Pre-order to post-order traversal

Pre-order = outputting the values of a binary tree in the order of the current node, then the left subtree, then the right subtree.

Post-order = outputting the values of a binary tree in the order of the left subtree, then the right subtree, the the current node.

In a binary search tree, the values of all nodes in the left subtree are less than the value of the current node; and alike for the right subtree. Hence if you know the start of a pre-order dump of a binary search tree (i.e. its root node's value), you can easily decompose the whole dump into the root node value, the values of the left subtree's nodes, and the values of the right subtree's nodes.

To output the tree in post-order, recursion and output reordering is applied. This task is left upon the reader.

you are given the pre-order traversal results. then put the values to a suitable binary search tree and just follow the post-order traversal algorithm for the obtained BST.

You are given the pre-order traversal of the tree, which is constructed by doing: output, traverse left, traverse right.

As the post-order traversal comes from a BST, you can deduce the in-order traversal (traverse left, output, traverse right) from the post-order traversal by sorting the numbers. In your example, the in-order traversal is 1, 2, 3, 4, 6, 7, 9, 10, 11.

From two traversals we can then construct the original tree. Let's use a simpler example for this:

• Pre-order: 2, 1, 4, 3
• In-order: 1, 2, 3, 4

The pre-order traversal gives us the root of the tree as 2. The in-order traversal tells us 1 falls into the left sub-tree and 3, 4 falls into the right sub-tree. The structure of the left sub-tree is trivial as it contains a single element. The right sub-tree's pre-order traversal is deduced by taking the order of the elements in this sub-tree from the original pre-order traversal: 4, 3. From this we know the root of the right sub-tree is 4 and from the in-order traversal (3, 4) we know that 3 falls into the left sub-tree. Our final tree looks like this:

  2
 / \
1   4
   /
  3

With the tree structure, we can get the post-order traversal by walking the tree: traverse left, traverse right, output. For this example, the post-order traversal is 1, 3, 4, 2.

To generalise the algorithm:

1. The first element in the pre-order traversal is the root of the tree. Elements less than the root form the left sub-tree. Elements greater than the root form the right sub-tree.
2. Find the structure of the left and right sub-trees using step 1 with a pre-order traversal that consists of the elements we worked out to be in that sub-tree placed in the order they appear in the original pre-order traversal.
3. Traverse the resulting tree in post-order to get the post-order traversal associated with the given pre-order traversal.

Using the above algorithm, the post-order traversal associated with the pre-order traversal in the question is: 1, 3, 4, 2, 9, 11, 10, 7, 6. Getting there is left as an exercise.

Based on Ondrej Tucny's answer. Valid for BST only
example:

     20  
    /  \  
   10  30  
   /\    \  
  6  15   35  

Preorder = 20 10 6 15 30 35
Post = 6 15 10 35 30 20

For a BST, In Preorder traversal; first element of array is 20. This is the root of our tree. All numbers in array which are lesser than 20 form its left subtree and greater numbers form right subtree.

//N = number of nodes in BST (size of traversal array)
int post[N] = {0}; 
int i =0;

void PretoPost(int pre[],int l,int r){
  if(l==r){post[i++] = pre[l]; return;}
  //pre[l] is root
  //Divide array in lesser numbers and greater numbers and then call this function on them recursively  
  for(int j=l+1;j<=r;j++) 
      if(pre[j]>pre[l])
          break;
  PretoPost(a,l+1,j-1); // add left node
  PretoPost(a,j,r); //add right node
  //root should go in the end
  post[i++] = pre[l]; 
  return;
 }

Please correct me if there is any mistake.