On Thu, Nov 27, 2008 at 13:18, dmitrey <dmitrey.kroshko@scipy.org> wrote:

hi Vicent,

Vicent Giner-Bosch wrote:
> Hello.
>
> I am looking for infomation about the Open Opt (OO) project, and I've
> been referred to this group.
>
> I've been reading the official documentation about OO, but it seems a
> little confusing to me.
>
> My question is:

1)

> if I want to use OO, what must I do?
>

2)

> In fact, if I want to develop a new optimization algorithm in Python,
> how can I use OO? I mean, in which part of the process can / should I
> use OO?
>

1 and 2 are two different questions.
1) If you want just use OO to find a solution of an optimization
problem, read Doc page and see the examples provided for each class.
Finance support for OO (it was GSoC for twice) had been finished and
there will hardly be any Doc extension in nearest future. Also, I just
don't see any reasons to provide alternative documentation, it's too
costly to maintain (keep up-to-date) several documentations.
2) To develop optimization algorithm you don't have to use OO, pure
Python, probably with numpy, will be enough.

> What are the advantages of using OO?

http://scipy.org/scipy/scikits/wiki/whyOpenOpt4user

> Is it just a "bunch" or library
> of available optimization algorithms, or does it also provide a
> general framework (for example, a general predefined Object Oriented
> structure, or some general functions in order to manage algorithms...)
>

The framework is similar to TOMOPT's TOMLAB.
It has some API funcs; those ones from user API are mentioned in Doc page.

> in order to build an test or run our own algorithms?
>
> What are the key features of OO?
>

I can't copy-paste here the info from OO website, moreover, you have
mentioned you have it read.

Regards, D.