Minimizing the free energy $\DeclareMathOperator{\tr}{tr}$
Let $H$ be a self-adjoint matrix and define the free energy as
$$
F(\Gamma)=\tr(H\Gamma+\Gamma \log \Gamma +(1-\Gamma) \log(1-\Gamma))
$$
where $\Gamma$ satisfies $0\le \Gamma \le 1$. I have seen physics papers suggest that $F(\Gamma)$ is minimized if we take
$$
\Gamma =\frac{1}{e^H +1}
$$
but I can't think of a rigorous proof.
EDIT. The "usual" non-rigrous method of proof would be to use variational method, i.e.,
\begin{align}
\delta F &=\tr\left(H\delta\Gamma+\log\left(\frac{\Gamma}{1-\Gamma}\right)\delta\Gamma \right)\\
0&=H+\log\left(\frac{\Gamma}{1-\Gamma}\right)\\
\Gamma &= \frac{1}{e^H+1} 
\end{align}
How would one make this argument rigorous?
EDIT 2. I just realized that $F(\Gamma)$ is convex, so I think that the above calculation is almost rigorous, except that $\delta\Gamma$ may not commute with $\Gamma$, as pointed out by @Sangchul Lee.
EDIT 3. Thank you @Sangchul Lee for the proof. I think I also found another proof, that may be a little more straightforward.
Let $\Gamma_0=1/(e^H+1)$ and let $0\le \Gamma_1 \le 1$. Let $\Gamma(t)=(1-t)\Gamma_0 +t\Gamma_1 =\Gamma_0 +t\Delta$ where $\Delta = \Gamma_1-\Gamma_0$. Let $f(x)=x \log x +(1-x) \log (1-x)$. Hence,
$$
F(\Gamma)-F(\Gamma_0) = \tr(tH\Delta)+\tr (f(\Gamma)-f(\Gamma_0))
$$
Since $f$ is convex, we can apply Klein's inequality and see that
$$
F(\Gamma)-F(\Gamma_0) \ge \tr(t\Delta (H+\log\Gamma_0 -\log (1-\Gamma_0))=0
$$
Also since $f$ is strictly convex, we see that $\Gamma_0$ is the unique global min.
By the way, not sure why this question was voted to close. Please vote to reopen if you think otherwise.
EDIT 4. After further looking into the proof of Klein's inequality, there is a subtle "problem" of taking the derivative of a trace function (which is not fully explained in the wikipedia article). @Sangchul Lee deals with this explicitly for this particular case, but I would like to think that it should be able to be done more generally. Hence, I posted another question here.
 A: In this answer, we will write
$$ D^+_{B}F(A) := \lim_{\epsilon \to 0^+} \frac{F(A+\epsilon B) - F(A)}{\epsilon} $$
whenever the limit exists. Then the following lemma will be useful:

Lemma. We have
$$ D^+_{B}\exp(A) = \int_{0}^{1} e^{sA}Be^{(1-s)A} \, \mathrm{d}s. $$

