I have read this solution, but I could not understand it.
It has shown $$\sigma_n\leqslant \frac 1n\sum_{j=1}^ks_j+\sup_{l\geqslant k}s_l,$$ but how does it go to $$\sup(\sigma_n)\leqslant \frac 1n\sum_{j=1}^ks_j+\sup_{l\geqslant k}s_l$$
I have thought of regarding the RHS as upper bound of $\sigma_n$, but I am not sure about this, since RHS varies as $n$ changes.
The writer wrote "take on both sides the limsup when $n \to \infty$", but I could not understand it. Please help.
First I will copy the relevant part of the answer you are talking about so that we have some context:
I would slightly change the formulation and the conclusion. (I think we need to add one more step to finish the conclusion. But I might have missed something obvious. Or maybe Davide considered the missing step easy and left it for the reader.)
So we have the inequality $$\sigma_n \le \frac 1n\sum_{j=1}^ks_j+\sup_{l\ge k}s_l.$$ This equality is true for any $n\ge k$.
If we take limit superior w.r.t. $n$ on both sides we get $$\limsup_{n\to\infty} \sigma_n \le \sup_{l\ge k}s_l.$$ (Since the first term on the RHS converges to zero for $n\to\infty$ and the second one does not depend on $n$.)
Now the above inequality is true for arbitrary $k$. So we also have
$$\limsup_{n\to\infty} \sigma_n \le \lim_{k\to\infty} \sup_{l\ge k}s_l = \limsup_{k\to\infty} s_k.$$