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: if I want to use OO, what must I do? 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? What are the advantages of using OO? 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...) in order to build an test or run our own algorithms? What are the key features of OO? I hope I've been clear enough about my questions. Any answer will be appreciated. Thank you very much in advance. -- Vicent Giner-Bosch, Valencia (Spain)
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:
hi Vicent, Vicent Giner-Bosch wrote: 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.
Dmitrey, Thank you for your clear answer. Here (http://scipy.org/scipy/scikits/wiki/whyOpenOpt4user and http://scipy.org/scipy/scikits/wiki/whereProfitsForOpenOptConnectedSolverOwn...) I see now that OpenOpt can be a useful tool for "connecting" different solvers, and to "speed" algorithms... But it doesn't provide any kind of general structure for building algorithms, as I thought in the begining. Anyway, no doubt it can be interesting in my research and development of new optimization algorithms. Thank you for the information. -- Vicent On Thu, Nov 27, 2008 at 13:18, dmitrey <dmitrey.kroshko@scipy.org> 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:
hi Vicent, Vicent Giner-Bosch wrote: 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.
On 11/27/2008 1:02 PM Vicent apparently wrote:
But it doesn't provide any kind of general structure for building algorithms, as I thought in the begining.
The GenericOpt component (under solvers) is supposed to supply such structure. However at the moment I do not see most of that code ... ? Not sure what happened here. Alan Isaac
On Nov 28, 3:55 pm, Alan G Isaac <ais...@american.edu> wrote:
On 11/27/2008 1:02 PM Vicent apparently wrote:
But it doesn't provide any kind of general structure for building algorithms, as I thought in the begining.
The GenericOpt component (under solvers) is supposed to supply such structure.
OK, it is good to know it. It is supposed that I can get benefits from using that existing general "structure", isn't it?
However at the moment I do not see most of that code ... ? Not sure what happened here.
I hope it can be solved... Thanks, A.I. -- Vicent
The GenericOpt component (under solvers) is supposed to supply such structure.
OK, it is good to know it.
It is supposed that I can get benefits from using that existing general "structure", isn't it?
Yes, it is. I've provided some basic blocks you can mix together to make the optimization procedure you want.
However at the moment I do not see most of that code ... ? Not sure what happened here.
I hope it can be solved...
There are some samples, for instance in the tests scripts. If you have more specifics questions, just ask, I'll try answer them. Matthieu -- Information System Engineer, Ph.D. Website: http://matthieu-brucher.developpez.com/ Blogs: http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn: http://www.linkedin.com/in/matthieubrucher
There are some samples, for instance in the tests scripts. If you have more specifics questions, just ask, I'll try answer them.
You have an explanation of the structure on the TRAC : http://scipy.org/scipy/scikits/wiki/Optimization There is a link to a tutorial, and the list of stuff that still needs to be implemented (and tested, of course). I didn't find the time to do that for the moment. My job takes a lot of my time, and I do not use the framework for it. Matthieu -- Information System Engineer, Ph.D. Website: http://matthieu-brucher.developpez.com/ Blogs: http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn: http://www.linkedin.com/in/matthieubrucher
http://scipy.org/scipy/scikits/wiki/OOClasses If you develop a new optimization algorithm in Python, of general interest, we could consider connecting it to OpenOpt. But there is an awful lot already there. Alan Isaac
participants (5)
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Alan G Isaac -
dmitrey -
Matthieu Brucher -
Vicent -
Vicent Giner-Bosch