[SciPy-user] lagrange multipliers in python
Sun Jun 17 11:19:22 CDT 2007
Since your problem includes inequality constraints, the simple method I
apply; it only works for problems involving only linear equality
To use the method, you need to identify the nullspace of your constraint
matrix, e.g., using
a singular value decomposition.
On 6/17/07, firstname.lastname@example.org <email@example.com> wrote:
> Hi Joachim,
> Joachim Dahl wrote:
> > If your function is too complicated to evaluate derivatives, chances
> > are that
> > it's not convex. But you're still going to need the first and second
> > order derivatives
> > for Newton's method...
> > If you want to solve
> > min. f(x)
> > s.t. A*x = b
> > you could first find a feasible point x0 satisfying A*x0 = b (e.g., the
> > least-norm solution to A*x = b) and parametrize all feasible points as
> > z = x0+ B*y
> > where B spans the nullspace of A, i.e., A*B = 0. Now you have an
> > unconstrained
> > problem
> > min. f( x0 + B*y )
> > over the new variable y.
> I still don't quite understand how to liminate linear equality
> constraints. Could you please point me to some web resources that
> describe this method in detail? Or what key words I should use if I want
> to google on the web?
> Xiao Jianfeng
> SciPy-user mailing list
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