Some binary tree implementations store data only at the
leaf nodes,
using the internal nodes to provide structure
to the tree.
By definition, a leaf node does not need to store pointers to its
(empty) children.
More generally, binary tree implementations might require some amount
of space for internal nodes, and a different amount for leaf nodes.
Thus, to compute the space required by such implementations, it is
useful to know the minimum and maximum fraction of the nodes that are
leaves in a tree containing \(n\) internal nodes.
Unfortunately, this fraction is not fixed.
A binary tree of \(n\) internal nodes might have only one leaf.
This occurs when the internal nodes are arranged in a chain ending
in a single leaf as shown in Figure 7.4.1.
In this example, the number of leaves is low because each
internal node has only one non-empty child.
To find an upper bound on the number of leaves for a tree of \(n\)
internal nodes, first note that the upper bound will occur when each
internal node has two non-empty children, that is, when the tree is
full.
However, this observation does not tell what shape of tree will yield
the highest percentage of non-empty leaves.
It turns out not to matter, because all full binary trees with
\(n\) internal nodes have the same number of leaves.
This fact allows us to compute the space requirements for a full
binary tree implementation whose leaves require a different amount of
space from its internal nodes.
Figure 7.4.1: A tree containing many internal nodes and a single leaf.
When analyzing the space requirements for a binary tree
implementation,
it is useful to know how many empty subtrees a tree contains.
A simple extension of the Full Binary Tree Theorem tells us exactly
how many empty subtrees there are in any binary tree, whether
full or not.
Here are two approaches to proving the following theorem, and
each suggests a useful way of thinking about binary trees.
