I'm stuck on a problem for my optimization class.
Minimize $f(x)$ st $x \in \Omega$ where $\Omega=\{x\in \mathbb{R^2}:x_1^2+x_2^2\geq 1\}$ and $ f(x)=x_2$.
a) Find all points that satisfy FONC
b) Which points from (a) satisfy SONC
c) Which points from (a) are local minimizers
I have $d^T\nabla f=\begin{bmatrix} 0 & d_2\end{bmatrix}^T$, so any value st $d_2\geq 0$ satisfies $d^T\nabla f\geq0$. I'm stuck from here.
I found an answer online that goes $l(x,\mu)=\nabla f(x)+\mu\Omega(x) \implies 0=\begin{bmatrix}1 & 0\end{bmatrix}^T+\begin{bmatrix}2\mu_1x_1 \\ 2\mu_1x_2\end{bmatrix} \implies 2\mu_1x_1=-1$ and $2\mu_1x_2=0$ therefore $x_1=0,x_2=0$. This answer doesn't make any sense to me as the gradient seems backwards and they got $x_1=0$ when its equation is equal to $-1$.
If anyone can explain, that would be really appreciated.