I have run into a problem relative to linear progamming. This problem is about extreme point and vetices of a polyheron. The question is that an extreme point and a vertex of a polyhedron is equivalent. I guess the answer is yes and I have proved a vertex is an extreme point. However, I have difficulty in proving an extreme point is a vertex.
I know one way to prove is using basic feasible solutions but my lecturer asked to prove not using that convention.
The definition of an extreme point of a polyheron is
Let $P$ be a polyhedron. A vector $x \in P$ is an extreme point of $P$ if we cannot find two vectors $y, z \in P$, both different from $x$, and a scalar $\lambda \in [0,1]$, such that $x=\lambda y +(1-\lambda) z$.
For a vertex, this following definition is know
Let $P$ be a polyhedron. A vector $x \in P$ is a vertex of $P$ of there exists some $c$ such that $c'x < c'y$ for all $y$ satisfying $y \in P$ and $y \neq x$.
Both two definitions come from the book "Introduction to Linear Optimization" by Dimistris Bertsimas.
Any help would be greatly appreciated.
For these kinds of problems, the geometry should guide you. There is an answer below, but first consider the following hint:
Below is a solution.
Suppose that $x$ is not a vertex. Then there is no such $c$; in particular, there are $x \neq y,z \in P$ and $0 \neq c \in \mathbb{R}^n$ so that $c^Tx < c^Ty$ and $c^Tx \geqslant c^Tz$. I claim this implies that $x \in \text{conv}(y,z)$.
Notice that $c^Tx$ is in $[c^Ty, c^Tz] \subset \mathbb{R}$. Then there is some $\lambda \geqslant 0$ so that:
$$\lambda c^Ty + (1-\lambda)c^Tz = c^Tx$$
By multilinearity of the dot product (alternatively, properties of matrix multiplication):
\begin{align*} \lambda c^Ty + (1-\lambda)c^Tz = c^Tx &\Longrightarrow c^T(\lambda y) + c^T ((1-\lambda)z) = c^T x\\ &\Longrightarrow c^T(\lambda y + (1-\lambda)z) = c^T x \end{align*}
Since $c \neq 0$, this implies $\lambda y + (1-\lambda)z = x$ for $y,z\neq x$ in $P$, so $x$ is not an extreme point.
Hence if $x$ is an extreme point, $x$ is a vertex.