The problem is this: Use the recursion-tree method to give a good asymptotic upper bound on $$ T(n) = 9T(\sqrt[3]n) + \Theta(1). $$ I am able to get the tree started and find a pattern with the sub-problems, but I am having difficulty finding the total cost of the running times throughout the tree. I cannot figure out how to get the number of sub-problems at depth $i$ when $n=1$. I have a feeling the answer is $O(\log_3 n)$, but I cannot verify that at the moment. Any help would be appreciated.
The recurrence can be written as $$T(n) = 9T(\sqrt[3]n) + C, $$ where $C$ is some constant, since any constant will always be treated as 1 asymptotically. My recursion tree is explained by each level below:
Level 0: This is the constant $C$
Level 1: $T(\sqrt[3]n)$ is written 9 times which represent the sub-problems of $C$. This adds up to $9C\sqrt[3]n$.
Level 2: Each of the 9 sub-problems from level 1 gets divided into 9 more sub-problems, which are each written as $T(\sqrt[9]n)$. All of these add up to $81C\sqrt[9]n$.
Sub-Problem Sizes and Nodes: The number of nodes at depth $i$ is $9^i$. We know that the sub-problem size for a node at depth $i$ is $n^{1/3^i}$. The problem size hits $n=1$ when this size equals 1. Solving for $i$ yields:
$$ (n^{1/3^i})^{3i} = 1^{3i} n = 1^{3i}. $$
This results in $n$ being 1 which doesn't give a logarithmic form!