Rooted Trees

31.1 Definitions for Rooted Trees

• DEFINITION: A tree is a set of nodes, perhaps empty. If not empty,
  there is a distinguished node r, called root and zero or more sub­trees
  T₁,T₂,.....T_k each of whose roots are connected by a directed edge to r.
  The root of each subtree is called a child of r, and r is called the parent of each child.
  
• DEFINITION: Nodes with no children are called leaf nodes. All other
  nodes are called interior nodes.
  
• DEFINITION: The degree of a node is the number of its children.
  The degree of a tree is the maximum degree of any of its nodes.
  
• DEFINITION: Nodes with the same parent are called siblings.
  
• There is such a thing as a NULL tree -- a tree with no nodes. Not every
  author allows this.
  
• Trees defined this way are "rooted'' trees. They have a distinguished root.
  Not all trees are considered to be rooted.
  
• Nodes are sometimes called vertices or points; edges are sometimes called lines.
  
• Note that the edges are directed edges. This means there is a direction
  associated with them. This definition has the edges directed to the root.
  Nobody draws the edges as directed edges, it's just understood.
  

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The figure shows a rooted tree with edges undirected from child to parent. The nodes are labelled for convenience. The tree has degree 3 since the node with maximum degree (the root, node A) has degree 3. The more traditional way to draw the tree is with undirected edges; this is also shown in the figure.

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Generic trees are usually shown in a schematic form with the subtrees indicated by triangles. The firgure below shows such a representation. The details of the subtrees are hidden in the "triangles'"

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• DEFINITION: A path from node n₁ to node n_k is a sequence of nodes
  n₁, n₂,....., n_k such that n_i is the parent of n _i+1 for 1 <= i < k.
  The length of this path is the number of edges on the path (namely k - 1)
  
• There is a path of length zero from every node to itself.
• In a tree there is one and only one path from the root to each node.
• DEFINITION: The depth of a node is the length of the path from
  the root to the node
  
• The root is at depth 0.
  
• DEFINITION: The depth of a tree is the depth of its deepest leaf.
  
• DEFINITION: The height of any node is the longest path from the
  node to any leaf.
  
• The height of any leaf is 0.
  
• DEFINITION: The height of a tree is the height of its root.
  
• The height and depth of a tree are equal.
  
• DEFINITION: If there is a path from node n_i to node n_j, then node n_i is an ancestor of node n_j and node n_j is a descendent of node n_i.
  
• Every node is both an ancestor and a descendent of itself. An ancestor
  (descendent) of a node which is not the node itself is called a proper
  ancestor (descendent) of the node.
  

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31.2 Theorems about Trees

Theorem There are n - 1 edges in any tree with n nodes Proof: Each edge connects a node to its parent. Every node except the root has a parent.

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31.3 First­Child, Next­Sibling Representation of General Rooted Trees

One could imagine representing a general node as a struct:
struct general_node
{
  Element_Type element;
  general_node * child1;
  general_node * child2;
  general_node * child3;
  general_node * child4;
  .
  .
  .
};

An obvious problem with this representation is that the number of
children would have to be large enough to cover all the expected cases.
This would usually mean wasting a lot of pointers.

A better representation is the ``first­child, next­sibling'' representation.
struct general_node
{
  Element_Type element;
  general_node *first_child;
  general_node *next_sibling;
};

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The figure below shows the "first­child, next­sibling" representation of the
rooted tree shown above.. The downward­pointing edges are the "first-child" edges. The
horizontal edges are the "next-sibling" edges.

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32 Binary Trees

• DEFINITION: A binary tree is a rooted tree in which no node can
  have more than two children, and the children are distinguished as left and right.
  
• Not every rooted tree with degree two is a binary tree. The children must
  be in order -- there is a "left'' child and a "right'' child.
  
• DEFINITION: A full binary tree is one in which every leaf node is at
  the same level, and every interior node has exactly two children.
  
• DEFINITION: A complete binary tree is a full binary tree except
  perhaps for the final level which is filled from left to right.
  

