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I am working on a way to find the minimum of a function J(Y) with the constraint set
C = {X E R^N such that gt(x) =<0 Vi E [1,n]}

Let L(Y, mu) = J(Y) + SIGMA m --> i = 1 muigi(Y) be the lagrangean of the problem.

I am having trouble proving the following theorem :

If is a saddle point of L, then and X minimises J on C.
Reciprocal:
If J and the gi are convex and differentiable in X, then for every solution X, there exists such that is a saddle point of the lagrangean L.

I would be grateful for a full proof of both the first and second parts of this theorem, in particular for the reciprocal.

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I am working on a way to find the minimum of a function J (Y) with the constraint set

Let be the langragean of the problem.

I am having trouble proving the following theorem :

If is a saddle point of L, then and X minimises J on C.
Reciprocal:
If J and the gi are convex and differentiable in X, then for every solution X( I think it should be an optimal solution) , there exists such that is a saddle point of the langrangean L.

Proof. First we need to write the primal problem and its Lagrangian Dual problem.

Primal Problem ...

#### Solution Summary

A theorem involving a Lagrangean and saddle points is proven. The solution is detailed and well presented.

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