# nonlinear optimization – Normal Cones and KKT Conditions

I’m trying to understand a statement from the book “Perturbation Analysis of Optimization Problems”, by Bonnans and Shapiro. Let me start by providing some context. In page 148, the authors write down the optimality conditions for the problem

$$min_{xin Q} f(x) text{ subject to } G(x)in K,$$

where $$Q$$ and $$K$$ are convex subsets of Banach spaces $$X$$ and $$Y$$, respectively, and $$fcolon Xto mathbb{R}$$, and $$Gcolon Xto Y$$.

In equation 3.8, the authors say that for some feasible point $$x_0$$, the optimality conditions are

$$xintext{argmin}_{xin Q} L(x,lambda) text{ and } lambdain N_K(G(x_0)),$$

Where $$L(x,lambda)colon= f(x) + langle lambda, G(X)rangle$$ is the lagrangian associated with the problem, and $$N_K(G(x_0))$$ is the normal cone of $$K$$ at the point $$x_0$$.

After that, in equation 3.9, they say that when $$K$$ is a cone, the condition $$lambdain N_K(G(x_0))$$ is equivalent to

$$G(x_0)in K,, lambda in K^circ, text{ and } langle lambda, G(x_0)rangle =0,$$

and that’s where I’m struggling. I see that these conditions resemble the classic KKT conditions and they
correspond to primal feasibility, dual feasibility, and complementarity, respectively. But I want to figure this out formally and hopefully be able to have some geometric interpretation of it.

Can someone help me out with this?