On bounding the average cost of top-down merge sort Let $A_n$ be the average number of comparisons to sort $n$ keys by merging them in a top-down fashion (see any algorithm textbook). It can he shown that
$$
A_0 = A_1 = 0;\quad A_n = A_{\lfloor{n/2}\rfloor} + A_{\lceil{n/2}\rceil} + n - \frac{\lfloor{n/2}\rfloor}{\lceil{n/2}\rceil+1} - \frac{\lceil{n/2}\rceil}{\lfloor{n/2}\rfloor+1}.
$$
(See Knuth's AOCP, for instance.)
Flajolet and Golin in 1993 used complex analysis (Mellin transforms) and Fourier analysis to find a precise asymptotic approximation of $A_n$. I am interested in finding lower and upper bounds on $A_n$ of the form $n\lg n + \alpha n + \beta$, where $\lg n$ is the binary logarithm, not using these powerful but complicated analytical approaches.
By distinguishing on the parity of $n$, we simply get
$$
A_{2p} = 2 A_{p} + 2p - 2 + \frac{2}{p+1};\quad A_{2p+1} = A_{p} + A_{p+1} + 2p - 1 + \frac{2}{p+2}.
$$
I tried difference equations, by letting $\Delta_n := A_{n+1} - A_{n}$, yielding
$$
\Delta_{2p} = \Delta_{p} + 1 + \frac{2}{p+2} - \frac{2}{p+1};\quad \Delta_{2p+1} = \Delta_p + 1.
$$
Then, I am stuck.
The same study for the maximum number of comparisons leads to simpler difference equations: $\Delta_{2p} = \Delta_{2p+1} = \Delta_{p} + 1$, which implies $\Delta_n = \lfloor{\lg n}\rfloor + 1$, to wit, the number of bits in the binary expansion of $n$. From there, a closed form for the maximum cost $\sum_{k=1}^{n-1}\Delta_k$ follows relatively easily (see Flajolet and Sedgewick, for instance).
Any idea how to bound $\Delta_k$ and $\sum_{k=1}^{n-1}\Delta_k$ in the present case?
 A: Inductively, if $A_p \le p \log_2 p + \alpha p + \beta$, then 
$A_{2p} \le ((2p) \log_2 (2p) + \alpha (2p)  + \beta) + (\beta -2 + \frac {2}{p+1})$
so we'll want $\beta-2 + \frac{2}{p+1} \le 0.$
The odd case is a little harder. Assume the inequality is true for $A_p,A_{p+1}.$
$A_{2p+1} =  A_p + A_{p+1} + 2p - 1 + \frac{2}{p+2}$
$ \le (p+1)\log_2 (p+1) + p ~\log_2 p + \alpha(2p+1) + \beta + \beta + 2p - 1  + \frac{2}{p+2}.$
Using $\log_2 x \le \frac{(x-(p+1/2))}{(p+1/2)\log 2} + \log_2 (p+1/2)$ at $x=p, p+1$:
$$A_{2p+1} \le (p+1)\log_2 (p+1/2) + p~\log_2p + \frac{p+1}{(2p+1)\log 2} - \frac {p}{(2p+1)\log 2} \\\ + \alpha(2p+1) + \beta + \beta + 2p - 1 + \frac{2}{p+2}$$
$$A_{2p+1} \le \bigg((2p+1)\log_2(2p+1) + \alpha(2p+1) + \beta \bigg) + \\\ \beta -2+ \frac{2}{p+2}+\frac{1}{(2p+1)\log 2}.$$
So, we'll also want $\beta - 2 + \frac{2}{p+2} + \frac{1}{(2p+1)\log 2} \le 0.$ Then if we choose $\alpha, \beta$ so that the base of the induction is satisfied, we get $A_n \le n \log_2 n + \alpha n + \beta.$
I think that you can get slightly better bounds by starting the induction higher, but let's start it at $p=1, A_2 = 1, A_3 = \frac 8 3.$ Then we want $4$ inequalities to be satisfied: 
$1 \le 2 \log_2 2 + 2\alpha + \beta$ 
$\frac 8 3 \le 3 \log_2 3 + 3\alpha + \beta$
$\beta -2 + 1 \le 0$
$\beta -2 + \frac 23 + \frac{1}{3 \log 2} \le 0$
and we want to minimize $\alpha$ and then minimize $\beta$ for that $\alpha$ subject to these constraints. If I calculate correctly, then the solution is $\alpha = -\frac76 + \frac{1}{6 \log 2} = -0.9263, \beta = \frac43 - \frac 1{3\log 2} = 0.8524,$ and $A_n \le n \log_2 n + \alpha n + \beta$ should be satisfied for all $n \ge 2$. You can get a slightly lower value of $\alpha$ if you don't need the formula to work for $n=2$.
