# Iterative preorder traversal

In last post Iterative inorder traversal , we learned how to do inorder traversal of binary tree without recursion or in iterative way. Today we will learn how to do iterative preorder traversal of binary tree. In preorder traversal, root node is processed before left and right subtrees. For example, preorder traversal of below tree would be [10,5,1,6,14,12,15], We already know how to implement preorder traversal in recursive way, let’s understand how to implement it in non-recursive way.

## Iterative preorder traversal : Thoughts

If we look at recursive implementation, we see we process the root node as soon as we reach it and then start with left subtree before touching anything on right subtree.

Once left subtree is processed, control goes to first node in right subtree. To emulate this behavior in non-recursive way, it is best to use a stack. What and when push and pop will happen on the stack?
Start with pushing the root node to stack. Traversal continues till there at least one node onto stack.

Pop the root node from stack,process it and push it’s right and left child on to stack. Why right child before left child? Because we want to process left subtree before right subtree. As at every node, we push it’s children onto stack, entire left subtree of node will be processed before right child is popped from the stack. Algorithm is very simple and is as follows.

1. Start with root node and push on to stack s
2. While there stack is not empty
1. Pop from stack `current ` = s.pop() and process the node.
2. Push `current.right` onto to stack.
3. Push `current.left` onto to stack.

### Iterative preorder traversal : example

Let’s take and example and see how it works. Given below tree, do preorder traversal on it without recursion. Let’s start from root node(10) and push it onto stack. current = node(10). Here loop starts, which check if there is node onto stack. If yes, it pops that out. s.pop will return node(10), we will print it and push it’s right and left child onto stack. Preorder traversal till now : . Since stack is not empty, pop from it.current= node(5). Print it and push it’s right and left child i.e node(6) and node(1) on stack. Again, stack is not empty, pop from stack. current  = node(1). Print node. There is no right and left child for this node, so we will not push anything on the stack. Stack is not empty yet, pop again. current= node(6). Print node. Similar to node(1), it also does not have right or left subtree, so nothing gets pushed onto stack. However, stack is not empty yet. Pop. Current = node(14). Print node, and as there are left and right children, push them onto stack as right child before left child. Stack is not empty, so pop from stack, current = node(12). Print it, as there are no children of node(12), push nothing to stack. Pop again from stack as it not empty. current = node(15). Print it. No children, so no need to push anything. At this point, stack becomes empty and we have traversed all node of tree also.

### Iterative preorder traversal : Implementation

```#include <stdio.h>
#include<stdlib.h>
#include<math.h>

struct node{
int value;
struct node *left;
struct node *right;
};
typedef struct node Node;

#define STACK_SIZE 10

typedef struct stack{
int top;
Node *items[STACK_SIZE];
}stack;

void push(stack *ms, Node *item){
if(ms->top < STACK_SIZE-1){
ms->items[++(ms->top)] = item;
}
else {
printf("Stack is full\n");
}
}

Node * pop (stack *ms){
if(ms->top > -1 ){
return ms->items[(ms->top)--];
}
else{
printf("Stack is empty\n");
}
}
Node * peek(stack ms){
if(ms.top < 0){
printf("Stack empty\n");
return 0;
}
return ms.items[ms.top];
}
int isEmpty(stack ms){
if(ms.top < 0) return 1;
else return 0;
}
void preorderTraversalWithoutRecursion(Node *root){
stack ms;
ms.top = -1;

if(root == NULL) return ;

Node *currentNode = NULL;
/* Step 1 : Start with root */
push(&ms,root);

while(!isEmpty(ms)){
/* Step 5 : Pop the node */
currentNode = pop(&ms);
/* Step 2 : Print the node */
printf("%d  ", currentNode->value);
/* Step 3: Push right child first */
if(currentNode->right){
push(&ms, currentNode->right);
}
/* Step 4: Push left child */
if(currentNode->left){
push(&ms, currentNode->left);
}
}
}

void preorder (Node *root){
if ( !root ) return;

printf("%d ", root->value );
preorder(root->left);
preorder(root->right);
}

Node * createNode(int value){
Node * newNode =  (Node *)malloc(sizeof(Node));

newNode->value = value;
newNode->right= NULL;
newNode->left = NULL;

return newNode;
}

Node * addNode(Node *node, int value){
if(node == NULL){
return createNode(value);
}
else{
if (node->value > value){
node->left = addNode(node->left, value);
}
else{
node->right = addNode(node->right, value);
}
}
return node;
}

/* Driver program for the function written above */
int main(){
Node *root = NULL;
//Creating a binary tree
root = addNode(root,30);
root = addNode(root,20);
root = addNode(root,15);
root = addNode(root,25);
root = addNode(root,40);
root = addNode(root,37);
root = addNode(root,45);

preorder(root);
printf("\n");

preorderTraversalWithoutRecursion(root);
return 0;
}
```

Complexity of iterative implementation of binary tree is O(n) as we will be visiting each node at least once. Also, there is added space complexity of stack which is O(n).

