If a smooth function defined on $\mathbb{R}^n$ is convex and bounded below, can we assert that its minima must be a connected set? If a function defined on $\mathbb{R}^n$ is

*

*infinitely differentiable,

*convex, i.e., $f\left( t x_1 + (1-t) x_2 \right) \le t f(x_1) + (1-t) f(x_2)$, and

*bounded below,

can we assert that its minima, if exist, form a connected set?

Background
I encounter this question when dealing with the maximum likelihood estimation of probabilistic graphical models.
The negative log-likelihood is bounded below by zero (since probabilities are bounded above), and its Hessian is positive semi-definite (but not positive definite).
By intuition, I guess its minima form a connected set, thus optimization of the loss function will give a good estimation of model parameters in principle, if the true model is not pathological.
Here is a specific example.
All variables are real.
The probability of an observed data point $s$ is given by
$$
P(s | h, J)
= \frac{1}{Z(h,J)} \exp\left( h^T s + \frac{1}{2} s^T J s \right)
= \frac{1}{Z(h,J)} \exp\left( \sum_{i=1}^{N} h_i s_i + \sum_{i=1}^N \sum_{j>i}^N J_{ij} s_i s_j \right).
$$
Here $s = (s_1, \cdots, s_N)^T$ and $s_i = \pm 1$,
$N$ is a given integer,
$h=(h_1, \cdots, h_N)^T$ is real,
and $J$ is a $N \times N$ real symmetric matrix whose diagonal element are zero, i.e.,
$$
J=
\begin{pmatrix}
  0      & J_{12} & \cdots & J_{1N} \\
  J_{12} & \ddots &        & J_{2N} \\
  \vdots &        & \ddots & \vdots \\
  J_{1N} & J_{2N} & \cdots & 0      \\
\end{pmatrix}.
$$
The normalization factor (partition function) is therefore
$$
Z(h,J) = \sum_{s_1=\pm 1} \cdots \sum_{s_N=\pm 1} \exp\left( h^T s + \frac{1}{2} s^T J s \right) .
$$
(FYI, this is just the generalized Ising model in physics or the Markov random field in statistics.)
The negative log-likelihood (loss function) for the above probability is
$$
L(h,J)
=
- h^T s - \frac{1}{2} s^T J s + \log Z(h,J) ,
$$
which is infinitely differentiable, convex with respect to $h$ and $J$, bounded below by zero.
 A: Firstly, $f$ may not have any minimum. For example, if $f(x) = e^x$, $f$ is smooth, bounded from below but does not have any minimum.
Moreover, suppose $f$ has a global minimum. By translation, suppose $f \geqslant 0$ and $\min f = 0$. Suppose there exists two point $x_1\neq x_2$ such that $f(x_1)=f(x_2) = 0$. Thus by convexity, for all $t \in [0,1]$, $f((1-t)x_1+tx_2) \leqslant (1-t)f(x_1)+tf(x_2) = 0$. As $f\geqslant 0$, $f((1-t)x_1+tx_2)=0$. Consequently, the entire interval $[x_1,x_2]$ minimize $f$, and thus, the set of $x$ such that $f(x) = 0$ is a convex subset of $\mathbb{R}^n$.
Notice that the smoothness of $f$ is never used here.
A: The set of all minimizers of a convex function is a convex set. (See proof below.) And of course, every convex set is connected.
Proof:
Suppose $x$ and $y$ are minimizers of a convex function $f$. So $f(x) = f(y) = f^*$, where $f^*$ is the minimum value of $f$.If $0 \leq \theta \leq 1$, and $z = \theta x + (1-\theta) y$, then
$$
f(z) \leq \theta f(x) + (1-\theta) f(y) = f^*.
$$
So $z$ is also a minimizer for $f$.
