We first present a naive algorithm that recursively iterates through all nodes.

Below is Python code implementing this naive algorithm, which we will soon optimize.

import sys def ask(cur): print cur sys.stdout.flush() return [int(_) for _ in raw_input().split()] def count(cur): # count size of subtree at cur if cur == 0: return 0 a, b = ask(cur) rgt = count(b) lft = count(a) return 1 + lft + rgt ans = count(1) print 'Answer %d' % ans

Of course, with $N \le 10^{100}$, this code is too slow!

We will make an optimization that makes use of the fact $lft - rgt \in \{0, 1\}$, where $lft$ and $rgt$ are the size of the left and right subtree of $cur$ (as used in the above code).

We do not need to compute $lft$ from scratch; we merely need to compute it given that it is either $rgt$ or $rgt + 1$.

In other words, we evaluate $lft = countGiven(a, rgt)$.

When computing $countGiven(cur, x)$, we use the same approach as before: compute the size of the left subtree of $cur$ and the right subtree of $cur$ (which we shall again name $lft$ and $rgt$), then evaluate $1 + lft + rgt$ to find the size of the subtree at $cur$.

In general, if $lft + rgt = z$, then $lft = \lceil \frac{z}{2}\rceil$ and $rgt = \lfloor\frac{z}{2}\rfloor$. This is very useful, as we know $z + 1$ is either $x$ or $x + 1$.

If $x$ is even, then $lft \in \{\lceil \frac{x - 1}{2}\rceil, \lceil \frac{x}{2}\rceil\} \implies lft = \frac{x}{2}$.

If $x$ is odd, then $rgt \in \{\lfloor\frac{x - 1}{2}\rfloor, \lfloor\frac{x}{2}\rfloor\}\implies rgt = \frac{x-1}{2}$.

Knowing $lft$ lets us recursively find $rgt = countGiven(b, lft - 1)$, and vice versa -- $lft = countGiven(a, rgt)$. Overall, this means we only need to recurse once to count the size of a tree given that its size is one of two options.

Below is code implementing this optimization.

def countGiven(cur, x): # given ret = x or ret = x + 1, find ret if cur == 0: return 0 a, b = ask(cur) # lft + rgt = x - 1 or lft + rgt = x if x % 2 == 1: rgt = (x - 1) / 2 lft = countGiven(a, rgt) return 1 + lft + rgt else: lft = x / 2 rgt = countGiven(b, lft - 1) return 1 + lft + rgt def count(cur): # count size of subtree at cur if cur == 0: return 0 a, b = ask(cur) rgt = count(b) lft = countGiven(a, rgt) return 1 + lft + rgt

We can analyze the number of queries this code makes -- we want it to be less than the allowed $70,000$.

In the maximum case, $N = 10^{100}$. Then the height $H \approx \lg_2 N = 100 \lg_2 10 < 100 \cdot \frac{10}{3} \approx 333$

The procedure $count$ is called once for each level. At a height $h$, $count$ spawns a $countGiven$ which makes $h$ queries, so overall, there are $1 + 2 + \ldots + 333 \approx 10^6/18 < 70000$ queries.

[Note from problem author: Trees of this sort are sometimes called "Braun trees" in the computing literature; they have a number of nice uses particularly in "functional" programming languages, since they are easy to manipulate in a recursive context. For example, to insert a new element in $O(\log n)$ time while maintaining the balancing condition, we can just insert it recursively into our left subtree and then swap the left and right children of the root.]