Stable strict local minimum implies local convexity

Solution 1:

I was thinking this has a chance in $\mathbb R$ but once you step in $\mathbb R^2$ there should be counterexamples. Let us have a look at the polynomial function: $f\colon (x,y)\in \mathbb R^2 \longmapsto x^4 + 12 x^2 y^2 + y^4 + (x+2y)^2$. You can factor it as $f(x,y) = (x^2+y^2)^2 + 10x^2y^2 + (x+2y)^2$, this establishes that $f(0,0) = 0$ is the unique minimum.

We should note that $f$ is not locally convex around $(0,0)$. For example you can check that, \begin{equation} \begin{bmatrix} 2 \\ -1 \end{bmatrix}^\top \nabla^2 f(t,t) \begin{bmatrix} 2 \\ -1 \end{bmatrix} = -12t^2 < 0, \end{equation} for all $t \neq 0$, no matter how small.

It seemed that there existed a unique minimum for a small enough linear perturbation, however upon closer inspection the gradient map is not injective. I have plotted (rotated and resized) $\nabla f(r\cos \theta, r\sin\theta)$ with $r$ fixed smaller and smaller, and with $\theta$ varying from $0$ to $2 \pi$. It seems to describe a deformed circle around $(0,0)$ (it actually becomes close to a segment) but those circles actually overlap (see the attached plot). In any case the equation is, \begin{equation} \begin{bmatrix} 4 x^3 + 24 x y^2 + 2 (x + 2 y) \\ 24 x^2 y + 4 y^3 + 4 (x + 2 y) \end{bmatrix} = \begin{bmatrix} -u \\ -v \end{bmatrix}. \end{equation}

Arcs of the gradient are drawn, $\nabla f(r \cos t, r \sin t)$, for $r=10^{-7}(1+n/4)$ with $n=1,\dots,10$ and $t = 0 \dots 2\pi$. The picture has been rotated and stretched to show clearly the overlap.