Please share if there is something wrong or missing. If you are willing to contribute and share your knowledge with thousands of learners across the world, please reach out to us at communications@algorithmsandme.com

# Binary search tree traversals

Most of the problems on binary search tree can be solved using one or other traversal of tree. There are three types of binary search tree traversals : Preorder traversal, Inorder traversal and Postorder traversal. Let’s discuss each one in detail.

## Preorder traversal of BST

Preorder traversal means traverse the root node first, then left subtree and at last traverse right subtree. For example, given below tree, preorder traversal will be 10,5,1,,6,19,17,21. As we already know that binary search tree is recursive data structure, any traversal can be solved recursively. Start with the root, then do a preorder traversal of left subtree and then at last do the preorder traversal of right subtree.

In above example,  after traversing root node(10), left subtree is traversed in preorder [5,1,6]. Same is true after traversing node(5).

### Preorder traversal implementation

```package com.company.BST;

import java.util.ArrayList;

/**
* Created by sangar on 10.5.18.
*/
public class BinarySearchTree {

private Node root;

public void BinarySearchTree(){
root = null;
}

public class Node {
private int value;
private  Node left;
private Node right;

public Node(int value) {
this.value = value;
this.left = null;
this.right = null;
}
}

public void insert(int value){
this.root =  insertNode(this.root, value);
}

private Node insertNode(Node root, int value){
if(root == null){
//if this node is root of tree
root = new Node(value);
}
else{
if(root.value > value){
//If root is greater than value, node should be added to left subtree
root.left = insertNode(root.left, value);
}
else{
//If root is less than value, node should be added to right subtree
root.right = insertNode(root.right, value);
}
}
return root;
}

private void inorder(Node root){
if(root == null) return;

if(root.left != null) inorder(root.left);
System.out.println(root.value);
if(root.right != null) inorder(root.right);
}
}
```

## Inorder traversal of BST

Inorder traversal means traverse left subtree first, then root node and at last traverse right subtree. For example, given below tree, inorder traversal will be 1,5,6,10,17,19,21. Again following the recursive strategy, if node has left child, do inorder traversal on left subtree, once inorder traversal of left subtree is done, visit root node and then again do inorder traversal of right subtree.

In above example,  before traversing root node(10), left subtree is traversed in inorder [1,5,6]. Same is true before traversing node(5).

### Inorder traversal implementation

```package com.company.BST;

import java.util.ArrayList;

/**
* Created by sangar on 10.5.18.
*/
public class BinarySearchTree {

private Node root;

public void BinarySearchTree(){
root = null;
}

public class Node {
private int value;
private  Node left;
private Node right;

public Node(int value) {
this.value = value;
this.left = null;
this.right = null;
}
}

public void insert(int value){
this.root =  insertNode(this.root, value);
}

private Node insertNode(Node root, int value){
if(root == null){
//if this node is root of tree
root = new Node(value);
}
else{
if(root.value > value){
//If root is greater than value, node should be added to left subtree
root.left = insertNode(root.left, value);
}
else{
//If root is less than value, node should be added to right subtree
root.right = insertNode(root.right, value);
}
}
return root;
}

private void inorder(Node root){
if(root == null) return;

if(root.left != null) inorder(root.left);
System.out.println(root.value);
if(root.right != null) inorder(root.right);
}
}

```

## Postorder traversal of BST

Postorder traversal means traverse left subtree first, then right subtree and at last visit root node. For example, given below tree, postorder traversal will be 1,6,5,17,21,19,10. Again following the recursive strategy, if node has left child, do postorder traversal on left subtree, then do postorder traversal of right subtree and finally visit root node.

In above example,  before traversing root node(10), left subtree is traversed in postorder [1,6,5] and then right subtree is traversed in postorder [17,21,19] and then finally node(10).

### Postorder traversal implementation

```package com.company.BST;

import java.util.ArrayList;

/**
* Created by sangar on 10.5.18.
*/
public class BinarySearchTree {

private Node root;

public void BinarySearchTree(){
root = null;
}

public class Node {
private int value;
private  Node left;
private Node right;

public Node(int value) {
this.value = value;
this.left = null;
this.right = null;
}
}

public void insert(int value){
this.root =  insertNode(this.root, value);
}

private Node insertNode(Node root, int value){
if(root == null){
//if this node is root of tree
root = new Node(value);
}
else{
if(root.value > value){
//If root is greater than value, node should be added to left subtree
root.left = insertNode(root.left, value);
}
else{
//If root is less than value, node should be added to right subtree
root.right = insertNode(root.right, value);
}
}
return root;
}

private void preOrder(Node root){
if(root == null) return;

System.out.println(root.value);
preOrder(root.left);
preOrder(root.right);
}
}
```

In next few posts, we will discuss problems which can be solved using these traversals.

Please share if there is something wrong or missing. If you want to contribute and share your knowledge with thousands of learners across the world, please reach out to us at communications@algorithmsandme.com