# Optimizing a symmetric function.

I have a set of $n$ variables $(x_1,\ldots, x_n)$ satisfying $x_i < n$ and $\sum x_i < n^{3/2}$. I want to maximize: $$\sum\limits_{i,j, i \neq j} \sqrt{x_i^{1/3} x_j^{1/3} (x_i^{1/3} + x_j^{1/3})^2 (x_i^{5/3} + x_j^{5/3})}$$

Is there a systematic way to approach these kinds of problems? I am fine with having an asymptotic answer.

My best upper bound is $O(n^3)$ which we can get by seeing that the expression is $< \sum\limits_{i,j, i \neq j} max(x_i, x_j)^{3/2} < n(\sum_i x_i^{3/2}) < n(n^2) = n^3$

My current best way to maximize is to set $\sqrt{n}$ $x_i$'s to $n/2$ and the other $n-\sqrt{n}$ $x_i$'s to $\sqrt{n}/2$. This gives $O(n^{35/12})$. Is there a way to prove this is optimal upto constant/polylogarithmic factors?

The idea is that there are $2$ parts of the expression, one which maximizes at $x_i = x_j$ and one which maximizes at some $x_i = n$(max value) and other $x_i = 0$. This is why we need to set $\sqrt{n}$ $x_i$'s to $n/2$ and the other $n-\sqrt{n}$ $x_i$'s to $\sqrt{n}/2$ which gives $O(n^{35/12})$.
To prove this we will separate the expression into 2 parts and maximize them independently. Let $m_{i, j} = max(x_i, x_j)$. Then,
$\sum\limits_{i,j, i \neq j} \sqrt{x_i^{1/3} x_j^{1/3} (x_i^{1/3} + x_j^{1/3})^2 (x_i^{5/3} + x_j^{5/3})} = O(\sum\limits_{i,j, i \neq j} \sqrt{x_i^{1/3} x_j^{1/3} (m_{i, j}^{1/3})^2 (m_{i, j}^{5/3})}) = O(\sum\limits_{i,j, i \neq j} \sqrt{x_i^{8/3} x_j^{1/3}}) = O(\sum\limits_{i,j, i \neq j} x_i^{4/3} x_j^{1/6}) = O((\sum x_i^{4/3})(\sum x_i^{1/6})) = O((\sqrt{n}n^{4/3})(n\sqrt{n}^{1/12})) = O(n^{35/12})$.
$\sum x_i^{4/3}$ and $\sum x_i^{1/6}$ are the expressions we maximized independently.