[SciPy-user] lagrange multipliers in python
Sun Jun 17 10:07:42 CDT 2007
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
> 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?
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