[SciPy-user] lagrange multipliers in python

dmitrey openopt@ukr....
Thu Jun 14 13:30:54 CDT 2007

afaik scipy hasn't NLP solvers with equality constraints, as well as CVXOPT.
I had seen somewhere a Python package (seems like binding to c-code) 
where rSQP had been implemented, it allows to have nonlin equality 
constraints. Try web search "python rsqp optimization solver" or "python 
sqp optimization solver"

for example visit
and python binding to the latter

However, I didn't use the ones.
Another one approach is use penalty coefficients (instead of Lagrange 
multipliers) with Naum Z. Shor r-alg implemented in scikits.openopt ralg 
solver (it doesn't contain c- or f-code, BSD lic.). It can handle 
gradient/subgradient provided by user and plot graphics output for NLP 
UC ralg solver.
Currently it's unconstrained, but it allows to handle very huge 
penalties rather well.

svn co http://svn.scipy.org/svn/scikits/trunk/openopt openopt
sudo python setup.py install

from scikits.openopt import NLP

however, it doesn't produce pyc-files in the site-packages directory while installation, you'd better to do it by hands now.
this is very preliminary version, only some months has been spent.


fdu.xiaojf@gmail.com wrote:
> Hi all,
> Sorry for the cross-posting.
> I'm trying to find the minimum of a multivariate function F(x1, x2, ...,
> xn) subject to multiple constraints G1(x1, x2, ..., xn) = 0, G2(...) =
> 0, ..., Gm(...) = 0.
> The conventional way is to construct a dummy function Q,
> $$Q(X, \Lambda) = F(X) + \lambda_1 G1(X) + \lambda_2 G2(X) + ... + \lambda_m 
> Gm(X)$$
> and then calculate the value of X and \Lambda when the gradient of function Q 
> equals 0.
> I think this is a routine work, so I want to know if there are available
> functions in python(mainly scipy) to do this? Or maybe there is already
> a better way in python?
> I have googled but haven't found helpful pages.
> Thanks a lot.
> Xiao Jianfeng
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