[SciPy-user] Would anyone connect fortran constrained linear least squares solver to Python?
Mon Jan 7 11:17:31 CST 2008
openopt development is a little bit frozen for now, since I still search
for a money support - I haven't got any since GSoC 2007 finish, but I
try to remain active as long as possible (as my mentor Alan G Isaac
recommended me), waiting for another one GSoC (according to rules I
should remain being student at ~April 11 and I intend to do my
graduation after the date, to take participation in GSoC 2008).
So all I do is eating low-hanging fruits: either connecting
well-documented solvers with python bridge already provided by someone,
or doing some other changes (minor ones, I have no possibilities to
start big jobs).
Unfortunately, NLPy still lacks precise convenient documentation. Would
you provide something like the one:
then I could provide openopt binding for the solver.
As for the netlib routine mentioned, of course I would gladly connect it
would someone provide fortran-python bridge for the routine. Taking into
account I have no experience of using f2py, SWIG, ctypes and other
similar Python <-> xxx bridges, I have neither possibility no willing to
spend my time and efforts for to provide it by myself.
Moreover, OO is 100% python-written (to prevent possible cross-platform
with installation) and fortran routines should go to scipy and/or other
software already containing C/Fortran code.
BTW, an OpenOpt user from Princeton informed me of willing to contribute
something, and then we stopped at connecting IPOPT (to OO), so 2-3 month
later (I hope) OO users will have IPOPT binding as well.
Best regards, Dmitrey
Dominique Orban wrote:
> On Dec 26, 2007 12:29 PM, dmitrey <email@example.com> wrote:
>> Hi all,
>> I had noticed (from traffic statistics): lots of people are interested
>> in linear least squares problems (LLSP). However, scipy has only
>> unconstrained LAPACK dGELSS/sGELSS.
>> Could anyone provide connection of the fortran-written solver to Python
>> (or connect it to scipy)?
>> (that one can handle linear eq and ineq constraints)
>> Then I would gladly provide connection of the one to scikits.openopt.
> Hi Dmitrey,
> Please note that NLPy now features a pure Python version of Michael
> Saunders' LSQR for unconstrained linear least-squares:
> Regarding the Netlib Fortran code, can't we use f2py?
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