Minimizing the following function
$f(x_1,x_2,\cdots,x_n)=\prod\limits_i^n x_i^{x_i}$
such that
$x_1+x_2+\cdots+x_n=P, 2\le x_i$ and $x_i$ are integers.
My attempt: In my opinion we obtain the result when all $x_i's$ are almost equal i.e. $|x_i-x_j|\le 0$ for all $i$ and $j$. I am trying to solve by Lagrange's multiplier and I obtained a system of equation which looks messy.
On
Using weighted AM-GM inequality, $$\frac{P}{\dfrac{x_1}{x_1}+\dfrac{x_2}{x_2}+\dots+\dfrac{x_n}{x_n}}\leq\Big(\prod_{i=1}^{n}x^{x_i}_i\Big)^\dfrac{1}{P}\leq \frac{x_1\cdot x_1+x_2\cdot x_2+\dots+x_n\cdot x_n}{P}\\\Rightarrow\frac{P}{n}\leq \Big(\prod_{i=1}^{n}x^{x_i}_i\Big)^\dfrac{1}{P}\leq\frac{x_1^2+x^2_2+\dots+x_n^2}{P}\leq \frac{(x_1+x_2+\dots+x_n)^2}{P}=P$$
Hence $$\Big(\dfrac{P}{n}\Big)^P\leq\Big(\prod_{i=1}^{n}x^{x_i}_i\Big)\leq P^P$$
try as a logarithm: $$ f(x_1,x_2,\cdots,x_n)=\prod_i^nx_i^{x_i} $$ log: $$ g(\vec x)=\log(f(\vec x))=\sum_i^n x_i \log(x_i) $$ Then lagrange method: $$ \mathcal L(\vec x)=g(\vec x)+\lambda(P-\sum_i^n x_i)=\sum_i^n x_i \log(x_i)+\lambda(P-\sum_i^n x_i)= $$ $$ =\sum_i^n x_i( \log(x_i) -\lambda)+\lambda P $$ now impose $\delta \mathcal L=0$.
The logarithm is strickly monotone so extremas are preserved.
EDIT: Full calculation: $$ 0=\delta\mathcal L=\delta\left\{\sum_i^n x_i( \log(x_i) -\lambda)+\lambda P\right\}= $$ $$ =\sum_i^n \delta \left\{x_i( \log(x_i) -\lambda)\right\}+\delta\lambda P= $$ $$ =\sum_i^n \left\{\delta x_i( \log(x_i) -\lambda) +x_i( \frac{\delta x_i}{x_i} -\delta \lambda) \right\}+\delta\lambda P= $$ $$ =\sum_i^n \delta x_i\left( \log(x_i) -\lambda +1\right)+\delta\lambda(P-\sum_i^n x_i) $$ This implies both: $$ \begin{cases} \log(x_i) -\lambda +1=0\\ P-\sum_i^n x_i=0 \end{cases}\implies\begin{cases} x_i =e^{\lambda-1}\equiv\hat x\\ P-\sum_i^n x_i=0 \end{cases}\implies x_i=\frac{P}{n} $$