Convexity of Variance Minimization

$$X$$ is a discrete random variable taking value $$x_n$$ with probability $$1/N$$ for $$n=1, \ldots,N$$. I would like to set the $$x_n$$ values in an optimization problem. My objective is to minimize the variance while satisfying a set of constraints.

So the problem is:

$$\begin{array}{ll} & \min\limits_{\{x_n\}_{n=1}^N}{\operatorname{Var}(X)} \\ & \text{s.t.} \ \ldots \end{array}$$

Denote $$x = \begin{pmatrix} x_1 & \ldots & x_n\end{pmatrix}^\top$$. So in this discrete distribution, the variance is: $$\operatorname E[X^2] -\operatorname E[X]^2 = \frac{1}{N} \sum_{n=1}^N x_n^2 - \frac{1}{N^2} \left( \sum_{n=1}^N x_n \right)^2 = \frac{x^\top x}{N} - \frac{(\mathbf{e}^\top x)(x^\top \mathbf{e})}{N^2}.$$

My questions are:

1. The variance is not convex, so minimization is hard, right? Is there a common method for this?
2. Is there any convex function which results in a low variance after being minimized?

Edit: The variance may be convex based on the comments. We can show that $$\operatorname E[X^2] -\operatorname E[X]^2 = \frac{x^\top \left( I - \tfrac{\mathbf{e}\mathbf{e}^\top}{N}\right) x}{N},$$ so to show this is convex, we need to show that $$I - \tfrac{\mathbf{e}\mathbf{e}^\top}{N}$$ is a p.s.d. matrix. That is itself a challenge. I need to see the proof of how this is p.s.d, or if one can properly write down how the variance can be shown as a closed-form norm expression as in the comments.

To show the variance is $$\ell^2$$-norm-representable we need to have $$a^\top a = I - \tfrac{\mathbf{e}\mathbf{e}^\top}{N}$$ for some $$a$$. Finding $$a$$ will also make it.

Edit-2: Ok, I got the comment. He follows the $$\operatorname{Var}(X) = \operatorname E[(X - \operatorname E[X])^2]$$ approach, then it is obvious.

• To me it looks like $\operatorname V(x)$ is a convex quadratic function, since $0\le\operatorname V(x) = x'Hx$ where $H$ is the Hessian of $\operatorname V(x)$. Sep 11 '19 at 17:46
• Also $\operatorname V(x)=\frac1N\left\|x-ee^\top x/N\right\|^2$ which clearly is convex. Sep 11 '19 at 17:54
• Minimizing $\left\|x-ee^\top x/N\right\|$ is easy using SOCP/conic quadratic optimization. See docs.mosek.com/modeling-cookbook/index.html Sep 11 '19 at 18:04
• @ErlingMOSEK Is it $\operatorname V(X) =\frac1N \left\|x - \frac{ee^\top x}N\right\|_2^2$ or $\operatorname V(X) = \frac1N\left\|\frac{x - ee^\top x}N \right\|_2^2$ ? Because I can not derive this. Thanks for the answer! Sep 11 '19 at 20:00
• To show why $A = I - \left( \frac{e e^T}{n}\right)$ is psd, show that $v^T A v \geq 0 \, \forall v$, where $v \in \mathbb{R}^n$. An inequality that will come in handy is $n\left(\sum\limits_{i=1}^{n} v_i^2\right) \geq \left(\sum\limits_{i=1}^{n} v_i \right)^2$, where $v_i$ is the the $i^{th}$ component of $v$. Sep 11 '19 at 22:41

It holds $$\begin{array}{rcl} \operatorname V(x) &= &\dfrac1N\left\| x-\dfrac{e^\top x}{N} e \right\|^2 \\ & = & \dfrac1N\left(x^\top x+\dfrac{(e^\top x)^2 e^\top e}{N^2}-2\dfrac{(e^\top x)^2}N\right) \\ & = & \dfrac{x^\top x}{N} - \dfrac{(e^\top x)^2}{N^2}. \end{array}$$ So you are minimizing the $$\ell^2$$-norm of an affine expression which is known to be convex.
The problem $$\begin{array}{lcl} \min & \dfrac{\|x-e u\|}{N} & \\ \mbox{s.t.} & \dfrac{e^\top x}{N} - u & = & 0 \\ \end{array}$$ provides a nice interpretation since $$u$$ is the average. Note the problem tries to make all the $$x$$ equal to the average value.
Alternatively the last problem can be stated as $$\begin{array}{lcl} \min & \dfrac{s}{N}&\\ \mbox{s.t.} & \dfrac{e^\top x}{N} - u &=0 \\ &(s;x-e u) &\in Q. \\ \end{array}$$ where $$Q$$ is a quadratic cone. This provides another convexity proof because the quadratic cone is convex. Hence, the problem can be solved using SOCP also known as conic quadratic optimization.