In a text book I am following, there is a definition of positive definite matrices, which I did not see before:
$x^TAx \geq \alpha x^Tx$
where $\alpha$ is a positive scalar and $x \in \mathbb{R^n}$.
The usual definition I know is for every non-zero vector $x \in \mathbb{R^n}$, it is $x^TAx > 0$.
These definitions must be equivalent. It is trivial to show that the first definition implies $x^TAx > 0$. But I failed to see a way to show the other way around of the equivalence. I thought of applying SVD to the matrix $A$ and tried to find a way to show that $x^TAx$ is always lower bounded by a $\alpha x^Tx$ with $\alpha$ being positive, using singular values and vectors of $A$ but couldn't move forward. What is the correct way of approach here?
(The other question in the board assumes symmetric matrices. This one does not).
Let $f(x)=x^TAx$. Furthermore let $f(x)>0$ for all non-zero $x$.
Let $C:=\{x \in \mathbb R^n: ||x||_2=1\}$. Then $C$ is compact and $f$ is continuous on $C$. Thus there is $x_0 \in C$ such that
$f(x_0) \le f(x)$ for all $x \in C$.
Now put $ \alpha =f(x_0)$. Then $\alpha >0$.
It is your turn to show that $x^TAx \geq \alpha x^Tx$ for all $x \in \mathbb R^n$.
Hint: for $t \in \mathbb R^n$ and $t \in \mathbb R$ we have $f(tx)=t^2f(x).$