Proof. We have
$$ D^+_{B}\exp(A)
= \sum_{n=1}^{\infty} \frac{1}{n!} D^+_{B}(A^n)
= \sum_{n=1}^{\infty} \frac{1}{n!} \sum_{k=0}^{n-1} A^k B A^{n-1-k}
= \sum_{k,l\geq 0} \frac{A^k B A^l}{(k+l+1)!}. $$
Then the claim follows from the beta integral $\int_{0}^{1} u^k(1-u)^l \, \mathrm{d}s = \frac{k!l!}{(k+l+1)!} $. $\square$
Now let $\Gamma_0$ and $\Gamma_1$ be self-adjoint matrices such that $0 \leq \Gamma_k \leq 1$ for $k = 0, 1$. Interpolate $\Gamma_0$ and $\Gamma_1$ by letting
$$ \Gamma_t = (1-t)\Gamma_0 + t\Gamma_1, \quad 0 \leq t \leq 1. $$
We also write $\Delta = \frac{\mathrm{d}}{\mathrm{d}t} \Gamma_t = \Gamma_1 - \Gamma_0$ since this will appear frequently. Then by the Frullani's integral and the functional calculus,
\begin{align*}
\frac{\mathrm{d}}{\mathrm{d}t} \log \Gamma_{t}
&=  \frac{\mathrm{d}}{\mathrm{d}t} \int_{0}^{\infty} \frac{e^{-x} - e^{-x\Gamma_t}}{x} \, \mathrm{d}x \\
&= \int_{0}^{\infty} (D^+_{\Delta}\exp)(-x\Gamma_t) \, \mathrm{d}x \\
&= \int_{0}^{\infty} \int_{0}^{1} e^{-sx\Gamma_t}\Delta e^{-(1-s)x\Gamma_t} \, \mathrm{d}s \mathrm{d}x.
\end{align*}
From this, we obtain
\begin{align*}
\operatorname{Tr}\left( \Gamma_t \frac{\mathrm{d}}{\mathrm{d}t} \log\Gamma_t \right)
&= \operatorname{Tr}\left( \int_{0}^{\infty} \int_{0}^{1} \Gamma_t e^{-sx\Gamma_t}\Delta e^{-(1-s)x\Gamma_t} \, \mathrm{d}s \mathrm{d}x \right) \\
&= \int_{0}^{\infty} \int_{0}^{1} \operatorname{Tr}\left( \Gamma_t e^{-sx\Gamma_t}\Delta e^{-(1-s)x\Gamma_t} \right) \, \mathrm{d}s \mathrm{d}x \\
&= \int_{0}^{\infty} \int_{0}^{1} \operatorname{Tr}\left( \Gamma_t e^{-x\Gamma_t}\Delta \right) \, \mathrm{d}s \mathrm{d}x \\
&= \operatorname{Tr}(\Delta),
\end{align*}
and similarly
$$ \operatorname{Tr}\left( (1-\Gamma_t) \frac{\mathrm{d}}{\mathrm{d}t} \log(1-\Gamma_t) \right) = -\operatorname{Tr}(\Delta). $$
So we obtain
$$ \frac{\mathrm{d}}{\mathrm{d}t} F(\Gamma_t)
= \operatorname{Tr}\left( H\Delta + \Delta\log(\Gamma_t) - \Delta\log(1-\Gamma_t) \right) \tag{1} $$
Differentiating both sides with respect to $t$ again,
\begin{align*}
\frac{\mathrm{d}^2}{\mathrm{d}t^2} F(\Gamma_t)
&= \operatorname{Tr}\left(\Delta \frac{\mathrm{d}}{\mathrm{d}t}\log(\Gamma_t) - \Delta \frac{\mathrm{d}}{\mathrm{d}t}\log(1-\Gamma_t) \right) \\
&= \operatorname{Tr}\left( \int_{0}^{\infty} \int_{0}^{1} \Delta e^{-sx\Gamma_t}\Delta e^{-(1-s)x\Gamma_t} \, \mathrm{d}s \mathrm{d}x \right) \\
&\qquad + \operatorname{Tr}\left( 
 \int_{0}^{\infty} \int_{0}^{1} \Delta e^{-sx(1-\Gamma_t)}\Delta e^{-(1-s)x(1-\Gamma_t)} \, \mathrm{d}s \mathrm{d}x \right) \\
&= \int_{0}^{\infty} \int_{0}^{1} \left\| e^{-\frac{1}{2}sx\Gamma_t}\Delta e^{-\frac{1}{2}(1-s)x\Gamma_t} \right\|^2 \, \mathrm{d}s \mathrm{d}x \\
&\qquad + \int_{0}^{\infty} \int_{0}^{1} \left\| e^{-\frac{1}{2}sx(1-\Gamma_t)}\Delta e^{-\frac{1}{2}(1-s)x(1-\Gamma_t)} \right\|^2 \, \mathrm{d}s \mathrm{d}x \tag{2}
\end{align*}
where $\| X \|^2 = \operatorname{Tr}(X^* X)$ is always a non-negative real number.
Now we are ready to prove the claim.

*

*From $\text{(2)}$, we know that $\frac{\mathrm{d}^2}{\mathrm{d}t^2} F(\Gamma_t) \geq 0$, which in turn implies that $F$ is convex.


*$\text{(1)}$ can be used to show that $\Gamma_{\text{m}} = \frac{1}{e^H + 1}$ is a local extremum of $F$. Then by the convexity, this is a local minimum of $F$.
Therefore $\Gamma_{\text{m}}$ minimizes $F$.
