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Coding Interview Patterns
  • Coding Interview Patterns
  • 1. Pattern: Sliding Window
    • 1.0 Introduction
    • 1.1 Maximum Sum Subarray of Size K (easy)
    • 1.2 Smallest Subarray with a given sum (easy)
    • 1.3 Longest Substring with K Distinct Characters (medium)
    • 1.4 Fruits into Baskets (medium)
    • 1.5 No-repeat Substring (hard)
    • 1.6 Longest Substring with Same Letters after Replacement (hard)
    • 1.7 Longest Subarray with Ones after Replacement (hard)
    • 1.8 - Permutation in a String (hard)
    • 1.9 String Anagrams (hard)
    • 1.10 Smallest Window containing Substring (hard)
    • 1.11 Words Concatenation (hard)
  • 2. Pattern: Two Pointers
    • 2.0 Introduction
    • 2.1 Pair with Target Sum (easy)
    • 2.2 Remove Duplicates (easy)
    • 2.3 Squaring a Sorted Array (easy)
    • 2.4 Triplet Sum to Zero (medium)
    • 2.5 Triplet Sum Close to Target (medium)
    • 2.6 Triplets with Smaller Sum (medium)
    • 2.7 Subarrays with Product Less than a Target (medium)
    • 2.8 Dutch National Flag Problem (medium)
    • 2.9 Comparing Strings containing Backspaces (medium)
    • 2.10 Minimum Window Sort (medium)
  • 7. Pattern: Tree Breadth First Search
    • 7.0 Introduction
    • 7.1 Binary Tree Level Order Traversal (easy)
    • 7.2 Reverse Level Order Traversal (easy)
    • 7.3 Zigzag Traversal (medium)
    • 7.4 Level Averages in a Binary Tree (easy)
    • 7.5 Minimum Depth of a Binary Tree (easy)
    • 7.6 Maximum Depth of Binary Tree (easy)
    • 7.7 Level Order Successor (easy)
    • 7.8 Connect Level Order Siblings (medium)
    • 7.9 Problem Challenge 1 - Connect All Level Order Siblings (medium)
    • 7.10 Problem Challenge 2 - Right View of a Binary Tree (easy)
  • 11. Pattern: Modified Binary Search
    • 11.1 Introduction
    • 11.2 Order-agnostic Binary Search (easy)
    • 11.3
  • 16. Pattern: Topological Sort (Graph)
    • 16.1 Introduction
    • 16.2 Topological Sort (medium)
    • 16.3 Tasks Scheduling (medium)
    • 16.4 Tasks Scheduling Order (medium)
  • Contributor Covenant Code of Conduct
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  1. 7. Pattern: Tree Breadth First Search

7.5 Minimum Depth of a Binary Tree (easy)

Previous7.4 Level Averages in a Binary Tree (easy)Next7.6 Maximum Depth of Binary Tree (easy)

Last updated 3 years ago

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Given a binary tree, find its minimum depth.

The minimum depth is the number of nodes along the shortest path from the root node down to the nearest leaf node.

Note: A leaf is a node with no children.

Example 1:

Input: root = [3,9,20,null,null,15,7]
Output: 2

Example 2:

Input: root = [2,null,3,null,4,null,5,null,6]
Output: 5

Constraints:

  • The number of nodes in the tree is in the range [0, 105].

  • -1000 <= Node.val <= 1000

import java.util.*;

class TreeNode {
  int val;
  TreeNode left;
  TreeNode right;

  TreeNode(int x) {
    val = x;
  }
};

class Main {
  public static int findDepth(TreeNode root) {
    if(root == null) return 0;
    Queue<TreeNode> queue = new LinkedList<>();
    queue.offer(root);
    int depth = 1;
    while(!queue.isEmpty()) {
      int levelSize = queue.size();
      for(int i = 0; i < levelSize; i++) {
        TreeNode currNode = queue.poll();

        if(currNode.left != null) queue.offer(currNode.left);
        if(currNode.right != null) queue.offer(currNode.right);
        if(currNode.left == null && currNode.right == null) {
          return depth;
        }

      }
      depth++;
    }
    return depth;
  }

  public static void main(String[] args) {
    TreeNode root = new TreeNode(12);
    root.left = new TreeNode(7);
    root.right = new TreeNode(1);
    root.right.left = new TreeNode(10);
    root.right.right = new TreeNode(5);
    System.out.println("Tree Minimum Depth: " + Main.findDepth(root));
    root.left.left = new TreeNode(9);
    root.right.left.left = new TreeNode(11);
    System.out.println("Tree Minimum Depth: " + Main.findDepth(root));
  }
}

The time complexity of the above algorithm is O(N), where ā€˜N’ is the total number of nodes in the tree. This is due to the fact that we traverse each node once.

The space complexity of the above algorithm will be O(N) which is required for the queue. Since we can have a maximum of N/2 nodes at any level (this could happen only at the lowest level), therefore we will need O(N) space to store them in the queue

Time complexity

Space complexity

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