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Here's the definition of a Tree Node for binary trees. It keeps pointers
to its left and right children.

// forward declaration
template <class Etype> class Binary_Search_Tree;
template <class Etype>
class Tree_Node
{
  protected:
    Etype Element;
    Tree_Node *Left;
    Tree_Node *Right;
    Tree_Node( Etype E = 0, Tree_Node *L = NULL, Tree_Node *R = NULL )
       : Element( E ), Left( L ), Right( R ) { }

    friend class Binary_Search_Tree<Etype>;
};

Binary Search Tree is made a friend of Tree Node.

The constructor takes an element and two pointers as arguments (with
default values for all). The constructor can be called in any of the following ways:

1. Tree_Node();
in this case Element will be 0, and both Left and Right children will
be NULL.

2. Tree_Node(elementvalue);
in this case, Element will have the value elementvalue, Left and
Right will be NULL.

3. Tree_Node(elementvalue, nodeptr1);
in this case, Element will have the value elementvalue, Left will
have the value nodeptr1 and Right will be NULL. < BR>
4. Tree_Node(elementvalue, nodeptr1, nodeptr2);
in this case, Element will have the value elementvalue, Left will
have the value nodeptr1 and Right will have the value nodeptr2.

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32.1 Theorems About Binary Trees

Theorem 32.1 If a binary tree (BT) of height h has t leaves, then h >= lg t

Proof: If h >= lg t, then equivalently t <= 2 ^h . We prove the latter by induction on h.
Base: h = 0 (single node). Therefore, t = 1 = 2⁰
IH: Assume true for every BT with height less than h
Let T be a BT of height h > 0 and with t leaf nodes. We consider two
possible cases:

Case 1: Root of T has only one child. Without loss of generality, let it be
the left child. Then the left subtree has height h - 1 and it has all
the leaf nodes of T. By IH, t <= 2 ^{h - 1} < 2^h

Case 2: Root has two children. Let t₁ and t₂ be the number of leaf nodes and h ₁ and h ₂ the heights
of the two subtrees, respectively. Since the leaf nodes of T are partitioned between the two subtrees,
t = t₁ + t₂.
By IH, t₁ <= 2^h₁ and t ₂ <= 2 ^{h ₂} . Also, h₁ <= h - 1 and h ₂ <= h - 1

Therefore,

      t <= 2^h₁ + 2^h₂
      t <= 2^{h - 1} + 2^{h - 1}
      t <= 2^h

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Theorem 32.2 A full binary tree (FBT) of height h has 2^h leaf nodes.

Proof: By induction on h
Base: A FBT of height 0 has 1 node (the root).
IH: Assume all full binary trees of height h, 0 < h <= H have 2 ^h leaf nodes.

For a FBT, T, of height H, create a new FBT T' by adding exactly two
children to each leaf of T . The height of T' is H + 1 and T' has twice the
nuber of leaf nodes as T , namely 2 X 2^H = 2 ^H+1.

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Theorem 32.3 The number of nodes in a full binary tree of height h is 2 ^h+1 - 1

Proof: The number of nodes is the sum of the number of nodes on each
level, l. Since there are 2^l nodes at each level, the total number of nodes is

SUM^h_t=0   2^t = 2^h+1 - 1
The equation is easily proven by induction.

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Theorem 32.4 If n is the number of nodes in a complete binary tree (CBT)
of height h, then 2^h <= n < 2^h+1

Proof: A CBT of height h has at least one and as many as 2^h nodes at
level h (the final level is not empty, and may be full). In any event, level
h - 1, (h > 0) ,must be full by definition of a CBT. The number of nodes in a
FBT of height h - 1 is 2^{(h - 1) + 1} - 1. (By Theorem 32.3 above)
A CBT of height h has at least one more node than a FBT of height h - 1.
Therefore, the number of nodes n in a CBT is bounded by

2^{(h - 1) + 1} - 1 + 1 <= n <= 2^h+1 - 1
Therefore,
2^h <= n < 2^h+1 Equivalently, we can say h <= lg n < h + 1.