly answer 
> them (and add them to the wiki)
> Matthieu
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