Tree Traversal Inorder Left Root Right in DSA C++ - Time & Space Complexity

Time Complexity: Tree Traversal Inorder Left Root Right

O(n)

Understanding Time Complexity

We want to understand how the time taken to visit all nodes in a tree grows as the tree gets bigger.

Specifically, we ask: How does the time to do an inorder traversal change with the number of nodes?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

void inorderTraversal(Node* root) {
    if (root == nullptr) return;
    inorderTraversal(root->left);
    // Process root node here
    inorderTraversal(root->right);
}
    

This code visits each node in the tree in the order: left child, root, then right child.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Recursive calls visiting each node once.
• How many times: Exactly once per node in the tree.

How Execution Grows With Input

Each node is visited once, so the total work grows directly with the number of nodes.

Input Size (n)	Approx. Operations
10	About 10 visits
100	About 100 visits
1000	About 1000 visits

Pattern observation: The work grows in a straight line as the tree size increases.

Final Time Complexity

Time Complexity: O(n)

This means the time to complete the traversal grows directly in proportion to the number of nodes.

Common Mistake

[X] Wrong: "Since recursion calls itself twice, the time doubles each time and becomes exponential."

[OK] Correct: Each node is visited only once, so the total work is just one visit per node, not repeated multiple times.

Interview Connect

Understanding this traversal's time complexity helps you explain how tree operations scale, a key skill in many coding challenges.

Self-Check

"What if we changed the traversal to preorder (root, left, right)? How would the time complexity change?"

Practice

1. What is the order of nodes visited in an inorder traversal of a binary tree?

easy

A. Right subtree, Root, Left subtree

B. Root, Left subtree, Right subtree

C. Left subtree, Root, Right subtree

D. Root, Right subtree, Left subtree

Solution

1. Step 1: Understand inorder traversal definition

   Inorder traversal visits nodes in the order: left child, then root, then right child.

2. Step 2: Apply to binary tree traversal

   This means we first visit all nodes in the left subtree, then the root node, then all nodes in the right subtree.

3. Final Answer:

   Left subtree, Root, Right subtree -> Option C

4. Quick Check:

   Inorder = Left, Root, Right [OK]

Hint: Remember: Inorder means Left, Root, then Right [OK]

Common Mistakes:

• Confusing inorder with preorder or postorder
• Visiting root before left subtree
• Visiting right subtree before root

2. Which of the following is the correct C++ function signature for inorder traversal of a binary tree node pointer Node* root?

easy

A. int inorderTraversal(Node* root)

B. int inorderTraversal(Node root)

C. void inorderTraversal(Node root)

D. void inorderTraversal(Node* root)

Solution

1. Step 1: Identify correct parameter type

   Traversal functions usually take a pointer to the root node to access the tree nodes.

2. Step 2: Identify correct return type

   Traversal functions typically return void because they print or process nodes without returning a value.

3. Final Answer:

   void inorderTraversal(Node* root) -> Option D

4. Quick Check:

   Pointer parameter and void return [OK]

Hint: Traversal functions use Node* and void return [OK]

Common Mistakes:

• Passing node by value instead of pointer
• Using int return type instead of void
• Missing pointer symbol * in parameter

3. Given the binary tree:

    2
   / \
  1   3

What is the output of the inorder traversal function that prints node values separated by spaces?

medium

A. 1 2 3

B. 2 1 3

C. 3 2 1

D. 1 3 2

Solution

1. Step 1: Traverse left subtree

   Start at root (2), go left to node (1), print 1 since it has no left child.

2. Step 2: Visit root and right subtree

   Return to root (2), print 2, then go right to node (3), print 3.

3. Final Answer:

   1 2 3 -> Option A

4. Quick Check:

   Inorder traversal output = 1 2 3 [OK]

Hint: Inorder prints nodes in ascending order for BST [OK]

Common Mistakes:

• Printing root before left subtree
• Printing right subtree before root
• Confusing preorder or postorder output

4. Consider this C++ inorder traversal code snippet:

void inorder(Node* root) {
  if (root == nullptr) return;
  inorder(root->left);
  std::cout << root->val << " ";
  inorder(root->right);
}

What is the error if the output is empty for a non-empty tree?

medium

A. The base case condition is incorrect

B. The function is not called with the root node

C. The print statement is missing

D. The traversal order is wrong

Solution

1. Step 1: Check base case correctness

   The base case correctly returns when root is nullptr, so no error here.

2. Step 2: Verify function call

   If output is empty, likely the function was never called with the actual root node of the tree.

3. Final Answer:

   The function is not called with the root node -> Option B

4. Quick Check:

   Missing initial call causes no output [OK]

Hint: Always call traversal with actual root node [OK]

Common Mistakes:

• Not calling traversal function at all
• Calling with nullptr instead of root
• Assuming print statement missing when it exists

5. You have a binary tree with nodes:

      4
     / \
    2   6
   / \ / \
  1  3 5  7

Using inorder traversal, which sequence of node values will you get?

hard

A. [1, 2, 3, 4, 5, 6, 7]

B. [4, 2, 1, 3, 6, 5, 7]

C. [7, 6, 5, 4, 3, 2, 1]

D. [1, 3, 2, 5, 7, 6, 4]

Solution

1. Step 1: Traverse left subtree of root (4)

   Visit left subtree rooted at 2: inorder visits 1, then 2, then 3.

2. Step 2: Visit root and right subtree

   After left subtree, visit root 4, then right subtree rooted at 6: visit 5, then 6, then 7.

3. Final Answer:

   [1, 2, 3, 4, 5, 6, 7] -> Option A

4. Quick Check:

   Inorder traversal of BST gives sorted sequence [OK]

Hint: Inorder traversal of BST returns sorted node values [OK]

Common Mistakes:

• Mixing preorder or postorder sequences
• Visiting root before left subtree
• Visiting right subtree before root