Threaded binary tree

A threaded binary tree is defined as follows:

"A binary tree is threaded by making all right child pointers that would normally be null point to the in-order successor of the node (if it exists), and all left child pointers that would normally be null point to the in-order predecessor of the node."[1]

This definition assumes the traversal order is the same as in-order traversal of the tree. However, pointers can instead (or in addition) be added to tree nodes, rather than replacing linked lists thus defined are also commonly called "threads", and can be used to enable traversal in any order(s) desired. For example, a tree whose nodes represent information about people might be sorted by name, but have extra threads allowing quick traversal in order of birth date, weight, or any other known characteristic.

Trees, including (but not limited to) binary search trees, can be used to store items in a particular order, such as the value of some property stored in each node, often called a key. One useful operation on such a tree is traversal: visiting all the items in order of the key.

A simple recursive traversal algorithm that visits each node of a binary search tree is the following. Assume t is a pointer to a node, or nil. "Visiting" t can mean performing any action on the node t or its contents.

One problem with this algorithm is that, because of its recursion, it uses stack space proportional to the height of a tree. If the tree is fairly balanced, this amounts to O(log n) space for a tree containing n elements. In the worst case, when the tree takes the form of a chain, the height of the tree is n so the algorithm takes O(n) space. A second problem is that all traversals must begin at the root when nodes have pointers only to their children. It is common to have a pointer to a particular node, but that is not sufficient to get back to the rest of the tree unless extra information is added, such as thread pointers.

In this approach, it may not be possible to tell whether the left and/or right pointers in a given node actually point to children, or are a consequence of threading. If the distinction is necessary, adding a single bit to each node is enough to record it.

In a 1968 textbook, Donald Knuth asked whether a non-recursive algorithm for in-order traversal exists, that uses no stack and leaves the tree unmodified.[2] One of the solutions to this problem is tree threading, presented by J. H. Morris in 1979.[3][4] In the 1969 follow-up edition,[5] Knuth attributed the threaded tree representation to Perlis and Thornton (1960).[6]

1. Single Threaded: each node is threaded towards either the in-order predecessor or successor (left or right).
2. Double threaded: each node is threaded towards both the in-order predecessor and successor (left and right).

In Python:

def parent(node):
    if node is node.tree.root:
        return None
    else:
        x = node
        y = node
        while True:
            if is_thread(y):
                p = y.right
                if p is None or p.left is not node:
                    p = x
                    while not is_thread(p.left):
                        p = p.left
                    p = p.left
                return p
            elif is_thread(x):
                p = x.left
                if p is None or p.right is not node:
                    p = y
                    while not is_thread(p.right):
                        p = p.right
                    p = p.right
                return p
            x = x.left
            y = y.right

Threads are reference to the predecessors and successors of the node according to an inorder traversal.

In-order traversal of the threaded tree is A,B,C,D,E,F,G,H,I, the predecessor of E is D, the successor of E is F.

Let's make the Threaded Binary tree out of a normal binary tree:

The in-order traversal for the above tree is — D B A E C. So, the respective Threaded Binary tree will be --

In an m-way threaded binary tree with n nodes, there are n*m - (n-1) void links.

As this is a non-recursive method for traversal, it has to be an iterative procedure; meaning, all the steps for the traversal of a node have to be under a loop so that the same can be applied to all the nodes in the tree.

We will consider the IN-ORDER traversal again. Here, for every node, we'll visit the left sub-tree (if it exists) first (if and only if we haven't visited it earlier); then we visit (i.e. print its value, in our case) the node itself and then the right sub-tree (if it exists). If the right sub-tree is not there, we check for the threaded link and make the threaded node the current node in consideration. Please, follow the example given below.

• GNU libavl 2.0.2, Section on threaded binary search trees