Convexity and minimum of a vector function Prove that the function $f:\mathbb{R}^n\to \mathbb{R}$ given by $f(x)=x^T \cdot x$ is strictly convex. Use this result to find the absolute minimum by equating the derivative to zero.
I am not sure how to prove that a vector function is convex. Is there a general method to do this? Also, I tried differentiating the function and I got $2x^T$dx as a result for the differential, which would mean that $2x^T$ is the derivative. However, does this give me the absolute minimum? Or did I make a mistake in differentiating? Thanks in advance.
 A: You may prove convexity from first principle. $f(x)$ is said to be strictly convex if and only if $f(px+qy)<pf(x)+qf(y)$ for any $x\not=y$ and for every $0<p<1 \ (q=1-p)$. This can be proved by verifying that
$$
pf(x)+qf(y)-f(px+qy)=pq(x-y)^T(x-y)=pq\|x-y\|^2>0,
$$
where $\|v\|$ denotes the length (i.e. Euclidean norm) of a vector $v$.
To find the minimum of $f$, note that $f(x)=x^Tx=\|x\|^2\ge0$ and $f(x)=0$ only when $x=0$. Hence the absolute minimum of $f$ occurs at the $x=0$. Calculus is not of much use here, because it can only prove that a certain point is a local minimum, but you are asked to find the absolute minima of $f$. But anyway, since you have $f'(x)=2x^T$, setting $f'(x)=0$ would give you back the critical point $x=0$. To show that $x=0$ is indeed an absolute minimum, you still need to argue that $f(x)\ge f(0)=0$ for every $x$.
Edit: It's worth mentioning (thanks to Dominic Michaelis) that every local minimum of a convex function is a global minimum, but in general, calculus is only helpful for screening out local minima among critical points. Extra work is often required to locate global minima.
A: When the Hessian Matrix of a function $f$ is positiv definit the function $f$ is strict convex. That should help you.
By the way what do you mean with $2x^T \, dx$
Let us first rephrase the definition of strict convexity. 
$$f(t \cdot x+(1-t)y)< t\cdot f(x) + (1-t)f(y)$$
As this is an easy example we will make it with the definition.
$$\sum_{i=1}^n (t\cdot x_i + (1-t)y_i)^2 =\sum_{i=1}^n t^2 x_i^2 + 2 (1-t)(t)(x_i \cdot y_i)+ (1-t)^2 y_i^2 $$
And the right hand side is 
$$\sum_{i=1} t x_i^2 + (1-t)y_i^2 $$
We can show that it is true for every coordinate (so we don't need the sum)
$$t^2 x^2 + 2(1-t)(t)(xy)+(1-t)^2 y^2< tx^2 + (1-t)y^2 $$
This is equivalent to 
$$0<t(1-t) x^2 + (1-t)(t) y^2 -2t(t-1)xy$$
We have in every term a $t(1-t)$ as $t\in(0,1)$ we can devide through it
$$0< x^2 -2 xy +y^2=(x-y)^2 $$
So we see this is true for every summand of the sum, and hence for the sum. 
There is a very nice way how to differentiate stuff like that (this one isn't rigorous as I do it).  We just use the product rule 
$$D(x^T x) = (x^T)' x + x^T x'= x^T (x)' + x^T=2 x^T$$ 
Using the symmetrie of $x^T x$. You could do it with the partial derivates too. 
The Hessian Matrix is $2\cdot I$ where $I$ is the unit matrix.
