$k\sum v_i v_i^T-\big(\sum v_i\big)\big(\sum v_i^T\big)\succeq 0$ My professor claimed that $$k\sum_{i=1}^k v_i v_i^T-\Big(\sum_{i=1}^k v_i\Big)\Big(\sum_{i=1}^k v_i^T\Big)\succeq 0,$$
holds for any family of vectors $\{v_1,\dots,v_k\}$, and can be shown using the Cauchy Schwarz inequality on the quadratic form.
I'm unsure whether it's necessary assume: $k$ is a positive integer, and $v_i$ are vectors of ones and zeros such that $\sum_{i=1}^k v_i=\vec{1}$. I don't think need to assume this due to the claim that it holds for any family of vectors. 
In trying to prove that the above is positive semidefinite, I get the quadratic form
$$\begin{align}
k\sum_{i=1}^k x^T v_i v_i^T x-x^T \Big(\sum_{i=1}^k v_i\Big)\Big(\sum_{i=1}^k v_i^T \Big)x
&=
k\sum_{i=1}^k x^T v_i v_i^T x-|\langle \sum_{i=1}^k v_i, x\rangle|^2\\
&\geq 
k\sum_{i=1}^k x^T v_i v_i^T x-\|x\|^2 \bigg\|\sum_{i=1}^k v_i\bigg\|^2\\
&\equiv
k\sum_{i=1}^k x^T v_i v_i^T x-x^Tx n\\
&=
x^T\big(k\sum_{i=1}^k v_i v_i^T-n\mathbb{I}\big)x\\
\end{align}$$
where $n\geq k$. I do not think this matrix in the parentheses is positive semidefinite, since its diagonals are negative. Can someone help me prove the claim of my professor?
 A: Indeed, the form of the $v_i$ is not needed, but we utilize the fact that $k$ is a positive integer:
\begin{align}
k\sum_{i=1}^k x^T v_i v_i^T x-x^T \left(\sum_{i=1}^k v_i\right)\left(\sum_{i=1}^k v_i^T \right)x
&=
\left(\sum_{i=1}^k 1\right) \left(\sum_{i=1}^k w_i^2\right) -\left(\sum_{i=1}^k w_i\right)^2,\textrm{ where }w_i\equiv x^Tv_i\\
&=\left\Vert1_k\right\Vert^2 \left\Vert w\right\Vert^2 - \left|\langle 1_k,w\rangle\right|^2, \textrm{ where }w\equiv(w_1,\dots,w_k)^T
\\
&\geq ^{CS}
\left|\langle 1_k,w\rangle\right|^2 - \left|\langle 1_k,w\rangle\right|^2\\
&=0\\
&\hspace{2.5in}\blacksquare
\end{align}
A: I guess that the vectors are row vectors of an $\mathbb{R}^n$. If the vectors are column vectors, take transposes of the difference and then follow the proof below, to prove that this transpose is positive semi-definite. Then, since $A$ is positive semidefinite if-f $A^{T}$ is, the desired result follows in the case of column vectors too. In the case of row vectors the inequality is equivalent to proving that
$$k\sum_{i=1}^k||u_i||^2\geq \left|\left|\sum_{i=1}^ku_i\right|\right|^2.\ \ \ (1)$$
Indeed, note that for the difference in your question the matrix multiplication $x($this difference$)x^{T}$ is equal to 
$$||x||^2\left(k\sum_{i=1}^k||u_i||^2-\left|\left|\sum_{i=1}^ku_i\right|\right|^2\right).$$ 
Examining $(1)$ at each coordinate, we conclude that it suffices to show that
$$k\sum_{i=1}^ka_i^2\geq \left(\sum_{i=1}^ka_i\right)^2\ \ \ (2)$$
for any $k$-tuple $(a_1,\ldots,a_k)$ of real numbers. Indeed, then we will have that
$$k\sum_{i=1}^ku_{ij}^2\geq \left(\sum_{i=1}^ku_{ij}\right)^2,\ \forall j=1,\cdots,n$$
and $(1)$ will follow by summing over all $j$'s. Now, $(2)$ can be easily proved by applying C-S to the vectors $(1,\ldots,1)$ and $(a_1,\ldots,a_k).$ An other proof of $(2)$ can be obtained by applying Jensen's inequality to the convex function $x\mapsto x^2.$
