# minimum of sum of strictly convex functions

Is the following statement true? If so, how can I find a proof?

Suppose that $f_1$ and $f_2$ are strictly convex functions on a convex set $X \subseteq \mathbb{R}^n$. If $f_1$ and $f_2$ have minimum, then $f = f_1+f_2$ has a minimum.

I understand that $f$ is strictly convex and bounded from below, because $f_1$ and $f_2$ have a unique minimum. But can it happen that $f$ will never attain its infimum?

• I had an answer but deleted it when I realized the subtle point here. I guess a counterexample would require $X$ to be open, and for $f$ to obtain its minimum on the boundary of $X$. If $X$ is closed, it certainly can't happen. – Michael Grant Jul 21 '16 at 15:44

Here is a counter-example for $n=2$.
Take $f_1(x) = x_1^2 + x_2^2$, $f_2(x) = 10(x_1-1)^2 + (x_2-1)^2$, both are clearly strictly convex with global minima at $p_1:=(0,0)$ and $p_2:=(1,1)$, respectively. The global minimum of $f_1+f_2$ is at $p_3:=(10/11, 1/2)$, which is not a convex combination of the minima of $f_1$ and $f_2$.
Now define $X$ to be an $\epsilon$-neigborhood of the convex hull of the global minima of $f_1$ and $f_2$: $$X = \{ (x_1,x_2): \ |x_1-x_2| < \epsilon\}.$$ For $\epsilon < 9/22$ the point $p_3$ is not in $X$. This is the only point in $\mathbb R^2$, where the gradient of $f_1+f_2$ is zero. Hence, $f_1+f_2$ has no minimum in the open set $X$.
The claim is true for $n=1$, though. Let $X$ be convex, hence an interval. Let $x_1$ and $x_2$ denote the minima of $f_1$ and $f_2$, respectively. Let me assume that $f_1$ and $f_2$ are continuously differentiable. I am sure that the claim is true in the general case as well.
Suppose $x_1<x_2$. By strict monotonicity of the gradient of strictly convex functions it follows $f_1'(x_2)>0$ and $f_2'(x_1)<0$. Hence, the derivative of $f_1+f_2$ changes sign on $X$. Thus there is a zero, which is the minimum of $x_1+x_2$.