[SciPy-user] lagrange multipliers in python
Mon Jun 18 08:28:29 CDT 2007
> Hi all,
> email@example.com wrote:
> > My last email was composed in a hurry, so let me describe my problem in
> > detail to make it clear.
> > I have to minimize a multivariate function F(x1, x2, ..., xn) subject to
> > multiple inequality constraints and equality constraints.
> > The number of variables(x1, x2, ..., xn) is 10 ~ 20, the number of
> > inequality constraints is the same with the number of variables( all
> > variables should be no less than 0). The number of equality constraints
> > is less than 8(mainly 4 or 5), and the equality constraints are linear.
> > My function is too complicated to get the expression of derivate easily, so
> > according to Joachim Dahl(firstname.lastname@example.org)'s post, it is probably
> > non-convex. But I think it's possible to calculate the first derivate
> > numerically.
> > I have tried scipy.optimize.fmin_l_bfgs_b(), which can handle bound constraints
> > but seems cannot handle equality constraints.
> > Mr. Markus Amann has kindly sent me a script written by him, which can
> > handle equality constraints and is easy to use. The method used by
> > Markus involves the calculation of Jacobian, which I don't
> > understand.(Sorry for my ignorance in this filed. My major is chemistry,
> > and I'm trying to learn some knowledge about numerical optimization.)
> > However, it seems that the script cannot handle inequality constraints.
> > (sorry if I was wrong).
> > I hope my bad English have described my problem clearly.
> > Any help will be greatly appreciated.
> > Best regards,
> > Xiao Jianfeng
> I have found COBYLA(http://www.jeannot.org/~js/code/index.en.html#COBYLA),
> and it has a Python interface, which make it very easy to use.
You seem not to be aware that cobyla is part of scipy:
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